BLD: minor adjustments to the talib sample algo

[MIG] Migrated buy_and_hodl and buy_low_sell_high to version 0.3 to work with Poloniex exchange

.gitignore

@@ -78,3 +78,7 @@ zipline.iml
 ./data
 
 TAGS
+
+python2
+python3
+scratch

README.rst

@@ -1,196 +1,3 @@
-========
-Catalyst
-========
-
-|version status|
-
-Catalyst is an algorithmic trading library for crypto-assets written in Python.
-It allows trading strategies to be easily expressed and backtested against historical data, providing analytics and insights regarding a particular strategy's performance.
-Catalyst will be expanded to support live-trading of crypto-assets in the coming months.
-Please visit `<enigma.co>`_ to learn about Catalyst, or refer to the 
-`whitepaper <https://www.enigma.co/enigma_catalyst.pdf>`_ for further technical details.
-
-Catalyst builds on top of the well-established `Zipline <https://github.com/quantopian/zipline>`_ project.
-We did our best to minimize structural changes to the general API to maximize compatibility with existing trading algorithms, developer knowledge, and tutorials.
-For now, please refer to the `Zipline API Docs <http://zipline.io>`_ as a general reference and bring any other questions you have to our #dev channel on `Slack <https://join.slack.com/enigmacatalyst/shared_invite/MTkzMjQ0MTg1NTczLTE0OTY3MjE3MDEtZGZmMTI5YzI3ZA>`_.
-
-Our primary contributions include the:
-
-- Introduction of an open trading calendar that permits simulation to allow trades on weekends, holidays, and outside of normal business hours.
-- Curation of OHLCV data bundle from `Poloniex's API <https://poloniex.com/support/api/>`_, which contains data in five-minute intervals as early as 2/19/2015.
-- Support for backtesting of daily trading strategies, support for five-minute backtesting is in development.
-- Addition of Bitcoin price (USDT_BTC) as a benchmark asset for comparing performance.
-
-Interested in getting involved?
-`Join us on Slack! <https://join.slack.com/enigmacatalyst/shared_invite/MTkzMjQ0MTg1NTczLTE0OTY3MjE3MDEtZGZmMTI5YzI3ZA>`_
-
-
-Installation
-============
-
-At the moment, Catalyst has some fairly specific and strict depedency requirements.
-We recommend the use of Python virtual environments if you wish to simplify the installation process, or otherwise isolate Catalyst's dependencies from your other projects.
-If you don't have ``virtualenv`` installed, see our later section on Virtual Environments.
-
-.. code-block:: bash
-
-    $ virtualenv catalyst-venv
-    $ source ./catalyst-venv/bin/activate
-    $ pip install enigma-catalyst
-
-**Note:** A successful installation will require several minutes in order to compile dependencies that expose C APIs.
-
-Dependencies
-------------
-
-Catalyst's depedencies can be found in the ``etc/requirements.txt`` file.
-If you need to install them outside of a typical ``pip install``, this is done using:
-
-.. code-block:: bash
-
-    $ pip install -r etc/requirements.txt
-
-Though not required by Catalyst directly, our example algorithms use matplotlib to visually display backtest results.
-If you wish to run any examples or use matplotlib during development, it can be installed using:
-
-.. code-block:: bash
-
-    $ pip install matplotlib
-
-**Note:** If you plan to use matplotlib and virtualenv on Mac OS X, see our later section for additional setup instructions.
-
-Getting Started
-===============
-
-The following code implements a simple buy and hold algorithm. The full source can be found in ``catalyst/examples/buy_and_hodl.py``.
-
-.. code:: python
-
-    import numpy as np
-    
-    from catalyst.api import (
-        order_target_value,
-        symbol,
-        record,
-        cancel_order,
-        get_open_orders,
-    )
-    
-    ASSET = 'USDT_BTC'
-    
-    TARGET_HODL_RATIO = 0.8
-    RESERVE_RATIO = 1.0 - TARGET_HODL_RATIO
-
-    def initialize(context):
-        context.is_buying = True
-        context.asset = symbol(ASSET)
-
-    def handle_data(context, data):
-        cash = context.portfolio.cash
-        target_hodl_value = TARGET_HODL_RATIO * context.portfolio.starting_cash
-        reserve_value = RESERVE_RATIO * context.portfolio.starting_cash
-        
-        # Cancel any outstanding orders from the previous day
-        orders = get_open_orders(context.asset) or []
-        for order in orders:
-            cancel_order(order)
-        
-        # Stop buying after passing reserve threshold
-        if cash <= reserve_value:
-            context.is_buying = False
-        
-        # Retrieve current price from pricing data
-        price = data[context.asset].price
-        
-        # Check if still buying and could (approximately) afford another purchase                    
-        if context.is_buying and cash > price:
-            # Place order to make position in asset equal to target_hodl_value
-            order_target_value(
-                context.asset,
-                target_hodl_value,
-                limit_price=1.1 * price,
-                stop_price=0.9 * price,
-            )
-        
-        # Record any state for later analysis
-        record(
-            price=price,
-            cash=context.portfolio.cash,
-            leverage=context.account.leverage,
-        )
-
-
-You can then run this algorithm using the Catalyst CLI. From the command
-line, run:
-
-.. code:: bash
-
-    $ catalyst ingest
-    $ catalyst run -f buy_and_hodl.py --start 2015-3-1 --end 2017-6-28 --capital-base 100000 -o bah.pickle
-
-This will download the crypto-asset price data from a poloniex bundle
-curated by Enigma in the specified time range and stream it through
-the algorithm and plot the resulting performance using matplotlib.
-
-You can find other examples in the ``catalyst/examples`` directory.
-
-Limitations
------------
-
-This project is currently in a pre-alpha state and has some limitations we'd like to address:
-
-- *Minimum Denomination:* The smallest tradable unit in Catalyst is equal to 1/1000th of a full coin. We plan to enable more granular increments, but have capped it at 1/1000th for the time being.
-- *Supported Assets:* Currently the poloniex bundle comes prepopulated with data for all 90 registered trading pairs. However, due to limitations in how portfolios are currently modeled, we recommend sticking to ``USDT_*`` trading pairs. USDT is an independent currency listed on Poloniex whose price is pegged to the US dollar. Currently, this list includes: ``USDT_BTC``, ``USDT_DASH``, ``USDT_ETC``, ``USDT_ETH``, ``USDT_LTC``, ``USDT_NXT``, ``USDT_REP``, ``USDT_STR``, ``USDT_XMR``, ``USDT_XRP``, and ``USDT_ZEC``. We plan to add support for basing your portfolio in arbitrary currencies and provide native support for modeling ForEx trades in the near future!
-
-Virtual Environments
-====================
-
-Here we will provide a brief tutorial for installing ``virtualenv`` and its basic usage.
-For more information regarding ``virtualenv``, please refer to this `virtualenv guide <http://python-guide-pt-br.readthedocs.io/en/latest/dev/virtualenvs/>`_.
-
-The ``virtualenv`` command can be installed using:
-
-.. code-block:: bash
-
-    $ pip install virtualenv
-
-To create a new virtual environment, choose a directory, e.g. ``/path/to/venv-dir``, where project-specific packages and files will be stored.  The environment is created by running:
-
-.. code-block:: bash
-
-    $ virtualenv /path/to/venv-dir
-
-To enter an environment, run the ``bin/activate`` script located in ``/path/to/venv-dir`` using:
-
-.. code-block:: bash
-
-    $ source /path/to/venv-dir/bin/activate
-
-Exiting an environment is accomplished using ``deactivate``, and removing it entirely is done by deleting ``/path/to/venv-dir``.
-
-OS X + virtualenv + matplotlib
--------------------------------------
-
-A note about using matplotlib in virtual enviroments on OS X: it may be necessary to run
-
-.. code-block:: python
-
-    echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
-
-in order to override the default ``macosx`` backend for your system, which may not be accessible from inside the virtual environment.
-This will allow Catalyst to open matplotlib charts from within a virtual environment, which is useful for displaying the performance of your backtests.  To learn more about matplotlib backends, please refer to the
-`matplotlib backend documentation <https://matplotlib.org/faq/usage_faq.html#what-is-a-backend>`_.
-
-Disclaimer
-==========
-
-Keep in mind that this project is still under active development, and is not recommended for production use in its current state.
-We are deeply committed to improving the overall user experience, reliability, and feature-set offered by Catalyst.
-If you have any suggestions, feedback, or general improvements regarding any of these topics, please let us know!
-
-Hello World,
-
-The Enigma Team
-
-.. |version status| image:: https://img.shields.io/pypi/pyversions/enigma-catalyst.svg
-   :target: https://pypi.python.org/pypi/enigma-catalyst
+All the documentation for `Catalyst <https://github.com/enigmampc/catalyst>`_
+can be found in the 
+`documentation website <https://enigmampc.github.io/catalyst>`_.

catalyst/__main__.py

@@ -8,6 +8,9 @@ import pandas as pd
 from six import text_type
 
 from catalyst.data import bundles as bundles_module
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.exchange_utils import delete_algo_folder
+from catalyst.exchange.factory import get_exchange
 from catalyst.utils.cli import Date, Timestamp
 from catalyst.utils.run_algo import _run, load_extensions
 
@@ -28,16 +31,17 @@ except NameError:
     '--strict-extensions/--non-strict-extensions',
     is_flag=True,
     help='If --strict-extensions is passed then catalyst will not run if it'
-    ' cannot load all of the specified extensions. If this is not passed or'
-    ' --non-strict-extensions is passed then the failure will be logged but'
-    ' execution will continue.',
+         ' cannot load all of the specified extensions. If this is not passed or'
+         ' --non-strict-extensions is passed then the failure will be logged but'
+         ' execution will continue.',
 )
 @click.option(
     '--default-extension/--no-default-extension',
     is_flag=True,
     default=True,
-    help="Don't load the default catalyst extension.py file in $ZIPLINE_HOME.",
+    help="Don't load the default catalyst extension.py file in $CATALYST_HOME.",
 )
+@click.version_option()
 def main(extension, strict_extensions, default_extension):
     """Top level catalyst entry point.
     """
@@ -64,6 +68,7 @@ def extract_option_object(option):
     option_object : click.Option
         The option object that this decorator will create.
     """
+
     @option
     def opt():
         pass
@@ -95,7 +100,9 @@ def ipython_only(option):
         def _(*args, **kwargs):
             kwargs[argname] = None
             return f(*args, **kwargs)
+
         return _
+
     return d
 
 
@@ -117,13 +124,13 @@ def ipython_only(option):
     '--define',
     multiple=True,
     help="Define a name to be bound in the namespace before executing"
-    " the algotext. For example '-Dname=value'. The value may be any python"
-    " expression. These are evaluated in order so they may refer to previously"
-    " defined names.",
+         " the algotext. For example '-Dname=value'. The value may be any python"
+         " expression. These are evaluated in order so they may refer to previously"
+         " defined names.",
 )
 @click.option(
     '--data-frequency',
-    type=click.Choice({'daily', '5-minute', 'minute'}),
+    type=click.Choice({'daily', 'minute'}),
     default='daily',
     show_default=True,
     help='The data frequency of the simulation.',
@@ -149,7 +156,7 @@ def ipython_only(option):
     default=pd.Timestamp.utcnow(),
     show_default=False,
     help='The date to lookup data on or before.\n'
-    '[default: <current-time>]'
+         '[default: <current-time>]'
 )
 @click.option(
     '-s',
@@ -170,7 +177,7 @@ def ipython_only(option):
     metavar='FILENAME',
     show_default=True,
     help="The location to write the perf data. If this is '-' the perf will"
-    " be written to stdout.",
+         " be written to stdout.",
 )
 @click.option(
     '--print-algo/--no-print-algo',
@@ -184,6 +191,23 @@ def ipython_only(option):
     default=None,
     help='Should the algorithm methods be resolved in the local namespace.'
 ))
+@click.option(
+    '-x',
+    '--exchange-name',
+    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the targeted exchange (supported: bitfinex, bittrex, poloniex).',
+)
+@click.option(
+    '-n',
+    '--algo-namespace',
+    help='A label assigned to the algorithm for data storage purposes.'
+)
+@click.option(
+    '-c',
+    '--base-currency',
+    help='The base currency used to calculate statistics '
+         '(e.g. usd, btc, eth).',
+)
 @click.pass_context
 def run(ctx,
         algofile,
@@ -197,9 +221,19 @@ def run(ctx,
         end,
         output,
         print_algo,
-        local_namespace):
+        local_namespace,
+        exchange_name,
+        algo_namespace,
+        base_currency):
     """Run a backtest for the given algorithm.
     """
+
+    if (algotext is not None) == (algofile is not None):
+        ctx.fail(
+            "must specify exactly one of '-f' / '--algofile' or"
+            " '-t' / '--algotext'",
+        )
+
     # check that the start and end dates are passed correctly
     if start is None and end is None:
         # check both at the same time to avoid the case where a user
@@ -213,11 +247,8 @@ def run(ctx,
     if end is None:
         ctx.fail("must specify an end date with '-e' / '--end'")
 
-    if (algotext is not None) == (algofile is not None):
-        ctx.fail(
-            "must specify exactly one of '-f' / '--algofile' or"
-            " '-t' / '--algotext'",
-        )
+    if exchange_name is None:
+        ctx.fail("must specify an exchange name '-x'")
 
     perf = _run(
         initialize=None,
@@ -238,6 +269,11 @@ def run(ctx,
         print_algo=print_algo,
         local_namespace=local_namespace,
         environ=os.environ,
+        live=False,
+        exchange=exchange_name,
+        algo_namespace=algo_namespace,
+        base_currency=base_currency,
+        live_graph=False
     )
 
     if output == '-':
@@ -265,11 +301,11 @@ def catalyst_magic(line, cell=None):
                 '--algotext', cell,
                 '--output', os.devnull,  # don't write the results by default
             ] + ([
-                # these options are set when running in line magic mode
-                # set a non None algo text to use the ipython user_ns
-                '--algotext', '',
-                '--local-namespace',
-            ] if cell is None else []) + line.split(),
+                     # these options are set when running in line magic mode
+                     # set a non None algo text to use the ipython user_ns
+                     '--algotext', '',
+                     '--local-namespace',
+                 ] if cell is None else []) + line.split(),
             '%s%%catalyst' % ((cell or '') and '%'),
             # don't use system exit and propogate errors to the caller
             standalone_mode=False,
@@ -281,15 +317,280 @@ def catalyst_magic(line, cell=None):
             raise ValueError('main returned non-zero status code: %d' % e.code)
 
 
+@main.command()
+@click.option(
+    '-f',
+    '--algofile',
+    default=None,
+    type=click.File('r'),
+    help='The file that contains the algorithm to run.',
+)
+@click.option(
+    '-t',
+    '--algotext',
+    help='The algorithm script to run.',
+)
+@click.option(
+    '-D',
+    '--define',
+    multiple=True,
+    help="Define a name to be bound in the namespace before executing"
+         " the algotext. For example '-Dname=value'. The value may be any python"
+         " expression. These are evaluated in order so they may refer to previously"
+         " defined names.",
+)
+@click.option(
+    '-o',
+    '--output',
+    default='-',
+    metavar='FILENAME',
+    show_default=True,
+    help="The location to write the perf data. If this is '-' the perf will"
+         " be written to stdout.",
+)
+@click.option(
+    '--print-algo/--no-print-algo',
+    is_flag=True,
+    default=False,
+    help='Print the algorithm to stdout.',
+)
+@ipython_only(click.option(
+    '--local-namespace/--no-local-namespace',
+    is_flag=True,
+    default=None,
+    help='Should the algorithm methods be resolved in the local namespace.'
+))
+@click.option(
+    '-x',
+    '--exchange-name',
+    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the targeted exchange (supported: bitfinex, bittrex, poloniex).',
+)
+@click.option(
+    '-n',
+    '--algo-namespace',
+    help='A label assigned to the algorithm for data storage purposes.'
+)
+@click.option(
+    '-c',
+    '--base-currency',
+    help='The base currency used to calculate statistics '
+         '(e.g. usd, btc, eth).',
+)
+@click.option(
+    '--live-graph/--no-live-graph',
+    is_flag=True,
+    default=False,
+    help='Display live graph.',
+)
+@click.pass_context
+def live(ctx,
+         algofile,
+         algotext,
+         define,
+         output,
+         print_algo,
+         local_namespace,
+         exchange_name,
+         algo_namespace,
+         base_currency,
+         live_graph):
+    """Trade live with the given algorithm.
+    """
+    if (algotext is not None) == (algofile is not None):
+        ctx.fail(
+            "must specify exactly one of '-f' / '--algofile' or"
+            " '-t' / '--algotext'",
+        )
+
+    if exchange_name is None:
+        ctx.fail("must specify an exchange name '-x'")
+    if algo_namespace is None:
+        ctx.fail("must specify an algorithm name '-n' in live execution mode")
+    if base_currency is None:
+        ctx.fail("must specify a base currency '-c' in live execution mode")
+
+    perf = _run(
+        initialize=None,
+        handle_data=None,
+        before_trading_start=None,
+        analyze=None,
+        algofile=algofile,
+        algotext=algotext,
+        defines=define,
+        data_frequency=None,
+        capital_base=None,
+        data=None,
+        bundle=None,
+        bundle_timestamp=None,
+        start=None,
+        end=None,
+        output=output,
+        print_algo=print_algo,
+        local_namespace=local_namespace,
+        environ=os.environ,
+        live=True,
+        exchange=exchange_name,
+        algo_namespace=algo_namespace,
+        base_currency=base_currency,
+        live_graph=live_graph
+    )
+
+    if output == '-':
+        click.echo(str(perf))
+    elif output != os.devnull:  # make the catalyst magic not write any data
+        perf.to_pickle(output)
+
+    return perf
+
+
+@main.command(name='ingest-exchange')
+@click.option(
+    '-x',
+    '--exchange-name',
+    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the exchange bundle to ingest (supported: bitfinex,'
+         ' bittrex, poloniex).',
+)
+@click.option(
+    '-f',
+    '--data-frequency',
+    type=click.Choice({'daily', 'minute', 'daily,minute', 'minute,daily'}),
+    default='daily',
+    show_default=True,
+    help='The data frequency of the desired OHLCV bars.',
+)
+@click.option(
+    '-s',
+    '--start',
+    default=None,
+    type=Date(tz='utc', as_timestamp=True),
+    help='The start date of the data range. (default: one year from end date)',
+)
+@click.option(
+    '-e',
+    '--end',
+    default=None,
+    type=Date(tz='utc', as_timestamp=True),
+    help='The end date of the data range. (default: today)',
+)
+@click.option(
+    '-i',
+    '--include-symbols',
+    default=None,
+    help='A list of symbols to ingest (optional comma separated list)',
+)
+@click.option(
+    '--exclude-symbols',
+    default=None,
+    help='A list of symbols to exclude from the ingestion '
+         '(optional comma separated list)',
+)
+@click.option(
+    '--show-progress/--no-show-progress',
+    default=True,
+    help='Print progress information to the terminal.'
+)
+@click.option(
+    '--verbose/--no-verbose`',
+    default=False,
+    help='Show a progress indicator for every currency pair.'
+)
+@click.option(
+    '--validate/--no-validate`',
+    default=False,
+    help='Report potential anomalies found in data bundles.'
+)
+def ingest_exchange(exchange_name, data_frequency, start, end,
+                    include_symbols, exclude_symbols, show_progress, verbose,
+                    validate):
+    """
+    Ingest data for the given exchange.
+    """
+
+    if exchange_name is None:
+        ctx.fail("must specify an exchange name '-x'")
+
+    exchange = get_exchange(exchange_name)
+    exchange_bundle = ExchangeBundle(exchange)
+
+    click.echo('Ingesting exchange bundle {}...'.format(exchange_name))
+    exchange_bundle.ingest(
+        data_frequency=data_frequency,
+        include_symbols=include_symbols,
+        exclude_symbols=exclude_symbols,
+        start=start,
+        end=end,
+        show_progress=show_progress,
+        show_breakdown=verbose,
+        show_report=validate
+    )
+
+
+@main.command(name='clean-algo')
+@click.option(
+    '-n',
+    '--algo-namespace',
+    help='The label of the algorithm to for which to clean the state.'
+)
+@click.pass_context
+def clean_algo(ctx, algo_namespace):
+    click.echo(
+        'Deleting the state folder of algo: {}...'.format(algo_namespace)
+    )
+    delete_algo_folder(algo_namespace)
+
+
+@main.command(name='clean-exchange')
+@click.option(
+    '-x',
+    '--exchange-name',
+    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the exchange bundle to ingest (supported: bitfinex,'
+         ' bittrex, poloniex).',
+)
+@click.option(
+    '-f',
+    '--data-frequency',
+    type=click.Choice({'daily', 'minute'}),
+    default=None,
+    help='The bundle data frequency to remove. If not specified, it will '
+         'remove both daily and minute bundles.',
+)
+@click.pass_context
+def clean_exchange(ctx, exchange_name, data_frequency):
+    """Clean up bundles from 'ingest-exchange'.
+    """
+
+    if exchange_name is None:
+        ctx.fail("must specify an exchange name '-x'")
+
+    exchange = get_exchange(exchange_name)
+    exchange_bundle = ExchangeBundle(exchange)
+
+    click.echo('Cleaning exchange bundle {}...'.format(exchange_name))
+    exchange_bundle.clean(
+        data_frequency=data_frequency,
+    )
+    click.echo('Done')
+
+
 @main.command()
 @click.option(
     '-b',
     '--bundle',
-    default='poloniex',
     metavar='BUNDLE-NAME',
-    show_default=True,
+    default=None,
+    show_default=False,
     help='The data bundle to ingest.',
 )
+@click.option(
+    '-x',
+    '--exchange-name',
+    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the exchange bundle to ingest (supported: bitfinex,'
+         ' bittrex, poloniex).',
+)
 @click.option(
     '-c',
     '--compile-locally',
@@ -308,9 +609,12 @@ def catalyst_magic(line, cell=None):
     default=True,
     help='Print progress information to the terminal.'
 )
-def ingest(bundle, compile_locally, assets_version, show_progress):
+@click.pass_context
+def ingest(ctx, bundle, exchange_name, compile_locally, assets_version,
+           show_progress):
     """Ingest the data for the given bundle.
     """
+
     bundles_module.ingest(
         bundle,
         os.environ,
@@ -330,19 +634,26 @@ def ingest(bundle, compile_locally, assets_version, show_progress):
     show_default=True,
     help='The data bundle to clean.',
 )
+@click.option(
+    '-x',
+    '--exchange_name',
+    metavar='EXCHANGE-NAME',
+    show_default=True,
+    help='The exchange bundle name to clean.',
+)
 @click.option(
     '-e',
     '--before',
     type=Timestamp(),
     help='Clear all data before TIMESTAMP.'
-    ' This may not be passed with -k / --keep-last',
+         ' This may not be passed with -k / --keep-last',
 )
 @click.option(
     '-a',
     '--after',
     type=Timestamp(),
     help='Clear all data after TIMESTAMP'
-    ' This may not be passed with -k / --keep-last',
+         ' This may not be passed with -k / --keep-last',
 )
 @click.option(
     '-k',
@@ -350,10 +661,10 @@ def ingest(bundle, compile_locally, assets_version, show_progress):
     type=int,
     metavar='N',
     help='Clear all but the last N downloads.'
-    ' This may not be passed with -e / --before or -a / --after',
+         ' This may not be passed with -e / --before or -a / --after',
 )
 def clean(bundle, before, after, keep_last):
-    """Clean up data downloaded with the ingest command.
+    """Clean up bundles from 'ingest'.
     """
     bundles_module.clean(
         bundle,

catalyst/algorithm.py

@@ -125,6 +125,7 @@ from catalyst.utils.factory import create_simulation_parameters
 from catalyst.utils.math_utils import (
     tolerant_equals,
     round_if_near_integer,
+    round_nearest
 )
 from catalyst.utils.pandas_utils import clear_dataframe_indexer_caches
 from catalyst.utils.preprocess import preprocess
@@ -133,15 +134,13 @@ from catalyst.utils.security_list import SecurityList
 import catalyst.protocol
 from catalyst.sources.requests_csv import PandasRequestsCSV
 
-from catalyst.gens.sim_engine import (
-    MinuteSimulationClock,
-    FiveMinuteSimulationClock,
-)
+from catalyst.gens.sim_engine import MinuteSimulationClock
 from catalyst.sources.benchmark_source import BenchmarkSource
 from catalyst.catalyst_warnings import ZiplineDeprecationWarning
 
+from catalyst.constants import LOG_LEVEL
 
-log = logbook.Logger("ZiplineLog")
+log = logbook.Logger("CatalystLog", level=LOG_LEVEL)
 
 
 class TradingAlgorithm(object):
@@ -173,7 +172,7 @@ class TradingAlgorithm(object):
     algo_filename : str, optional
         The filename for the algoscript. This will be used in exception
         tracebacks. default: '<string>'.
-    data_frequency : {'daily', '5-minute', 'minute'}, optional
+    data_frequency : {'daily', 'minute'}, optional
         The duration of the bars.
     instant_fill : bool, optional
         Whether to fill orders immediately or on next bar. default: False
@@ -226,7 +225,7 @@ class TradingAlgorithm(object):
             script : str
                 Algoscript that contains initialize and
                 handle_data function definition.
-            data_frequency : {'daily', '5-minute', 'minute'}
+            data_frequency : {'daily', 'minute'}
                The duration of the bars.
             capital_base : float <default: 1.0e5>
                How much capital to start with.
@@ -434,8 +433,6 @@ class TradingAlgorithm(object):
         if get_loader is not None:
             if data_frequency == 'daily':
                 all_dates = self.trading_calendar.all_sessions
-            elif data_frequency == '5-minute':
-                all_dates = self.trading_calendar.all_five_minutes
             elif data_frequency == 'minute':
                 all_dates = self.trading_calendar.all_minutes
             else:
@@ -444,9 +441,6 @@ class TradingAlgorithm(object):
                     'data frequency: {}'.format(data_frequency)
                 )
 
-            print 'first_dates:', all_dates[:10]
-            print 'last_dates:', all_dates[:-10]
-
             self.engine = SimplePipelineEngine(
                 get_loader,
                 all_dates,
@@ -470,7 +464,7 @@ class TradingAlgorithm(object):
         self._in_before_trading_start = True
 
         with handle_non_market_minutes(data) if \
-                self.data_frequency in ('minute', '5-minute') else ExitStack():
+                self.data_frequency == 'minute' else ExitStack():
             self._before_trading_start(self, data)
 
         self._in_before_trading_start = False
@@ -526,11 +520,10 @@ class TradingAlgorithm(object):
         market_closes = trading_o_and_c['market_close']
         minutely_emission = False
 
-        if self.sim_params.data_frequency in set(('minute', '5-minute')):
+        if self.sim_params.data_frequency == 'minute':
             market_opens = trading_o_and_c['market_open']
 
-            minutely_emission = self.sim_params.emission_rate in \
-                set(('minute', '5-minute'))
+            minutely_emission = self.sim_params.emission_rate == 'minute'
         else:
             # in daily mode, we want to have one bar per session, timestamped
             # as the last minute of the session.
@@ -554,15 +547,6 @@ class TradingAlgorithm(object):
             'UTC',
         )
 
-        if self.sim_params.data_frequency == '5-minute':
-            return FiveMinuteSimulationClock(
-                self.sim_params.sessions,
-                execution_opens,
-                execution_closes,
-                before_trading_start_minutes,
-                minute_emission=minutely_emission,
-            )
-
         return MinuteSimulationClock(
             self.sim_params.sessions,
             execution_opens,
@@ -694,8 +678,6 @@ class TradingAlgorithm(object):
                     time_count = times.nunique()
                     if time_count == 1:
                         self.sim_params.data_frequency = 'daily'
-                    elif time_count == 288:
-                        self.sim_params.data_frequency = '5-minute'
                     else:
                         self.sim_params.data_frequency = 'minute'
 
@@ -717,8 +699,6 @@ class TradingAlgorithm(object):
 
                 if self.sim_params.data_frequency == 'daily':
                     equity_reader_arg = 'equity_daily_reader'
-                elif self.sim_params.data_frequency == '5-minute':
-                    equity_daily_reader = 'equity_5_minute_reader'
                 elif self.sim_params.data_frequency == 'minute':
                     equity_reader_arg = 'equity_minute_reader'
                 equity_reader = PanelBarReader(
@@ -962,9 +942,9 @@ class TradingAlgorithm(object):
                   The arena from the simulation parameters. This will normally
                   be ``'backtest'`` but some systems may use this distinguish
                   live trading from backtesting.
-              data_frequency : {'daily', '5-minute', 'minute'}
+              data_frequency : {'daily', 'minute'}
                   data_frequency tells the algorithm if it is running with
-                  daily, minute, or five-minute mode.
+                  daily or minute mode.
               start : datetime
                   The start date for the simulation.
               end : datetime
@@ -1138,19 +1118,12 @@ class TradingAlgorithm(object):
                           'date_rule. You should use keyword argument '
                           'time_rule= when calling schedule_function without '
                           'specifying a date_rule', stacklevel=3)
-        
-        freq = self.sim_params.data_frequency
 
         date_rule = date_rule or date_rules.every_day()
-        if freq is 'daily':
-            # ignore time rule in daily mode
-            time_rule = time_rules.every_minute()
-        else:
-            # use provided time rule or default to every minute or 5 minutes
-            # based on desired data frequency.
-            time_rule = time_rule or (time_rules.every_5_minutes()
-                                      if freq is '5-minute' else
-                                      time_rules.every_minute())
+        time_rule = ((time_rule or time_rules.every_minute())
+                     if self.sim_params.data_frequency == 'minute' else
+                     # If we are in daily mode the time_rule is ignored.
+                     time_rules.every_minute())
 
         # Check the type of the algorithm's schedule before pulling calendar
         # Note that the ExchangeTradingSchedule is currently the only
@@ -1491,7 +1464,7 @@ class TradingAlgorithm(object):
 
     def _calculate_order(self, asset, amount,
                          limit_price=None, stop_price=None, style=None):
-        amount = self.round_order(amount)
+        amount = self.round_order(amount, asset)
 
         # Raises a ZiplineError if invalid parameters are detected.
         self.validate_order_params(asset,
@@ -1508,16 +1481,13 @@ class TradingAlgorithm(object):
         return amount, style
 
     @staticmethod
-    def round_order(amount):
+    def round_order(amount, asset):
         """
-        Convert number of shares to an integer.
-
-        By default, truncates to the integer share count that's either within
-        .0001 of amount or closer to zero.
-
-        E.g. 3.9999 -> 4.0; 5.5 -> 5.0; -5.5 -> -5.0
+        Converts the number of shares to the smallest tradable lot size for
+        the asset being ordered.
+        
         """
-        return int(round_if_near_integer(amount))
+        return round_nearest(amount, asset.min_trade_size)
 
     def validate_order_params(self,
                               asset,
@@ -1553,7 +1523,6 @@ class TradingAlgorithm(object):
                              self.updated_portfolio(),
                              self.get_datetime(),
                              self.trading_client.current_data)
-
     @staticmethod
     def __convert_order_params_for_blotter(limit_price, stop_price, style):
         """
@@ -1825,7 +1794,7 @@ class TradingAlgorithm(object):
 
     @data_frequency.setter
     def data_frequency(self, value):
-        assert value in ('daily', '5-minute', 'minute')
+        assert value in ('daily', 'minute')
         self.sim_params.data_frequency = value
 
     @api_method

catalyst/assets/_assets.pyx

@@ -17,34 +17,36 @@
 """
 Cythonized Asset object.
 """
+import hashlib
+
 cimport cython
 from cpython.number cimport PyNumber_Index
 from cpython.object cimport (
-    Py_EQ,
-    Py_NE,
-    Py_GE,
-    Py_LE,
-    Py_GT,
-    Py_LT,
+Py_EQ,
+Py_NE,
+Py_GE,
+Py_LE,
+Py_GT,
+Py_LT,
 )
 from cpython cimport bool
 
+import pandas as pd
+from datetime import timedelta
 import numpy as np
 from numpy cimport int64_t
 import warnings
 cimport numpy as np
 
 from catalyst.utils.calendars import get_calendar
-
+from catalyst.exchange.exchange_errors import InvalidSymbolError, SidHashError
 
 # IMPORTANT NOTE: You must change this template if you change
 # Asset.__reduce__, or else we'll attempt to unpickle an old version of this
 # class
 CACHE_FILE_TEMPLATE = '/tmp/.%s-%s.v7.cache'
 
-
 cdef class Asset:
-
     cdef readonly int sid
     # Cached hash of self.sid
     cdef int sid_hash
@@ -59,6 +61,7 @@ cdef class Asset:
 
     cdef readonly object exchange
     cdef readonly object exchange_full
+    cdef readonly object min_trade_size
 
     _kwargnames = frozenset({
         'sid',
@@ -70,18 +73,20 @@ cdef class Asset:
         'auto_close_date',
         'exchange',
         'exchange_full',
+        'min_trade_size',
     })
 
     def __init__(self,
-                 int sid, # sid is required
-                 object exchange, # exchange is required
+                 int sid,  # sid is required
+                 object exchange,  # exchange is required
                  object symbol="",
                  object asset_name="",
                  object start_date=None,
                  object end_date=None,
                  object first_traded=None,
                  object auto_close_date=None,
-                 object exchange_full=None):
+                 object exchange_full=None,
+                 object min_trade_size=None):
 
         self.sid = sid
         self.sid_hash = hash(sid)
@@ -94,6 +99,7 @@ cdef class Asset:
         self.end_date = end_date
         self.first_traded = first_traded
         self.auto_close_date = auto_close_date
+        self.min_trade_size = min_trade_size
 
     def __int__(self):
         return self.sid
@@ -148,7 +154,8 @@ cdef class Asset:
 
     def __repr__(self):
         attrs = ('symbol', 'asset_name', 'exchange',
-                 'start_date', 'end_date', 'first_traded', 'auto_close_date')
+                 'start_date', 'end_date', 'first_traded', 'auto_close_date',
+                 'min_trade_size')
         tuples = ((attr, repr(getattr(self, attr, None)))
                   for attr in attrs)
         strings = ('%s=%s' % (t[0], t[1]) for t in tuples)
@@ -170,7 +177,8 @@ cdef class Asset:
                                  self.end_date,
                                  self.first_traded,
                                  self.auto_close_date,
-                                 self.exchange_full))
+                                 self.exchange_full,
+                                 self.min_trade_size))
 
     cpdef to_dict(self):
         """
@@ -186,6 +194,7 @@ cdef class Asset:
             'auto_close_date': self.auto_close_date,
             'exchange': self.exchange,
             'exchange_full': self.exchange_full,
+            'min_trade_size': self.min_trade_size
         }
 
     @classmethod
@@ -230,13 +239,11 @@ cdef class Asset:
         calendar = get_calendar(self.exchange)
         return calendar.is_open_on_minute(dt_minute)
 
-
 cdef class Equity(Asset):
-
     def __repr__(self):
         attrs = ('symbol', 'asset_name', 'exchange',
                  'start_date', 'end_date', 'first_traded', 'auto_close_date',
-                 'exchange_full')
+                 'exchange_full', 'min_trade_size')
         tuples = ((attr, repr(getattr(self, attr, None)))
                   for attr in attrs)
         strings = ('%s=%s' % (t[0], t[1]) for t in tuples)
@@ -250,8 +257,8 @@ cdef class Equity(Asset):
         """
         def __get__(self):
             warnings.warn("The security_start_date property will soon be "
-            "retired. Please use the start_date property instead.",
-            DeprecationWarning)
+                          "retired. Please use the start_date property instead.",
+                          DeprecationWarning)
             return self.start_date
 
     property security_end_date:
@@ -261,8 +268,8 @@ cdef class Equity(Asset):
         """
         def __get__(self):
             warnings.warn("The security_end_date property will soon be "
-            "retired. Please use the end_date property instead.",
-            DeprecationWarning)
+                          "retired. Please use the end_date property instead.",
+                          DeprecationWarning)
             return self.end_date
 
     property security_name:
@@ -272,13 +279,11 @@ cdef class Equity(Asset):
         """
         def __get__(self):
             warnings.warn("The security_name property will soon be "
-            "retired. Please use the asset_name property instead.",
-            DeprecationWarning)
+                          "retired. Please use the asset_name property instead.",
+                          DeprecationWarning)
             return self.asset_name
 
-
 cdef class Future(Asset):
-
     cdef readonly object root_symbol
     cdef readonly object notice_date
     cdef readonly object expiration_date
@@ -303,8 +308,8 @@ cdef class Future(Asset):
     })
 
     def __init__(self,
-                 int sid, # sid is required
-                 object exchange, # exchange is required
+                 int sid,  # sid is required
+                 object exchange,  # exchange is required
                  object symbol="",
                  object root_symbol="",
                  object asset_name="",
@@ -388,6 +393,204 @@ cdef class Future(Asset):
         super_dict['multiplier'] = self.multiplier
         return super_dict
 
+cdef class TradingPair(Asset):
+    cdef readonly float leverage
+    cdef readonly object market_currency
+    cdef readonly object base_currency
+    cdef readonly object end_daily
+    cdef readonly object end_minute
+    cdef readonly object exchange_symbol
+
+    _kwargnames = frozenset({
+        'sid',
+        'symbol',
+        'asset_name',
+        'start_date',
+        'end_date',
+        'first_traded',
+        'auto_close_date',
+        'exchange',
+        'exchange_full',
+        'leverage',
+        'market_currency',
+        'base_currency',
+        'end_daily',
+        'end_minute',
+        'exchange_symbol',
+        'min_trade_size'
+    })
+    def __init__(self,
+                 object symbol,
+                 object exchange,
+                 object start_date=None,
+                 object asset_name=None,
+                 int sid=0,
+                 float leverage=1.0,
+                 object end_daily=None,
+                 object end_minute=None,
+                 object end_date=None,
+                 object exchange_symbol=None,
+                 object first_traded=None,
+                 object auto_close_date=None,
+                 object exchange_full=None,
+                 object min_trade_size=None):
+        """
+        Replicates the Asset constructor with some built-in conventions
+        and a new 'leverage' attribute.
+
+        Symbol
+        ------
+        Catalyst defines its own set of "universal" symbols to reference
+        trading pairs across exchanges. This is required because exchanges
+        are not adhering to a universal symbolism. For example, Bitfinex
+        uses the BTC symbol for Bitcon while Kraken uses XBT. In addition,
+        pairs are sometimes presented differently. For example, Bitfinex
+        puts the market currency before the base currency without a
+        separator, Bittrex puts the base currency first and uses a dash
+        seperator.
+
+        Here is the Catalyst convention: [Market Currency]_[Base Currency]
+        For example: btc_usd, eth_btc, neo_eth, ltc_eur.
+
+        The symbol for each currency (e.g. btc, eth, ltc) is generally
+        aligned with the Bittrex exchange.
+
+        Sid
+        ---
+        The sid of each asset is calculated based on a numeric hash of the
+        universal symbol. This simple approach avoids maintaining a mapping
+        of sids.
+
+        Leverage
+        --------
+        In contrast with equities, crypto exchanges generally assign
+        leverage values to specific trading pairs. Pairs with the
+        highest volume and market cap generally benefit from high leverage.
+        New currencies from ICO generally cannot be leveraged.
+
+        The leverage value is either None or and integer.
+
+        Leverage allows you to open a larger position with a smaller amount
+        of funds. For example, if you open a $5,000 position in BTC/USD
+        with 5:1 leverage, only one-fifth of this amount, or $1000, will be
+        tied to the position from your balance. Your remaining balance will
+        be available for opening more positions. If you open this same
+        position with 2:1 leverage, $2,500 of your balance will be tied to
+        the position. If you open with 1:1 leverage, $5,000 of your balance
+        will be tied to the position.
+
+        :param symbol:
+        :param exchange:
+        :param start_date:
+        :param asset_name:
+        :param sid:
+        :param leverage:
+        :param end_daily
+        :param end_minute
+        :param end_date:
+        :param exchange_symbol:
+        :param first_traded:
+        :param auto_close_date:
+        :param exchange_full:
+        :param min_trade_size:
+        """
+
+        symbol = symbol.lower()
+        try:
+            self.market_currency, self.base_currency = symbol.split('_')
+        except Exception as e:
+            raise InvalidSymbolError(symbol=symbol, error=e)
+
+        if sid == 0 or sid is None:
+            try:
+                # sid = abs(hash(symbol)) % (10 ** 4)
+                # TODO: try to encode the symbol in the main scope
+                sid = int(
+                    hashlib.sha256(symbol.encode('utf-8')).hexdigest(), 16
+                ) % 10 ** 6
+            except Exception as e:
+                raise SidHashError(symbol=symbol)
+
+        if asset_name is None:
+            asset_name = ' / '.join(symbol.split('_')).upper()
+
+        if start_date is None:
+            start_date = pd.Timestamp.utcnow()
+
+        if end_date is None:
+            end_date = pd.Timestamp.utcnow() + timedelta(days=365)
+
+        super().__init__(
+            sid,
+            exchange,
+            symbol=symbol,
+            asset_name=asset_name,
+            start_date=start_date,
+            end_date=end_date,
+            first_traded=first_traded,
+            auto_close_date=auto_close_date,
+            exchange_full=exchange_full,
+            min_trade_size=min_trade_size
+        )
+
+        self.leverage = leverage
+        self.end_daily = end_daily
+        self.end_minute = end_minute
+        self.exchange_symbol = exchange_symbol
+
+    def __repr__(self):
+        return 'Trading Pair {symbol}({sid}) Exchange: {exchange}, ' \
+               'Introduced On: {start_date}, ' \
+               'Market Currency: {market_currency}, ' \
+               'Base Currency: {base_currency}, ' \
+               'Exchange Leverage: {leverage}, ' \
+               'Minimum Trade Size: {min_trade_size} ' \
+               'Last daily ingestion: {end_daily} ' \
+               'Last minutely ingestion: {end_minute}'.format(
+            symbol=self.symbol,
+            sid=self.sid,
+            exchange=self.exchange,
+            start_date=self.start_date,
+            market_currency=self.market_currency,
+            base_currency=self.base_currency,
+            leverage=self.leverage,
+            min_trade_size=self.min_trade_size,
+            end_daily=self.end_daily,
+            end_minute=self.end_minute
+        )
+
+    def is_exchange_open(self, dt_minute):
+        """
+        Parameters
+        ----------
+        dt_minute: pd.Timestamp (UTC, tz-aware)
+            The minute to check.
+
+        Returns
+        -------
+        boolean: whether the asset's exchange is open at the given minute.
+        """
+        #TODO: consider implementing to spot holds
+        return True
+
+    cpdef __reduce__(self):
+        """
+        Function used by pickle to determine how to serialize/deserialize this
+        class.  Should return a tuple whose first element is self.__class__,
+        and whose second element is a tuple of all the attributes that should
+        be serialized/deserialized during pickling.
+        """
+        return (self.__class__, (self.symbol,
+                                 self.exchange,
+                                 self.start_date,
+                                 self.asset_name,
+                                 self.sid,
+                                 self.leverage,
+                                 self.end_date,
+                                 self.first_traded,
+                                 self.auto_close_date,
+                                 self.exchange_full,
+                                 self.min_trade_size))
 
 def make_asset_array(int size, Asset asset):
     cdef np.ndarray out = np.empty([size], dtype=object)

catalyst/assets/asset_db_schema.py

@@ -39,7 +39,8 @@ equities = sa.Table(
     sa.Column('first_traded', sa.Integer),
     sa.Column('auto_close_date', sa.Integer),
     sa.Column('exchange', sa.Text),
-    sa.Column('exchange_full', sa.Text)
+    sa.Column('exchange_full', sa.Text),
+    sa.Column('min_trade_size', sa.Float)
 )
 
 equity_symbol_mappings = sa.Table(

catalyst/assets/asset_writer.py

@@ -73,6 +73,7 @@ _equities_defaults = {
     'exchange': None,
     # optional, something like "New York Stock Exchange"
     'exchange_full': None,
+    'min_trade_size': 1
 }
 
 # Default values for the futures DataFrame
@@ -390,6 +391,8 @@ class AssetDBWriter(object):
                   The date on which to close any positions in this asset.
               exchange : str
                   The exchange where this asset is traded.
+              min_trade_size: float, optional
+                  The minimum denomination this asset can be traded.
 
             The index of this dataframe should contain the sids.
         futures : pd.DataFrame, optional

catalyst/assets/assets.py

@@ -76,7 +76,9 @@ from catalyst.utils.numpy_utils import as_column
 from catalyst.utils.preprocess import preprocess
 from catalyst.utils.sqlite_utils import group_into_chunks, coerce_string_to_eng
 
-log = Logger('assets.py')
+from catalyst.constants import LOG_LEVEL
+
+log = Logger('assets.py', level=LOG_LEVEL)
 
 # A set of fields that need to be converted to strings before building an
 # Asset to avoid unicode fields

catalyst/constants.py

@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+
+import logbook
+
+LOG_LEVEL = logbook.INFO
+
+DATE_TIME_FORMAT = '%Y-%m-%d %H:%M'
+
+AUTO_INGEST = False

catalyst/curate/poloniex.py

@@ -1,22 +1,22 @@
 import json, time, csv
 from datetime import datetime
 import pandas as pd
-import os
-import time
-import requests
-import logbook
+import os, time, shutil, requests, logbook
+from catalyst.exchange.exchange_utils import get_exchange_symbols_filename
 
-DT_START        = time.mktime(datetime(2010, 1, 1, 0, 0).timetuple())
-CSV_OUT_FOLDER  = '/var/tmp/catalyst/data/poloniex/'
+
+DT_START        = int(time.mktime(datetime(2010, 1, 1, 0, 0).timetuple()))
+DT_END          = pd.to_datetime('today').value // 10 ** 9
+CSV_OUT_FOLDER  = os.environ.get('CSV_OUT_FOLDER', '/efs/exchanges/poloniex/')
 CONN_RETRIES    = 2
 
 logbook.StderrHandler().push_application()
 log = logbook.Logger(__name__)
 
 class PoloniexCurator(object):
-    """
+    '''
     OHLCV data feed generator for crypto data. Based on Poloniex market data
-    """
+    '''
 
     _api_path       = 'https://poloniex.com/public?'
     currency_pairs  = []
@@ -26,10 +26,15 @@ class PoloniexCurator(object):
             try:
                 os.makedirs(CSV_OUT_FOLDER)
             except Exception as e:
-                log.error('Failed to create data folder: %s' % CSV_OUT_FOLDER)
+                log.error('Failed to create data folder: {}'.format(
+                            CSV_OUT_FOLDER))
                 log.exception(e)
 
+
     def get_currency_pairs(self):
+        '''
+        Retrieves and returns all currency pairs from the exchange 
+        '''
         url = self._api_path + 'command=returnTicker'
 
         try:
@@ -45,100 +50,327 @@ class PoloniexCurator(object):
             self.currency_pairs.append(ticker)
         self.currency_pairs.sort()
 
-        log.debug('Currency pairs retrieved successfully: %d' % (len(self.currency_pairs)))
+        log.debug('Currency pairs retrieved successfully: {}'.format(
+                    len(self.currency_pairs)
+                    ))
 
-    def _get_start_date(self, csv_fn):
-        ''' Function returns latest appended date, if the file has been previously written
-            the last line is an empty one, so we have to read the second to last line 
+
+
+    def _retrieve_tradeID_date(self, row):
+        '''
+        Helper function that reads tradeID and date fields from CSV readline
+        '''
+        tId = int(row.split(',')[0])
+        d   = pd.to_datetime(row.split(',')[1], 
+                             infer_datetime_format=True).value // 10 ** 9
+        return tId, d
+
+
+    def retrieve_trade_history(self, currencyPair, start=DT_START, 
+                               end=DT_END, temp=None):
+        '''
+        Retrieves TradeHistory from exchange for a given currencyPair 
+        between start and end dates. If no start date is provided, uses 
+        a system-wide one (beginning of time for cryptotrading).
+        If no end date is provided, 'now' is used.
+
+        Stores results in CSV file on disk.
+        
+        This function is called recursively to work around the 
+        limitations imposed by the provider API.
+        '''
+        csv_fn = CSV_OUT_FOLDER + 'crypto_trades-' + currencyPair + '.csv'
+
+        '''
+        Check what data we already have on disk, reading first and last 
+        lines from file. Data is stored on file from NEWEST to OLDEST.
         '''
         try:
             with open(csv_fn, 'ab+') as f: 
-                f.seek(0, os.SEEK_END)              # First check file is not zero size
-                if(f.tell() > 2):
-                    f.seek(-2, os.SEEK_END)         # Jump to the second last byte.
+                f.seek(0, os.SEEK_END)
+                if(f.tell() > 2):                   # Check file size is not 0
+                    f.seek(0)                       # Go to start to read 
+                    last_tradeID, end_file = self._retrieve_tradeID_date(f.readline())
+                    f.seek(-2, os.SEEK_END)         # Jump to the 2nd last byte
                     while f.read(1) != b"\n":       # Until EOL is found...
                         f.seek(-2, os.SEEK_CUR)     # ...jump back the read byte plus one more.
-                    lastrow = f.readline() 
-                    return int(lastrow.split(',')[0]) + 300
+                    first_tradeID, start_file = self._retrieve_tradeID_date(f.readline())
+
+                    if( end_file + 3600 * 6 > DT_END and ( first_tradeID == 1
+                        or (currencyPair == 'BTC_HUC' and first_tradeID ==     2) 
+                        or (currencyPair == 'BTC_RIC' and first_tradeID ==     2) 
+                        or (currencyPair == 'BTC_XCP' and first_tradeID ==     2)
+                        or (currencyPair == 'BTC_NAV' and first_tradeID ==  4569)
+                        or (currencyPair == 'BTC_POT' and first_tradeID == 23511) ) ):
+                        return
 
         except Exception as e:
-            log.error('Error opening file: %s' % csv_fn)
+            log.error('Error opening file: {}'.format(csv_fn))
             log.exception(e)
 
-        return DT_START
+        '''
+        Poloniex API limits querying TradeHistory to intervals smaller 
+        than 1 month, so we make sure that start date is never more than
+        1 month apart from end date
+        '''
+        if( end - start > 2419200 ): # 60s/min * 60min/hr * 24hr/day * 28days     
+            newstart = end - 2419200
+        else:
+            newstart = start
 
-    def get_data(self, currencyPair, start, end=9999999999, period=300):
-        url = self._api_path + 'command=returnChartData&currencyPair=' + currencyPair + '&start=' + str(start) + '&end=' + str(end) + '&period=' + str(period)
+        log.debug('{}: Retrieving from {} to {}\t {} - {}'.format(
+                    currencyPair, str(newstart), str(end),
+                    time.ctime(newstart), time.ctime(end)))
 
-        try:
-            response = requests.get(url)
-        except Exception as e:
-            log.error('Failed to retrieve candlestick chart data for %s' % currencyPair)
-            log.exception(e)
+        url = '{path}command=returnTradeHistory&currencyPair={pair}' \
+              '&start={start}&end={end}'.format(
+                    path = self._api_path,
+                    pair = currencyPair,
+                    start = str(newstart),
+                    end = str(end)
+                )
+        print url
+
+        attempts = 0
+        success = 0
+        while attempts < CONN_RETRIES:
+            try:
+                response = requests.get(url)
+            except Exception as e:
+                log.error('Failed to retrieve trade history data for {}'.format(
+                            currencyPair
+                            ))
+                log.exception(e)
+                attempts += 1
+            else:
+                try:
+                    if isinstance(response.json(), dict) and response.json()['error']:
+                        log.error('Failed to to retrieve trade history data '
+                                  'for {}: {}'.format(
+                                    currencyPair,
+                                    response.json()['error']
+                                    ))
+                        attempts += 1
+                except Exception as e:
+                    log.exception(e)
+                    attempts += 1
+                else:
+                    success = 1
+                    break
+
+        if not success:
             return None
 
-        return response.json()
 
-    '''
-    Pulls latest data for a single pair
-    '''
-    def append_data_single_pair(self, currencyPair, repeat=0):
-        log.debug('Getting data for %s' % currencyPair)
-        csv_fn = CSV_OUT_FOLDER + 'crypto_prices-' + currencyPair + '.csv'
-        start  = self._get_start_date(csv_fn)
-        # Only fetch data if more than 5min have passed since last fetch
-        if (time.time() > start):
-            data   = self.get_data(currencyPair, start)
-            if data is not None:
-                try: 
-                    with open(csv_fn, 'ab') as csvfile:
-                        csvwriter = csv.writer(csvfile)
-                        for item in data:
-                            if item['date'] == 0:
-                                continue
-                            csvwriter.writerow([
-                                item['date'],
-                                item['open'],
-                                item['high'],
-                                item['low'],
-                                item['close'],
-                                item['volume'],
-                            ])
-                except Exception as e:
-                    log.error('Error opening %s' % csv_fn)
-                    log.exception(e)
-            elif (repeat < CONN_RETRIES):
-                    log.debug('Retrying: attemt %d' % (repeat+1) )
-                    self.append_data_single_pair(currencyPair, repeat + 1)    
+        '''
+        If we get to transactionId == 1, and we already have that on 
+        disk, we got to the end of TradeHistory for this coin.
+        '''
+        if('first_tradeID' in locals() 
+                and response.json()[-1]['tradeID'] == first_tradeID): 
+            return
 
-    '''
-    Pulls latest data for all currency pairs
-    '''
-    def append_data(self):
-        for currencyPair in self.currency_pairs:
-            self.append_data_single_pair(currencyPair)
-            # Rate limit is 6 calls per second, sleep 1sec/6 to be safe
-            time.sleep(0.17)
+        '''
+            There are primarily two scenarios:
+                a) There is newer data available that we need to add at 
+                   the beginning of the file. We'll retrieve all what we 
+                   need until we get to what we already have, writing it 
+                   to a temporary file; and we will write that at the 
+                   beginning of our existing file.
+                b) We are going back in time, appending at the end of 
+                   our existing TradeHistory until the first transaction 
+                   for this currencyPair
+        '''
+        try: 
+            if( 'end_file' in locals() and end_file + 3600 < end):
+                if (temp is None):
+                    temp = os.tmpfile()
+                tempcsv = csv.writer(temp)
+                for item in response.json():
+                    if( item['tradeID'] <= last_tradeID ):
+                        continue
+                    tempcsv.writerow([
+                        item['tradeID'],
+                        item['date'],
+                        item['type'],
+                        item['rate'],
+                        item['amount'],
+                        item['total'],
+                        item['globalTradeID']        
+                    ])
+                if( response.json()[-1]['tradeID'] > last_tradeID ):
+                    end = pd.to_datetime( response.json()[-1]['date'], 
+                                infer_datetime_format=True).value // 10 ** 9
+                    self.retrieve_trade_history(currencyPair, start, 
+                                                end, temp=temp) 
+                else:
+                    with open(csv_fn,'rb+') as f:
+                        shutil.copyfileobj(f,temp)
+                        f.seek(0)
+                        temp.seek(0)
+                        shutil.copyfileobj(temp,f)
+                    temp.close()
+                    end = start_file
+            else:
+                with open(csv_fn, 'ab') as csvfile:
+                    csvwriter = csv.writer(csvfile)
+                    for item in response.json():
+                        if( 'first_tradeID' in locals() 
+                                and item['tradeID'] >= first_tradeID ):
+                            continue
+                        csvwriter.writerow([
+                            item['tradeID'],
+                            item['date'],
+                            item['type'],
+                            item['rate'],
+                            item['amount'],
+                            item['total'],
+                            item['globalTradeID']
+                        ])
+                end = pd.to_datetime(response.json()[-1]['date'], 
+                            infer_datetime_format=True).value // 10 ** 9
 
-    '''
-    Returns a data frame for all pairs, or for the requests currency pair.
-    Makes sure data is up to date
-    '''
-    def to_dataframe(self, start, end, currencyPair=None):
-        csv_fn = CSV_OUT_FOLDER + 'crypto_prices-' + currencyPair + '.csv'
-        last_date = self._get_start_date(csv_fn)
-        if last_date + 300 < end or not os.path.exists(csv_fn):
-            # get latest data
-            self.append_data_single_pair(currencyPair)
+        except Exception as e:
+            log.error('Error opening {}'.format(csv_fn))
+            log.exception(e)
 
-        # CSV holds the latest snapshot
-        df = pd.read_csv(csv_fn, names=['date', 'open', 'high', 'low', 'close', 'volume'])
-        df['date']=pd.to_datetime(df['date'],unit='s')
+        '''
+            If we got here, we aren't done yet. Call recursively with 
+            'end' times that go sequentially back in time.
+        '''
+        self.retrieve_trade_history(currencyPair, start, end)
+
+
+
+    def generate_ohlcv(self, df):
+        '''
+        Generates OHLCV dataframe from a dataframe containing all TradeHistory
+        by resampling with 1-minute period
+        '''
+        df.set_index('date', inplace=True)               # Index by date
+        vol = df['total'].to_frame('volume')             # set Vol aside
+        df.drop('total', axis=1, inplace=True)           # Drop volume data 
+        ohlc = df.resample('T').ohlc()                   # Resample OHLC 1min
+        ohlc.columns = ohlc.columns.map(lambda t: t[1])  # Raname columns by dropping 'rate'
+        closes = ohlc['close'].fillna(method='pad')      # Pad fwd missing 'close'
+        ohlc = ohlc.apply(lambda x: x.fillna(closes))    # Fill N/A with last close
+        vol = vol.resample('T').sum().fillna(0)          # Add volumes by bin
+        ohlcv = pd.concat([ohlc,vol], axis=1)            # Concatenate OHLC + Vol
+        return ohlcv
+
+
+
+    def write_ohlcv_file(self, currencyPair): 
+        '''
+        Generates OHLCV data file with 1minute bars from TradeHistory on disk
+        '''       
+        csv_trades = CSV_OUT_FOLDER + 'crypto_trades-' + currencyPair + '.csv'
+        csv_1min   = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
+        if( os.path.getmtime(csv_1min) > time.time() - 7200 ):
+            log.debug(currencyPair+': 1min data file already up to date. '
+                      'Delete the file if you want to rebuild it.')
+        else:
+            df = pd.read_csv(csv_trades, 
+                                names=['tradeID',
+                                       'date',
+                                       'type',
+                                       'rate',
+                                       'amount',
+                                       'total',
+                                       'globalTradeID'], 
+                                dtype = {'tradeID': int, 
+                                         'date': str, 
+                                         'type': str, 
+                                         'rate': float, 
+                                         'amount': float, 
+                                         'total': float, 
+                                         'globalTradeID': int } 
+                                )
+            df.drop(['tradeID','type','amount','globalTradeID'], 
+                    axis=1, inplace=True)
+            df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True) 
+            ohlcv = self.generate_ohlcv(df)
+            try: 
+                with open(csv_1min, 'w') as csvfile:
+                    csvwriter = csv.writer(csvfile)
+                    for item in ohlcv.itertuples():
+                        if item.Index == 0:
+                            continue
+                        csvwriter.writerow([
+                            item.Index.value // 10 ** 9,
+                            item.open,
+                            item.high,
+                            item.low,
+                            item.close,
+                            item.volume,
+                        ])
+            except Exception as e:
+                log.error('Error opening {}'.format(csv_fn))
+                log.exception(e)
+            log.debug('{}: Generated 1min OHLCV data.'.format(currencyPair))
+
+
+
+    def onemin_to_dataframe(self, currencyPair, start, end):
+        '''
+        Returns a data frame for a given currencyPair from data on disk
+        '''
+        csv_fn     = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
+        df         = pd.read_csv(csv_fn, names=['date', 
+                                                'open', 
+                                                'high', 
+                                                'low', 
+                                                'close', 
+                                                'volume']
+                                )
+        df['date'] = pd.to_datetime(df['date'],unit='s')
         df.set_index('date', inplace=True)
+        return df[start : end]
+
+
+    def generate_symbols_json(self, filename=None):
+        '''
+        Generates a symbols.json file with corresponding start_date 
+        for each currencyPair
+        '''
+        symbol_map = {}
+
+        if(filename is None):
+            filename = get_exchange_symbols_filename('poloniex')
+
+        with open(filename, 'w') as symbols:
+            for currencyPair in self.currency_pairs:
+                start = None
+                csv_fn     = '{}crypto_trades-{}.csv'.format(
+                                CSV_OUT_FOLDER, currencyPair)
+                with open(csv_fn, 'r') as f: 
+                    f.seek(0, os.SEEK_END)
+                    if(f.tell() > 2):               # Check file size is not 0
+                        f.seek(-2, os.SEEK_END)     # Jump to 2nd last byte
+                        while f.read(1) != b"\n":   # Until EOL is found...
+                            f.seek(-2, os.SEEK_CUR) # ...jump back the read byte plus one more.
+                        start = pd.to_datetime( f.readline().split(',')[1], 
+                                                infer_datetime_format=True)
+
+                if(start is None):
+                    start = time.gmtime()
+                base, market = currencyPair.lower().split('_')
+                symbol = '{market}_{base}'.format( market=market, base=base )
+                symbol_map[currencyPair] = dict(
+                    symbol = symbol,
+                    start_date = start.strftime("%Y-%m-%d")
+                )
+            json.dump(symbol_map, symbols, sort_keys=True, indent=2, 
+                        separators=(',',':'))    
 
-        return df[datetime.fromtimestamp(start):datetime.fromtimestamp(end-1)]
 
 if __name__ == '__main__':
     pc = PoloniexCurator()
     pc.get_currency_pairs()
-    pc.append_data()
+    #pc.generate_symbols_json()
+    
+    for currencyPair in pc.currency_pairs:
+        pc.retrieve_trade_history(currencyPair)
+        log.debug('{} up to date.'.format(currencyPair))
+        pc.write_ohlcv_file(currencyPair)
+

catalyst/data/_equities.pyx

@@ -215,13 +215,13 @@ cpdef _read_bcolz_data(ctable_t table,
                 else:
                     continue
 
-        if column_name in ['open', 'high', 'low', 'close']:
+        if column_name in ['open', 'high', 'low', 'close', 'volume']:
             where_nan = (outbuf == 0)
-            outbuf_as_float = outbuf.astype(float64) * .000001
+            outbuf_as_float = outbuf.astype(float64) * .000000001
             outbuf_as_float[where_nan] = NAN
             results.append(outbuf_as_float)
-        elif column_name != 'volume':
-            results.append(outbuf.astype(uint32))
+        elif column_name in ['volume']:
+            results.append(outbuf.astype(float64) * .000000001)
         else:
             results.append(outbuf)
     return results

catalyst/data/_minute_bar_internal.pyx

@@ -35,17 +35,6 @@ def minute_value(ndarray[long_t, ndim=1] market_opens,
 
     return market_opens[q] + r
 
-@cython.cdivision(True)
-def five_minute_value(ndarray[long_t, ndim=1] market_opens,
-                      Py_ssize_t pos,
-                      short five_minutes_per_day):
-
-    cdef short q, r
-    q = cython.cdiv(pos, five_minutes_per_day)
-    r = cython.cmod(pos, five_minutes_per_day)
-
-    return market_opens[q] + r
-
 def find_position_of_minute(ndarray[long_t, ndim=1] market_opens,
                             ndarray[long_t, ndim=1] market_closes,
                             long_t minute_val,
@@ -99,26 +88,6 @@ def find_position_of_minute(ndarray[long_t, ndim=1] market_opens,
 
     return (market_open_loc * minutes_per_day) + delta
 
-def find_position_of_five_minute(ndarray[long_t, ndim=1] market_opens,
-                                 ndarray[long_t, ndim=1] market_closes,
-                                 long_t five_minute_val,
-                                 short five_minutes_per_day,
-                                 bool forward_fill):
-
-    cdef Py_ssize_t market_open_loc, market_open, delta
-
-    market_open_loc = \
-        searchsorted(market_opens, five_minute_val, side='right') - 1
-    market_open = market_opens[market_open_loc]
-    market_close = market_closes[market_open_loc]
-
-    if not forward_fill and ((five_minute_val - market_open) >= five_minutes_per_day):
-        raise ValueError("Given five minutes is not between an open and a close")
-
-    delta = int_min(five_minute_val - market_open, market_close - market_open)
-
-    return (market_open_loc * five_minutes_per_day) + delta
-
 def find_last_traded_position_internal(
         ndarray[long_t, ndim=1] market_opens,
         ndarray[long_t, ndim=1] market_closes,
@@ -189,50 +158,3 @@ def find_last_traded_position_internal(
     # found a trade event
     return -1
 
-def find_last_traded_five_minute_position_internal(
-        ndarray[long_t, ndim=1] market_opens,
-        ndarray[long_t, ndim=1] market_closes,
-        long_t end_five_minute,
-        long_t start_five_minute,
-        volumes,
-        short five_minutes_per_day):
-    cdef Py_ssize_t minute_pos, current_minute, q
-
-    five_minute_pos = int_min(
-        find_position_of_five_minute(
-            market_opens,
-            market_closes,
-            end_five_minute,
-            five_minutes_per_day,
-            True,
-        ),
-        len(volumes) - 1,
-    )
-
-    while five_minute_pos >= 0:
-        current_five_minute = five_minute_value(
-            market_opens, five_minute_pos, five_minutes_per_day
-        )
-
-        q = cython.cdiv(five_minute_pos, five_minutes_per_day)
-        if current_five_minute > market_closes[q]:
-            five_minute_pos = find_position_of_five_minute(
-                market_opens,
-                market_closes,
-                market_closes[q],
-                five_minutes_per_day,
-                False,
-            )
-            continue
-
-        if current_five_minute < start_five_minute:
-            return -1
-
-        if volumes[five_minute_pos] != 0:
-            return five_minute_pos
-
-        five_minute_pos -= 1
-
-    # we've gone to the beginning of this asset's range, and still haven't
-    # found a trade event
-    return -1

catalyst/data/bundles/base.py

@@ -30,8 +30,10 @@ from catalyst.utils.cli import (
 )
 from catalyst.utils.memoize import lazyval
 
+from catalyst.constants import LOG_LEVEL
+
 logbook.StderrHandler().push_application()
-log = logbook.Logger(__name__)
+log = logbook.Logger(__name__, level=LOG_LEVEL)
 
 DEFAULT_RETRIES = 5
 
@@ -60,10 +62,6 @@ class BaseBundle(object):
     def minutes_per_day(self):
         raise NotImplementedError()
 
-    @lazyval
-    def five_minutes_per_day(self):
-        raise NotImplementedError()
-
     @lazyval
     def frequencies(self):
         raise NotImplementedError()
@@ -115,7 +113,6 @@ class BaseBundle(object):
                environ,
                asset_db_writer,
                minute_bar_writer,
-               five_minute_bar_writer,
                daily_bar_writer,
                adjustment_writer,
                calendar,
@@ -162,7 +159,7 @@ class BaseBundle(object):
 
                 # Post-process metadata using cached symbol frames, and write to
                 # disk.  This metadata must be written before any attempt to write
-                # either minute or 5-minute data.
+                # minute data.
                 metadata = self._post_process_metadata(
                     raw_metadata,
                     cache,
@@ -170,24 +167,6 @@ class BaseBundle(object):
                 )
                 asset_db_writer.write(metadata)
 
-                # Compile 5-minute symbol data if bundle supports 5-minute mode and
-                # persist the dataset to disk.
-                if '5-minute' in self.frequencies:
-                    five_minute_bar_writer.write(
-                        self._fetch_symbol_iter(
-                            api_key,
-                            cache,
-                            symbol_map,
-                            calendar,
-                            start_session,
-                            end_session,
-                            '5-minute',
-                            retries,
-                        ),
-                        length=len(symbol_map),
-                        show_progress=show_progress,
-                    )
-
                 # Compile minute symbol data if bundle supports minute mode and
                 # persist the dataset to disk.
                 if 'minute' in self.frequencies:
@@ -296,14 +275,12 @@ class BaseBundle(object):
                     except Exception as e:
                         log.exception(
                             'Failed to load metadata from {}. '
-                            'Retrying.'.format(
-                                name=self.name,
-                            )
+                            'Retrying.'.format(self.name)
                         ) 
                 else:
                     raise ValueError(
-                        'Failed to download metadata page %d after %d '
-                        'attempts.'.format(page_number, retries),
+                        'Failed to download metadata page {} after {} '
+                        'attempts.'.format(page_number, retries)
                     )
 
 
@@ -313,7 +290,8 @@ class BaseBundle(object):
 
             # Apply selective asset filtering, useful for benchmark
             # ingestion.
-            raw = raw[raw.symbol.isin(self._asset_filter)]
+            if self._asset_filter:
+                raw = raw[raw.symbol.isin(self._asset_filter)]
 
             # Update cached value for key.
             cache[key] = raw
@@ -490,7 +468,7 @@ class BaseBundle(object):
                     data_frequency,
                 )
                 raw_data.index = pd.to_datetime(raw_data.index, utc=True)
-                raw_data.index = raw_data.index.tz_localize('UTC')
+                #raw_data.index = raw_data.index.tz_localize('UTC')
 
                 # Filter incoming data to fit start and end sessions.
                 raw_data = raw_data[

catalyst/data/bundles/base_pricing.py

@@ -24,6 +24,7 @@ class BasePricingBundle(BaseBundle):
             ('start_date', 'datetime64[ns]'),
             ('end_date', 'datetime64[ns]'),
             ('ac_date', 'datetime64[ns]'),
+            ('min_trade_size', 'float'),
         ]
 
     @lazyval
@@ -46,10 +47,6 @@ class BaseCryptoPricingBundle(BasePricingBundle):
     def minutes_per_day(self):
         return 1440
 
-    @lazyval
-    def five_minutes_per_day(self):
-        return 288
-
     @property
     def splits(self):
         return []
@@ -67,10 +64,6 @@ class BaseEquityPricingBundle(BasePricingBundle):
     def minutes_per_day(self):
         return 390
 
-    @lazyval
-    def five_minutes_per_day(self):
-        return 78
-
     @property
     def splits(self):
         return self._splits

catalyst/data/bundles/core.py

@@ -17,10 +17,6 @@ from ..us_equity_pricing import (
     SQLiteAdjustmentReader,
     SQLiteAdjustmentWriter,
 )
-from ..five_minute_bars import (
-    BcolzFiveMinuteBarReader,
-    BcolzFiveMinuteBarWriter,
-)
 from ..minute_bars import (
     BcolzMinuteBarReader,
     BcolzMinuteBarWriter,
@@ -54,11 +50,6 @@ def minute_path(bundle_name, timestr, environ=None):
         environ=environ,
     )
 
-def five_minute_path(bundle_name, timestr, environ=None):
-    return pth.data_path(
-        five_minute_relative(bundle_name, timestr, environ),
-        environ=environ,
-    )
 
 def daily_path(bundle_name, timestr, environ=None):
     return pth.data_path(
@@ -90,13 +81,11 @@ def cache_relative(bundle_name, timestr, environ=None):
 
 
 def daily_relative(bundle_name, timestr, environ=None):
-    return bundle_name, timestr, 'daily.bcolz'
+    return bundle_name, timestr, 'daily_equities.bcolz'
 
-def five_minute_relative(bundle_name, timestr, environ=None):
-    return bundle_name, timestr, 'five_minute.bcolz'
 
 def minute_relative(bundle_name, timestr, environ=None):
-    return bundle_name, timestr, 'minute.bcolz'
+    return bundle_name, timestr, 'minute_equities.bcolz'
 
 
 def asset_db_relative(bundle_name, timestr, environ=None, db_version=None):
@@ -206,14 +195,13 @@ RegisteredBundle = namedtuple(
      'start_session',
      'end_session',
      'minutes_per_day',
-     'five_minutes_per_day',
      'ingest',
      'create_writers']
 )
 
 BundleData = namedtuple(
     'BundleData',
-    'asset_finder minute_bar_reader five_minute_bar_reader daily_bar_reader '
+    'asset_finder minute_bar_reader daily_bar_reader '
     'adjustment_reader',
 )
 
@@ -303,7 +291,6 @@ def _make_bundle_core():
             bundle.ingest,
             calendar_name=bundle.calendar_name,
             minutes_per_day=bundle.minutes_per_day,
-            five_minutes_per_day=bundle.five_minutes_per_day,
             start_session=start_session,
             end_session=end_session,
             create_writers=create_writers,
@@ -316,7 +303,6 @@ def _make_bundle_core():
                  start_session=None,
                  end_session=None,
                  minutes_per_day=1440,
-                 five_minutes_per_day=288,
                  create_writers=True):
         """Register a data bundle ingest function.
 
@@ -397,7 +383,6 @@ def _make_bundle_core():
             start_session=start_session,
             end_session=end_session,
             minutes_per_day=minutes_per_day,
-            five_minutes_per_day=five_minutes_per_day,
             ingest=f,
             create_writers=create_writers,
         )
@@ -496,16 +481,6 @@ def _make_bundle_core():
                 # that it can compute the adjustment ratios for the dividends.
                 daily_bar_writer.write(())
 
-                five_minute_bar_writer = BcolzFiveMinuteBarWriter(
-                    wd.ensure_dir(*five_minute_relative(
-                        name, timestr, environ=environ)
-                    ),
-                    calendar,
-                    start_session,
-                    end_session,
-                    five_minutes_per_day=bundle.five_minutes_per_day,
-                )
-
                 minute_bar_writer = BcolzMinuteBarWriter(
                     wd.ensure_dir(*minute_relative(
                         name, timestr, environ=environ)
@@ -532,7 +507,6 @@ def _make_bundle_core():
                 )
             else:
                 daily_bar_writer = None
-                five_minute_bar_writer = None
                 minute_bar_writer = None
                 asset_db_writer = None
                 adjustment_db_writer = None
@@ -544,7 +518,6 @@ def _make_bundle_core():
                 environ,
                 asset_db_writer,
                 minute_bar_writer,
-                five_minute_bar_writer,
                 daily_bar_writer,
                 adjustment_db_writer,
                 calendar,
@@ -631,9 +604,6 @@ def _make_bundle_core():
             minute_bar_reader=BcolzMinuteBarReader(
                 minute_path(name, timestr, environ=environ),
             ),
-            five_minute_bar_reader=BcolzFiveMinuteBarReader(
-                five_minute_path(name, timestr, environ=environ),
-            ),
             daily_bar_reader=BcolzDailyBarReader(
                 daily_path(name, timestr, environ=environ),
             ),

catalyst/data/bundles/poloniex.py

@@ -13,6 +13,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import sys
+
 from datetime import datetime
 
 import pandas as pd
@@ -23,6 +25,8 @@ from catalyst.data.bundles.core import register_bundle
 from catalyst.data.bundles.base_pricing import BaseCryptoPricingBundle
 from catalyst.utils.memoize import lazyval
 
+from catalyst.curate.poloniex import PoloniexCurator
+
 class PoloniexBundle(BaseCryptoPricingBundle):
     @lazyval
     def name(self):
@@ -36,14 +40,13 @@ class PoloniexBundle(BaseCryptoPricingBundle):
     def frequencies(self):
         return set((
             'daily',
-            '5-minute',
+            'minute',
         ))
 
     @lazyval
     def tar_url(self):
         return (
-            'https://www.dropbox.com/s/9naqffawnq8o4r2/'
-            'poloniex-bundle.tar?dl=1'
+            'https://s3.amazonaws.com/enigmaco/catalyst-bundles/poloniex/poloniex-bundle.tar.gz'
         )
 
     @lazyval
@@ -76,12 +79,14 @@ class PoloniexBundle(BaseCryptoPricingBundle):
         start_date = sym_data.index[0]
         end_date = sym_data.index[-1]
         ac_date = end_date + pd.Timedelta(days=1)
+        min_trade_size = 0.00000001
 
         return (
             sym_md.symbol,
             start_date,
             end_date,
             ac_date,
+            min_trade_size,
         )
 
     def fetch_raw_symbol_frame(self,
@@ -91,19 +96,30 @@ class PoloniexBundle(BaseCryptoPricingBundle):
                                start_date,
                                end_date,
                                frequency):
-        raw = pd.read_json(
-            self._format_data_url(
-                api_key,
-                symbol,
-                start_date,
-                end_date,
-                frequency,
-            ),
-            orient='records',
-        )
-        raw.set_index('date', inplace=True)
 
-        scale = 1000.0
+        # TODO: replace this with direct exchange call
+        # The end date and frequency should be used to calculate the number of bars
+        if(frequency == 'minute'):
+            pc = PoloniexCurator()
+            raw = pc.onemin_to_dataframe(symbol, start_date, end_date)
+
+        else:
+            raw = pd.read_json(
+                self._format_data_url(
+                    api_key,
+                    symbol,
+                    start_date,
+                    end_date,
+                    frequency,
+                ),
+                orient='records',
+            )
+            raw.set_index('date', inplace=True)
+
+        # BcolzDailyBarReader introduces a 1/1000 factor in the way pricing is stored
+        # on disk, which we compensate here to get the right pricing amounts
+        # ref: data/us_equity_pricing.py
+        scale = 1
         raw.loc[:, 'open'] /= scale
         raw.loc[:, 'high'] /= scale
         raw.loc[:, 'low'] /= scale
@@ -132,7 +148,6 @@ class PoloniexBundle(BaseCryptoPricingBundle):
                          data_frequency):
         period_map = {
             'daily': 86400,
-            '5-minute': 300,
         }
 
         try:
@@ -151,8 +166,23 @@ class PoloniexBundle(BaseCryptoPricingBundle):
         return self._format_polo_query(query_params)
     
     def _format_polo_query(self, query_params):
+        # TODO: got against the exchange object
         return 'https://poloniex.com/public?{query}'.format(
             query=urlencode(query_params),
         )
 
-register_bundle(PoloniexBundle, ['USDT_BTC'])
+''' 
+As a second parameter, you can pass an array of currency pairs 
+that will be processed as an asset_filter to only process that 
+subset of assets in the bundle, such as:
+register_bundle(PoloniexBundle, ['USDT_BTC',])
+
+For a production environment make sure to use (to bundle all pairs):
+register_bundle(PoloniexBundle)
+'''
+
+if 'ingest' in sys.argv and '-c' in sys.argv:
+    register_bundle(PoloniexBundle)
+else:
+    register_bundle(PoloniexBundle, create_writers=False)
+

catalyst/data/bundles/quandl.py

@@ -40,7 +40,9 @@ from catalyst.utils.cli import maybe_show_progress
 
 from . import core as bundles
 
-log = Logger(__name__)
+from catalyst.constants import LOG_LEVEL
+
+log = Logger(__name__, level=LOG_LEVEL)
 seconds_per_call = (pd.Timedelta('10 minutes') / 2000).total_seconds()
 
 class QuandlBundle(BaseEquityPricingBundle):

catalyst/data/data_portal.py

@@ -42,7 +42,6 @@ from catalyst.assets.roll_finder import (
 )
 from catalyst.data.dispatch_bar_reader import (
     AssetDispatchMinuteBarReader,
-    AssetDispatchFiveMinuteBarReader,
     AssetDispatchSessionBarReader
 )
 from catalyst.data.resample import (
@@ -69,7 +68,9 @@ from catalyst.errors import (
     HistoryWindowStartsBeforeData,
 )
 
-log = Logger('DataPortal')
+from catalyst.constants import LOG_LEVEL
+
+log = Logger('DataPortal', level=LOG_LEVEL)
 
 BASE_FIELDS = frozenset([
     "open",
@@ -120,10 +121,6 @@ class DataPortal(object):
         daily data backtests or daily history calls in a minute backetest.
         If a daily bar reader is not provided but a minute bar reader is,
         the minutes will be rolled up to serve the daily requests.
-    five_minute_reader : BcolzFiveMinuteBarReader, optional
-        The five minute bar reader for equities. This will be used to service
-        5-minute data backtests or five-minute history calls.  This can be used
-        to serve daily calls if no daily bar reader is provided.
     minute_reader : BcolzMinuteBarReader, optional
         The minute bar reader for equities. This will be used to service
         minute data backtests or minute history calls. This can be used
@@ -150,7 +147,6 @@ class DataPortal(object):
                  trading_calendar,
                  first_trading_day,
                  daily_reader=None,
-                 five_minute_reader=None,
                  minute_reader=None,
                  future_daily_reader=None,
                  future_minute_reader=None,
@@ -202,7 +198,6 @@ class DataPortal(object):
                 reader.last_available_dt
                 for reader in [
                     minute_reader,
-                    five_minute_reader,
                     future_minute_reader,
                 ]
                 if reader is not None
@@ -214,8 +209,6 @@ class DataPortal(object):
 
         aligned_minute_reader = self._ensure_reader_aligned(
             minute_reader)
-        aligned_five_minute_reader = self._ensure_reader_aligned(
-            five_minute_reader)
         aligned_session_reader = self._ensure_reader_aligned(
             daily_reader)
         aligned_future_minute_reader = self._ensure_reader_aligned(
@@ -229,13 +222,10 @@ class DataPortal(object):
         }
 
         aligned_minute_readers = {}
-        aligned_five_minute_readers = {}
         aligned_session_readers = {}
 
         if aligned_minute_reader is not None:
             aligned_minute_readers[Equity] = aligned_minute_reader
-        if aligned_five_minute_reader is not None:
-            aligned_five_minute_readers[Equity] = aligned_five_minute_reader
         if aligned_session_reader is not None:
             aligned_session_readers[Equity] = aligned_session_reader
 
@@ -267,13 +257,6 @@ class DataPortal(object):
             self._last_available_minute,
         )
 
-        _dispatch_five_minute_reader = AssetDispatchFiveMinuteBarReader(
-            self.trading_calendar,
-            self.asset_finder,
-            aligned_five_minute_readers,
-            self._last_available_minute,
-        )
-
         _dispatch_session_reader = AssetDispatchSessionBarReader(
             self.trading_calendar,
             self.asset_finder,
@@ -283,7 +266,6 @@ class DataPortal(object):
 
         self._pricing_readers = {
             'minute': _dispatch_minute_reader,
-            '5-minute': _dispatch_five_minute_reader,
             'daily': _dispatch_session_reader,
         }
 
@@ -719,17 +701,6 @@ class DataPortal(object):
             spot_value=result
         )
 
-
-    def _get_five_minute_spot_value(self, asset, column, dt, ffill=False):
-        return self._get_minutely_spot_value(
-            asset,
-            column,
-            dt,
-            ffill, 
-            '5-minute',
-        )
-        
-
     def _get_minute_spot_value(self, asset, column, dt, ffill=False):
         return self._get_minutely_spot_value(
             asset,

catalyst/data/dispatch_bar_reader.py

@@ -18,6 +18,7 @@ from numpy import (
     full,
     nan,
     int64,
+    float64,
     zeros
 )
 from six import iteritems, with_metaclass
@@ -70,7 +71,9 @@ class AssetDispatchBarReader(with_metaclass(ABCMeta)):
         return self._dt_window_size(start_dt, end_dt), num_sids
 
     def _make_raw_array_out(self, field, shape):
-        if field != 'volume' and field != 'sid':
+        if field == 'volume':
+            out = zeros(shape, dtype=float64)
+        elif field != 'sid':
             out = full(shape, nan)
         else:
             out = zeros(shape, dtype=int64)
@@ -135,12 +138,6 @@ class AssetDispatchMinuteBarReader(AssetDispatchBarReader):
     def _dt_window_size(self, start_dt, end_dt):
         return len(self.trading_calendar.minutes_in_range(start_dt, end_dt))
 
-
-class AssetDispatchFiveMinuteBarReader(AssetDispatchBarReader):
-
-    def _dt_window_size(self, start_dt, end_dt):
-        return len(self.trading_calendar.five_minutes_in_range(start_dt, end_dt))
-
 class AssetDispatchSessionBarReader(AssetDispatchBarReader):
 
     def _dt_window_size(self, start_dt, end_dt):

catalyst/data/history_loader.py

@@ -38,7 +38,7 @@ from catalyst.utils.numpy_utils import float64_dtype
 from catalyst.utils.pandas_utils import find_in_sorted_index
 
 # Default number of decimal places used for rounding asset prices.
-DEFAULT_ASSET_PRICE_DECIMALS = 3
+DEFAULT_ASSET_PRICE_DECIMALS = 9
 
 
 class HistoryCompatibleUSEquityAdjustmentReader(object):

catalyst/data/loader.py

@@ -12,41 +12,38 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import datetime
 import os
 from collections import OrderedDict
 
 import logbook
 import pandas as pd
-import numpy as np
-from pandas_datareader.data import DataReader
-import datetime
-import time
 import pytz
+from pandas_datareader.data import DataReader
 from six import iteritems
 from six.moves.urllib_error import HTTPError
 
-from .benchmarks import get_benchmark_returns
+from catalyst.utils.calendars import get_calendar
 from . import treasuries, treasuries_can
+from .benchmarks import get_benchmark_returns
+from ..utils.deprecate import deprecated
 from ..utils.paths import (
     cache_root,
     data_root,
 )
-from ..utils.deprecate import deprecated
 
-from catalyst.data.bundles.poloniex import PoloniexBundle
-from catalyst.utils.calendars import get_calendar
+from catalyst.constants import LOG_LEVEL
 
-
-logger = logbook.Logger('Loader')
+logger = logbook.Logger('Loader', level=LOG_LEVEL)
 
 # Mapping from index symbol to appropriate bond data
 INDEX_MAPPING = {
     'SPY':
-    (treasuries, 'treasury_curves.csv', 'www.federalreserve.gov'),
+        (treasuries, 'treasury_curves.csv', 'www.federalreserve.gov'),
     '^GSPTSE':
-    (treasuries_can, 'treasury_curves_can.csv', 'bankofcanada.ca'),
+        (treasuries_can, 'treasury_curves_can.csv', 'bankofcanada.ca'),
     '^FTSE':  # use US treasuries until UK bonds implemented
-    (treasuries, 'treasury_curves.csv', 'www.federalreserve.gov'),
+        (treasuries, 'treasury_curves.csv', 'www.federalreserve.gov'),
 }
 
 ONE_HOUR = pd.Timedelta(hours=1)
@@ -93,20 +90,28 @@ def has_data_for_dates(series_or_df, first_date, last_date):
     dts = series_or_df.index
     if not isinstance(dts, pd.DatetimeIndex):
         raise TypeError("Expected a DatetimeIndex, but got %s." % type(dts))
-    first, last = dts[[0, -1]]
-    return (first <= first_date) and (last >= last_date)
+    first, last = dts[[0, -1]].tz_localize(None)
+    return (first <= first_date.tz_localize(None)) and (
+        last >= last_date.tz_localize(None))
 
-def load_crypto_market_data(trading_day=None,
-                            trading_days=None,
-                            bm_symbol='USDT_BTC',
-                            environ=None):
+
+def load_crypto_market_data(trading_day=None, trading_days=None,
+                            bm_symbol=None, bundle=None, bundle_data=None,
+                            environ=None, exchange=None, start_dt=None,
+                            end_dt=None):
     if trading_day is None:
         trading_day = get_calendar('OPEN').trading_day
-    if trading_days is None:
-        trading_days = get_calendar('OPEN').all_sessions
 
-    first_date = trading_days[0]
-    now = pd.Timestamp.utcnow()
+    # TODO: consider making configurable
+    bm_symbol = 'btc_usdt'
+    # if trading_days is None:
+    #    trading_days = get_calendar('OPEN').schedule
+
+    # if start_dt is None:
+    start_dt = get_calendar('OPEN').first_trading_session
+
+    if end_dt is None:
+        end_dt = pd.Timestamp.utcnow()
 
     # We expect to have benchmark and treasury data that's current up until
     # **two** full trading days prior to the most recently completed trading
@@ -122,29 +127,54 @@ def load_crypto_market_data(trading_day=None,
 
     # We'll attempt to download new data if the latest entry in our cache is
     # before this date.
-    last_date = trading_days[trading_days.get_loc(now, method='ffill') - 2]
+    '''
+    if(bundle_data):
+        # If we are using the bundle to retrieve the cryptobenchmark, find the last
+        # date for which there is trading data in the bundle
+        asset = bundle_data.asset_finder.lookup_symbol(symbol=bm_symbol,as_of_date=None)
+        ix = bundle_data.daily_bar_reader._last_rows[asset.sid]
+        last_date = pd.to_datetime(bundle_data.daily_bar_reader._spot_col('day')[ix],unit='s')
+    else:
+        last_date = trading_days[trading_days.get_loc(now, method='ffill') - 2]
+    '''
+    last_date = trading_days[trading_days.get_loc(end_dt, method='ffill') - 1]
 
-    br = ensure_crypto_benchmark_data(
-        bm_symbol,
-        first_date,
-        last_date,
-        now,
-        # We need the trading_day to figure out the close prior to the first
-        # date so that we can compute returns for the first date.
-        trading_day,
-        environ,
-    )
+    if exchange is None:
+        # This is exceptional, since placing the import at the module scope
+        #  breaks things and it's only needed here
+        from catalyst.exchange.poloniex.poloniex import Poloniex
+        exchange = Poloniex('', '', '')
+
+    benchmark_asset = exchange.get_asset(bm_symbol)
+
+    # exchange.get_history_window() already ensures that we have the right data
+    # for the right dates
+    br = exchange.get_history_window(
+        assets=[benchmark_asset],
+        end_dt=last_date,
+        bar_count=pd.Timedelta(last_date - start_dt).days,
+        frequency='1d',
+        field='close',
+        data_frequency='daily')
+    br.columns = ['close']
+    br = br.pct_change(1).iloc[1:]
+    br.loc[start_dt] = 0
+    br = br.sort_index()
+
+    # Override first_date for treasury data since we have it for many more years
+    # and is independent of crypto data
+    first_date_treasury = pd.Timestamp('1990-01-02', tz='UTC')
     tc = ensure_treasury_data(
         bm_symbol,
-        first_date,
+        first_date_treasury,
         last_date,
-        now,
+        end_dt,
         environ,
     )
-    benchmark_returns = br[br.index.slice_indexer(first_date, last_date)]
-    treasury_curves = tc[tc.index.slice_indexer(first_date, last_date)]
+    benchmark_returns = br[br.index.slice_indexer(start_dt, last_date)]
+    treasury_curves = tc[
+        tc.index.slice_indexer(first_date_treasury, last_date)]
     return benchmark_returns, treasury_curves
-    
 
 
 def load_market_data(trading_day=None, trading_days=None, bm_symbol='SPY',
@@ -232,20 +262,22 @@ def load_market_data(trading_day=None, trading_days=None, bm_symbol='SPY',
     treasury_curves = tc[tc.index.slice_indexer(first_date, last_date)]
     return benchmark_returns, treasury_curves
 
+
 def ensure_crypto_benchmark_data(symbol,
                                  first_date,
                                  last_date,
                                  now,
                                  trading_day,
+                                 bundle,
+                                 bundle_data,
                                  environ=None):
-
     filename = get_benchmark_filename(symbol)
 
     logger.info(
         ('Loading benchmark data for {symbol!r} '
-            'from {first_date} to {last_date}'),
+         'from {first_date} to {last_date}'),
         symbol=symbol,
-        first_date=first_date - trading_day,
+        first_date=first_date,
         last_date=last_date
     )
 
@@ -258,34 +290,65 @@ def ensure_crypto_benchmark_data(symbol,
         environ,
     )
 
-
     if data is not None:
         return data
 
     # If no cached data was found or it was missing any dates then download the
     # necessary data.
-    logger.info(
-        ('Downloading benchmark data for {symbol!r} '
-            'from {first_date} to {last_date}'),
-        symbol=symbol,
-        first_date=first_date - trading_day,
-        last_date=last_date
-    )
 
-    # Load benchmark symbol from Poloniex API
-    try:
-        bundle = PoloniexBundle()
-        bench_raw = bundle._fetch_symbol_frame(
-            None,
-            symbol,
-            get_calendar(bundle.calendar_name),
-            first_date,
-            last_date,
-            'daily',
-        )
-    except (OSError, IOError, HTTPError):
-        logger.exception('Failed to fetch new crypto benchmark returns')
-        raise
+    if (bundle == 'poloniex'):
+        '''
+        If we're using the Poloniex bundle, we'll get the benchmark from the bundle
+        instead of downloading it from Poloniex every time we need it.
+        Poloniex has a captcha for API queries originating from outside the US that 
+        prevents users abroad from getting Catalyst to work
+        '''
+        logger.info(
+            (
+                'Retrieving benchmark data from bundle for {symbol!r} from {first_date} to {last_date}'),
+            symbol=symbol, first_date=first_date, last_date=last_date)
+
+        asset = bundle_data.asset_finder.lookup_symbol(symbol=symbol,
+                                                       as_of_date=None)
+        fields = ['day', 'close']
+        raw = bundle_data.daily_bar_reader.load_raw_arrays(
+            columns=fields,
+            start_date=first_date - trading_day,
+            end_date=last_date,
+            assets=[asset, ])
+        bench_raw = pd.concat([pd.DataFrame(raw[0], columns=['date']),
+                               pd.DataFrame(raw[1], columns=['close'])],
+                              axis=1)
+        bench_raw['date'] = pd.to_datetime(bench_raw['date'], unit='s')
+        bench_raw.set_index('date', inplace=True)
+        bench_raw.sort_index(inplace=True)
+        bench_raw = bench_raw[
+                    pd.to_datetime(first_date - trading_day):pd.to_datetime(
+                        last_date)]
+
+    else:
+        # This is how it used to be: downloading the benchmark everytime. 
+        # Leaving this code here to be repurposed in the future for other bundles.
+        logger.info(
+            (
+                'Downloading benchmark data for {symbol!r} from {first_date} to {last_date}'),
+            symbol=symbol, first_date=first_date, last_date=last_date)
+
+        raise DeprecationWarning('poloniex bundle deprecated')
+        # Load benchmark symbol from Poloniex API
+        # try:
+        #     bundle = PoloniexBundle()
+        #     bench_raw = bundle._fetch_symbol_frame(
+        #         None,
+        #         symbol,
+        #         get_calendar(bundle.calendar_name),
+        #         first_date - trading_day,
+        #         last_date,
+        #         'daily',
+        #     )
+        # except (OSError, IOError, HTTPError):
+        #     logger.exception('Failed to fetch new crypto benchmark returns')
+        #     raise
 
     # select close column and compute percent change between days
     daily_close = bench_raw[['close']]
@@ -344,7 +407,7 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
     # necessary data.
     logger.info(
         ('Downloading benchmark data for {symbol!r} '
-            'from {first_date} to {last_date}'),
+         'from {first_date} to {last_date}'),
         symbol=symbol,
         first_date=first_date - trading_day,
         last_date=last_date
@@ -364,6 +427,7 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
         logger.warn("Still don't have expected data after redownload!")
     return data
 
+
 def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
                           environ=None):
     """
@@ -404,7 +468,7 @@ def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
     # necessary data.
     logger.info(
         ('Downloading benchmark data for {symbol!r} '
-            'from {first_date} to {last_date}'),
+         'from {first_date} to {last_date}'),
         symbol=symbol,
         first_date=first_date - trading_day,
         last_date=last_date
@@ -478,11 +542,6 @@ def ensure_treasury_data(symbol, first_date, last_date, now, environ=None):
 
 def _load_cached_data(filename, first_date, last_date, now, resource_name,
                       environ=None):
-    if resource_name == 'benchmark':
-        from_csv = pd.Series.from_csv
-    else:
-        from_csv = pd.DataFrame.from_csv
-
     # Path for the cache.
     path = get_data_filepath(filename, environ)
 
@@ -490,8 +549,11 @@ def _load_cached_data(filename, first_date, last_date, now, resource_name,
     # yet, so don't try to read from 'path'.
     if os.path.exists(path):
         try:
-            data = from_csv(path)
-            data.index = pd.to_datetime(data.index).tz_localize('UTC')
+            data = pd.DataFrame.from_csv(path)
+            if data.empty:
+                raise ValueError("File is empty.")
+            data.index = pd.to_datetime(data.index, infer_datetime_format=True,
+                                        errors='coerce').tz_localize('UTC')
             if has_data_for_dates(data, first_date, last_date):
                 return data
 
@@ -517,7 +579,7 @@ def _load_cached_data(filename, first_date, last_date, now, resource_name,
             )
 
     logger.info(
-        "Cache at {path} does not have data from {start} to {end}.\n",
+        "Cache at {path} does not have data from {start} to {end}.",
         start=first_date,
         end=last_date,
         path=path,

catalyst/data/minute_bars.py

@@ -39,20 +39,21 @@ from catalyst.data._minute_bar_internal import (
 from catalyst.gens.sim_engine import NANOS_IN_MINUTE
 
 from catalyst.data.bar_reader import BarReader, NoDataOnDate
-from catalyst.data.us_equity_pricing import check_uint32_safe
+from catalyst.data.us_equity_pricing import check_uint64_safe
 from catalyst.utils.calendars import get_calendar
 from catalyst.utils.cli import maybe_show_progress
 from catalyst.utils.memoize import lazyval
 
+from catalyst.constants import LOG_LEVEL
 
-logger = logbook.Logger('MinuteBars')
+logger = logbook.Logger('MinuteBars', level=LOG_LEVEL)
 
 US_EQUITIES_MINUTES_PER_DAY = 390
 FUTURES_MINUTES_PER_DAY = 1440
 
 DEFAULT_EXPECTEDLEN = US_EQUITIES_MINUTES_PER_DAY * 252 * 15
 
-OHLC_RATIO = 1000
+OHLC_RATIO = 100000000
 
 
 class BcolzMinuteOverlappingData(Exception):
@@ -114,15 +115,15 @@ def _sid_subdir_path(sid):
 
 
 def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
-    """Adapt OHLCV columns into uint32 columns.
+    """Adapt OHLCV columns into uint64 columns.
 
     Parameters
     ----------
     cols : dict
         A dict mapping each column name (open, high, low, close, volume)
-        to a float column to convert to uint32.
+        to a float column to convert to uint64.
     scale_factor : int
-        Factor to use to scale float values before converting to uint32.
+        Factor to use to scale float values before converting to uint64.
     sid : int
         Sid of the relevant asset, for logging.
     invalid_data_behavior : str
@@ -135,6 +136,7 @@ def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
     scaled_highs = np.nan_to_num(cols['high']) * scale_factor
     scaled_lows = np.nan_to_num(cols['low']) * scale_factor
     scaled_closes = np.nan_to_num(cols['close']) * scale_factor
+    scaled_volumes = np.nan_to_num(cols['volume']) * scale_factor
 
     exclude_mask = np.zeros_like(scaled_opens, dtype=bool)
 
@@ -143,11 +145,12 @@ def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
         ('high', scaled_highs),
         ('low', scaled_lows),
         ('close', scaled_closes),
+        ('volume', scaled_volumes),
     ]:
         max_val = scaled_col.max()
 
         try:
-            check_uint32_safe(max_val, col_name)
+            check_uint64_safe(max_val, col_name)
         except ValueError:
             if invalid_data_behavior == 'raise':
                 raise
@@ -155,20 +158,20 @@ def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
             if invalid_data_behavior == 'warn':
                 logger.warn(
                     'Values for sid={}, col={} contain some too large for '
-                    'uint32 (max={}), filtering them out',
+                    'uint64 (max={}), filtering them out',
                     sid, col_name, max_val,
                 )
 
             # We want to exclude all rows that have an unsafe value in
             # this column.
-            exclude_mask &= (scaled_col >= np.iinfo(np.uint32).max)
+            exclude_mask &= (scaled_col >= np.iinfo(np.uint64).max)
 
     # Convert all cols to uint32.
-    opens = scaled_opens.astype(np.uint32)
-    highs = scaled_highs.astype(np.uint32)
-    lows = scaled_lows.astype(np.uint32)
-    closes = scaled_closes.astype(np.uint32)
-    volumes = cols['volume'].astype(np.uint32)
+    opens = scaled_opens.astype(np.uint64)
+    highs = scaled_highs.astype(np.uint64)
+    lows = scaled_lows.astype(np.uint64)
+    closes = scaled_closes.astype(np.uint64)
+    volumes = scaled_volumes.astype(np.uint64)
 
     # Exclude rows with unsafe values by setting to zero.
     opens[exclude_mask] = 0
@@ -260,14 +263,14 @@ class BcolzMinuteBarMetadata(object):
             )
 
     def __init__(
-        self,
-        default_ohlc_ratio,
-        ohlc_ratios_per_sid,
-        calendar,
-        start_session,
-        end_session,
-        minutes_per_day,
-        version=FORMAT_VERSION,
+            self,
+            default_ohlc_ratio,
+            ohlc_ratios_per_sid,
+            calendar,
+            start_session,
+            end_session,
+            minutes_per_day,
+            version=FORMAT_VERSION,
     ):
         self.calendar = calendar
         self.start_session = start_session
@@ -288,7 +291,7 @@ class BcolzMinuteBarMetadata(object):
         ohlc_ratio : int
             The default ratio by which to multiply the pricing data to
             convert the floats from floats to an integer to fit within
-            the np.uint32. If ohlc_ratios_per_sid is None or does not
+            the np.uint64. If ohlc_ratios_per_sid is None or does not
             contain a mapping for a given sid, this ratio is used.
         ohlc_ratios_per_sid : dict
              A dict mapping each sid in the output to the factor by
@@ -340,10 +343,10 @@ class BcolzMinuteBarMetadata(object):
             'first_trading_day': str(self.start_session.date()),
             'market_opens': (
                 market_opens.values.astype('datetime64[m]').
-                astype(np.int64).tolist()),
+                    astype(np.int64).tolist()),
             'market_closes': (
                 market_closes.values.astype('datetime64[m]').
-                astype(np.int64).tolist()),
+                    astype(np.int64).tolist()),
         }
         with open(self.metadata_path(rootdir), 'w+') as fp:
             json.dump(metadata, fp)
@@ -372,13 +375,13 @@ class BcolzMinuteBarWriter(object):
         The last trading session in the data set.
     default_ohlc_ratio : int, optional
         The default ratio by which to multiply the pricing data to
-        convert from floats to integers that fit within np.uint32. If
+        convert from floats to integers that fit within np.uint64. If
         ohlc_ratios_per_sid is None or does not contain a mapping for a
-        given sid, this ratio is used. Default is OHLC_RATIO (1000).
+        given sid, this ratio is used. Default is OHLC_RATIO (10^8).
     ohlc_ratios_per_sid : dict, optional
         A dict mapping each sid in the output to the ratio by which to
         multiply the pricing data to convert the floats from floats to
-        an integer to fit within the np.uint32.
+        an integer to fit within the np.uint64.
     expectedlen : int, optional
         The expected length of the dataset, used when creating the initial
         bcolz ctable.
@@ -401,11 +404,9 @@ class BcolzMinuteBarWriter(object):
     Each individual asset's data is stored as a bcolz table with a column for
     each pricing field: (open, high, low, close, volume)
 
-    The open, high, low, and close columns are integers which are 1000 times
+    The open, high, low, close and volume columns are integers which are 10^8 times
     the quoted price, so that the data can represented and stored as an
-    np.uint32, supporting market prices quoted up to the thousands place.
-
-    volume is a np.uint32 with no mutation of the tens place.
+    np.uint64, supporting market prices quoted up to the 1/10^8-th place.
 
     The 'index' for each individual asset are a repeating period of minutes of
     length `minutes_per_day` starting from each market open.
@@ -573,7 +574,7 @@ class BcolzMinuteBarWriter(object):
         if not os.path.exists(sid_containing_dirname):
             # Other sids may have already created the containing directory.
             os.makedirs(sid_containing_dirname)
-        initial_array = np.empty(0, np.uint32)
+        initial_array = np.empty(0, np.uint64)
         table = ctable(
             rootdir=path,
             columns=[
@@ -610,7 +611,7 @@ class BcolzMinuteBarWriter(object):
         minute_offset = len(table) % self._minutes_per_day
         num_to_prepend = numdays * self._minutes_per_day - minute_offset
 
-        prepend_array = np.zeros(num_to_prepend, np.uint32)
+        prepend_array = np.zeros(num_to_prepend, np.uint64)
         # Fill all OHLCV with zeros.
         table.append([prepend_array] * 5)
         table.flush()
@@ -815,11 +816,11 @@ class BcolzMinuteBarWriter(object):
 
         minutes_count = all_minutes_in_window.size
 
-        open_col = np.zeros(minutes_count, dtype=np.uint32)
-        high_col = np.zeros(minutes_count, dtype=np.uint32)
-        low_col = np.zeros(minutes_count, dtype=np.uint32)
-        close_col = np.zeros(minutes_count, dtype=np.uint32)
-        vol_col = np.zeros(minutes_count, dtype=np.uint32)
+        open_col = np.zeros(minutes_count, dtype=np.uint64)
+        high_col = np.zeros(minutes_count, dtype=np.uint64)
+        low_col = np.zeros(minutes_count, dtype=np.uint64)
+        close_col = np.zeros(minutes_count, dtype=np.uint64)
+        vol_col = np.zeros(minutes_count, dtype=np.uint64)
 
         dt_ixs = np.searchsorted(all_minutes_in_window.values,
                                  dts.astype('datetime64[ns]'))
@@ -914,10 +915,10 @@ class BcolzMinuteBarReader(MinuteBarReader):
         )
         self._schedule = self.calendar.schedule[slicer]
         self._market_opens = self._schedule.market_open
-        self._market_open_values = self._market_opens.values.\
+        self._market_open_values = self._market_opens.values. \
             astype('datetime64[m]').astype(np.int64)
         self._market_closes = self._schedule.market_close
-        self._market_close_values = self._market_closes.values.\
+        self._market_close_values = self._market_closes.values. \
             astype('datetime64[m]').astype(np.int64)
 
         self._default_ohlc_inverse = 1.0 / metadata.default_ohlc_ratio
@@ -1125,8 +1126,8 @@ class BcolzMinuteBarReader(MinuteBarReader):
             else:
                 return np.nan
 
-        if field != 'volume':
-            value *= self._ohlc_ratio_inverse_for_sid(sid)
+        # if field != 'volume':
+        value *= self._ohlc_ratio_inverse_for_sid(sid)
         return value
 
     def get_last_traded_dt(self, asset, dt):
@@ -1248,7 +1249,7 @@ class BcolzMinuteBarReader(MinuteBarReader):
             if field != 'volume':
                 out = np.full(shape, np.nan)
             else:
-                out = np.zeros(shape, dtype=np.uint32)
+                out = np.zeros(shape, dtype=np.float64)
 
             for i, sid in enumerate(sids):
                 carray = self._open_minute_file(field, sid)
@@ -1256,17 +1257,17 @@ class BcolzMinuteBarReader(MinuteBarReader):
                 if indices_to_exclude is not None:
                     for excl_start, excl_stop in indices_to_exclude[::-1]:
                         excl_slice = np.s_[
-                            excl_start - start_idx:excl_stop - start_idx + 1]
+                                     excl_start - start_idx:excl_stop - start_idx + 1]
                         values = np.delete(values, excl_slice)
 
                 where = values != 0
                 # first slice down to len(where) because we might not have
                 # written data for all the minutes requested
-                if field != 'volume':
-                    out[:len(where), i][where] = (
-                        values[where] * self._ohlc_ratio_inverse_for_sid(sid))
-                else:
-                    out[:len(where), i][where] = values[where]
+                # if field != 'volume':
+                out[:len(where), i][where] = (
+                    values[where] * self._ohlc_ratio_inverse_for_sid(sid))
+                # else:
+                #    out[:len(where), i][where] = values[where]
 
             results.append(out)
         return results
@@ -1319,9 +1320,9 @@ class H5MinuteBarUpdateWriter(object):
 
     def __init__(self, path, complevel=None, complib=None):
         self._complevel = complevel if complevel \
-            is not None else self._COMPLEVEL
+                                       is not None else self._COMPLEVEL
         self._complib = complib if complib \
-            is not None else self._COMPLIB
+                                   is not None else self._COMPLIB
         self._path = path
 
     def write(self, frames):
@@ -1353,6 +1354,7 @@ class H5MinuteBarUpdateReader(MinuteBarUpdateReader):
     path : str
         The path of the HDF5 file from which to source data.
     """
+
     def __init__(self, path):
         self._panel = pd.read_hdf(path)
 

catalyst/data/resample.py

@@ -156,7 +156,10 @@ class DailyHistoryAggregator(object):
             cache = self._caches[field] = (session, market_open, {})
 
         _, market_open, entries = cache
-        market_open = market_open.tz_localize('UTC')
+        try:
+            market_open = market_open.tz_localize('UTC')
+        except TypeError:
+            market_open = market_open.tz_convert('UTC')
         if dt != market_open:
             prev_dt = dt_value - self._one_min
         else:

catalyst/data/us_equity_pricing.py

@@ -11,6 +11,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+from __future__ import division     # Python2 req to have division of ints yield float
+
 from errno import ENOENT
 from functools import partial
 from os import remove
@@ -80,8 +83,9 @@ from catalyst.utils.cli import (
 from ._equities import _compute_row_slices, _read_bcolz_data
 from ._adjustments import load_adjustments_from_sqlite
 
+from catalyst.constants import LOG_LEVEL
 
-logger = logbook.Logger('UsEquityPricing')
+logger = logbook.Logger('UsEquityPricing', level=LOG_LEVEL)
 
 OHLC = frozenset(['open', 'high', 'low', 'close'])
 OHLCV = frozenset(['open', 'high', 'low', 'close', 'volume'])
@@ -116,6 +120,8 @@ SQLITE_STOCK_DIVIDEND_PAYOUT_COLUMN_DTYPES = {
 UINT32_MAX = iinfo(uint32).max
 UINT64_MAX = iinfo(uint64).max
 
+PRICE_ADJUSTMENT_FACTOR = 1000000000    # Provides 9 decimals resolution. Also affects _equities.pyx L220
+
 
 def check_uint32_safe(value, colname):
     if value >= UINT32_MAX:
@@ -433,11 +439,11 @@ class BcolzDailyBarWriter(object):
             return raw_data
 
         winsorise_uint64(raw_data, invalid_data_behavior, 'volume', *OHLC)
-        processed = (raw_data[list(OHLC)] * 1000000).astype('uint64')
+        processed = (raw_data[list(OHLC)] * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
         dates = raw_data.index.values.astype('datetime64[s]')
         check_uint32_safe(dates.max().view(np.int64), 'day')
         processed['day'] = dates.astype('uint32')
-        processed['volume'] = raw_data.volume.astype('uint64')
+        processed['volume'] = (raw_data.volume * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
         return ctable.fromdataframe(processed)
 
 
@@ -490,9 +496,8 @@ class BcolzDailyBarReader(SessionBarReader):
 
     The data in these columns is interpreted as follows:
 
-    - Price columns ('open', 'high', 'low', 'close') are interpreted as 1000 *
-      as-traded dollar value.
-    - Volume is interpreted as as-traded volume.
+    - Price columns ('open', 'high', 'low', 'close') and Volume are interpreted 
+      as 10^9 * as-traded dollar value.
     - Day is interpreted as seconds since midnight UTC, Jan 1, 1970.
     - Id is the asset id of the row.
 
@@ -519,7 +524,6 @@ class BcolzDailyBarReader(SessionBarReader):
         # Need to test keeping the entire array in memory for the course of a
         # process first.
         self._spot_cols = {}
-        self.PRICE_ADJUSTMENT_FACTOR = 0.001
         self._read_all_threshold = read_all_threshold
 
     @lazyval
@@ -759,13 +763,10 @@ class BcolzDailyBarReader(SessionBarReader):
         """
         ix = self.sid_day_index(sid, dt)
         price = self._spot_col(field)[ix]
-        if field != 'volume':
-            if price == 0:
-                return nan
-            else:
-                return price * 0.001
+        if field != 'volume' and price == 0:
+            return nan
         else:
-            return price
+            return price / PRICE_ADJUSTMENT_FACTOR
 
 
 class PanelBarReader(SessionBarReader):

catalyst/examples/arbitrage_with_interface.py

@@ -0,0 +1,275 @@
+from logbook import Logger
+
+from catalyst.api import (
+    record,
+    order,
+    symbol,
+    get_open_orders
+)
+from catalyst.exchange.stats_utils import get_pretty_stats
+from catalyst.utils.run_algo import run_algorithm
+
+algo_namespace = 'arbitrage_eth_btc'
+log = Logger(algo_namespace)
+
+
+def initialize(context):
+    log.info('initializing arbitrage algorithm')
+
+    # The context contains a new "exchanges" attribute which is a dictionary
+    # of exchange objects by exchange name. This allow easy access to the
+    # exchanges.
+    context.buying_exchange = context.exchanges['poloniex']
+    context.selling_exchange = context.exchanges['bitfinex']
+
+    context.trading_pair_symbol = 'eth_btc'
+    context.trading_pairs = dict()
+
+    # Note the second parameter of the symbol() method
+    # Passing the exchange name here returns a TradingPair object including
+    # the exchange information. This allow all other operations using
+    # the TradingPair to target the correct exchange.
+    context.trading_pairs[context.buying_exchange] = \
+        symbol('eth_btc', context.buying_exchange.name)
+
+    context.trading_pairs[context.selling_exchange] = \
+        symbol(context.trading_pair_symbol, context.selling_exchange.name)
+
+    context.entry_points = [
+        dict(gap=0.03, amount=0.05),
+        dict(gap=0.04, amount=0.1),
+        dict(gap=0.05, amount=0.5),
+    ]
+    context.exit_points = [
+        dict(gap=-0.02, amount=0.5),
+    ]
+
+    context.SLIPPAGE_ALLOWED = 0.02
+    pass
+
+
+def place_orders(context, amount, buying_price, selling_price, action):
+    """
+    This method will always place two orders of the same amount to keep
+    the currency position the same as it moves between the two exchanges.
+
+    :param context: TradingAlgorithm
+    :param amount: float
+        The trading pair amount to trade on both exchanges.
+    :param buying_price: float
+        The current trading pair price on the buying exchange.
+    :param selling_price: float
+        The current trading pair price on the selling exchange.
+    :param action: string
+        "enter": buys on the buying exchange and sells on the selling exchange
+        "exit": buys on the selling exchange and sells on the buying exchange
+
+    :return:
+    """
+    if action == 'enter':
+        enter_exchange = context.buying_exchange
+        entry_price = buying_price
+
+        exit_exchange = context.selling_exchange
+        exit_price = selling_price
+
+    elif action == 'exit':
+        enter_exchange = context.selling_exchange
+        entry_price = selling_price
+
+        exit_exchange = context.buying_exchange
+        exit_price = buying_price
+
+    else:
+        raise ValueError('invalid order action')
+
+    base_currency = enter_exchange.base_currency
+    base_currency_amount = enter_exchange.portfolio.cash
+
+    exit_balances = exit_exchange.get_balances()
+    exit_currency = context.trading_pairs[
+        context.selling_exchange].market_currency
+
+    if exit_currency in exit_balances:
+        market_currency_amount = exit_balances[exit_currency]
+    else:
+        log.warn(
+            'the selling exchange {exchange_name} does not hold '
+            'currency {currency}'.format(
+                exchange_name=exit_exchange.name,
+                currency=exit_currency
+            )
+        )
+        return
+
+    if base_currency_amount < (amount * entry_price):
+        adj_amount = base_currency_amount / entry_price
+        log.warn(
+            'not enough {base_currency} ({base_currency_amount}) to buy '
+            '{amount}, adjusting the amount to {adj_amount}'.format(
+                base_currency=base_currency,
+                base_currency_amount=base_currency_amount,
+                amount=amount,
+                adj_amount=adj_amount
+            )
+        )
+        amount = adj_amount
+
+    elif market_currency_amount < amount:
+        log.warn(
+            'not enough {currency} ({currency_amount}) to sell '
+            '{amount}, aborting'.format(
+                currency=exit_currency,
+                currency_amount=market_currency_amount,
+                amount=amount
+            )
+        )
+        return
+
+    adj_buy_price = entry_price * (1 + context.SLIPPAGE_ALLOWED)
+    log.info(
+        'buying {amount} {trading_pair} on {exchange_name} with price '
+        'limit {limit_price}'.format(
+            amount=amount,
+            trading_pair=context.trading_pair_symbol,
+            exchange_name=enter_exchange.name,
+            limit_price=adj_buy_price
+        )
+    )
+    order(
+        asset=context.trading_pairs[enter_exchange],
+        amount=amount,
+        limit_price=adj_buy_price
+    )
+
+    adj_sell_price = exit_price * (1 - context.SLIPPAGE_ALLOWED)
+    log.info(
+        'selling {amount} {trading_pair} on {exchange_name} with price '
+        'limit {limit_price}'.format(
+            amount=-amount,
+            trading_pair=context.trading_pair_symbol,
+            exchange_name=exit_exchange.name,
+            limit_price=adj_sell_price
+        )
+    )
+    order(
+        asset=context.trading_pairs[exit_exchange],
+        amount=-amount,
+        limit_price=adj_sell_price
+    )
+    pass
+
+
+def handle_data(context, data):
+    log.info('handling bar {}'.format(data.current_dt))
+
+    buying_price = data.current(
+        context.trading_pairs[context.buying_exchange], 'price')
+
+    log.info('price on buying exchange {exchange}: {price}'.format(
+        exchange=context.buying_exchange.name.upper(),
+        price=buying_price,
+    ))
+
+    selling_price = data.current(
+        context.trading_pairs[context.selling_exchange], 'price')
+
+    log.info('price on selling exchange {exchange}: {price}'.format(
+        exchange=context.selling_exchange.name.upper(),
+        price=selling_price,
+    ))
+
+    # If for example,
+    #   selling price = 50
+    #   buying price = 25
+    #   expected gap = 1
+
+    # If follows that,
+    #   selling price - buying price / buying price
+    #   50 - 25 / 25 = 1
+    gap = (selling_price - buying_price) / buying_price
+    log.info(
+        'the price gap: {gap} ({gap_percent}%)'.format(
+            gap=gap,
+            gap_percent=gap * 100
+        )
+    )
+    record(buying_price=buying_price, selling_price=selling_price, gap=gap)
+
+    # Waiting for orders to close before initiating new ones
+    for exchange in context.trading_pairs:
+        asset = context.trading_pairs[exchange]
+
+        orders = get_open_orders(asset)
+        if orders:
+            log.info(
+                'found {order_count} open orders on {exchange_name} '
+                'skipping bar until all open orders execute'.format(
+                    order_count=len(orders),
+                    exchange_name=exchange.name
+                )
+            )
+            return
+
+    # Consider the least ambitious entry point first
+    # Override of wider gap is found
+    entry_points = sorted(
+        context.entry_points,
+        key=lambda point: point['gap'],
+    )
+
+    buy_amount = None
+    for entry_point in entry_points:
+        if gap > entry_point['gap']:
+            buy_amount = entry_point['amount']
+
+    if buy_amount:
+        log.info('found buy trigger for amount: {}'.format(buy_amount))
+        place_orders(
+            context=context,
+            amount=buy_amount,
+            buying_price=buying_price,
+            selling_price=selling_price,
+            action='enter'
+        )
+
+    else:
+        # Consider the narrowest exit gap first
+        # Override of wider gap is found
+        exit_points = sorted(
+            context.exit_points,
+            key=lambda point: point['gap'],
+            reverse=True
+        )
+
+        sell_amount = None
+        for exit_point in exit_points:
+            if gap < exit_point['gap']:
+                sell_amount = exit_point['amount']
+
+        if sell_amount:
+            log.info('found sell trigger for amount: {}'.format(sell_amount))
+            place_orders(
+                context=context,
+                amount=sell_amount,
+                buying_price=buying_price,
+                selling_price=selling_price,
+                action='exit'
+            )
+
+
+def analyze(context, stats):
+    log.info('the daily stats:\n{}'.format(get_pretty_stats(stats)))
+    pass
+
+
+run_algorithm(
+    initialize=initialize,
+    handle_data=handle_data,
+    analyze=analyze,
+    exchange_name='poloniex,bitfinex',
+    live=True,
+    algo_namespace=algo_namespace,
+    base_currency='btc',
+    live_graph=False
+)

catalyst/examples/buy_and_hodl.py

@@ -23,9 +23,8 @@ from catalyst.api import (
     get_open_orders,
 )
 
-
 def initialize(context):
-    context.ASSET_NAME = 'USDT_BTC'
+    context.ASSET_NAME = 'BTC_USDT'
     context.TARGET_HODL_RATIO = 0.8
     context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
 
@@ -42,8 +41,6 @@ def initialize(context):
 def handle_data(context, data):
     context.i += 1
 
-    print 'i:', context.i
-
     starting_cash = context.portfolio.starting_cash
     target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
     reserve_value = context.RESERVE_RATIO * starting_cash
@@ -52,14 +49,14 @@ def handle_data(context, data):
     orders = get_open_orders(context.asset) or []
     for order in orders:
         cancel_order(order)
-    
+
     # Stop buying after passing the reserve threshold
     cash = context.portfolio.cash
     if cash <= reserve_value:
         context.is_buying = False
 
     # Retrieve current asset price from pricing data
-    price = data[context.asset].price
+    price = data.current(context.asset, 'price')
 
     # Check if still buying and could (approximately) afford another purchase
     if context.is_buying and cash > price:
@@ -73,6 +70,7 @@ def handle_data(context, data):
 
     record(
         price=price,
+        volume=data.current(context.asset, 'volume'),
         cash=cash,
         starting_cash=context.portfolio.starting_cash,
         leverage=context.account.leverage,
@@ -80,12 +78,13 @@ def handle_data(context, data):
 
 def analyze(context=None, results=None):
     import matplotlib.pyplot as plt
+
     # Plot the portfolio and asset data.
-    ax1 = plt.subplot(511)
+    ax1 = plt.subplot(611)
     results[['portfolio_value']].plot(ax=ax1)
     ax1.set_ylabel('Portfolio Value (USD)')
 
-    ax2 = plt.subplot(512, sharex=ax1)
+    ax2 = plt.subplot(612, sharex=ax1)
     ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
     (context.TICK_SIZE * results[['price']]).plot(ax=ax2)
 
@@ -101,11 +100,11 @@ def analyze(context=None, results=None):
         color='g',
     )
 
-    ax3 = plt.subplot(513, sharex=ax1)
+    ax3 = plt.subplot(613, sharex=ax1)
     results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
     ax3.set_ylabel('Leverage ')
 
-    ax4 = plt.subplot(514, sharex=ax1)
+    ax4 = plt.subplot(614, sharex=ax1)
     results[['starting_cash', 'cash']].plot(ax=ax4)
     ax4.set_ylabel('Cash (USD)')
 
@@ -119,7 +118,7 @@ def analyze(context=None, results=None):
         'benchmark_period_return',
     ]]
 
-    ax5 = plt.subplot(515, sharex=ax1)
+    ax5 = plt.subplot(615, sharex=ax1)
     results[[
         'treasury',
         'algorithm',
@@ -127,8 +126,12 @@ def analyze(context=None, results=None):
     ]].plot(ax=ax5)
     ax5.set_ylabel('Percent Change')
 
+    ax6 = plt.subplot(616, sharex=ax1)
+    results[['volume']].plot(ax=ax6)
+    ax6.set_ylabel('Volume (mCoins/5min)')
+
     plt.legend(loc=3)
 
     # Show the plot.
     plt.gcf().set_size_inches(18, 8)
-    plt.show()
+    plt.show()

catalyst/examples/buy_btc_simple.py

@@ -0,0 +1,29 @@
+'''
+	This is a very simple example referenced in the beginner's tutorial:
+	https://enigmampc.github.io/catalyst/beginner-tutorial.html
+
+	Run this example, by executing the following from your terminal:
+	catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle
+
+	If you want to run this code using another exchange, make sure that 
+	the asset is available on that exchange. For example, if you were to run 
+	it for exchange Poloniex, you would need to edit the following line:
+
+		context.asset = symbol('btc_usdt')     # note 'usdt' instead of 'usd'
+
+	and specify exchange poloniex as follows:
+
+	catalyst run -f buy_btc_simple.py -x poloniex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle
+
+	To see which assets are available on each exchange, visit:
+	https://www.enigma.co/catalyst/status
+'''
+
+from catalyst.api import order, record, symbol
+
+def initialize(context):
+    context.asset = symbol('btc_usd')
+
+def handle_data(context, data):
+    order(context.asset, 1)
+    record(btc = data.current(context.asset, 'price'))

catalyst/examples/buy_low_sell_high.py

@@ -0,0 +1,158 @@
+'''
+This algorithm requires an additional library (ta-lib) beyond those required by catalyst.
+Install it first by running: 
+$ pip install TA-Lib
+
+If you get build errors like "fatal error: ta-lib/ta_libc.h: No such file or directory"
+it typically means that it can't find the underlying TA-Lib library and needs to be installed.
+See https://mrjbq7.github.io/ta-lib/install.html for instructions on how to install 
+the required dependencies.
+'''
+
+import talib
+from logbook import Logger
+
+from catalyst.api import (
+    order,
+    order_target_percent,
+    symbol,
+    record,
+    get_open_orders,
+)
+from catalyst.exchange.stats_utils import get_pretty_stats
+
+algo_namespace = 'buy_low_sell_high_xrp'
+log = Logger(algo_namespace)
+
+
+def initialize(context):
+    log.info('initializing algo')
+    context.ASSET_NAME = 'XRP_USDT'
+    context.asset = symbol(context.ASSET_NAME)
+
+    context.TARGET_POSITIONS = 5000
+    context.PROFIT_TARGET = 0.1
+    context.SLIPPAGE_ALLOWED = 0.05
+
+    context.retry_check_open_orders = 10
+    context.retry_update_portfolio = 10
+    context.retry_order = 5
+
+    context.swallow_errors = True
+
+    context.errors = []
+    pass
+
+
+def _handle_data(context, data):
+    prices = data.history(
+        context.asset,
+        fields='price',
+        bar_count=20,
+        frequency='15m'
+    )
+
+    rsi = talib.RSI(prices.values, timeperiod=14)[-1]
+    log.info('got rsi: {}'.format(rsi))
+
+    # Buying more when RSI is low, this should lower our cost basis
+    if rsi <= 30:
+        buy_increment = 50
+    elif rsi <= 40:
+        buy_increment = 20
+    elif rsi <= 70:
+        buy_increment = 5
+    else:
+        buy_increment = None
+
+    cash = context.portfolio.cash
+    log.info('base currency available: {cash}'.format(cash=cash))
+
+    price = data.current(context.asset, 'price')
+    log.info('got price {price}'.format(price=price))
+
+    record(
+        price=price,
+        rsi=rsi,
+    )
+
+    orders = get_open_orders(context.asset)
+    if orders:
+        log.info('skipping bar until all open orders execute')
+        return
+
+    is_buy = False
+    cost_basis = None
+    if context.asset in context.portfolio.positions:
+        position = context.portfolio.positions[context.asset]
+
+        cost_basis = position.cost_basis
+        log.info(
+            'found {amount} positions with cost basis {cost_basis}'.format(
+                amount=position.amount,
+                cost_basis=cost_basis
+            )
+        )
+
+        if position.amount >= context.TARGET_POSITIONS:
+            log.info('reached positions target: {}'.format(position.amount))
+            return
+
+        if price < cost_basis:
+            is_buy = True
+        elif position.amount > 0 and \
+                        price > cost_basis * (1 + context.PROFIT_TARGET):
+            profit = (price * position.amount) - (cost_basis * position.amount)
+            log.info('closing position, taking profit: {}'.format(profit))
+            order_target_percent(
+                asset=context.asset,
+                target=0,
+                limit_price=price * (1 - context.SLIPPAGE_ALLOWED),
+            )
+        else:
+            log.info('no buy or sell opportunity found')
+    else:
+        is_buy = True
+
+    if is_buy:
+        if buy_increment is None:
+            log.info('the rsi is too high to consider buying {}'.format(rsi))
+            return
+
+        if price * buy_increment > cash:
+            log.info('not enough base currency to consider buying')
+            return
+
+        log.info(
+            'buying position cheaper than cost basis {} < {}'.format(
+                price,
+                cost_basis
+            )
+        )
+        order(
+            asset=context.asset,
+            amount=buy_increment,
+            limit_price=price * (1 + context.SLIPPAGE_ALLOWED)
+        )
+
+
+def handle_data(context, data):
+    log.info('handling bar {}'.format(data.current_dt))
+    try:
+        _handle_data(context, data)
+    except Exception as e:
+        log.warn('aborting the bar on error {}'.format(e))
+        context.errors.append(e)
+
+    log.info('completed bar {}, total execution errors {}'.format(
+        data.current_dt,
+        len(context.errors)
+    ))
+
+    if len(context.errors) > 0:
+        log.info('the errors:\n{}'.format(context.errors))
+
+
+def analyze(context, stats):
+    log.info('the daily stats:\n{}'.format(get_pretty_stats(stats)))
+    pass

catalyst/examples/buy_low_sell_high_live.py

@@ -0,0 +1,168 @@
+import talib
+from logbook import Logger
+
+import pandas as pd
+from catalyst.api import (
+    order,
+    order_target_percent,
+    symbol,
+    record,
+    get_open_orders,
+)
+from catalyst.exchange.stats_utils import get_pretty_stats
+from catalyst.utils.run_algo import run_algorithm
+
+algo_namespace = 'buy_the_dip_live'
+log = Logger('buy low sell high')
+
+
+def initialize(context):
+    log.info('initializing algo')
+    context.ASSET_NAME = 'btc_usdt'
+    context.asset = symbol(context.ASSET_NAME)
+
+    context.TARGET_POSITIONS = 30
+    context.PROFIT_TARGET = 0.1
+    context.SLIPPAGE_ALLOWED = 0.02
+
+    context.retry_check_open_orders = 10
+    context.retry_update_portfolio = 10
+    context.retry_order = 5
+
+    context.errors = []
+    pass
+
+
+def _handle_data(context, data):
+    price = data.current(context.asset, 'price')
+    log.info('got price {price}'.format(price=price))
+
+    prices = data.history(
+        context.asset,
+        fields='price',
+        bar_count=20,
+        frequency='1d'
+    )
+    rsi = talib.RSI(prices.values, timeperiod=14)[-1]
+    log.info('got rsi: {}'.format(rsi))
+
+    # Buying more when RSI is low, this should lower our cost basis
+    if rsi <= 30:
+        buy_increment = 1
+    elif rsi <= 40:
+        buy_increment = 0.5
+    elif rsi <= 70:
+        buy_increment = 0.2
+    else:
+        buy_increment = 0.1
+
+    cash = context.portfolio.cash
+    log.info('base currency available: {cash}'.format(cash=cash))
+
+    record(
+        price=price,
+        rsi=rsi,
+    )
+
+    orders = get_open_orders(context.asset)
+    if orders:
+        log.info('skipping bar until all open orders execute')
+        return
+
+    is_buy = False
+    cost_basis = None
+    if context.asset in context.portfolio.positions:
+        position = context.portfolio.positions[context.asset]
+
+        cost_basis = position.cost_basis
+        log.info(
+            'found {amount} positions with cost basis {cost_basis}'.format(
+                amount=position.amount,
+                cost_basis=cost_basis
+            )
+        )
+
+        if position.amount >= context.TARGET_POSITIONS:
+            log.info('reached positions target: {}'.format(position.amount))
+            return
+
+        if price < cost_basis:
+            is_buy = True
+        elif position.amount > 0 and \
+                        price > cost_basis * (1 + context.PROFIT_TARGET):
+            profit = (price * position.amount) - (cost_basis * position.amount)
+            log.info('closing position, taking profit: {}'.format(profit))
+            order_target_percent(
+                asset=context.asset,
+                target=0,
+                limit_price=price * (1 - context.SLIPPAGE_ALLOWED),
+            )
+        else:
+            log.info('no buy or sell opportunity found')
+    else:
+        is_buy = True
+
+    if is_buy:
+        if buy_increment is None:
+            log.info('the rsi is too high to consider buying {}'.format(rsi))
+            return
+
+        if price * buy_increment > cash:
+            log.info('not enough base currency to consider buying')
+            return
+
+        log.info(
+            'buying position cheaper than cost basis {} < {}'.format(
+                price,
+                cost_basis
+            )
+        )
+        order(
+            asset=context.asset,
+            amount=buy_increment,
+            limit_price=price * (1 + context.SLIPPAGE_ALLOWED)
+        )
+
+
+def handle_data(context, data):
+    log.info('handling bar {}'.format(data.current_dt))
+    # try:
+    _handle_data(context, data)
+    # except Exception as e:
+    #     log.warn('aborting the bar on error {}'.format(e))
+    #     context.errors.append(e)
+
+    log.info('completed bar {}, total execution errors {}'.format(
+        data.current_dt,
+        len(context.errors)
+    ))
+
+    if len(context.errors) > 0:
+        log.info('the errors:\n{}'.format(context.errors))
+
+
+def analyze(context, stats):
+    log.info('the daily stats:\n{}'.format(get_pretty_stats(stats)))
+    pass
+
+
+run_algorithm(
+    capital_base=100000,
+    initialize=initialize,
+    handle_data=handle_data,
+    analyze=analyze,
+    exchange_name='poloniex',
+    start=pd.to_datetime('2017-5-01', utc=True),
+    end=pd.to_datetime('2017-10-16', utc=True),
+    base_currency='usdt',
+    data_frequency='daily'
+)
+# run_algorithm(
+#     initialize=initialize,
+#     handle_data=handle_data,
+#     analyze=analyze,
+#     exchange_name='poloniex',
+#     live=True,
+#     algo_namespace=algo_namespace,
+#     base_currency='btc'
+# )

catalyst/examples/mean_reversion.py

@@ -0,0 +1,283 @@
+# For this example, we're going to write a simple momentum script.  When the 
+# stock goes up quickly, we're going to buy; when it goes down quickly, we're
+# going to sell.  Hopefully we'll ride the waves.
+from datetime import timedelta
+
+import pandas as pd
+import talib
+# To run an algorithm in Catalyst, you need two functions: initialize and
+# handle_data.
+from logbook import Logger
+from talib.common import MA_Type
+
+from catalyst import run_algorithm
+from catalyst.api import symbol, record, order_target_percent, \
+    get_open_orders
+# We give a name to the algorithm which Catalyst will use to persist its state.
+# In this example, Catalyst will create the `.catalyst/data/live_algos`
+# directory. If we stop and start the algorithm, Catalyst will resume its
+# state using the files included in the folder.
+from catalyst.exchange.stats_utils import extract_transactions, trend_direction
+
+algo_namespace = 'mean_reversion'
+log = Logger(algo_namespace)
+
+
+def initialize(context):
+    # This initialize function sets any data or variables that you'll use in
+    # your algorithm.  For instance, you'll want to define the trading pair (or
+    # trading pairs) you want to backtest.  You'll also want to define any
+    # parameters or values you're going to use.
+
+    # In our example, we're looking at Ether in USD Tether.
+    context.eth_btc = symbol('neo_usd')
+    context.base_price = None
+    context.current_day = None
+    context.trigger = None
+
+
+def handle_data(context, data):
+    # This handle_data function is where the real work is done.  Our data is
+    # minute-level tick data, and each minute is called a frame.  This function
+    # runs on each frame of the data.
+
+    # We flag the first period of each day.
+    # Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
+    # would only execute once. This method works with minute and daily
+    # frequencies.
+    today = data.current_dt.floor('1D')
+    if today != context.current_day:
+        context.traded_today = False
+        context.current_day = today
+
+    # We're computing the volume-weighted-average-price of the security
+    # defined above, in the context.eth_btc variable.  For this example, we're 
+    # using three bars on the 15 min bars.
+
+    # The frequency attribute determine the bar size. We use this convention
+    # for the frequency alias:
+    # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
+    prices = data.history(
+        context.eth_btc,
+        fields='close',
+        bar_count=50,
+        frequency='15T'
+    )
+
+    # Ta-lib calculates various technical indicator based on price and
+    # volume arrays.
+
+    # In this example, we are comp
+    rsi = talib.RSI(prices.values, timeperiod=14)
+    upper, middle, lower = talib.BBANDS(
+        prices.values,
+        timeperiod=20,
+        nbdevup=2,
+        nbdevdn=2,
+        matype=MA_Type.EMA
+    )
+
+    # We need a variable for the current price of the security to compare to
+    # the average. Since we are requesting two fields, data.current()
+    # returns a DataFrame with
+    current = data.current(context.eth_btc, fields=['close', 'volume'])
+    price = current['close']
+
+    # If base_price is not set, we use the current value. This is the
+    # price at the first bar which we reference to calculate price_change.
+    if context.base_price is None:
+        context.base_price = price
+
+    price_change = (price - context.base_price) / context.base_price
+    cash = context.portfolio.cash
+
+    # Now that we've collected all current data for this frame, we use
+    # the record() method to save it. This data will be available as
+    # a parameter of the analyze() function for further analysis.
+    record(
+        price=price,
+        volume=current['volume'],
+        upper_band=upper[-1],
+        lower_band=lower[-1],
+        price_change=price_change,
+        rsi=rsi[-1],
+        cash=cash
+    )
+
+    # We are trying to avoid over-trading by limiting our trades to
+    # one per day.
+    if context.traded_today:
+        return
+
+    # Since we are using limit orders, some orders may not execute immediately
+    # we wait until all orders are executed before considering more trades.
+    orders = get_open_orders(context.eth_btc)
+    if len(orders) > 0:
+        return
+
+    # Exit if we cannot trade
+    if not data.can_trade(context.eth_btc):
+        return
+
+    # Another powerful built-in feature of the Catalyst backtester is the
+    # portfolio object.  The portfolio object tracks your positions, cash,
+    # cost basis of specific holdings, and more.  In this line, we calculate
+    # how long or short our position is at this minute.   
+    pos_amount = context.portfolio.positions[context.eth_btc].amount
+
+    # In this example, we're using a trigger instead of buying directly after
+    # a signal. Since this is mean reversion, our signals go against the
+    # momentum. Using a trigger allow us to spot the opportunity but trade
+    # only when a trade reversal begins.
+    if context.trigger is not None:
+        # The tread_direction() method determines the trend based on the last
+        # two bars of the series.
+        direction = trend_direction(rsi)
+        if context.trigger[1] == 'buy' and direction == 'up':
+            log.info(
+                '{}: buying - price: {}, rsi: {}, bband: {}'.format(
+                    data.current_dt, price, rsi[-1], lower[-1]
+                )
+            )
+            order_target_percent(context.eth_btc, 1)
+            context.traded_today = True
+            context.trigger = None
+
+        elif context.trigger[1] == 'sell' and direction == 'down':
+            log.info(
+                '{}: selling - price: {}, rsi: {}, bband: {}'.format(
+                    data.current_dt, price, rsi[-1], upper[-1]
+                )
+            )
+            order_target_percent(context.eth_btc, 0)
+            context.traded_today = True
+            context.trigger = None
+
+        # If we found a signal but no trade reversal within two hours, we
+        # reset the trigger.
+        elif context.trigger[0] + timedelta(hours=2) < data.current_dt:
+            context.trigger = None
+
+    else:
+        # Determining the entry and exit signals based on RSI and SMA
+        if rsi[-1] <= 30 and pos_amount == 0:
+            context.trigger = (data.current_dt, 'buy')
+
+        elif rsi[-1] >= 80 and pos_amount > 0:
+            context.trigger = (data.current_dt, 'sell')
+
+
+def analyze(context=None, perf=None):
+    import matplotlib.pyplot as plt
+
+    # The base currency of the algo exchange
+    base_currency = context.exchanges.values()[0].base_currency.upper()
+
+    # Plot the portfolio value over time.
+    ax1 = plt.subplot(611)
+    perf.loc[:, 'portfolio_value'].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio Value ({})'.format(base_currency))
+
+    # Plot the price increase or decrease over time.
+    ax2 = plt.subplot(612, sharex=ax1)
+    perf.loc[:, 'price'].plot(ax=ax2, label='Price')
+    perf.loc[:, 'upper_band'].plot(ax=ax2, label='Upper')
+    perf.loc[:, 'lower_band'].plot(ax=ax2, label='Lower')
+
+    ax2.set_ylabel('{asset} ({base})'.format(
+        asset=context.eth_btc.symbol, base=base_currency
+    ))
+
+    transaction_df = extract_transactions(perf)
+    if not transaction_df.empty:
+        buy_df = transaction_df[transaction_df['amount'] > 0]
+        sell_df = transaction_df[transaction_df['amount'] < 0]
+        ax2.scatter(
+            buy_df.index.to_pydatetime(),
+            perf.loc[buy_df.index, 'price'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax2.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index, 'price'],
+            marker='v',
+            s=100,
+            c='red',
+            label=''
+        )
+
+    ax4 = plt.subplot(613, sharex=ax1)
+    perf.loc[:, 'cash'].plot(
+        ax=ax4, label='Base Currency ({})'.format(base_currency)
+    )
+    ax4.set_ylabel('Cash ({})'.format(base_currency))
+
+    perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
+
+    ax5 = plt.subplot(614, sharex=ax1)
+    perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
+    ax5.set_ylabel('Percent Change')
+
+    ax6 = plt.subplot(615, sharex=ax1)
+    perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
+    ax6.axhline(70, color='darkgoldenrod')
+    ax6.axhline(30, color='darkgoldenrod')
+
+    if not transaction_df.empty:
+        ax6.scatter(
+            buy_df.index.to_pydatetime(),
+            perf.loc[buy_df.index, 'rsi'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax6.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index, 'rsi'],
+            marker='v',
+            s=100,
+            c='red',
+            label=''
+        )
+    plt.legend(loc=3)
+
+    # Show the plot.
+    plt.gcf().set_size_inches(18, 8)
+    plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    # The execution mode: backtest or live
+    MODE = 'backtest'
+
+    if MODE == 'backtest':
+        # catalyst run -f catalyst/examples/mean_reversion_simple.py -x poloniex -s 2017-7-1 -e 2017-7-31 -c usdt -n mean-reversion --data-frequency minute --capital-base 10000
+        run_algorithm(
+            capital_base=1,
+            data_frequency='minute',
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='bitfinex',
+            algo_namespace=algo_namespace,
+            base_currency='usd',
+            start=pd.to_datetime('2017-10-1', utc=True),
+            end=pd.to_datetime('2017-11-13', utc=True),
+        )
+
+    elif MODE == 'live':
+        run_algorithm(
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='bitfinex',
+            live=True,
+            algo_namespace=algo_namespace,
+            base_currency='usd',
+            live_graph=True
+        )

catalyst/examples/mean_reversion_simple.py

@@ -0,0 +1,248 @@
+# For this example, we're going to write a simple momentum script.  When the 
+# stock goes up quickly, we're going to buy; when it goes down quickly, we're
+# going to sell.  Hopefully we'll ride the waves.
+from datetime import timedelta
+
+import pandas as pd
+import talib
+# To run an algorithm in Catalyst, you need two functions: initialize and
+# handle_data.
+from logbook import Logger
+from talib.common import MA_Type
+
+from catalyst import run_algorithm
+from catalyst.api import symbol, record, order_target_percent, \
+    get_open_orders
+# We give a name to the algorithm which Catalyst will use to persist its state.
+# In this example, Catalyst will create the `.catalyst/data/live_algos`
+# directory. If we stop and start the algorithm, Catalyst will resume its
+# state using the files included in the folder.
+from catalyst.exchange.stats_utils import extract_transactions, trend_direction
+
+algo_namespace = 'mean_reversion_simple'
+log = Logger(algo_namespace)
+
+
+def initialize(context):
+    # This initialize function sets any data or variables that you'll use in
+    # your algorithm.  For instance, you'll want to define the trading pair (or
+    # trading pairs) you want to backtest.  You'll also want to define any
+    # parameters or values you're going to use.
+
+    # In our example, we're looking at Ether in USD Tether.
+    context.eth_btc = symbol('neo_usd')
+    context.base_price = None
+    context.current_day = None
+
+
+def handle_data(context, data):
+    # This handle_data function is where the real work is done.  Our data is
+    # minute-level tick data, and each minute is called a frame.  This function
+    # runs on each frame of the data.
+
+    # We flag the first period of each day.
+    # Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
+    # would only execute once. This method works with minute and daily
+    # frequencies.
+    today = data.current_dt.floor('1D')
+    if today != context.current_day:
+        context.traded_today = False
+        context.current_day = today
+
+    # We're computing the volume-weighted-average-price of the security
+    # defined above, in the context.eth_btc variable.  For this example, we're 
+    # using three bars on the 15 min bars.
+
+    # The frequency attribute determine the bar size. We use this convention
+    # for the frequency alias:
+    # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
+    prices = data.history(
+        context.eth_btc,
+        fields='close',
+        bar_count=50,
+        frequency='15T'
+    )
+
+    # Ta-lib calculates various technical indicator based on price and
+    # volume arrays.
+
+    # In this example, we are comp
+    rsi = talib.RSI(prices.values, timeperiod=14)
+
+    # We need a variable for the current price of the security to compare to
+    # the average. Since we are requesting two fields, data.current()
+    # returns a DataFrame with
+    current = data.current(context.eth_btc, fields=['close', 'volume'])
+    price = current['close']
+
+    # If base_price is not set, we use the current value. This is the
+    # price at the first bar which we reference to calculate price_change.
+    if context.base_price is None:
+        context.base_price = price
+
+    price_change = (price - context.base_price) / context.base_price
+    cash = context.portfolio.cash
+
+    # Now that we've collected all current data for this frame, we use
+    # the record() method to save it. This data will be available as
+    # a parameter of the analyze() function for further analysis.
+    record(
+        price=price,
+        volume=current['volume'],
+        price_change=price_change,
+        rsi=rsi[-1],
+        cash=cash
+    )
+
+    # We are trying to avoid over-trading by limiting our trades to
+    # one per day.
+    if context.traded_today:
+        return
+
+    # Since we are using limit orders, some orders may not execute immediately
+    # we wait until all orders are executed before considering more trades.
+    orders = get_open_orders(context.eth_btc)
+    if len(orders) > 0:
+        return
+
+    # Exit if we cannot trade
+    if not data.can_trade(context.eth_btc):
+        return
+
+    # Another powerful built-in feature of the Catalyst backtester is the
+    # portfolio object.  The portfolio object tracks your positions, cash,
+    # cost basis of specific holdings, and more.  In this line, we calculate
+    # how long or short our position is at this minute.   
+    pos_amount = context.portfolio.positions[context.eth_btc].amount
+
+    if rsi[-1] <= 30 and pos_amount == 0:
+        log.info(
+            '{}: buying - price: {}, rsi: {}'.format(
+                data.current_dt, price, rsi[-1]
+            )
+        )
+        order_target_percent(context.eth_btc, 1)
+        context.traded_today = True
+
+    elif rsi[-1] >= 80 and pos_amount > 0:
+        log.info(
+            '{}: selling - price: {}, rsi: {}'.format(
+                data.current_dt, price, rsi[-1]
+            )
+        )
+        order_target_percent(context.eth_btc, 0)
+        context.traded_today = True
+
+
+def analyze(context=None, perf=None):
+    import matplotlib.pyplot as plt
+
+    # The base currency of the algo exchange
+    base_currency = context.exchanges.values()[0].base_currency.upper()
+
+    # Plot the portfolio value over time.
+    ax1 = plt.subplot(611)
+    perf.loc[:, 'portfolio_value'].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio Value ({})'.format(base_currency))
+
+    # Plot the price increase or decrease over time.
+    ax2 = plt.subplot(612, sharex=ax1)
+    perf.loc[:, 'price'].plot(ax=ax2, label='Price')
+
+    ax2.set_ylabel('{asset} ({base})'.format(
+        asset=context.eth_btc.symbol, base=base_currency
+    ))
+
+    transaction_df = extract_transactions(perf)
+    if not transaction_df.empty:
+        buy_df = transaction_df[transaction_df['amount'] > 0]
+        sell_df = transaction_df[transaction_df['amount'] < 0]
+        ax2.scatter(
+            buy_df.index.to_pydatetime(),
+            perf.loc[buy_df.index, 'price'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax2.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index, 'price'],
+            marker='v',
+            s=100,
+            c='red',
+            label=''
+        )
+
+    ax4 = plt.subplot(613, sharex=ax1)
+    perf.loc[:, 'cash'].plot(
+        ax=ax4, label='Base Currency ({})'.format(base_currency)
+    )
+    ax4.set_ylabel('Cash ({})'.format(base_currency))
+
+    perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
+
+    ax5 = plt.subplot(614, sharex=ax1)
+    perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
+    ax5.set_ylabel('Percent Change')
+
+    ax6 = plt.subplot(615, sharex=ax1)
+    perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
+    ax6.axhline(70, color='darkgoldenrod')
+    ax6.axhline(30, color='darkgoldenrod')
+
+    if not transaction_df.empty:
+        ax6.scatter(
+            buy_df.index.to_pydatetime(),
+            perf.loc[buy_df.index, 'rsi'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax6.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index, 'rsi'],
+            marker='v',
+            s=100,
+            c='red',
+            label=''
+        )
+    plt.legend(loc=3)
+
+    # Show the plot.
+    plt.gcf().set_size_inches(18, 8)
+    plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    # The execution mode: backtest or live
+    MODE = 'backtest'
+
+    if MODE == 'backtest':
+        # catalyst run -f catalyst/examples/mean_reversion_simple.py -x poloniex -s 2017-7-1 -e 2017-7-31 -c usdt -n mean-reversion --data-frequency minute --capital-base 10000
+        run_algorithm(
+            capital_base=10000,
+            data_frequency='minute',
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='bitfinex',
+            algo_namespace=algo_namespace,
+            base_currency='usd',
+            start=pd.to_datetime('2017-10-1', utc=True),
+            end=pd.to_datetime('2017-11-10', utc=True),
+        )
+
+    elif MODE == 'live':
+        run_algorithm(
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='bitfinex',
+            live=True,
+            algo_namespace=algo_namespace,
+            base_currency='usd',
+            live_graph=True
+        )

catalyst/examples/rsi_profit_target.py

@@ -0,0 +1,276 @@
+from datetime import timedelta
+
+import pandas as pd
+import numpy as np
+import talib
+from logbook import Logger
+
+from catalyst.api import (
+    order,
+    symbol,
+    record,
+    get_open_orders,
+)
+from catalyst.exchange.stats_utils import crossover, crossunder
+from catalyst.utils.run_algo import run_algorithm
+
+algo_namespace = 'rsi'
+log = Logger(algo_namespace)
+
+
+def initialize(context):
+    log.info('initializing algo')
+    context.asset = symbol('eth_btc')
+    context.base_price = None
+
+    context.MAX_HOLDINGS = 0.2
+    context.RSI_OVERSOLD = 30
+    context.RSI_OVERSOLD_BBANDS = 45
+    context.RSI_OVERBOUGHT_BBANDS = 55
+    context.SLIPPAGE_ALLOWED = 0.03
+
+    context.TARGET = 0.15
+    context.STOP_LOSS = 0.1
+    context.STOP = 0.03
+    context.position = None
+
+    context.last_bar = None
+
+    context.errors = []
+    pass
+
+
+def _handle_buy_sell_decision(context, data, signal, price):
+    orders = get_open_orders(context.asset)
+    if len(orders) > 0:
+        log.info('skipping bar until all open orders execute')
+        return
+
+    positions = context.portfolio.positions
+    if context.position is None and context.asset in positions:
+        position = positions[context.asset]
+        context.position = dict(
+            cost_basis=position['cost_basis'],
+            amount=position['amount'],
+            stop=None
+        )
+
+    action = None
+    if context.position is not None:
+        cost_basis = context.position['cost_basis']
+        amount = context.position['amount']
+        log.info(
+            'found {amount} positions with cost basis {cost_basis}'.format(
+                amount=amount,
+                cost_basis=cost_basis
+            )
+        )
+        stop = context.position['stop']
+
+        target = cost_basis * (1 + context.TARGET)
+        if price >= target:
+            context.position['cost_basis'] = price
+            context.position['stop'] = context.STOP
+
+        stop_target = context.STOP_LOSS if stop is None else context.STOP
+        if price < cost_basis * (1 - stop_target):
+            log.info('executing stop loss')
+            order(
+                asset=context.asset,
+                amount=-amount,
+                limit_price=price * (1 - context.SLIPPAGE_ALLOWED),
+            )
+            action = 0
+            context.position = None
+
+    else:
+        if signal == 'long':
+            log.info('opening position')
+            buy_amount = context.MAX_HOLDINGS / price
+            order(
+                asset=context.asset,
+                amount=buy_amount,
+                limit_price=price * (1 + context.SLIPPAGE_ALLOWED),
+            )
+            context.position = dict(
+                cost_basis=price,
+                amount=buy_amount,
+                stop=None
+            )
+            action = 0
+
+
+def _handle_data_rsi_only(context, data):
+    price = data.current(context.asset, 'close')
+    log.info('got price {price}'.format(price=price))
+
+    if price is np.nan:
+        log.warn('no pricing data')
+        return
+
+    if context.base_price is None:
+        context.base_price = price
+
+    try:
+        prices = data.history(
+            context.asset,
+            fields='price',
+            bar_count=17,
+            frequency='30T'
+        )
+    except Exception as e:
+        log.warn('historical data not available: '.format(e))
+        return
+
+    rsi = talib.RSI(prices.values, timeperiod=16)[-1]
+    log.info('got rsi {}'.format(rsi))
+
+    signal = None
+    if rsi < context.RSI_OVERSOLD:
+        signal = 'long'
+
+    # Making sure that the price is still current
+    price = data.current(context.asset, 'close')
+    cash = context.portfolio.cash
+    log.info(
+        'base currency available: {cash}, cap: {cap}'.format(
+            cash=cash,
+            cap=context.MAX_HOLDINGS
+        )
+    )
+    volume = data.current(context.asset, 'volume')
+    price_change = (price - context.base_price) / context.base_price
+    record(
+        price=price,
+        price_change=price_change,
+        rsi=rsi,
+        volume=volume,
+        cash=cash,
+        starting_cash=context.portfolio.starting_cash,
+        leverage=context.account.leverage,
+    )
+
+    _handle_buy_sell_decision(context, data, signal, price)
+
+
+def handle_data(context, data):
+    dt = data.current_dt
+
+    if context.last_bar is None or (
+                context.last_bar + timedelta(minutes=15)) <= dt:
+        context.last_bar = dt
+    else:
+        return
+
+    log.info('BAR {}'.format(dt))
+    try:
+        _handle_data_rsi_only(context, data)
+    except Exception as e:
+        log.warn('aborting the bar on error {}'.format(e))
+        context.errors.append(e)
+
+    if len(context.errors) > 0:
+        log.info('the errors:\n{}'.format(context.errors))
+
+
+def analyze(context=None, results=None):
+    import matplotlib.pyplot as plt
+
+    base_currency = context.exchanges.values()[0].base_currency.upper()
+    # Plot the portfolio and asset data.
+    ax1 = plt.subplot(611)
+    results.loc[:, 'portfolio_value'].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio Value ({})'.format(base_currency))
+
+    ax2 = plt.subplot(612, sharex=ax1)
+    results.loc[:, 'price'].plot(ax=ax2)
+    ax2.set_ylabel('{asset} ({base})'.format(
+        asset=context.asset.symbol, base=base_currency
+    ))
+
+    trans = results.loc[[t != [] for t in results.transactions], :]
+    buys = trans.loc[[t[0]['amount'] > 0 for t in trans.transactions], :]
+    sells = trans.loc[[t[0]['amount'] < 0 for t in trans.transactions], :]
+    # buys = results.loc[results['action'] == 1, :]
+    # sells = results.loc[results['action'] == 0, :]
+
+    ax2.plot(
+        buys.index,
+        results.loc[buys.index, 'price'],
+        '^',
+        markersize=10,
+        color='g',
+    )
+    ax2.plot(
+        sells.index,
+        results.loc[sells.index, 'price'],
+        'v',
+        markersize=10,
+        color='r',
+    )
+
+    ax3 = plt.subplot(613, sharex=ax1)
+    results.loc[:, ['alpha', 'beta']].plot(ax=ax3)
+    ax3.set_ylabel('Alpha / Beta ')
+
+    ax4 = plt.subplot(614, sharex=ax1)
+    results.loc[:, ['starting_cash', 'cash']].plot(ax=ax4)
+    ax4.set_ylabel('Base Currency ({})'.format(base_currency))
+
+    results['algorithm'] = results.loc[:, 'algorithm_period_return']
+
+    ax5 = plt.subplot(615, sharex=ax1)
+    results.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
+    ax5.set_ylabel('Percent Change')
+
+    ax6 = plt.subplot(616, sharex=ax1)
+    results.loc[:, 'rsi'].plot(ax=ax6)
+    ax6.set_ylabel('RSI')
+
+    ax6.plot(
+        buys.index,
+        results.loc[buys.index, 'rsi'],
+        '^',
+        markersize=10,
+        color='g',
+    )
+    ax6.plot(
+        sells.index,
+        results.loc[sells.index, 'rsi'],
+        'v',
+        markersize=10,
+        color='r',
+    )
+
+    plt.legend(loc=3)
+
+    # Show the plot.
+    plt.gcf().set_size_inches(18, 8)
+    plt.show()
+    pass
+
+
+run_algorithm(
+    initialize=initialize,
+    handle_data=handle_data,
+    analyze=analyze,
+    exchange_name='bittrex',
+    live=True,
+    algo_namespace=algo_namespace,
+    base_currency='btc',
+    live_graph=False
+)
+
+# Backtest
+# run_algorithm(
+#     capital_base=0.5,
+#     data_frequency='minute',
+#     initialize=initialize,
+#     handle_data=handle_data,
+#     analyze=analyze,
+#     exchange_name='poloniex',
+#     algo_namespace=algo_namespace,
+#     base_currency='btc',
+#     start=pd.to_datetime('2017-9-1', utc=True),
+#     end=pd.to_datetime('2017-10-1', utc=True),
+# )

catalyst/examples/simple_loop.py

@@ -0,0 +1,52 @@
+import talib
+import pandas as pd
+
+from catalyst import run_algorithm
+from catalyst.api import symbol
+
+
+def initialize(context):
+    print('initializing')
+    context.asset = symbol('swift_btc')
+
+
+def handle_data(context, data):
+    print('handling bar: {}'.format(data.current_dt))
+
+    price = data.current(context.asset, 'close')
+    print('got price {price}'.format(price=price))
+
+    try:
+        prices = data.history(
+            context.asset,
+            fields='price',
+            bar_count=15,
+            frequency='1D'
+        )
+        rsi = talib.RSI(prices.values, timeperiod=14)[-1]
+        print('got rsi: {}'.format(rsi))
+    except Exception as e:
+        print(e)
+
+
+run_algorithm(
+    capital_base=250,
+    start=pd.to_datetime('2015-4-1', utc=True),
+    end=pd.to_datetime('2017-11-1', utc=True),
+    data_frequency='daily',
+    initialize=initialize,
+    handle_data=handle_data,
+    analyze=None,
+    exchange_name='bittrex',
+    algo_namespace='simple_loop',
+    base_currency='btc'
+)
+# run_algorithm(
+#     initialize=initialize,
+#     handle_data=handle_data,
+#     analyze=None,
+#     exchange_name='poloniex',
+#     live=True,
+#     algo_namespace='simple_loop',
+#     base_currency='eth',
+#     live_graph=False

catalyst/examples/talib_simple.py

@@ -0,0 +1,364 @@
+# Run Command
+# catalyst run --start 2017-1-1 --end 2017-11-1 -o talib_simple.pickle -f talib_simple.py -x poloniex
+# 
+# Description
+# Simple TALib Example showing how to use various indicators in you strategy
+# Based loosly on https://github.com/mellertson/talib-macd-example/blob/master/talib-macd-matplotlib-example.py
+
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import talib as ta
+from logbook import Logger
+from matplotlib.dates import date2num
+from matplotlib.finance import candlestick_ohlc
+
+from catalyst import run_algorithm
+from catalyst.api import (
+    order,
+    order_target_percent,
+    symbol,
+)
+from catalyst.exchange.stats_utils import get_pretty_stats
+
+algo_namespace = 'talib_sample'
+log = Logger(algo_namespace)
+
+
+def initialize(context):
+    log.info('Starting TALib Simple Example')
+
+    context.ASSET_NAME = 'BTC_USDT'
+    context.asset = symbol(context.ASSET_NAME)
+
+    context.ORDER_SIZE = 10
+    context.SLIPPAGE_ALLOWED = 0.05
+
+    context.swallow_errors = True
+    context.errors = []
+
+    # Bars to look at per iteration should be bigger than SMA_SLOW
+    context.BARS = 365
+    context.COUNT = 0
+
+    # Technical Analysis Settings
+    context.SMA_FAST = 50
+    context.SMA_SLOW = 100
+    context.RSI_PERIOD = 14
+    context.RSI_OVER_BOUGHT = 80
+    context.RSI_OVER_SOLD = 20
+    context.RSI_AVG_PERIOD = 15
+    context.MACD_FAST = 12
+    context.MACD_SLOW = 26
+    context.MACD_SIGNAL = 9
+    context.STOCH_K = 14
+    context.STOCH_D = 3
+    context.STOCH_OVER_BOUGHT = 80
+    context.STOCH_OVER_SOLD = 20
+
+    pass
+
+
+def _handle_data(context, data):
+    # Get price, open, high, low, close
+    prices = data.history(
+        context.asset,
+        bar_count=context.BARS,
+        fields=['price', 'open', 'high', 'low', 'close'],
+        frequency='1d')
+
+    # Create a analysis data frame
+    analysis = pd.DataFrame(index=prices.index)
+
+    # SMA FAST
+    analysis['sma_f'] = ta.SMA(prices.close.as_matrix(), context.SMA_FAST)
+    # SMA SLOW
+    analysis['sma_s'] = ta.SMA(prices.close.as_matrix(), context.SMA_SLOW)
+
+    # Relative Strength Index
+    analysis['rsi'] = ta.RSI(prices.close.as_matrix(), context.RSI_PERIOD)
+    # RSI SMA
+    analysis['sma_r'] = ta.SMA(analysis.rsi.as_matrix(),
+                               context.RSI_AVG_PERIOD)
+
+    # MACD, MACD Signal, MACD Histogram
+    analysis['macd'], analysis['macdSignal'], analysis['macdHist'] = ta.MACD(
+        prices.close.as_matrix(), fastperiod=context.MACD_FAST,
+        slowperiod=context.MACD_SLOW, signalperiod=context.MACD_SIGNAL)
+
+    # Stochastics %K %D 
+    # %K = (Current Close - Lowest Low)/(Highest High - Lowest Low) * 100
+    # %D = 3-day SMA of %K
+    analysis['stoch_k'], analysis['stoch_d'] = ta.STOCH(
+        prices.high.as_matrix(), prices.low.as_matrix(),
+        prices.close.as_matrix(), slowk_period=context.STOCH_K,
+        slowd_period=context.STOCH_D)
+
+    # SMA FAST over SLOW Crossover
+    analysis['sma_test'] = np.where(analysis.sma_f > analysis.sma_s, 1, 0)
+
+    # MACD over Signal Crossover
+    analysis['macd_test'] = np.where((analysis.macd > analysis.macdSignal), 1,
+                                     0)
+
+    # Stochastics OVER BOUGHT & Decreasing
+    analysis['stoch_over_bought'] = np.where(
+        (analysis.stoch_k > context.STOCH_OVER_BOUGHT) & (
+            analysis.stoch_k > analysis.stoch_k.shift(1)), 1, 0)
+
+    # Stochastics OVER SOLD & Increasing
+    analysis['stoch_over_sold'] = np.where(
+        (analysis.stoch_k < context.STOCH_OVER_SOLD) & (
+            analysis.stoch_k > analysis.stoch_k.shift(1)), 1, 0)
+
+    # RSI OVER BOUGHT & Decreasing
+    analysis['rsi_over_bought'] = np.where(
+        (analysis.rsi > context.RSI_OVER_BOUGHT) & (
+            analysis.rsi < analysis.rsi.shift(1)), 1, 0)
+
+    # RSI OVER SOLD & Increasing
+    analysis['rsi_over_sold'] = np.where(
+        (analysis.rsi < context.RSI_OVER_SOLD) & (
+            analysis.rsi > analysis.rsi.shift(1)), 1, 0)
+
+    # Save the prices and analysis to send to analyze
+    context.prices = prices
+    context.analysis = analysis
+    context.price = data.current(context.asset, 'price')
+
+    makeOrders(context, analysis)
+
+    # Log the values of this bar
+    logAnalysis(analysis)
+
+
+def handle_data(context, data):
+    log.info('handling bar {}'.format(data.current_dt))
+    try:
+        _handle_data(context, data)
+    except Exception as e:
+        log.warn('aborting the bar on error {}'.format(e))
+        context.errors.append(e)
+
+    log.info('completed bar {}, total execution errors {}'.format(
+        data.current_dt,
+        len(context.errors)
+    ))
+
+    if len(context.errors) > 0:
+        log.info('the errors:\n{}'.format(context.errors))
+
+
+def analyze(context, results):
+    # Save results in CSV file
+    filename = os.path.splitext(os.path.basename('talib_simple'))[0]
+    results.to_csv(filename + '.csv')
+
+    log.info('the daily stats:\n{}'.format(get_pretty_stats(results)))
+    chart(context, context.prices, context.analysis, results)
+    pass
+
+
+def makeOrders(context, analysis):
+    if context.asset in context.portfolio.positions:
+
+        # Current position
+        position = context.portfolio.positions[context.asset]
+
+        if (position == 0):
+            log.info('Position Zero')
+            return
+
+        # Cost Basis
+        cost_basis = position.cost_basis
+
+        log.info(
+            'Holdings: {amount} @ {cost_basis}'.format(
+                amount=position.amount,
+                cost_basis=cost_basis
+            )
+        )
+
+        # Sell when holding and got sell singnal
+        if isSell(context, analysis):
+            profit = (context.price * position.amount) - (
+                cost_basis * position.amount)
+            order_target_percent(
+                asset=context.asset,
+                target=0,
+                limit_price=context.price * (1 - context.SLIPPAGE_ALLOWED),
+            )
+            log.info(
+                'Sold {amount} @ {price} Profit: {profit}'.format(
+                    amount=position.amount,
+                    price=context.price,
+                    profit=profit
+                )
+            )
+        else:
+            log.info('no buy or sell opportunity found')
+    else:
+        # Buy when not holding and got buy signal
+        if isBuy(context, analysis):
+            order(
+                asset=context.asset,
+                amount=context.ORDER_SIZE,
+                limit_price=context.price * (1 + context.SLIPPAGE_ALLOWED)
+            )
+            log.info(
+                'Bought {amount} @ {price}'.format(
+                    amount=context.ORDER_SIZE,
+                    price=context.price
+                )
+            )
+
+
+def isBuy(context, analysis):
+    # Bullish SMA Crossover
+    if (getLast(analysis, 'sma_test') == 1):
+        # Bullish MACD
+        if (getLast(analysis, 'macd_test') == 1):
+            return True
+
+    # # Bullish Stochastics
+    # if(getLast(analysis, 'stoch_over_sold') == 1):
+    #     return True
+
+    # # Bullish RSI
+    # if(getLast(analysis, 'rsi_over_sold') == 1):
+    #     return True
+
+    return False
+
+
+def isSell(context, analysis):
+    # Bearish SMA Crossover
+    if (getLast(analysis, 'sma_test') == 0):
+        # Bearish MACD
+        if (getLast(analysis, 'macd_test') == 0):
+            return True
+
+    # # Bearish Stochastics
+    # if(getLast(analysis, 'stoch_over_bought') == 0):
+    #     return True
+
+    # # Bearish RSI
+    # if(getLast(analysis, 'rsi_over_bought') == 0):
+    #     return True
+
+    return False
+
+
+def chart(context, prices, analysis, results):
+    results.portfolio_value.plot()
+
+    # Data for matplotlib finance plot
+    dates = date2num(prices.index.to_pydatetime())
+
+    # Create the Open High Low Close Tuple
+    prices_ohlc = [tuple([dates[i],
+                          prices.open[i],
+                          prices.high[i],
+                          prices.low[i],
+                          prices.close[i]]) for i in range(len(dates))]
+
+    fig = plt.figure(figsize=(14, 18))
+
+    # Draw the candle sticks
+    ax1 = fig.add_subplot(411)
+    ax1.set_ylabel(context.ASSET_NAME, size=20)
+    candlestick_ohlc(ax1, prices_ohlc, width=0.4, colorup='g', colordown='r')
+
+    # Draw Moving Averages
+    analysis.sma_f.plot(ax=ax1, c='r')
+    analysis.sma_s.plot(ax=ax1, c='g')
+
+    # RSI
+    ax2 = fig.add_subplot(412)
+    ax2.set_ylabel('RSI', size=12)
+    analysis.rsi.plot(ax=ax2, c='g',
+                      label='Period: ' + str(context.RSI_PERIOD))
+    analysis.sma_r.plot(ax=ax2, c='r',
+                        label='MA: ' + str(context.RSI_AVG_PERIOD))
+    ax2.axhline(y=30, c='b')
+    ax2.axhline(y=50, c='black')
+    ax2.axhline(y=70, c='b')
+    ax2.set_ylim([0, 100])
+    handles, labels = ax2.get_legend_handles_labels()
+    ax2.legend(handles, labels)
+
+    # Draw MACD computed with Talib
+    ax3 = fig.add_subplot(413)
+    ax3.set_ylabel('MACD: ' + str(context.MACD_FAST) + ', ' + str(
+        context.MACD_SLOW) + ', ' + str(context.MACD_SIGNAL), size=12)
+    analysis.macd.plot(ax=ax3, color='b', label='Macd')
+    analysis.macdSignal.plot(ax=ax3, color='g', label='Signal')
+    analysis.macdHist.plot(ax=ax3, color='r', label='Hist')
+    ax3.axhline(0, lw=2, color='0')
+    handles, labels = ax3.get_legend_handles_labels()
+    ax3.legend(handles, labels)
+
+    # Stochastic plot
+    ax4 = fig.add_subplot(414)
+    ax4.set_ylabel('Stoch (k,d)', size=12)
+    analysis.stoch_k.plot(ax=ax4, label='stoch_k:' + str(context.STOCH_K),
+                          color='r')
+    analysis.stoch_d.plot(ax=ax4, label='stoch_d:' + str(context.STOCH_D),
+                          color='g')
+    handles, labels = ax4.get_legend_handles_labels()
+    ax4.legend(handles, labels)
+    ax4.axhline(y=20, c='b')
+    ax4.axhline(y=50, c='black')
+    ax4.axhline(y=80, c='b')
+
+    plt.show()
+
+
+def logAnalysis(analysis):
+    # Log only the last value in the array
+    log.info('- sma_f:          {:.2f}'.format(getLast(analysis, 'sma_f')))
+    log.info('- sma_s:          {:.2f}'.format(getLast(analysis, 'sma_s')))
+
+    log.info('- rsi:            {:.2f}'.format(getLast(analysis, 'rsi')))
+    log.info('- sma_r:          {:.2f}'.format(getLast(analysis, 'sma_r')))
+
+    log.info('- macd:           {:.2f}'.format(getLast(analysis, 'macd')))
+    log.info(
+        '- macdSignal:     {:.2f}'.format(getLast(analysis, 'macdSignal')))
+    log.info('- macdHist:       {:.2f}'.format(getLast(analysis, 'macdHist')))
+
+    log.info('- stoch_k:        {:.2f}'.format(getLast(analysis, 'stoch_k')))
+    log.info('- stoch_d:        {:.2f}'.format(getLast(analysis, 'stoch_d')))
+
+    log.info('- sma_test:       {}'.format(getLast(analysis, 'sma_test')))
+    log.info('- macd_test:      {}'.format(getLast(analysis, 'macd_test')))
+
+    log.info('- stoch_over_bought:   {}'.format(
+        getLast(analysis, 'stoch_over_bought')))
+    log.info(
+        '- stoch_over_sold:   {}'.format(getLast(analysis, 'stoch_over_sold')))
+
+    log.info('- rsi_over_bought:       {}'.format(
+        getLast(analysis, 'rsi_over_bought')))
+    log.info(
+        '- rsi_over_sold:       {}'.format(getLast(analysis, 'rsi_over_sold')))
+
+
+def getLast(arr, name):
+    return arr[name][arr[name].index[-1]]
+
+
+if __name__ == '__main__':
+    run_algorithm(
+        capital_base=10000,
+        data_frequency='daily',
+        initialize=initialize,
+        handle_data=handle_data,
+        analyze=analyze,
+        exchange_name='poloniex',
+        base_currency='usdt',
+        start=pd.to_datetime('2016-11-1', utc=True),
+        end=pd.to_datetime('2017-11-10', utc=True),
+    )

catalyst/exchange/asset_finder_exchange.py

@@ -0,0 +1,93 @@
+from logbook import Logger
+
+from catalyst.constants import LOG_LEVEL
+
+log = Logger('AssetFinderExchange', level=LOG_LEVEL)
+
+
+class AssetFinderExchange(object):
+    def __init__(self):
+        self._asset_cache = {}
+
+    @property
+    def sids(self):
+        """
+        This seems to be used to pre-fetch assets.
+        I don't think that we need this for live-trading.
+        Leaving the list empty.
+        """
+        return list()
+
+    def retrieve_all(self, sids, default_none=False):
+        """
+        Retrieve all assets in `sids`.
+
+        Parameters
+        ----------
+        sids : iterable of int
+            Assets to retrieve.
+        default_none : bool
+            If True, return None for failed lookups.
+            If False, raise `SidsNotFound`.
+
+        Returns
+        -------
+        assets : list[Asset or None]
+            A list of the same length as `sids` containing Assets (or Nones)
+            corresponding to the requested sids.
+
+        Raises
+        ------
+        SidsNotFound
+            When a requested sid is not found and default_none=False.
+        """
+        for sid in sids:
+            if sid in self._asset_cache:
+                log.debug('got asset from cache: {}'.format(sid))
+            else:
+                log.debug('fetching asset: {}'.format(sid))
+        return list()
+
+    def lookup_symbol(self, symbol, exchange, as_of_date=None, fuzzy=False):
+        """Lookup an asset by symbol.
+
+        Parameters
+        ----------
+        symbol : str
+            The ticker symbol to resolve.
+        as_of_date : datetime or None
+            Look up the last owner of this symbol as of this datetime.
+            If ``as_of_date`` is None, then this can only resolve the equity
+            if exactly one equity has ever owned the ticker.
+        fuzzy : bool, optional
+            Should fuzzy symbol matching be used? Fuzzy symbol matching
+            attempts to resolve differences in representations for
+            shareclasses. For example, some people may represent the ``A``
+            shareclass of ``BRK`` as ``BRK.A``, where others could write
+            ``BRK_A``.
+
+        Returns
+        -------
+        equity : Asset
+            The equity that held ``symbol`` on the given ``as_of_date``, or the
+            only equity to hold ``symbol`` if ``as_of_date`` is None.
+
+        Raises
+        ------
+        SymbolNotFound
+            Raised when no equity has ever held the given symbol.
+        MultipleSymbolsFound
+            Raised when no ``as_of_date`` is given and more than one equity
+            has held ``symbol``. This is also raised when ``fuzzy=True`` and
+            there are multiple candidates for the given ``symbol`` on the
+            ``as_of_date``.
+        """
+        log.debug('looking up symbol: {} {}'.format(symbol, exchange.name))
+
+        key = ','.join([exchange.name, symbol])
+        if key in self._asset_cache:
+            return self._asset_cache[key]
+        else:
+            asset = exchange.get_asset(symbol)
+            self._asset_cache[key] = asset
+            return asset

catalyst/exchange/bitfinex/bitfinex.py

@@ -0,0 +1,700 @@
+import base64
+import datetime
+import hashlib
+import hmac
+import json
+import re
+import time
+
+import numpy as np
+import pandas as pd
+import pytz
+import requests
+import six
+from catalyst.assets._assets import TradingPair
+from logbook import Logger
+
+from catalyst.exchange.exchange import Exchange
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.exchange_errors import (
+    ExchangeRequestError,
+    InvalidHistoryFrequencyError,
+    InvalidOrderStyle, OrderCancelError)
+from catalyst.exchange.exchange_execution import ExchangeLimitOrder, \
+    ExchangeStopLimitOrder, ExchangeStopOrder
+from catalyst.exchange.exchange_utils import get_exchange_symbols_filename, \
+    download_exchange_symbols, get_symbols_string
+from catalyst.finance.order import Order, ORDER_STATUS
+from catalyst.protocol import Account
+
+# Trying to account for REST api instability
+# https://stackoverflow.com/questions/15431044/can-i-set-max-retries-for-requests-request
+requests.adapters.DEFAULT_RETRIES = 20
+
+BITFINEX_URL = 'https://api.bitfinex.com'
+
+from catalyst.constants import LOG_LEVEL
+
+log = Logger('Bitfinex', level=LOG_LEVEL)
+warning_logger = Logger('AlgoWarning')
+
+
+class Bitfinex(Exchange):
+    def __init__(self, key, secret, base_currency, portfolio=None):
+        self.url = BITFINEX_URL
+        self.key = key
+        self.secret = secret.encode('UTF-8')
+        self.name = 'bitfinex'
+        self.color = 'green'
+        self.assets = {}
+        self.load_assets()
+        self.base_currency = base_currency
+        self._portfolio = portfolio
+        self.minute_writer = None
+        self.minute_reader = None
+
+        # The candle limit for each request
+        self.num_candles_limit = 1000
+
+        # Max is 90 but playing it safe
+        # https://www.bitfinex.com/posts/188
+        self.max_requests_per_minute = 80
+        self.request_cpt = dict()
+
+        self.bundle = ExchangeBundle(self)
+
+    def _request(self, operation, data, version='v1'):
+        payload_object = {
+            'request': '/{}/{}'.format(version, operation),
+            'nonce': '{0:f}'.format(time.time() * 1000000),
+            # convert to string
+            'options': {}
+        }
+
+        if data is None:
+            payload_dict = payload_object
+        else:
+            payload_dict = payload_object.copy()
+            payload_dict.update(data)
+
+        payload_json = json.dumps(payload_dict)
+        if six.PY3:
+            payload = base64.b64encode(bytes(payload_json, 'utf-8'))
+        else:
+            payload = base64.b64encode(payload_json)
+
+        m = hmac.new(self.secret, payload, hashlib.sha384)
+        m = m.hexdigest()
+
+        # headers
+        headers = {
+            'X-BFX-APIKEY': self.key,
+            'X-BFX-PAYLOAD': payload,
+            'X-BFX-SIGNATURE': m
+        }
+
+        if data is None:
+            request = requests.get(
+                '{url}/{version}/{operation}'.format(
+                    url=self.url,
+                    version=version,
+                    operation=operation
+                ), data={},
+                headers=headers)
+        else:
+            request = requests.post(
+                '{url}/{version}/{operation}'.format(
+                    url=self.url,
+                    version=version,
+                    operation=operation
+                ),
+                headers=headers)
+
+        return request
+
+    def _get_v2_symbol(self, asset):
+        pair = asset.symbol.split('_')
+        symbol = 't' + pair[0].upper() + pair[1].upper()
+        return symbol
+
+    def _get_v2_symbols(self, assets):
+        """
+        Workaround to support Bitfinex v2
+        TODO: Might require a separate asset dictionary
+
+        :param assets:
+        :return:
+        """
+
+        v2_symbols = []
+        for asset in assets:
+            v2_symbols.append(self._get_v2_symbol(asset))
+
+        return v2_symbols
+
+    def _create_order(self, order_status):
+        """
+        Create a Catalyst order object from a Bitfinex order dictionary
+        :param order_status:
+        :return: Order
+        """
+        if order_status['is_cancelled']:
+            status = ORDER_STATUS.CANCELLED
+        elif not order_status['is_live']:
+            log.info('found executed order {}'.format(order_status))
+            status = ORDER_STATUS.FILLED
+        else:
+            status = ORDER_STATUS.OPEN
+
+        amount = float(order_status['original_amount'])
+        filled = float(order_status['executed_amount'])
+
+        if order_status['side'] == 'sell':
+            amount = -amount
+            filled = -filled
+
+        price = float(order_status['price'])
+        order_type = order_status['type']
+
+        stop_price = None
+        limit_price = None
+
+        # TODO: is this comprehensive enough?
+        if order_type.endswith('limit'):
+            limit_price = price
+        elif order_type.endswith('stop'):
+            stop_price = price
+
+        executed_price = float(order_status['avg_execution_price'])
+
+        # TODO: bitfinex does not specify comission. I could calculate it but not sure if it's worth it.
+        commission = None
+
+        date = pd.Timestamp.utcfromtimestamp(float(order_status['timestamp']))
+        date = pytz.utc.localize(date)
+        order = Order(
+            dt=date,
+            asset=self.assets[order_status['symbol']],
+            amount=amount,
+            stop=stop_price,
+            limit=limit_price,
+            filled=filled,
+            id=str(order_status['id']),
+            commission=commission
+        )
+        order.status = status
+
+        return order, executed_price
+
+    def get_balances(self):
+        log.debug('retrieving wallets balances')
+        try:
+            self.ask_request()
+            response = self._request('balances', None)
+            balances = response.json()
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'message' in balances:
+            raise ExchangeRequestError(
+                error='unable to fetch balance {}'.format(balances['message'])
+            )
+
+        std_balances = dict()
+        for balance in balances:
+            currency = balance['currency'].lower()
+            std_balances[currency] = float(balance['available'])
+
+        return std_balances
+
+    @property
+    def account(self):
+        account = Account()
+
+        account.settled_cash = None
+        account.accrued_interest = None
+        account.buying_power = None
+        account.equity_with_loan = None
+        account.total_positions_value = None
+        account.total_positions_exposure = None
+        account.regt_equity = None
+        account.regt_margin = None
+        account.initial_margin_requirement = None
+        account.maintenance_margin_requirement = None
+        account.available_funds = None
+        account.excess_liquidity = None
+        account.cushion = None
+        account.day_trades_remaining = None
+        account.leverage = None
+        account.net_leverage = None
+        account.net_liquidation = None
+
+        return account
+
+    @property
+    def time_skew(self):
+        # TODO: research the time skew conditions
+        return pd.Timedelta('0s')
+
+    def get_account(self):
+        # TODO: fetch account data and keep in cache
+        return None
+
+    def get_candles(self, freq, assets, bar_count=None,
+                    start_dt=None, end_dt=None):
+        """
+        Retrieve OHLVC candles from Bitfinex
+
+        :param data_frequency:
+        :param assets:
+        :param bar_count:
+        :return:
+
+        Available Frequencies
+        ---------------------
+        '1m', '5m', '15m', '30m', '1h', '3h', '6h', '12h', '1D', '7D', '14D',
+         '1M'
+        """
+        log.debug(
+            'retrieving {bars} {freq} candles on {exchange} from '
+            '{end_dt} for markets {symbols}, '.format(
+                bars=bar_count,
+                freq=freq,
+                exchange=self.name,
+                end_dt=end_dt,
+                symbols=get_symbols_string(assets)
+            )
+        )
+
+        allowed_frequencies = ['1T', '5T', '15T', '30T', '60T', '180T',
+                               '360T', '720T', '1D', '7D', '14D', '30D']
+        if freq not in allowed_frequencies:
+            raise InvalidHistoryFrequencyError(frequency=freq)
+
+        freq_match = re.match(r'([0-9].*)(T|H|D)', freq, re.M | re.I)
+        if freq_match:
+            number = int(freq_match.group(1))
+            unit = freq_match.group(2)
+
+            if unit == 'T':
+                if number in [60, 180, 360, 720]:
+                    number = number / 60
+                    converted_unit = 'h'
+                else:
+                    converted_unit = 'm'
+            else:
+                converted_unit = unit
+
+            frequency = '{}{}'.format(number, converted_unit)
+
+        else:
+            raise InvalidHistoryFrequencyError(frequency=freq)
+
+        # Making sure that assets are iterable
+        asset_list = [assets] if isinstance(assets, TradingPair) else assets
+        ohlc_map = dict()
+        for asset in asset_list:
+            symbol = self._get_v2_symbol(asset)
+            url = '{url}/v2/candles/trade:{frequency}:{symbol}'.format(
+                url=self.url,
+                frequency=frequency,
+                symbol=symbol
+            )
+
+            if bar_count:
+                is_list = True
+                url += '/hist?limit={}'.format(int(bar_count))
+
+                def get_ms(date):
+                    epoch = datetime.datetime.utcfromtimestamp(0)
+                    epoch = epoch.replace(tzinfo=pytz.UTC)
+
+                    return (date - epoch).total_seconds() * 1000.0
+
+                if start_dt is not None:
+                    start_ms = get_ms(start_dt)
+                    url += '&start={0:f}'.format(start_ms)
+
+                if end_dt is not None:
+                    end_ms = get_ms(end_dt)
+                    url += '&end={0:f}'.format(end_ms)
+
+            else:
+                is_list = False
+                url += '/last'
+
+            try:
+                self.ask_request()
+                response = requests.get(url)
+            except Exception as e:
+                raise ExchangeRequestError(error=e)
+
+            if 'error' in response.content:
+                raise ExchangeRequestError(
+                    error='Unable to retrieve candles: {}'.format(
+                        response.content)
+                )
+
+            candles = response.json()
+
+            def ohlc_from_candle(candle):
+                last_traded = pd.Timestamp.utcfromtimestamp(
+                    candle[0] / 1000.0)
+                last_traded = last_traded.replace(tzinfo=pytz.UTC)
+                ohlc = dict(
+                    open=np.float64(candle[1]),
+                    high=np.float64(candle[3]),
+                    low=np.float64(candle[4]),
+                    close=np.float64(candle[2]),
+                    volume=np.float64(candle[5]),
+                    price=np.float64(candle[2]),
+                    last_traded=last_traded
+                )
+                return ohlc
+
+            if is_list:
+                ohlc_bars = []
+                # We can to list candles from old to new
+                for candle in reversed(candles):
+                    ohlc = ohlc_from_candle(candle)
+                    ohlc_bars.append(ohlc)
+
+                ohlc_map[asset] = ohlc_bars
+
+            else:
+                ohlc = ohlc_from_candle(candles)
+                ohlc_map[asset] = ohlc
+
+        return ohlc_map[assets] \
+            if isinstance(assets, TradingPair) else ohlc_map
+
+    def create_order(self, asset, amount, is_buy, style):
+        """
+        Creating order on the exchange.
+
+        :param asset:
+        :param amount:
+        :param is_buy:
+        :param style:
+        :return:
+        """
+        exchange_symbol = self.get_symbol(asset)
+        if isinstance(style, ExchangeLimitOrder) \
+                or isinstance(style, ExchangeStopLimitOrder):
+            price = style.get_limit_price(is_buy)
+            order_type = 'limit'
+
+        elif isinstance(style, ExchangeStopOrder):
+            price = style.get_stop_price(is_buy)
+            order_type = 'stop'
+
+        else:
+            raise InvalidOrderStyle(exchange=self.name,
+                                    style=style.__class__.__name__)
+
+        req = dict(
+            symbol=exchange_symbol,
+            amount=str(float(abs(amount))),
+            price="{:.20f}".format(float(price)),
+            side='buy' if is_buy else 'sell',
+            type='exchange ' + order_type,  # TODO: support margin trades
+            exchange=self.name,
+            is_hidden=False,
+            is_postonly=False,
+            use_all_available=0,
+            ocoorder=False,
+            buy_price_oco=0,
+            sell_price_oco=0
+        )
+
+        date = pd.Timestamp.utcnow()
+        try:
+            self.ask_request()
+            response = self._request('order/new', req)
+            order_status = response.json()
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'message' in order_status:
+            raise ExchangeRequestError(
+                error='unable to create Bitfinex order {}'.format(
+                    order_status['message'])
+            )
+
+        order_id = str(order_status['id'])
+        order = Order(
+            dt=date,
+            asset=asset,
+            amount=amount,
+            stop=style.get_stop_price(is_buy),
+            limit=style.get_limit_price(is_buy),
+            id=order_id
+        )
+
+        return order
+
+    def get_open_orders(self, asset=None):
+        """Retrieve all of the current open orders.
+
+        Parameters
+        ----------
+        asset : Asset
+            If passed and not None, return only the open orders for the given
+            asset instead of all open orders.
+
+        Returns
+        -------
+        open_orders : dict[list[Order]] or list[Order]
+            If no asset is passed this will return a dict mapping Assets
+            to a list containing all the open orders for the asset.
+            If an asset is passed then this will return a list of the open
+            orders for this asset.
+        """
+        try:
+            self.ask_request()
+            response = self._request('orders', None)
+            order_statuses = response.json()
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'message' in order_statuses:
+            raise ExchangeRequestError(
+                error='Unable to retrieve open orders: {}'.format(
+                    order_statuses['message'])
+            )
+
+        orders = []
+        for order_status in order_statuses:
+            order, executed_price = self._create_order(order_status)
+            if asset is None or asset == order.sid:
+                orders.append(order)
+
+        return orders
+
+    def get_order(self, order_id):
+        """Lookup an order based on the order id returned from one of the
+        order functions.
+
+        Parameters
+        ----------
+        order_id : str
+            The unique identifier for the order.
+
+        Returns
+        -------
+        order : Order
+            The order object.
+        """
+        try:
+            self.ask_request()
+            response = self._request(
+                'order/status', {'order_id': int(order_id)})
+            order_status = response.json()
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'message' in order_status:
+            raise ExchangeRequestError(
+                error='Unable to retrieve order status: {}'.format(
+                    order_status['message'])
+            )
+        return self._create_order(order_status)
+
+    def cancel_order(self, order_param):
+        """Cancel an open order.
+
+        Parameters
+        ----------
+        order_param : str or Order
+            The order_id or order object to cancel.
+        """
+        order_id = order_param.id \
+            if isinstance(order_param, Order) else order_param
+
+        try:
+            self.ask_request()
+            response = self._request('order/cancel', {'order_id': order_id})
+            status = response.json()
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'message' in status:
+            raise OrderCancelError(
+                order_id=order_id,
+                exchange=self.name,
+                error=status['message']
+            )
+
+    def tickers(self, assets):
+        """
+        Fetch ticket data for assets
+        https://docs.bitfinex.com/v2/reference#rest-public-tickers
+
+        :param assets:
+        :return:
+        """
+        symbols = self._get_v2_symbols(assets)
+        log.debug('fetching tickers {}'.format(symbols))
+
+        try:
+            self.ask_request()
+            response = requests.get(
+                '{url}/v2/tickers?symbols={symbols}'.format(
+                    url=self.url,
+                    symbols=','.join(symbols),
+                )
+            )
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'error' in response.content:
+            raise ExchangeRequestError(
+                error='Unable to retrieve tickers: {}'.format(
+                    response.content)
+            )
+
+        try:
+            tickers = response.json()
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        ticks = dict()
+        for index, ticker in enumerate(tickers):
+            if not len(ticker) == 11:
+                raise ExchangeRequestError(
+                    error='Invalid ticker in response: {}'.format(ticker)
+                )
+
+            ticks[assets[index]] = dict(
+                timestamp=pd.Timestamp.utcnow(),
+                bid=ticker[1],
+                ask=ticker[3],
+                last_price=ticker[7],
+                low=ticker[10],
+                high=ticker[9],
+                volume=ticker[8],
+            )
+
+        log.debug('got tickers {}'.format(ticks))
+        return ticks
+
+    def generate_symbols_json(self, filename=None, source_dates=False):
+        symbol_map = {}
+
+        if not source_dates:
+            fn, r = download_exchange_symbols(self.name)
+            with open(fn) as data_file:
+                cached_symbols = json.load(data_file)
+
+        response = self._request('symbols', None)
+
+        for symbol in response.json():
+            if (source_dates):
+                start_date = self.get_symbol_start_date(symbol)
+            else:
+                try:
+                    start_date = cached_symbols[symbol]['start_date']
+                except KeyError as e:
+                    start_date = time.strftime('%Y-%m-%d')
+
+            try:
+                end_daily = cached_symbols[symbol]['end_daily']
+            except KeyError as e:
+                end_daily = 'N/A'
+
+            try:
+                end_minute = cached_symbols[symbol]['end_minute']
+            except KeyError as e:
+                end_minute = 'N/A'
+
+            symbol_map[symbol] = dict(
+                symbol=symbol[:-3] + '_' + symbol[-3:],
+                start_date=start_date,
+                end_daily=end_daily,
+                end_minute=end_minute,
+            )
+
+        if (filename is None):
+            filename = get_exchange_symbols_filename(self.name)
+
+        with open(filename, 'w') as f:
+            json.dump(symbol_map, f, sort_keys=True, indent=2,
+                      separators=(',', ':'))
+
+    def get_symbol_start_date(self, symbol):
+
+        print(symbol)
+        symbol_v2 = 't' + symbol.upper()
+
+        """
+            For each symbol we retrieve candles with Monhtly resolution
+            We get the first month, and query again with daily resolution
+            around that date, and we get the first date
+        """
+        url = '{url}/v2/candles/trade:1M:{symbol}/hist'.format(
+            url=self.url,
+            symbol=symbol_v2
+        )
+
+        try:
+            self.ask_request()
+            response = requests.get(url)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        """
+            If we don't get any data back for our monthly-resolution query
+            it means that symbol started trading less than a month ago, so
+            arbitrarily set the ref. date to 15 days ago to be safe with
+            +/- 31 days
+        """
+        if (len(response.json())):
+            startmonth = response.json()[-1][0]
+        else:
+            startmonth = int((time.time() - 15 * 24 * 3600) * 1000)
+
+        """
+            Query again with daily resolution setting the start and end around
+            the startmonth we got above. Avoid end dates greater than now: time.time()
+        """
+        url = '{url}/v2/candles/trade:1D:{symbol}/hist?start={start}&end={end}'.format(
+            url=self.url,
+            symbol=symbol_v2,
+            start=startmonth - 3600 * 24 * 31 * 1000,
+            end=min(startmonth + 3600 * 24 * 31 * 1000,
+                    int(time.time() * 1000))
+        )
+
+        try:
+            self.ask_request()
+            response = requests.get(url)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        return time.strftime('%Y-%m-%d',
+                             time.gmtime(int(response.json()[-1][0] / 1000)))
+
+    def get_orderbook(self, asset, order_type='all', limit=100):
+        exchange_symbol = asset.exchange_symbol
+        try:
+            self.ask_request()
+            # TODO: implement limit
+            response = self._request(
+                'book/{}'.format(exchange_symbol), None)
+            data = response.json()
+
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        # TODO: filter by type
+        result = dict()
+        for order_type in data:
+            result[order_type] = []
+
+            for entry in data[order_type]:
+                result[order_type].append(dict(
+                    rate=float(entry['price']),
+                    quantity=float(entry['amount'])
+                ))
+
+        return result

catalyst/exchange/bitfinex/symbols.json

@@ -0,0 +1,127 @@
+{
+  "neobtc": {
+    "symbol": "neo_btc",
+    "start_date": "2017-09-07",
+    "precision": 5
+  },
+  "neousd": {
+    "symbol": "neo_usd",
+    "start_date": "2017-09-07"
+  },
+  "neoeth": {
+    "symbol": "neo_eth",
+    "start_date": "2017-09-07"
+  },
+  "btcusd": {
+    "symbol": "btc_usd",
+    "start_date": "2010-01-01"
+  },
+  "bchusd": {
+    "symbol": "bch_usd",
+    "start_date": "2010-01-01"
+  },
+  "ltcusd": {
+    "symbol": "ltc_usd",
+    "start_date": "2010-01-01"
+  },
+  "ltcbtc": {
+    "symbol": "ltc_btc",
+    "start_date": "2010-01-01"
+  },
+  "ethusd": {
+    "symbol": "eth_usd",
+    "start_date": "2017-01-01"
+  },
+  "ethbtc": {
+    "symbol": "eth_btc",
+    "start_date": "2017-01-01"
+  },
+  "etcbtc": {
+    "symbol": "etc_btc",
+    "start_date": "2017-01-01"
+  },
+  "etcusd": {
+    "symbol": "etc_usd",
+    "start_date": "2017-01-01"
+  },
+  "rrtusd": {
+    "symbol": "rrt_usd",
+    "start_date": "2010-01-01"
+  },
+  "rrtbtc": {
+    "symbol": "rrt_btc",
+    "start_date": "2010-01-01"
+  },
+  "zecusd": {
+    "symbol": "zec_usd",
+    "start_date": "2010-01-01"
+  },
+  "zecbtc": {
+    "symbol": "zec_btc",
+    "start_date": "2010-01-01"
+  },
+  "xmrusd": {
+    "symbol": "xmr_usd",
+    "start_date": "2010-01-01"
+  },
+  "xmrbtc": {
+    "symbol": "xmr_btc",
+    "start_date": "2010-01-01"
+  },
+  "dshusd": {
+    "symbol": "dsh_usd",
+    "start_date": "2010-01-01"
+  },
+  "dshbtc": {
+    "symbol": "dsh_btc",
+    "start_date": "2010-01-01"
+  },
+  "bccbtc": {
+    "symbol": "bcc_btc",
+    "start_date": "2010-01-01"
+  },
+  "bcubtc": {
+    "symbol": "bcu_btc",
+    "start_date": "2010-01-01"
+  },
+  "bccusd": {
+    "symbol": "bcc_usd",
+    "start_date": "2010-01-01"
+  },
+  "bcuusd": {
+    "symbol": "bcu_usd",
+    "start_date": "2010-01-01"
+  },
+  "xrpusd": {
+    "symbol": "xrp_usd",
+    "start_date": "2010-01-01"
+  },
+  "xrpbtc": {
+    "symbol": "xrp_btc",
+    "start_date": "2010-01-01"
+  },
+  "iotusd": {
+    "symbol": "iot_usd",
+    "start_date": "2010-01-01"
+  },
+  "iotbtc": {
+    "symbol": "iot_btc",
+    "start_date": "2010-01-01"
+  },
+  "ioteth": {
+    "symbol": "iot_eth",
+    "start_date": "2010-01-01"
+  },
+  "eosusd": {
+    "symbol": "eos_usd",
+    "start_date": "2010-01-01"
+  },
+  "eosbtc": {
+    "symbol": "eos_btc",
+    "start_date": "2010-01-01"
+  },
+  "eoseth": {
+    "symbol": "eos_eth",
+    "start_date": "2010-01-01"
+  }
+}

catalyst/exchange/bittrex/bittrex.py

@@ -0,0 +1,413 @@
+import json
+import time
+
+import pandas as pd
+from catalyst.assets._assets import TradingPair
+from logbook import Logger
+from six.moves import urllib
+
+from catalyst.constants import LOG_LEVEL
+from catalyst.exchange.bittrex.bittrex_api import Bittrex_api
+from catalyst.exchange.exchange import Exchange
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.exchange_errors import InvalidHistoryFrequencyError, \
+    ExchangeRequestError, InvalidOrderStyle, OrderNotFound, OrderCancelError, \
+    CreateOrderError
+from catalyst.exchange.exchange_utils import get_exchange_symbols_filename, \
+    download_exchange_symbols, get_symbols_string
+from catalyst.finance.execution import LimitOrder, StopLimitOrder
+from catalyst.finance.order import Order, ORDER_STATUS
+
+# TODO: consider using this: https://github.com/mondeja/bittrex_v2
+
+log = Logger('Bittrex', level=LOG_LEVEL)
+
+URL2 = 'https://bittrex.com/Api/v2.0'
+
+
+class Bittrex(Exchange):
+    def __init__(self, key, secret, base_currency, portfolio=None):
+        self.api = Bittrex_api(key=key, secret=secret)
+        self.name = 'bittrex'
+        self.color = 'blue'
+        self.base_currency = base_currency
+        self._portfolio = portfolio
+
+        self.num_candles_limit = 2000
+
+        # Not sure what the rate limit is but trying to play it safe
+        # https://bitcoin.stackexchange.com/questions/53778/bittrex-api-rate-limit
+        self.max_requests_per_minute = 60
+        self.request_cpt = dict()
+
+        self.minute_writer = None
+        self.minute_reader = None
+
+        self.assets = dict()
+        self.load_assets()
+
+        self.bundle = ExchangeBundle(self)
+
+    @property
+    def account(self):
+        pass
+
+    @property
+    def time_skew(self):
+        # TODO: research the time skew conditions
+        return pd.Timedelta('0s')
+
+    def sanitize_curency_symbol(self, exchange_symbol):
+        """
+        Helper method used to build the universal pair.
+        Include any symbol mapping here if appropriate.
+
+        :param exchange_symbol:
+        :return universal_symbol:
+        """
+        return exchange_symbol.lower()
+
+    def get_balances(self):
+        balances = self.api.getbalances()
+        try:
+            log.debug('retrieving wallet balances')
+            self.ask_request()
+
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        std_balances = dict()
+        try:
+            for balance in balances:
+                currency = balance['Currency'].lower()
+                std_balances[currency] = balance['Available']
+
+        except TypeError:
+            raise ExchangeRequestError(error=balances)
+
+        return std_balances
+
+    def create_order(self, asset, amount, is_buy, style):
+        log.info('creating {} order'.format('buy' if is_buy else 'sell'))
+        exchange_symbol = self.get_symbol(asset)
+
+        if isinstance(style, LimitOrder) or isinstance(style, StopLimitOrder):
+            if isinstance(style, StopLimitOrder):
+                log.warn('{} will ignore the stop price'.format(self.name))
+
+            price = style.get_limit_price(is_buy)
+            try:
+                self.ask_request()
+                if is_buy:
+                    order_status = self.api.buylimit(exchange_symbol, amount,
+                                                     price)
+                else:
+                    order_status = self.api.selllimit(exchange_symbol,
+                                                      abs(amount), price)
+            except Exception as e:
+                raise ExchangeRequestError(error=e)
+
+            if 'uuid' in order_status:
+                order_id = order_status['uuid']
+                order = Order(
+                    dt=pd.Timestamp.utcnow(),
+                    asset=asset,
+                    amount=amount,
+                    stop=style.get_stop_price(is_buy),
+                    limit=style.get_limit_price(is_buy),
+                    id=order_id
+                )
+                return order
+            else:
+                if order_status == 'INSUFFICIENT_FUNDS':
+                    log.warn('not enough funds to create order')
+                    return None
+                elif order_status == 'DUST_TRADE_DISALLOWED_MIN_VALUE_50K_SAT':
+                    log.warn('Your order is too small, order at least 50K'
+                             ' Satoshi')
+                    return None
+                else:
+                    raise CreateOrderError(
+                        exchange=self.name,
+                        error=order_status
+                    )
+        else:
+            raise InvalidOrderStyle(exchange=self.name,
+                                    style=style.__class__.__name__)
+
+    def get_open_orders(self, asset):
+        symbol = self.get_symbol(asset)
+        try:
+            self.ask_request()
+            open_orders = self.api.getopenorders(symbol)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        orders = list()
+        for order_status in open_orders:
+            order = self._create_order(order_status)
+            orders.append(order)
+
+        return orders
+
+    def _create_order(self, order_status):
+        log.info(
+            'creating catalyst order from Bittrex {}'.format(order_status))
+        if order_status['CancelInitiated']:
+            status = ORDER_STATUS.CANCELLED
+        elif order_status['Closed'] is not None:
+            status = ORDER_STATUS.FILLED
+        else:
+            status = ORDER_STATUS.OPEN
+
+        date = pd.to_datetime(order_status['Opened'], utc=True)
+        amount = order_status['Quantity']
+        filled = amount - order_status['QuantityRemaining']
+        order = Order(
+            dt=date,
+            asset=self.assets[order_status['Exchange']],
+            amount=amount,
+            stop=None,  # Not yet supported by Bittrex
+            limit=order_status['Limit'],
+            filled=filled,
+            id=order_status['OrderUuid'],
+            commission=order_status['CommissionPaid']
+        )
+        order.status = status
+
+        executed_price = order_status['PricePerUnit']
+
+        return order, executed_price
+
+    def get_order(self, order_id):
+        log.info('retrieving order {}'.format(order_id))
+        try:
+            self.ask_request()
+            order_status = self.api.getorder(order_id)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if order_status is None:
+            raise OrderNotFound(order_id=order_id, exchange=self.name)
+
+        return self._create_order(order_status)
+
+    def cancel_order(self, order_param):
+        order_id = order_param.id \
+            if isinstance(order_param, Order) else order_param
+        log.info('cancelling order {}'.format(order_id))
+
+        try:
+            self.ask_request()
+            status = self.api.cancel(order_id)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'message' in status:
+            raise OrderCancelError(
+                order_id=order_id,
+                exchange=self.name,
+                error=status['message']
+            )
+
+    def get_candles(self, freq, assets, bar_count=None,
+                    start_dt=None, end_dt=None):
+        """
+        Supported Intervals
+        -------------------
+        day, oneMin, fiveMin, thirtyMin, hour
+
+        :param freq:
+        :param assets:
+        :param bar_count:
+        :param start_dt
+        :param end_dt
+        :return:
+        """
+
+        # TODO: this has no effect at the moment
+        if end_dt is None:
+            end_dt = pd.Timestamp.utcnow()
+
+        log.debug(
+            'retrieving {bars} {freq} candles on {exchange} from '
+            '{end_dt} for markets {symbols}, '.format(
+                bars=bar_count,
+                freq=freq,
+                exchange=self.name,
+                end_dt=end_dt,
+                symbols=get_symbols_string(assets)
+            )
+        )
+
+        if freq == '1T':
+            frequency = 'oneMin'
+        elif freq == '5T':
+            frequency = 'fiveMin'
+        elif freq == '30T':
+            frequency = 'thirtyMin'
+        elif freq == '60T':
+            frequency = 'hour'
+        elif freq == '1D':
+            frequency = 'day'
+        else:
+            raise InvalidHistoryFrequencyError(frequency=freq)
+
+        # Making sure that assets are iterable
+        asset_list = [assets] if isinstance(assets, TradingPair) else assets
+        for asset in asset_list:
+            end = int(time.mktime(end_dt.timetuple()))
+            url = '{url}/pub/market/GetTicks?marketName={symbol}' \
+                  '&tickInterval={frequency}&_={end}'.format(
+                url=URL2,
+                symbol=self.get_symbol(asset),
+                frequency=frequency,
+                end=end
+            )
+
+            try:
+                data = json.loads(urllib.request.urlopen(url).read().decode())
+            except Exception as e:
+                raise ExchangeRequestError(error=e)
+
+            if data['message']:
+                raise ExchangeRequestError(
+                    error='Unable to fetch candles {}'.format(data['message'])
+                )
+
+            candles = data['result']
+
+            def ohlc_from_candle(candle):
+                ohlc = dict(
+                    open=candle['O'],
+                    high=candle['H'],
+                    low=candle['L'],
+                    close=candle['C'],
+                    volume=candle['V'],
+                    price=candle['C'],
+                    last_traded=pd.to_datetime(candle['T'], utc=True)
+                )
+                return ohlc
+
+            ordered_candles = list(reversed(candles))
+            ohlc_map = dict()
+            if bar_count is None:
+                ohlc_map[asset] = ohlc_from_candle(ordered_candles[0])
+            else:
+                # TODO: optimize
+                ohlc_bars = []
+                for candle in ordered_candles[:bar_count]:
+                    ohlc = ohlc_from_candle(candle)
+                    ohlc_bars.append(ohlc)
+
+                ohlc_map[asset] = ohlc_bars
+
+        return ohlc_map[assets] \
+            if isinstance(assets, TradingPair) else ohlc_map
+
+    def tickers(self, assets):
+        """
+        As of v1.1, Bittrex only allows one ticker at the time.
+        So we have to make multiple calls to fetch multiple assets.
+
+        :param assets:
+        :return:
+        """
+        log.info('retrieving tickers')
+
+        ticks = dict()
+        for asset in assets:
+            symbol = self.get_symbol(asset)
+            try:
+                self.ask_request()
+                ticker = self.api.getticker(symbol)
+            except Exception as e:
+                raise ExchangeRequestError(error=e)
+
+            # TODO: catch invalid ticker
+            ticks[asset] = dict(
+                timestamp=pd.Timestamp.utcnow(),
+                bid=ticker['Bid'],
+                ask=ticker['Ask'],
+                last_price=ticker['Last']
+            )
+
+        log.debug('got tickers {}'.format(ticks))
+        return ticks
+
+    def get_account(self):
+        log.info('retrieving account data')
+        pass
+
+    def generate_symbols_json(self, filename=None):
+        symbol_map = {}
+
+        fn, r = download_exchange_symbols(self.name)
+        with open(fn) as data_file:
+            cached_symbols = json.load(data_file)
+
+        markets = self.api.getmarkets()
+        for market in markets:
+            exchange_symbol = market['MarketName']
+            symbol = '{market}_{base}'.format(
+                market=self.sanitize_curency_symbol(market['MarketCurrency']),
+                base=self.sanitize_curency_symbol(market['BaseCurrency'])
+            )
+
+            try:
+                end_daily = cached_symbols[exchange_symbol]['end_daily']
+            except KeyError as e:
+                end_daily = 'N/A'
+
+            try:
+                end_minute = cached_symbols[exchange_symbol]['end_minute']
+            except KeyError as e:
+                end_minute = 'N/A'
+
+            symbol_map[exchange_symbol] = dict(
+                symbol=symbol,
+                start_date=pd.to_datetime(market['Created'],
+                                          utc=True).strftime("%Y-%m-%d"),
+                end_daily=end_daily,
+                end_minute=end_minute,
+            )
+
+        if (filename is None):
+            filename = get_exchange_symbols_filename(self.name)
+
+        with open(filename, 'w') as f:
+            json.dump(symbol_map, f, sort_keys=True, indent=2,
+                      separators=(',', ':'))
+
+    def get_orderbook(self, asset, order_type='all', limit=100):
+        if order_type == 'all':
+            order_type = 'both'
+        elif order_type == 'bid':
+            order_type = 'buy'
+        elif order_type == 'ask':
+            order_type = 'sell'
+        else:
+            raise ValueError('invalid type')
+
+        exchange_symbol = asset.exchange_symbol
+        data = self.api.getorderbook(
+            market=exchange_symbol,
+            type=order_type,
+            depth=100
+        )
+
+        result = dict()
+        for exchange_type in data:
+            if exchange_type == 'buy':
+                order_type = 'bids'
+            elif exchange_type == 'sell':
+                order_type = 'asks'
+
+            result[order_type] = []
+            for entry in data[exchange_type]:
+                result[order_type].append(dict(
+                    rate=entry['Rate'],
+                    quantity=entry['Quantity']
+                ))
+
+        return result

catalyst/exchange/bittrex/bittrex_api.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python
+import json
+import time
+import hmac
+import hashlib
+import ssl
+
+# Workaround for backwards compatibility
+# https://stackoverflow.com/questions/3745771/urllib-request-in-python-2-7
+from six.moves import urllib
+
+urlopen = urllib.request.urlopen
+
+
+class Bittrex_api(object):
+    def __init__(self, key, secret):
+        self.key = key
+        self.secret = secret
+        self.public = ['getmarkets', 'getcurrencies', 'getticker',
+                       'getmarketsummaries', 'getmarketsummary',
+                       'getorderbook', 'getmarkethistory']
+        self.market = ['buylimit', 'buymarket', 'selllimit', 'sellmarket',
+                       'cancel', 'getopenorders']
+        self.account = ['getbalances', 'getbalance', 'getdepositaddress',
+                        'withdraw', 'getorder', 'getorderhistory',
+                        'getwithdrawalhistory', 'getdeposithistory']
+
+    def query(self, method, values={}):
+        if method in self.public:
+            url = 'https://bittrex.com/api/v1.1/public/'
+        elif method in self.market:
+            url = 'https://bittrex.com/api/v1.1/market/'
+        elif method in self.account:
+            url = 'https://bittrex.com/api/v1.1/account/'
+        else:
+            return 'Something went wrong, sorry.'
+
+        url += method + '?' + urllib.parse.urlencode(values)
+
+        if method not in self.public:
+            url += '&apikey=' + self.key
+            url += '&nonce=' + str(int(time.time()))
+
+            signature = hmac.new(self.secret.encode('utf-8'),
+                                 url.encode('utf-8'),
+                                 hashlib.sha512).hexdigest()
+            headers = {'apisign': signature}
+        else:
+            headers = {}
+
+        req = urllib.request.Request(url, headers=headers)
+        response = json.loads(urlopen(
+            req, context=ssl._create_unverified_context()).read())
+
+        if response["result"]:
+            return response["result"]
+        else:
+            return response["message"]
+
+    def getmarkets(self):
+        return self.query('getmarkets')
+
+    def getcurrencies(self):
+        return self.query('getcurrencies')
+
+    def getticker(self, market):
+        return self.query('getticker', {'market': market})
+
+    def getmarketsummaries(self):
+        return self.query('getmarketsummaries')
+
+    def getmarketsummary(self, market):
+        return self.query('getmarketsummary', {'market': market})
+
+    def getorderbook(self, market, type, depth=20):
+        return self.query('getorderbook',
+                          {'market': market, 'type': type, 'depth': depth})
+
+    def getmarkethistory(self, market, count=20):
+        return self.query('getmarkethistory',
+                          {'market': market, 'count': count})
+
+    def buylimit(self, market, quantity, rate):
+        return self.query('buylimit', {'market': market, 'quantity': quantity,
+                                       'rate': rate})
+
+    def buymarket(self, market, quantity):
+        return self.query('buymarket',
+                          {'market': market, 'quantity': quantity})
+
+    def selllimit(self, market, quantity, rate):
+        return self.query('selllimit', {'market': market, 'quantity': quantity,
+                                        'rate': rate})
+
+    def sellmarket(self, market, quantity):
+        return self.query('sellmarket',
+                          {'market': market, 'quantity': quantity})
+
+    def cancel(self, uuid):
+        return self.query('cancel', {'uuid': uuid})
+
+    def getopenorders(self, market):
+        return self.query('getopenorders', {'market': market})
+
+    def getbalances(self):
+        return self.query('getbalances')
+
+    def getbalance(self, currency):
+        return self.query('getbalance', {'currency': currency})
+
+    def getdepositaddress(self, currency):
+        return self.query('getdepositaddress', {'currency': currency})
+
+    def withdraw(self, currency, quantity, address):
+        return self.query('withdraw',
+                          {'currency': currency, 'quantity': quantity,
+                           'address': address})
+
+    def getorder(self, uuid):
+        return self.query('getorder', {'uuid': uuid})
+
+    def getorderhistory(self, market, count):
+        return self.query('getorderhistory',
+                          {'market': market, 'count': count})
+
+    def getwithdrawalhistory(self, currency, count):
+        return self.query('getwithdrawalhistory',
+                          {'currency': currency, 'count': count})
+
+    def getdeposithistory(self, currency, count):
+        return self.query('getdeposithistory',
+                          {'currency': currency, 'count': count})

catalyst/exchange/bittrex/extensions-example.py

@@ -0,0 +1,7 @@
+from catalyst.data.bundles import register
+from catalyst.exchange.exchange_bundle import exchange_bundle
+
+symbols = (
+    'neo_btc',
+)
+register('exchange_bitfinex', exchange_bundle('bitfinex', symbols))

catalyst/exchange/bundle_utils.py

@@ -0,0 +1,319 @@
+import calendar
+import os
+import tarfile
+from datetime import timedelta, datetime, date
+
+import numpy as np
+import pandas as pd
+import pytz
+
+from catalyst.data.bundles.core import download_without_progress
+from catalyst.exchange.exchange_utils import get_exchange_bundles_folder
+
+EXCHANGE_NAMES = ['bitfinex', 'bittrex', 'poloniex']
+API_URL = 'http://data.enigma.co/api/v1'
+
+
+def get_date_from_ms(ms):
+    """
+    The date from the number of miliseconds from the epoch.
+
+    Parameters
+    ----------
+    ms: int
+
+    Returns
+    -------
+    datetime
+
+    """
+    return datetime.fromtimestamp(ms / 1000.0)
+
+
+def get_seconds_from_date(date):
+    """
+    The number of seconds from the epoch.
+
+    Parameters
+    ----------
+    date: datetime
+
+    Returns
+    -------
+    int
+
+    """
+    epoch = datetime.utcfromtimestamp(0)
+    epoch = epoch.replace(tzinfo=pytz.UTC)
+
+    return int((date - epoch).total_seconds())
+
+
+def get_bcolz_chunk(exchange_name, symbol, data_frequency, period):
+    """
+    Download and extract a bcolz bundle.
+
+    Parameters
+    ----------
+    exchange_name: str
+    symbol: str
+    data_frequency: str
+    period: str
+
+    Returns
+    -------
+    str
+        Filename: bitfinex-daily-neo_eth-2017-10.tar.gz
+
+    """
+    root = get_exchange_bundles_folder(exchange_name)
+    name = '{exchange}-{frequency}-{symbol}-{period}'.format(
+        exchange=exchange_name,
+        frequency=data_frequency,
+        symbol=symbol,
+        period=period
+    )
+    path = os.path.join(root, name)
+
+    if not os.path.isdir(path):
+        url = 'https://s3.amazonaws.com/enigmaco/catalyst-bundles/' \
+              'exchange-{exchange}/{name}.tar.gz'.format(
+            exchange=exchange_name,
+            name=name
+        )
+
+        bytes = download_without_progress(url)
+        with tarfile.open('r', fileobj=bytes) as tar:
+            tar.extractall(path)
+
+    return path
+
+
+def get_delta(periods, data_frequency):
+    """
+    Get a time delta based on the specified data frequency.
+
+    Parameters
+    ----------
+    periods: int
+    data_frequency: str
+
+    Returns
+    -------
+    timedelta
+
+    """
+    return timedelta(minutes=periods) \
+        if data_frequency == 'minute' else timedelta(days=periods)
+
+
+def get_periods_range(start_dt, end_dt, freq):
+    """
+    Get a date range for the specified parameters.
+
+    Parameters
+    ----------
+    start_dt: datetime
+    end_dt: datetime
+    freq: str
+
+    Returns
+    -------
+    DateTimeIndex
+
+    """
+    if freq == 'minute':
+        freq = 'T'
+
+    elif freq == 'daily':
+        freq = 'D'
+
+    return pd.date_range(start_dt, end_dt, freq=freq)
+
+
+def get_periods(start_dt, end_dt, freq):
+    """
+    The number of periods in the specified range.
+
+    Parameters
+    ----------
+    start_dt: datetime
+    end_dt: datetime
+    freq: str
+
+    Returns
+    -------
+    int
+
+    """
+    return len(get_periods_range(start_dt, end_dt, freq))
+
+
+def get_start_dt(end_dt, bar_count, data_frequency, include_first=True):
+    """
+    The start date based on specified end date and data frequency.
+
+    Parameters
+    ----------
+    end_dt: datetime
+    bar_count: int
+    data_frequency: str
+
+    Returns
+    -------
+    datetime
+
+    """
+    periods = bar_count
+    if periods > 1:
+        delta = get_delta(periods, data_frequency)
+        start_dt = end_dt - delta
+
+        if not include_first:
+            start_dt += get_delta(1, data_frequency)
+    else:
+        start_dt = end_dt
+
+    return start_dt
+
+
+def get_period_label(dt, data_frequency):
+    """
+    The period label for the specified date and frequency.
+
+    Parameters
+    ----------
+    dt: datetime
+    data_frequency: str
+
+    Returns
+    -------
+    str
+
+    """
+    return '{}-{:02d}'.format(dt.year, dt.month) if data_frequency == 'minute' \
+        else '{}'.format(dt.year)
+
+
+def get_month_start_end(dt, first_day=None, last_day=None):
+    """
+    The first and last day of the month for the specified date.
+
+    Parameters
+    ----------
+    dt: datetime
+    first_day: datetime
+    last_day: datetime
+
+    Returns
+    -------
+    datetime, datetime
+
+    """
+    month_range = calendar.monthrange(dt.year, dt.month)
+
+    if first_day:
+        month_start = first_day
+    else:
+        month_start = pd.to_datetime(datetime(
+            dt.year, dt.month, 1, 0, 0, 0, 0
+        ), utc=True)
+
+    if last_day:
+        month_end = last_day
+    else:
+        month_end = pd.to_datetime(datetime(
+            dt.year, dt.month, month_range[1], 23, 59, 0, 0
+        ), utc=True)
+
+        if month_end > pd.Timestamp.utcnow():
+            month_end = pd.Timestamp.utcnow().floor('1D')
+
+    return month_start, month_end
+
+
+def get_year_start_end(dt, first_day=None, last_day=None):
+    """
+    The first and last day of the year for the specified date.
+
+    Parameters
+    ----------
+
+    dt: datetime
+    first_day: datetime
+    last_day: datetime
+
+    Returns
+    -------
+    datetime, datetime
+
+    """
+    year_start = first_day if first_day \
+        else pd.to_datetime(date(dt.year, 1, 1), utc=True)
+    year_end = last_day if last_day \
+        else pd.to_datetime(date(dt.year, 12, 31), utc=True)
+
+    if year_end > pd.Timestamp.utcnow():
+        year_end = pd.Timestamp.utcnow().floor('1D')
+
+    return year_start, year_end
+
+
+def get_df_from_arrays(arrays, periods):
+    """
+    A DataFrame from the specified OHCLV arrays.
+
+    Parameters
+    ----------
+    arrays: Object
+    periods: DateTimeIndex
+
+    Returns
+    -------
+    DataFrame
+
+    """
+    ohlcv = dict()
+    for index, field in enumerate(
+            ['open', 'high', 'low', 'close', 'volume']):
+        ohlcv[field] = arrays[index].flatten()
+
+    df = pd.DataFrame(
+        data=ohlcv,
+        index=periods
+    )
+    return df
+
+
+def range_in_bundle(asset, start_dt, end_dt, reader):
+    """
+    Evaluate whether price data of an asset is included has been ingested in
+    the exchange bundle for the given date range.
+
+    Parameters
+    ----------
+    asset: TradingPair
+    start_dt: datetime
+    end_dt: datetime
+    reader: BcolzBarMinuteReader
+
+    Returns
+    -------
+    bool
+
+    """
+    has_data = True
+    dates = [start_dt, end_dt]
+
+    while dates and has_data:
+        try:
+            dt = dates.pop(0)
+            close = reader.get_value(asset.sid, dt, 'close')
+
+            if np.isnan(close):
+                has_data = False
+
+        except Exception as e:
+            has_data = False
+
+    return has_data

catalyst/exchange/exchange.py

@@ -0,0 +1,950 @@
+import abc
+from abc import ABCMeta, abstractmethod, abstractproperty
+from datetime import timedelta
+from time import sleep
+
+import numpy as np
+import pandas as pd
+from catalyst.assets._assets import TradingPair
+from logbook import Logger
+
+from catalyst.constants import LOG_LEVEL
+from catalyst.data.data_portal import BASE_FIELDS
+from catalyst.exchange.bundle_utils import get_start_dt, \
+    get_delta, get_periods, get_periods_range
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.exchange_errors import MismatchingBaseCurrencies, \
+    InvalidOrderStyle, BaseCurrencyNotFoundError, SymbolNotFoundOnExchange, \
+    PricingDataNotLoadedError, \
+    NoDataAvailableOnExchange
+from catalyst.exchange.exchange_execution import ExchangeStopLimitOrder, \
+    ExchangeLimitOrder, ExchangeStopOrder
+from catalyst.exchange.exchange_portfolio import ExchangePortfolio
+from catalyst.exchange.exchange_utils import get_exchange_symbols, \
+    get_frequency, resample_history_df
+from catalyst.finance.order import ORDER_STATUS
+from catalyst.finance.transaction import Transaction
+from catalyst.utils.deprecate import deprecated
+
+log = Logger('Exchange', level=LOG_LEVEL)
+
+
+class Exchange:
+    __metaclass__ = ABCMeta
+
+    def __init__(self):
+        self.name = None
+        self.assets = {}
+        self._portfolio = None
+        self.minute_writer = None
+        self.minute_reader = None
+        self.base_currency = None
+
+        self.num_candles_limit = None
+        self.max_requests_per_minute = None
+        self.request_cpt = None
+        self.bundle = ExchangeBundle(self)
+
+    @property
+    def positions(self):
+        return self.portfolio.positions
+
+    @property
+    def portfolio(self):
+        """
+        The exchange portfolio
+
+        Returns
+        -------
+        ExchangePortfolio
+        """
+        if self._portfolio is None:
+            self._portfolio = ExchangePortfolio(
+                start_date=pd.Timestamp.utcnow()
+            )
+            self.synchronize_portfolio()
+
+        return self._portfolio
+
+    @abstractproperty
+    def account(self):
+        pass
+
+    @abstractproperty
+    def time_skew(self):
+        pass
+
+    def is_open(self, dt):
+        """
+        Is the exchange open
+
+        Parameters
+        ----------
+        dt: Timestamp
+
+        Returns
+        -------
+        bool
+
+        """
+        # TODO: implement for each exchange.
+        return True
+
+    def ask_request(self):
+        """
+        Asks permission to issue a request to the exchange.
+        The primary purpose is to avoid hitting rate limits.
+
+        The application will pause if the maximum requests per minute
+        permitted by the exchange is exceeded.
+
+        Returns
+        -------
+        bool
+
+        """
+        now = pd.Timestamp.utcnow()
+        if not self.request_cpt:
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+
+        cpt_date = list(self.request_cpt.keys())[0]
+        cpt = self.request_cpt[cpt_date]
+
+        if now > cpt_date + timedelta(minutes=1):
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+
+        if cpt >= self.max_requests_per_minute:
+            delta = now - cpt_date
+
+            sleep_period = 60 - delta.total_seconds()
+            sleep(sleep_period)
+
+            now = pd.Timestamp.utcnow()
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+        else:
+            self.request_cpt[cpt_date] += 1
+
+    def get_symbol(self, asset):
+        """
+        The the exchange specific symbol of the specified market.
+
+        Parameters
+        ----------
+        asset: TradingPair
+
+        Returns
+        -------
+        str
+
+        """
+        symbol = None
+
+        for key in self.assets:
+            if not symbol and self.assets[key].symbol == asset.symbol:
+                symbol = key
+
+        if not symbol:
+            raise ValueError('Currency %s not supported by exchange %s' %
+                             (asset['symbol'], self.name.title()))
+
+        return symbol
+
+    def get_symbols(self, assets):
+        """
+        Get a list of symbols corresponding to each given asset.
+
+        Parameters
+        ----------
+        assets: list[TradingPair]
+
+        Returns
+        -------
+        list[str]
+
+        """
+        symbols = []
+        for asset in assets:
+            symbols.append(self.get_symbol(asset))
+
+        return symbols
+
+    def get_assets(self, symbols=None):
+        """
+        The list of markets for the specified symbols.
+
+        Parameters
+        ----------
+        symbols: list[str]
+
+        Returns
+        -------
+        list[TradingPair]
+
+        """
+        assets = []
+
+        if symbols is not None:
+            for symbol in symbols:
+                asset = self.get_asset(symbol)
+                assets.append(asset)
+        else:
+            for key in self.assets:
+                assets.append(self.assets[key])
+
+        return assets
+
+    def get_asset(self, symbol):
+        """
+        The market for the specified symbol.
+
+        Parameters
+        ----------
+        symbol: str
+
+        Returns
+        -------
+        TradingPair
+
+        """
+        asset = None
+
+        for key in self.assets:
+            if not asset and self.assets[key].symbol.lower() == symbol.lower():
+                asset = self.assets[key]
+
+        if not asset:
+            supported_symbols = [
+                pair.symbol for pair in list(self.assets.values())
+            ]
+
+            raise SymbolNotFoundOnExchange(
+                symbol=symbol,
+                exchange=self.name.title(),
+                supported_symbols=supported_symbols
+            )
+
+        return asset
+
+    def fetch_symbol_map(self):
+        return get_exchange_symbols(self.name)
+
+    def load_assets(self):
+        """
+        Populate the 'assets' attribute with a dictionary of Assets.
+        The key of the resulting dictionary is the exchange specific
+        currency pair symbol. The universal symbol is contained in the
+        'symbol' attribute of each asset.
+
+        Notes
+        -----
+        The sid of each asset is calculated based on a numeric hash of the
+        universal symbol. This simple approach avoids maintaining a mapping
+        of sids.
+
+        This method can be overridden if an exchange offers equivalent data
+        via its api.
+
+        """
+        symbol_map = self.fetch_symbol_map()
+        for exchange_symbol in symbol_map:
+            asset = symbol_map[exchange_symbol]
+
+            if 'start_date' in asset:
+                start_date = pd.to_datetime(asset['start_date'], utc=True)
+            else:
+                start_date = None
+
+            if 'end_date' in asset:
+                end_date = pd.to_datetime(asset['end_date'], utc=True)
+            else:
+                end_date = None
+
+            if 'leverage' in asset:
+                leverage = asset['leverage']
+            else:
+                leverage = 1.0
+
+            if 'asset_name' in asset:
+                asset_name = asset['asset_name']
+            else:
+                asset_name = None
+
+            if 'min_trade_size' in asset:
+                min_trade_size = asset['min_trade_size']
+            else:
+                min_trade_size = 0.0000001
+
+            if 'end_daily' in asset and asset['end_daily'] != 'N/A':
+                end_daily = pd.to_datetime(asset['end_daily'], utc=True)
+            else:
+                end_daily = None
+
+            if 'end_minute' in asset and asset['end_minute'] != 'N/A':
+                end_minute = pd.to_datetime(asset['end_minute'], utc=True)
+            else:
+                end_minute = None
+
+            trading_pair = TradingPair(
+                symbol=asset['symbol'],
+                exchange=self.name,
+                start_date=start_date,
+                end_date=end_date,
+                leverage=leverage,
+                asset_name=asset_name,
+                min_trade_size=min_trade_size,
+                end_daily=end_daily,
+                end_minute=end_minute,
+                exchange_symbol=exchange_symbol
+            )
+
+            self.assets[exchange_symbol] = trading_pair
+
+    def check_open_orders(self):
+        """
+        Loop through the list of open orders in the Portfolio object.
+        For each executed order found, create a transaction and apply to the
+        Portfolio.
+
+        Returns
+        -------
+        list[Transaction]
+
+        """
+        transactions = list()
+        if self.portfolio.open_orders:
+            for order_id in list(self.portfolio.open_orders):
+                log.debug('found open order: {}'.format(order_id))
+
+                order, executed_price = self.get_order(order_id)
+                log.debug('got updated order {} {}'.format(
+                    order, executed_price))
+
+                if order.status == ORDER_STATUS.FILLED:
+                    transaction = Transaction(
+                        asset=order.asset,
+                        amount=order.amount,
+                        dt=pd.Timestamp.utcnow(),
+                        price=executed_price,
+                        order_id=order.id,
+                        commission=order.commission
+                    )
+                    transactions.append(transaction)
+
+                    self.portfolio.execute_order(order, transaction)
+
+                elif order.status == ORDER_STATUS.CANCELLED:
+                    self.portfolio.remove_order(order)
+
+                else:
+                    delta = pd.Timestamp.utcnow() - order.dt
+                    log.info(
+                        'order {order_id} still open after {delta}'.format(
+                            order_id=order_id,
+                            delta=delta
+                        )
+                    )
+        return transactions
+
+    def get_spot_value(self, assets, field, dt=None, data_frequency='minute'):
+        """
+        Public API method that returns a scalar value representing the value
+        of the desired asset's field at either the given dt.
+
+        Parameters
+        ----------
+        assets : Asset, ContinuousFuture, or iterable of same.
+            The asset or assets whose data is desired.
+        field : {'open', 'high', 'low', 'close', 'volume',
+                 'price', 'last_traded'}
+            The desired field of the asset.
+        dt : pd.Timestamp
+            The timestamp for the desired value.
+        data_frequency : str
+            The frequency of the data to query; i.e. whether the data is
+            'daily' or 'minute' bars
+
+        Returns
+        -------
+        value : float, int, or pd.Timestamp
+            The spot value of ``field`` for ``asset`` The return type is based
+            on the ``field`` requested. If the field is one of 'open', 'high',
+            'low', 'close', or 'price', the value will be a float. If the
+            ``field`` is 'volume' the value will be a int. If the ``field`` is
+            'last_traded' the value will be a Timestamp.
+
+        Bitfinex timeframes
+        -------------------
+        Available values: '1m', '5m', '15m', '30m', '1h', '3h', '6h', '12h',
+         '1D', '7D', '14D', '1M'
+        """
+        if field not in BASE_FIELDS:
+            raise KeyError('Invalid column: {}'.format(field))
+
+        values = []
+        for asset in assets:
+            value = self.get_single_spot_value(asset, field, data_frequency)
+            values.append(value)
+
+        return values
+
+    def get_single_spot_value(self, asset, field, data_frequency):
+        """
+        Similar to 'get_spot_value' but for a single asset
+
+        Notes
+        -----
+        We're writing each minute bar to disk using zipline's machinery.
+        This is especially useful when running multiple algorithms
+        concurrently. By using local data when possible, we try to reaching
+        request limits on exchanges.
+
+        Parameters
+        ----------
+        asset: TradingPair
+        field: str
+        data_frequency: str
+
+        Returns
+        -------
+        float
+            The spot value of the given asset / field
+
+        """
+        log.debug(
+            'fetching spot value {field} for symbol {symbol}'.format(
+                symbol=asset.symbol,
+                field=field
+            )
+        )
+
+        freq = '1T' if data_frequency == 'minute' else '1D'
+        ohlc = self.get_candles(freq, asset)
+        if field not in ohlc:
+            raise KeyError('Invalid column: %s' % field)
+
+        value = ohlc[field]
+        log.debug('got spot value: {}'.format(value))
+
+        return value
+
+    def get_series_from_candles(self, candles, start_dt, end_dt,
+                                data_frequency, field, previous_value=None):
+        """
+        Get a series of field data for the specified candles.
+
+        Parameters
+        ----------
+        candles: list[dict[str, float]]
+        start_dt: datetime
+        end_dt: datetime
+        data_frequency: str
+        field: str
+        previous_value: float
+
+        Returns
+        -------
+        Series
+
+        """
+        dates = [candle['last_traded'] for candle in candles]
+        values = [candle[field] for candle in candles]
+        series = pd.Series(values, index=dates)
+
+        periods = get_periods_range(
+            start_dt, end_dt, data_frequency
+        )
+        # TODO: ensure that this working as expected, if not use fillna
+        series = series.reindex(
+            periods,
+            method='ffill',
+            fill_value=previous_value,
+        )
+
+        return series
+
+    @deprecated
+    def get_history_window_direct(self,
+                                  assets,
+                                  end_dt,
+                                  bar_count,
+                                  frequency,
+                                  field,
+                                  data_frequency=None,
+                                  ffill=True):
+
+        """
+        Public API method that returns a dataframe containing the requested
+        history window.  Data is fully adjusted.
+
+        Parameters
+        ----------
+        assets : list[TradingPair]
+            The assets whose data is desired.
+
+        end_dt: datetime
+            The date of the last bar
+
+        bar_count: int
+            The number of bars desired.
+
+        frequency: string
+            "1d" or "1m"
+
+        field: string
+            The desired field of the asset.
+
+        data_frequency: string
+            The frequency of the data to query; i.e. whether the data is
+            'daily' or 'minute' bars.
+
+        # TODO: fill how?
+        ffill: boolean
+            Forward-fill missing values. Only has effect if field
+            is 'price'.
+
+        Returns
+        -------
+        DataFrame
+            A dataframe containing the requested data.
+
+        """
+        start_dt = get_start_dt(end_dt, bar_count, data_frequency)
+
+        # The get_history method supports multiple asset
+        candles = self.get_candles(
+            data_frequency=frequency,
+            assets=assets,
+            bar_count=bar_count,
+            start_dt=start_dt,
+            end_dt=end_dt
+        )
+        candle_series = self.get_series_from_candles(
+            candles=candles,
+            start_dt=start_dt,
+            end_dt=end_dt,
+            data_frequency=frequency,
+            field=field,
+        )
+
+        df = pd.DataFrame(candle_series)
+        return df
+
+    def get_history_window(self,
+                           assets,
+                           end_dt,
+                           bar_count,
+                           frequency,
+                           field,
+                           data_frequency=None,
+                           ffill=True):
+
+        """
+        Public API method that returns a dataframe containing the requested
+        history window.  Data is fully adjusted.
+
+        Parameters
+        ----------
+        assets : list[TradingPair]
+            The assets whose data is desired.
+
+        end_dt: datetime
+            The date of the last bar.
+
+        bar_count: int
+            The number of bars desired.
+
+        frequency: string
+            "1d" or "1m"
+
+        field: string
+            The desired field of the asset.
+
+        data_frequency: string
+            The frequency of the data to query; i.e. whether the data is
+            'daily' or 'minute' bars.
+
+        # TODO: fill how?
+        ffill: boolean
+            Forward-fill missing values. Only has effect if field
+            is 'price'.
+
+        Returns
+        -------
+        DataFrame
+            A dataframe containing the requested data.
+
+        """
+        freq, candle_size, unit, data_frequency = get_frequency(
+            frequency, data_frequency
+        )
+        adj_bar_count = candle_size * bar_count
+        try:
+            series = self.bundle.get_history_window_series_and_load(
+                assets=assets,
+                end_dt=end_dt,
+                bar_count=adj_bar_count,
+                field=field,
+                data_frequency=data_frequency
+            )
+        except (PricingDataNotLoadedError, NoDataAvailableOnExchange):
+            series = dict()
+
+        for asset in assets:
+            if asset not in series or series[asset].index[-1] < end_dt:
+                # Adding bars too recent to be contained in the consolidated
+                # exchanges bundles. We go directly against the exchange
+                # to retrieve the candles.
+                start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency)
+                trailing_dt = \
+                    series[asset].index[-1] + get_delta(1, data_frequency) \
+                        if asset in series else start_dt
+
+                # The get_history method supports multiple asset
+                # Use the original frequency to let each api optimize
+                # the size of result sets
+                trailing_bar_count = get_periods(
+                    trailing_dt, end_dt, freq
+                )
+                candles = self.get_candles(
+                    freq=freq,
+                    assets=asset,
+                    bar_count=trailing_bar_count,
+                    start_dt=start_dt,
+                    end_dt=end_dt
+                )
+
+                last_value = series[asset].iloc(0) if asset in series \
+                    else np.nan
+
+                # Create a series with the common data_frequency, ffill
+                # missing values
+                candle_series = self.get_series_from_candles(
+                    candles=candles,
+                    start_dt=trailing_dt,
+                    end_dt=end_dt,
+                    data_frequency=data_frequency,
+                    field=field,
+                    previous_value=last_value
+                )
+
+                if asset in series:
+                    series[asset].append(candle_series)
+
+                else:
+                    series[asset] = candle_series
+
+        df = resample_history_df(pd.DataFrame(series), freq, field)
+        # TODO: consider this more carefully
+        df.dropna(inplace=True)
+
+        return df
+
+    def synchronize_portfolio(self):
+        """
+        Update the portfolio cash and position balances based on the
+        latest ticker prices.
+
+        """
+        log.debug('synchronizing portfolio with exchange {}'.format(self.name))
+        balances = self.get_balances()
+
+        base_position_available = balances[self.base_currency] \
+            if self.base_currency in balances else None
+
+        if base_position_available is None:
+            raise BaseCurrencyNotFoundError(
+                base_currency=self.base_currency,
+                exchange=self.name.title()
+            )
+
+        portfolio = self._portfolio
+        portfolio.cash = base_position_available
+        log.debug('found base currency balance: {}'.format(portfolio.cash))
+
+        if portfolio.starting_cash is None:
+            portfolio.starting_cash = portfolio.cash
+
+        if portfolio.positions:
+            assets = list(portfolio.positions.keys())
+            tickers = self.tickers(assets)
+
+            portfolio.positions_value = 0.0
+            for asset in tickers:
+                # TODO: convert if the position is not in the base currency
+                ticker = tickers[asset]
+                position = portfolio.positions[asset]
+                position.last_sale_price = ticker['last_price']
+                position.last_sale_date = ticker['timestamp']
+
+                portfolio.positions_value += \
+                    position.amount * position.last_sale_price
+                portfolio.portfolio_value = \
+                    portfolio.positions_value + portfolio.cash
+
+    def order(self, asset, amount, limit_price=None, stop_price=None,
+              style=None):
+        """Place an order.
+
+        Parameters
+        ----------
+        asset : TradingPair
+            The asset that this order is for.
+
+        amount : int
+            The amount of shares to order. If ``amount`` is positive, this is
+            the number of shares to buy or cover. If ``amount`` is negative,
+            this is the number of shares to sell or short.
+
+        limit_price : float, optional
+            The limit price for the order.
+
+        stop_price : float, optional
+            The stop price for the order.
+
+        style : ExecutionStyle, optional
+            The execution style for the order.
+
+        Returns
+        -------
+        order_id : str or None
+            The unique identifier for this order, or None if no order was
+            placed.
+
+        Notes
+        -----
+        The ``limit_price`` and ``stop_price`` arguments provide shorthands for
+        passing common execution styles. Passing ``limit_price=N`` is
+        equivalent to ``style=LimitOrder(N)``. Similarly, passing
+        ``stop_price=M`` is equivalent to ``style=StopOrder(M)``, and passing
+        ``limit_price=N`` and ``stop_price=M`` is equivalent to
+        ``style=StopLimitOrder(N, M)``. It is an error to pass both a ``style``
+        and ``limit_price`` or ``stop_price``.
+
+        See Also
+        --------
+        :class:`catalyst.finance.execution.ExecutionStyle`
+        :func:`catalyst.api.order_value`
+        :func:`catalyst.api.order_percent`
+
+        """
+        if amount == 0:
+            log.warn('skipping order amount of 0')
+            return None
+
+        if asset.base_currency != self.base_currency.lower():
+            raise MismatchingBaseCurrencies(
+                base_currency=asset.base_currency,
+                algo_currency=self.base_currency
+            )
+
+        is_buy = (amount > 0)
+
+        if limit_price is not None and stop_price is not None:
+            style = ExchangeStopLimitOrder(limit_price, stop_price,
+                                           exchange=self.name)
+        elif limit_price is not None:
+            style = ExchangeLimitOrder(limit_price, exchange=self.name)
+
+        elif stop_price is not None:
+            style = ExchangeStopOrder(stop_price, exchange=self.name)
+
+        elif style is not None:
+            raise InvalidOrderStyle(exchange=self.name.title(),
+                                    style=style.__class__.__name__)
+        else:
+            raise ValueError('Incomplete order data.')
+
+        display_price = limit_price if limit_price is not None else stop_price
+        log.debug(
+            'issuing {side} order of {amount} {symbol} for {type}: {price}'.format(
+                side='buy' if is_buy else 'sell',
+                amount=amount,
+                symbol=asset.symbol,
+                type=style.__class__.__name__,
+                price='{}{}'.format(display_price, asset.base_currency)
+            )
+        )
+        order = self.create_order(asset, amount, is_buy, style)
+        if order:
+            self._portfolio.create_order(order)
+            return order.id
+        else:
+            return None
+
+    # The methods below must be implemented for each exchange.
+    @abstractmethod
+    def get_balances(self):
+        """
+        Retrieve wallet balances for the exchange.
+
+        Returns
+        -------
+        dict[TradingPair, float]
+
+        """
+        pass
+
+    @abstractmethod
+    def create_order(self, asset, amount, is_buy, style):
+        """
+        Place an order on the exchange.
+
+        Parameters
+        ----------
+        asset: TradingPair
+            The target market.
+
+        amount: float
+            The amount of shares to order. If ``amount`` is positive, this is
+            the number of shares to buy or cover. If ``amount`` is negative,
+            this is the number of shares to sell or short.
+
+        is_buy: bool
+            Is it a buy order?
+
+        style: ExecutionStyle
+
+        Returns
+        -------
+        Order
+
+        """
+        pass
+
+    @abstractmethod
+    def get_open_orders(self, asset):
+        """Retrieve all of the current open orders.
+
+        Parameters
+        ----------
+        asset : Asset
+            If passed and not None, return only the open orders for the given
+            asset instead of all open orders.
+
+        Returns
+        -------
+        open_orders : dict[list[Order]] or list[Order]
+            If no asset is passed this will return a dict mapping Assets
+            to a list containing all the open orders for the asset.
+            If an asset is passed then this will return a list of the open
+            orders for this asset.
+        """
+        pass
+
+    @abstractmethod
+    def get_order(self, order_id):
+        """Lookup an order based on the order id returned from one of the
+        order functions.
+
+        Parameters
+        ----------
+        order_id : str
+            The unique identifier for the order.
+
+        Returns
+        -------
+        order : Order
+            The order object.
+        execution_price: float
+            The execution price per share of the order
+        """
+        pass
+
+    @abstractmethod
+    def cancel_order(self, order_param):
+        """Cancel an open order.
+
+        Parameters
+        ----------
+        order_param : str or Order
+            The order_id or order object to cancel.
+        """
+        pass
+
+    @abstractmethod
+    def get_candles(self, freq, assets, bar_count=None,
+                    start_dt=None, end_dt=None):
+        """
+        Retrieve OHLCV candles for the given assets
+
+        Parameters
+        ----------
+        freq: str
+            The frequency alias per convention:
+            http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
+
+        assets: list[TradingPair]
+            The targeted assets.
+
+        bar_count: int
+            The number of bar desired. (default 1)
+
+        end_dt: datetime, optional
+            The last bar date.
+
+        start_dt: datetime, optional
+            The first bar date.
+
+        Returns
+        -------
+        dict[TradingPair, dict[str, Object]]
+            A dictionary of OHLCV candles. Each TradingPair instance is
+            mapped to a list of dictionaries with this structure:
+                open: float
+                high: float
+                low: float
+                close: float
+                volume: float
+                last_traded: datetime
+
+            See definition here:
+                http://www.investopedia.com/terms/o/ohlcchart.asp
+        """
+        pass
+
+    @abc.abstractmethod
+    def tickers(self, assets):
+        """
+        Retrieve current tick data for the given assets
+
+        Parameters
+        ----------
+        assets: list[TradingPair]
+
+        Returns
+        -------
+        list[dict[str, float]
+
+        """
+        pass
+
+    @abc.abstractmethod
+    def get_account(self):
+        """
+        Retrieve the account parameters.
+        """
+        pass
+
+    @abc.abstractmethod
+    def get_orderbook(self, asset, order_type, limit):
+        """
+        Retrieve the the orderbook for the given trading pair.
+
+        Parameters
+        ----------
+        asset: TradingPair
+        order_type: str
+            The type of orders: bid, ask or all
+        limit: int
+
+        Returns
+        -------
+        list[dict[str, float]
+        """
+        pass

catalyst/exchange/exchange_algorithm.py

@@ -0,0 +1,799 @@
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import pickle
+import signal
+import sys
+from collections import deque
+from datetime import timedelta
+from os import listdir
+from os.path import isfile, join
+from time import sleep
+
+import logbook
+import pandas as pd
+from catalyst.assets._assets import TradingPair
+
+import catalyst.protocol as zp
+from catalyst.algorithm import TradingAlgorithm
+from catalyst.constants import LOG_LEVEL
+from catalyst.errors import OrderInBeforeTradingStart
+from catalyst.exchange.exchange_blotter import ExchangeBlotter
+from catalyst.exchange.exchange_errors import (
+    ExchangeRequestError,
+    ExchangePortfolioDataError,
+    ExchangeTransactionError,
+    OrphanOrderError)
+from catalyst.exchange.exchange_execution import ExchangeStopLimitOrder, \
+    ExchangeLimitOrder, ExchangeStopOrder
+from catalyst.exchange.exchange_utils import save_algo_object, get_algo_object, \
+    get_algo_folder, get_algo_df, \
+    save_algo_df
+from catalyst.exchange.live_graph_clock import LiveGraphClock
+from catalyst.exchange.simple_clock import SimpleClock
+from catalyst.exchange.stats_utils import get_pretty_stats
+from catalyst.finance.execution import MarketOrder
+from catalyst.finance.performance.period import calc_period_stats
+from catalyst.gens.tradesimulation import AlgorithmSimulator
+from catalyst.utils.api_support import (
+    api_method,
+    disallowed_in_before_trading_start)
+from catalyst.utils.input_validation import error_keywords, ensure_upper_case, \
+    expect_types
+from catalyst.utils.math_utils import round_nearest
+from catalyst.utils.preprocess import preprocess
+
+log = logbook.Logger('exchange_algorithm', level=LOG_LEVEL)
+
+
+class ExchangeAlgorithmExecutor(AlgorithmSimulator):
+    def __init__(self, *args, **kwargs):
+        super(self.__class__, self).__init__(*args, **kwargs)
+
+
+class ExchangeTradingAlgorithmBase(TradingAlgorithm):
+    def __init__(self, *args, **kwargs):
+        self.exchanges = kwargs.pop('exchanges', None)
+
+        super(ExchangeTradingAlgorithmBase, self).__init__(*args, **kwargs)
+
+    def round_order(self, amount, asset):
+        """
+        We need fractions with cryptocurrencies
+
+        :param amount:
+        :return:
+        """
+        return round_nearest(amount, asset.min_trade_size)
+
+    @api_method
+    @preprocess(symbol_str=ensure_upper_case)
+    def symbol(self, symbol_str, exchange_name=None):
+        """Lookup an Equity by its ticker symbol.
+
+        Parameters
+        ----------
+        symbol_str : str
+            The ticker symbol for the equity to lookup.
+        exchange_name: str
+            The name of the exchange containing the symbol
+
+        Returns
+        -------
+        equity : Equity
+            The equity that held the ticker symbol on the current
+            symbol lookup date.
+
+        Raises
+        ------
+        SymbolNotFound
+            Raised when the symbols was not held on the current lookup date.
+
+        See Also
+        --------
+        :func:`catalyst.api.set_symbol_lookup_date`
+        """
+        # If the user has not set the symbol lookup date,
+        # use the end_session as the date for sybmol->sid resolution.
+
+        _lookup_date = self._symbol_lookup_date \
+            if self._symbol_lookup_date is not None \
+            else self.sim_params.end_session
+
+        if exchange_name is None:
+            exchange = list(self.exchanges.values())[0]
+        else:
+            exchange = self.exchanges[exchange_name]
+
+        return self.asset_finder.lookup_symbol(
+            symbol=symbol_str,
+            exchange=exchange,
+            as_of_date=_lookup_date
+        )
+
+    def prepare_period_stats(self, start_dt, end_dt):
+        """
+        Creates a dictionary representing the state of the tracker.
+
+        Parameters
+        ----------
+        start_dt: datetime
+        end_dt: datetime
+
+        Notes
+        -----
+        I rewrote this in an attempt to better control the stats.
+        I don't want things to happen magically through complex logic
+        pertaining to backtesting.
+
+        """
+        tracker = self.perf_tracker
+        period = tracker.todays_performance
+
+        pos_stats = period.position_tracker.stats()
+        period_stats = calc_period_stats(pos_stats, period.ending_cash)
+
+        stats = dict(
+            period_start=tracker.period_start,
+            period_end=tracker.period_end,
+            capital_base=tracker.capital_base,
+            progress=tracker.progress,
+            ending_value=period.ending_value,
+            ending_exposure=period.ending_exposure,
+            capital_used=period.cash_flow,
+            starting_value=period.starting_value,
+            starting_exposure=period.starting_exposure,
+            starting_cash=period.starting_cash,
+            ending_cash=period.ending_cash,
+            portfolio_value=period.ending_cash + period.ending_value,
+            pnl=period.pnl,
+            returns=period.returns,
+            period_open=period.period_open,
+            period_close=period.period_close,
+            gross_leverage=period_stats.gross_leverage,
+            net_leverage=period_stats.net_leverage,
+            short_exposure=pos_stats.short_exposure,
+            long_exposure=pos_stats.long_exposure,
+            short_value=pos_stats.short_value,
+            long_value=pos_stats.long_value,
+            longs_count=pos_stats.longs_count,
+            shorts_count=pos_stats.shorts_count,
+        )
+
+        # Merging cumulative risk
+        stats.update(tracker.cumulative_risk_metrics.to_dict())
+
+        # Merging latest recorded variables
+        stats.update(self.recorded_vars)
+
+        stats['positions'] = period.position_tracker.get_positions_list()
+
+        # we want the key to be absent, not just empty
+        # Only include transactions for given dt
+        stats['transactions'] = []
+        for date in period.processed_transactions:
+            if start_dt <= date < end_dt:
+                transactions = period.processed_transactions[date]
+                for t in transactions:
+                    stats['transactions'].append(t.to_dict())
+
+        stats['orders'] = []
+        for date in period.orders_by_modified:
+            if start_dt <= date < end_dt:
+                orders = period.orders_by_modified[date]
+                for order in orders:
+                    stats['orders'].append(orders[order].to_dict())
+
+        return stats
+
+
+class ExchangeTradingAlgorithmBacktest(ExchangeTradingAlgorithmBase):
+    def __init__(self, *args, **kwargs):
+        super(ExchangeTradingAlgorithmBacktest, self).__init__(*args, **kwargs)
+
+        self.frame_stats = list()
+        self.blotter = ExchangeBlotter(
+            data_frequency=self.data_frequency,
+            # Default to NeverCancel in catalyst
+            cancel_policy=self.cancel_policy,
+        )
+        log.info('initialized trading algorithm in backtest mode')
+
+    def _calculate_order(self, asset, amount,
+                         limit_price=None, stop_price=None, style=None):
+        # Raises a ZiplineError if invalid parameters are detected.
+        self.validate_order_params(asset,
+                                   amount,
+                                   limit_price,
+                                   stop_price,
+                                   style)
+
+        # Convert deprecated limit_price and stop_price parameters to use
+        # ExecutionStyle objects.
+        style = self.__convert_order_params_for_blotter(limit_price,
+                                                        stop_price,
+                                                        style)
+        return amount, style
+
+    @staticmethod
+    def __convert_order_params_for_blotter(limit_price, stop_price, style):
+        """
+        Helper method for converting deprecated limit_price and stop_price
+        arguments into ExecutionStyle instances.
+
+        This function assumes that either style == None or (limit_price,
+        stop_price) == (None, None).
+        """
+        if style:
+            assert (limit_price, stop_price) == (None, None)
+            return style
+        if limit_price and stop_price:
+            return ExchangeStopLimitOrder(limit_price, stop_price)
+        if limit_price:
+            return ExchangeLimitOrder(limit_price)
+        if stop_price:
+            return ExchangeStopOrder(stop_price)
+        else:
+            return MarketOrder()
+
+    def handle_data(self, data):
+        super(ExchangeTradingAlgorithmBacktest, self).handle_data(data)
+
+        minute_stats = self.prepare_period_stats(
+            data.current_dt, data.current_dt + timedelta(minutes=1))
+        self.frame_stats.append(minute_stats)
+
+    def analyze(self, perf):
+        stats = pd.DataFrame(self.frame_stats)
+        stats.set_index('period_close', inplace=True, drop=False)
+
+        super(ExchangeTradingAlgorithmBacktest, self).analyze(stats)
+
+
+class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
+    def __init__(self, *args, **kwargs):
+        self.algo_namespace = kwargs.pop('algo_namespace', None)
+        self.live_graph = kwargs.pop('live_graph', None)
+
+        self._clock = None
+        self.minute_stats = deque(maxlen=60)
+
+        self.pnl_stats = get_algo_df(self.algo_namespace, 'pnl_stats')
+
+        self.custom_signals_stats = \
+            get_algo_df(self.algo_namespace, 'custom_signals_stats')
+
+        self.exposure_stats = \
+            get_algo_df(self.algo_namespace, 'exposure_stats')
+
+        self.is_running = True
+
+        self.retry_check_open_orders = 5
+        self.retry_synchronize_portfolio = 5
+        self.retry_get_open_orders = 5
+        self.retry_order = 2
+        self.retry_delay = 5
+
+        self.stats_minutes = 5
+
+        super(ExchangeTradingAlgorithmLive, self).__init__(*args, **kwargs)
+
+        signal.signal(signal.SIGINT, self.signal_handler)
+
+        log.info('initialized trading algorithm in live mode')
+
+    def signal_handler(self, signal, frame):
+        """
+        Handles the keyboard interruption signal.
+
+        Parameters
+        ----------
+        signal
+        frame
+
+        Returns
+        -------
+
+        """
+        self.is_running = False
+
+        if self._analyze is None:
+            log.info('Interruption signal detected {}, exiting the '
+                     'algorithm'.format(signal))
+
+        else:
+            log.info('Interruption signal detected {}, calling `analyze()` '
+                     'before exiting the algorithm'.format(signal))
+
+            algo_folder = get_algo_folder(self.algo_namespace)
+            folder = join(algo_folder, 'daily_perf')
+            files = [f for f in listdir(folder) if isfile(join(folder, f))]
+
+            daily_perf_list = []
+            for item in files:
+                filename = join(folder, item)
+                with open(filename, 'rb') as handle:
+                    daily_perf_list.append(pickle.load(handle))
+
+            stats = pd.DataFrame(daily_perf_list)
+
+            self.analyze(stats)
+
+        sys.exit(0)
+
+    @property
+    def clock(self):
+        if self._clock is None:
+            return self._create_clock()
+        else:
+            return self._clock
+
+    def _create_clock(self):
+
+        # The calendar's execution times are the minutes over which we actually
+        # want to run the clock. Typically the execution times simply adhere to
+        # the market open and close times. In the case of the futures calendar,
+        # for example, we only want to simulate over a subset of the full 24
+        # hour calendar, so the execution times dictate a market open time of
+        # 6:31am US/Eastern and a close of 5:00pm US/Eastern.
+
+        # In our case, we are trading around the clock, so the market close
+        # corresponds to the last minute of the day.
+
+        # This method is taken from TradingAlgorithm.
+        # The clock has been replaced to use RealtimeClock
+        # TODO: should we apply a time skew? not sure to understand the utility.
+
+        log.debug('creating clock')
+        if self.live_graph:
+            self._clock = LiveGraphClock(
+                self.sim_params.sessions,
+                context=self
+            )
+        else:
+            self._clock = SimpleClock(
+                self.sim_params.sessions,
+            )
+
+        return self._clock
+
+    def _create_generator(self, sim_params):
+        if self.perf_tracker is None:
+            self.perf_tracker = get_algo_object(
+                algo_name=self.algo_namespace,
+                key='perf_tracker'
+            )
+
+        # Call the simulation trading algorithm for side-effects:
+        # it creates the perf tracker
+        TradingAlgorithm._create_generator(self, sim_params)
+        self.trading_client = ExchangeAlgorithmExecutor(
+            self,
+            sim_params,
+            self.data_portal,
+            self.clock,
+            self._create_benchmark_source(),
+            self.restrictions,
+            universe_func=self._calculate_universe
+        )
+
+        return self.trading_client.transform()
+
+    def updated_portfolio(self):
+        """
+        We skip the entire performance tracker business and update the
+        portfolio directly.
+
+        Returns
+        -------
+        ExchangePortfolio
+
+        """
+        # TODO: build cumulative portfolio
+        return self.perf_tracker.get_portfolio(False)
+
+    def updated_account(self):
+        return self.perf_tracker.get_account(False)
+
+    def _synchronize_portfolio(self, attempt_index=0):
+        try:
+            for exchange_name in self.exchanges:
+                exchange = self.exchanges[exchange_name]
+
+                exchange.synchronize_portfolio()
+
+                # Applying the updated last_sales_price to the positions
+                # in the performance tracker. This seems a bit redundant
+                # but it will make sense when we have multiple exchange portfolios
+                # feeding into the same performance tracker.
+                tracker = self.perf_tracker.todays_performance.position_tracker
+                for asset in exchange.portfolio.positions:
+                    position = exchange.portfolio.positions[asset]
+                    tracker.update_position(
+                        asset=asset,
+                        last_sale_date=position.last_sale_date,
+                        last_sale_price=position.last_sale_price
+                    )
+        except ExchangeRequestError as e:
+            log.warn(
+                'update portfolio attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_synchronize_portfolio:
+                sleep(self.retry_delay)
+                self._synchronize_portfolio(attempt_index + 1)
+            else:
+                raise ExchangePortfolioDataError(
+                    data_type='update-portfolio',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    def _check_open_orders(self, attempt_index=0):
+        try:
+            orders = list()
+            for exchange_name in self.exchanges:
+                exchange = self.exchanges[exchange_name]
+                exchange_orders = exchange.check_open_orders()
+
+                orders += exchange_orders
+
+            return orders
+        except ExchangeRequestError as e:
+            log.warn(
+                'check open orders attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_check_open_orders:
+                sleep(self.retry_delay)
+                return self._check_open_orders(attempt_index + 1)
+            else:
+                raise ExchangePortfolioDataError(
+                    data_type='order-status',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    def add_pnl_stats(self, period_stats):
+        """
+        Save p&l stats.
+
+        Parameters
+        ----------
+        period_stats
+
+        Returns
+        -------
+
+        """
+        starting = period_stats['starting_cash']
+        current = period_stats['portfolio_value']
+        appreciation = (current / starting) - 1
+        perc = (appreciation * 100) if current != 0 else 0
+
+        log.debug('adding pnl stats: {:6f}%'.format(perc))
+
+        df = pd.DataFrame(
+            data=[dict(performance=perc)],
+            index=[period_stats['period_close']]
+        )
+        self.pnl_stats = pd.concat([self.pnl_stats, df])
+
+        save_algo_df(self.algo_namespace, 'pnl_stats', self.pnl_stats)
+
+    def add_custom_signals_stats(self, period_stats):
+        """
+        Save custom signals stats.
+
+        Parameters
+        ----------
+        period_stats
+
+        Returns
+        -------
+
+        """
+        log.debug('adding custom signals stats: {}'.format(self.recorded_vars))
+        df = pd.DataFrame(
+            data=[self.recorded_vars],
+            index=[period_stats['period_close']],
+        )
+        self.custom_signals_stats = pd.concat([self.custom_signals_stats, df])
+
+        save_algo_df(self.algo_namespace, 'custom_signals_stats',
+                     self.custom_signals_stats)
+
+    def add_exposure_stats(self, period_stats):
+        """
+        Save exposure stats.
+
+        Parameters
+        ----------
+        period_stats
+
+        Returns
+        -------
+
+        """
+        data = dict(
+            long_exposure=period_stats['long_exposure'],
+            base_currency=period_stats['ending_cash']
+        )
+        log.debug('adding exposure stats: {}'.format(data))
+
+        df = pd.DataFrame(
+            data=[data],
+            index=[period_stats['period_close']],
+        )
+        self.exposure_stats = pd.concat([self.exposure_stats, df])
+
+        save_algo_df(self.algo_namespace, 'exposure_stats',
+                     self.exposure_stats)
+
+    def handle_data(self, data):
+        """
+        Wrapper around the handle_data method of each algo.
+
+        Parameters
+        ----------
+        data
+
+        """
+        if not self.is_running:
+            return
+
+        self._synchronize_portfolio()
+
+        transactions = self._check_open_orders()
+        for transaction in transactions:
+            self.perf_tracker.process_transaction(transaction)
+
+        if self._handle_data:
+            self._handle_data(self, data)
+
+        # Unlike trading controls which remain constant unless placing an
+        # order, account controls can change each bar. Thus, must check
+        # every bar no matter if the algorithm places an order or not.
+        self.validate_account_controls()
+
+        try:
+            # Since the clock runs 24/7, I trying to disable the daily
+            # Performance tracker and keep only minute and cumulative
+            self.perf_tracker.update_performance()
+
+            minute_stats = self.prepare_period_stats(
+                data.current_dt, data.current_dt + timedelta(minutes=1))
+
+            # Saving the last hour in memory
+            self.minute_stats.append(minute_stats)
+
+            self.add_pnl_stats(minute_stats)
+            if self.recorded_vars:
+                self.add_custom_signals_stats(minute_stats)
+                recorded_cols = list(self.recorded_vars.keys())
+            else:
+                recorded_cols = None
+
+            self.add_exposure_stats(minute_stats)
+
+            print_df = pd.DataFrame(list(self.minute_stats))
+            log.info(
+                'statistics for the last {stats_minutes} minutes:\n{stats}'.format(
+                    stats_minutes=self.stats_minutes,
+                    stats=get_pretty_stats(
+                        stats_df=print_df,
+                        recorded_cols=recorded_cols,
+                        num_rows=self.stats_minutes
+                    )
+                ))
+
+            today = pd.to_datetime('today', utc=True)
+            daily_stats = self.prepare_period_stats(
+                start_dt=today,
+                end_dt=pd.Timestamp.utcnow()
+            )
+            save_algo_object(
+                algo_name=self.algo_namespace,
+                key=today.strftime('%Y-%m-%d'),
+                obj=daily_stats,
+                rel_path='daily_perf'
+            )
+
+        except Exception as e:
+            log.warn('unable to calculate performance: {}'.format(e))
+
+        # TODO: pickle does not seem to work in python 3
+        try:
+            save_algo_object(
+                algo_name=self.algo_namespace,
+                key='perf_tracker',
+                obj=self.perf_tracker
+            )
+        except Exception as e:
+            log.warn('unable to save minute perfs to disk: {}'.format(e))
+
+        try:
+            for exchange_name in self.exchanges:
+                exchange = self.exchanges[exchange_name]
+                save_algo_object(
+                    algo_name=self.algo_namespace,
+                    key='portfolio_{}'.format(exchange_name),
+                    obj=exchange.portfolio
+                )
+        except Exception as e:
+            log.warn('unable to save portfolio to disk: {}'.format(e))
+
+    def _order(self,
+               asset,
+               amount,
+               limit_price=None,
+               stop_price=None,
+               style=None,
+               attempt_index=0):
+        try:
+            exchange = self.exchanges[asset.exchange]
+            return exchange.order(asset, amount, limit_price,
+                                  stop_price,
+                                  style)
+        except ExchangeRequestError as e:
+            log.warn(
+                'order attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_order:
+                sleep(self.retry_delay)
+                return self._order(
+                    asset, amount, limit_price, stop_price, style,
+                    attempt_index + 1)
+            else:
+                raise ExchangeTransactionError(
+                    transaction_type='order',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    @api_method
+    @disallowed_in_before_trading_start(OrderInBeforeTradingStart())
+    @expect_types(asset=TradingPair)
+    def order(self,
+              asset,
+              amount,
+              limit_price=None,
+              stop_price=None,
+              style=None):
+        """
+        We use the exchange specific portfolio to place orders.
+        The cumulative portfolio does not contain open orders but exchange
+        portfolios do.
+
+        Parameters
+        ----------
+        asset: TradingPair
+        amount: float
+        limit_price: float
+        stop_price: float
+        style: Style
+        order: Order
+            The catalyst order object or None
+        """
+        amount, style = self._calculate_order(asset, amount,
+                                              limit_price, stop_price,
+                                              style)
+
+        order_id = self._order(asset, amount, limit_price, stop_price, style)
+
+        exchange = self.exchanges[asset.exchange]
+        exchange_portfolio = exchange.portfolio
+        if order_id is not None:
+
+            if order_id in exchange_portfolio.open_orders:
+                order = exchange_portfolio.open_orders[order_id]
+                self.perf_tracker.process_order(order)
+                return order
+
+            else:
+                raise OrphanOrderError(
+                    order_id=order_id,
+                    exchange=exchange.name
+                )
+        else:
+            log.warn('unable to order {} {} on exchange {}'.format(
+                amount, asset.symbol, asset.exchange))
+            return None
+
+    @api_method
+    def batch_market_order(self, share_counts):
+        raise NotImplementedError()
+
+    def _get_open_orders(self, asset=None, attempt_index=0):
+        try:
+            if asset:
+                exchange = self.exchanges[asset.exchange]
+                return exchange.get_open_orders(asset)
+
+            else:
+                open_orders = []
+                for exchange_name in self.exchanges:
+                    exchange = self.exchanges[exchange_name]
+                    exchange_orders = exchange.get_open_orders()
+                    open_orders.append(exchange_orders)
+
+                return open_orders
+        except ExchangeRequestError as e:
+            log.warn(
+                'open orders attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_get_open_orders:
+                sleep(self.retry_delay)
+                return self._get_open_orders(asset, attempt_index + 1)
+            else:
+                raise ExchangePortfolioDataError(
+                    data_type='open-orders',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    @error_keywords(sid='Keyword argument `sid` is no longer supported for '
+                        'get_open_orders. Use `asset` instead.')
+    @api_method
+    def get_open_orders(self, asset=None):
+        """Retrieve all of the current open orders.
+
+        Parameters
+        ----------
+        asset : Asset
+            If passed and not None, return only the open orders for the given
+            asset instead of all open orders.
+
+        Returns
+        -------
+        open_orders : dict[list[Order]] or list[Order]
+            If no asset is passed this will return a dict mapping Assets
+            to a list containing all the open orders for the asset.
+            If an asset is passed then this will return a list of the open
+            orders for this asset.
+        """
+        return self._get_open_orders(asset)
+
+    @api_method
+    def get_order(self, order_id, exchange_name):
+        """Lookup an order based on the order id returned from one of the
+        order functions.
+
+        Parameters
+        ----------
+        order_id : str
+            The unique identifier for the order.
+
+        Returns
+        -------
+        order : Order
+            The order object.
+        execution_price: float
+            The execution price per share of the order
+        """
+        exchange = self.exchanges[exchange_name]
+        return exchange.get_order(order_id)
+
+    @api_method
+    def cancel_order(self, order_param, exchange_name):
+        """Cancel an open order.
+
+        Parameters
+        ----------
+        order_param : str or Order
+            The order_id or order object to cancel.
+        """
+        exchange = self.exchanges[exchange_name]
+
+        order_id = order_param
+        if isinstance(order_param, zp.Order):
+            order_id = order_param.id
+
+        exchange.cancel_order(order_id)

catalyst/exchange/exchange_bcolz.py

@@ -0,0 +1,98 @@
+import numpy as np
+
+from catalyst import get_calendar
+from catalyst.data.minute_bars import BcolzMinuteBarReader, \
+    BcolzMinuteBarWriter
+
+
+class BcolzExchangeBarWriter(BcolzMinuteBarWriter):
+    def __init__(self, *args, **kwargs):
+        self._data_frequency = kwargs.pop('data_frequency', None)
+        kwargs.pop('minutes_per_day', None)
+        kwargs.pop('calendar', None)
+
+        end_session = kwargs.pop('end_session', None)
+        if end_session is not None:
+            end_session = end_session.floor('1d')
+
+        minutes_per_day = 1440 if self._data_frequency == 'minute' else 1
+        default_ohlc_ratio = kwargs.pop('default_ohlc_ratio', 100000000)
+        calendar = get_calendar('OPEN')
+
+        super(BcolzExchangeBarWriter, self) \
+            .__init__(*args, **dict(kwargs,
+                                    minutes_per_day=minutes_per_day,
+                                    default_ohlc_ratio=default_ohlc_ratio,
+                                    calendar=calendar,
+                                    end_session=end_session
+                                    ))
+
+
+class BcolzExchangeBarReader(BcolzMinuteBarReader):
+    def __init__(self, *args, **kwargs):
+        self._data_frequency = kwargs.pop('data_frequency', None)
+
+        super(BcolzExchangeBarReader, self).__init__(*args, **kwargs)
+
+    @property
+    def data_frequency(self):
+        return self._data_frequency
+
+    def load_raw_arrays(self, fields, start_dt, end_dt, sids):
+        """
+        Parameters
+        ----------
+        fields : list of str
+           'open', 'high', 'low', 'close', or 'volume'
+        start_dt: Timestamp
+           Beginning of the window range.
+        end_dt: Timestamp
+           End of the window range.
+        sids : list of int
+           The asset identifiers in the window.
+
+        Returns
+        -------
+        list of np.ndarray
+            A list with an entry per field of ndarrays with shape
+            (minutes in range, sids) with a dtype of float64, containing the
+            values for the respective field over start and end dt range.
+        """
+        start_idx = self._find_position_of_minute(start_dt)
+        end_idx = self._find_position_of_minute(end_dt)
+
+        periods = self.calendar.minutes_in_range(start_dt, end_dt) \
+            if self.data_frequency == 'minute' \
+            else self.calendar.sessions_in_range(start_dt, end_dt)
+
+        num_days = len(periods)
+        shape = num_days, len(sids)
+
+        all_fields = fields[:]
+        if len(all_fields) == 1 and all_fields[0] == 'volume':
+            all_fields.insert(0, 'close')
+
+        mask = None
+        data = []
+        for field in all_fields:
+            if field != 'volume':
+                out = np.full(shape, np.nan)
+            else:
+                out = np.zeros(shape, dtype=np.float64)
+
+            for i, sid in enumerate(sids):
+                carray = self._open_minute_file(field, sid)
+                a = carray[start_idx:end_idx + 1]
+
+                if mask is None:
+                    mask = a != 0
+
+                inverse_ratio = self._ohlc_ratio_inverse_for_sid(sid)
+                out[:len(mask), i][mask] = (
+                    a[mask] * inverse_ratio
+                )
+
+            if field in fields:
+                data.append(out)
+
+        return data

catalyst/exchange/exchange_blotter.py

@@ -0,0 +1,134 @@
+from catalyst.assets._assets import TradingPair
+from logbook import Logger
+
+from catalyst.constants import LOG_LEVEL
+from catalyst.finance.blotter import Blotter
+from catalyst.finance.commission import CommissionModel
+from catalyst.finance.slippage import SlippageModel
+from catalyst.finance.transaction import create_transaction
+
+log = Logger('exchange_blotter', level=LOG_LEVEL)
+
+# It seems like we need to accept greater slippage risk in cryptos
+# Orders won't often close at Equity levels.
+# TODO: should work with set_commission and set_slippage
+DEFAULT_SLIPPAGE_SPREAD = 0.0001
+DEFAULT_MAKER_FEE = 0.0015
+DEFAULT_TAKER_FEE = 0.0025
+
+
+class TradingPairFeeSchedule(CommissionModel):
+    """
+    Calculates a commission for a transaction based on a per percentage fee.
+
+    Parameters
+    ----------
+    fee : float, optional
+        The percentage fee.
+    """
+
+    def __init__(self,
+                 maker_fee=DEFAULT_MAKER_FEE,
+                 taker_fee=DEFAULT_TAKER_FEE):
+        self.maker_fee = maker_fee
+        self.taker_fee = taker_fee
+
+    def __repr__(self):
+        return (
+            '{class_name}(maker_fee={maker_fee}, '
+            'taker_fee={taker_fee})'.format(
+                class_name=self.__class__.__name__,
+                maker_fee=self.maker_fee,
+                taker_fee=self.taker_fee,
+            )
+        )
+
+    def calculate(self, order, transaction):
+        """
+        Calculate the final fee based on the order parameters.
+
+        :param order:
+        :param transaction:
+
+        :return float:
+            The total commission.
+        """
+        cost = abs(transaction.amount) * transaction.price
+
+        # Assuming just the taker fee for now
+        fee = cost * self.taker_fee
+        return fee
+
+
+class TradingPairFixedSlippage(SlippageModel):
+    """
+    Model slippage as a fixed spread.
+
+    Parameters
+    ----------
+    spread : float, optional
+        spread / 2 will be added to buys and subtracted from sells.
+    """
+
+    def __init__(self, spread=DEFAULT_SLIPPAGE_SPREAD):
+        super(TradingPairFixedSlippage, self).__init__()
+        self.spread = spread
+
+    def __repr__(self):
+        return '{class_name}(spread={spread})'.format(
+            class_name=self.__class__.__name__, spread=self.spread,
+        )
+
+    def simulate(self, data, asset, orders_for_asset):
+        self._volume_for_bar = 0
+
+        price = data.current(asset, 'close')
+
+        dt = data.current_dt
+        for order in orders_for_asset:
+            if order.open_amount == 0:
+                continue
+
+            order.check_triggers(price, dt)
+            if not order.triggered:
+                log.debug('order has not reached the trigger at current '
+                          'price {}'.format(price))
+                continue
+
+            execution_price, execution_volume = self.process_order(data, order)
+
+            transaction = create_transaction(
+                order, dt, execution_price, execution_volume
+            )
+
+            self._volume_for_bar += abs(transaction.amount)
+            yield order, transaction
+
+    def process_order(self, data, order):
+        price = data.current(order.asset, 'close')
+
+        if order.amount > 0:
+            # Buy order
+            adj_price = price * (1 + self.spread)
+        else:
+            # Sell order
+            adj_price = price * (1 - self.spread)
+
+        log.debug('added slippage to price: {} => {}'.format(price, adj_price))
+
+        return adj_price, order.amount
+
+
+class ExchangeBlotter(Blotter):
+    def __init__(self, *args, **kwargs):
+        super(ExchangeBlotter, self).__init__(*args, **kwargs)
+
+        # Using the equity models for now
+        # We may be able to define more sophisticated models based on the fee
+        # structure of each exchange.
+        self.slippage_models = {
+            TradingPair: TradingPairFixedSlippage()
+        }
+        self.commission_models = {
+            TradingPair: TradingPairFeeSchedule()
+        }

catalyst/exchange/exchange_bundle.py

@@ -0,0 +1,928 @@
+import os
+import shutil
+from functools import partial
+from itertools import chain
+from operator import is_not
+
+import numpy as np
+import pandas as pd
+from catalyst.assets._assets import TradingPair
+from datetime import datetime, timedelta
+from logbook import Logger
+from pytz import UTC
+from six import itervalues
+
+from catalyst import get_calendar
+from catalyst.constants import DATE_TIME_FORMAT, AUTO_INGEST
+from catalyst.constants import LOG_LEVEL
+from catalyst.data.minute_bars import BcolzMinuteOverlappingData, \
+    BcolzMinuteBarMetadata
+from catalyst.exchange.bundle_utils import range_in_bundle, \
+    get_bcolz_chunk, get_month_start_end, \
+    get_year_start_end, get_df_from_arrays, get_start_dt, get_period_label
+from catalyst.exchange.exchange_bcolz import BcolzExchangeBarReader, \
+    BcolzExchangeBarWriter
+from catalyst.exchange.exchange_errors import EmptyValuesInBundleError, \
+    TempBundleNotFoundError, \
+    NoDataAvailableOnExchange, \
+    PricingDataNotLoadedError
+from catalyst.exchange.exchange_utils import get_exchange_folder
+from catalyst.utils.cli import maybe_show_progress
+from catalyst.utils.paths import ensure_directory
+
+log = Logger('exchange_bundle', level=LOG_LEVEL)
+
+BUNDLE_NAME_TEMPLATE = os.path.join('{root}', '{frequency}_bundle')
+
+
+def _cachpath(symbol, type_):
+    return '-'.join([symbol, type_])
+
+
+class ExchangeBundle:
+    def __init__(self, exchange):
+        self.exchange = exchange
+        self.minutes_per_day = 1440
+        self.default_ohlc_ratio = 1000000
+        self._writers = dict()
+        self._readers = dict()
+        self.calendar = get_calendar('OPEN')
+
+    def get_assets(self, include_symbols, exclude_symbols):
+        # TODO: filter exclude symbols assets
+        if include_symbols is not None:
+            include_symbols_list = include_symbols.split(',')
+
+            return self.exchange.get_assets(include_symbols_list)
+
+        else:
+            return self.exchange.get_assets()
+
+    def get_reader(self, data_frequency, path=None):
+        """
+        Get a data writer object, either a new object or from cache
+
+        Returns
+        -------
+        BcolzMinuteBarReader | BcolzDailyBarReader
+
+        """
+        if path is None:
+            root = get_exchange_folder(self.exchange.name)
+            path = BUNDLE_NAME_TEMPLATE.format(
+                root=root,
+                frequency=data_frequency
+            )
+
+        if path in self._readers and self._readers[path] is not None:
+            return self._readers[path]
+
+        try:
+            self._readers[path] = BcolzExchangeBarReader(
+                rootdir=path,
+                data_frequency=data_frequency
+            )
+        except IOError:
+            self._readers[path] = None
+
+        return self._readers[path]
+
+    def update_metadata(self, writer, start_dt, end_dt):
+        pass
+
+    def get_writer(self, start_dt, end_dt, data_frequency):
+        """
+        Get a data writer object, either a new object or from cache
+
+        Returns
+        -------
+        BcolzMinuteBarWriter | BcolzDailyBarWriter
+
+        """
+        root = get_exchange_folder(self.exchange.name)
+        path = BUNDLE_NAME_TEMPLATE.format(
+            root=root,
+            frequency=data_frequency
+        )
+
+        if path in self._writers:
+            return self._writers[path]
+
+        ensure_directory(path)
+
+        if len(os.listdir(path)) > 0:
+
+            metadata = BcolzMinuteBarMetadata.read(path)
+
+            write_metadata = False
+            if start_dt < metadata.start_session:
+                write_metadata = True
+                start_session = start_dt
+            else:
+                start_session = metadata.start_session
+
+            if end_dt > metadata.end_session:
+                write_metadata = True
+
+                end_session = end_dt
+            else:
+                end_session = metadata.end_session
+
+            self._writers[path] = \
+                BcolzExchangeBarWriter(
+                    rootdir=path,
+                    start_session=start_session,
+                    end_session=end_session,
+                    write_metadata=write_metadata,
+                    data_frequency=data_frequency
+                )
+        else:
+            self._writers[path] = BcolzExchangeBarWriter(
+                rootdir=path,
+                start_session=start_dt,
+                end_session=end_dt,
+                write_metadata=True,
+                data_frequency=data_frequency
+            )
+
+        return self._writers[path]
+
+    def filter_existing_assets(self, assets, start_dt, end_dt, data_frequency):
+        """
+        For each asset, get the close on the start and end dates of the chunk.
+            If the data exists, the chunk ingestion is complete.
+            If any data is missing we ingest the data.
+
+        Parameters
+        ----------
+        assets: list[TradingPair]
+            The assets is scope.
+        start_dt: datetime
+            The chunk start date.
+        end_dt: datetime
+            The chunk end date.
+        data_frequency: str
+
+        Returns
+        -------
+        list[TradingPair]
+            The assets missing from the bundle
+        """
+        reader = self.get_reader(data_frequency)
+        missing_assets = []
+        for asset in assets:
+            has_data = range_in_bundle(asset, start_dt, end_dt, reader)
+
+            if not has_data:
+                missing_assets.append(asset)
+
+        return missing_assets
+
+    def _write(self, data, writer, data_frequency):
+        try:
+            writer.write(
+                data=data,
+                show_progress=False,
+                invalid_data_behavior='raise'
+            )
+        except BcolzMinuteOverlappingData as e:
+            log.debug('chunk already exists: {}'.format(e))
+        except Exception as e:
+            log.warn('error when writing data: {}, trying again'.format(e))
+
+            # This is workaround, there is an issue with empty
+            # session_label when using a newly created writer
+            del self._writers[writer._rootdir]
+
+            writer = self.get_writer(writer._start_session,
+                                     writer._end_session, data_frequency)
+            writer.write(
+                data=data,
+                show_progress=False,
+                invalid_data_behavior='raise'
+            )
+
+    def get_calendar_periods_range(self, start_dt, end_dt, data_frequency):
+        """
+        Get a list of dates for the specified range.
+
+        Parameters
+        ----------
+        start_dt: datetime
+        end_dt: datetime
+        data_frequency: str
+
+        Returns
+        -------
+        list[datetime]
+
+        """
+        return self.calendar.minutes_in_range(start_dt, end_dt) \
+            if data_frequency == 'minute' \
+            else self.calendar.sessions_in_range(start_dt, end_dt)
+
+    def _spot_empty_periods(self, ohlcv_df, asset, data_frequency,
+                            empty_rows_behavior):
+        problems = []
+
+        nan_rows = ohlcv_df[ohlcv_df.isnull().T.any().T].index
+        if len(nan_rows) > 0:
+            dates = []
+            for row_date in nan_rows.values:
+                row_date = pd.to_datetime(row_date, utc=True)
+                if row_date > asset.start_date:
+                    dates.append(row_date)
+
+            if len(dates) > 0:
+                end_dt = asset.end_minute if data_frequency == 'minute' \
+                    else asset.end_daily
+
+                problem = '{name} ({start_dt} to {end_dt}) has empty ' \
+                          'periods: {dates}'.format(
+                    name=asset.symbol,
+                    start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
+                    end_dt=end_dt.strftime(DATE_TIME_FORMAT),
+                    dates=[date.strftime(DATE_TIME_FORMAT) for date in dates]
+                )
+                if empty_rows_behavior == 'warn':
+                    log.warn(problem)
+
+                elif empty_rows_behavior == 'raise':
+                    raise EmptyValuesInBundleError(
+                        name=asset.symbol,
+                        end_minute=end_dt,
+                        dates=dates
+                    )
+
+                else:
+                    ohlcv_df.dropna(inplace=True)
+
+            else:
+                problem = None
+
+            problems.append(problem)
+
+        return problems
+
+    def _spot_duplicates(self, ohlcv_df, asset, data_frequency, threshold):
+        # TODO: work in progress
+        series = ohlcv_df.reset_index().groupby('close')['index'].apply(
+            np.array
+        )
+
+        ref_delta = timedelta(minutes=1) if data_frequency == 'minute' \
+            else timedelta(days=1)
+
+        dups = series.loc[lambda values: [len(x) > 10 for x in values]]
+
+        for index, dates in dups.iteritems():
+            prev_date = None
+            for date in dates:
+                if prev_date is not None:
+                    delta = (date - prev_date) / 1e9
+                    if delta == ref_delta.seconds:
+                        log.info('pex')
+
+                prev_date = date
+
+        problems = []
+        for index, dates in dups.iteritems():
+            end_dt = asset.end_minute if data_frequency == 'minute' \
+                else asset.end_daily
+
+            problem = '{name} ({start_dt} to {end_dt}) has {threshold} ' \
+                      'identical close values on: {dates}'.format(
+                name=asset.symbol,
+                start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
+                end_dt=end_dt.strftime(DATE_TIME_FORMAT),
+                threshold=threshold,
+                dates=[pd.to_datetime(date).strftime(DATE_TIME_FORMAT)
+                       for date in dates]
+            )
+
+            problems.append(problem)
+
+        return problems
+
+    def ingest_df(self, ohlcv_df, data_frequency, asset, writer,
+                  empty_rows_behavior='warn', duplicates_threshold=None):
+        """
+        Ingest a DataFrame of OHLCV data for a given market.
+
+        Parameters
+        ----------
+        ohlcv_df: DataFrame
+        data_frequency: str
+        asset: TradingPair
+        writer:
+        empty_rows_behavior: str
+
+        """
+        problems = []
+        if empty_rows_behavior is not 'ignore':
+            problems += self._spot_empty_periods(
+                ohlcv_df, asset, data_frequency, empty_rows_behavior
+            )
+
+        # if duplicates_threshold is not None:
+        #     problems += self._spot_duplicates(
+        #         ohlcv_df, asset, data_frequency, duplicates_threshold
+        #     )
+
+        data = []
+        if not ohlcv_df.empty:
+            ohlcv_df.sort_index(inplace=True)
+            data.append((asset.sid, ohlcv_df))
+
+        self._write(data, writer, data_frequency)
+
+        return problems
+
+    def ingest_ctable(self, asset, data_frequency, period,
+                      writer, empty_rows_behavior='strip',
+                      duplicates_threshold=100, cleanup=False):
+        """
+        Merge a ctable bundle chunk into the main bundle for the exchange.
+
+        Parameters
+        ----------
+        asset: TradingPair
+        data_frequency: str
+        period: str
+        writer:
+        empty_rows_behavior: str
+            Ensure that the bundle does not have any missing data.
+
+        cleanup: bool
+            Remove the temp bundle directory after ingestion.
+
+        Returns
+        -------
+        list[str]
+            A list of problems which occurred during ingestion.
+
+        """
+        problems = []
+
+        # Download and extract the bundle
+        path = get_bcolz_chunk(
+            exchange_name=self.exchange.name,
+            symbol=asset.symbol,
+            data_frequency=data_frequency,
+            period=period
+        )
+
+        reader = self.get_reader(data_frequency, path=path)
+        if reader is None:
+            try:
+                log.warn('the reader is unable to use bundle: {}, '
+                         'deleting it.'.format(path))
+                shutil.rmtree(path)
+
+            except Exception as e:
+                log.warn('unable to remove temp bundle: {}'.format(e))
+
+            raise TempBundleNotFoundError(path=path)
+
+        start_dt = reader.first_trading_day
+        end_dt = reader.last_available_dt
+
+        if data_frequency == 'daily':
+            end_dt = end_dt - pd.Timedelta(hours=23, minutes=59)
+
+        arrays = None
+        try:
+            arrays = reader.load_raw_arrays(
+                sids=[asset.sid],
+                fields=['open', 'high', 'low', 'close', 'volume'],
+                start_dt=start_dt,
+                end_dt=end_dt
+            )
+        except Exception as e:
+            log.warn('skipping ctable for {} from {} to {}: {}'.format(
+                asset.symbol, start_dt, end_dt, e
+            ))
+
+        if not arrays:
+            return reader._rootdir
+
+        periods = self.get_calendar_periods_range(
+            start_dt, end_dt, data_frequency
+        )
+        df = get_df_from_arrays(arrays, periods)
+        problems += self.ingest_df(
+            ohlcv_df=df,
+            data_frequency=data_frequency,
+            asset=asset,
+            writer=writer,
+            empty_rows_behavior=empty_rows_behavior,
+            duplicates_threshold=duplicates_threshold
+        )
+
+        if cleanup:
+            log.debug(
+                'removing bundle folder following ingestion: {}'.format(
+                    reader._rootdir)
+            )
+            shutil.rmtree(reader._rootdir)
+
+        return filter(partial(is_not, None), problems)
+
+    def get_adj_dates(self, start, end, assets, data_frequency):
+        """
+        Contains a date range to the trading availability of the specified
+        markets.
+
+        Parameters
+        ----------
+        start: datetime
+        end: datetime
+        assets: list[TradingPair]
+        data_frequency: str
+
+        Returns
+        -------
+        datetime, datetime
+        """
+        earliest_trade = None
+        last_entry = None
+        for asset in assets:
+            if earliest_trade is None or earliest_trade > asset.start_date:
+                if asset.start_date >= self.calendar.first_session:
+                    earliest_trade = asset.start_date
+
+                else:
+                    earliest_trade = self.calendar.first_session
+
+            end_asset = asset.end_minute if data_frequency == 'minute' else \
+                asset.end_daily
+            if end_asset is not None:
+                if last_entry is None or end_asset > last_entry:
+                    last_entry = end_asset
+
+            else:
+                end = None
+                last_entry = None
+
+        if start is None or \
+                (earliest_trade is not None and earliest_trade > start):
+            start = earliest_trade
+
+        if end is None or (last_entry is not None and end > last_entry):
+            end = last_entry
+
+        if end is None or start is None or start > end:
+            raise NoDataAvailableOnExchange(
+                exchange=[asset.exchange for asset in assets],
+                symbol=[asset.symbol for asset in assets],
+                data_frequency=data_frequency,
+            )
+
+        return start, end
+
+    def prepare_chunks(self, assets, data_frequency, start_dt, end_dt):
+        """
+        Split a price data request into chunks corresponding to individual
+        bundles.
+
+        Parameters
+        ----------
+        assets: list[TradingPair]
+        data_frequency: str
+        start_dt: datetime
+        end_dt: datetime
+
+        Returns
+        -------
+        dict[TradingPair, list[dict(str, Object]]]
+
+        """
+        get_start_end = get_month_start_end \
+            if data_frequency == 'minute' else get_year_start_end
+
+        # Get a reader for the main bundle to verify if data exists
+        reader = self.get_reader(data_frequency)
+
+        chunks = dict()
+        for asset in assets:
+            try:
+                # Checking if the the asset has price data in the specified
+                # date range
+                adj_start, adj_end = self.get_adj_dates(
+                    start_dt, end_dt, [asset], data_frequency
+                )
+
+            except NoDataAvailableOnExchange as e:
+                # If not, we continue to the next asset
+                log.debug('skipping {}: {}'.format(asset.symbol, e))
+                continue
+
+            dates = pd.date_range(
+                start=get_period_label(adj_start, data_frequency),
+                end=get_period_label(adj_end, data_frequency),
+                freq='MS' if data_frequency == 'minute' else 'AS',
+                tz=UTC
+            )
+
+            # Adjusting the last date of the range to avoid
+            # going over the asset's trading bounds
+            dates.values[0] = adj_start
+            dates.values[-1] = adj_end
+
+            chunks[asset] = []
+            for index, dt in enumerate(dates):
+                period_start, period_end = get_start_end(
+                    dt=dt,
+                    first_day=dt if index == 0 else None,
+                    last_day=dt if index == len(dates) - 1 else None
+                )
+
+                # Currencies don't always start trading at midnight.
+                # Checking the last minute of the day instead.
+                range_start = period_start.replace(hour=23, minute=59) \
+                    if data_frequency == 'minute' else period_start
+
+                # Checking if the data already exists in the bundle
+                # for the date range of the chunk. If not, we create
+                # a chunk for ingestion.
+                has_data = range_in_bundle(
+                    asset, range_start, period_end, reader
+                )
+                if not has_data:
+                    period = get_period_label(dt, data_frequency)
+                    chunk = dict(
+                        asset=asset,
+                        period=period,
+                    )
+                    chunks[asset].append(chunk)
+
+            # We sort the chunks by end date to ingest most recent data first
+            chunks[asset].sort(
+                key=lambda chunk: pd.to_datetime(chunk['period'])
+            )
+
+        return chunks
+
+    def ingest_assets(self, assets, data_frequency, start_dt=None, end_dt=None,
+                      show_progress=False, show_breakdown=False,
+                      show_report=False):
+        """
+        Determine if data is missing from the bundle and attempt to ingest it.
+
+        Parameters
+        ----------
+        assets: list[TradingPair]
+        data_frequency: str
+        start_dt: datetime
+        end_dt: datetime
+        show_progress: bool
+        show_breakdown: bool
+
+        """
+        if start_dt is None:
+            start_dt = self.calendar.first_session
+
+        if end_dt is None:
+            end_dt = pd.Timestamp.utcnow()
+
+        get_start_end = get_month_start_end \
+            if data_frequency == 'minute' else get_year_start_end
+
+        # Assign the first and last day of the period
+        start_dt, _ = get_start_end(start_dt)
+        _, end_dt = get_start_end(end_dt)
+
+        chunks = self.prepare_chunks(
+            assets=assets,
+            data_frequency=data_frequency,
+            start_dt=start_dt,
+            end_dt=end_dt
+        )
+
+        problems = []
+        # This is the common writer for the entire exchange bundle
+        # we want to give an end_date far in time
+        writer = self.get_writer(start_dt, end_dt, data_frequency)
+        if show_breakdown:
+            for asset in chunks:
+                with maybe_show_progress(
+                        chunks[asset],
+                        show_progress,
+                        label='Ingesting {frequency} price data for '
+                              '{symbol} on {exchange}'.format(
+                            exchange=self.exchange.name,
+                            frequency=data_frequency,
+                            symbol=asset.symbol
+                        )) as it:
+                    for chunk in it:
+                        problems += self.ingest_ctable(
+                            asset=chunk['asset'],
+                            data_frequency=data_frequency,
+                            period=chunk['period'],
+                            writer=writer,
+                            empty_rows_behavior='strip',
+                            cleanup=True
+                        )
+        else:
+            all_chunks = list(chain.from_iterable(itervalues(chunks)))
+
+            # We sort the chunks by end date to ingest most recent data first
+            all_chunks.sort(
+                key=lambda chunk: pd.to_datetime(chunk['period'])
+            )
+            with maybe_show_progress(
+                    all_chunks,
+                    show_progress,
+                    label='Ingesting {frequency} price data on '
+                          '{exchange}'.format(
+                        exchange=self.exchange.name,
+                        frequency=data_frequency,
+                    )) as it:
+                for chunk in it:
+                    problems += self.ingest_ctable(
+                        asset=chunk['asset'],
+                        data_frequency=data_frequency,
+                        period=chunk['period'],
+                        writer=writer,
+                        empty_rows_behavior='strip',
+                        cleanup=True
+                    )
+
+        if show_report and len(problems) > 0:
+            log.info('problems during ingestion:{}\n'.format(
+                '\n'.join(problems)
+            ))
+
+    def ingest(self, data_frequency, include_symbols=None,
+               exclude_symbols=None, start=None, end=None,
+               show_progress=True, show_breakdown=True, show_report=True):
+        """
+        Inject data based on specified parameters.
+
+        Parameters
+        ----------
+        data_frequency: str
+        include_symbols: str
+        exclude_symbols: str
+        start: datetime
+        end: datetime
+        show_progress: bool
+        environ:
+
+        """
+        assets = self.get_assets(include_symbols, exclude_symbols)
+
+        for frequency in data_frequency.split(','):
+            self.ingest_assets(assets, frequency, start, end,
+                               show_progress, show_breakdown, show_report)
+
+    def get_history_window_series_and_load(self,
+                                           assets,
+                                           end_dt,
+                                           bar_count,
+                                           field,
+                                           data_frequency,
+                                           algo_end_dt=None
+                                           ):
+        """
+        Retrieve price data history, ingest missing data.
+
+        Parameters
+        ----------
+        assets: list[TradingPair]
+        end_dt: datetime
+        bar_count: int
+        field: str
+        data_frequency: str
+        algo_end_dt: datetime
+
+        Returns
+        -------
+        Series
+
+        """
+        if AUTO_INGEST:
+            try:
+                series = self.get_history_window_series(
+                    assets=assets,
+                    end_dt=end_dt,
+                    bar_count=bar_count,
+                    field=field,
+                    data_frequency=data_frequency
+                )
+                return pd.DataFrame(series)
+
+            except PricingDataNotLoadedError:
+                start_dt = get_start_dt(end_dt, bar_count, data_frequency)
+                log.info(
+                    'pricing data for {symbol} not found in range '
+                    '{start} to {end}, updating the bundles.'.format(
+                        symbol=[asset.symbol for asset in assets],
+                        start=start_dt,
+                        end=end_dt
+                    )
+                )
+                self.ingest_assets(
+                    assets=assets,
+                    start_dt=start_dt,
+                    end_dt=algo_end_dt,
+                    data_frequency=data_frequency,
+                    show_progress=True,
+                    show_breakdown=True
+                )
+                series = self.get_history_window_series(
+                    assets=assets,
+                    end_dt=end_dt,
+                    bar_count=bar_count,
+                    field=field,
+                    data_frequency=data_frequency,
+                    reset_reader=True
+                )
+                return series
+
+        else:
+            series = self.get_history_window_series(
+                assets=assets,
+                end_dt=end_dt,
+                bar_count=bar_count,
+                field=field,
+                data_frequency=data_frequency
+            )
+            return pd.DataFrame(series)
+
+    def get_spot_values(self,
+                        assets,
+                        field,
+                        dt,
+                        data_frequency,
+                        reset_reader=False
+                        ):
+        """
+        The spot values for the gives assets, field and date. Reads from
+        the exchange data bundle.
+
+        Parameters
+        ----------
+        assets: list[TradingPair]
+        field: str
+        dt: pd.Timestamp
+        data_frequency: str
+        reset_reader:
+
+        Returns
+        -------
+        float
+
+        """
+        values = []
+        try:
+            reader = self.get_reader(data_frequency)
+            if reset_reader:
+                del self._readers[reader._rootdir]
+                reader = self.get_reader(data_frequency)
+
+            for asset in assets:
+                value = reader.get_value(
+                    sid=asset.sid,
+                    dt=dt,
+                    field=field
+                )
+                values.append(value)
+
+            return values
+
+        except Exception:
+            symbols = [asset.symbol for asset in assets]
+            raise PricingDataNotLoadedError(
+                field=field,
+                first_trading_day=min([asset.start_date for asset in assets]),
+                exchange=self.exchange.name,
+                symbols=symbols,
+                symbol_list=','.join(symbols),
+                data_frequency=data_frequency,
+                start_dt=dt,
+                end_dt=dt
+            )
+
+    def get_history_window_series(self,
+                                  assets,
+                                  end_dt,
+                                  bar_count,
+                                  field,
+                                  data_frequency,
+                                  reset_reader=False):
+        start_dt = get_start_dt(end_dt, bar_count, data_frequency, False)
+        start_dt, end_dt = self.get_adj_dates(
+            start_dt, end_dt, assets, data_frequency
+        )
+
+        reader = self.get_reader(data_frequency)
+        if reset_reader:
+            del self._readers[reader._rootdir]
+            reader = self.get_reader(data_frequency)
+
+        if reader is None:
+            symbols = [asset.symbol for asset in assets]
+            raise PricingDataNotLoadedError(
+                field=field,
+                first_trading_day=min([asset.start_date for asset in assets]),
+                exchange=self.exchange.name,
+                symbols=symbols,
+                symbol_list=','.join(symbols),
+                data_frequency=data_frequency,
+                start_dt=start_dt,
+                end_dt=end_dt
+            )
+
+        for asset in assets:
+            asset_start_dt, asset_end_dt = self.get_adj_dates(
+                start_dt, end_dt, assets, data_frequency
+            )
+
+            in_bundle = range_in_bundle(
+                asset, asset_start_dt, asset_end_dt, reader
+            )
+            if not in_bundle:
+                raise PricingDataNotLoadedError(
+                    field=field,
+                    first_trading_day=asset.start_date,
+                    exchange=self.exchange.name,
+                    symbols=asset.symbol,
+                    symbol_list=asset.symbol,
+                    data_frequency=data_frequency,
+                    start_dt=asset_start_dt,
+                    end_dt=asset_end_dt
+                )
+
+        series = dict()
+        try:
+            arrays = reader.load_raw_arrays(
+                sids=[asset.sid for asset in assets],
+                fields=[field],
+                start_dt=start_dt,
+                end_dt=end_dt
+            )
+
+        except Exception:
+            symbols = [asset.symbol.encode('utf-8') for asset in assets]
+            raise PricingDataNotLoadedError(
+                field=field,
+                first_trading_day=min([asset.start_date for asset in assets]),
+                exchange=self.exchange.name,
+                symbols=symbols,
+                symbol_list=','.join(symbols),
+                data_frequency=data_frequency,
+                start_dt=start_dt,
+                end_dt=end_dt
+            )
+
+        periods = self.get_calendar_periods_range(
+            start_dt, end_dt, data_frequency
+        )
+
+        for asset_index, asset in enumerate(assets):
+            asset_values = arrays[asset_index]
+
+            value_series = pd.Series(asset_values.flatten(), index=periods)
+            series[asset] = value_series
+
+        return series
+
+    def clean(self, data_frequency):
+        """
+        Removing the bundle data from the catalyst folder.
+
+        Parameters
+        ----------
+        data_frequency: str
+
+        """
+        log.debug('cleaning exchange {}, frequency {}'.format(
+            self.exchange.name, data_frequency
+        ))
+        root = get_exchange_folder(self.exchange.name)
+
+        symbols = os.path.join(root, 'symbols.json')
+        if os.path.isfile(symbols):
+            os.remove(symbols)
+
+        temp_bundles = os.path.join(root, 'temp_bundles')
+
+        if os.path.isdir(temp_bundles):
+            log.debug('removing folder and content: {}'.format(temp_bundles))
+            shutil.rmtree(temp_bundles)
+            log.debug('{} removed'.format(temp_bundles))
+
+        frequencies = ['daily', 'minute'] if data_frequency is None \
+            else [data_frequency]
+
+        for frequency in frequencies:
+            label = '{}_bundle'.format(frequency)
+            frequency_bundle = os.path.join(root, label)
+
+            if os.path.isdir(frequency_bundle):
+                log.debug(
+                    'removing folder and content: {}'.format(frequency_bundle)
+                )
+                shutil.rmtree(frequency_bundle)
+                log.debug('{} removed'.format(frequency_bundle))

catalyst/exchange/exchange_data_portal.py

@@ -0,0 +1,405 @@
+import abc
+from time import sleep
+
+import numpy as np
+import pandas as pd
+from catalyst.assets._assets import TradingPair
+from logbook import Logger
+
+from catalyst.constants import LOG_LEVEL, AUTO_INGEST
+from catalyst.data.data_portal import DataPortal
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.exchange_errors import (
+    ExchangeRequestError,
+    ExchangeBarDataError,
+    PricingDataNotLoadedError)
+from catalyst.exchange.exchange_utils import get_frequency, resample_history_df
+
+log = Logger('DataPortalExchange', level=LOG_LEVEL)
+
+
+class DataPortalExchangeBase(DataPortal):
+    def __init__(self, *args, **kwargs):
+
+        self.exchanges = kwargs.pop('exchanges', None)
+        # TODO: put somewhere accessible by each algo
+        self.retry_get_history_window = 5
+        self.retry_get_spot_value = 5
+        self.retry_delay = 5
+
+        super(DataPortalExchangeBase, self).__init__(*args, **kwargs)
+
+    def _get_history_window(self,
+                            assets,
+                            end_dt,
+                            bar_count,
+                            frequency,
+                            field,
+                            data_frequency,
+                            ffill=True,
+                            attempt_index=0):
+        try:
+            exchange_assets = dict()
+            for asset in assets:
+                if asset.exchange not in exchange_assets:
+                    exchange_assets[asset.exchange] = list()
+
+                exchange_assets[asset.exchange].append(asset)
+
+            if len(exchange_assets) > 1:
+                df_list = []
+                for exchange_name in exchange_assets:
+                    exchange = self.exchanges[exchange_name]
+                    assets = exchange_assets[exchange_name]
+
+                    df_exchange = self.get_exchange_history_window(
+                        exchange,
+                        assets,
+                        end_dt,
+                        bar_count,
+                        frequency,
+                        field,
+                        data_frequency,
+                        ffill)
+
+                    df_list.append(df_exchange)
+
+                # Merging the values values of each exchange
+                return pd.concat(df_list)
+
+            else:
+                exchange = self.exchanges[list(exchange_assets.keys())[0]]
+                return self.get_exchange_history_window(
+                    exchange,
+                    assets,
+                    end_dt,
+                    bar_count,
+                    frequency,
+                    field,
+                    data_frequency,
+                    ffill)
+
+        except ExchangeRequestError as e:
+            log.warn(
+                'get history attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_get_history_window:
+                sleep(self.retry_delay)
+                return self._get_history_window(assets,
+                                                end_dt,
+                                                bar_count,
+                                                frequency,
+                                                field,
+                                                data_frequency,
+                                                ffill,
+                                                attempt_index + 1)
+            else:
+                raise ExchangeBarDataError(
+                    data_type='history',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    def get_history_window(self,
+                           assets,
+                           end_dt,
+                           bar_count,
+                           frequency,
+                           field,
+                           data_frequency=None,
+                           ffill=True):
+
+        if field == 'price':
+            field = 'close'
+
+        return self._get_history_window(assets,
+                                        end_dt,
+                                        bar_count,
+                                        frequency,
+                                        field,
+                                        data_frequency,
+                                        ffill)
+
+    @abc.abstractmethod
+    def get_exchange_history_window(self,
+                                    exchange,
+                                    assets,
+                                    end_dt,
+                                    bar_count,
+                                    frequency,
+                                    field,
+                                    data_frequency,
+                                    ffill=True):
+        pass
+
+    def _get_spot_value(self, assets, field, dt, data_frequency,
+                        attempt_index=0):
+        try:
+            if isinstance(assets, TradingPair):
+                exchange = self.exchanges[assets.exchange]
+                spot_values = self.get_exchange_spot_value(
+                    exchange, [assets], field, dt, data_frequency)
+
+                if not spot_values:
+                    return np.nan
+
+                return spot_values[0]
+
+            else:
+                exchange_assets = dict()
+                for asset in assets:
+                    if asset.exchange not in exchange_assets:
+                        exchange_assets[asset.exchange] = list()
+
+                    exchange_assets[asset.exchange].append(asset)
+
+                if len(list(exchange_assets.keys())) == 1:
+                    exchange = self.exchanges[list(exchange_assets.keys())[0]]
+                    return self.get_exchange_spot_value(
+                        exchange, assets, field, dt, data_frequency)
+
+                else:
+                    spot_values = []
+                    for exchange_name in exchange_assets:
+                        exchange = self.exchanges[exchange_name]
+                        assets = exchange_assets[exchange_name]
+                        exchange_spot_values = self.get_exchange_spot_value(
+                            exchange,
+                            assets,
+                            field,
+                            dt,
+                            data_frequency
+                        )
+                        if len(assets) == 1:
+                            spot_values.append(exchange_spot_values)
+                        else:
+                            spot_values += exchange_spot_values
+
+                    return spot_values
+
+        except ExchangeRequestError as e:
+            log.warn(
+                'get spot value attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_get_spot_value:
+                sleep(self.retry_delay)
+                return self._get_spot_value(assets, field, dt, data_frequency,
+                                            attempt_index + 1)
+            else:
+                raise ExchangeBarDataError(
+                    data_type='spot',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    def get_spot_value(self, assets, field, dt, data_frequency):
+        if field == 'price':
+            field = 'close'
+
+        return self._get_spot_value(assets, field, dt, data_frequency)
+
+    @abc.abstractmethod
+    def get_exchange_spot_value(self, exchange, assets, field, dt,
+                                data_frequency):
+        return
+
+    def get_adjusted_value(self, asset, field, dt,
+                           perspective_dt,
+                           data_frequency,
+                           spot_value=None):
+        # TODO: does this pertain to cryptocurrencies?
+        log.warn('get_adjusted_value is not implemented yet!')
+        return spot_value
+
+
+class DataPortalExchangeLive(DataPortalExchangeBase):
+    def __init__(self, *args, **kwargs):
+        super(DataPortalExchangeLive, self).__init__(*args, **kwargs)
+
+    def get_exchange_history_window(self,
+                                    exchange,
+                                    assets,
+                                    end_dt,
+                                    bar_count,
+                                    frequency,
+                                    field,
+                                    data_frequency,
+                                    ffill=True):
+        """
+        Fetching price history window from the exchange.
+
+        Parameters
+        ----------
+        exchange: Exchange
+        assets: list[TradingPair]
+        end_dt: datetime
+        bar_count: int
+        frequency: str
+        field: str
+        data_frequency: str
+        ffill: bool
+
+        Returns
+        -------
+        DataFrame
+
+        """
+        df = exchange.get_history_window(
+            assets,
+            end_dt,
+            bar_count,
+            frequency,
+            field,
+            data_frequency,
+            ffill)
+        return df
+
+    def get_exchange_spot_value(self, exchange, assets, field, dt,
+                                data_frequency):
+        """
+        A spot value for the exchange.
+
+        Parameters
+        ----------
+        exchange: Exchange
+        assets: list[TradingPair]
+        field: str
+        dt: datetime
+        data_frequency: str
+
+        Returns
+        -------
+        float
+
+        """
+        exchange_spot_values = exchange.get_spot_value(
+            assets, field, dt, data_frequency)
+
+        return exchange_spot_values
+
+
+class DataPortalExchangeBacktest(DataPortalExchangeBase):
+    def __init__(self, *args, **kwargs):
+        super(DataPortalExchangeBacktest, self).__init__(*args, **kwargs)
+
+        self.exchange_bundles = dict()
+
+        self.history_loaders = dict()
+        self.minute_history_loaders = dict()
+
+        for exchange_name in self.exchanges:
+            exchange = self.exchanges[exchange_name]
+            self.exchange_bundles[exchange_name] = ExchangeBundle(exchange)
+
+    def _get_first_trading_day(self, assets):
+        first_date = None
+        for asset in assets:
+            if first_date is None or asset.start_date > first_date:
+                first_date = asset.start_date
+        return first_date
+
+    def get_exchange_history_window(self,
+                                    exchange,
+                                    assets,
+                                    end_dt,
+                                    bar_count,
+                                    frequency,
+                                    field,
+                                    data_frequency,
+                                    ffill=True):
+        """
+        Fetching price history window from the exchange bundle.
+
+        Parameters
+        ----------
+        exchange: Exchange
+        assets: list[TradingPair]
+        end_dt: datetime
+        bar_count: int
+        frequency: str
+        field: str
+        data_frequency: str
+        ffill: bool
+
+        Returns
+        -------
+        DataFrame
+
+        """
+        bundle = self.exchange_bundles[exchange.name]  # type: ExchangeBundle
+
+        freq, candle_size, unit, adj_data_frequency = get_frequency(
+            frequency, data_frequency
+        )
+        adj_bar_count = candle_size * bar_count
+
+        if data_frequency == 'minute' and adj_data_frequency == 'daily':
+            end_dt = end_dt.floor('1D')
+
+        series = bundle.get_history_window_series_and_load(
+            assets=assets,
+            end_dt=end_dt,
+            bar_count=adj_bar_count,
+            field=field,
+            data_frequency=adj_data_frequency,
+            algo_end_dt=self._last_available_session,
+        )
+
+        df = resample_history_df(pd.DataFrame(series), freq, field)
+        return df
+
+    def get_exchange_spot_value(self,
+                                exchange,
+                                assets,
+                                field,
+                                dt,
+                                data_frequency
+                                ):
+        """
+        A spot value for the exchange bundle. Try to ingest data if not in
+        the bundle.
+
+        Parameters
+        ----------
+        exchange: Exchange
+        assets: list[TradingPair]
+        field: str
+        dt: datetime
+        data_frequency: str
+
+        Returns
+        -------
+        float
+
+        """
+        bundle = self.exchange_bundles[exchange.name]
+        if data_frequency == 'daily':
+            dt = dt.floor('1D')
+        else:
+            dt = dt.floor('1 min')
+
+        if AUTO_INGEST:
+            try:
+                return bundle.get_spot_values(
+                    assets, field, dt, data_frequency
+                )
+            except PricingDataNotLoadedError:
+                log.info(
+                    'pricing data for {symbol} not found on {dt}'
+                    ', updating the bundles.'.format(
+                        symbol=[asset.symbol for asset in assets],
+                        dt=dt
+                    )
+                )
+                bundle.ingest_assets(
+                    assets=assets,
+                    start_dt=self._first_trading_day,
+                    end_dt=self._last_available_session,
+                    data_frequency=data_frequency,
+                    show_progress=True
+                )
+                return bundle.get_spot_values(
+                    assets, field, dt, data_frequency, True
+                )
+        else:
+            return bundle.get_spot_values(assets, field, dt, data_frequency)

catalyst/exchange/exchange_errors.py

@@ -0,0 +1,229 @@
+import sys
+import traceback
+
+from catalyst.errors import ZiplineError
+
+
+def silent_except_hook(exctype, excvalue, exctraceback):
+    if exctype in [PricingDataBeforeTradingError, PricingDataNotLoadedError,
+                   SymbolNotFoundOnExchange, NoDataAvailableOnExchange,
+                   ExchangeAuthEmpty]:
+        fn = traceback.extract_tb(exctraceback)[-1][0]
+        ln = traceback.extract_tb(exctraceback)[-1][1]
+        print("Error traceback: {1} (line {2})\n"
+              "{0.__name__}:  {3}".format(exctype, fn, ln, excvalue))
+    else:
+        sys.__excepthook__(exctype, excvalue, exctraceback)
+
+
+sys.excepthook = silent_except_hook
+
+
+class ExchangeRequestError(ZiplineError):
+    msg = (
+        'Request failed: {error}'
+    ).strip()
+
+
+class ExchangeRequestErrorTooManyAttempts(ZiplineError):
+    msg = (
+        'Request failed: {error}, giving up after {attempts} attempts'
+    ).strip()
+
+
+class ExchangeBarDataError(ZiplineError):
+    msg = (
+        'Unable to retrieve bar data: {data_type}, ' +
+        'giving up after {attempts} attempts: {error}'
+    ).strip()
+
+
+class ExchangePortfolioDataError(ZiplineError):
+    msg = (
+        'Unable to retrieve portfolio data: {data_type}, ' +
+        'giving up after {attempts} attempts: {error}'
+    ).strip()
+
+
+class ExchangeTransactionError(ZiplineError):
+    msg = (
+        'Unable to execute transaction: {transaction_type}, ' +
+        'giving up after {attempts} attempts: {error}'
+    ).strip()
+
+
+class ExchangeNotFoundError(ZiplineError):
+    msg = (
+        'Exchange {exchange_name} not found. Please specify exchanges '
+        'supported by Catalyst and verify spelling for accuracy.'
+    ).strip()
+
+
+class ExchangeAuthNotFound(ZiplineError):
+    msg = (
+        'Please create an auth.json file containing the api token and key for '
+        'exchange {exchange}. Place the file here: {filename}'
+    ).strip()
+
+
+class ExchangeAuthEmpty(ZiplineError):
+    msg = (
+        'Please enter your API token key and secret for exchange {exchange} '
+        'in the following file: {filename}'
+    ).strip()
+
+
+class ExchangeSymbolsNotFound(ZiplineError):
+    msg = (
+        'Unable to download or find a local copy of symbols.json for exchange '
+        '{exchange}. The file should be here: {filename}'
+    ).strip()
+
+
+class AlgoPickleNotFound(ZiplineError):
+    msg = (
+        'Pickle not found for algo {algo} in path {filename}'
+    ).strip()
+
+
+class InvalidHistoryFrequencyAlias(ZiplineError):
+    msg = (
+        'Invalid frequency alias {freq}. Valid suffixes are M (minute) '
+        'and D (day). For example, these aliases would be valid '
+        '1M, 5M, 1D.'
+    ).strip()
+
+
+class InvalidHistoryFrequencyError(ZiplineError):
+    msg = (
+        'Frequency {frequency} not supported by the exchange.'
+    ).strip()
+
+
+class MismatchingFrequencyError(ZiplineError):
+    msg = (
+        'Bar aggregate frequency {frequency} not compatible with '
+        'data frequency {data_frequency}.'
+    ).strip()
+
+
+class InvalidSymbolError(ZiplineError):
+    msg = (
+        'Invalid trading pair symbol: {symbol}. '
+        'Catalyst symbols must follow this convention: '
+        '[Market Currency]_[Base Currency]. For example: eth_usd, btc_usd, '
+        'neo_eth, ubq_btc. Error details: {error}'
+    ).strip()
+
+
+class InvalidOrderStyle(ZiplineError):
+    msg = (
+        'Order style {style} not supported by exchange {exchange}.'
+    ).strip()
+
+
+class CreateOrderError(ZiplineError):
+    msg = (
+        'Unable to create order on exchange {exchange} {error}.'
+    ).strip()
+
+
+class OrderNotFound(ZiplineError):
+    msg = (
+        'Order {order_id} not found on exchange {exchange}.'
+    ).strip()
+
+
+class OrphanOrderError(ZiplineError):
+    msg = (
+        'Order {order_id} found in exchange {exchange} but not tracked by '
+        'the algorithm.'
+    ).strip()
+
+
+class OrphanOrderReverseError(ZiplineError):
+    msg = (
+        'Order {order_id} tracked by algorithm, but not found in exchange {exchange}.'
+    ).strip()
+
+
+class OrderCancelError(ZiplineError):
+    msg = (
+        'Unable to cancel order {order_id} on exchange {exchange} {error}.'
+    ).strip()
+
+
+class SidHashError(ZiplineError):
+    msg = (
+        'Unable to hash sid from symbol {symbol}.'
+    ).strip()
+
+
+class BaseCurrencyNotFoundError(ZiplineError):
+    msg = (
+        'Algorithm base currency {base_currency} not found in exchange '
+        '{exchange}.'
+    ).strip()
+
+
+class MismatchingBaseCurrencies(ZiplineError):
+    msg = (
+        'Unable to trade with base currency {base_currency} when the '
+        'algorithm uses {algo_currency}.'
+    ).strip()
+
+
+class MismatchingBaseCurrenciesExchanges(ZiplineError):
+    msg = (
+        'Unable to trade with base currency {base_currency} when the '
+        'exchange {exchange_name} users {exchange_currency}.'
+    ).strip()
+
+
+class SymbolNotFoundOnExchange(ZiplineError):
+    """
+    Raised when a symbol() call contains a non-existent symbol.
+    """
+    msg = ('Symbol {symbol} not found on exchange {exchange}. '
+           'Choose from: {supported_symbols}').strip()
+
+
+class BundleNotFoundError(ZiplineError):
+    msg = ('Unable to find bundle data for exchange {exchange} and '
+           'data frequency {data_frequency}.'
+           'Please ingest some price data.'
+           'See `catalyst ingest-exchange --help` for details.').strip()
+
+
+class TempBundleNotFoundError(ZiplineError):
+    msg = ('Temporary bundle not found in: {path}.').strip()
+
+
+class EmptyValuesInBundleError(ZiplineError):
+    msg = ('{name} with end minute {end_minute} has empty rows '
+           'in ranges: {dates}').strip()
+
+
+class PricingDataBeforeTradingError(ZiplineError):
+    msg = ('Pricing data for trading pairs {symbols} on exchange {exchange} '
+           'starts on {first_trading_day}, but you are either trying to trade or '
+           'retrieve pricing data on {dt}. Adjust your dates accordingly.').strip()
+
+
+class PricingDataNotLoadedError(ZiplineError):
+    msg = ('Missing data for {exchange} {symbols} in date range '
+           '[{start_dt} - {end_dt}]'
+           '\nPlease run: `catalyst ingest-exchange -x {exchange} -f '
+           '{data_frequency} -i {symbol_list}`. See catalyst documentation '
+           'for details.').strip()
+
+
+class ApiCandlesError(ZiplineError):
+    msg = ('Unable to fetch candles from the remote API: {error}.').strip()
+
+
+class NoDataAvailableOnExchange(ZiplineError):
+    msg = (
+        'Requested data for trading pair {symbol} is not available on exchange {exchange} '
+        'in `{data_frequency}` frequency at this time. '
+        'Check `http://enigma.co/catalyst/status` for market coverage.').strip()

catalyst/exchange/exchange_execution.py

@@ -0,0 +1,67 @@
+from catalyst.finance.execution import LimitOrder, StopOrder, StopLimitOrder
+
+
+class ExchangeLimitOrder(LimitOrder):
+    def get_limit_price(self, is_buy):
+        """
+        We may be trading Satoshis with 8 decimals, we cannot round numbers.
+
+        Parameters
+        ----------
+        is_buy: bool
+
+        Returns
+        -------
+        float
+
+        """
+        return self.limit_price
+
+
+class ExchangeStopOrder(StopOrder):
+    def get_stop_price(self, is_buy):
+        """
+        We may be trading Satoshis with 8 decimals, we cannot round numbers.
+
+        Parameters
+        ----------
+        is_buy: bool
+
+        Returns
+        -------
+        float
+
+        """
+        return self.stop_price
+
+
+class ExchangeStopLimitOrder(StopLimitOrder):
+    def get_limit_price(self, is_buy):
+        """
+        We may be trading Satoshis with 8 decimals, we cannot round numbers.
+
+        Parameters
+        ----------
+        is_buy: bool
+
+        Returns
+        -------
+        float
+
+        """
+        return self.limit_price
+
+    def get_stop_price(self, is_buy):
+        """
+        We may be trading Satoshis with 8 decimals, we cannot round numbers.
+
+        Parameters
+        ----------
+        is_buy: bool
+
+        Returns
+        -------
+        float
+
+        """
+        return self.stop_price

catalyst/exchange/exchange_portfolio.py

@@ -0,0 +1,140 @@
+import numpy as np
+from logbook import Logger
+
+from catalyst.constants import LOG_LEVEL
+from catalyst.protocol import Portfolio, Positions, Position
+from catalyst.utils.deprecate import deprecated
+
+log = Logger('ExchangePortfolio', level=LOG_LEVEL)
+
+
+class ExchangePortfolio(Portfolio):
+    """
+    Since the goal is to support multiple exchanges, it makes sense to
+    include additional stats in the portfolio object.
+
+    Instead of relying on the performance tracker, each exchange portfolio
+    tracks its own holding. This offers a separation between tracking an
+    exchange and the statistics of the algorithm.
+    """
+
+    def __init__(self, start_date, starting_cash=None):
+        self.capital_used = 0.0
+        self.starting_cash = starting_cash
+        self.portfolio_value = starting_cash
+        self.pnl = 0.0
+        self.returns = 0.0
+        self.cash = starting_cash
+        self.positions = Positions()
+        self.start_date = start_date
+        self.positions_value = 0.0
+        self.open_orders = dict()
+
+    def create_order(self, order):
+        """
+        Create an open order and store in memory.
+
+        Parameters
+        ----------
+        order: Order
+
+        """
+        log.debug('creating order {}'.format(order.id))
+        self.open_orders[order.id] = order
+
+        order_position = self.positions[order.asset] \
+            if order.asset in self.positions else None
+
+        if order_position is None:
+            order_position = Position(order.asset)
+            self.positions[order.asset] = order_position
+
+        order_position.amount += order.amount
+        log.debug('open order added to portfolio')
+
+    def execute_order(self, order, transaction):
+        """
+        Update the open orders and positions to apply an executed order.
+
+        Unlike with backtesting, we do not need to add slippage and fees.
+        The executed price includes transaction fees.
+
+        Parameters
+        ----------
+        order: Order
+        transaction: Transaction
+
+        """
+        log.debug('executing order {}'.format(order.id))
+        del self.open_orders[order.id]
+
+        order_position = self.positions[order.asset] \
+            if order.asset in self.positions else None
+
+        if order_position is None:
+            raise ValueError(
+                'Trying to execute order for a position not held: %s' % order.id
+            )
+
+        self.capital_used += order.amount * transaction.price
+
+        if order.amount > 0:
+            if order_position.cost_basis > 0:
+                order_position.cost_basis = np.average(
+                    [order_position.cost_basis, transaction.price],
+                    weights=[order_position.amount, order.amount]
+                )
+            else:
+                order_position.cost_basis = transaction.price
+
+        log.debug('updated portfolio with executed order')
+
+    @deprecated
+    def execute_transaction(self, transaction):
+        # TODO: almost duplicate of execute_order. Not sure why Poloniex needs this.
+        log.debug('executing transaction {}'.format(transaction.order_id))
+
+        order_position = self.positions[transaction.asset] \
+            if transaction.asset in self.positions else None
+
+        if order_position is None:
+            raise ValueError(
+                'Trying to execute transaction for a position not held: %s' % transaction.order_id
+            )
+
+        self.capital_used += transaction.amount * transaction.price
+
+        if transaction.amount > 0:
+            if order_position.cost_basis > 0:
+                order_position.cost_basis = np.average(
+                    [order_position.cost_basis, transaction.price],
+                    weights=[order_position.amount, transaction.amount]
+                )
+            else:
+                order_position.cost_basis = transaction.price
+
+        log.debug('updated portfolio with executed order')
+
+    def remove_order(self, order):
+        """
+        Removing an open order.
+
+        Parameters
+        ----------
+        order: Order
+
+        """
+        log.info('removing cancelled order {}'.format(order.id))
+        del self.open_orders[order.id]
+
+        order_position = self.positions[order.asset] \
+            if order.asset in self.positions else None
+
+        if order_position is None:
+            raise ValueError(
+                'Trying to remove order for a position not held: %s' % order.id
+            )
+
+        order_position.amount -= order.amount
+
+        log.debug('removed order from portfolio')

catalyst/exchange/exchange_utils.py

@@ -0,0 +1,503 @@
+import json
+import os
+import pickle
+import re
+import shutil
+from datetime import date, datetime
+
+import pandas as pd
+from catalyst.assets._assets import TradingPair
+from six.moves.urllib import request
+
+from catalyst.exchange.exchange_errors import ExchangeSymbolsNotFound, \
+    InvalidHistoryFrequencyError, InvalidHistoryFrequencyAlias
+from catalyst.utils.paths import data_root, ensure_directory, \
+    last_modified_time
+
+SYMBOLS_URL = 'https://s3.amazonaws.com/enigmaco/catalyst-exchanges/' \
+              '{exchange}/symbols.json'
+
+
+def get_exchange_folder(exchange_name, environ=None):
+    """
+    The root path of an exchange folder.
+
+    Parameters
+    ----------
+    exchange_name: str
+    environ:
+
+    Returns
+    -------
+    str
+
+    """
+    if not environ:
+        environ = os.environ
+
+    root = data_root(environ)
+    exchange_folder = os.path.join(root, 'exchanges', exchange_name)
+    ensure_directory(exchange_folder)
+
+    return exchange_folder
+
+
+def get_exchange_symbols_filename(exchange_name, environ=None):
+    """
+    The absolute path of the exchange's symbol.json file.
+
+    Parameters
+    ----------
+    exchange_name:
+    environ:
+
+    Returns
+    -------
+    str
+
+    """
+    exchange_folder = get_exchange_folder(exchange_name, environ)
+    return os.path.join(exchange_folder, 'symbols.json')
+
+
+def download_exchange_symbols(exchange_name, environ=None):
+    """
+    Downloads the exchange's symbols.json from the repository.
+
+    Parameters
+    ----------
+    exchange_name: str
+    environ:
+
+    Returns
+    -------
+    str
+
+    """
+    filename = get_exchange_symbols_filename(exchange_name)
+    url = SYMBOLS_URL.format(exchange=exchange_name)
+    response = request.urlretrieve(url=url, filename=filename)
+    return response
+
+
+def get_exchange_symbols(exchange_name, environ=None):
+    """
+    The de-serialized content of the exchange's symbols.json.
+
+    Parameters
+    ----------
+    exchange_name: str
+    environ:
+
+    Returns
+    -------
+    Object
+
+    """
+    filename = get_exchange_symbols_filename(exchange_name)
+
+    if not os.path.isfile(filename) or \
+                    pd.Timedelta(pd.Timestamp('now',
+                                              tz='UTC') - last_modified_time(
+                        filename)).days > 1:
+        download_exchange_symbols(exchange_name, environ)
+
+    if os.path.isfile(filename):
+        with open(filename) as data_file:
+            data = json.load(data_file)
+            return data
+    else:
+        raise ExchangeSymbolsNotFound(
+            exchange=exchange_name,
+            filename=filename
+        )
+
+
+def get_symbols_string(assets):
+    """
+    A concatenated string of symbols from a list of assets.
+
+    Parameters
+    ----------
+    assets: list[TradingPair]
+
+    Returns
+    -------
+    str
+
+    """
+    array = [assets] if isinstance(assets, TradingPair) else assets
+    return ', '.join([asset.symbol for asset in array])
+
+
+def get_exchange_auth(exchange_name, environ=None):
+    """
+    The de-serialized contend of the exchange's auth.json file.
+
+    Parameters
+    ----------
+    exchange_name: str
+    environ:
+
+    Returns
+    -------
+    Object
+
+    """
+    exchange_folder = get_exchange_folder(exchange_name, environ)
+    filename = os.path.join(exchange_folder, 'auth.json')
+
+    if os.path.isfile(filename):
+        with open(filename) as data_file:
+            data = json.load(data_file)
+            return data
+    else:
+        data = dict(name=exchange_name, key='', secret='')
+        with open(filename, 'w') as f:
+            json.dump(data, f, sort_keys=False, indent=2,
+                      separators=(',', ':'))
+            return data
+
+
+def delete_algo_folder(algo_name, environ=None):
+    """
+    Delete the folder containing the algo state.
+
+    Parameters
+    ----------
+    algo_name: str
+    environ:
+
+    Returns
+    -------
+    str
+
+    """
+    folder = get_algo_folder(algo_name, environ)
+    shutil.rmtree(folder)
+
+
+def get_algo_folder(algo_name, environ=None):
+    """
+    The algorithm root folder of the algorithm.
+
+    Parameters
+    ----------
+    algo_name: str
+    environ:
+
+    Returns
+    -------
+    str
+
+    """
+    if not environ:
+        environ = os.environ
+
+    root = data_root(environ)
+    algo_folder = os.path.join(root, 'live_algos', algo_name)
+    ensure_directory(algo_folder)
+
+    return algo_folder
+
+
+def get_algo_object(algo_name, key, environ=None, rel_path=None):
+    """
+    The de-serialized object of the algo name and key.
+
+    Parameters
+    ----------
+    algo_name: str
+    key: str
+    environ:
+    rel_path: str
+
+    Returns
+    -------
+    Object
+
+    """
+    if algo_name is None:
+        return None
+
+    folder = get_algo_folder(algo_name, environ)
+
+    if rel_path is not None:
+        folder = os.path.join(folder, rel_path)
+
+    filename = os.path.join(folder, key + '.p')
+
+    if os.path.isfile(filename):
+        try:
+            with open(filename, 'rb') as handle:
+                return pickle.load(handle)
+        except Exception as e:
+            return None
+    else:
+        return None
+
+
+def save_algo_object(algo_name, key, obj, environ=None, rel_path=None):
+    """
+    Serialize and save an object by algo name and key.
+
+    Parameters
+    ----------
+    algo_name: str
+    key: str
+    obj: Object
+    environ:
+    rel_path: str
+
+    """
+    folder = get_algo_folder(algo_name, environ)
+
+    if rel_path is not None:
+        folder = os.path.join(folder, rel_path)
+        ensure_directory(folder)
+
+    filename = os.path.join(folder, key + '.p')
+
+    with open(filename, 'wb') as handle:
+        pickle.dump(obj, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+
+def get_algo_df(algo_name, key, environ=None, rel_path=None):
+    """
+    The de-serialized DataFrame of an algo name and key.
+
+    Parameters
+    ----------
+    algo_name: str
+    key: str
+    environ:
+    rel_path: str
+
+    Returns
+    -------
+    DataFrame
+
+    """
+    folder = get_algo_folder(algo_name, environ)
+
+    if rel_path is not None:
+        folder = os.path.join(folder, rel_path)
+
+    filename = os.path.join(folder, key + '.csv')
+
+    if os.path.isfile(filename):
+        try:
+            with open(filename, 'rb') as handle:
+                return pd.read_csv(handle, index_col=0, parse_dates=True)
+        except IOError:
+            return pd.DataFrame()
+    else:
+        return pd.DataFrame()
+
+
+def save_algo_df(algo_name, key, df, environ=None, rel_path=None):
+    """
+    Serialize to csv and save a DataFrame by algo name and key.
+
+    Parameters
+    ----------
+    algo_name: str
+    key: str
+    df: pd.DataFrame
+    environ:
+    rel_path: str
+
+    """
+    folder = get_algo_folder(algo_name, environ)
+    if rel_path is not None:
+        folder = os.path.join(folder, rel_path)
+        ensure_directory(folder)
+
+    filename = os.path.join(folder, key + '.csv')
+
+    with open(filename, 'wt') as handle:
+        df.to_csv(handle, encoding='UTF_8')
+
+
+def get_exchange_minute_writer_root(exchange_name, environ=None):
+    """
+    The minute writer folder for the exchange.
+
+    Parameters
+    ----------
+    exchange_name: str
+    environ:
+
+    Returns
+    -------
+    BcolzExchangeBarWriter
+
+    """
+    exchange_folder = get_exchange_folder(exchange_name, environ)
+
+    minute_data_folder = os.path.join(exchange_folder, 'minute_data')
+    ensure_directory(minute_data_folder)
+
+    return minute_data_folder
+
+
+def get_exchange_bundles_folder(exchange_name, environ=None):
+    """
+    The temp folder for bundle downloads by algo name.
+
+    Parameters
+    ----------
+    exchange_name: str
+    environ:
+
+    Returns
+    -------
+    str
+
+    """
+    exchange_folder = get_exchange_folder(exchange_name, environ)
+
+    temp_bundles = os.path.join(exchange_folder, 'temp_bundles')
+    ensure_directory(temp_bundles)
+
+    return temp_bundles
+
+
+def perf_serial(obj):
+    """
+    JSON serializer for objects not serializable by default json code
+
+    Parameters
+    ----------
+    obj: Object
+
+    Returns
+    -------
+    str
+
+    """
+    if isinstance(obj, (datetime, date)):
+        return obj.isoformat()
+
+    raise TypeError("Type %s not serializable" % type(obj))
+
+
+def get_common_assets(exchanges):
+    """
+    The assets available in all specified exchanges.
+
+    Parameters
+    ----------
+    exchanges: list[Exchange]
+
+    Returns
+    -------
+    list[TradingPair]
+
+    """
+    symbols = []
+    for exchange_name in exchanges:
+        s = [asset.symbol for asset in exchanges[exchange_name].get_assets()]
+        symbols.append(s)
+
+    inter_symbols = set.intersection(*map(set, symbols))
+
+    assets = []
+    for symbol in inter_symbols:
+        for exchange_name in exchanges:
+            asset = exchanges[exchange_name].get_asset(symbol)
+            assets.append(asset)
+
+    return assets
+
+
+def get_frequency(freq, data_frequency):
+    """
+    Get the frequency parameters.
+
+    Notes
+    -----
+    We're trying to use Pandas convention for frequency aliases.
+
+    Parameters
+    ----------
+    freq: str
+    data_frequency: str
+
+    Returns
+    -------
+    str, int, str, str
+
+    """
+    if freq == 'minute':
+        unit = 'T'
+        candle_size = 1
+
+    elif freq == 'daily':
+        unit = 'D'
+        candle_size = 1
+
+    else:
+        freq_match = re.match(r'([0-9].*)?(m|M|d|D|h|H|T)', freq, re.M | re.I)
+        if freq_match:
+            candle_size = int(freq_match.group(1)) if freq_match.group(1) \
+                else 1
+            unit = freq_match.group(2)
+
+        else:
+            raise InvalidHistoryFrequencyError(frequency=freq)
+
+    if unit.lower() == 'd':
+        alias = '{}D'.format(candle_size)
+
+        if data_frequency == 'minute':
+            data_frequency = 'daily'
+
+    elif unit.lower() == 'm' or unit == 'T':
+        alias = '{}T'.format(candle_size)
+
+        if data_frequency == 'daily':
+            data_frequency = 'minute'
+
+    # elif unit.lower() == 'h':
+    #     candle_size = candle_size * 60
+    #
+    #     alias = '{}T'.format(candle_size)
+    #     if data_frequency == 'daily':
+    #         data_frequency = 'minute'
+
+    else:
+        raise InvalidHistoryFrequencyAlias(freq=freq)
+
+    return alias, candle_size, unit, data_frequency
+
+
+def resample_history_df(df, freq, field):
+    """
+    Resample the OHCLV DataFrame using the specified frequency.
+
+    Parameters
+    ----------
+    df: DataFrame
+    freq: str
+    field: str
+
+    Returns
+    -------
+    DataFrame
+
+    """
+    if field == 'open':
+        agg = 'first'
+    elif field == 'high':
+        agg = 'max'
+    elif field == 'low':
+        agg = 'min'
+    elif field == 'close':
+        agg = 'last'
+    elif field == 'volume':
+        agg = 'sum'
+    else:
+        raise ValueError('Invalid field.')
+
+    return df.resample(freq).agg(agg)

catalyst/exchange/factory.py

@@ -0,0 +1,43 @@
+from catalyst.exchange.bitfinex.bitfinex import Bitfinex
+from catalyst.exchange.bittrex.bittrex import Bittrex
+from catalyst.exchange.exchange_errors import ExchangeNotFoundError
+from catalyst.exchange.exchange_utils import get_exchange_auth
+from catalyst.exchange.poloniex.poloniex import Poloniex
+
+
+def get_exchange(exchange_name, base_currency=None):
+    exchange_auth = get_exchange_auth(exchange_name)
+    if exchange_name == 'bitfinex':
+        return Bitfinex(
+            key=exchange_auth['key'],
+            secret=exchange_auth['secret'],
+            base_currency=base_currency,
+            portfolio=None
+        )
+
+    elif exchange_name == 'bittrex':
+        return Bittrex(
+            key=exchange_auth['key'],
+            secret=exchange_auth['secret'],
+            base_currency=base_currency,
+            portfolio=None
+        )
+
+    elif exchange_name == 'poloniex':
+        return Poloniex(
+            key=exchange_auth['key'],
+            secret=exchange_auth['secret'],
+            base_currency=base_currency,
+            portfolio=None
+        )
+
+    else:
+        raise ExchangeNotFoundError(exchange_name=exchange_name)
+
+
+def get_exchanges(exchange_names):
+    exchanges = dict()
+    for exchange_name in exchange_names:
+        exchanges[exchange_name] = get_exchange(exchange_name)
+
+    return exchanges

catalyst/exchange/live_graph_clock.py

@@ -0,0 +1,233 @@
+import pandas as pd
+from catalyst.gens.sim_engine import (
+    BAR,
+    SESSION_START
+)
+from logbook import Logger
+
+from catalyst.constants import LOG_LEVEL
+from catalyst.exchange.exchange_errors import \
+    MismatchingBaseCurrenciesExchanges
+
+log = Logger('LiveGraphClock', level=LOG_LEVEL)
+
+
+class LiveGraphClock(object):
+    """Realtime clock for live trading.
+
+    This class is a drop-in replacement for
+    :class:`zipline.gens.sim_engine.MinuteSimulationClock`.
+
+    This mixes the clock with a live graph.
+
+    Notes
+    -----
+    This seemingly awkward approach allows us to run the program using a single
+    thread. This is important because Matplotlib does not play nice with
+    multi-threaded environments. Zipline probably does not either.
+
+
+    Matplotlib has a pause() method which is a wrapper around time.sleep()
+    used in the SimpleClock. The key difference is that users
+    can still interact with the chart during the pause cycles. This is
+    what enables us to keep a single thread. This is also why we are not using
+    the 'animate' callback of Matplotlib. We need to direct access to the
+    __iter__ method in order to yield events to Zipline.
+
+    The :param:`time_skew` parameter represents the time difference between
+    the exchange and the live trading machine's clock. It's not used currently.
+    """
+
+    def __init__(self, sessions, context, time_skew=pd.Timedelta('0s')):
+
+        global mdates, plt  # TODO: Could be cleaner
+        import matplotlib.dates as mdates
+        from matplotlib import pyplot as plt
+        from matplotlib import style
+
+        self.sessions = sessions
+        self.time_skew = time_skew
+        self._last_emit = None
+        self._before_trading_start_bar_yielded = True
+        self.context = context
+        self.fmt = mdates.DateFormatter('%Y-%m-%d %H:%M')
+
+        style.use('dark_background')
+
+        fig = plt.figure()
+        fig.canvas.set_window_title('Enigma Catalyst: {}'.format(
+            self.context.algo_namespace))
+
+        self.ax_pnl = fig.add_subplot(311)
+
+        self.ax_custom_signals = fig.add_subplot(312, sharex=self.ax_pnl)
+
+        self.ax_exposure = fig.add_subplot(313, sharex=self.ax_pnl)
+
+        if len(context.minute_stats) > 0:
+            self.draw_pnl()
+            self.draw_custom_signals()
+            self.draw_exposure()
+
+        # rotates and right aligns the x labels, and moves the bottom of the
+        # axes up to make room for them
+        fig.autofmt_xdate()
+        fig.subplots_adjust(hspace=0.5)
+
+        plt.tight_layout()
+        plt.ion()
+        plt.show()
+
+    def format_ax(self, ax):
+        """
+        Trying to assign reasonable parameters to the time axis.
+
+        Parameters
+        ----------
+        ax:
+
+        """
+        # TODO: room for improvement
+        ax.xaxis.set_major_locator(mdates.DayLocator(interval=1))
+        ax.xaxis.set_major_formatter(self.fmt)
+
+        locator = mdates.HourLocator(interval=4)
+        locator.MAXTICKS = 5000
+        ax.xaxis.set_minor_locator(locator)
+
+        datemin = pd.Timestamp.utcnow()
+        ax.set_xlim(datemin)
+
+        ax.grid(True)
+
+    def set_legend(self, ax):
+        """
+        Set legend on the chart.
+
+        Parameters
+        ----------
+        ax
+
+        """
+        ax.legend(loc='upper left', ncol=1, fontsize=10, numpoints=1)
+
+    def draw_pnl(self):
+        """
+        Draw p&l line on the chart.
+
+        """
+        ax = self.ax_pnl
+        df = self.context.pnl_stats
+
+        ax.clear()
+        ax.set_title('Performance')
+        ax.plot(df.index, df['performance'], '-',
+                color='green',
+                linewidth=1.0,
+                label='Performance'
+                )
+
+        def perc(val):
+            return '{:2f}'.format(val)
+
+        ax.format_ydata = perc
+
+        self.set_legend(ax)
+        self.format_ax(ax)
+
+    def draw_custom_signals(self):
+        """
+        Draw custom signals on the chart.
+
+        """
+        ax = self.ax_custom_signals
+        df = self.context.custom_signals_stats
+
+        colors = ['blue', 'green', 'red', 'black', 'orange', 'yellow', 'pink']
+
+        ax.clear()
+        ax.set_title('Custom Signals')
+        for index, column in enumerate(df.columns.values.tolist()):
+            ax.plot(df.index, df[column], '-',
+                    color=colors[index],
+                    linewidth=1.0,
+                    label=column
+                    )
+
+        self.set_legend(ax)
+        self.format_ax(ax)
+
+    def draw_exposure(self):
+        """
+        Draw exposure line on the chart.
+
+        """
+        ax = self.ax_exposure
+        context = self.context
+        df = context.exposure_stats
+
+        # TODO: list exchanges in graph
+        base_currency = None
+        positions = []
+        for exchange_name in context.exchanges:
+            exchange = context.exchanges[exchange_name]
+
+            if not base_currency:
+                base_currency = exchange.base_currency
+            elif base_currency != exchange.base_currency:
+                raise MismatchingBaseCurrenciesExchanges(
+                    base_currency=base_currency,
+                    exchange_name=exchange.name,
+                    exchange_currency=exchange.base_currency
+                )
+
+            positions += exchange.portfolio.positions
+
+        ax.clear()
+        ax.set_title('Exposure')
+        ax.plot(df.index, df['base_currency'], '-',
+                color='green',
+                linewidth=1.0,
+                label='Base Currency: {}'.format(base_currency.upper())
+                )
+
+        symbols = []
+        for position in positions:
+            symbols.append(position.symbol)
+
+        ax.plot(df.index, df['long_exposure'], '-',
+                color='blue',
+                linewidth=1.0,
+                label='Long Exposure: {}'.format(', '.join(symbols).upper()))
+
+        self.set_legend(ax)
+        self.format_ax(ax)
+
+    def __iter__(self):
+        yield pd.Timestamp.utcnow(), SESSION_START
+
+        while True:
+            current_time = pd.Timestamp.utcnow()
+            current_minute = current_time.floor('1 min')
+
+            if self._last_emit is None or current_minute > self._last_emit:
+                log.debug('emitting minutely bar: {}'.format(current_minute))
+
+                self._last_emit = current_minute
+                yield current_minute, BAR
+
+                try:
+                    self.draw_pnl()
+                    self.draw_custom_signals()
+                    self.draw_exposure()
+
+                    plt.draw()
+                except Exception as e:
+                    log.warn('Unable to update the graph: {}'.format(e))
+
+            else:
+                # I can't use the "animate" reactive approach here because
+                # I need to yield from the main loop.
+
+                # Workaround: https://stackoverflow.com/a/33050617/814633
+                plt.pause(1)

catalyst/exchange/poloniex/poloniex.py

@@ -0,0 +1,651 @@
+import json
+import json
+import time
+from collections import defaultdict
+
+import numpy as np
+import pandas as pd
+import pytz
+from catalyst.assets._assets import TradingPair
+from logbook import Logger
+# import six
+from six import iteritems
+
+from catalyst.constants import LOG_LEVEL
+# from websocket import create_connection
+from catalyst.exchange.exchange import Exchange
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.exchange_errors import (
+    ExchangeRequestError,
+    InvalidHistoryFrequencyError,
+    InvalidOrderStyle, OrphanOrderReverseError)
+from catalyst.exchange.exchange_execution import ExchangeLimitOrder, \
+    ExchangeStopLimitOrder
+from catalyst.exchange.exchange_utils import get_exchange_symbols_filename, \
+    download_exchange_symbols, get_symbols_string
+from catalyst.exchange.poloniex.poloniex_api import Poloniex_api
+from catalyst.finance.order import Order, ORDER_STATUS
+from catalyst.finance.transaction import Transaction
+from catalyst.protocol import Account
+
+log = Logger('Poloniex', level=LOG_LEVEL)
+
+
+class Poloniex(Exchange):
+    def __init__(self, key, secret, base_currency, portfolio=None):
+        self.api = Poloniex_api(key=key, secret=secret)
+        self.name = 'poloniex'
+        self.assets = {}
+        self.load_assets()
+        self.base_currency = base_currency
+        self._portfolio = portfolio
+        self.minute_writer = None
+        self.minute_reader = None
+        self.transactions = defaultdict(list)
+
+        self.num_candles_limit = 2000
+        self.max_requests_per_minute = 60
+        self.request_cpt = dict()
+
+        self.bundle = ExchangeBundle(self)
+
+    def sanitize_curency_symbol(self, exchange_symbol):
+        """
+        Helper method used to build the universal pair.
+        Include any symbol mapping here if appropriate.
+
+        :param exchange_symbol:
+        :return universal_symbol:
+        """
+        return exchange_symbol.lower()
+
+    def _create_order(self, order_status):
+        """
+        Create a Catalyst order object from the Exchange order dictionary
+        :param order_status:
+        :return: Order
+        """
+        # if order_status['is_cancelled']:
+        #    status = ORDER_STATUS.CANCELLED
+        # elif not order_status['is_live']:
+        #    log.info('found executed order {}'.format(order_status))
+        #    status = ORDER_STATUS.FILLED
+        # else:
+        status = ORDER_STATUS.OPEN
+
+        amount = float(order_status['amount'])
+        # filled = float(order_status['executed_amount'])
+        filled = None
+
+        if order_status['type'] == 'sell':
+            amount = -amount
+            # filled = -filled
+
+        price = float(order_status['rate'])
+        order_type = order_status['type']
+
+        stop_price = None
+        limit_price = None
+
+        # TODO: is this comprehensive enough?
+        # if order_type.endswith('limit'):
+        #    limit_price = price
+        # elif order_type.endswith('stop'):
+        #    stop_price = price
+
+        # executed_price = float(order_status['avg_execution_price'])
+        executed_price = price
+
+        # TODO: bitfinex does not specify comission. I could calculate it but not sure if it's worth it.
+        commission = None
+
+        # date = pd.Timestamp.utcfromtimestamp(float(order_status['timestamp']))
+        # date = pytz.utc.localize(date)
+        date = None
+
+        order = Order(
+            dt=date,
+            asset=self.assets[order_status['symbol']],
+            # No such field in Poloniex
+            amount=amount,
+            stop=stop_price,
+            limit=limit_price,
+            filled=filled,
+            id=str(order_status['orderNumber']),
+            commission=commission
+        )
+        order.status = status
+
+        return order, executed_price
+
+    def get_balances(self):
+        balances = self.api.returnbalances()
+        try:
+            log.debug('retrieving wallets balances')
+        except Exception as e:
+            log.debug(e)
+            raise ExchangeRequestError(error=e)
+
+        if 'error' in balances:
+            raise ExchangeRequestError(
+                error='unable to fetch balance {}'.format(balances['error'])
+            )
+
+        std_balances = dict()
+        for (key, value) in iteritems(balances):
+            currency = key.lower()
+            std_balances[currency] = float(value)
+
+        return std_balances
+
+    @property
+    def account(self):
+        account = Account()
+
+        account.settled_cash = None
+        account.accrued_interest = None
+        account.buying_power = None
+        account.equity_with_loan = None
+        account.total_positions_value = None
+        account.total_positions_exposure = None
+        account.regt_equity = None
+        account.regt_margin = None
+        account.initial_margin_requirement = None
+        account.maintenance_margin_requirement = None
+        account.available_funds = None
+        account.excess_liquidity = None
+        account.cushion = None
+        account.day_trades_remaining = None
+        account.leverage = None
+        account.net_leverage = None
+        account.net_liquidation = None
+
+        return account
+
+    @property
+    def time_skew(self):
+        # TODO: research the time skew conditions
+        return pd.Timedelta('0s')
+
+    def get_account(self):
+        # TODO: fetch account data and keep in cache
+        return None
+
+    def get_candles(self, freq, assets, bar_count=None,
+                    start_dt=None, end_dt=None):
+        """
+        Retrieve OHLVC candles from Poloniex
+
+        :param freq:
+        :param assets:
+        :param bar_count:
+        :return:
+
+        Available Frequencies
+        ---------------------
+        '5m', '15m', '30m', '2h', '4h', '1D'
+        """
+
+        if end_dt is None:
+            end_dt = pd.Timestamp.utcnow()
+
+        log.debug(
+            'retrieving {bars} {freq} candles on {exchange} from '
+            '{end_dt} for markets {symbols}, '.format(
+                bars=bar_count,
+                freq=freq,
+                exchange=self.name,
+                end_dt=end_dt,
+                symbols=get_symbols_string(assets)
+            )
+        )
+
+        if freq == '1T' and (bar_count == 1 or bar_count is None):
+            # TODO: use the order book instead
+            # We use the 5m to fetch the last bar
+            frequency = 300
+        elif freq == '5T':
+            frequency = 300
+        elif freq == '15T':
+            frequency = 900
+        elif freq == '30T':
+            frequency = 1800
+        elif freq == '120T':
+            frequency = 7200
+        elif freq == '240T':
+            frequency = 14400
+        elif freq == '1D':
+            frequency = 86400
+        else:
+            # Poloniex does not offer 1m data candles
+            # It is likely to error out there frequently
+            raise InvalidHistoryFrequencyError(frequency=freq)
+
+        # Making sure that assets are iterable
+        asset_list = [assets] if isinstance(assets, TradingPair) else assets
+        ohlc_map = dict()
+
+        for asset in asset_list:
+
+            # TODO: what's wrong with this?
+            # end = int(time.mktime(end_dt.timetuple()))
+            end = int(time.time())
+            if bar_count is None:
+                start = end - 2 * frequency
+            else:
+                start = end - bar_count * frequency
+
+            try:
+                response = self.api.returnchartdata(
+                    self.get_symbol(asset), frequency, start, end
+                )
+            except Exception as e:
+                raise ExchangeRequestError(error=e)
+
+            if 'error' in response:
+                raise ExchangeRequestError(
+                    error='Unable to retrieve candles: {}'.format(
+                        response.content)
+                )
+
+            def ohlc_from_candle(candle):
+                last_traded = pd.Timestamp.utcfromtimestamp(candle['date'])
+                last_traded = last_traded.replace(tzinfo=pytz.UTC)
+
+                ohlc = dict(
+                    open=np.float64(candle['open']),
+                    high=np.float64(candle['high']),
+                    low=np.float64(candle['low']),
+                    close=np.float64(candle['close']),
+                    volume=np.float64(candle['volume']),
+                    price=np.float64(candle['close']),
+                    last_traded=last_traded
+                )
+
+                return ohlc
+
+            if bar_count is None:
+                ohlc_map[asset] = ohlc_from_candle(response[0])
+            else:
+                ohlc_bars = []
+                for candle in response:
+                    ohlc = ohlc_from_candle(candle)
+                    ohlc_bars.append(ohlc)
+                ohlc_map[asset] = ohlc_bars
+
+        return ohlc_map[assets] \
+            if isinstance(assets, TradingPair) else ohlc_map
+
+    def create_order(self, asset, amount, is_buy, style):
+        """
+        Creating order on the exchange.
+
+        :param asset:
+        :param amount:
+        :param is_buy:
+        :param style:
+        :return:
+        """
+        exchange_symbol = self.get_symbol(asset)
+
+        if isinstance(style, ExchangeLimitOrder) or isinstance(style,
+                                                               ExchangeStopLimitOrder):
+            if isinstance(style, ExchangeStopLimitOrder):
+                log.warn('{} will ignore the stop price'.format(self.name))
+
+            price = style.get_limit_price(is_buy)
+
+            try:
+                if (is_buy):
+                    response = self.api.buy(exchange_symbol, amount, price)
+                else:
+                    response = self.api.sell(exchange_symbol, -amount, price)
+            except Exception as e:
+                raise ExchangeRequestError(error=e)
+
+            date = pd.Timestamp.utcnow()
+
+            if ('orderNumber' in response):
+                order_id = str(response['orderNumber'])
+                order = Order(
+                    dt=date,
+                    asset=asset,
+                    amount=amount,
+                    stop=style.get_stop_price(is_buy),
+                    limit=style.get_limit_price(is_buy),
+                    id=order_id
+                )
+                return order
+            else:
+                log.warn(
+                    '{} order failed: {}'.format('buy' if is_buy else 'sell',
+                                                 response['error']))
+                return None
+        else:
+            raise InvalidOrderStyle(exchange=self.name,
+                                    style=style.__class__.__name__)
+
+    def get_open_orders(self, asset='all'):
+        """Retrieve all of the current open orders.
+
+        Parameters
+        ----------
+        asset : Asset
+            If passed and not 'all', return only the open orders for the given
+            asset instead of all open orders.
+
+        Returns
+        -------
+        open_orders : dict[list[Order]] or list[Order]
+            If 'all' is passed this will return a dict mapping Assets
+            to a list containing all the open orders for the asset.
+            If an asset is passed then this will return a list of the open
+            orders for this asset.
+        """
+
+        return self.portfolio.open_orders
+
+        """
+            TODO: Why going to the exchange if we already have this info locally?
+                  And why creating all these Orders if we later discard them?
+        """
+
+        try:
+            if (asset == 'all'):
+                response = self.api.returnopenorders('all')
+            else:
+                response = self.api.returnopenorders(self.get_symbol(asset))
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'error' in response:
+            raise ExchangeRequestError(
+                error='Unable to retrieve open orders: {}'.format(
+                    order_statuses['message'])
+            )
+
+        print(self.portfolio.open_orders)
+
+        # TODO: Need to handle openOrders for 'all'
+        orders = list()
+        for order_status in response:
+            order, executed_price = self._create_order(
+                order_status)  # will Throw error b/c Polo doesn't track order['symbol']
+            if asset is None or asset == order.sid:
+                orders.append(order)
+
+        return orders
+
+    def get_order(self, order_id):
+        """Lookup an order based on the order id returned from one of the
+        order functions.
+
+        Parameters
+        ----------
+        order_id : str
+            The unique identifier for the order.
+
+        Returns
+        -------
+        order : Order
+            The order object.
+        """
+
+        try:
+            order = self._portfolio.open_orders[order_id]
+        except Exception as e:
+            raise OrphanOrderError(order_id=order_id, exchange=self.name)
+
+        return order
+
+        # TODO: Need to decide whether we fetch orders locally or from exchnage
+        # The code below is ignored
+
+        try:
+            response = self.api.returnopenorders(self.get_symbol(order.sid))
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        for o in response:
+            if (int(o['orderNumber']) == int(order_id)):
+                return order
+
+        return None
+
+    def cancel_order(self, order_param):
+        """Cancel an open order.
+
+        Parameters
+        ----------
+        order_param : str or Order
+            The order_id or order object to cancel.
+        """
+
+        if (isinstance(order_param, Order)):
+            order = order_param
+        else:
+            order = self._portfolio.open_orders[order_param]
+
+        try:
+            response = self.api.cancelorder(order.id)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'error' in response:
+            log.info(
+                'Unable to cancel order {order_id} on exchange {exchange} {error}.'.format(
+                    order_id=order.id,
+                    exchange=self.name,
+                    error=response['error']
+                ))
+
+            # raise OrderCancelError(
+            #    order_id=order.id,
+            #    exchange=self.name,
+            #    error=response['error']
+            # )
+
+        self.portfolio.remove_order(order)
+
+    def tickers(self, assets):
+        """
+        Fetch ticket data for assets
+        https://docs.bitfinex.com/v2/reference#rest-public-tickers
+
+        :param assets:
+        :return:
+        """
+        symbols = self.get_symbols(assets)
+
+        log.debug('fetching tickers {}'.format(symbols))
+
+        try:
+            response = self.api.returnticker()
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'error' in response:
+            raise ExchangeRequestError(
+                error='Unable to retrieve tickers: {}'.format(
+                    response['error'])
+            )
+
+        ticks = dict()
+
+        for index, symbol in enumerate(symbols):
+            ticks[assets[index]] = dict(
+                timestamp=pd.Timestamp.utcnow(),
+                bid=float(response[symbol]['highestBid']),
+                ask=float(response[symbol]['lowestAsk']),
+                last_price=float(response[symbol]['last']),
+                low=float(response[symbol]['lowestAsk']),
+                # TODO: Polo does not provide low
+                high=float(response[symbol]['highestBid']),
+                # TODO: Polo does not provide high
+                volume=float(response[symbol]['baseVolume']),
+            )
+
+        log.debug('got tickers {}'.format(ticks))
+        return ticks
+
+    def generate_symbols_json(self, filename=None, source_dates=False):
+        symbol_map = {}
+
+        if not source_dates:
+            fn, r = download_exchange_symbols(self.name)
+            with open(fn) as data_file:
+                cached_symbols = json.load(data_file)
+
+        response = self.api.returnticker()
+
+        for exchange_symbol in response:
+            base, market = self.sanitize_curency_symbol(exchange_symbol).split(
+                '_')
+            symbol = '{market}_{base}'.format(market=market, base=base)
+
+            if (source_dates):
+                start_date = self.get_symbol_start_date(exchange_symbol)
+            else:
+                try:
+                    start_date = cached_symbols[exchange_symbol]['start_date']
+                except KeyError as e:
+                    start_date = time.strftime('%Y-%m-%d')
+
+            try:
+                end_daily = cached_symbols[exchange_symbol]['end_daily']
+            except KeyError as e:
+                end_daily = 'N/A'
+
+            try:
+                end_minute = cached_symbols[exchange_symbol]['end_minute']
+            except KeyError as e:
+                end_minute = 'N/A'
+
+            symbol_map[exchange_symbol] = dict(
+                symbol=symbol,
+                start_date=start_date,
+                end_daily=end_daily,
+                end_minute=end_minute,
+            )
+
+        if (filename is None):
+            filename = get_exchange_symbols_filename(self.name)
+
+        with open(filename, 'w') as f:
+            json.dump(symbol_map, f, sort_keys=True, indent=2,
+                      separators=(',', ':'))
+
+    def get_symbol_start_date(self, symbol):
+        try:
+            r = self.api.returnchartdata(symbol, 86400, pd.to_datetime(
+                '2010-1-1').value // 10 ** 9)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        return time.strftime('%Y-%m-%d', time.gmtime(int(r[0]['date'])))
+
+    def check_open_orders(self):
+        """
+        Need to override this function for Poloniex:
+
+        Loop through the list of open orders in the Portfolio object.
+        Check if any transactions have been executed:
+            If so, create a transaction and apply to the Portfolio.
+        Check if the order is still open:
+            If not, remove it from open orders
+
+        :return:
+        transactions: Transaction[]
+        """
+        transactions = list()
+        if self.portfolio.open_orders:
+            for order_id in list(self.portfolio.open_orders):
+
+                order = self._portfolio.open_orders[order_id]
+                log.debug('found open order: {}'.format(order_id))
+
+                try:
+                    order_open = self.get_order(order_id)
+                except Exception as e:
+                    raise ExchangeRequestError(error=e)
+
+                if (order_open):
+                    delta = pd.Timestamp.utcnow() - order.dt
+                    log.info(
+                        'order {order_id} still open after {delta}'.format(
+                            order_id=order_id,
+                            delta=delta)
+                    )
+
+                try:
+                    response = self.api.returnordertrades(order_id)
+                except Exception as e:
+                    raise ExchangeRequestError(error=e)
+
+                if ('error' in response):
+                    if (not order_open):
+                        raise OrphanOrderReverseError(order_id=order_id,
+                                                      exchange=self.name)
+                else:
+                    for tx in response:
+                        """
+                            We maintain a list of dictionaries of transactions that correspond to
+                            partially filled orders, indexed by order_id. Every time we query 
+                            executed transactions from the exchange, we check if we had that 
+                            transaction for that order already. If not, we process it.
+
+                            When an order if fully filled, we flush the dict of transactions  
+                            associated with that order.
+                        """
+                        if (not filter(
+                                lambda item: item['order_id'] == tx['tradeID'],
+                                self.transactions[order_id])):
+                            log.debug(
+                                'Got new transaction for order {}: amount {}, price {}'.format(
+                                    order_id, tx['amount'], tx['rate']))
+                            tx['amount'] = float(tx['amount'])
+                            if (tx['type'] == 'sell'):
+                                tx['amount'] = -tx['amount']
+                            transaction = Transaction(
+                                asset=order.asset,
+                                amount=tx['amount'],
+                                dt=pd.to_datetime(tx['date'], utc=True),
+                                price=float(tx['rate']),
+                                order_id=tx['tradeID'],
+                                # it's a misnomer, but keeping it for compatibility
+                                commission=float(tx['fee'])
+                            )
+                            self.transactions[order_id].append(transaction)
+                            self.portfolio.execute_transaction(transaction)
+                            transactions.append(transaction)
+
+                    if (not order_open):
+                        """
+                            Since transactions have been executed individually
+                            the only thing left to do is remove them from list of open_orders
+                        """
+                        del self.portfolio.open_orders[order_id]
+                        del self.transactions[order_id]
+
+        return transactions
+
+    def get_orderbook(self, asset, order_type='all'):
+        exchange_symbol = asset.exchange_symbol
+        data = self.api.returnOrderBook(market=exchange_symbol)
+
+        result = dict()
+        for order_type in data:
+            # TODO: filter by type
+            if order_type != 'asks' and order_type != 'bids':
+                continue
+
+            result[order_type] = []
+            for entry in data[order_type]:
+                if len(entry) == 2:
+                    result[order_type].append(
+                        dict(
+                            rate=float(entry[0]),
+                            quantity=float(entry[1])
+                        )
+                    )
+        return result

catalyst/exchange/poloniex/poloniex_api.py

@@ -0,0 +1,215 @@
+#!/usr/bin/env python
+import json
+import time
+import hmac
+import hashlib
+import ssl
+
+from six.moves import urllib
+
+# Workaround for backwards compatibility
+# https://stackoverflow.com/questions/3745771/urllib-request-in-python-2-7
+urlopen = urllib.request.urlopen
+
+
+class Poloniex_api(object):
+    def __init__(self, key, secret):
+        self.key = key
+        self.secret = secret
+
+        self.max_requests_per_second = 6
+        self.request_cpt = dict()
+
+        self.public = ['returnTicker', 'return24Volume', 'returnOrderBook',
+                       'returnTradeHistory', 'returnChartData',
+                       'returnCurrencies', 'returnLoanOrders']
+        self.trading = ['returnBalances', 'returnCompleteBalances',
+                        'returnDepositAddresses',
+                        'generateNewAddress', 'returnDepositsWithdrawals',
+                        'returnOpenOrders',
+                        'returnTradeHistory', 'returnOrderTrades',
+                        'buy', 'sell', 'cancelOrder', 'moveOrder',
+                        'withdraw', 'returnFeeInfo',
+                        'returnAvailableAccountBalances',
+                        'returnTradableBalances', 'transferBalance',
+                        'returnMarginAccountSummary', 'marginBuy',
+                        'marginSell',
+                        'getMarginPosition', 'closeMarginPosition',
+                        'createLoanOffer',
+                        'cancelLoanOffer', 'returnOpenLoanOffers',
+                        'returnActiveLoans',
+                        'returnLendingHistory', 'toggleAutoRenew']
+
+    def ask_request(self):
+        """
+        Asks permission to issue a request to the exchange.
+        The primary purpose is to avoid hitting rate limits.
+
+        The application will pause if the maximum requests per minute
+        permitted by the exchange is exceeded.
+
+        :return boolean:
+
+        """
+        now = time.time()
+        if not self.request_cpt:
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+
+        cpt_date = list(self.request_cpt.keys())[0]
+        cpt = self.request_cpt[cpt_date]
+
+        if now > cpt_date + 1:
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+
+        if cpt >= self.max_requests_per_second:
+
+            time.sleep(1)
+
+            now = time.time()
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+        else:
+            self.request_cpt[cpt_date] += 1
+
+    def query(self, method, req={}):
+
+        if method in self.public:
+            url = 'https://poloniex.com/public?command=' + method + '&' + \
+                  urllib.parse.urlencode(req)
+            headers = {}
+            post_data = None
+        elif method in self.trading:
+            url = 'https://poloniex.com/tradingApi'
+            req['command'] = method
+            req['nonce'] = int(time.time() * 1000)
+            post_data = urllib.parse.urlencode(req)
+
+            signature = hmac.new(self.secret.encode('utf-8'),
+                                 post_data.encode('utf-8'),
+                                 hashlib.sha512).hexdigest()
+            headers = {'Sign': signature, 'Key': self.key}
+
+            post_data = post_data.encode('utf-8')
+        else:
+            raise ValueError(
+                'Method "' + method + '" not found in neither the Public API '
+                                      'or Trading API endpoints'
+            )
+
+        self.ask_request()
+        req = urllib.request.Request(
+            url,
+            data=post_data,
+            headers=headers,
+        )
+        return json.loads(
+            urlopen(req, context=ssl._create_unverified_context()).read())
+
+    def returnticker(self):
+        return self.query('returnTicker', {})
+
+    def return24volume(self):
+        return self.query('return24Volume', {})
+
+    def returnOrderBook(self, market='all'):
+        return self.query('returnOrderBook', {'currencyPair': market})
+
+    def returntradehistory(self, market, start=None, end=None):
+        if (start is not None and end is not None):
+            return self.query('returntradehistory',
+                              {'currencyPair': market, 'start': start,
+                               'end': end})
+        else:
+            return self.query('returntradehistory', {'currencyPair': market})
+
+    def returnchartdata(self, market, period, start, end=9999999999):
+        return self.query('returnChartData',
+                          {'currencyPair': market, 'period': period,
+                           'start': start, 'end': end})
+
+    def returncurrencies(self):
+        return self.query('returnCurrencies', {})
+
+    def returnloadorders(self, market):
+        return self.query('returnLoanOrders', {'currency': market})
+
+    def returnbalances(self):
+        return self.query('returnBalances')
+
+    def returncompletebalances(self, account):
+        if (account):
+            return self.query('returnCompleteBalances', {'account': account})
+        else:
+            return self.query('returnCompleteBalances')
+
+    def returndepositaddresses(self):
+        return self.query('returnDepositAddresses')
+
+    def generatenewaddress(self, currency):
+        return self.query('generateNewAddress', {'currency': currency})
+
+    def returnDepositsWithdrawals(self, start, end):
+        return self.query('returnDepositsWithdrawals',
+                          {'start': start, 'end': end})
+
+    def returnopenorders(self, market):
+        return self.query('returnOpenOrders', {'currencyPair': market})
+
+    def returntradehistory(self, market):
+        # TODO: optional start and/or end and limit
+        return self.query('returnTradeHistory', {'currencyPair': market})
+
+    def returnordertrades(self, ordernumber):
+        return self.query('returnOrderTrades', {'orderNumber': ordernumber})
+
+    def buy(self, market, amount, rate, fillorkill=0, immediateorcancel=0,
+            postonly=0):
+        if (fillorkill):
+            return self.query('buy', {'currencyPair': market, 'rate': rate,
+                                      'amount': amount,
+                                      'fillOrKill': fillorkill, })
+        elif (immediateorcancel):
+            return self.query('buy', {'currencyPair': market, 'rate': rate,
+                                      'amount': amount,
+                                      'immediateOrCancel': immediateorcancel, })
+        elif (postonly):
+            return self.query('buy', {'currencyPair': market, 'rate': rate,
+                                      'amount': amount,
+                                      'postOnly': postonly, })
+        else:
+            return self.query('buy', {'currencyPair': market, 'rate': rate,
+                                      'amount': amount, })
+
+    def sell(self, market, amount, rate, fillorkill=0, immediateorcancel=0,
+             postonly=0):
+        if (fillorkill):
+            return self.query('sell', {'currencyPair': market, 'rate': rate,
+                                       'amount': amount,
+                                       'fillOrKill': fillorkill, })
+        elif (immediateorcancel):
+            return self.query('sell', {'currencyPair': market, 'rate': rate,
+                                       'amount': amount,
+                                       'immediateOrCancel': immediateorcancel, })
+        elif (postonly):
+            return self.query('sell', {'currencyPair': market, 'rate': rate,
+                                       'amount': amount,
+                                       'postOnly': postonly, })
+        else:
+            return self.query('sell', {'currencyPair': market, 'rate': rate,
+                                       'amount': amount, })
+
+    def cancelorder(self, ordernumber):
+        return self.query('cancelOrder', {'orderNumber': ordernumber})
+
+    def withdraw(self, currency, quantity, address):
+        return self.query('withdraw',
+                          {'currency': currency, 'amount': quantity,
+                           'address': address})
+
+    def returnfeeinfo(self):
+        return self.query('returnFeeInfo')

catalyst/exchange/simple_clock.py

@@ -0,0 +1,60 @@
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from time import sleep
+
+import pandas as pd
+from catalyst.gens.sim_engine import (
+    BAR,
+    SESSION_START
+)
+from logbook import Logger
+
+from catalyst.constants import LOG_LEVEL
+
+log = Logger('ExchangeClock', level=LOG_LEVEL)
+
+
+class SimpleClock(object):
+    """Realtime clock for live trading.
+
+    This class is a drop-in replacement for
+    :class:`zipline.gens.sim_engine.MinuteSimulationClock`.
+
+    This is a stripped down version because crypto exchanges run around the clock.
+
+    The :param:`time_skew` parameter represents the time difference between
+    the Broker and the live trading machine's clock.
+    """
+
+    def __init__(self, sessions, time_skew=pd.Timedelta("0s")):
+
+        self.sessions = sessions
+        self.time_skew = time_skew
+        self._last_emit = None
+        self._before_trading_start_bar_yielded = True
+
+    def __iter__(self):
+        yield pd.Timestamp.utcnow(), SESSION_START
+
+        while True:
+            current_time = pd.Timestamp.utcnow()
+            current_minute = current_time.floor('1 min')
+
+            if self._last_emit is None or current_minute > self._last_emit:
+                log.debug('emitting minutely bar: {}'.format(current_minute))
+
+                self._last_emit = current_minute
+                yield current_minute, BAR
+            else:
+                sleep(1)

catalyst/exchange/stats_utils.py

@@ -0,0 +1,210 @@
+import numbers
+
+import numpy as np
+import pandas as pd
+
+
+def trend_direction(series):
+    if series[-1] is np.nan or series[-1] is np.nan:
+        return None
+
+    if series[-1] > series[-2]:
+        return 'up'
+    else:
+        return 'down'
+
+
+def crossover(source, target):
+    """
+    The `x`-series is defined as having crossed over `y`-series if the value
+    of `x` is greater than the value of `y` and the value of `x` was less than
+    the value of `y` on the bar immediately preceding the current bar.
+
+    Parameters
+    ----------
+    source: Series
+    target: Series
+
+    Returns
+    -------
+    bool
+
+    """
+    if source[-1] is np.nan or source[-2] is np.nan \
+            or target[-1] is np.nan or target[-2] is np.nan:
+        return False
+
+    if source[-1] > target[-1] and source[-2] < target[-2]:
+        return True
+    else:
+        return False
+
+
+def crossunder(source, target):
+    """
+    The `x`-series is defined as having crossed under `y`-series if the value
+    of `x` is less than the value of `y` and the value of `x` was greater than
+    the value of `y` on the bar immediately preceding the current bar.
+
+    Parameters
+    ----------
+    source: Series
+    target: Series
+
+    Returns
+    -------
+    bool
+
+    """
+    if isinstance(target, numbers.Number):
+        if source[-1] is np.nan or source[-2] is np.nan \
+                or target is np.nan:
+            return False
+
+        if source[-1] < target <= source[-2]:
+            return True
+        else:
+            return False
+    else:
+        if source[-1] is np.nan or source[-2] is np.nan \
+                or target[-1] is np.nan or target[-2] is np.nan:
+            return False
+
+        if source[-1] < target[-1] and source[-2] >= target[-2]:
+            return True
+        else:
+            return False
+
+
+def vwap(df):
+    """
+    Volume-weighted average price (VWAP) is a ratio generally used by
+    institutional investors and mutual funds to make buys and sells so as not
+    to disturb the market prices with large orders. It is the average share
+    price of a stock weighted against its trading volume within a particular
+    time frame, generally one day.
+
+    Read more: Volume Weighted Average Price - VWAP
+    https://www.investopedia.com/terms/v/vwap.asp#ixzz4xt922daE
+
+    Parameters
+    ----------
+    df: pd.DataFrame
+
+    Returns
+    -------
+
+    """
+    if 'close' not in df.columns or 'volume' not in df.columns:
+        raise ValueError('price data must include `volume` and `close`')
+
+    vol_sum = np.nansum(df['volume'].values)
+
+    try:
+        ret = np.nansum(df['close'].values * df['volume'].values) / vol_sum
+    except ZeroDivisionError:
+        ret = np.nan
+
+    return ret
+
+
+def get_pretty_stats(stats_df, recorded_cols=None, num_rows=10):
+    """
+    Format and print the last few rows of a statistics DataFrame.
+    See the pyfolio project for the data structure.
+
+    Parameters
+    ----------
+    stats_df: DataFrame
+    num_rows: int
+
+    Returns
+    -------
+    str
+
+    """
+    stats_df.set_index('period_close', drop=True, inplace=True)
+    stats_df.dropna(axis=1, how='all', inplace=True)
+
+    pd.set_option('display.expand_frame_repr', False)
+    pd.set_option('precision', 3)
+    pd.set_option('display.width', 1000)
+    pd.set_option('display.max_colwidth', 1000)
+
+    columns = ['starting_cash', 'ending_cash', 'portfolio_value',
+               'pnl', 'long_exposure', 'short_exposure', 'orders',
+               'transactions', 'positions']
+
+    if recorded_cols is not None:
+        for column in recorded_cols:
+            columns.append(column)
+
+    def format_positions(positions):
+        parts = []
+        for position in positions:
+            msg = '{amount:.2f}{market} cost basis {cost_basis:.4f}{base}'.format(
+                amount=position['amount'],
+                market=position['sid'].market_currency,
+                cost_basis=position['cost_basis'],
+                base=position['sid'].base_currency
+            )
+            parts.append(msg)
+        return ', '.join(parts)
+
+    formatters = {
+        'orders': lambda orders: len(orders),
+        'transactions': lambda transactions: len(transactions),
+        'returns': lambda returns: "{0:.4f}".format(returns),
+        'positions': format_positions
+    }
+
+    return stats_df.tail(num_rows).to_string(
+        columns=columns,
+        formatters=formatters
+    )
+
+
+def df_to_string(df):
+    """
+    Create a formatted str representation of the DataFrame.
+
+    Parameters
+    ----------
+    df: DataFrame
+
+    Returns
+    -------
+    str
+
+    """
+    pd.set_option('display.expand_frame_repr', False)
+    pd.set_option('precision', 8)
+    pd.set_option('display.width', 1000)
+    pd.set_option('display.max_colwidth', 1000)
+
+    return df.to_string()
+
+
+def extract_transactions(perf):
+    """
+    Compute indexes for buy and sell transactions
+
+    Parameters
+    ----------
+    perf: DataFrame
+        The algo performance DataFrame.
+
+    Returns
+    -------
+    DataFrame
+        A DataFrame of transactions.
+
+    """
+    trans_list = perf.transactions.values
+    all_trans = [t for sublist in trans_list for t in sublist]
+    all_trans.sort(key=lambda t: t['dt'])
+
+    transactions = pd.DataFrame(all_trans)
+    if not transactions.empty:
+        transactions.set_index('dt', inplace=True, drop=True)
+    return transactions

catalyst/finance/blotter.py

@@ -34,7 +34,9 @@ from catalyst.finance.commission import (
 from catalyst.finance.cancel_policy import NeverCancel
 from catalyst.utils.input_validation import expect_types
 
-log = Logger('Blotter')
+from catalyst.constants import LOG_LEVEL
+
+log = Logger('Blotter', level=LOG_LEVEL)
 warning_logger = Logger('AlgoWarning')
 
 

catalyst/finance/controls.py

@@ -24,7 +24,9 @@ from catalyst.errors import (
     TradingControlViolation,
 )
 
-log = logbook.Logger('TradingControl')
+from catalyst.constants import LOG_LEVEL
+
+log = logbook.Logger('TradingControl', level=LOG_LEVEL)
 
 
 class TradingControl(with_metaclass(abc.ABCMeta)):

catalyst/finance/execution.py

@@ -77,6 +77,7 @@ class LimitOrder(ExecutionStyle):
     Execution style representing an order to be executed at a price equal to or
     better than a specified limit price.
     """
+
     def __init__(self, limit_price, exchange=None):
         """
         Store the given price.
@@ -99,6 +100,7 @@ class StopOrder(ExecutionStyle):
     Execution style representing an order to be placed once the market price
     reaches a specified stop price.
     """
+
     def __init__(self, stop_price, exchange=None):
         """
         Store the given price.
@@ -121,6 +123,7 @@ class StopLimitOrder(ExecutionStyle):
     Execution style representing a limit order to be placed with a specified
     limit price once the market reaches a specified stop price.
     """
+
     def __init__(self, limit_price, stop_price, exchange=None):
         """
         Store the given prices
@@ -144,31 +147,20 @@ class StopLimitOrder(ExecutionStyle):
 def asymmetric_round_price_to_penny(price, prefer_round_down,
                                     diff=(0.0095 - .005)):
     """
-    Asymmetric rounding function for adjusting prices to two places in a way
-    that "improves" the price.  For limit prices, this means preferring to
-    round down on buys and preferring to round up on sells.  For stop prices,
-    it means the reverse.
+    Modified the original function because we do not want to round
+    prices on crypto exchange.
 
-    If prefer_round_down == True:
-        When .05 below to .95 above a penny, use that penny.
-    If prefer_round_down == False:
-        When .95 below to .05 above a penny, use that penny.
+    Parameters
+    ----------
+    price: float
+
+    Returns
+    -------
+    float
 
-    In math-speak:
-    If prefer_round_down: [<X-1>.0095, X.0195) -> round to X.01.
-    If not prefer_round_down: (<X-1>.0005, X.0105] -> round to X.01.
     """
-    # Subtracting an epsilon from diff to enforce the open-ness of the upper
-    # bound on buys and the lower bound on sells.  Using the actual system
-    # epsilon doesn't quite get there, so use a slightly less epsilon-ey value.
-    epsilon = float_info.epsilon * 10
-    diff = diff - epsilon
-
-    # relies on rounding half away from zero, unlike numpy's bankers' rounding
-    rounded = round(price - (diff if prefer_round_down else -diff), 2)
-    if zp_math.tolerant_equals(rounded, 0.0):
-        return 0.0
-    return rounded
+    # TODO: consider overriding outside of the original function
+    return price
 
 
 def check_stoplimit_prices(price, label):

catalyst/finance/performance/period.py

@@ -88,7 +88,10 @@ from six import itervalues, iteritems
 
 import catalyst.protocol as zp
 
-log = logbook.Logger('Performance')
+from catalyst.constants import LOG_LEVEL
+
+log = logbook.Logger('Performance', level=LOG_LEVEL)
+
 TRADE_TYPE = zp.DATASOURCE_TYPE.TRADE
 
 

catalyst/finance/performance/position.py

@@ -40,7 +40,9 @@ import logbook
 from catalyst.assets import Future, Asset
 from catalyst.utils.input_validation import expect_types
 
-log = logbook.Logger('Performance')
+from catalyst.constants import LOG_LEVEL
+
+log = logbook.Logger('Performance', level=LOG_LEVEL)
 
 
 class Position(object):

catalyst/finance/performance/position_tracker.py

@@ -32,7 +32,9 @@ from catalyst.assets import (
 )
 from . position import positiondict
 
-log = logbook.Logger('Performance')
+from catalyst.constants import LOG_LEVEL
+
+log = logbook.Logger('Performance', level=LOG_LEVEL)
 
 
 PositionStats = namedtuple('PositionStats',

catalyst/finance/performance/tracker.py

@@ -70,7 +70,9 @@ import catalyst.finance.risk as risk
 
 from . position_tracker import PositionTracker
 
-log = logbook.Logger('Performance')
+from catalyst.constants import LOG_LEVEL
+
+log = logbook.Logger('Performance', level=LOG_LEVEL)
 
 
 class PerformanceTracker(object):
@@ -111,27 +113,11 @@ class PerformanceTracker(object):
                     self.treasury_curves,
                     self.trading_calendar
                 )
-        elif self.emission_rate == '5-minute':
-            self.all_benchmark_returns = pd.Series(
-                index=pd.date_range(
-                    self.sim_params.first_open,
-                    self.sim_params.last_close,
-                    freq='5min'
-                ),
-            )
-            self.cumulative_risk_metrics = \
-                risk.RiskMetricsCumulative(
-                    self.sim_params,
-                    self.treasury_curves,
-                    self.trading_calendar,
-                    create_first_day_stats=True,
-                )
         elif self.emission_rate == 'minute':
             self.all_benchmark_returns = pd.Series(index=pd.date_range(
                 self.sim_params.first_open, self.sim_params.last_close,
                 freq='Min')
             )
-
             self.cumulative_risk_metrics = \
                 risk.RiskMetricsCumulative(
                     self.sim_params,

catalyst/finance/risk/cumulative.py

@@ -22,7 +22,7 @@ from pandas.tseries.tools import normalize_date
 
 from six import iteritems
 
-from . risk import (
+from .risk import (
     check_entry,
     choose_treasury
 )
@@ -37,9 +37,10 @@ from empyrical import (
     sharpe_ratio,
     sortino_ratio,
 )
+import warnings
+from catalyst.constants import LOG_LEVEL
 
-log = logbook.Logger('Risk Cumulative')
-
+log = logbook.Logger('Risk Cumulative', level=LOG_LEVEL)
 
 choose_treasury = functools.partial(choose_treasury, lambda *args: '10year',
                                     compound=False)
@@ -143,6 +144,8 @@ class RiskMetricsCumulative(object):
         self.num_trading_days = 0
 
     def update(self, dt, algorithm_returns, benchmark_returns, leverage):
+        warnings.filterwarnings('error')
+
         # Keep track of latest dt for use in to_dict and other methods
         # that report current state.
         self.latest_dt = dt
@@ -189,9 +192,12 @@ class RiskMetricsCumulative(object):
             if len(self.benchmark_returns) == 1:
                 self.benchmark_returns = np.append(0.0, self.benchmark_returns)
 
-        self.benchmark_cumulative_returns[dt_loc] = cum_returns(
-            self.benchmark_returns
-        )[-1]
+        try:
+            self.benchmark_cumulative_returns[dt_loc] = cum_returns(
+                self.benchmark_returns
+            )[-1]
+        except Exception as e:
+            log.debug('cumulative returns error: {}'.format(e))
 
         benchmark_cumulative_returns_to_date = \
             self.benchmark_cumulative_returns[:dt_loc + 1]
@@ -266,10 +272,15 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
         self.downside_risk[dt_loc] = downside_risk(
             self.algorithm_returns
         )
-        self.sortino[dt_loc] = sortino_ratio(
-            self.algorithm_returns,
-            _downside_risk=self.downside_risk[dt_loc]
-        )
+
+        try:
+            self.sortino[dt_loc] = sortino_ratio(
+                self.algorithm_returns,
+                _downside_risk=self.downside_risk[dt_loc]
+            )
+        except Exception as e:
+            log.debug('sortino ratio error: {}'.format(e))
+
         self.information[dt_loc] = information_ratio(
             self.algorithm_returns,
             self.benchmark_returns,
@@ -281,6 +292,8 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
         self.max_leverage = self.calculate_max_leverage()
         self.max_leverages[dt_loc] = self.max_leverage
 
+        warnings.resetwarnings()
+
     def to_dict(self):
         """
         Creates a dictionary representing the state of the risk report.
@@ -292,18 +305,18 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
         rval = {
             'trading_days': self.num_trading_days,
             'benchmark_volatility':
-            self.benchmark_volatility[dt_loc],
+                self.benchmark_volatility[dt_loc],
             'algo_volatility':
-            self.algorithm_volatility[dt_loc],
+                self.algorithm_volatility[dt_loc],
             'treasury_period_return': self.treasury_period_return,
             # Though the two following keys say period return,
             # they would be more accurately called the cumulative return.
             # However, the keys need to stay the same, for now, for backwards
             # compatibility with existing consumers.
             'algorithm_period_return':
-            self.algorithm_cumulative_returns[dt_loc],
+                self.algorithm_cumulative_returns[dt_loc],
             'benchmark_period_return':
-            self.benchmark_cumulative_returns[dt_loc],
+                self.benchmark_cumulative_returns[dt_loc],
             'beta': self.beta[dt_loc],
             'alpha': self.alpha[dt_loc],
             'sharpe': self.sharpe[dt_loc],

catalyst/finance/risk/period.py

@@ -36,7 +36,9 @@ from empyrical import (
     sortino_ratio
 )
 
-log = logbook.Logger('Risk Period')
+from catalyst.constants import LOG_LEVEL
+
+log = logbook.Logger('Risk Period', level=LOG_LEVEL)
 
 choose_treasury = functools.partial(risk.choose_treasury,
                                     risk.select_treasury_duration)

catalyst/finance/risk/report.py

@@ -63,7 +63,9 @@ from dateutil.relativedelta import relativedelta
 
 from . period import RiskMetricsPeriod
 
-log = logbook.Logger('Risk Report')
+from catalyst.constants import LOG_LEVEL
+
+log = logbook.Logger('Risk Report', level=LOG_LEVEL)
 
 
 class RiskReport(object):

catalyst/finance/risk/risk.py

@@ -61,7 +61,9 @@ Risk Report
 import logbook
 import numpy as np
 
-log = logbook.Logger('Risk')
+from catalyst.constants import LOG_LEVEL
+
+log = logbook.Logger('Risk', level=LOG_LEVEL)
 
 
 TREASURY_DURATIONS = [
@@ -158,7 +160,7 @@ def choose_treasury(select_treasury, treasury_curves, start_session,
                 )
                 break
 
-        if search_day:
+        if search_day and trading_calendar.name != 'OPEN':          # Supress warning for 'OPEN' calendar
             if (search_dist is None or search_dist > 1) and \
                     search_days[0] <= end_session <= search_days[-1]:
                 message = "No rate within 1 trading day of end date = \

catalyst/finance/slippage.py

@@ -41,6 +41,7 @@ DEFAULT_EQUITY_VOLUME_SLIPPAGE_BAR_LIMIT = 0.025
 DEFAULT_FUTURE_VOLUME_SLIPPAGE_BAR_LIMIT = 0.05
 
 
+
 class LiquidityExceeded(Exception):
     pass
 
@@ -205,20 +206,22 @@ class VolumeShareSlippage(SlippageModel):
     def process_order(self, data, order):
         volume = data.current(order.asset, "volume")
 
+        min_trade_size = order.asset.min_trade_size
+
         max_volume = self.volume_limit * volume
 
         # price impact accounts for the total volume of transactions
         # created against the current minute bar
         remaining_volume = max_volume - self.volume_for_bar
-        if remaining_volume < 1:
+        if remaining_volume < min_trade_size:
             # we can't fill any more transactions
             raise LiquidityExceeded()
 
         # the current order amount will be the min of the
         # volume available in the bar or the open amount.
-        cur_volume = int(min(remaining_volume, abs(order.open_amount)))
+        cur_volume = min(remaining_volume, abs(order.open_amount))
 
-        if cur_volume < 1:
+        if cur_volume < min_trade_size:
             return None, None
 
         # tally the current amount into our total amount ordered.

catalyst/finance/trading.py

@@ -26,7 +26,9 @@ from catalyst.data.loader import load_market_data
 from catalyst.utils.calendars import get_calendar
 from catalyst.utils.memoize import remember_last
 
-log = logbook.Logger('Trading')
+from catalyst.constants import LOG_LEVEL
+
+log = logbook.Logger('Trading', level=LOG_LEVEL)
 
 
 DEFAULT_CAPITAL_BASE = 1e5

catalyst/finance/transaction.py

@@ -65,14 +65,10 @@ def create_transaction(order, dt, price, amount):
     # floor the amount to protect against non-whole number orders
     # TODO: Investigate whether we can add a robust check in blotter
     # and/or tradesimulation, as well.
-    amount_magnitude = int(abs(amount))
-
-    if amount_magnitude < 1:
-        raise Exception("Transaction magnitude must be at least 1.")
 
     transaction = Transaction(
         asset=order.asset,
-        amount=int(amount),
+        amount=amount,
         dt=dt,
         price=price,
         order_id=order.id

catalyst/gens/sim_engine.pyx

@@ -20,9 +20,7 @@ cimport cython
 from cpython cimport bool
 
 cdef np.int64_t _nanos_in_minute = 60000000000
-cdef np.int64_t _nanos_in_five_minutes = 5 * _nanos_in_minute
 NANOS_IN_MINUTE = _nanos_in_minute
-NANOS_IN_FIVE_MINUTES = _nanos_in_five_minutes
 
 cpdef enum:
     BAR = 0
@@ -117,24 +115,3 @@ cdef class MinuteSimulationClock:
             yield minute, BAR
             if minute_emission:
                 yield minute, MINUTE_END
-
-cdef class FiveMinuteSimulationClock(MinuteSimulationClock):
-    @cython.boundscheck(False)
-    @cython.wraparound(False)
-    cdef dict calc_minutes_by_session(self):
-        cdef dict five_minutes_by_session
-        cdef int session_idx
-        cdef np.int64_t session_nano
-        cdef np.ndarray[np.int64_t, ndim=1] five_minutes_nanos
-
-        five_minutes_by_session = {}
-        for session_idx, session_nano in enumerate(self.sessions_nanos):
-            five_minutes_nanos = np.arange(
-                self.market_opens_nanos[session_idx],
-                self.market_closes_nanos[session_idx],
-                _nanos_in_five_minutes
-            )
-            five_minutes_by_session[session_nano] = pd.to_datetime(
-                five_minutes_nanos, utc=True, box=True
-            )
-        return five_minutes_by_session

catalyst/gens/tradesimulation.py

@@ -27,14 +27,15 @@ from catalyst.gens.sim_engine import (
     BEFORE_TRADING_START_BAR
 )
 
-log = Logger('Trade Simulation')
+from catalyst.constants import LOG_LEVEL
+
+log = Logger('Trade Simulation', level=LOG_LEVEL)
 
 
 class AlgorithmSimulator(object):
 
     EMISSION_TO_PERF_KEY_MAP = {
         'minute': 'minute_perf',
-        '5-minute': '5_minute_perf',
         'daily': 'daily_perf'
     }
 
@@ -202,7 +203,7 @@ class AlgorithmSimulator(object):
             stack.enter_context(self.processor)
             stack.enter_context(ZiplineAPI(self.algo))
 
-            if algo.data_frequency in set(('minute', '5-minute')):
+            if algo.data_frequency == 'minute':
                 def execute_order_cancellation_policy():
                     algo.blotter.execute_cancel_policy(SESSION_END)
 

catalyst/pipeline/loaders/crypto_pricing_loader.py

@@ -41,11 +41,7 @@ class CryptoPricingLoader(PipelineLoader):
             reader = bundle.daily_bar_reader
             all_sessions = cal.all_sessions
 
-        elif data_frequency == '5-minute':
-            reader = bundle.five_minute_bar_reader
-            all_sessions = cal.all_five_minutes
-
-        elif daily_bar_reader == 'minute':
+        elif data_frequency == 'minute':
             reader = bundle.minute_bar_reader
             all_sessions = cal.all_minutes
 
@@ -106,12 +102,6 @@ class CryptoPricingLoader(PipelineLoader):
 
 
 def _shift_dates(dates, start_date, end_date, shift):
-    print 'dates.head:\n', dates[:10]
-    print 'dates.tail:\n', dates[:-10]
-
-    print 'start_date:', start_date
-    print 'end_date:', end_date
-    print 'shift:', shift
 
     try:
         start = dates.get_loc(start_date)

catalyst/pipeline/loaders/equity_pricing_loader.py

@@ -40,8 +40,6 @@ class USEquityPricingLoader(PipelineLoader):
 
         if data_frequency == 'daily':
             reader = bundle.daily_bar_reader
-        elif data_frequency == '5-minute':
-            reader = bundle.five_minute_bar_reader
         elif daily_bar_reader == 'minute':
             reader = bundle.minute_bar_reader
         else:
@@ -53,9 +51,6 @@ class USEquityPricingLoader(PipelineLoader):
 
         if data_frequency == 'daily':
             all_sessions = cal.all_sessions
-        elif data_frequency == '5-minute':
-            reader = bundle.five_minute_bar_reader
-            all_sessions = cal.all_five_minutes
         elif daily_bar_reader == 'minute':
             reader = bundle.minute_bar_reader
             all_sessions = cal.all_minutes

catalyst/sources/benchmark_source.py

@@ -51,10 +51,7 @@ class BenchmarkSource(object):
         elif benchmark_returns is not None:
             daily_series = benchmark_returns[sessions[0]:sessions[-1]]
 
-            print 'BENCHMARK_RETURNS'
-
             if self.emission_rate == "minute":
-                print 'BENCHMARK_RETURNS minute'
                 # we need to take the env's benchmark returns, which are daily,
                 # and resample them to minute
                 minutes = trading_calendar.minutes_for_sessions_in_range(
@@ -68,29 +65,20 @@ class BenchmarkSource(object):
                 )
 
                 self._precalculated_series = minute_series
-            elif self.emission_rate == '5-minute':
-                print 'BENCHMARK_RETURNS 5-minute'
-                five_minutes = \
-                    trading_calendar.five_minutes_for_sessions_in_range(
-                        sessions[0],
-                        sessions[-1],
-                    )
-
-                five_minute_series = daily_series.reindex(
-                    index=five_minutes,
-                    method='ffill',
-                )
-                
-                self._precalculated_series = five_minute_series
             else:
-                print 'BENCHMARK_RETURNS daily'
                 self._precalculated_series = daily_series
         else:
             raise Exception("Must provide either benchmark_asset or "
                             "benchmark_returns.")
 
     def get_value(self, dt):
-        return self._precalculated_series.loc[dt]
+        try:
+            series = self._precalculated_series
+            value = series.loc[dt]
+            return value
+        except Exception:
+            # TODO: workaround, find permanent fix
+            return 0
 
     def get_range(self, start_dt, end_dt):
         return self._precalculated_series.loc[start_dt:end_dt]
@@ -173,24 +161,8 @@ class BenchmarkSource(object):
                 ffill=True
             )[asset]
 
-            return benchmark_series.pct_change()[1:]
-        elif self.emission_rate == '5-minute':
-            five_minutes = trading_calendar.five_minutes_for_sessions_in_range(
-                self.sessions[0], self.sessions[-1]
-            )
-            benchmark_series = data_portal.get_history_window(
-                [asset],
-                five_minutes[-1],
-                bar_count=len(five_minutes) + 1,
-                frequency='5m',
-                field='price',
-                data_frequency=self.emission_rate,
-                ffill=True,
-            )[asset]
-
             return benchmark_series.pct_change()[1:]
         else:
-            print '----------------------------------------'
             start_date = asset.start_date
             if start_date < trading_days[0]:
                 # get the window of close prices for benchmark_asset from the

catalyst/sources/requests_csv.py

@@ -23,7 +23,9 @@ from catalyst.protocol import (
 )
 from catalyst.assets import Equity
 
-logger = Logger('Requests Source Logger')
+from catalyst.constants import LOG_LEVEL
+
+logger = Logger('Requests Source Logger', level=LOG_LEVEL)
 
 
 def roll_dts_to_midnight(dts, trading_day):

catalyst/support/issue_44.py

@@ -0,0 +1,109 @@
+import pandas as pd
+from catalyst import run_algorithm
+from catalyst.exchange.exchange_utils import get_exchange_symbols
+
+from catalyst.api import (
+    symbols,
+)
+
+
+def initialize(context):
+    context.i = -1
+    context.base_currency = 'btc'
+
+
+def handle_data(context, data):
+    lookback = 60 * 24 * 7  # (minutes, hours, days)
+    context.i += 1
+    if context.i < lookback:
+        return
+
+    today = context.blotter.current_dt.strftime('%Y-%m-%d %H:%M:%S')
+
+    try:
+        # update universe everyday
+        new_day = 60 * 24
+        if not context.i % new_day:
+            context.universe = universe(context, today)
+
+        # get data every 30 minutes
+        minutes = 30
+        if not context.i % minutes and context.universe:
+            for coin in context.coins:
+                pair = str(coin.symbol)
+
+                # ohlcv data
+                open = data.history(coin, 'open', lookback,
+                                    '1m').ffill().bfill().resample(
+                    '30T').first()
+                high = data.history(coin, 'high', lookback,
+                                    '1m').ffill().bfill().resample('30T').max()
+                low = data.history(coin, 'low', lookback,
+                                   '1m').ffill().bfill().resample('30T').min()
+                close = data.history(coin, 'price', lookback,
+                                     '1m').ffill().bfill().resample(
+                    '30T').last()
+                volume = data.history(coin, 'volume', lookback,
+                                      '1m').ffill().bfill().resample(
+                    '30T').sum()
+
+                print(today, pair, close[-1])
+
+    except Exception as e:
+        print(e)
+
+
+def analyze(context=None, results=None):
+    pass
+
+
+def universe(context, today):
+    json_symbols = get_exchange_symbols('poloniex')
+    poloniex_universe_df = pd.DataFrame.from_dict(
+        json_symbols).transpose().astype(str)
+    poloniex_universe_df['base_currency'] = poloniex_universe_df.apply(
+        lambda row: row.symbol.split('_')[1],
+        axis=1)
+    poloniex_universe_df['market_currency'] = poloniex_universe_df.apply(
+        lambda row: row.symbol.split('_')[0],
+        axis=1)
+    poloniex_universe_df = poloniex_universe_df[
+        poloniex_universe_df['base_currency'] == context.base_currency]
+    poloniex_universe_df = poloniex_universe_df[
+        poloniex_universe_df.symbol != 'gas_btc']
+
+    # Markets currently not working on Catalyst 0.3.1
+    # 2017-01-01
+    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'bcn_btc']
+    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'burst_btc']
+    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'dgb_btc']
+    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'doge_btc']
+    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'emc2_btc']
+    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'pink_btc']
+    # poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.symbol != 'sc_btc']
+    print(poloniex_universe_df.head())
+
+    date = str(today).split(' ')[0]
+
+    poloniex_universe_df = poloniex_universe_df[
+        poloniex_universe_df.start_date < date]
+    context.coins = symbols(*poloniex_universe_df.symbol)
+    print(len(poloniex_universe_df))
+    return poloniex_universe_df.symbol.tolist()
+
+
+if __name__ == '__main__':
+    start_date = pd.to_datetime('2017-01-01', utc=True)
+    end_date = pd.to_datetime('2017-10-15', utc=True)
+
+    performance = run_algorithm(start=start_date, end=end_date,
+                                capital_base=10000.0,
+                                initialize=initialize,
+                                handle_data=handle_data,
+                                analyze=analyze,
+                                exchange_name='poloniex',
+                                data_frequency='minute',
+                                base_currency='btc',
+                                live=False,
+                                live_graph=False,
+                                algo_namespace='test')

catalyst/support/issue_47.py

@@ -0,0 +1,140 @@
+"""
+Requires Catalyst version 0.3.0 or above
+Tested on Catalyst version 0.3.2
+
+These example aims to provide and easy way for users to learn how to collect data from the different exchanges.
+You simply need to specify the exchange and the market that you want to focus on.
+You will all see how to create a universe and filter it base on the exchange and the market you desire.
+
+The example prints out the closing price of all the pairs for a given market-exchange every 30 minutes.
+The example also contains the ohlcv minute data for the past seven days which could be used to create indicators
+Use this as the backbone to create your own trading strategies.
+
+Variables lookback date and date are used to ensure data for a coin existed on the lookback period specified.
+"""
+
+import numpy as np
+import pandas as pd
+from datetime import timedelta
+from catalyst import run_algorithm
+from catalyst.exchange.exchange_utils import get_exchange_symbols
+
+from catalyst.api import (
+    symbols,
+)
+
+
+def initialize(context):
+    context.i = -1  # counts the minutes
+    context.exchange = 'poloniex'  # must match the exchange specified in run_algorithm
+    context.base_currency = 'eth'  # must match the base currency specified in run_algorithm
+
+
+def handle_data(context, data):
+    lookback = 60 * 24 * 7  # (minutes, hours, days) of how far to lookback in the data history
+    context.i += 1
+
+    # current date formatted into a string
+    today = context.blotter.current_dt
+    date, time = today.strftime('%Y-%m-%d %H:%M:%S').split(' ')
+    lookback_date = today - timedelta(days=(
+    lookback / (60 * 24)))  # subtract the amount of days specified in lookback
+    lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[
+        0]  # get only the date as a string
+
+    # update universe everyday
+    new_day = 60 * 24
+    if not context.i % new_day:
+        context.universe = universe(context, lookback_date, date)
+
+    # get data every 30 minutes
+    minutes = 30
+    if not context.i % minutes and context.universe:
+        # we iterate for every pair in the current universe
+        for coin in context.coins:
+            pair = str(coin.symbol)
+
+            # 30 minute interval ohlcv data (the standard data required for candlestick or indicators/signals)
+            # 30T means 30 minutes re-sampling of one minute data. change to your desire time interval.
+            open = fill(data.history(coin, 'open', bar_count=lookback,
+                                     frequency='1m')).resample('30T').first()
+            high = fill(data.history(coin, 'high', bar_count=lookback,
+                                     frequency='1m')).resample('30T').max()
+            low = fill(data.history(coin, 'low', bar_count=lookback,
+                                    frequency='1m')).resample('30T').min()
+            close = fill(data.history(coin, 'price', bar_count=lookback,
+                                      frequency='1m')).resample('30T').last()
+            volume = fill(data.history(coin, 'volume', bar_count=lookback,
+                                       frequency='1m')).resample('30T').sum()
+
+            # close[-1] is the equivalent to current price
+            # displays the minute price for each pair every 30 minutes
+            print(
+            today, pair, open[-1], high[-1], low[-1], close[-1], volume[-1])
+
+            # ----------------------------------------------------------------------------------------------------------
+            # -------------------------------------- Insert Your Strategy Here -----------------------------------------
+            # ----------------------------------------------------------------------------------------------------------
+
+
+def analyze(context=None, results=None):
+    pass
+
+
+# Get the universe for a given exchange and a given base_currency market
+# Example: Poloniex BTC Market
+def universe(context, lookback_date, current_date):
+    json_symbols = get_exchange_symbols(
+        context.exchange)  # get all the pairs for the exchange
+    universe_df = pd.DataFrame.from_dict(json_symbols).transpose().astype(
+        str)  # convert into a dataframe
+    universe_df['base_currency'] = universe_df.apply(
+        lambda row: row.symbol.split('_')[1],
+        axis=1)
+    universe_df['market_currency'] = universe_df.apply(
+        lambda row: row.symbol.split('_')[0],
+        axis=1)
+    # Filter all the exchange pairs to only the ones for a give base currency
+    universe_df = universe_df[
+        universe_df['base_currency'] == context.base_currency]
+
+    # Filter all the pairs to ensure that pair existed in the current date range
+    universe_df = universe_df[universe_df.start_date < lookback_date]
+    universe_df = universe_df[universe_df.end_daily >= current_date]
+    context.coins = symbols(
+        *universe_df.symbol)  # convert all the pairs to symbols
+    print(universe_df.head(), len(universe_df))
+    return universe_df.symbol.tolist()
+
+
+# Replace all NA, NAN or infinite values with its nearest value
+def fill(series):
+    if isinstance(series, pd.Series):
+        return series.replace([np.inf, -np.inf], np.nan).ffill().bfill()
+    elif isinstance(series, np.ndarray):
+        return pd.Series(series).replace([np.inf, -np.inf],
+                                         np.nan).ffill().bfill().values
+    else:
+        return series
+
+
+if __name__ == '__main__':
+    start_date = pd.to_datetime('2017-01-01', utc=True)
+    end_date = pd.to_datetime('2017-10-15', utc=True)
+
+    performance = run_algorithm(start=start_date, end=end_date,
+                                capital_base=10000.0,
+                                initialize=initialize,
+                                handle_data=handle_data,
+                                analyze=analyze,
+                                exchange_name='poloniex',
+                                data_frequency='minute',
+                                base_currency='eth',
+                                live=False,
+                                live_graph=False,
+                                algo_namespace='simple_universe')
+
+"""
+Run in Terminal (inside catalyst environment):
+python simple_universe.py
+"""

catalyst/support/issue_55.py

@@ -0,0 +1,42 @@
+import talib
+import pandas as pd
+
+from catalyst import run_algorithm
+from catalyst.api import symbol
+
+
+def initialize(context):
+    print('initializing')
+    context.asset = symbol('xcp_btc')
+
+
+def handle_data(context, data):
+    print('handling bar: {}'.format(data.current_dt))
+
+    price = data.current(context.asset, 'close')
+    print('got price {price}'.format(price=price))
+
+    try:
+        prices = data.history(
+            context.asset,
+            fields='close',
+            bar_count=1,
+            frequency='1D'
+        )
+        print('got {} price entries\n'.format(len(prices), prices))
+    except Exception as e:
+        print(e)
+
+
+run_algorithm(
+    capital_base=1,
+    start=pd.to_datetime('2015-3-2', utc=True),
+    end=pd.to_datetime('2017-8-31', utc=True),
+    data_frequency='daily',
+    initialize=initialize,
+    handle_data=handle_data,
+    analyze=None,
+    exchange_name='poloniex',
+    algo_namespace='issue_55',
+    base_currency='btc'
+)

catalyst/support/issue_57.py

@@ -0,0 +1,46 @@
+import talib
+import pandas as pd
+
+from catalyst import run_algorithm
+from catalyst.api import symbol
+
+
+def initialize(context):
+    print('initializing')
+    context.asset = symbol('btc_usdt')
+
+
+def handle_data(context, data):
+    print('handling bar: {}'.format(data.current_dt))
+
+    price = data.current(context.asset, 'close')
+    print('got price {price}'.format(price=price))
+
+    try:
+        prices = data.history(
+            context.asset,
+            fields='close',
+            bar_count=60,
+            frequency='1D'
+        )
+        print('got {} price entries\n'.format(len(prices), prices))
+    except Exception as e:
+        print(e)
+
+
+run_algorithm(
+    capital_base=1,
+    start=pd.to_datetime('2016-2-11', utc=True),
+    end=pd.to_datetime('2017-8-31', utc=True),
+    data_frequency='daily',
+    initialize=initialize,
+    handle_data=handle_data,
+    analyze=None,
+    exchange_name='bittrex',
+    algo_namespace='issue_57',
+    base_currency='btc'
+<<<<<<< HEAD
+)
+=======
+)
+>>>>>>> develop

catalyst/support/rodrigo_1.py

@@ -0,0 +1,153 @@
+import pandas as pd
+from logbook import Logger, DEBUG
+
+from catalyst import run_algorithm
+from catalyst.api import (schedule_function, order_target_percent, symbol,
+                          date_rules, get_open_orders, cancel_order, record,
+                          set_commission, set_slippage)
+
+log = Logger('rodrigo_1', level=DEBUG)
+"""
+The initialize function sets any data or variables that 
+you'll use in your algorithm. 
+It's only called once at the beginning of your algorithm.
+"""
+
+
+def initialize(context):
+    # Select asset of interest
+    context.asset = symbol('BTC_USD')
+
+    # set_commission(TradingPairFeeSchedule(maker_fee=0.5, taker_fee=0.5))
+    # set_slippage(TradingPairFixedSlippage(spread=0.5))
+    # Set up a rebalance method to run every day
+    schedule_function(rebalance, date_rule=date_rules.every_day())
+
+
+"""
+Rebalance function scheduled to run once per day.
+"""
+
+
+def rebalance(context, data):
+    # To make market decisions, we're calculating the token's
+    # moving average for the last 5 days.
+
+    # We get the price history for the last 5 days.
+    price_history = data.history(context.asset, fields='price', bar_count=5,
+                                 frequency='1d')
+
+    # Then we take an average of those 5 days.
+    average_price = price_history.mean()
+
+    # We also get the coin's current price.
+    price = data.current(context.asset, 'price')
+
+    # Cancel any outstanding orders
+    orders = get_open_orders(context.asset) or []
+    for order in orders:
+        cancel_order(order)
+
+    # If our coin is currently listed on a major exchange
+    if data.can_trade(context.asset):
+        # If the current price is 1% above the 5-day average price,
+        # we open a long position. If the current price is below the
+        # average price, then we want to close our position to 0 shares.
+        if price > (1.01 * average_price):
+            # Place the buy order (positive means buy, negative means sell)
+            order_target_percent(context.asset, .99)
+            log.info("Buying %s" % (context.asset.symbol))
+        elif price < average_price:
+            # Sell all of our shares by setting the target position to zero
+            order_target_percent(context.asset, 0)
+            log.info("Selling %s" % (context.asset.symbol))
+
+    # Use the record() method to track up to five custom signals.
+    # Record Apple's current price and the average price over the last
+    # five days.
+    cash = context.portfolio.cash
+    leverage = context.account.leverage
+
+    record(price=price, average_price=average_price, cash=cash,
+           leverage=leverage)
+
+
+def analyze(context=None, results=None):
+    import matplotlib.pyplot as plt
+
+    # Plot the portfolio and asset data.
+    ax1 = plt.subplot(511)
+    results[['portfolio_value']].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio Value (USD)')
+
+    ax2 = plt.subplot(512, sharex=ax1)
+    ax2.set_ylabel('{asset} (USD)'.format(asset=context.asset))
+    (results[[
+        'price',
+    ]]).plot(ax=ax2)
+
+    trans = results.ix[[t != [] for t in results.transactions]]
+    buys = trans.ix[
+        [t[0]['amount'] > 0 for t in trans.transactions]
+    ]
+    sells = trans.ix[
+        [t[0]['amount'] < 0 for t in trans.transactions]
+    ]
+
+    ax2.plot(
+        buys.index,
+        results.price[buys.index],
+        '^',
+        markersize=10,
+        color='g',
+    )
+    ax2.plot(
+        sells.index,
+        results.price[sells.index],
+        'v',
+        markersize=10,
+        color='r',
+    )
+
+    ax3 = plt.subplot(513, sharex=ax1)
+    results[['leverage']].plot(ax=ax3)
+    ax3.set_ylabel('Leverage ')
+
+    ax4 = plt.subplot(514, sharex=ax1)
+    results[['cash']].plot(ax=ax4)
+    ax4.set_ylabel('Cash (USD)')
+
+    results[[
+        'algorithm',
+        'benchmark',
+    ]] = results[[
+        'algorithm_period_return',
+        'benchmark_period_return',
+    ]]
+
+    ax5 = plt.subplot(515, sharex=ax1)
+    results[[
+        'algorithm',
+        'benchmark',
+    ]].plot(ax=ax5)
+    ax5.set_ylabel('Percent Change')
+
+    plt.legend(loc=3)
+
+    # Show the plot.
+    plt.gcf().set_size_inches(18, 8)
+    plt.show()
+
+
+run_algorithm(
+    capital_base=100000,
+    start=pd.to_datetime('2017-1-1', utc=True),
+    end=pd.to_datetime('2017-10-22', utc=True),
+    data_frequency='minute',
+    initialize=initialize,
+    handle_data=None,
+    analyze=analyze,
+    exchange_name='bitfinex',
+    algo_namespace='rodrigo_1',
+    base_currency='usd'
+)

catalyst/utils/calendars/exchange_calendar_open.py

@@ -1,6 +1,7 @@
 from datetime import time
 from pytz import timezone
 
+from pandas import Timestamp
 from pandas.tseries.offsets import DateOffset
 
 from catalyst.utils.memoize import lazyval
@@ -28,3 +29,6 @@ class OpenExchangeCalendar(TradingCalendar):
     @lazyval
     def day(self):
         return DateOffset(days=1)
+
+    def __init__(self, *args, **kwargs):
+        super(OpenExchangeCalendar, self).__init__(start=Timestamp('2015-3-1', tz='UTC'), **kwargs)

catalyst/utils/calendars/trading_calendar.py

@@ -117,9 +117,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
 
         self._trading_minutes_nanos = self.all_minutes.values.\
             astype(np.int64)
-        
-        self._trading_five_minutes_nanos = self.all_five_minutes.values.\
-            astype(np.int64)
 
         self.first_trading_session = _all_days[0]
         self.last_trading_session = _all_days[-1]
@@ -182,18 +179,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
         """
         return int(self._minutes_per_session[start_session:end_session].sum())
 
-    @lazyval
-    def _five_minutes_per_session(self):
-        diff = self.schedule.market_close - self.schedule.market_open
-        diff = diff.astype('timedelta64[m]')
-        return (diff + 1) // 5
-
-    def five_minutes_count_for_sessions_in_range(self,
-                                                 start_session,
-                                                 end_session):
-        five_mins = self._five_minutes_per_session[start_session:end_session]
-        return int(five_mins.sum())
-
     @property
     def regular_holidays(self):
         """
@@ -386,10 +371,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
         idx = next_divider_idx(self._trading_minutes_nanos, dt.value)
         return self.all_minutes[idx]
 
-    def next_five_minute(self, dt):
-        idx = next_divider_idx(self._trading_five_minutes_nanos, dt.values)
-        return self.all_five_mintutes[idx]
-
     def previous_minute(self, dt):
         """
         Given a dt, return the previous exchange minute.
@@ -484,12 +465,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
             end_minute=self.schedule.at[session_label, 'market_close'],
         )
 
-    def five_minutes_for_session(self, session_label):
-        return self.five_minutes_in_range(
-            start_five_minute=self.schedule.at[session_label, 'market_open'],
-            end_five_minute=self.schedule.at[session_label, 'market_close'],
-        )
-
     def minutes_window(self, start_dt, count):
         start_dt_nanos = start_dt.value
         all_minutes_nanos = self._trading_minutes_nanos
@@ -591,20 +566,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
 
         return abs(end_idx - start_idx)
 
-    def five_minutes_in_range(self, start_five_minute, end_five_minute):
-        start_idx = searchsorted(self._trading_five_minutes_nanos,
-                                 start_five_minute.value)
-
-        end_idx = searchsorted(self._trading_five_minutes_nanos,
-                               end_five_minute.value)
-
-        if end_five_minute.value == self._trading_five_minutes_nanos[end_idx]:
-            # if the end minute is a market minute, increase by 1
-            end_idx += 1
-
-        return self.all_five_minutes[start_idx:end_idx]
-        
-
     def minutes_in_range(self, start_minute, end_minute):
         """
         Given start and end minutes, return all the calendar minutes
@@ -662,15 +623,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
 
         return self.minutes_in_range(first_minute, last_minute)
 
-    def five_minutes_for_sessions_in_range(self,
-                                           start_session_label,
-                                           end_session_label):
-
-        first_minute, _ = self.open_and_close_for_session(start_session_label)
-        _, last_minute = self.open_and_close_for_session(end_session_label)
-
-        return self.five_minutes_in_range(first_minute, last_minute)
-
     def open_and_close_for_session(self, session_label):
         """
         Returns a tuple of timestamps of the open and close of the session
@@ -777,13 +729,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
 
         return DatetimeIndex(all_minutes).tz_localize("UTC")
 
-    @lazyval
-    def all_five_minutes(self):
-        """
-        Returns a DatetimeIndex representing all the five minutes in this calendar.
-        """
-        return self._all_minutes_with_interval(5) 
-
     @lazyval
     def all_minutes(self):
         """

catalyst/utils/events.py

@@ -602,7 +602,6 @@ class date_rules(object):
 class time_rules(object):
     market_open = AfterOpen
     market_close = BeforeClose
-    every_5_minutes = Always
     every_minute = Always
 
 

catalyst/utils/math_utils.py

@@ -17,6 +17,8 @@ import math
 
 from numpy import isnan
 
+def round_nearest(x, a):
+    return round(round(x / a) * a, -int(math.floor(math.log10(a))))
 
 def tolerant_equals(a, b, atol=10e-7, rtol=10e-7, equal_nan=False):
     """Check if a and b are equal with some tolerance.

catalyst/utils/paths.py

@@ -126,7 +126,7 @@ def catalyst_root(environ=None):
 
     root = environ.get('ZIPLINE_ROOT', None)
     if root is None:
-        root = expanduser('~/.catalyst')
+        root = os.path.join(expanduser('~'),'.catalyst')
 
     return root
 

catalyst/utils/run_algo.py

@@ -1,34 +1,52 @@
 import os
-import re
-from runpy import run_path
 import sys
 import warnings
+from datetime import timedelta
+from runpy import run_path
+from time import sleep
 
 import click
+import pandas as pd
+
+from catalyst.exchange.bittrex.bittrex import Bittrex
+from catalyst.exchange.bitfinex.bitfinex import Bitfinex
+from catalyst.exchange.poloniex.poloniex import Poloniex
+
 try:
     from pygments import highlight
     from pygments.lexers import PythonLexer
     from pygments.formatters import TerminalFormatter
+
     PYGMENTS = True
 except:
     PYGMENTS = False
 from toolz import valfilter, concatv
 from functools import partial
 
-from catalyst.algorithm import TradingAlgorithm
-from catalyst.data.bundles.core import load
-from catalyst.data.data_portal import DataPortal
-from catalyst.data.loader import load_crypto_market_data
 from catalyst.finance.trading import TradingEnvironment
-from catalyst.pipeline.data import USEquityPricing, CryptoPricing
-from catalyst.pipeline.loaders import (
-    USEquityPricingLoader,
-    CryptoPricingLoader,
-)
 from catalyst.utils.calendars import get_calendar
 from catalyst.utils.factory import create_simulation_parameters
+from catalyst.data.loader import load_crypto_market_data
 import catalyst.utils.paths as pth
 
+from catalyst.exchange.exchange_algorithm import ExchangeTradingAlgorithmLive, \
+    ExchangeTradingAlgorithmBacktest
+from catalyst.exchange.exchange_data_portal import DataPortalExchangeLive, \
+    DataPortalExchangeBacktest
+from catalyst.exchange.asset_finder_exchange import AssetFinderExchange
+from catalyst.exchange.exchange_portfolio import ExchangePortfolio
+from catalyst.exchange.exchange_errors import (
+    ExchangeRequestError, ExchangeAuthEmpty,
+    ExchangeRequestErrorTooManyAttempts,
+    BaseCurrencyNotFoundError, ExchangeNotFoundError)
+from catalyst.exchange.exchange_utils import get_exchange_auth, \
+    get_algo_object, get_exchange_folder
+from logbook import Logger
+
+from catalyst.constants import LOG_LEVEL
+
+log = Logger('run_algo', level=LOG_LEVEL)
+
 
 class _RunAlgoError(click.ClickException, ValueError):
     """Signal an error that should have a different message if invoked from
@@ -68,7 +86,12 @@ def _run(handle_data,
          output,
          print_algo,
          local_namespace,
-         environ):
+         environ,
+         live,
+         exchange,
+         algo_namespace,
+         base_currency,
+         live_graph):
     """Run a backtest for the given algorithm.
 
     This is shared between the cli and :func:`catalyst.run_algo`.
@@ -117,108 +140,188 @@ def _run(handle_data,
         else:
             click.echo(algotext)
 
-    if bundle is not None:
-        bundles = bundle.split(',')
+    mode = 'live' if live else 'backtest'
+    log.info('running algo in {mode} mode'.format(mode=mode))
 
-        def get_trading_env_and_data(bundles):
-            env = data = None
+    exchange_name = exchange
+    if exchange_name is None:
+        raise ValueError('Please specify at least one exchange.')
 
-            b = 'poloniex'
-            if len(bundles) == 0:
-                return env, data
-            elif len(bundles) == 1:
-                b = bundles[0]
+    exchange_list = [x.strip().lower() for x in exchange.split(',')]
 
-            bundle_data = load(
-                b,
-                environ,
-                bundle_timestamp,
+    exchanges = dict()
+    for exchange_name in exchange_list:
+
+        # Looking for the portfolio from the cache first
+        portfolio = get_algo_object(
+            algo_name=algo_namespace,
+            key='portfolio_{}'.format(exchange_name),
+            environ=environ
+        )
+
+        if portfolio is None:
+            portfolio = ExchangePortfolio(
+                start_date=pd.Timestamp.utcnow()
             )
 
-            prefix, connstr = re.split(
-                r'sqlite:///',
-                str(bundle_data.asset_finder.engine.url),
-                maxsplit=1,
+        # This corresponds to the json file containing api token info
+        exchange_auth = get_exchange_auth(exchange_name)
+
+        if live and (exchange_auth['key'] == '' or exchange_auth['secret'] == ''):
+            raise ExchangeAuthEmpty(
+                    exchange=exchange_name.title(), 
+                    filename=os.path.join(get_exchange_folder(exchange_name, environ), 'auth.json') )
+
+        if exchange_name == 'bitfinex':
+            exchanges[exchange_name] = Bitfinex(
+                key=exchange_auth['key'],
+                secret=exchange_auth['secret'],
+                base_currency=base_currency,
+                portfolio=portfolio
             )
-            if prefix:
-                raise ValueError(
-                    "invalid url %r, must begin with 'sqlite:///'" %
-                    str(bundle_data.asset_finder.engine.url),
+        elif exchange_name == 'bittrex':
+            exchanges[exchange_name] = Bittrex(
+                key=exchange_auth['key'],
+                secret=exchange_auth['secret'],
+                base_currency=base_currency,
+                portfolio=portfolio
+            )
+        elif exchange_name == 'poloniex':
+            exchanges[exchange_name] = Poloniex(
+                key=exchange_auth['key'],
+                secret=exchange_auth['secret'],
+                base_currency=base_currency,
+                portfolio=portfolio
+            )
+        else:
+            raise ExchangeNotFoundError(exchange_name=exchange_name)
+
+    open_calendar = get_calendar('OPEN')
+
+    env = TradingEnvironment(
+        load=partial(
+            load_crypto_market_data,
+            environ=environ,
+            start_dt=start,
+            end_dt=end
+        ),
+        environ=environ,
+        exchange_tz='UTC',
+        asset_db_path=None  # We don't need an asset db, we have exchanges
+    )
+    env.asset_finder = AssetFinderExchange()
+    choose_loader = None  # TODO: use the DataPortal for in the algorithm class for this
+
+    if live:
+        start = pd.Timestamp.utcnow()
+
+        # TODO: fix the end data.
+        end = start + timedelta(hours=8760)
+
+        data = DataPortalExchangeLive(
+            exchanges=exchanges,
+            asset_finder=env.asset_finder,
+            trading_calendar=open_calendar,
+            first_trading_day=pd.to_datetime('today', utc=True)
+        )
+
+        def fetch_capital_base(exchange, attempt_index=0):
+            """
+            Fetch the base currency amount required to bootstrap
+            the algorithm against the exchange.
+
+            The algorithm cannot continue without this value.
+
+            :param exchange: the targeted exchange
+            :param attempt_index:
+            :return capital_base: the amount of base currency available for
+            trading
+            """
+            try:
+                log.debug('retrieving capital base in {} to bootstrap '
+                          'exchange {}'.format(base_currency, exchange_name))
+                balances = exchange.get_balances()
+            except ExchangeRequestError as e:
+                if attempt_index < 20:
+                    log.warn(
+                        'could not retrieve balances on {}: {}'.format(
+                            exchange.name, e
+                        )
+                    )
+                    sleep(5)
+                    return fetch_capital_base(exchange, attempt_index + 1)
+
+                else:
+                    raise ExchangeRequestErrorTooManyAttempts(
+                        attempts=attempt_index,
+                        error=e
+                    )
+
+            if base_currency in balances:
+                return balances[base_currency]
+            else:
+                raise BaseCurrencyNotFoundError(
+                    base_currency=base_currency,
+                    exchange=exchange_name
                 )
 
-            open_calendar = get_calendar('OPEN')
+        capital_base = 0
+        for exchange_name in exchanges:
+            exchange = exchanges[exchange_name]
+            capital_base += fetch_capital_base(exchange)
 
-            env = TradingEnvironment(
-                load=partial(load_crypto_market_data, environ=environ),
-                bm_symbol='USDT_BTC',
-                trading_calendar=open_calendar,
-                asset_db_path=connstr,
-                environ=environ,
-            )
+        sim_params = create_simulation_parameters(
+            start=start,
+            end=end,
+            capital_base=capital_base,
+            emission_rate='minute',
+            data_frequency='minute'
+        )
 
-            first_trading_day = bundle_data.minute_bar_reader.first_trading_day
-
-            data = DataPortal(
-                env.asset_finder,
-                open_calendar,
-                first_trading_day=first_trading_day,
-                minute_reader=bundle_data.minute_bar_reader,
-                five_minute_reader=bundle_data.five_minute_bar_reader,
-                daily_reader=bundle_data.daily_bar_reader,
-                adjustment_reader=bundle_data.adjustment_reader,
-            )
-
-            return env, data
-
-        def get_loader_for_bundle(b):
-            bundle_data = load(
-                b,
-                environ,
-                bundle_timestamp,
-            )
-
-            if b == 'poloniex':
-                return CryptoPricingLoader(
-                           bundle_data,
-                           data_frequency,
-                           CryptoPricing,
-                       )
-            elif b == 'quandl':
-                return USEquityPricingLoader(
-                           bundle_data,
-                           data_frequency,
-                           USEquityPricing,
-                       )
-            raise ValueError(
-                "No PipelineLoader registered for bundle %s." % b
-            )
-
-        loaders = [get_loader_for_bundle(b) for b in bundles]
-        env, data = get_trading_env_and_data(bundles)
-
-        def choose_loader(column):
-            for loader in loaders:
-                if column in loader.columns:
-                    return loader
-            raise ValueError(
-                "No PipelineLoader registered for column %s." % column
-            )
+        # TODO: use the constructor instead
+        sim_params._arena = 'live'
 
+        algorithm_class = partial(
+            ExchangeTradingAlgorithmLive,
+            exchanges=exchanges,
+            algo_namespace=algo_namespace,
+            live_graph=live_graph
+        )
     else:
-        env = TradingEnvironment(environ=environ)
-        choose_loader = None
+        # Removed the existing Poloniex fork to keep things simple
+        # We can add back the complexity if required.
 
-    perf = TradingAlgorithm(
-        namespace=namespace,
-        env=env,
-        get_pipeline_loader=choose_loader,
-        sim_params=create_simulation_parameters(
+        # I don't think that we should have arbitrary price data bundles
+        # Instead, we should center this data around exchanges.
+        # We still need to support bundles for other misc data, but we
+        # can handle this later.
+
+        data = DataPortalExchangeBacktest(
+            exchanges=exchanges,
+            asset_finder=None,
+            trading_calendar=open_calendar,
+            first_trading_day=start,
+            last_available_session=end
+        )
+
+        sim_params = create_simulation_parameters(
             start=start,
             end=end,
             capital_base=capital_base,
             data_frequency=data_frequency,
             emission_rate=data_frequency,
-        ),
+        )
+
+        algorithm_class = partial(
+            ExchangeTradingAlgorithmBacktest,
+            exchanges=exchanges
+        )
+
+    perf = algorithm_class(
+        namespace=namespace,
+        env=env,
+        get_pipeline_loader=choose_loader,
+        sim_params=sim_params,
         **{
             'initialize': initialize,
             'handle_data': handle_data,
@@ -294,10 +397,10 @@ def load_extensions(default, extensions, strict, environ, reload=False):
             _loaded_extensions.add(ext)
 
 
-def run_algorithm(start,
-                  end,
-                  initialize,
-                  capital_base,
+def run_algorithm(initialize,
+                  capital_base=None,
+                  start=None,
+                  end=None,
                   handle_data=None,
                   before_trading_start=None,
                   analyze=None,
@@ -308,7 +411,12 @@ def run_algorithm(start,
                   default_extension=True,
                   extensions=(),
                   strict_extensions=True,
-                  environ=os.environ):
+                  environ=os.environ,
+                  live=False,
+                  exchange_name=None,
+                  base_currency=None,
+                  algo_namespace=None,
+                  live_graph=False):
     """Run a trading algorithm.
 
     Parameters
@@ -362,6 +470,12 @@ def run_algorithm(start,
     environ : mapping[str -> str], optional
         The os environment to use. Many extensions use this to get parameters.
         This defaults to ``os.environ``.
+    live: execute live trading
+    exchange_conn: The exchange connection parameters
+
+    Supported Exchanges
+    -------------------
+    bitfinex
 
     Returns
     -------
@@ -374,25 +488,30 @@ def run_algorithm(start,
     """
     load_extensions(default_extension, extensions, strict_extensions, environ)
 
-    non_none_data = valfilter(bool, {
-        'data': data is not None,
-        'bundle': bundle is not None,
-    })
-    if not non_none_data:
-        # if neither data nor bundle are passed use 'quantopian-quandl'
-        bundle = 'quantopian-quandl'
+    # I'm not sure that we need this since the modified DataPortal
+    # does not require extensions to be explicitly loaded.
 
-    elif len(non_none_data) != 1:
-        raise ValueError(
-            'must specify one of `data`, `data_portal`, or `bundle`,'
-            ' got: %r' % non_none_data,
-        )
+    # This will be useful for arbitrary non-pricing bundles but we may
+    # need to modify the logic.
+    if not live:
+        non_none_data = valfilter(bool, {
+            'data': data is not None,
+            'bundle': bundle is not None,
+        })
+        if not non_none_data:
+            # if neither data nor bundle are passed use 'quantopian-quandl'
+            bundle = 'quantopian-quandl'
 
-    elif 'bundle' not in non_none_data and bundle_timestamp is not None:
-        raise ValueError(
-            'cannot specify `bundle_timestamp` without passing `bundle`',
-        )
+        elif len(non_none_data) != 1:
+            raise ValueError(
+                'must specify one of `data`, `data_portal`, or `bundle`,'
+                ' got: %r' % non_none_data,
+            )
 
+        elif 'bundle' not in non_none_data and bundle_timestamp is not None:
+            raise ValueError(
+                'cannot specify `bundle_timestamp` without passing `bundle`',
+            )
     return _run(
         handle_data=handle_data,
         initialize=initialize,
@@ -412,4 +531,9 @@ def run_algorithm(start,
         print_algo=False,
         local_namespace=False,
         environ=environ,
+        live=live,
+        exchange=exchange_name,
+        algo_namespace=algo_namespace,
+        base_currency=base_currency,
+        live_graph=live_graph
     )

docs/CNAME

@@ -1 +1 @@
-www.zipline.io
+enigma-catalyst.readthedocs.io

docs/live-trading-blueprint.md

@@ -0,0 +1,207 @@
+<h1>Live Trading Blueprint</h1>
+The purpose of this document is to allow project contributors navigate
+through the ongoing live trading implementation.
+
+<h2>Components</h2>
+At a high level, the following components have been implemented to coerce
+zipline into live trading.
+
+<h3>Exchange</h3>
+
+*catalyst/exchange*
+
+Exchange is a new package introducing cryptocurrency
+exchanges to zipline. The package contains mostly new implementations
+of existing components, adapted to characteristics of exchanges.
+
+Here are some key characteristics which make cryptocurrency exchanges
+exchanges different compared to equity brokers.
+* They trade around the clock.
+* Currency symbols are inconsistent across exchanges.
+* They trade currency pairs (i.e. the base currency is not always be USD).
+This is a paradigm shift in context of zipline. Additional
+business logic will be required to manage the portfolio data and orders.
+* The price of a single asset might vary across exchanges. This means
+arbitrage opportunities. Consequently, to extract maximum alpha, the
+platform should not only support multiple exchanges, but also multiple
+exchanges per algorithm.
+* The fee model is usually more complex than that of an equity broker.
+It can vary drastically between exchanges.
+* There are no splits, mergers, etc. to worry about.
+* A complete order book is usually available, the platform should
+offer access to it order to help traders reduce slippage.
+
+<h3>New Components</h3>
+These components of the exchange package were added to the zipline
+sources.
+
+<h4>Exchange</h4>
+
+*catalyst/exchange/exchange.py*
+
+Abstract class which acts as an interface for the implementation of
+various exchanges. It also contains logic common to all exchanges.
+
+<h4>Bitfinex</h4>
+
+*catalyst/exchange/bitfinex.py*
+
+The Bitfinex exchange implementation. It extends the Exchange class.
+
+<h4>DataPortalExchange</h4>
+
+*catalyst/exchange/data_portal_exchange.py*
+
+Extends the zipline DataPortal to route spot data to the exchange.
+This is critical because it allows the algoritm to request data in
+real-time.
+
+For example, `data.current(asset, 'price')` retrieves the current price
+of the asset, not the price at the time of yielding the bar this
+is critical to minimize slippage.
+
+At the time of writing, it only supports spot data but I believe that
+it should be extended to historical data as well. Some exchanges
+have better historical data APIs than others. This will need to
+be considered during each individual implementation.
+
+<h4>ExchangeClock</h4>
+
+*catalyst/exchange/exchange_clock.py*
+
+An implementation to the zipline Clock which runs 24/7. It yields a
+bar every minute.
+
+<h4>AssetFinderExchange</h4>
+
+*catalyst/exchange/asset_finder_exchange.py*
+
+An alternate implementation of AssetFinder which locates each asset
+against the exchanges instead of bundle databases.
+
+For example, `symbol('eth_usd')` should return an Ethereum/USD asset
+regardless of currency notation of the target exchange.
+
+To acheive this, I have created a dictionary of currencies for the
+Bitfinex exchange. Here is what it looks like.
+* Each key represents the exchange specific symbol.
+* The symbol attribute represents the abstract symbol common across
+all exchanges for the given currency.
+* The start_date attribute should correspond to its first trading day
+on the exchange.
+
+```json
+{
+  "btcusd": {
+    "symbol": "btc_usd",
+    "start_date": "2010-01-01"
+  },
+  "ltcusd": {
+    "symbol": "ltc_usd",
+    "start_date": "2010-01-01"
+  },
+  "ltcbtc": {
+    "symbol": "ltc_btc",
+    "start_date": "2010-01-01"
+  },
+  "ethusd": {
+    "symbol": "eth_usd",
+    "start_date": "2010-01-01"
+  },
+  "ethbtc": {
+    "symbol": "eth_btc",
+    "start_date": "2010-01-01"
+  }
+}
+```
+
+<h4>ExchangeTradingAlgorithm</h4>
+
+*catalyst/exchange/algorithm_exchange.py*
+
+Extends the TradingAlgorithm class which orchestrates the api
+operations. This class brings together most of the components
+described above.
+
+<h3>Modified Components</h3>
+
+The following components have been modified to include conditional
+business logic to enable live trading.
+
+<h4>run_algorithm</h4>
+
+*catalyst/utils/run_algo.py*
+
+The run_algorithm interface is an entry point to execute an
+algorithm in zipline. This component was already modified for
+the catalyst concurrency bundles. I added conditional logic
+which should not interfere with backtesting.
+
+In a nutshell, the run_algorithm method now contains three additional
+parameters:
+* live: If True, zipline will attempt to trade live. If False or not
+specified, it will run a backtest as normal.
+* algo_namespace: An arbitrary namespace for the current algorithm.
+It will be used to persist data between runs.
+* exchange_conn: A dictionary containing the attributes required
+to instantiate an exchange. Here is an example for Bitfinex:
+
+```python
+exchange_conn = dict(
+    name='bitfinex',
+    key='',
+    secret=b'',
+    base_currency='usd'
+)
+```
+
+The following sample algorithm uses the run_algorithm interface:
+
+*catalyst/examples/buy_and_hold_live.py*
+
+<h2>Portfolio Management</h2>
+
+Zipline has a Portfolio class containing key metrics used by zipline
+for, but not only, these reasons:
+
+* Placing orders: When placing orders (e.g. order_target_percent),
+zipline queries the portfolio to assess the size of current positions,
+cash available, etc.
+* Measuring performance: The portfolio contains attributes like
+cost basis of each asset, p&l, etc. which zipline uses to compute all
+of its performance criteria.
+
+When backtesting, zipline automatically updates the Portfolio object
+of its corresponding algorithm. When live trading, these updates should
+be the responsibility of the exchange as it holds the truth for:
+
+* Executed price of each order (including fees and slippage)
+* Partial / failed orders
+* Cash (i.e. base currency) available
+* Cost basis of each position
+
+If each exchange account had a one-to-one relationship with an
+algorithm, portfolio metrics could be retrieved directly from the
+exchange without persisting any data to the algorithm. However,
+doing this would have at least the following drawbacks:
+
+* It may not be reasonable to ask users to dedicate an
+exchange account to a single algorithm. Exchanges are not easy
+to partition.
+* If an exchange account contains existing positions, the calculated
+cost basis would correspond to all positions, not just those
+initiated by the algorithm.
+* It would not be possible impose trading limits on algorithms.
+
+It follows that Portfolio metrics should be calculated using a strategic
+combination of the exchange data and algorithm activity. While tracking
+the activity of an algorithm works well in backtesting, it is more
+challenging during live trading. A live algorithm might run over
+several months. It might have to stop and start for many reasons.
+This means that the platform should have the ability to persist
+algorithm activity in order to be reliable.
+
+In the interest of time, I will start by persisting algorithm
+activity in memory. Data will be lost when the algorithm execution stops.
+The intent it to offer a simple basis from which to implement data
+persistence strategies in the future.

docs/source/conf.py

@@ -1,7 +1,7 @@
 import sys
 import os
 
-from zipline import __version__ as version
+#from catalyst import __version__ as version
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
@@ -21,14 +21,14 @@ extensions = [
 
 
 extlinks = {
-    'issue': ('https://github.com/quantopian/zipline/issues/%s', '#'),
-    'commit': ('https://github.com/quantopian/zipline/commit/%s', ''),
+    'issue': ('https://github.com/enigmampc/catalyst/issues/%s', '#'),
+    'commit': ('https://github.com/enigmampc/catalyst/commit/%s', ''),
 }
 
 # -- Docstrings ---------------------------------------------------------------
 
-extensions += ['numpydoc']
-numpydoc_show_class_members = False
+#extensions += ['numpydoc']
+#numpydoc_show_class_members = False
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['.templates']
@@ -40,11 +40,12 @@ source_suffix = '.rst'
 master_doc = 'index'
 
 # General information about the project.
-project = u'Zipline'
-copyright = u'2016, Quantopian Inc.'
+project = u'Catalyst'
+copyright = u'2017, Enigma MPC, Inc.'
 
 # The full version, including alpha/beta/rc tags, but excluding the commit hash
-release = version.split('+', 1)[0]
+#release = version.split('+', 1)[0]
+release = '0.3'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -84,7 +85,7 @@ html_show_sphinx = True
 html_show_copyright = True
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'ziplinedoc'
+htmlhelp_basename = 'catalystdoc'
 
 intersphinx_mapping = {
     'http://docs.python.org/dev': None,
@@ -93,6 +94,6 @@ intersphinx_mapping = {
     'pandas': ('http://pandas.pydata.org/pandas-docs/stable/', None),
 }
 
-doctest_global_setup = "import zipline"
+doctest_global_setup = "import catalyst"
 
 todo_include_todos = True

docs/source/development-guidelines.rst

@@ -1,21 +1,17 @@
 Development Guidelines
 ======================
-This page is intended for developers of Zipline, people who want to contribute to the Zipline codebase or documentation, or people who want to install from source and make local changes to their copy of Zipline.
+This page is intended for developers of Catalyst, people who want to contribute to the Catalyst codebase or documentation, or people who want to install from source and make local changes to their copy of Catalyst.
 
-All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We `track issues`__ on `GitHub`__ and also have a `mailing list`__ where you can ask questions.
-
-__ https://github.com/quantopian/zipline/issues
-__ https://github.com/
-__ https://groups.google.com/forum/#!forum/zipline
+All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We `track issues <https://github.com/enigmampc/catalyst/issues>`_ on `GitHub <https://github.com/enigmampc/catalyst>`_ and also have a `discord group <https://discord.gg/SJK32GY>`_ where you can ask questions.
 
 Creating a Development Environment
 ----------------------------------
 
-First, you'll need to clone Zipline by running:
+First, you'll need to clone Catalyst by running:
 
 .. code-block:: bash
 
-   $ git clone git@github.com:your-github-username/zipline.git
+   $ git clone git@github.com:enigmampc/catalyst.git
 
 Then check out to a new branch where you can make your changes:
 
@@ -23,15 +19,13 @@ Then check out to a new branch where you can make your changes:
 		
    $ git checkout -b some-short-descriptive-name
 
-If you don't already have them, you'll need some C library dependencies. You can follow the `install guide`__ to get the appropriate dependencies.
-
-__ install.html
+If you don't already have them, you'll need some C library dependencies. You can follow the `install guide <install.html>`_ to get the appropriate dependencies.
 
 The following section assumes you already have virtualenvwrapper and pip installed on your system. Suggested installation of Python library dependencies used for development:
 
 .. code-block:: bash
 
-   $ mkvirtualenv zipline
+   $ mkvirtualenv catalyst
    $ ./etc/ordered_pip.sh ./etc/requirements.txt
    $ pip install -r ./etc/requirements_dev.txt
    $ pip install -r ./etc/requirements_blaze.txt 
@@ -42,104 +36,39 @@ Finally, you can build the C extensions by running:
 
    $ python setup.py build_ext --inplace
 
-To finish, make sure `tests`__ pass.
+.. To finish, make sure `tests`__ pass.
 
-__ #style-guide-running-tests
+.. __ #style-guide-running-tests
 
-If you get an error running nosetests after setting up a fresh virtualenv, please try running
+.. If you get an error running nosetests after setting up a fresh virtualenv, please try running
 
-.. code-block:: bash
+.. code-block
 
-   # where zipline is the name of your virtualenv
-   $ deactivate zipline
-   $ workon zipline
+..   # where zipline is the name of your virtualenv
+..   $ deactivate zipline
+..   $ workon zipline
 
 
-Development with Docker
+.. Development with Docker
+.. -----------------------
+
+..If you want to work with zipline using a `Docker`__ container, you'll need to build the ``Dockerfile`` in the Zipline root directory, and then build ``Dockerfile-dev``. Instructions for building both containers can be found in ``Dockerfile`` and ``Dockerfile-dev``, respectively.
+
+.. __ https://docs.docker.com/get-started/
+   
+Git Branching Structure
 -----------------------
 
-If you want to work with zipline using a `Docker`__ container, you'll need to build the ``Dockerfile`` in the Zipline root directory, and then build ``Dockerfile-dev``. Instructions for building both containers can be found in ``Dockerfile`` and ``Dockerfile-dev``, respectively.
+If you want to contribute to the codebase of Catalyst, familiarize yourself with our branching structure, a fairly standardized one for that matter, that follows what is documented in the following article: `A successful Git branching model <http://nvie.com/posts/a-successful-git-branching-model/>`_. To contribute, create your local branch and submit a Pull Request (PR) to the **develop** branch.
 
-__ https://docs.docker.com/get-started/
+.. image:: https://camo.githubusercontent.com/9bde6fb64a9542a572e0e2017cbb58d9d2c440ac/687474703a2f2f6e7669652e636f6d2f696d672f6769742d6d6f64656c4032782e706e67
 
-
-Style Guide & Running Tests
----------------------------
-
-We use `flake8`__ for checking style requirements and `nosetests`__ to run Zipline tests. Our `continuous integration`__ tools will run these commands.
-
-__ http://flake8.pycqa.org/en/latest/
-__ http://nose.readthedocs.io/en/latest/
-__ https://en.wikipedia.org/wiki/Continuous_integration
-
-Before submitting patches or pull requests, please ensure that your changes pass when running:
-
-.. code-block:: bash
-
-   $ flake8 zipline tests
-
-In order to run tests locally, you'll need `TA-lib`__, which you can install on Linux by running:
-
-__ https://mrjbq7.github.io/ta-lib/install.html
-
-.. code-block:: bash
-
-   $ wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
-   $ tar -xvzf ta-lib-0.4.0-src.tar.gz
-   $ cd ta-lib/
-   $ ./configure --prefix=/usr
-   $ make
-   $ sudo make install
-
-And for ``TA-lib`` on OS X you can just run:
-
-.. code-block:: bash
-
-   $ brew install ta-lib
-
-Then run ``pip install`` TA-lib:
-
-.. code-block:: bash
-
-   $ pip install -r ./etc/requirements_talib.txt
-
-You should now be free to run tests:
-
-.. code-block:: bash
-		
-   $ nosetests
-
-
-Continuous Integration
-----------------------
-
-We use `Travis CI`__ for Linux-64 bit builds and `AppVeyor`__ for Windows-64 bit builds.
-
-.. note::
-
-   We do not currently have CI for OSX-64 bit builds. 32-bit builds may work but are not included in our integration tests.
-
-__ https://travis-ci.org/quantopian/zipline
-__ https://ci.appveyor.com/project/quantopian/zipline
-
-
-Packaging
----------
-To learn about how we build Zipline conda packages, you can read `this`__ section in our release process notes.
-
-__ release-process.html#uploading-conda-packages
-   
 Contributing to the Docs
 ------------------------
 
-If you'd like to contribute to the documentation on zipline.io, you can navigate to ``docs/source/`` where each `reStructuredText`__ (``.rst``) file is a separate section there. To add a section, create a new file called ``some-descriptive-name.rst`` and add ``some-descriptive-name`` to ``appendix.rst``. To edit a section, simply open up one of the existing files, make your changes, and save them.
-
-__ https://en.wikipedia.org/wiki/ReStructuredText
-
-We use `Sphinx`__ to generate documentation for Zipline, which you will need to install by running:
-
-__ http://www.sphinx-doc.org/en/stable/
+If you'd like to contribute to the documentation on enigmampc.github.io, you can navigate to ``docs/source/`` where each `reStructuredText <https://en.wikipedia.org/wiki/ReStructuredText>`_ file is a separate section there. To add a section, create a new file called ``some-descriptive-name.rst`` and add ``some-descriptive-name`` to ``index.rst``. To edit a section, simply open up one of the existing files, make your changes, and save them.
 
+We use `Sphinx <http://www.sphinx-doc.org/en/stable/>`_ to generate documentation for Catalyst, which you will need to install by running:
 
 .. code-block:: bash
 
@@ -149,7 +78,7 @@ To build and view the docs locally, run:
 
 .. code-block:: bash
 
-   # assuming you're in the Zipline root directory
+   # assuming you're in the Catalyst root directory
    $ cd docs
    $ make html
    $ {BROWSER} build/html/index.html
@@ -162,7 +91,7 @@ Standard prefixes to start a commit message:
 
 .. code-block:: text
 
-   BLD: change related to building Zipline
+   BLD: change related to building Catalyst
    BUG: bug fix
    DEP: deprecate something, or remove a deprecated object
    DEV: development tool or utility
@@ -172,15 +101,13 @@ Standard prefixes to start a commit message:
    REV: revert an earlier commit
    STY: style fix (whitespace, PEP8, flake8, etc)
    TST: addition or modification of tests
-   REL: related to releasing Zipline
+   REL: related to releasing Catalyst
    PERF: performance enhancements
 
 
 Some commit style guidelines:
 
-Commit lines should be no longer than `72 characters`__. The first line of the commit should include one of the above prefixes. There should be an empty line between the commit subject and the body of the commit. In general, the message should be in the imperative tense. Best practice is to include not only what the change is, but why the change was made.
-
-__ https://git-scm.com/book/en/v2/Distributed-Git-Contributing-to-a-Project
+Commit lines should be no longer than `72 characters <https://git-scm.com/book/en/v2/Distributed-Git-Contributing-to-a-Project>`_. The first line of the commit should include one of the above prefixes. There should be an empty line between the commit subject and the body of the commit. In general, the message should be in the imperative tense. Best practice is to include not only what the change is, but why the change was made.
 
 **Example:**
 
@@ -203,8 +130,6 @@ __ https://git-scm.com/book/en/v2/Distributed-Git-Contributing-to-a-Project
 Formatting Docstrings
 ---------------------
 
-When adding or editing docstrings for classes, functions, etc, we use `numpy`__ as the canonical reference.
-
-__ https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
+When adding or editing docstrings for classes, functions, etc, we use `numpy <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`_ as the canonical reference.
 
 

docs/source/index.rst

@@ -1,12 +1,23 @@
-.. include:: ../../README.rst
+.. include:: welcome.rst
+|
+|
+Table of Contents
+-----------------
 
 .. toctree::
    :maxdepth: 1
 
    install
    beginner-tutorial
-   bundles
+   jupyter
+   live-trading
+   naming-convention
+   videos
+   resources
    development-guidelines
-   appendix
-   release-process
    releases
+..   bundles
+..   development-guidelines
+..   appendix
+..   release-process
+