fix typos

BLD: Dockerfile[-dev]: s/quantopian/enigmampc/g

.gitignore

@@ -40,6 +40,7 @@ develop-eggs
 coverage.xml
 htmlcov
 nosetests.xml
+.python-version
 
 # C Extensions
 *.o

Dockerfile

@@ -1,11 +1,11 @@
 #
 # Dockerfile for an image with the currently checked out version of catalyst installed. To build:
 #
-#    docker build -t quantopian/catalyst .
+#    docker build -t enigmampc/catalyst .
 #
 # To run the container:
 #
-#    docker run -v /path/to/your/notebooks:/projects -v ~/.catalyst:/root/.catalyst -p 8888:8888/tcp --name catalyst -it quantopian/catalyst
+#    docker run -v /path/to/your/notebooks:/projects -v ~/.catalyst:/root/.catalyst -p 8888:8888/tcp --name catalyst -it enigmampc/catalyst
 #
 # To access Jupyter when running docker locally (you may need to add NAT rules):
 #

Dockerfile-dev

@@ -1,15 +1,15 @@
 #
 # Dockerfile for an image with the currently checked out version of catalyst installed. To build:
 #
-#    docker build -t quantopian/catalystdev -f Dockerfile-dev .
+#    docker build -t enigmampc/catalystdev -f Dockerfile-dev .
 #
-# Note: the dev build requires a quantopian/catalyst image, which you can build as follows:
+# Note: the dev build requires a enigmampc/catalyst image, which you can build as follows:
 #
-#    docker build -t quantopian/catalyst -f Dockerfile .
+#    docker build -t enigmampc/catalyst -f Dockerfile .
 #
 # To run the container:
 #
-#    docker run -v /path/to/your/notebooks:/projects -v ~/.catalyst:/root/.catalyst -p 8888:8888/tcp --name catalystdev -it quantopian/catalystdev
+#    docker run -v /path/to/your/notebooks:/projects -v ~/.catalyst:/root/.catalyst -p 8888:8888/tcp --name catalystdev -it enigmampc/catalystdev
 #
 # To access Jupyter when running docker locally (you may need to add NAT rules):
 #
@@ -25,7 +25,7 @@
 #
 #    docker exec -it catalystdev catalyst run -f /projects/my_algo.py --start 2015-1-1 --end 2016-1-1 /projects/result.pickle
 #
-FROM quantopian/catalyst
+FROM enigmampc/catalyst
 
 WORKDIR /catalyst
 

README.rst

@@ -1,10 +1,11 @@
-.. image:: https://s3.amazonaws.com/enigmaco-docs/enigma-catalyst.jpg
+.. image:: https://s3.amazonaws.com/enigmaco-docs/enigma-catalyst.png
     :target: https://enigmampc.github.io/catalyst
     :align: center
     :alt: Enigma | Catalyst
 
 |version tag|
 |version status|
+|forum|
 |discord|
 |twitter|
 
@@ -17,16 +18,16 @@ insights regarding a particular strategy's performance. Catalyst also supports
 live-trading of crypto-assets starting with three exchanges (Bitfinex, Bittrex, 
 and Poloniex) with more being added over time. Catalyst empowers users to share 
 and curate data and build profitable, data-driven investment strategies. Please 
-visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst, or 
-refer to the `whitepaper <https://www.enigma.co/enigma_catalyst.pdf>`_ for 
-further technical details.
+visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst.
 
 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. Join us on 
-`Discord <https://discord.gg/SJK32GY>`_ where we have a *#catalyst_dev* channel 
-for questions around Catalyst, algorithmic trading and technical support.
+existing trading algorithms, developer knowledge, and tutorials. Join us on the 
+`Catalyst Forum <https://catalyst.enigma.co/>`_ for questions around Catalyst, 
+algorithmic trading and technical support. We also have a 
+`Discord <https://discord.gg/SJK32GY>`_ group with the *#catalyst_dev* and 
+*#catalyst_setup* dedicated channels.
 
 Overview
 ========
@@ -62,6 +63,9 @@ Go to our `Documentation Website <https://enigmampc.github.io/catalyst/>`_.
 
 .. |version status| image:: https://img.shields.io/pypi/pyversions/enigma-catalyst.svg
    :target: https://pypi.python.org/pypi/enigma-catalyst
+   
+.. |forum| image:: https://img.shields.io/badge/forum-join-green.svg
+   :target: https://catalyst.enigma.co/
 
 .. |discord| image:: https://img.shields.io/badge/discord-join%20chat-green.svg
    :target: https://discordapp.com/invite/SJK32GY

catalyst/__main__.py

@@ -3,8 +3,10 @@ import os
 from functools import wraps
 
 import click
+import sys
 import logbook
 import pandas as pd
+from catalyst.marketplace.marketplace import Marketplace
 from six import text_type
 
 from catalyst.data import bundles as bundles_module
@@ -257,7 +259,7 @@ def run(ctx,
     if capital_base is None:
         ctx.fail("must specify a capital base with '--capital-base'")
 
-    click.echo('Running in backtesting mode.')
+    click.echo('Running in backtesting mode.', sys.stdout)
 
     perf = _run(
         initialize=None,
@@ -282,13 +284,15 @@ def run(ctx,
         exchange=exchange_name,
         algo_namespace=algo_namespace,
         base_currency=base_currency,
+        analyze_live=None,
         live_graph=False,
         simulate_orders=True,
+        auth_aliases=None,
         stats_output=None,
     )
 
     if output == '-':
-        click.echo(str(perf))
+        click.echo(str(perf), sys.stdout)
     elif output != os.devnull:  # make the catalyst magic not write any data
         perf.to_pickle(output)
 
@@ -312,11 +316,11 @@ 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,
@@ -393,6 +397,12 @@ def catalyst_magic(line, cell=None):
     help='The base currency used to calculate statistics '
          '(e.g. usd, btc, eth).',
 )
+@click.option(
+    '-e',
+    '--end',
+    type=Date(tz='utc', as_timestamp=True),
+    help='An optional end date at which to stop the execution.',
+)
 @click.option(
     '--live-graph/--no-live-graph',
     is_flag=True,
@@ -406,6 +416,15 @@ def catalyst_magic(line, cell=None):
     help='Simulating orders enable the paper trading mode. No orders will be '
          'sent to the exchange unless set to false.',
 )
+@click.option(
+    '--auth-aliases',
+    default=None,
+    help='Authentication file aliases for the specified exchanges. By default,'
+         'each exchange uses the "auth.json" file in the exchange folder. '
+         'Specifying an "auth2" alias would use "auth2.json". It should be '
+         'specified like this: "[exchange_name],[alias],..." For example, '
+         '"binance,auth2" or "binance,auth2,bittrex,auth2".',
+)
 @click.pass_context
 def live(ctx,
          algofile,
@@ -418,7 +437,9 @@ def live(ctx,
          exchange_name,
          algo_namespace,
          base_currency,
+         end,
          live_graph,
+         auth_aliases,
          simulate_orders):
     """Trade live with the given algorithm.
     """
@@ -441,10 +462,10 @@ def live(ctx,
         ctx.fail("must specify a capital base with '--capital-base'")
 
     if simulate_orders:
-        click.echo('Running in paper trading mode.')
+        click.echo('Running in paper trading mode.', sys.stdout)
 
     else:
-        click.echo('Running in live trading mode.')
+        click.echo('Running in live trading mode.', sys.stdout)
 
     perf = _run(
         initialize=None,
@@ -460,7 +481,7 @@ def live(ctx,
         bundle=None,
         bundle_timestamp=None,
         start=None,
-        end=None,
+        end=end,
         output=output,
         print_algo=print_algo,
         local_namespace=local_namespace,
@@ -470,12 +491,14 @@ def live(ctx,
         algo_namespace=algo_namespace,
         base_currency=base_currency,
         live_graph=live_graph,
+        analyze_live=None,
         simulate_orders=simulate_orders,
+        auth_aliases=auth_aliases,
         stats_output=None,
     )
 
     if output == '-':
-        click.echo(str(perf))
+        click.echo(str(perf), sys.stdout)
     elif output != os.devnull:  # make the catalyst magic not write any data
         perf.to_pickle(output)
 
@@ -557,7 +580,8 @@ def ingest_exchange(ctx, exchange_name, data_frequency, start, end,
 
     exchange_bundle = ExchangeBundle(exchange_name)
 
-    click.echo('Ingesting exchange bundle {}...'.format(exchange_name))
+    click.echo('Trying to ingest exchange bundle {}...'.format(exchange_name),
+               sys.stdout)
     exchange_bundle.ingest(
         data_frequency=data_frequency,
         include_symbols=include_symbols,
@@ -580,10 +604,11 @@ def ingest_exchange(ctx, exchange_name, data_frequency, start, end,
 @click.pass_context
 def clean_algo(ctx, algo_namespace):
     click.echo(
-        'Cleaning algo state: {}'.format(algo_namespace)
+        'Cleaning algo state: {}'.format(algo_namespace),
+        sys.stdout
     )
     delete_algo_folder(algo_namespace)
-    click.echo('Done')
+    click.echo('Done', sys.stdout)
 
 
 @main.command(name='clean-exchange')
@@ -610,11 +635,12 @@ def clean_exchange(ctx, exchange_name, data_frequency):
 
     exchange_bundle = ExchangeBundle(exchange_name)
 
-    click.echo('Cleaning exchange bundle {}...'.format(exchange_name))
+    click.echo('Cleaning exchange bundle {}...'.format(exchange_name),
+               sys.stdout)
     exchange_bundle.clean(
         data_frequency=data_frequency,
     )
-    click.echo('Done')
+    click.echo('Done', sys.stdout)
 
 
 @main.command()
@@ -735,7 +761,132 @@ def bundles():
         # because there were no entries, print a single message indicating that
         # no ingestions have yet been made.
         for timestamp in ingestions or ["<no ingestions>"]:
-            click.echo("%s %s" % (bundle, timestamp))
+            click.echo("%s %s" % (bundle, timestamp), sys.stdout)
+
+
+@main.group()
+@click.pass_context
+def marketplace(ctx):
+    """Access the Enigma Data Marketplace to:\n
+    - Register and Publish new datasets (seller-side)\n
+    - Subscribe and Ingest premium datasets (buyer-side)\n
+    """
+    pass
+
+
+@marketplace.command()
+@click.pass_context
+def ls(ctx):
+    """List all available datasets.
+    """
+    click.echo('Listing of available data sources on the marketplace:',
+               sys.stdout)
+    marketplace = Marketplace()
+    marketplace.list()
+
+
+@marketplace.command()
+@click.option(
+    '--dataset',
+    default=None,
+    help='The name of the dataset to ingest from the Data Marketplace.',
+)
+@click.pass_context
+def subscribe(ctx, dataset):
+    """Subscribe to an existing dataset.
+    """
+    marketplace = Marketplace()
+    marketplace.subscribe(dataset)
+
+
+@marketplace.command()
+@click.option(
+    '--dataset',
+    default=None,
+    help='The name of the dataset to ingest from the Data Marketplace.',
+)
+@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.pass_context
+def ingest(ctx, dataset, data_frequency, start, end):
+    """Ingest a dataset (requires subscription).
+    """
+    marketplace = Marketplace()
+    marketplace.ingest(dataset, data_frequency, start, end)
+
+
+@marketplace.command()
+@click.option(
+    '--dataset',
+    default=None,
+    help='The name of the dataset to ingest from the Data Marketplace.',
+)
+@click.pass_context
+def clean(ctx, dataset):
+    """Clean/Remove local data for a given dataset.
+    """
+    marketplace = Marketplace()
+    marketplace.clean(dataset)
+
+
+@marketplace.command()
+@click.pass_context
+def register(ctx):
+    """Register a new dataset.
+    """
+    marketplace = Marketplace()
+    marketplace.register()
+
+
+@marketplace.command()
+@click.option(
+    '--dataset',
+    default=None,
+    help='The name of the Marketplace dataset to publish data for.',
+)
+@click.option(
+    '--datadir',
+    default=None,
+    help='The folder that contains the CSV data files to publish.',
+)
+@click.option(
+    '--watch/--no-watch',
+    is_flag=True,
+    default=False,
+    help='Whether to watch the datadir for live data.',
+)
+@click.pass_context
+def publish(ctx, dataset, datadir, watch):
+    """Publish data for a registered dataset.
+    """
+    marketplace = Marketplace()
+    if dataset is None:
+        ctx.fail("must specify a dataset to publish data for "
+                 " with '--dataset'\n")
+    if datadir is None:
+        ctx.fail("must specify a datadir where to find the files to publish "
+                 " with '--datadir'\n")
+    marketplace.publish(dataset, datadir, watch)
 
 
 if __name__ == '__main__':

catalyst/_protocol.pyx

@@ -16,7 +16,6 @@ import warnings
 from contextlib import contextmanager
 from functools import wraps
 
-from pandas.tslib import normalize_date
 import pandas as pd
 import numpy as np
 
@@ -564,7 +563,7 @@ cdef class BarData:
             })
 
     cdef bool _is_stale_for_asset(self, asset, dt, adjusted_dt, data_portal):
-        session_label = normalize_date(dt) # FIXME
+        session_label = dt.normalize_date() # FIXME
 
         if not asset.is_alive_for_session(session_label):
             return False

catalyst/algorithm.py

@@ -21,7 +21,6 @@ import logbook
 import pytz
 import pandas as pd
 from contextlib2 import ExitStack
-from pandas.tseries.tools import normalize_date
 import numpy as np
 
 from itertools import chain, repeat
@@ -939,7 +938,7 @@ class TradingAlgorithm(object):
             The field to query. The options have the following meanings:
               arena : str
                   The arena from the simulation parameters. This will normally
-                  be ``'backtest'`` but some systems may use this distinguish
+                  be ``backtest`` but some systems may use this distinguish
                   live trading from backtesting.
               data_frequency : {'daily', 'minute'}
                   data_frequency tells the algorithm if it is running with
@@ -954,7 +953,7 @@ class TradingAlgorithm(object):
                   The platform that the code is running on. By default this
                   will be the string 'catalyst'. This can allow algorithms to
                   know if they are running on the Quantopian platform instead.
-              * : dict[str -> any]
+              \* : dict[str -> any]
                   Returns all of the fields in a dictionary.
 
         Returns
@@ -1032,7 +1031,7 @@ class TradingAlgorithm(object):
             argument is the name of the column in the preprocessed dataframe
             containing the symbols. This will be used along with the date
             information to map the sids in the asset finder.
-        **kwargs
+        \*\*kwargs
             Forwarded to :func:`pandas.read_csv`.
 
         Returns
@@ -1156,7 +1155,7 @@ class TradingAlgorithm(object):
 
         Parameters
         ----------
-        **kwargs
+        \*\*kwargs
             The names and values to record.
 
         Notes
@@ -1273,7 +1272,7 @@ class TradingAlgorithm(object):
 
         Parameters
         ----------
-        *args : iterable[str]
+        \*args : iterable[str]
             The ticker symbols to lookup.
 
         Returns
@@ -1345,7 +1344,7 @@ class TradingAlgorithm(object):
         # Make sure the asset exists, and that there is a last price for it.
         # FIXME: we should use BarData's can_trade logic here, but I haven't
         # yet found a good way to do that.
-        normalized_date = normalize_date(self.datetime)
+        normalized_date = self.datetime.normalize()
 
         if normalized_date < asset.start_date:
             raise CannotOrderDelistedAsset(
@@ -1392,7 +1391,7 @@ class TradingAlgorithm(object):
             )
 
         if asset.auto_close_date:
-            day = normalize_date(self.get_datetime())
+            day = self.get_datetime().normalize()
 
             if day > min(asset.end_date, asset.auto_close_date):
                 # If we are after the asset's end date or auto close date, warn
@@ -2475,7 +2474,7 @@ class TradingAlgorithm(object):
         """
         Internal implementation of `pipeline_output`.
         """
-        today = normalize_date(self.get_datetime())
+        today = self.get_datetime().normalize()
         data = NO_DATA = object()
         try:
             data = self._pipeline_cache.unwrap(today)

catalyst/api.pyi

@@ -34,6 +34,7 @@ def attach_pipeline(pipeline, name, chunks=None):
     :func:`catalyst.api.pipeline_output`
     """
 
+
 def batch_market_order(share_counts):
     """Place a batch market order for multiple assets.
 
@@ -48,6 +49,7 @@ def batch_market_order(share_counts):
         Index of ids for newly-created orders.
     """
 
+
 def cancel_order(order_param):
     """Cancel an open order.
 
@@ -57,7 +59,9 @@ def cancel_order(order_param):
         The order_id or order object to cancel.
     """
 
-def continuous_future(root_symbol_str, offset=0, roll='volume', adjustment='mul'):
+
+def continuous_future(root_symbol_str, offset=0, roll='volume',
+                      adjustment='mul'):
     """Create a specifier for a continuous contract.
 
     Parameters
@@ -81,7 +85,10 @@ def continuous_future(root_symbol_str, offset=0, roll='volume', adjustment='mul'
         The continuous future specifier.
     """
 
-def fetch_csv(url, pre_func=None, post_func=None, date_column='date', date_format=None, timezone='UTC', symbol=None, mask=True, symbol_column=None, special_params_checker=None, **kwargs):
+
+def fetch_csv(url, pre_func=None, post_func=None, date_column='date',
+              date_format=None, timezone='UTC', symbol=None, mask=True,
+              symbol_column=None, special_params_checker=None, **kwargs):
     """Fetch a csv from a remote url and register the data so that it is
     queryable from the ``data`` object.
 
@@ -125,6 +132,7 @@ def fetch_csv(url, pre_func=None, post_func=None, date_column='date', date_forma
         A requests source that will pull data from the url specified.
     """
 
+
 def future_symbol(symbol):
     """Lookup a futures contract with a given symbol.
 
@@ -144,6 +152,7 @@ def future_symbol(symbol):
         Raised when no contract named 'symbol' is found.
     """
 
+
 def get_datetime(tz=None):
     """
 Returns the current simulation datetime.
@@ -159,6 +168,7 @@ dt : datetime
     The current simulation datetime converted to ``tz``.
     """
 
+
 def get_environment(field='platform'):
     """Query the execution environment.
 
@@ -198,6 +208,7 @@ def get_environment(field='platform'):
         Raised when ``field`` is not a valid option.
     """
 
+
 def get_order(order_id):
     """Lookup an order based on the order id returned from one of the
     order functions.
@@ -213,10 +224,12 @@ def get_order(order_id):
         The order object.
     """
 
+
 def history(bar_count, frequency, field, ffill=True):
     """DEPRECATED: use ``data.history`` instead.
     """
 
+
 def order(asset, amount, limit_price=None, stop_price=None, style=None):
     """Place an order.
 
@@ -258,7 +271,9 @@ def order(asset, amount, limit_price=None, stop_price=None, style=None):
     :func:`catalyst.api.order_percent`
     """
 
-def order_percent(asset, percent, limit_price=None, stop_price=None, style=None):
+
+def order_percent(asset, percent, limit_price=None, stop_price=None,
+                  style=None):
     """Place an order in the specified asset corresponding to the given
     percent of the current portfolio value.
 
@@ -293,6 +308,7 @@ def order_percent(asset, percent, limit_price=None, stop_price=None, style=None)
     :func:`catalyst.api.order_value`
     """
 
+
 def order_target(asset, target, limit_price=None, stop_price=None, style=None):
     """Place an order to adjust a position to a target number of shares. If
     the position doesn't already exist, this is equivalent to placing a new
@@ -344,7 +360,9 @@ def order_target(asset, target, limit_price=None, stop_price=None, style=None):
     :func:`catalyst.api.order_target_value`
     """
 
-def order_target_percent(asset, target, limit_price=None, stop_price=None, style=None):
+
+def order_target_percent(asset, target, limit_price=None, stop_price=None,
+                         style=None):
     """Place an order to adjust a position to a target percent of the
     current portfolio value. If the position doesn't already exist, this is
     equivalent to placing a new order. If the position does exist, this is
@@ -396,7 +414,9 @@ def order_target_percent(asset, target, limit_price=None, stop_price=None, style
     :func:`catalyst.api.order_target_value`
     """
 
-def order_target_value(asset, target, limit_price=None, stop_price=None, style=None):
+
+def order_target_value(asset, target, limit_price=None, stop_price=None,
+                       style=None):
     """Place an order to adjust a position to a target value. If
     the position doesn't already exist, this is equivalent to placing a new
     order. If the position does exist, this is equivalent to placing an
@@ -448,6 +468,7 @@ def order_target_value(asset, target, limit_price=None, stop_price=None, style=N
     :func:`catalyst.api.order_target_percent`
     """
 
+
 def order_value(asset, value, limit_price=None, stop_price=None, style=None):
     """Place an order by desired value rather than desired number of
     shares.
@@ -488,6 +509,7 @@ def order_value(asset, value, limit_price=None, stop_price=None, style=None):
     :func:`catalyst.api.order_percent`
     """
 
+
 def pipeline_output(name):
     """Get the results of the pipeline that was attached with the name:
     ``name``.
@@ -514,6 +536,7 @@ def pipeline_output(name):
     :meth:`catalyst.pipeline.engine.PipelineEngine.run_pipeline`
     """
 
+
 def record(*args, **kwargs):
     """Track and record values each day.
 
@@ -529,7 +552,9 @@ def record(*args, **kwargs):
     :func:`~catalyst.run_algorithm`.
     """
 
-def schedule_function(func, date_rule=None, time_rule=None, half_days=True, calendar=None):
+
+def schedule_function(func, date_rule=None, time_rule=None, half_days=True,
+                      calendar=None):
     """Schedules a function to be called according to some timed rules.
 
     Parameters
@@ -549,6 +574,7 @@ def schedule_function(func, date_rule=None, time_rule=None, half_days=True, cale
     :class:`catalyst.api.time_rules`
     """
 
+
 def set_asset_restrictions(restrictions, on_error='fail'):
     """Set a restriction on which assets can be ordered.
 
@@ -562,6 +588,7 @@ def set_asset_restrictions(restrictions, on_error='fail'):
     catalyst.finance.asset_restrictions.Restrictions
     """
 
+
 def set_benchmark(benchmark):
     """Set the benchmark asset.
 
@@ -576,6 +603,7 @@ def set_benchmark(benchmark):
     automatically reinvested.
     """
 
+
 def set_cancel_policy(cancel_policy):
     """Sets the order cancellation policy for the simulation.
 
@@ -590,6 +618,7 @@ def set_cancel_policy(cancel_policy):
     :class:`catalyst.api.NeverCancel`
     """
 
+
 def set_commission(commission):
     """Sets the commission model for the simulation.
 
@@ -605,6 +634,7 @@ def set_commission(commission):
     :class:`catalyst.finance.commission.PerDollar`
     """
 
+
 def set_do_not_order_list(restricted_list, on_error='fail'):
     """Set a restriction on which assets can be ordered.
 
@@ -614,11 +644,13 @@ def set_do_not_order_list(restricted_list, on_error='fail'):
         The assets that cannot be ordered.
     """
 
+
 def set_long_only(on_error='fail'):
     """Set a rule specifying that this algorithm cannot take short
     positions.
     """
 
+
 def set_max_leverage(max_leverage):
     """Set a limit on the maximum leverage of the algorithm.
 
@@ -629,6 +661,7 @@ def set_max_leverage(max_leverage):
         be no maximum.
     """
 
+
 def set_max_order_count(max_count, on_error='fail'):
     """Set a limit on the number of orders that can be placed in a single
     day.
@@ -639,7 +672,9 @@ def set_max_order_count(max_count, on_error='fail'):
         The maximum number of orders that can be placed on any single day.
     """
 
-def set_max_order_size(asset=None, max_shares=None, max_notional=None, on_error='fail'):
+
+def set_max_order_size(asset=None, max_shares=None, max_notional=None,
+                       on_error='fail'):
     """Set a limit on the number of shares and/or dollar value of any single
     order placed for sid.  Limits are treated as absolute values and are
     enforced at the time that the algo attempts to place an order for sid.
@@ -658,7 +693,9 @@ def set_max_order_size(asset=None, max_shares=None, max_notional=None, on_error=
         The maximum value that can be ordered at one time.
     """
 
-def set_max_position_size(asset=None, max_shares=None, max_notional=None, on_error='fail'):
+
+def set_max_position_size(asset=None, max_shares=None, max_notional=None,
+                          on_error='fail'):
     """Set a limit on the number of shares and/or dollar value held for the
     given sid. Limits are treated as absolute values and are enforced at
     the time that the algo attempts to place an order for sid. This means
@@ -681,6 +718,7 @@ def set_max_position_size(asset=None, max_shares=None, max_notional=None, on_err
         The maximum value to hold for an asset.
     """
 
+
 def set_slippage(slippage):
     """Set the slippage model for the simulation.
 
@@ -694,6 +732,7 @@ def set_slippage(slippage):
     :class:`catalyst.finance.slippage.SlippageModel`
     """
 
+
 def set_symbol_lookup_date(dt):
     """Set the date for which symbols will be resolved to their assets
     (symbols may map to different firms or underlying assets at
@@ -705,6 +744,7 @@ def set_symbol_lookup_date(dt):
         The new symbol lookup date.
     """
 
+
 def sid(sid):
     """Lookup an Asset by its unique asset identifier.
 
@@ -724,6 +764,7 @@ def sid(sid):
         When a requested ``sid`` does not map to any asset.
     """
 
+
 def symbol(symbol_str):
     """Lookup an Equity by its ticker symbol.
 
@@ -748,6 +789,7 @@ def symbol(symbol_str):
     :func:`catalyst.api.set_symbol_lookup_date`
     """
 
+
 def symbols(*args):
     """Lookup multuple Equities as a list.
 
@@ -773,3 +815,18 @@ def symbols(*args):
     :func:`catalyst.api.set_symbol_lookup_date`
     """
 
+
+def get_dataset(ds_name, start=None, end=None):
+    """
+    Lookup a data source from the marketplace
+
+    Parameters
+    ----------
+    ds_name: str
+    start: pd.Timestamp
+    end: pd.Timestamp
+
+    Returns
+    -------
+
+    """

catalyst/assets/_assets.pyx

@@ -630,23 +630,28 @@ cdef class TradingPair(Asset):
         and whose second element is a tuple of all the attributes that should
         be serialized/deserialized during pickling.
         """
-        #TODO: make sure that all fields set there
+        # added arguments for catalyst
         return (self.__class__, (self.symbol,
                                  self.exchange,
                                  self.start_date,
                                  self.asset_name,
                                  self.sid,
                                  self.leverage,
+                                 self.end_daily,
+                                 self.end_minute,
                                  self.end_date,
+                                 self.exchange_symbol,
                                  self.first_traded,
                                  self.auto_close_date,
                                  self.exchange_full,
                                  self.min_trade_size,
                                  self.max_trade_size,
+                                 self.maker,
+                                 self.taker,
                                  self.lot,
                                  self.decimals,
-                                 self.taker,
-                                 self.maker))
+                                 self.trading_state,
+                                 self.data_source))
 
 def make_asset_array(int size, Asset asset):
     cdef np.ndarray out = np.empty([size], dtype=object)

catalyst/constants.py

@@ -15,4 +15,32 @@ SYMBOLS_URL = 'https://s3.amazonaws.com/enigmaco/catalyst-exchanges/' \
 DATE_TIME_FORMAT = '%Y-%m-%d %H:%M'
 DATE_FORMAT = '%Y-%m-%d'
 
+try:
+    ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+except Exception as e:
+    print('unable to get catalyst path: {}'.format(e))
+
 AUTO_INGEST = False
+
+AUTH_SERVER = 'https://data.enigma.co'
+
+ETH_REMOTE_NODE = 'https://mainnet.infura.io'
+
+MARKETPLACE_CONTRACT = 'https://raw.githubusercontent.com/enigmampc/' \
+                       'catalyst/master/catalyst/marketplace/' \
+                       'contract_marketplace_address.txt'
+
+MARKETPLACE_CONTRACT_ABI = 'https://raw.githubusercontent.com/enigmampc/' \
+                           'catalyst/master/catalyst/marketplace/' \
+                           'contract_marketplace_abi.json'
+
+ENIGMA_CONTRACT = 'https://raw.githubusercontent.com/enigmampc/' \
+                  'catalyst/master/catalyst/marketplace/' \
+                  'contract_enigma_address.txt'
+
+ENIGMA_CONTRACT_ABI = 'https://raw.githubusercontent.com/enigmampc/' \
+                      'catalyst/master/catalyst/marketplace/' \
+                      'contract_enigma_abi.json'
+
+SUPPORTED_WALLETS = ['metamask', 'ledger', 'trezor', 'bitbox', 'keystore',
+                     'key']

catalyst/data/data_portal.py

@@ -20,7 +20,6 @@ import numpy as np
 from numpy import float64, int64, nan
 import pandas as pd
 from pandas import isnull
-from pandas.tslib import normalize_date
 from six import iteritems
 from six.moves import reduce
 
@@ -439,7 +438,7 @@ class DataPortal(object):
                     (isinstance(asset, (Asset, ContinuousFuture))))
 
     def _get_fetcher_value(self, asset, field, dt):
-        day = normalize_date(dt)
+        day = dt.normalize()
 
         try:
             return \
@@ -1130,7 +1129,7 @@ class DataPortal(object):
         if self._asset_start_dates[sid] > dt:
             raise NoTradeDataAvailableTooEarly(
                 sid=sid,
-                dt=normalize_date(dt),
+                dt=dt.normalize(),
                 start_dt=start_date
             )
 
@@ -1138,7 +1137,7 @@ class DataPortal(object):
         if self._asset_end_dates[sid] < dt:
             raise NoTradeDataAvailableTooLate(
                 sid=sid,
-                dt=normalize_date(dt),
+                dt=dt.normalize(),
                 end_dt=end_date
             )
 
@@ -1262,7 +1261,7 @@ class DataPortal(object):
         if self._extra_source_df is None:
             return []
 
-        day = normalize_date(dt)
+        day = dt.normalize()
 
         if day in self._extra_source_df.index:
             assets = self._extra_source_df.loc[day]['sid']

catalyst/data/dispatch_bar_reader.py

@@ -88,11 +88,11 @@ class AssetDispatchBarReader(with_metaclass(ABCMeta)):
         if self._last_available_dt is not None:
             return self._last_available_dt
         else:
-            return min(r.last_available_dt for r in self._readers.values())
+            return min(r.last_available_dt for r in list(self._readers.values()))
 
     @lazyval
     def first_trading_day(self):
-        return max(r.first_trading_day for r in self._readers.values())
+        return max(r.first_trading_day for r in list(self._readers.values()))
 
     def get_value(self, sid, dt, field):
         asset = self._asset_finder.retrieve_asset(sid)

catalyst/data/history_loader.py

@@ -21,7 +21,6 @@ from abc import (
 from numpy import concatenate
 from lru import LRU
 from pandas import isnull
-from pandas.tslib import normalize_date
 from toolz import sliding_window
 
 from six import with_metaclass
@@ -93,8 +92,8 @@ class HistoryCompatibleUSEquityAdjustmentReader(object):
             The adjustments as a dict of loc -> Float64Multiply
         """
         sid = int(asset)
-        start = normalize_date(dts[0])
-        end = normalize_date(dts[-1])
+        start = dts[0].normalize()
+        end = dts[-1].normalize()
         adjs = {}
         if field != 'volume':
             mergers = self._adjustments_reader.get_adjustments_for_sid(

catalyst/data/loader.py

@@ -101,7 +101,7 @@ def load_crypto_market_data(trading_day=None, trading_days=None,
         trading_day = get_calendar('OPEN').trading_day
 
     # TODO: consider making configurable
-    bm_symbol = 'btc_usdt'
+    bm_symbol = 'btc_usd'
     # if trading_days is None:
     #    trading_days = get_calendar('OPEN').schedule
 
@@ -144,8 +144,9 @@ def load_crypto_market_data(trading_day=None, trading_days=None,
         #  breaks things and it's only needed here
         from catalyst.exchange.utils.factory import get_exchange
         exchange = get_exchange(
-            exchange_name='poloniex', base_currency='usdt'
+            exchange_name='bitfinex', base_currency='usd'
         )
+        exchange.init()
 
     benchmark_asset = exchange.get_asset(bm_symbol)
 

catalyst/data/us_equity_pricing.py

@@ -49,7 +49,6 @@ from pandas import (
     to_datetime,
     Timestamp,
 )
-from pandas.tslib import iNaT
 from six import (
     iteritems,
     string_types,
@@ -422,7 +421,7 @@ class BcolzDailyBarWriter(object):
         )
 
         full_table.attrs['first_trading_day'] = (
-            earliest_date if earliest_date is not None else iNaT
+            earliest_date if earliest_date is not None else NaT
         )
 
         full_table.attrs['first_row'] = first_row

catalyst/examples/buy_and_hodl.py

@@ -23,7 +23,7 @@ from catalyst.api import (order_target_value, symbol, record,
 
 
 def initialize(context):
-    context.ASSET_NAME = 'btc_usd'
+    context.ASSET_NAME = 'btc_usdt'
     context.TARGET_HODL_RATIO = 0.8
     context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
 
@@ -140,9 +140,9 @@ if __name__ == '__main__':
         initialize=initialize,
         handle_data=handle_data,
         analyze=analyze,
-        exchange_name='bitfinex',
+        exchange_name='poloniex',
         algo_namespace='buy_and_hodl',
-        base_currency='usd',
+        base_currency='usdt',
         start=pd.to_datetime('2015-03-01', utc=True),
         end=pd.to_datetime('2017-10-31', utc=True),
     )

catalyst/examples/buy_btc_simple.py

@@ -27,7 +27,7 @@ import pandas as pd
 
 
 def initialize(context):
-    context.asset = symbol('btc_usd')
+    context.asset = symbol('btc_usdt')
 
 
 def handle_data(context, data):
@@ -41,9 +41,9 @@ if __name__ == '__main__':
         data_frequency='daily',
         initialize=initialize,
         handle_data=handle_data,
-        exchange_name='bitfinex',
+        exchange_name='poloniex',
         algo_namespace='buy_and_hodl',
-        base_currency='usd',
+        base_currency='usdt',
         start=pd.to_datetime('2015-03-01', utc=True),
         end=pd.to_datetime('2017-10-31', utc=True),
     )

catalyst/examples/buy_low_sell_high.py

@@ -7,7 +7,6 @@ from catalyst.api import (
     order_target_percent,
     symbol,
     record,
-    get_open_orders,
 )
 from catalyst.exchange.utils.stats_utils import get_pretty_stats
 from catalyst.utils.run_algo import run_algorithm
@@ -60,7 +59,7 @@ def _handle_data(context, data):
         rsi=rsi,
     )
 
-    orders = get_open_orders(context.asset)
+    orders = context.blotter.open_orders
     if orders:
         log.info('skipping bar until all open orders execute')
         return
@@ -143,14 +142,14 @@ def analyze(context, stats):
 
 
 if __name__ == '__main__':
-    live = False
+    live = True
     if live:
         run_algorithm(
-            capital_base=0.001,
+            capital_base=1000,
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='binance',
+            exchange_name='bittrex',
             live=True,
             algo_namespace=algo_namespace,
             base_currency='btc',

catalyst/examples/dual_moving_average.py

@@ -4,8 +4,7 @@ import pandas as pd
 from logbook import Logger
 
 from catalyst import run_algorithm
-from catalyst.api import (record, symbol, order_target_percent,
-                          get_open_orders)
+from catalyst.api import (record, symbol, order_target_percent,)
 from catalyst.exchange.utils.stats_utils import extract_transactions
 
 NAMESPACE = 'dual_moving_average'
@@ -32,16 +31,18 @@ def handle_data(context, data):
     # moving average with the appropriate parameters. We choose to use
     # minute bars for this simulation -> freq="1m"
     # Returns a pandas dataframe.
-    short_mavg = data.history(context.asset,
+    short_data = data.history(context.asset,
                               'price',
                               bar_count=short_window,
-                              frequency="1m",
-                              ).mean()
-    long_mavg = data.history(context.asset,
+                              frequency="1T",
+                              )
+    short_mavg = short_data.mean()
+    long_data = data.history(context.asset,
                              'price',
                              bar_count=long_window,
-                             frequency="1m",
-                             ).mean()
+                             frequency="1T",
+                             )
+    long_mavg = long_data.mean()
 
     # Let's keep the price of our asset in a more handy variable
     price = data.current(context.asset, 'price')
@@ -61,7 +62,7 @@ def handle_data(context, data):
 
     # 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.asset)
+    orders = context.blotter.open_orders
     if len(orders) > 0:
         return
 
@@ -82,9 +83,9 @@ def handle_data(context, data):
 
 
 def analyze(context, perf):
-
     # Get the base_currency that was passed as a parameter to the simulation
-    base_currency = context.exchanges.values()[0].base_currency.upper()
+    exchange = list(context.exchanges.values())[0]
+    base_currency = exchange.base_currency.upper()
 
     # First chart: Plot portfolio value using base_currency
     ax1 = plt.subplot(411)
@@ -92,7 +93,7 @@ def analyze(context, perf):
     ax1.legend_.remove()
     ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
     start, end = ax1.get_ylim()
-    ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+    ax1.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
     # Second chart: Plot asset price, moving averages and buys/sells
     ax2 = plt.subplot(412, sharex=ax1)
@@ -103,9 +104,9 @@ def analyze(context, perf):
     ax2.set_ylabel('{asset}\n({base})'.format(
         asset=context.asset.symbol,
         base=base_currency
-        ))
+    ))
     start, end = ax2.get_ylim()
-    ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+    ax2.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
     transaction_df = extract_transactions(perf)
     if not transaction_df.empty:
@@ -135,19 +136,20 @@ def analyze(context, perf):
     ax3.legend_.remove()
     ax3.set_ylabel('Percent Change')
     start, end = ax3.get_ylim()
-    ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+    ax3.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
     # Fourth chart: Plot our cash
     ax4 = plt.subplot(414, sharex=ax1)
     perf.cash.plot(ax=ax4)
     ax4.set_ylabel('Cash\n({})'.format(base_currency))
     start, end = ax4.get_ylim()
-    ax4.yaxis.set_ticks(np.arange(0, end, end/5))
+    ax4.yaxis.set_ticks(np.arange(0, end, end / 5))
 
     plt.show()
 
 
 if __name__ == '__main__':
+    
     run_algorithm(
             capital_base=1000,
             data_frequency='minute',

catalyst/examples/marketplace/github-research.py

@@ -0,0 +1,70 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from catalyst import run_algorithm
+from catalyst.api import symbol, get_dataset
+
+START = '2017-01-01'
+END = '2017-12-31'
+
+
+def initialize(context):
+    pass
+
+
+def handle_data(context, data):
+    context.github = get_dataset('github')
+    context.github.sort_index(level=0, inplace=True)
+
+    context.zec = data.history(symbol('zec_usdt'),
+                               ['price', ],
+                               bar_count=365,
+                               frequency="1d")
+    context.xmr = data.history(symbol('xmr_usdt'),
+                               ['price', ],
+                               bar_count=365,
+                               frequency="1d")
+
+
+def analyze(context=None, results=None):
+    ax1 = plt.subplot(211)
+    idx = pd.IndexSlice
+    df = context.github.loc[START:END].loc[
+            idx[:, [b'ZEC']], ['commits']].reset_index(
+                level='symbol', drop=True)
+    df.plot(ax=ax1, color='blue')
+    ax1.legend(loc=2)
+    ax1.set_title('Zcash')
+    ax2 = ax1.twinx()
+    context.zec['price'].loc[START:END].plot(ax=ax2, color='green')
+    ax2.legend(loc=1)
+
+    ax3 = plt.subplot(212)
+    idx = pd.IndexSlice
+    df = context.github.loc[START:END].loc[
+            idx[:, [b'XMR']], ['commits']].reset_index(
+                level='symbol', drop=True)
+    df.plot(ax=ax3, color='blue')
+    ax3.legend(loc=2)
+    ax3.set_title('Monero')
+    ax4 = ax3.twinx()
+    context.xmr['price'].loc[START:END].plot(ax=ax4, color='green')
+    ax4.legend(loc=1)
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    run_algorithm(
+            capital_base=1000,
+            data_frequency='daily',
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='poloniex',
+            algo_namespace='algo-github',
+            base_currency='usdt',
+            live=False,
+            start=pd.to_datetime(END, utc=True),
+            end=pd.to_datetime(END, utc=True),
+        )

catalyst/examples/marketplace/mean_reversion_by_marketcap.py

@@ -0,0 +1,237 @@
+# 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.
+import os
+import tempfile
+import time
+
+import pandas as pd
+import talib
+from logbook import Logger
+
+from catalyst import run_algorithm
+from catalyst.api import symbol, record, order_target_percent, get_dataset
+from catalyst.exchange.utils.stats_utils import set_print_settings, \
+    get_pretty_stats
+# 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.utils.paths import ensure_directory
+
+NAMESPACE = 'mean_reversion_simple'
+log = Logger(NAMESPACE)
+
+
+# To run an algorithm in Catalyst, you need two functions: initialize and
+# handle_data.
+
+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 Neo in Ether.
+    df = get_dataset('testmarketcap2')  # type: pd.DataFrame
+
+    # Picking a specific date in our DataFrame
+    first_dt = df.index.get_level_values(0)[0]
+    # Since we use a MultiIndex with date / symbol, picking a date will
+    # result in a new DataFrame for the selected date with a single
+    # symbol index
+    df = df.xs(first_dt, level=0)
+    # Keep only the top coins by market cap
+    df = df.loc[df['market_cap_usd'].isin(df['market_cap_usd'].nlargest(100))]
+
+    set_print_settings()
+
+    df.sort_values(by=['market_cap_usd'], ascending=True, inplace=True)
+    print('the marketplace data:\n{}'.format(df))
+
+    # Pick the 5 assets with the lowest market cap for trading
+    quote_currency = 'eth'
+    exchange = context.exchanges[next(iter(context.exchanges))]
+    symbols = [a.symbol for a in exchange.assets
+               if a.start_date < context.datetime]
+    context.assets = []
+    for currency, price in df['market_cap_usd'].iteritems():
+        if len(context.assets) >= 5:
+            break
+
+        s = '{}_{}'.format(currency.decode('utf-8'), quote_currency)
+        if s in symbols:
+            context.assets.append(symbol(s))
+
+    context.base_price = None
+    context.current_day = None
+
+    context.RSI_OVERSOLD = 55
+    context.RSI_OVERBOUGHT = 60
+    context.CANDLE_SIZE = '5T'
+
+    context.start_time = time.time()
+
+
+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 = dict()
+        context.current_day = today
+
+    # Preparing dictionaries for asset-level data points
+    volumes = dict()
+    rsis = dict()
+    price_values = dict()
+    cash = context.portfolio.cash
+
+    for asset in context.assets:
+        # We're computing the volume-weighted-average-price of the security
+        # defined above, in the context.assets 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(
+            asset,
+            fields='close',
+            bar_count=50,
+            frequency=context.CANDLE_SIZE
+        )
+
+        # 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(asset, 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 asset not in context.base_price:
+        #     context.base_price[asset] = price
+        #
+        # base_price = context.base_price[asset]
+        # price_change = (price - base_price) / base_price
+
+        # Tracking the relevant data
+        volumes[asset] = current['volume']
+        rsis[asset] = rsi[-1]
+        price_values[asset] = price
+        # price_changes[asset] = price_change
+
+        # We are trying to avoid over-trading by limiting our trades to
+        # one per day.
+        if asset in context.traded_today:
+            continue
+
+        # Exit if we cannot trade
+        if not data.can_trade(asset):
+            continue
+
+        # 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[asset].amount
+
+        if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
+            log.info(
+                '{}: buying - price: {}, rsi: {}'.format(
+                    data.current_dt, price, rsi[-1]
+                )
+            )
+            # Set a style for limit orders,
+            limit_price = price * 1.005
+            target = 1.0 / len(context.assets)
+            order_target_percent(
+                asset, target, limit_price=limit_price
+            )
+            context.traded_today[asset] = True
+
+        elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
+            log.info(
+                '{}: selling - price: {}, rsi: {}'.format(
+                    data.current_dt, price, rsi[-1]
+                )
+            )
+            limit_price = price * 0.995
+            order_target_percent(
+                asset, 0, limit_price=limit_price
+            )
+            context.traded_today[asset] = True
+
+    # 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(
+        current_price=price_values,
+        volume=volumes,
+        rsi=rsis,
+        cash=cash,
+    )
+
+
+def analyze(context=None, perf=None):
+    stats = get_pretty_stats(perf)
+    print('the algo stats:\n{}'.format(stats))
+    pass
+
+
+if __name__ == '__main__':
+    # The execution mode: backtest or live
+    live = False
+
+    if live:
+        run_algorithm(
+            capital_base=0.1,
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='poloniex',
+            live=True,
+            algo_namespace=NAMESPACE,
+            base_currency='btc',
+            live_graph=False,
+            simulate_orders=False,
+            stats_output=None,
+        )
+
+    else:
+        folder = os.path.join(
+            tempfile.gettempdir(), 'catalyst', NAMESPACE
+        )
+        ensure_directory(folder)
+
+        timestr = time.strftime('%Y%m%d-%H%M%S')
+        out = os.path.join(folder, '{}.p'.format(timestr))
+        # catalyst run -f catalyst/examples/mean_reversion_simple.py \
+        #    -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion \
+        #   --data-frequency minute --capital-base 10000
+        run_algorithm(
+            capital_base=100,
+            data_frequency='minute',
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='poloniex',
+            algo_namespace=NAMESPACE,
+            base_currency='eth',
+            start=pd.to_datetime('2017-10-01', utc=True),
+            end=pd.to_datetime('2017-10-15', utc=True),
+        )
+        log.info('saved perf stats: {}'.format(out))

catalyst/examples/mean_reversion_simple.py

@@ -33,18 +33,18 @@ def initialize(context):
     # parameters or values you're going to use.
 
     # In our example, we're looking at Neo in Ether.
-    context.market = symbol('eth_btc')
+    context.market = symbol('bnb_eth')
     context.base_price = None
     context.current_day = None
 
-    context.RSI_OVERSOLD = 50
-    context.RSI_OVERBOUGHT = 65
-    context.CANDLE_SIZE = '5T'
+    context.RSI_OVERSOLD = 60
+    context.RSI_OVERBOUGHT = 70
+    context.CANDLE_SIZE = '15T'
 
     context.start_time = time.time()
 
-    # context.set_commission(maker=0.1, taker=0.2)
-    context.set_slippage(spread=0.0001)
+    context.set_commission(maker=0.001, taker=0.002)
+    context.set_slippage(spread=0.001)
 
 
 def handle_data(context, data):
@@ -114,7 +114,7 @@ def handle_data(context, data):
     # TODO: retest with open orders
     # 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.market)
+    orders = context.blotter.open_orders
     if len(orders) > 0:
         log.info('exiting because orders are open: {}'.format(orders))
         return
@@ -161,7 +161,7 @@ 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()
+    base_currency = list(context.exchanges.values())[0].base_currency.upper()
 
     # Plot the portfolio value over time.
     ax1 = plt.subplot(611)
@@ -244,21 +244,22 @@ def analyze(context=None, perf=None):
 
 if __name__ == '__main__':
     # The execution mode: backtest or live
-    live = False
+    live = True
 
     if live:
         run_algorithm(
-            capital_base=0.03,
+            capital_base=0.1,
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='poloniex',
+            exchange_name='binance',
             live=True,
             algo_namespace=NAMESPACE,
-            base_currency='btc',
+            base_currency='eth',
             live_graph=False,
             simulate_orders=False,
             stats_output=None,
+            # auth_aliases=dict(poloniex='auth2')
         )
 
     else:
@@ -273,14 +274,14 @@ if __name__ == '__main__':
         #    -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion \
         #   --data-frequency minute --capital-base 10000
         run_algorithm(
-            capital_base=0.1,
+            capital_base=0.035,
             data_frequency='minute',
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
             exchange_name='bitfinex',
             algo_namespace=NAMESPACE,
-            base_currency='eth',
+            base_currency='btc',
             start=pd.to_datetime('2017-10-01', utc=True),
             end=pd.to_datetime('2017-11-10', utc=True),
             output=out

catalyst/examples/mean_reversion_simple_custom_fees.py

@@ -0,0 +1,288 @@
+# 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.
+import os
+import tempfile
+import time
+
+import numpy as np
+import pandas as pd
+import talib
+from logbook import Logger
+
+from catalyst import run_algorithm
+from catalyst.api import symbol, record, order_target_percent, get_open_orders
+from catalyst.exchange.utils.stats_utils import extract_transactions
+# 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.utils.paths import ensure_directory
+
+NAMESPACE = 'mean_reversion_simple'
+log = Logger(NAMESPACE)
+
+
+# To run an algorithm in Catalyst, you need two functions: initialize and
+# handle_data.
+
+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 Neo in Ether.
+    context.market = symbol('eth_btc')
+    context.base_price = None
+    context.current_day = None
+
+    context.RSI_OVERSOLD = 50
+    context.RSI_OVERBOUGHT = 60
+    context.CANDLE_SIZE = '5T'
+
+    context.start_time = time.time()
+
+    context.set_commission(maker=0.001, taker=0.002)
+    # context.set_slippage(spread=0.001)
+
+
+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.market 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.market,
+        fields='close',
+        bar_count=50,
+        frequency=context.CANDLE_SIZE
+    )
+
+    # 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.market, 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(
+        volume=current['volume'],
+        price=price,
+        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
+
+    # TODO: retest with open orders
+    # 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.market)
+    if len(orders) > 0:
+        log.info('exiting because orders are open: {}'.format(orders))
+        return
+
+    # Exit if we cannot trade
+    if not data.can_trade(context.market):
+        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.market].amount
+
+    if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
+        log.info(
+            '{}: buying - price: {}, rsi: {}'.format(
+                data.current_dt, price, rsi[-1]
+            )
+        )
+        # Set a style for limit orders,
+        limit_price = price * 1.005
+        order_target_percent(
+            context.market, 1, limit_price=limit_price
+        )
+        context.traded_today = True
+
+    elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
+        log.info(
+            '{}: selling - price: {}, rsi: {}'.format(
+                data.current_dt, price, rsi[-1]
+            )
+        )
+        limit_price = price * 0.995
+        order_target_percent(
+            context.market, 0, limit_price=limit_price
+        )
+        context.traded_today = True
+
+
+def analyze(context=None, perf=None):
+    end = time.time()
+    log.info('elapsed time: {}'.format(end - context.start_time))
+
+    import matplotlib.pyplot as plt
+    # The base currency of the algo exchange
+    base_currency = list(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\nValue\n({})'.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}\n({base})'.format(
+        asset=context.market.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.floor('1 min'), 'price'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax2.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index.floor('1 min'), '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\n({})'.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\nChange')
+
+    ax6 = plt.subplot(615, sharex=ax1)
+    perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
+    ax6.set_ylabel('RSI')
+    ax6.axhline(context.RSI_OVERBOUGHT, color='darkgoldenrod')
+    ax6.axhline(context.RSI_OVERSOLD, color='darkgoldenrod')
+
+    if not transaction_df.empty:
+        ax6.scatter(
+            buy_df.index.to_pydatetime(),
+            perf.loc[buy_df.index.floor('1 min'), 'rsi'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax6.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index.floor('1 min'), 'rsi'],
+            marker='v',
+            s=100,
+            c='red',
+            label=''
+        )
+    plt.legend(loc=3)
+    start, end = ax6.get_ylim()
+    ax6.yaxis.set_ticks(np.arange(0, end, end / 5))
+
+    # Show the plot.
+    plt.gcf().set_size_inches(18, 8)
+    plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    # The execution mode: backtest or live
+    live = False
+
+    if live:
+        run_algorithm(
+            capital_base=0.025,
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='poloniex',
+            live=True,
+            algo_namespace=NAMESPACE,
+            base_currency='btc',
+            live_graph=False,
+            simulate_orders=False,
+            stats_output=None,
+        )
+
+    else:
+        folder = os.path.join(
+            tempfile.gettempdir(), 'catalyst', NAMESPACE
+        )
+        ensure_directory(folder)
+
+        timestr = time.strftime('%Y%m%d-%H%M%S')
+        out = os.path.join(folder, '{}.p'.format(timestr))
+        # catalyst run -f catalyst/examples/mean_reversion_simple.py \
+        #    -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion \
+        #   --data-frequency minute --capital-base 10000
+        run_algorithm(
+            capital_base=0.1,
+            data_frequency='minute',
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='bitfinex',
+            algo_namespace=NAMESPACE,
+            base_currency='eth',
+            start=pd.to_datetime('2017-10-01', utc=True),
+            end=pd.to_datetime('2017-11-10', utc=True),
+            output=out
+        )
+        log.info('saved perf stats: {}'.format(out))

catalyst/examples/portfolio_optimization.py

@@ -66,7 +66,7 @@ def handle_data(context, data):
         # Define portfolio optimization parameters
         n_portfolios = 50000
         results_array = np.zeros((3 + context.nassets, n_portfolios))
-        for p in xrange(n_portfolios):
+        for p in range(n_portfolios):
             weights = np.random.random(context.nassets)
             weights /= np.sum(weights)
             w = np.asmatrix(weights)
@@ -146,4 +146,5 @@ if __name__ == '__main__':
                             start=start,
                             end=end,
                             exchange_name='poloniex',
-                            capital_base=100000, )
+                            capital_base=100000,
+                            base_currency='usdt', )

catalyst/examples/rsi_profit_target.py

@@ -175,7 +175,7 @@ def handle_data(context, data):
 def analyze(context=None, results=None):
     import matplotlib.pyplot as plt
 
-    base_currency = context.exchanges.values()[0].base_currency.upper()
+    base_currency = list(context.exchanges.values())[0].base_currency.upper()
     # Plot the portfolio and asset data.
     ax1 = plt.subplot(611)
     results.loc[:, 'portfolio_value'].plot(ax=ax1)

catalyst/examples/simple_loop.py

@@ -57,7 +57,7 @@ def analyze(context, perf):
     log.info('the stats: {}'.format(get_pretty_stats(perf)))
 
     # The base currency of the algo exchange
-    base_currency = context.exchanges.values()[0].base_currency.upper()
+    base_currency = list(context.exchanges.values())[0].base_currency.upper()
 
     # Plot the portfolio value over time.
     ax1 = plt.subplot(611)
@@ -114,7 +114,7 @@ def analyze(context, perf):
 
 
 if __name__ == '__main__':
-    mode = 'backtest'
+    mode = 'live'
 
     if mode == 'backtest':
         run_algorithm(

catalyst/examples/simple_universe.py

@@ -41,8 +41,8 @@ from catalyst.exchange.utils.exchange_utils import get_exchange_symbols
 
 def initialize(context):
     context.i = -1  # minute counter
-    context.exchange = context.exchanges.values()[0].name.lower()
-    context.base_currency = context.exchanges.values()[0].base_currency.lower()
+    context.exchange = list(context.exchanges.values())[0].name.lower()
+    context.base_currency = list(context.exchanges.values())[0].base_currency.lower()
 
 
 def handle_data(context, data):
@@ -65,7 +65,7 @@ def handle_data(context, data):
     minutes = 30
 
     # get lookback_days of history data: that is 'lookback' number of bins
-    lookback = one_day_in_minutes / minutes * lookback_days
+    lookback = int(one_day_in_minutes / minutes * lookback_days)
     if not context.i % minutes and context.universe:
         # we iterate for every pair in the current universe
         for coin in context.coins:

catalyst/exchange/ccxt/ccxt_exchange.py

@@ -6,23 +6,29 @@ from collections import defaultdict
 import ccxt
 import pandas as pd
 import six
-from catalyst.assets._assets import TradingPair
-from ccxt import ExchangeNotAvailable, InvalidOrder
+from ccxt import InvalidOrder, NetworkError, \
+    ExchangeError
 from logbook import Logger
 from six import string_types
 
 from catalyst.algorithm import MarketOrder
+from catalyst.assets._assets import TradingPair
 from catalyst.constants import LOG_LEVEL
 from catalyst.exchange.exchange import Exchange
 from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import InvalidHistoryFrequencyError, \
     ExchangeSymbolsNotFound, ExchangeRequestError, InvalidOrderStyle, \
-    ExchangeNotFoundError, CreateOrderError, InvalidHistoryTimeframeError
+    ExchangeNotFoundError, CreateOrderError, InvalidHistoryTimeframeError, \
+    UnsupportedHistoryFrequencyError
 from catalyst.exchange.exchange_execution import ExchangeLimitOrder
 from catalyst.exchange.utils.exchange_utils import mixin_market_params, \
-    from_ms_timestamp, get_epoch, get_exchange_folder, get_catalyst_symbol, \
+    get_exchange_folder, get_catalyst_symbol, \
     get_exchange_auth
+from catalyst.exchange.utils.datetime_utils import from_ms_timestamp, \
+    get_epoch, \
+    get_periods_range
 from catalyst.finance.order import Order, ORDER_STATUS
+from catalyst.finance.transaction import Transaction
 
 log = Logger('CCXT', level=LOG_LEVEL)
 
@@ -37,7 +43,8 @@ SUPPORTED_EXCHANGES = dict(
 
 
 class CCXT(Exchange):
-    def __init__(self, exchange_name, key, secret, base_currency):
+    def __init__(self, exchange_name, key,
+                 secret, password, base_currency):
         log.debug(
             'finding {} in CCXT exchanges:\n{}'.format(
                 exchange_name, ccxt.exchanges
@@ -54,7 +61,9 @@ class CCXT(Exchange):
             self.api = exchange_attr({
                 'apiKey': key,
                 'secret': secret,
+                'password': password,
             })
+            self.api.enableRateLimit = True
 
         except Exception:
             raise ExchangeNotFoundError(exchange_name=exchange_name)
@@ -70,6 +79,7 @@ class CCXT(Exchange):
         self.max_requests_per_minute = 60
         self.low_balance_threshold = 0.1
         self.request_cpt = dict()
+        self._common_symbols = dict()
 
         self.bundle = ExchangeBundle(self.name)
         self.markets = None
@@ -105,7 +115,12 @@ class CCXT(Exchange):
                 with open(filename, 'w+') as f:
                     json.dump(self.markets, f, indent=4)
 
-            except ExchangeNotAvailable as e:
+            except (ExchangeError, NetworkError) as e:
+                log.warn(
+                    'unable to fetch markets {}: {}'.format(
+                        self.name, e
+                    )
+                )
                 raise ExchangeRequestError(error=e)
 
         self.load_assets()
@@ -175,6 +190,9 @@ class CCXT(Exchange):
                 if data_frequency == 'minute' and not freq.endswith('T'):
                     continue
 
+                elif data_frequency == 'hourly' and not freq.endswith('D'):
+                    continue
+
                 elif data_frequency == 'daily' and not freq.endswith('D'):
                     continue
 
@@ -210,6 +228,21 @@ class CCXT(Exchange):
         )
         return market
 
+    def substitute_currency_code(self, currency, source='catalyst'):
+        if source == 'catalyst':
+            currency = currency.upper()
+
+            key = self.api.common_currency_code(currency)
+            self._common_symbols[key] = currency.lower()
+            return key
+
+        else:
+            if currency in self._common_symbols:
+                return self._common_symbols[currency]
+
+            else:
+                return currency.lower()
+
     def get_symbol(self, asset_or_symbol, source='catalyst'):
         """
         The CCXT symbol.
@@ -217,6 +250,7 @@ class CCXT(Exchange):
         Parameters
         ----------
         asset_or_symbol
+        source
 
         Returns
         -------
@@ -226,7 +260,13 @@ class CCXT(Exchange):
         if source == 'ccxt':
             if isinstance(asset_or_symbol, string_types):
                 parts = asset_or_symbol.split('/')
-                return '{}_{}'.format(parts[0].lower(), parts[1].lower())
+                base_currency = self.substitute_currency_code(
+                    parts[0], source
+                )
+                quote_currency = self.substitute_currency_code(
+                    parts[1], source
+                )
+                return '{}_{}'.format(base_currency, quote_currency)
 
             else:
                 return asset_or_symbol.symbol
@@ -237,7 +277,13 @@ class CCXT(Exchange):
             ) else asset_or_symbol.symbol
 
             parts = symbol.split('_')
-            return '{}/{}'.format(parts[0].upper(), parts[1].upper())
+            base_currency = self.substitute_currency_code(
+                parts[0], source
+            )
+            quote_currency = self.substitute_currency_code(
+                parts[1], source
+            )
+            return '{}/{}'.format(base_currency, quote_currency)
 
     @staticmethod
     def map_frequency(value, source='ccxt', raise_error=True):
@@ -362,7 +408,7 @@ class CCXT(Exchange):
             timeframe, source='ccxt', raise_error=raise_error
         )
 
-    def get_candles(self, freq, assets, bar_count=None, start_dt=None,
+    def get_candles(self, freq, assets, bar_count=1, start_dt=None,
                     end_dt=None):
         is_single = (isinstance(assets, TradingPair))
         if is_single:
@@ -371,17 +417,39 @@ class CCXT(Exchange):
         symbols = self.get_symbols(assets)
         timeframe = CCXT.get_timeframe(freq)
 
-        ms = None
-        if start_dt is not None:
-            delta = start_dt - get_epoch()
-            ms = int(delta.total_seconds()) * 1000
+        if timeframe not in self.api.timeframes:
+            freqs = [CCXT.get_frequency(t) for t in self.api.timeframes]
+            raise UnsupportedHistoryFrequencyError(
+                exchange=self.name,
+                freq=freq,
+                freqs=freqs,
+            )
+
+        if start_dt is not None and end_dt is not None:
+            raise ValueError(
+                'Please provide either start_dt or end_dt, not both.'
+            )
+
+        if start_dt is None:
+            if end_dt is None:
+                end_dt = pd.Timestamp.utcnow()
+
+            dt_range = get_periods_range(
+                end_dt=end_dt,
+                periods=bar_count,
+                freq=freq,
+            )
+            start_dt = dt_range[0]
+
+        delta = start_dt - get_epoch()
+        since = int(delta.total_seconds()) * 1000
 
         candles = dict()
-        for asset in assets:
+        for index, asset in enumerate(assets):
             ohlcvs = self.api.fetch_ohlcv(
-                symbol=symbols[0],
+                symbol=symbols[index],
                 timeframe=timeframe,
-                since=ms,
+                since=since,
                 limit=bar_count,
                 params={}
             )
@@ -398,6 +466,9 @@ class CCXT(Exchange):
                     close=ohlcv[4],
                     volume=ohlcv[5]
                 ))
+            candles[asset] = sorted(
+                candles[asset], key=lambda c: c['last_traded']
+            )
 
         if is_single:
             return six.next(six.itervalues(candles))
@@ -408,6 +479,7 @@ class CCXT(Exchange):
     def _fetch_symbol_map(self, is_local):
         try:
             return self.fetch_symbol_map(is_local)
+
         except ExchangeSymbolsNotFound:
             return None
 
@@ -559,8 +631,12 @@ class CCXT(Exchange):
             for key in balances:
                 balances_lower[key.lower()] = balances[key]
 
-        except Exception as e:
-            log.debug('error retrieving balances: {}', e)
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to fetch balance {}: {}'.format(
+                    self.name, e
+                )
+            )
             raise ExchangeRequestError(error=e)
 
         return balances_lower
@@ -682,18 +758,23 @@ class CCXT(Exchange):
 
         side = 'buy' if amount > 0 else 'sell'
         if hasattr(self.api, 'amount_to_lots'):
-            adj_amount = self.api.amount_to_lots(
-                symbol=symbol,
-                amount=abs(amount),
-            )
-            if adj_amount != abs(amount):
-                log.info(
-                    'adjusted order amount {} to {} based on lot size'.format(
-                        abs(amount), adj_amount,
+            # TODO: is this right?
+            if self.api.markets is None:
+                self.api.load_markets()
+
+            # https://github.com/ccxt/ccxt/issues/1483
+            adj_amount = round(abs(amount), asset.decimals)
+            market = self.api.markets[symbol]
+            if 'lots' in market and market['lots'] > amount:
+                raise CreateOrderError(
+                    exchange=self.name,
+                    e='order amount lower than the smallest lot: {}'.format(
+                        amount
                     )
                 )
+
         else:
-            adj_amount = abs(amount)
+            adj_amount = round(abs(amount), asset.decimals)
 
         try:
             result = self.api.create_order(
@@ -703,14 +784,34 @@ class CCXT(Exchange):
                 amount=adj_amount,
                 price=price
             )
-        except ExchangeNotAvailable as e:
-            log.debug('unable to create order: {}'.format(e))
-            raise ExchangeRequestError(error=e)
-
         except InvalidOrder as e:
             log.warn('the exchange rejected the order: {}'.format(e))
             raise CreateOrderError(exchange=self.name, error=e)
 
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to create order {} / {}: {}'.format(
+                    self.name, symbol, e
+                )
+            )
+            raise ExchangeRequestError(error=e)
+
+        exchange_amount = None
+        if 'amount' in result and result['amount'] != adj_amount:
+            exchange_amount = result['amount']
+
+        elif 'info' in result:
+            if 'origQty' in result['info']:
+                exchange_amount = float(result['info']['origQty'])
+
+        if exchange_amount:
+            log.info(
+                'order amount adjusted by {} from {} to {}'.format(
+                    self.name, adj_amount, exchange_amount
+                )
+            )
+            adj_amount = exchange_amount
+
         if 'info' not in result:
             raise ValueError('cannot use order without info attribute')
 
@@ -735,18 +836,128 @@ class CCXT(Exchange):
                 limit=None,
                 params=dict()
             )
-        except Exception as e:
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to fetch open orders {} / {}: {}'.format(
+                    self.name, asset.symbol, e
+                )
+            )
             raise ExchangeRequestError(error=e)
 
         orders = []
         for order_status in result:
-            order, executed_price = self._create_order(order_status)
+            order, _ = self._create_order(order_status)
             if asset is None or asset == order.sid:
                 orders.append(order)
 
         return orders
 
-    def get_order(self, order_id, asset_or_symbol=None):
+    def _process_order_fallback(self, order):
+        """
+        Fallback method for exchanges which do not play nice with
+        fetch-my-trades. Apparently, about 60% of exchanges will return
+        the correct executed values with this method. Others will support
+        fetch-my-trades.
+
+        Parameters
+        ----------
+        order: Order
+
+        Returns
+        -------
+        float
+
+        """
+        exc_order, price = self.get_order(
+            order.id, order.asset, return_price=True
+        )
+        order.status = exc_order.status
+        order.commission = exc_order.commission
+        order.filled = exc_order.amount
+
+        transactions = []
+        if exc_order.status == ORDER_STATUS.FILLED:
+            if order.amount > exc_order.amount:
+                log.warn(
+                    'executed order amount {} differs '
+                    'from original'.format(
+                        exc_order.amount, order.amount
+                    )
+                )
+
+            order.check_triggers(
+                price=price,
+                dt=exc_order.dt,
+            )
+            transaction = Transaction(
+                asset=order.asset,
+                amount=order.amount,
+                dt=pd.Timestamp.utcnow(),
+                price=price,
+                order_id=order.id,
+                commission=order.commission,
+            )
+            transactions.append(transaction)
+
+        return transactions
+
+    def process_order(self, order):
+        # TODO: move to parent class after tracking features in the parent
+        if not self.api.has['fetchMyTrades']:
+            return self._process_order_fallback(order)
+
+        try:
+            all_trades = self.get_trades(order.asset)
+        except ExchangeRequestError as e:
+            log.warn(
+                'unable to fetch account trades, trying an alternate '
+                'method to find executed order {} / {}: {}'.format(
+                    order.id, order.asset.symbol, e
+                )
+            )
+            return self._process_order_fallback(order)
+
+        transactions = []
+        trades = [t for t in all_trades if t['order'] == order.id]
+        if not trades:
+            log.debug(
+                'order {} / {} not found in trades'.format(
+                    order.id, order.asset.symbol
+                )
+            )
+            return transactions
+
+        trades.sort(key=lambda t: t['timestamp'], reverse=False)
+        order.filled = 0
+        order.commission = 0
+        for trade in trades:
+            # status property will update automatically
+            filled = trade['amount'] * order.direction
+            order.filled += filled
+
+            commission = 0
+            if 'fee' in trade and 'cost' in trade['fee']:
+                commission = trade['fee']['cost']
+                order.commission += commission
+
+            order.check_triggers(
+                price=trade['price'],
+                dt=pd.to_datetime(trade['timestamp'], unit='ms', utc=True),
+            )
+            transaction = Transaction(
+                asset=order.asset,
+                amount=filled,
+                dt=pd.Timestamp.utcnow(),
+                price=trade['price'],
+                order_id=order.id,
+                commission=commission
+            )
+            transactions.append(transaction)
+
+        order.broker_order_id = ', '.join([t['id'] for t in trades])
+        return transactions
+
+    def get_order(self, order_id, asset_or_symbol=None, return_price=False):
         if asset_or_symbol is None:
             log.debug(
                 'order not found in memory, the request might fail '
@@ -758,12 +969,22 @@ class CCXT(Exchange):
             order_status = self.api.fetch_order(id=order_id, symbol=symbol)
             order, executed_price = self._create_order(order_status)
 
-        except Exception as e:
+            if return_price:
+                return order, executed_price
+
+            else:
+                return order
+
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to fetch order {} / {}: {}'.format(
+                    self.name, order_id, e
+                )
+            )
             raise ExchangeRequestError(error=e)
 
-        return order, executed_price
-
-    def cancel_order(self, order_param, asset_or_symbol=None):
+    def cancel_order(self, order_param,
+                     asset_or_symbol=None, params={}):
         order_id = order_param.id \
             if isinstance(order_param, Order) else order_param
 
@@ -775,12 +996,18 @@ class CCXT(Exchange):
         try:
             symbol = self.get_symbol(asset_or_symbol) \
                 if asset_or_symbol is not None else None
-            self.api.cancel_order(id=order_id, symbol=symbol)
+            self.api.cancel_order(id=order_id,
+                                  symbol=symbol, params= params)
 
-        except Exception as e:
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to cancel order {} / {}: {}'.format(
+                    self.name, order_id, e
+                )
+            )
             raise ExchangeRequestError(error=e)
 
-    def tickers(self, assets):
+    def tickers(self, assets, on_ticker_error='raise'):
         """
         Retrieve current tick data for the given assets
 
@@ -793,48 +1020,70 @@ class CCXT(Exchange):
         list[dict[str, float]
 
         """
-        tickers = dict()
-        try:
-            for asset in assets:
-                symbol = self.get_symbol(asset)
-                # TODO: use fetch_tickers() for efficiency
-                # I tried using fetch_tickers() but noticed some
-                # inconsistencies, see issue:
-                # https://github.com/ccxt/ccxt/issues/870
-                ticker = self.api.fetch_ticker(symbol=symbol)
-                if not ticker:
-                    log.warn('ticker not found for {} {}'.format(
-                        self.name, symbol
-                    ))
-                    continue
+        if len(assets) == 1:
+            try:
+                symbol = self.get_symbol(assets[0])
+                log.debug('fetching single ticker: {}'.format(symbol))
+                results = dict()
+                results[symbol] = self.api.fetch_ticker(symbol=symbol)
 
-                ticker['last_traded'] = from_ms_timestamp(ticker['timestamp'])
-
-                if 'last_price' not in ticker:
-                    # TODO: any more exceptions?
-                    ticker['last_price'] = ticker['last']
-
-                if 'baseVolume' in ticker and ticker['baseVolume'] is not None:
-                    # Using the volume represented in the base currency
-                    ticker['volume'] = ticker['baseVolume']
-
-                elif 'info' in ticker and 'bidQty' in ticker['info'] \
-                        and 'askQty' in ticker['info']:
-                    ticker['volume'] = float(ticker['info']['bidQty']) + \
-                                       float(ticker['info']['askQty'])
-
-                else:
-                    ticker['volume'] = 0
-
-                tickers[asset] = ticker
-
-        except ExchangeNotAvailable as e:
-            log.warn(
-                'unable to fetch ticker: {} {}'.format(
-                    self.name, asset.symbol
+            except (ExchangeError, NetworkError,) as e:
+                log.warn(
+                    'unable to fetch ticker {} / {}: {}'.format(
+                        self.name, symbol, e
+                    )
                 )
-            )
-            raise ExchangeRequestError(error=e)
+                raise ExchangeRequestError(error=e)
+
+        elif len(assets) > 1:
+            symbols = self.get_symbols(assets)
+            try:
+                log.debug('fetching multiple tickers: {}'.format(symbols))
+                results = self.api.fetch_tickers(symbols=symbols)
+
+            except (ExchangeError, NetworkError) as e:
+                log.warn(
+                    'unable to fetch tickers {} / {}: {}'.format(
+                        self.name, symbols, e
+                    )
+                )
+                raise ExchangeRequestError(error=e)
+        else:
+            raise ValueError('Cannot request tickers with not assets.')
+
+        tickers = dict()
+        for asset in assets:
+            symbol = self.get_symbol(asset)
+            if symbol not in results:
+                msg = 'ticker not found {} / {}'.format(
+                    self.name, symbol
+                )
+                log.warn(msg)
+                if on_ticker_error == 'warn':
+                    continue
+                else:
+                    raise ExchangeRequestError(error=msg)
+
+            ticker = results[symbol]
+            ticker['last_traded'] = from_ms_timestamp(ticker['timestamp'])
+
+            if 'last_price' not in ticker:
+                # TODO: any more exceptions?
+                ticker['last_price'] = ticker['last']
+
+            if 'baseVolume' in ticker and ticker['baseVolume'] is not None:
+                # Using the volume represented in the base currency
+                ticker['volume'] = ticker['baseVolume']
+
+            elif 'info' in ticker and 'bidQty' in ticker['info'] \
+                and 'askQty' in ticker['info']:
+                ticker['volume'] = float(ticker['info']['bidQty']) + \
+                                   float(ticker['info']['askQty'])
+
+            else:
+                ticker['volume'] = 0
+
+            tickers[asset] = ticker
 
         return tickers
 
@@ -864,3 +1113,27 @@ class CCXT(Exchange):
                 ))
 
         return result
+
+    def get_trades(self, asset, my_trades=True, start_dt=None, limit=100):
+        if not my_trades:
+            raise NotImplemented(
+                'get_trades only supports "my trades"'
+            )
+
+        # TODO: is it possible to sort this? Limit is useless otherwise.
+        ccxt_symbol = self.get_symbol(asset)
+        try:
+            trades = self.api.fetch_my_trades(
+                symbol=ccxt_symbol,
+                since=start_dt,
+                limit=limit,
+            )
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to fetch trades {} / {}: {}'.format(
+                    self.name, asset.symbol, e
+                )
+            )
+            raise ExchangeRequestError(error=e)
+
+        return trades

catalyst/exchange/exchange.py

@@ -5,20 +5,22 @@ from time import sleep
 
 import numpy as np
 import pandas as pd
-from logbook import Logger
-
 from catalyst.constants import LOG_LEVEL
 from catalyst.data.data_portal import BASE_FIELDS
 from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import MismatchingBaseCurrencies, \
     SymbolNotFoundOnExchange, \
     PricingDataNotLoadedError, \
-    NoDataAvailableOnExchange, NoValueForField, LastCandleTooEarlyError, \
+    NoDataAvailableOnExchange, NoValueForField, \
+    NoCandlesReceivedFromExchange, \
     TickerNotFoundError, NotEnoughCashError
-from catalyst.exchange.utils.bundle_utils import get_start_dt, \
-    get_delta, get_periods, get_periods_range
+from catalyst.exchange.utils.datetime_utils import get_delta, \
+    get_periods_range, \
+    get_periods, get_start_dt, get_frequency, \
+    get_candles_number_from_minutes
 from catalyst.exchange.utils.exchange_utils import get_exchange_symbols, \
-    get_frequency, resample_history_df, has_bundle
+    resample_history_df, has_bundle, get_candles_df
+from logbook import Logger
 
 log = Logger('Exchange', level=LOG_LEVEL)
 
@@ -178,6 +180,7 @@ class Exchange:
         if symbols is None:
             # Make a distinct list of all symbols
             symbols = list(set([asset.symbol for asset in self.assets]))
+            symbols.sort()
 
             if quote_currency is not None:
                 for symbol in symbols[:]:
@@ -196,12 +199,8 @@ class Exchange:
                 )
                 assets.append(asset)
 
-            except SymbolNotFoundOnExchange:
-                log.debug(
-                    'skipping non-existent market {} {}'.format(
-                        self.name, symbol
-                    )
-                )
+            except SymbolNotFoundOnExchange as e:
+                log.warn(e)
         return assets
 
     def get_asset(self, symbol, data_frequency=None, is_exchange_symbol=False,
@@ -234,11 +233,15 @@ class Exchange:
         """
         asset = None
 
+        # TODO: temp mapping, fix to use a single symbol convention
+        og_symbol = symbol
+        symbol = self.get_symbol(symbol) if not is_exchange_symbol else symbol
         log.debug(
             'searching assets for: {} {}'.format(
                 self.name, symbol
             )
         )
+        # TODO: simplify and loose the loop
         for a in self.assets:
             if asset is not None:
                 break
@@ -250,7 +253,8 @@ class Exchange:
             elif data_frequency is not None:
                 applies = (
                     (
-                        data_frequency == 'minute' and a.end_minute is not None)
+                        data_frequency == 'minute' and
+                        a.end_minute is not None)
                     or (
                         data_frequency == 'daily' and a.end_daily is not None)
                 )
@@ -260,7 +264,8 @@ class Exchange:
 
             # The symbol provided may use the Catalyst or the exchange
             # convention
-            key = a.exchange_symbol if is_exchange_symbol else a.symbol
+            key = a.exchange_symbol if \
+                is_exchange_symbol else self.get_symbol(a)
             if not asset and key.lower() == symbol.lower():
                 if applies:
                     asset = a
@@ -276,7 +281,7 @@ class Exchange:
             supported_symbols = sorted([a.symbol for a in self.assets])
 
             raise SymbolNotFoundOnExchange(
-                symbol=symbol,
+                symbol=og_symbol,
                 exchange=self.name.title(),
                 supported_symbols=supported_symbols
             )
@@ -434,7 +439,7 @@ class Exchange:
         series = pd.Series(values, index=dates)
 
         periods = get_periods_range(
-            start_dt, end_dt, data_frequency
+            start_dt=start_dt, end_dt=end_dt, freq=data_frequency
         )
         # TODO: ensure that this working as expected, if not use fillna
         series = series.reindex(
@@ -496,47 +501,62 @@ class Exchange:
 
         """
         freq, candle_size, unit, data_frequency = get_frequency(
-            frequency, data_frequency
+            frequency, data_frequency, supported_freqs=['T', 'D', 'H']
         )
-        adj_bar_count = candle_size * bar_count
 
-        start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency)
+        # we want to avoid receiving empty candles
+        # so we request more than needed
+        # TODO: consider defining a const per asset
+        # and/or some retry mechanism (in each iteration request more data)
+        kExtra_minutes_candles = 150
+        requested_bar_count = bar_count + \
+            get_candles_number_from_minutes(unit,
+                                            candle_size,
+                                            kExtra_minutes_candles)
 
         # The get_history method supports multiple asset
         candles = self.get_candles(
             freq=freq,
             assets=assets,
-            bar_count=bar_count,
-            start_dt=start_dt if not is_current else None,
+            bar_count=requested_bar_count,
             end_dt=end_dt if not is_current else None,
         )
 
-        series = dict()
+        # candles sanity check - verify no empty candles were received:
         for asset in candles:
-            asset_series = self.get_series_from_candles(
-                candles=candles[asset],
-                start_dt=start_dt,
-                end_dt=end_dt,
-                data_frequency=frequency,
-                field=field,
-            )
-            if end_dt is not None:
-                delta = get_delta(candle_size, data_frequency)
-                adj_end_dt = end_dt - delta
-                last_traded = asset_series.index[-1]
+            if not candles[asset]:
+                raise NoCandlesReceivedFromExchange(
+                    bar_count=requested_bar_count,
+                    end_dt=end_dt,
+                    asset=asset,
+                    exchange=self.name)
 
-                if last_traded < adj_end_dt:
-                    raise LastCandleTooEarlyError(
-                        last_traded=last_traded,
-                        end_dt=adj_end_dt,
-                        exchange=self.name,
-                    )
-            series[asset] = asset_series
+        # for avoiding unnecessary forward fill end_dt is taken back one second
+        forward_fill_till_dt = end_dt - timedelta(seconds=1)
+
+        series = get_candles_df(candles=candles,
+                                field=field,
+                                freq=frequency,
+                                bar_count=requested_bar_count,
+                                end_dt=forward_fill_till_dt)
+
+        # TODO: consider how to approach this edge case
+        # delta_candle_size = candle_size * 60 if unit == 'H' else candle_size
+        # Checking to make sure that the dates match
+        # delta = get_delta(delta_candle_size, data_frequency)
+        # adj_end_dt = end_dt - delta
+        # last_traded = asset_series.index[-1]
+        # if last_traded < adj_end_dt:
+        #    raise LastCandleTooEarlyError(
+        #        last_traded=last_traded,
+        #        end_dt=adj_end_dt,
+        #        exchange=self.name,
+        #    )
 
         df = pd.DataFrame(series)
         df.dropna(inplace=True)
 
-        return df
+        return df.tail(bar_count)
 
     def get_history_window_with_bundle(self,
                                        assets,
@@ -584,11 +604,12 @@ class Exchange:
             A dataframe containing the requested data.
 
         """
+        # TODO: this function needs some work,
+        # we're currently using it just for benchmark data
         freq, candle_size, unit, data_frequency = get_frequency(
-            frequency, data_frequency
+            frequency, data_frequency, supported_freqs=['T', 'D']
         )
         adj_bar_count = candle_size * bar_count
-
         try:
             series = self.bundle.get_history_window_series_and_load(
                 assets=assets,
@@ -610,20 +631,19 @@ class Exchange:
                 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
+                    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_bars = 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
+                    end_dt=end_dt,
+                    bar_count=trailing_bars if trailing_bars < 500 else 500,
                 )
 
                 last_value = series[asset].iloc(0) if asset in series \
@@ -661,7 +681,8 @@ class Exchange:
         else:
             return free, False
 
-    def sync_positions(self, positions, cash=None, check_balances=False):
+    def sync_positions(self, positions, cash=None,
+                       check_balances=False):
         """
         Update the portfolio cash and position balances based on the
         latest ticker prices.
@@ -699,8 +720,8 @@ class Exchange:
                     )
 
         positions_value = 0.0
-        if positions is not None:
-            assets = set([position.asset for position in positions])
+        if positions:
+            assets = list(set([position.asset for position in positions]))
             tickers = self.tickers(assets)
 
             for position in positions:
@@ -901,7 +922,24 @@ class Exchange:
         pass
 
     @abstractmethod
-    def cancel_order(self, order_param, symbol_or_asset=None):
+    def process_order(self, order):
+        """
+        Similar to get_order but looks only for executed orders.
+
+        Parameters
+        ----------
+        order: Order
+
+        Returns
+        -------
+        float
+            Avg execution price
+
+        """
+
+    @abstractmethod
+    def cancel_order(self, order_param,
+                     symbol_or_asset=None, params={}):
         """Cancel an open order.
 
         Parameters
@@ -910,12 +948,12 @@ class Exchange:
             The order_id or order object to cancel.
         symbol_or_asset: str|TradingPair
             The catalyst symbol, some exchanges need this
+        params:
         """
         pass
 
     @abstractmethod
-    def get_candles(self, freq, assets, bar_count=None,
-                    start_dt=None, end_dt=None):
+    def get_candles(self, freq, assets, bar_count, start_dt=None, end_dt=None):
         """
         Retrieve OHLCV candles for the given assets
 
@@ -955,13 +993,15 @@ class Exchange:
         pass
 
     @abc.abstractmethod
-    def tickers(self, assets):
+    def tickers(self, assets, on_ticker_error='raise'):
         """
         Retrieve current tick data for the given assets
 
         Parameters
         ----------
         assets: list[TradingPair]
+        on_ticker_error: str [raise|warn]
+            How to handle an error when retrieving a single ticker.
 
         Returns
         -------
@@ -980,7 +1020,7 @@ class Exchange:
     @abc.abstractmethod
     def get_orderbook(self, asset, order_type, limit):
         """
-        Retrieve the the orderbook for the given trading pair.
+        Retrieve the orderbook for the given trading pair.
 
         Parameters
         ----------
@@ -994,3 +1034,20 @@ class Exchange:
         list[dict[str, float]
         """
         pass
+
+    @abc.abstractmethod
+    def get_trades(self, asset, my_trades, start_dt, limit):
+        """
+        Retrieve a list of trades.
+
+        Parameters
+        ----------
+        my_trades: bool
+            List only my trades.
+        start_dt
+        limit
+
+        Returns
+        -------
+
+        """

catalyst/exchange/exchange_algorithm.py

@@ -16,13 +16,11 @@ import signal
 import sys
 from datetime import timedelta
 from os import listdir
-from os.path import isfile, join
-
-import logbook
-import pandas as pd
-from redo import retry
+from os.path import isfile, join, exists
 
 import catalyst.protocol as zp
+import logbook
+import pandas as pd
 from catalyst.algorithm import TradingAlgorithm
 from catalyst.constants import LOG_LEVEL
 from catalyst.exchange.exchange_blotter import ExchangeBlotter
@@ -38,17 +36,21 @@ from catalyst.exchange.utils.exchange_utils import (
     get_algo_folder,
     get_algo_df,
     save_algo_df,
+    clear_frame_stats_directory,
+    remove_old_files,
     group_assets_by_exchange, )
-from catalyst.exchange.utils.stats_utils import get_pretty_stats, stats_to_s3, \
-    stats_to_algo_folder
+from catalyst.exchange.utils.stats_utils import \
+    get_pretty_stats, stats_to_s3, stats_to_algo_folder
 from catalyst.finance.execution import MarketOrder
 from catalyst.finance.performance import PerformanceTracker
 from catalyst.finance.performance.period import calc_period_stats
 from catalyst.gens.tradesimulation import AlgorithmSimulator
+from catalyst.marketplace.marketplace import Marketplace
 from catalyst.utils.api_support import api_method
 from catalyst.utils.input_validation import error_keywords, ensure_upper_case
 from catalyst.utils.math_utils import round_nearest
 from catalyst.utils.preprocess import preprocess
+from redo import retry
 
 log = logbook.Logger('exchange_algorithm', level=LOG_LEVEL)
 
@@ -67,8 +69,8 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
 
         self.current_day = None
 
-        if self.simulate_orders is None \
-                and self.sim_params.arena == 'backtest':
+        if self.simulate_orders is None and \
+                self.sim_params.arena == 'backtest':
             self.simulate_orders = True
 
         # Operations with retry features
@@ -93,6 +95,8 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
             attempts=self.attempts,
         )
 
+        self._marketplace = None
+
     @staticmethod
     def __convert_order_params_for_blotter(limit_price, stop_price, style):
         """
@@ -130,7 +134,7 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
 
     @api_method
     def set_commission(self, maker=None, taker=None):
-        key = self.blotter.commission_models.keys()[0]
+        key = list(self.blotter.commission_models.keys())[0]
         if maker is not None:
             self.blotter.commission_models[key].maker = maker
 
@@ -139,7 +143,7 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
 
     @api_method
     def set_slippage(self, spread=None):
-        key = self.blotter.slippage_models.keys()[0]
+        key = list(self.blotter.slippage_models.keys())[0]
         if spread is not None:
             self.blotter.slippage_models[key].spread = spread
 
@@ -159,6 +163,25 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
                                                         style)
         return amount, style
 
+    def _calculate_order_target_amount(self, asset, target):
+        """
+        removes order amounts so we won't run into issues
+        when two orders are placed one after the other.
+        it then proceeds to removing positions amount at TradingAlgorithm
+        :param asset:
+        :param target:
+        :return: target
+        """
+        if asset in self.blotter.open_orders:
+            for open_order in self.blotter.open_orders[asset]:
+                current_amount = open_order.amount
+                target -= current_amount
+
+        target = super(ExchangeTradingAlgorithmBase, self). \
+            _calculate_order_target_amount(asset, target)
+
+        return target
+
     def round_order(self, amount, asset):
         """
         We need fractions with cryptocurrencies
@@ -168,6 +191,15 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
         """
         return round_nearest(amount, asset.min_trade_size)
 
+    @api_method
+    def get_dataset(self, data_source_name, start=None, end=None):
+        if self._marketplace is None:
+            self._marketplace = Marketplace()
+
+        return self._marketplace.get_dataset(
+            data_source_name, start, end,
+        )
+
     @api_method
     @preprocess(symbol_str=ensure_upper_case)
     def symbol(self, symbol_str, exchange_name=None):
@@ -271,9 +303,9 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
         # Merging latest recorded variables
         stats.update(self.recorded_vars)
 
-        stats['positions'] = cum.position_tracker.get_positions_list()
-
         period = tracker.todays_performance
+        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'] = []
@@ -304,6 +336,7 @@ class ExchangeTradingAlgorithmBacktest(ExchangeTradingAlgorithmBase):
         super(ExchangeTradingAlgorithmBacktest, self).__init__(*args, **kwargs)
 
         self.frame_stats = list()
+        self.state = {}
         log.info('initialized trading algorithm in backtest mode')
 
     def is_last_frame_of_day(self, data):
@@ -351,23 +384,40 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         self.live_graph = kwargs.pop('live_graph', None)
         self.stats_output = kwargs.pop('stats_output', None)
         self._analyze_live = kwargs.pop('analyze_live', None)
+        self.end = kwargs.pop('end', None)
 
         self._clock = None
         self.frame_stats = list()
 
-        self.pnl_stats = get_algo_df(self.algo_namespace, 'pnl_stats')
+        # erase the frame_stats folder to avoid overloading the disk
+        error = clear_frame_stats_directory(self.algo_namespace)
+        if error:
+            log.warning(error)
 
-        self.custom_signals_stats = \
-            get_algo_df(self.algo_namespace, 'custom_signals_stats')
+        # in order to save paper & live files separately
+        self.mode_name = 'paper' if kwargs['simulate_orders'] else 'live'
 
-        self.exposure_stats = \
-            get_algo_df(self.algo_namespace, 'exposure_stats')
+        self.pnl_stats = get_algo_df(
+                self.algo_namespace,
+                'pnl_stats_{}'.format(self.mode_name),
+            )
+
+        self.custom_signals_stats = get_algo_df(
+                self.algo_namespace,
+                'custom_signals_stats_{}'.format(self.mode_name)
+            )
+
+        self.exposure_stats = get_algo_df(
+                self.algo_namespace,
+                'exposure_stats_{}'.format(self.mode_name)
+            )
 
         self.is_running = True
 
         self.stats_minutes = 1
 
         self._last_orders = []
+        self._last_open_orders = []
         self.trading_client = None
 
         super(ExchangeTradingAlgorithmLive, self).__init__(*args, **kwargs)
@@ -378,9 +428,20 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             log.warn("Can't initialize signal handler inside another thread."
                      "Exit should be handled by the user.")
 
-        log.info('initialized trading algorithm in live mode')
-
     def interrupt_algorithm(self):
+        """
+
+        when algorithm comes to an end this function is called.
+        extracts the stats and calls analyze.
+        after finishing, it exits the run.
+
+        Parameters
+        ----------
+
+        Returns
+        -------
+
+        """
         self.is_running = False
 
         if self._analyze is None:
@@ -390,17 +451,31 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             log.info('Exiting the algorithm. Calling `analyze()` '
                      'before exiting the algorithm.')
 
+            # add the last day stats which is not saved in the directory
+            current_stats = pd.DataFrame(self.frame_stats)
+            current_stats.set_index('period_close', drop=False, inplace=True)
+
+            # get the location of the directory
             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))]
+            folder = join(algo_folder, 'frame_stats')
 
-            daily_perf_list = []
-            for item in files:
-                filename = join(folder, item)
-                with open(filename, 'rb') as handle:
-                    daily_perf_list.append(pickle.load(handle))
+            if exists(folder):
+                files = [f for f in listdir(folder) if isfile(join(folder, f))]
 
-            stats = pd.DataFrame(daily_perf_list)
+                period_stats_list = []
+                for item in files:
+                    filename = join(folder, item)
+
+                    with open(filename, 'rb') as handle:
+                        perf_period = pickle.load(handle)
+                        period_stats_list.extend(perf_period)
+
+                stats = pd.DataFrame(period_stats_list)
+                stats.set_index('period_close', drop=False, inplace=True)
+
+                stats = pd.concat([stats, current_stats])
+            else:
+                stats = current_stats
 
             self.analyze(stats)
 
@@ -460,43 +535,69 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
 
         return self._clock
 
-    def get_generator(self):
-        if self.trading_client is not None:
-            return self.trading_client.transform()
+    def _init_trading_client(self):
+        """
+        This replaces Ziplines `_create_generator` method. The main difference
+        is that we are restoring performance tracker objects if available.
+        This allows us to stop/start algos without loosing their state.
+
+        """
+        self.state = get_algo_object(
+            algo_name=self.algo_namespace,
+            key='context.state_{}'.format(self.mode_name),
+        )
+        if self.state is None:
+            self.state = {}
 
-        perf = None
         if self.perf_tracker is None:
+            # Note from the Zipline dev:
+            # HACK: When running with the `run` method, we set perf_tracker to
+            # None so that it will be overwritten here.
             tracker = self.perf_tracker = PerformanceTracker(
                 sim_params=self.sim_params,
                 trading_calendar=self.trading_calendar,
                 env=self.trading_environment,
             )
-
             # Set the dt initially to the period start by forcing it to change.
             self.on_dt_changed(self.sim_params.start_session)
 
+            new_position_tracker = tracker.position_tracker
+            tracker.position_tracker = None
+
             # Unpacking the perf_tracker and positions if available
-            perf = get_algo_object(
+            cum_perf = get_algo_object(
                 algo_name=self.algo_namespace,
-                key='cumulative_performance',
+                key='cumulative_performance_{}'.format(self.mode_name),
             )
+            if cum_perf is not None:
+                tracker.cumulative_performance = cum_perf
+                # Ensure single common position tracker
+                tracker.position_tracker = cum_perf.position_tracker
+
+            today = pd.Timestamp.utcnow().floor('1D')
+            todays_perf = get_algo_object(
+                algo_name=self.algo_namespace,
+                key=today.strftime('%Y-%m-%d'),
+                rel_path='daily_performance_{}'.format(self.mode_name),
+            )
+            if todays_perf is not None:
+                # Ensure single common position tracker
+                if tracker.position_tracker is not None:
+                    todays_perf.position_tracker = tracker.position_tracker
+                else:
+                    tracker.position_tracker = todays_perf.position_tracker
+
+                tracker.todays_performance = todays_perf
+
+            if tracker.position_tracker is None:
+                # Use a new position_tracker if not is found in the state
+                tracker.position_tracker = new_position_tracker
 
         if not self.initialized:
+            # Calls the initialize function of the algorithm
             self.initialize(*self.initialize_args, **self.initialize_kwargs)
             self.initialized = True
 
-        # Call the simulation trading algorithm for side-effects:
-        # it creates the perf tracker
-        # TradingAlgorithm._create_generator(self, self.sim_params)
-        if perf is not None:
-            tracker.cumulative_performance = perf
-
-            period = self.perf_tracker.todays_performance
-            period.starting_cash = perf.ending_cash
-            period.starting_exposure = perf.ending_exposure
-            period.starting_value = perf.ending_value
-            period.position_tracker = perf.position_tracker
-
         self.trading_client = ExchangeAlgorithmExecutor(
             algo=self,
             sim_params=self.sim_params,
@@ -506,6 +607,11 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             restrictions=self.restrictions,
             universe_func=self._calculate_universe,
         )
+
+    def get_generator(self):
+        if self.trading_client is None:
+            self._init_trading_client()
+
         return self.trading_client.transform()
 
     def updated_portfolio(self):
@@ -523,10 +629,6 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         positions, returning the available cash, and raising error
         if the data goes out of sync.
 
-        Parameters
-        ----------
-        attempt_index: int
-
         Returns
         -------
         float
@@ -559,10 +661,17 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             if base_currency is None:
                 base_currency = exchange.base_currency
 
+            orders = []
+            for asset in self.blotter.open_orders:
+                asset_orders = self.blotter.open_orders[asset]
+                if asset_orders:
+                    orders += asset_orders
+
+            required_cash = self.portfolio.cash if not orders else None
             cash, positions_value = exchange.sync_positions(
                 positions=exchange_positions,
                 check_balances=check_balances,
-                cash=self.portfolio.cash,
+                cash=required_cash,
             )
             total_cash += cash
             total_positions_value += positions_value
@@ -606,7 +715,11 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         )
         self.pnl_stats = pd.concat([self.pnl_stats, df])
 
-        save_algo_df(self.algo_namespace, 'pnl_stats', self.pnl_stats)
+        save_algo_df(
+            self.algo_namespace,
+            'pnl_stats_{}'.format(self.mode_name),
+            self.pnl_stats,
+        )
 
     def add_custom_signals_stats(self, period_stats):
         """
@@ -627,8 +740,11 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         )
         self.custom_signals_stats = pd.concat([self.custom_signals_stats, df])
 
-        save_algo_df(self.algo_namespace, 'custom_signals_stats',
-                     self.custom_signals_stats)
+        save_algo_df(
+            self.algo_namespace,
+            'custom_signals_stats_{}'.format(self.mode_name),
+            self.custom_signals_stats,
+        )
 
     def add_exposure_stats(self, period_stats):
         """
@@ -655,9 +771,43 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         self.exposure_stats = pd.concat([self.exposure_stats, df])
 
         save_algo_df(
-            self.algo_namespace, 'exposure_stats', self.exposure_stats
+            self.algo_namespace,
+            'exposure_stats_{}'.format(self.mode_name),
+            self.exposure_stats
         )
 
+    def nullify_frame_stats(self, now):
+        """
+
+        Save all period_stats to local directory
+        erase old files from the folder and nullify
+        self.frame_stats
+
+        Parameters
+        ----------
+        now: Timestamp
+
+        Returns
+        -------
+
+        """
+        save_algo_object(
+            algo_name=self.algo_namespace,
+            key=now.floor('1D').strftime('%Y-%m-%d'),
+            obj=self.frame_stats,
+            rel_path='frame_stats'
+        )
+
+        error = remove_old_files(
+            algo_name=self.algo_namespace,
+            today=now,
+            rel_path='frame_stats'
+        )
+        if error:
+            log.warning(error)
+
+        self.frame_stats = list()
+
     def handle_data(self, data):
         """
         Wrapper around the handle_data method of each algo.
@@ -670,17 +820,27 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         if not self.is_running:
             return
 
+        if self.end is not None and self.end < data.current_dt:
+            log.info('Algorithm has reached specified end time. Finishing...')
+            self.interrupt_algorithm()
+
         # Resetting the frame stats every day to minimize memory footprint
         today = data.current_dt.floor('1D')
         if self.current_day is not None and today > self.current_day:
-            self.frame_stats = list()
+            self.nullify_frame_stats(now=data.current_dt)
 
         self.performance_needs_update = False
-        new_orders = self.perf_tracker.todays_performance.orders_by_id.keys()
-        if new_orders != self._last_orders:
+        last_orders_list = list(self.blotter.orders.keys())
+        open_orders_list = list(self.blotter.open_orders.keys())
+
+        if last_orders_list != self._last_orders or \
+                open_orders_list != self._last_open_orders:
             self.performance_needs_update = True
 
-        self._last_orders = new_orders
+        # Saving current order positions
+        # to detect changes in the next frame
+        self._last_orders = copy.deepcopy(last_orders_list)
+        self._last_open_orders = copy.deepcopy(open_orders_list)
 
         if self.performance_needs_update:
             self.perf_tracker.update_performance()
@@ -697,7 +857,7 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             self.portfolio_needs_update = False
 
         log.info(
-            'got totals from exchanges, cash: {} positions: {}'.format(
+            'portfolio balances, cash: {}, positions: {}'.format(
                 cash, positions_value
             )
         )
@@ -709,18 +869,34 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         # every bar no matter if the algorithm places an order or not.
         self.validate_account_controls()
 
+        self._save_algo_state(data)
+        self.current_day = data.current_dt.floor('1D')
+
+    def _save_algo_state(self, data):
+        today = data.current_dt.floor('1D')
         try:
             self._save_stats_csv(self._process_stats(data))
         except Exception as e:
             log.warn('unable to calculate performance: {}'.format(e))
 
+        log.debug('saving cumulative performance object')
         save_algo_object(
             algo_name=self.algo_namespace,
-            key='cumulative_performance',
+            key='cumulative_performance_{}'.format(self.mode_name),
             obj=self.perf_tracker.cumulative_performance,
         )
-
-        self.current_day = data.current_dt.floor('1D')
+        log.debug('saving todays performance object')
+        save_algo_object(
+            algo_name=self.algo_namespace,
+            key=today.strftime('%Y-%m-%d'),
+            obj=self.perf_tracker.todays_performance,
+            rel_path='daily_performance_{}'.format(self.mode_name)
+        )
+        log.debug('saving context.state object')
+        save_algo_object(
+            algo_name=self.algo_namespace,
+            key='context.state_{}'.format(self.mode_name),
+            obj=self.state)
 
     def _process_stats(self, data):
         today = data.current_dt.floor('1D')
@@ -736,6 +912,8 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         # Saving the last hour in memory
         self.frame_stats.append(frame_stats)
 
+        # creating and saving the pnl_stats into the local
+        # directory
         self.add_pnl_stats(frame_stats)
         if self.recorded_vars:
             self.add_custom_signals_stats(frame_stats)
@@ -763,12 +941,6 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             start_dt=today,
             end_dt=data.current_dt
         )
-        save_algo_object(
-            algo_name=self.algo_namespace,
-            key=today.strftime('%Y-%m-%d'),
-            obj=daily_stats,
-            rel_path='daily_perf'
-        )
 
         return recorded_cols
 
@@ -779,6 +951,7 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             csv_bytes = stats_to_algo_folder(
                 stats=self.frame_stats,
                 algo_namespace=self.algo_namespace,
+                folder_name='stats_{}'.format(self.mode_name),
                 recorded_cols=recorded_cols,
             )
         except Exception as e:
@@ -806,6 +979,13 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         raise NotImplementedError()
 
     def _get_open_orders(self, asset=None):
+        if self.simulate_orders:
+            raise ValueError(
+                'The get_open_orders() method only works in live mode. '
+                'The purpose is to list open orders on the exchange '
+                'regardless who placed them. To list the open orders of '
+                'this algo, use `context.blotter.open_orders`.'
+            )
         if asset:
             exchange = self.exchanges[asset.exchange]
             return exchange.get_open_orders(asset)
@@ -839,13 +1019,15 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             If an asset is passed then this will return a list of the open
             orders for this asset.
         """
+        # TODO: should this be a shortcut to the open orders in the blotter?
         return retry(
             action=self._get_open_orders,
             attempts=self.attempts['get_open_orders_attempts'],
             sleeptime=self.attempts['retry_sleeptime'],
             retry_exceptions=(ExchangeRequestError,),
             cleanup=lambda: log.warn('Fetching open orders again.'),
-            args=(asset,))
+            args=(asset,)
+        )
 
     @api_method
     def get_order(self, order_id, exchange_name):
@@ -874,13 +1056,19 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             args=(order_id,))
 
     @api_method
-    def cancel_order(self, order_param, exchange_name):
+    def cancel_order(self, order_param, exchange_name,
+                     symbol=None, params={}):
         """Cancel an open order.
 
         Parameters
         ----------
         order_param : str or Order
             The order_id or order object to cancel.
+
+        exchange_name: name of exchange from
+                        which you want to cancel the order
+        symbol:
+        params:
         """
         exchange = self.exchanges[exchange_name]
 
@@ -894,4 +1082,4 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             sleeptime=self.attempts['retry_sleeptime'],
             retry_exceptions=(ExchangeRequestError,),
             cleanup=lambda: log.warn('cancelling order again.'),
-            args=(order_id,))
+            args=(order_id, symbol, params))

catalyst/exchange/exchange_asset_finder.py

@@ -1,8 +1,7 @@
 import pandas as pd
-from logbook import Logger
-
 from catalyst.constants import LOG_LEVEL
 from catalyst.exchange.utils.factory import find_exchanges
+from logbook import Logger
 
 log = Logger('ExchangeAssetFinder', level=LOG_LEVEL)
 

catalyst/exchange/exchange_blotter.py

@@ -1,8 +1,9 @@
+import numpy as np
 import pandas as pd
-from catalyst.assets._assets import TradingPair
 from logbook import Logger
 from redo import retry
 
+from catalyst.assets._assets import TradingPair
 from catalyst.constants import LOG_LEVEL
 from catalyst.exchange.exchange_errors import ExchangeRequestError
 from catalyst.finance.blotter import Blotter
@@ -42,6 +43,11 @@ class TradingPairFeeSchedule(CommissionModel):
             )
         )
 
+    def get_maker_taker(self, asset):
+        maker = self.maker if self.maker is not None else asset.maker
+        taker = self.taker if self.taker is not None else asset.taker
+        return maker, taker
+
     def calculate(self, order, transaction):
         """
         Calculate the final fee based on the order parameters.
@@ -55,15 +61,14 @@ class TradingPairFeeSchedule(CommissionModel):
         cost = abs(transaction.amount) * transaction.price
 
         asset = order.asset
-        maker = self.maker if self.maker is not None else asset.maker
-        taker = self.taker if self.taker is not None else asset.taker
+        maker, taker = self.get_maker_taker(asset)
 
         multiplier = taker
         if order.limit is not None:
             multiplier = maker \
                 if ((order.amount > 0 and order.limit < transaction.price)
                     or (order.amount < 0 and order.limit > transaction.price)) \
-                   and order.limit_reached else taker
+                and order.limit_reached else taker
 
         fee = cost * multiplier
         return fee
@@ -90,7 +95,6 @@ class TradingPairFixedSlippage(SlippageModel):
 
     def simulate(self, data, asset, orders_for_asset):
         self._volume_for_bar = 0
-
         price = data.current(asset, 'close')
 
         dt = data.current_dt
@@ -100,18 +104,20 @@ class TradingPairFixedSlippage(SlippageModel):
 
             order.check_triggers(price, dt)
             if not order.triggered:
-                log.debug('order has not reached the trigger at current '
-                          'price {}'.format(price))
+                log.info(
+                    'order has not reached the trigger at current '
+                    'price {}'.format(price)
+                )
                 continue
 
             execution_price, execution_volume = self.process_order(data, order)
+            if execution_price is not None:
+                transaction = create_transaction(
+                    order, dt, execution_price, execution_volume
+                )
 
-            transaction = create_transaction(
-                order, dt, execution_price, execution_volume
-            )
-
-            self._volume_for_bar += abs(transaction.amount)
-            yield order, transaction
+                self._volume_for_bar += abs(transaction.amount)
+                yield order, transaction
 
     def process_order(self, data, order):
         price = data.current(order.asset, 'close')
@@ -202,34 +208,29 @@ class ExchangeBlotter(Blotter):
             for order in self.open_orders[asset]:
                 log.debug('found open order: {}'.format(order.id))
 
-                new_order, executed_price = exchange.get_order(order.id, asset)
-                log.debug(
-                    'got updated order {} {}'.format(
-                        new_order, executed_price
+                transactions = exchange.process_order(order)
+                # This is a temporary measure, we should really update all
+                # trades, not just when the order gets filled. I just think
+                # that this is safer until we have a robust way to track
+                # the trades already processed by the algo. We can't loose
+                # them if the algo shuts down.
+                if transactions and order.status == ORDER_STATUS.FILLED:
+                    avg_price = np.average(
+                        a=[t.price for t in transactions],
+                        weights=[t.amount for t in transactions],
                     )
-                )
-                order.status = new_order.status
-
-                if order.status == ORDER_STATUS.FILLED:
-                    order.commission = new_order.commission
-                    if order.amount != new_order.amount:
-                        log.warn(
-                            'executed order amount {} differs '
-                            'from original'.format(
-                                new_order.amount, order.amount
-                            )
+                    ostatus = 'filled' if order.open_amount == 0 else 'partial'
+                    log.info(
+                        '{} order {} / {}: {}, avg price: {}'.format(
+                            ostatus,
+                            order.id,
+                            asset.symbol,
+                            order.filled,
+                            avg_price,
                         )
-                        order.amount = new_order.amount
-
-                    transaction = Transaction(
-                        asset=order.asset,
-                        amount=order.amount,
-                        dt=pd.Timestamp.utcnow(),
-                        price=executed_price,
-                        order_id=order.id,
-                        commission=order.commission
                     )
-                    yield order, transaction
+                    for transaction in transactions:
+                        yield order, transaction
 
                 elif order.status == ORDER_STATUS.CANCELLED:
                     yield order, None
@@ -237,9 +238,12 @@ class ExchangeBlotter(Blotter):
                 else:
                     delta = pd.Timestamp.utcnow() - order.dt
                     log.info(
-                        'order {order_id} still open after {delta}'.format(
+                        '{exchange} order {order_id} for {symbol} still open '
+                        'after {delta}'.format(
+                            exchange=exchange.name,
                             order_id=order.id,
-                            delta=delta
+                            delta=delta,
+                            symbol=order.asset.symbol,
                         )
                     )
 
@@ -250,7 +254,6 @@ class ExchangeBlotter(Blotter):
 
         for order, txn in self.check_open_orders():
             order.dt = txn.dt
-
             transactions.append(txn)
 
             if not order.open:

catalyst/exchange/exchange_bundle.py

@@ -8,12 +8,8 @@ from operator import is_not
 import numpy as np
 import pandas as pd
 import pytz
-from catalyst.assets._assets import TradingPair
-from logbook import Logger
-from pytz import UTC
-from six import itervalues
-
 from catalyst import get_calendar
+from catalyst.assets._assets import TradingPair
 from catalyst.constants import DATE_TIME_FORMAT, AUTO_INGEST
 from catalyst.constants import LOG_LEVEL
 from catalyst.data.minute_bars import BcolzMinuteOverlappingData, \
@@ -25,13 +21,16 @@ from catalyst.exchange.exchange_errors import EmptyValuesInBundleError, \
     NoDataAvailableOnExchange, \
     PricingDataNotLoadedError, DataCorruptionError, PricingDataValueError
 from catalyst.exchange.utils.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, \
-    get_delta, get_assets
+    get_bcolz_chunk, get_df_from_arrays, get_assets
+from catalyst.exchange.utils.datetime_utils import get_start_dt, \
+    get_period_label, get_month_start_end, get_year_start_end
 from catalyst.exchange.utils.exchange_utils import get_exchange_folder, \
     save_exchange_symbols, mixin_market_params, get_catalyst_symbol
 from catalyst.utils.cli import maybe_show_progress
 from catalyst.utils.paths import ensure_directory
+from logbook import Logger
+from pytz import UTC
+from six import itervalues
 
 log = Logger('exchange_bundle', level=LOG_LEVEL)
 
@@ -233,12 +232,12 @@ class ExchangeBundle:
 
                 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])
+                                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)
@@ -599,8 +598,9 @@ class ExchangeBundle:
         # 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(
+            if chunks:
+                for asset in chunks:
+                    with maybe_show_progress(
                         chunks[asset],
                         show_progress,
                         label='Ingesting {frequency} price data for '
@@ -608,6 +608,30 @@ class ExchangeBundle:
                             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
+            if all_chunks:
+                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(
@@ -618,30 +642,6 @@ class ExchangeBundle:
                             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(
@@ -831,7 +831,6 @@ class ExchangeBundle:
                                            field,
                                            data_frequency,
                                            algo_end_dt=None,
-                                           trailing_bar_count=None,
                                            force_auto_ingest=False
                                            ):
         """
@@ -859,7 +858,6 @@ class ExchangeBundle:
                     bar_count=bar_count,
                     field=field,
                     data_frequency=data_frequency,
-                    trailing_bar_count=trailing_bar_count,
                 )
                 return pd.DataFrame(series)
 
@@ -888,7 +886,6 @@ class ExchangeBundle:
                     field=field,
                     data_frequency=data_frequency,
                     reset_reader=True,
-                    trailing_bar_count=trailing_bar_count,
                 )
                 return series
 
@@ -899,7 +896,6 @@ class ExchangeBundle:
                 bar_count=bar_count,
                 field=field,
                 data_frequency=data_frequency,
-                trailing_bar_count=trailing_bar_count,
             )
             return pd.DataFrame(series)
 
@@ -963,17 +959,12 @@ class ExchangeBundle:
                                   bar_count,
                                   field,
                                   data_frequency,
-                                  trailing_bar_count=None,
                                   reset_reader=False):
         start_dt = get_start_dt(end_dt, bar_count, data_frequency, False)
         start_dt, _ = self.get_adj_dates(
             start_dt, end_dt, assets, data_frequency
         )
 
-        if trailing_bar_count:
-            delta = get_delta(trailing_bar_count, data_frequency)
-            end_dt += delta
-
         # This is an attempt to resolve some caching with the reader
         # when auto-ingesting data.
         # TODO: needs more work

catalyst/exchange/exchange_data_portal.py

@@ -3,17 +3,17 @@ import abc
 import numpy as np
 import pandas as pd
 from catalyst.assets._assets import TradingPair
-from logbook import Logger
-from redo import retry
-
 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,
     PricingDataNotLoadedError)
-from catalyst.exchange.utils.exchange_utils import get_frequency, \
-    resample_history_df, group_assets_by_exchange
+from catalyst.exchange.utils.exchange_utils import resample_history_df, \
+    group_assets_by_exchange
+from catalyst.exchange.utils.datetime_utils import get_frequency, get_start_dt
+from logbook import Logger
+from redo import retry
 
 log = Logger('DataPortalExchange', level=LOG_LEVEL)
 
@@ -292,13 +292,13 @@ class DataPortalExchangeBacktest(DataPortalExchangeBase):
         DataFrame
 
         """
+        # TODO: verify that the exchange supports the timeframe
         bundle = self.exchange_bundles[exchange_name]  # type: ExchangeBundle
 
         freq, candle_size, unit, adj_data_frequency = get_frequency(
-            frequency, data_frequency
+            frequency, data_frequency, supported_freqs=['T', 'D']
         )
         adj_bar_count = candle_size * bar_count
-        trailing_bar_count = candle_size - 1
 
         if data_frequency == 'minute' and adj_data_frequency == 'daily':
             end_dt = end_dt.floor('1D')
@@ -310,10 +310,10 @@ class DataPortalExchangeBacktest(DataPortalExchangeBase):
             field=field,
             data_frequency=adj_data_frequency,
             algo_end_dt=self._last_available_session,
-            trailing_bar_count=trailing_bar_count,
         )
 
-        df = resample_history_df(pd.DataFrame(series), freq, field)
+        start_dt = get_start_dt(end_dt, adj_bar_count, adj_data_frequency)
+        df = resample_history_df(pd.DataFrame(series), freq, field, start_dt)
         return df
 
     def get_exchange_spot_value(self,

catalyst/exchange/exchange_errors.py

@@ -100,6 +100,13 @@ class InvalidHistoryFrequencyError(ZiplineError):
     ).strip()
 
 
+class UnsupportedHistoryFrequencyError(ZiplineError):
+    msg = (
+        '{exchange} does not support candle frequency {freq}, please choose '
+        'from: {freqs}.'
+    ).strip()
+
+
 class InvalidHistoryTimeframeError(ZiplineError):
     msg = (
         'CCXT timeframe {timeframe} not supported by the exchange.'
@@ -315,3 +322,10 @@ class BalanceTooLowError(ZiplineError):
         'add positions to hold a free amount greater than {amount}, or clean '
         'the state of this algo and restart.'
     ).strip()
+
+
+class NoCandlesReceivedFromExchange(ZiplineError):
+    msg = (
+        'Although requesting {bar_count} candles until {end_dt} of asset {asset}, '
+        'an empty list of candles was received for {exchange}.'
+    ).strip()

catalyst/exchange/exchange_portfolio.py

@@ -1,8 +1,7 @@
 import numpy as np
-from logbook import Logger
-
 from catalyst.constants import LOG_LEVEL
 from catalyst.protocol import Portfolio, Positions, Position
+from logbook import Logger
 
 log = Logger('ExchangePortfolio', level=LOG_LEVEL)
 

catalyst/exchange/exchange_pricing_loader.py

@@ -11,12 +11,6 @@
 # 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 logbook import Logger
-from numpy import (
-    iinfo,
-    uint32,
-)
-
 from catalyst.constants import LOG_LEVEL
 from catalyst.data.us_equity_pricing import BcolzDailyBarReader
 from catalyst.errors import NoFurtherDataError
@@ -26,6 +20,11 @@ from catalyst.pipeline.data import DataSet, Column
 from catalyst.pipeline.loaders.base import PipelineLoader
 from catalyst.utils.calendars import get_calendar
 from catalyst.utils.numpy_utils import float64_dtype
+from logbook import Logger
+from numpy import (
+    iinfo,
+    uint32,
+)
 
 UINT32_MAX = iinfo(uint32).max
 

catalyst/exchange/live_graph_clock.py

@@ -1,13 +1,12 @@
 import pandas as pd
+from catalyst.constants import LOG_LEVEL
+from catalyst.exchange.utils.stats_utils import prepare_stats
 from catalyst.gens.sim_engine import (
     BAR,
     SESSION_START
 )
 from logbook import Logger
 
-from catalyst.constants import LOG_LEVEL
-from catalyst.exchange.utils.stats_utils import prepare_stats
-
 log = Logger('LiveGraphClock', level=LOG_LEVEL)
 
 

catalyst/exchange/simple_clock.py

@@ -14,14 +14,13 @@
 from time import sleep
 
 import pandas as pd
+from catalyst.constants import LOG_LEVEL
 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)
 
 

catalyst/exchange/utils/bundle_utils.py

@@ -1,11 +1,18 @@
-import calendar
 import os
 import tarfile
-from datetime import timedelta, datetime, date
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+
+from catalyst.data.bundles.core import download_without_progress
+from catalyst.exchange.utils.exchange_utils import get_exchange_bundles_folder
+import os
+import tarfile
+from datetime import datetime
 
 import numpy as np
 import pandas as pd
-import pytz
 
 from catalyst.data.bundles.core import download_without_progress
 from catalyst.exchange.utils.exchange_utils import get_exchange_bundles_folder
@@ -14,41 +21,6 @@ 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.
@@ -78,8 +50,8 @@ def get_bcolz_chunk(exchange_name, symbol, data_frequency, period):
     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)
+            exchange=exchange_name,
+            name=name)
 
         bytes = download_without_progress(url)
         with tarfile.open('r', fileobj=bytes) as tar:
@@ -88,178 +60,6 @@ def get_bcolz_chunk(exchange_name, symbol, data_frequency, period):
     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
-
-    """
-    if data_frequency == 'minute':
-        return '{}-{:02d}'.format(dt.year, dt.month)
-    else:
-        return '{}'.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.

catalyst/exchange/utils/datetime_utils.py

@@ -0,0 +1,362 @@
+import calendar
+import math
+import re
+from datetime import datetime, timedelta, date
+
+import pandas as pd
+import pytz
+
+from catalyst.exchange.exchange_errors import InvalidHistoryFrequencyError, \
+    InvalidHistoryFrequencyAlias
+
+
+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_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(freq, start_dt=None, end_dt=None, periods=None):
+    """
+    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'
+
+    if start_dt is not None and end_dt is not None and periods is None:
+
+        return pd.date_range(start_dt, end_dt, freq=freq)
+
+    elif periods is not None and (start_dt is not None or end_dt is not None):
+        _, unit_periods, unit, _ = get_frequency(freq)
+        adj_periods = periods * unit_periods
+
+        # TODO: standardize time aliases to avoid any mapping
+        unit = 'd' if unit == 'D' else 'h' if unit == 'H' else 'm'
+        delta = pd.Timedelta(adj_periods, unit)
+
+        if start_dt is not None:
+            return pd.date_range(
+                start=start_dt,
+                end=start_dt + delta,
+                freq=freq,
+                closed='left',
+            )
+
+        else:
+            return pd.date_range(
+                start=end_dt - delta,
+                end=end_dt,
+                freq=freq,
+            )
+
+    else:
+        raise ValueError(
+            'Choose only two parameters between start_dt, end_dt '
+            'and periods.'
+        )
+
+
+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=start_dt, end_dt=end_dt, freq=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
+    include_first
+
+    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
+
+    """
+    if data_frequency == 'minute':
+        return '{}-{:02d}'.format(dt.year, dt.month)
+    else:
+        return '{}'.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_frequency(freq, data_frequency=None, supported_freqs=['D', 'H', 'T']):
+    """
+    Takes an arbitrary candle size (e.g. 15T) and converts to the lowest
+    common denominator supported by the data bundles (e.g. 1T). The data
+    bundles only support 1T and 1D frequencies. If another frequency
+    is requested, Catalyst must request the underlying data and resample.
+
+    Notes
+    -----
+    We're trying to use Pandas convention for frequency aliases.
+
+    Parameters
+    ----------
+    freq: str
+    data_frequency: str
+
+    Returns
+    -------
+    str, int, str, str
+
+    """
+    if data_frequency is None:
+        data_frequency = 'daily' if freq.upper().endswith('D') else 'minute'
+
+    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)
+
+    # TODO: some exchanges support H and W frequencies but not bundles
+    # Find a way to pass-through these parameters to exchanges
+    # but resample from minute or daily in backtest mode
+    # see catalyst/exchange/ccxt/ccxt_exchange.py:242 for mapping between
+    # Pandas offet aliases (used by Catalyst) and the CCXT timeframes
+    if unit.lower() == 'd':
+        unit = 'D'
+        alias = '{}D'.format(candle_size)
+
+        if data_frequency == 'minute':
+            data_frequency = 'daily'
+
+    elif unit.lower() == 'm' or unit == 'T':
+        unit = 'T'
+        alias = '{}T'.format(candle_size)
+        data_frequency = 'minute'
+
+    elif unit.lower() == 'h':
+        data_frequency = 'minute'
+
+        if 'H' in supported_freqs:
+            unit = 'H'
+            alias = '{}H'.format(candle_size)
+        else:
+            candle_size = candle_size * 60
+            alias = '{}T'.format(candle_size)
+
+    else:
+        raise InvalidHistoryFrequencyAlias(freq=freq)
+
+    return alias, candle_size, unit, data_frequency
+
+
+def from_ms_timestamp(ms):
+    return pd.to_datetime(ms, unit='ms', utc=True)
+
+
+def get_epoch():
+    return pd.to_datetime('1970-1-1', utc=True)
+
+
+def get_candles_number_from_minutes(unit, candle_size, minutes):
+    """
+    Get the number of bars needed for the given time interval
+    in minutes.
+
+    Notes
+    -----
+    Supports only "T", "D" and "H" units
+
+    Parameters
+    ----------
+    unit: str
+    candle_size : int
+    minutes: int
+
+    Returns
+    -------
+    int
+
+    """
+    if unit == "T":
+        res = (float(minutes) / candle_size)
+    elif unit == "H":
+        res = (minutes / 60.0) / candle_size
+    else:  # unit == "D"
+        res = (minutes / 1440.0) / candle_size
+
+    return int(math.ceil(res))

catalyst/exchange/utils/exchange_utils.py

@@ -2,7 +2,6 @@ import hashlib
 import json
 import os
 import pickle
-import re
 import shutil
 from datetime import date, datetime
 
@@ -12,8 +11,7 @@ from six import string_types
 from six.moves.urllib import request
 
 from catalyst.constants import DATE_FORMAT, SYMBOLS_URL
-from catalyst.exchange.exchange_errors import ExchangeSymbolsNotFound, \
-    InvalidHistoryFrequencyError, InvalidHistoryFrequencyAlias
+from catalyst.exchange.exchange_errors import ExchangeSymbolsNotFound
 from catalyst.exchange.utils.serialization_utils import ExchangeJSONEncoder, \
     ExchangeJSONDecoder
 from catalyst.utils.paths import data_root, ensure_directory, \
@@ -128,9 +126,12 @@ def get_exchange_symbols(exchange_name, is_local=False, environ=None):
     filename = get_exchange_symbols_filename(exchange_name, is_local)
 
     if not is_local and (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)
+            pd.Timestamp('now', tz='UTC') - last_modified_time(
+            filename)).days > 1):
+        try:
+            download_exchange_symbols(exchange_name, environ)
+        except Exception:
+            pass
 
     if os.path.isfile(filename):
         with open(filename) as data_file:
@@ -190,7 +191,7 @@ def get_symbols_string(assets):
     return ', '.join([asset.symbol for asset in array])
 
 
-def get_exchange_auth(exchange_name, environ=None):
+def get_exchange_auth(exchange_name, alias=None, environ=None):
     """
     The de-serialized contend of the exchange's auth.json file.
 
@@ -205,7 +206,8 @@ def get_exchange_auth(exchange_name, environ=None):
 
     """
     exchange_folder = get_exchange_folder(exchange_name, environ)
-    filename = os.path.join(exchange_folder, 'auth.json')
+    name = 'auth' if alias is None else alias
+    filename = os.path.join(exchange_folder, '{}.json'.format(name))
 
     if os.path.isfile(filename):
         with open(filename) as data_file:
@@ -271,6 +273,7 @@ def get_algo_object(algo_name, key, environ=None, rel_path=None, how='pickle'):
     key: str
     environ:
     rel_path: str
+    how: str
 
     Returns
     -------
@@ -314,6 +317,7 @@ def save_algo_object(algo_name, key, obj, environ=None, rel_path=None,
     obj: Object
     environ:
     rel_path: str
+    how: str
 
     """
     folder = get_algo_folder(algo_name, environ)
@@ -390,6 +394,71 @@ def save_algo_df(algo_name, key, df, environ=None, rel_path=None):
         df.to_csv(handle, encoding='UTF_8')
 
 
+def clear_frame_stats_directory(algo_name):
+    """
+    remove the outdated directory
+    to avoid overloading the disk
+
+    Parameters
+    ----------
+    algo_name: str
+
+    Returns
+    -------
+    error: str
+
+    """
+    error = None
+    algo_folder = get_algo_folder(algo_name)
+    folder = os.path.join(algo_folder, 'frame_stats')
+    if os.path.exists(folder):
+        try:
+            shutil.rmtree(folder)
+        except OSError:
+            error = 'unable to remove {}, the analyze ' \
+                    'data will be inconsistent'.format(folder)
+    return error
+
+
+def remove_old_files(algo_name, today, rel_path, environ=None):
+    """
+    remove old files from a directory
+    to avoid overloading the disk
+
+    Parameters
+    ----------
+    algo_name: str
+    today: Timestamp
+    rel_path: str
+    environ:
+
+    Returns
+    -------
+    error: str
+
+    """
+
+    error = None
+    algo_folder = get_algo_folder(algo_name, environ)
+    folder = os.path.join(algo_folder, rel_path)
+    ensure_directory(folder)
+
+    # run on all files in the folder
+    for f in os.listdir(folder):
+        try:
+            file_path = os.path.join(folder, f)
+            creation_unix = os.path.getctime(file_path)
+            creation_time = pd.to_datetime(creation_unix, unit='s', utc=True)
+
+            # if the file is older than 30 days erase it
+            if today - pd.DateOffset(30) > creation_time:
+                os.unlink(file_path)
+        except OSError:
+            error = 'unable to erase files in {}'.format(folder)
+
+    return error
+
+
 def get_exchange_minute_writer_root(exchange_name, environ=None):
     """
     The minute writer folder for the exchange.
@@ -510,73 +579,7 @@ def get_common_assets(exchanges):
     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)
-
-    # TODO: some exchanges support H and W frequencies but not bundles
-    # Find a way to pass-through these parameters to exchanges
-    # but resample from minute or daily in backtest mode
-    # see catalyst/exchange/ccxt/ccxt_exchange.py:242 for mapping between
-    # Pandas offet aliases (used by Catalyst) and the CCXT timeframes
-    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):
+def resample_history_df(df, freq, field, start_dt=None):
     """
     Resample the OHCLV DataFrame using the specified frequency.
 
@@ -604,7 +607,16 @@ def resample_history_df(df, freq, field):
     else:
         raise ValueError('Invalid field.')
 
-    resampled_df = df.resample(freq).agg(agg)
+    resampled_df = df.resample(
+        freq, closed='left', label='left'
+    ).agg(agg)  # type: pd.DataFrame
+
+    # Because the samples are closed left, we get one more candle at
+    # the beginning then the requested number for bars. Removing this
+    # candle to avoid confusion.
+    if start_dt and not resampled_df.empty:
+        resampled_df = resampled_df[resampled_df.index >= start_dt]
+
     return resampled_df
 
 
@@ -630,8 +642,9 @@ def mixin_market_params(exchange_name, params, market):
         params['maker'] = 0.001
         params['taker'] = 0.002
 
-    elif 'maker' in market and 'taker' in market \
-            and market['maker'] is not None and market['taker'] is not None:
+    elif 'maker' in market and 'taker' in market and \
+            market['maker'] is not None and market['taker'] is not None:
+
         params['maker'] = market['maker']
         params['taker'] = market['taker']
 
@@ -649,14 +662,6 @@ def mixin_market_params(exchange_name, params, market):
                 params['lot'] = params['min_trade_size']
 
 
-def from_ms_timestamp(ms):
-    return pd.to_datetime(ms, unit='ms', utc=True)
-
-
-def get_epoch():
-    return pd.to_datetime('1970-1-1', utc=True)
-
-
 def group_assets_by_exchange(assets):
     exchange_assets = dict()
     for asset in assets:
@@ -711,23 +716,36 @@ def save_asset_data(folder, df, decimals=8):
             )
 
 
-def get_candles_df(candles, field, freq, bar_count, end_dt,
-                   previous_value=None):
+def forward_fill_df_if_needed(df, periods):
+    df = df.reindex(periods)
+    # volume should always be 0 (if there were no trades in this interval)
+    df['volume'] = df['volume'].fillna(0.0)
+    # ie pull the last close into this close
+    df['close'] = df.fillna(method='pad')
+    # now copy the close that was pulled down from the last timestep
+    # into this row, across into o/h/l
+    df['open'] = df['open'].fillna(df['close'])
+    df['low'] = df['low'].fillna(df['close'])
+    df['high'] = df['high'].fillna(df['close'])
+    return df
+
+
+def transform_candles_to_df(candles):
+    return pd.DataFrame(candles).set_index('last_traded')
+
+
+def get_candles_df(candles, field, freq, bar_count, end_dt):
     all_series = dict()
+
     for asset in candles:
-        periods = pd.date_range(end=end_dt, periods=bar_count, freq=freq)
+        asset_df = transform_candles_to_df(candles[asset])
+        rounded_end_dt = end_dt.floor(freq)
+        periods = pd.date_range(end=rounded_end_dt,
+                                periods=bar_count,
+                                freq=freq)
+        asset_df = forward_fill_df_if_needed(asset_df, periods)
 
-        dates = [candle['last_traded'] for candle in candles[asset]]
-        values = [candle[field] for candle in candles[asset]]
-        series = pd.Series(values, index=dates)
-
-        series = series.reindex(
-            periods,
-            method='ffill',
-            fill_value=previous_value,
-        )
-        series.sort_index(inplace=True)
-        all_series[asset] = series
+        all_series[asset] = pd.Series(asset_df[field])
 
     df = pd.DataFrame(all_series)
     df.dropna(inplace=True)

catalyst/exchange/utils/factory.py

@@ -1,25 +1,24 @@
 import os
 
-from logbook import Logger
-
 from catalyst.constants import LOG_LEVEL
 from catalyst.exchange.ccxt.ccxt_exchange import CCXT
 from catalyst.exchange.exchange import Exchange
 from catalyst.exchange.exchange_errors import ExchangeAuthEmpty
 from catalyst.exchange.utils.exchange_utils import get_exchange_auth, \
     get_exchange_folder, is_blacklist
+from logbook import Logger
 
 log = Logger('factory', level=LOG_LEVEL)
 exchange_cache = dict()
 
 
 def get_exchange(exchange_name, base_currency=None, must_authenticate=False,
-                 skip_init=False):
+                 skip_init=False, auth_alias=None):
     key = (exchange_name, base_currency)
     if key in exchange_cache:
         return exchange_cache[key]
 
-    exchange_auth = get_exchange_auth(exchange_name)
+    exchange_auth = get_exchange_auth(exchange_name, alias=auth_alias)
 
     has_auth = (exchange_auth['key'] != '' and exchange_auth['secret'] != '')
     if must_authenticate and not has_auth:
@@ -34,6 +33,8 @@ def get_exchange(exchange_name, base_currency=None, must_authenticate=False,
         exchange_name=exchange_name,
         key=exchange_auth['key'],
         secret=exchange_auth['secret'],
+        password=exchange_auth['password'] if 'password'
+                                              in exchange_auth.keys() else '',
         base_currency=base_currency,
     )
     exchange_cache[key] = exchange

catalyst/exchange/utils/serialization_utils.py

@@ -3,9 +3,8 @@ import re
 from json import JSONEncoder
 
 import pandas as pd
-from six import string_types
-
 from catalyst.constants import DATE_TIME_FORMAT
+from six import string_types
 
 
 class ExchangeJSONEncoder(json.JSONEncoder):

catalyst/exchange/utils/stats_utils.py

@@ -8,9 +8,9 @@ import time
 import numpy as np
 import pandas as pd
 from catalyst.assets._assets import TradingPair
-
 from catalyst.exchange.utils.exchange_utils import get_algo_folder
 from catalyst.utils.paths import data_root, ensure_directory
+from operator import itemgetter
 
 s3_conn = []
 mailgun = []
@@ -44,7 +44,7 @@ def crossover(source, target):
     """
     if isinstance(target, numbers.Number):
         if source[-1] is np.nan or source[-2] is np.nan \
-                or target is np.nan:
+            or target is np.nan:
             return False
 
         if source[-1] >= target > source[-2]:
@@ -54,7 +54,7 @@ def crossover(source, target):
 
     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:
+            or target[-1] is np.nan or target[-2] is np.nan:
             return False
 
         if source[-1] > target[-1] and source[-2] < target[-2]:
@@ -81,7 +81,7 @@ def crossunder(source, target):
     """
     if isinstance(target, numbers.Number):
         if source[-1] is np.nan or source[-2] is np.nan \
-                or target is np.nan:
+            or target is np.nan:
             return False
 
         if source[-1] < target <= source[-2]:
@@ -90,7 +90,7 @@ def crossunder(source, target):
             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:
+            or target[-1] is np.nan or target[-2] is np.nan:
             return False
 
         if source[-1] < target[-1] and source[-2] >= target[-2]:
@@ -229,7 +229,10 @@ def prepare_stats(stats, recorded_cols=list()):
                                               asset_values)
 
     df = pd.DataFrame(stats)
-
+    df['orders'] = df['orders'].apply(lambda orders: len(orders))
+    df['transactions'] = df['transactions'].apply(
+        lambda transactions: len(transactions)
+    )
     index_cols = [
         'period_close', 'starting_cash', 'ending_cash', 'portfolio_value',
         'pnl', 'long_exposure', 'short_exposure', 'orders', 'transactions',
@@ -241,11 +244,6 @@ def prepare_stats(stats, recorded_cols=list()):
         for column in recorded_cols:
             index_cols.append(column)
 
-    df['orders'] = df['orders'].apply(lambda orders: len(orders))
-    df['transactions'] = df['transactions'].apply(
-        lambda transactions: len(transactions)
-    )
-
     if asset_cols:
         columns = asset_cols
         df.set_index(index_cols, drop=True, inplace=True)
@@ -261,7 +259,14 @@ def prepare_stats(stats, recorded_cols=list()):
     return df, columns
 
 
-def get_pretty_stats(stats, recorded_cols=None, num_rows=10):
+def set_print_settings():
+    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)
+
+
+def get_pretty_stats(stats, recorded_cols=None, num_rows=10, show_tail=True):
     """
     Format and print the last few rows of a statistics DataFrame.
     See the pyfolio project for the data structure.
@@ -280,18 +285,18 @@ def get_pretty_stats(stats, recorded_cols=None, num_rows=10):
 
     """
     if isinstance(stats, pd.DataFrame):
-        stats = stats.T.to_dict().values()
+        stats = list(stats.T.to_dict().values())
+        stats.sort(key=itemgetter('period_close'))
+
+    if len(stats) > num_rows:
+        display_stats = stats[-num_rows:] if show_tail else stats[0:num_rows]
+    else:
+        display_stats = stats
 
-    display_stats = stats[-num_rows:] if len(stats) > num_rows else stats
     df, columns = prepare_stats(
         display_stats, recorded_cols=recorded_cols
     )
-
-    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)
-
+    set_print_settings()
     return df.to_string(columns=columns)
 
 
@@ -352,9 +357,13 @@ def stats_to_s3(uri, stats, algo_namespace, recorded_cols=None,
     pid = os.getpid()
 
     parts = uri.split('//')
-    obj = s3.Object(parts[1], '{}/{}-{}-{}.csv'.format(
-        folder, timestr, algo_namespace, pid
-    ))
+    path = '{folder}/{algo}/{time}-{algo}-{pid}.csv'.format(
+        folder=folder,
+        algo=algo_namespace,
+        time=timestr,
+        pid=pid,
+    )
+    obj = s3.Object(parts[1], path)
     obj.put(Body=bytes_to_write)
 
 
@@ -387,7 +396,8 @@ def email_error(algo_name, dt, e, environ=None):
             )})
 
 
-def stats_to_algo_folder(stats, algo_namespace, recorded_cols=None):
+def stats_to_algo_folder(stats, algo_namespace,
+                         folder_name, recorded_cols=None):
     """
     Saves the performance stats to the algo local folder.
 
@@ -395,6 +405,7 @@ def stats_to_algo_folder(stats, algo_namespace, recorded_cols=None):
     ----------
     stats: list[Object]
     algo_namespace: str
+    folder_name: str
     recorded_cols: list[str]
 
     Returns
@@ -407,7 +418,7 @@ def stats_to_algo_folder(stats, algo_namespace, recorded_cols=None):
     timestr = time.strftime('%Y%m%d')
     folder = get_algo_folder(algo_namespace)
 
-    stats_folder = os.path.join(folder, 'stats')
+    stats_folder = os.path.join(folder, folder_name)
     ensure_directory(stats_folder)
 
     filename = os.path.join(stats_folder, '{}.csv'.format(timestr))
@@ -439,6 +450,17 @@ def df_to_string(df):
     return df.to_string()
 
 
+def extract_orders(perf):
+    order_list = perf.orders.values
+    all_orders = [t for sublist in order_list for t in sublist]
+    all_orders.sort(key=lambda o: o['dt'])
+
+    orders = pd.DataFrame(all_orders)
+    if not orders.empty:
+        orders.set_index('dt', inplace=True, drop=True)
+    return orders
+
+
 def extract_transactions(perf):
     """
     Compute indexes for buy and sell transactions

catalyst/exchange/utils/test_utils.py

@@ -3,7 +3,6 @@ import random
 import tempfile
 
 from catalyst.assets._assets import TradingPair
-
 from catalyst.exchange.utils.exchange_utils import get_exchange_folder
 from catalyst.exchange.utils.factory import find_exchanges
 from catalyst.utils.paths import ensure_directory
@@ -63,14 +62,14 @@ def output_df(df, assets, name=None):
 
     """
     if isinstance(assets, TradingPair):
-        exchange_folder = assets.exchange
-        asset_folder = assets.symbol
+        asset_folder = '{}_{}'.format(assets.exchange, assets.symbol)
     else:
-        exchange_folder = ','.join([asset.exchange for asset in assets])
-        asset_folder = ','.join([asset.symbol for asset in assets])
+        asset_folder = ','.join(
+            ['{}_{}'.format(a.exchange, a.symbol) for a in assets]
+        )
 
     folder = os.path.join(
-        tempfile.gettempdir(), 'catalyst', exchange_folder, asset_folder
+        tempfile.gettempdir(), 'catalyst', asset_folder
     )
     ensure_directory(folder)
 
@@ -80,4 +79,4 @@ def output_df(df, assets, name=None):
     path = os.path.join(folder, '{}.csv'.format(name))
     df.to_csv(path)
 
-    return path
+    return path, folder

catalyst/finance/performance/tracker.py

@@ -62,7 +62,6 @@ from __future__ import division
 import logbook
 
 import pandas as pd
-from pandas.tseries.tools import normalize_date
 
 from catalyst.finance.performance.period import PerformancePeriod
 from catalyst.errors import NoFurtherDataError
@@ -344,7 +343,7 @@ class PerformanceTracker(object):
         """
         self.position_tracker.sync_last_sale_prices(dt, False, data_portal)
         self.update_performance()
-        todays_date = normalize_date(dt)
+        todays_date = dt.normalize()
         account = self.get_account(False)
 
         bench_returns = self.all_benchmark_returns.loc[todays_date:dt]

catalyst/finance/risk/cumulative.py

@@ -18,7 +18,6 @@ import logbook
 import numpy as np
 
 import pandas as pd
-from pandas.tseries.tools import normalize_date
 
 from six import iteritems
 
@@ -80,7 +79,7 @@ class RiskMetricsCumulative(object):
         # on the first day.
         self.day_before_start = self.start_session - self.sessions.freq
 
-        last_day = normalize_date(sim_params.end_session)
+        last_day = sim_params.end_session.normalize()
         if last_day not in self.sessions:
             last_day = pd.tseries.index.DatetimeIndex(
                 [last_day]

catalyst/finance/risk/period.py

@@ -29,13 +29,15 @@ from .risk import check_entry
 from empyrical import (
     alpha_beta_aligned,
     annual_volatility,
-    cum_returns,
     downside_risk,
     information_ratio,
-    max_drawdown,
     sharpe_ratio,
     sortino_ratio
 )
+from catalyst.patches.stats import (
+    max_drawdown,
+    cum_returns,
+)
 
 from catalyst.constants import LOG_LEVEL
 

catalyst/finance/trading.py

@@ -16,7 +16,6 @@ from functools import partial
 
 import logbook
 import pandas as pd
-from pandas.tslib import normalize_date
 from six import string_types
 from sqlalchemy import create_engine
 
@@ -95,11 +94,24 @@ class TradingEnvironment(object):
         if not trading_calendar:
             trading_calendar = get_calendar("NYSE")
 
-        self.benchmark_returns, self.treasury_curves = load(
-            trading_calendar.day,
-            trading_calendar.schedule.index,
-            self.bm_symbol,
-        )
+        # todo: uncomment and add a well defined benchmark
+        # self.benchmark_returns, self.treasury_curves = load(
+        #     trading_calendar.day,
+        #     trading_calendar.schedule.index,
+        #     self.bm_symbol,
+        #     exchange=exchange,
+        # )
+
+        start_data = get_calendar('OPEN').first_trading_session
+        end_data = pd.Timestamp.utcnow()
+        treasure_cols = ['1month', '3month', '6month', '1year', '2year',
+                         '3year', '5year', '7year', '10year', '20year', '30year']
+        self.benchmark_returns = pd.DataFrame(data=0.001,
+                                              index=pd.date_range(start_data, end_data),
+                                              columns=['close'])
+        self.treasury_curves = pd.DataFrame(data=0.001,
+                                            index=pd.date_range(start_data, end_data),
+                                            columns=treasure_cols)
 
         self.exchange_tz = exchange_tz
 
@@ -151,8 +163,8 @@ class SimulationParameters(object):
         # chop off any minutes or hours on the given start and end dates,
         # as we only support session labels here (and we represent session
         # labels as midnight UTC).
-        self._start_session = normalize_date(start_session)
-        self._end_session = normalize_date(end_session)
+        self._start_session = start_session.normalize()
+        self._end_session = end_session.normalize()
         self._capital_base = capital_base
 
         self._emission_rate = emission_rate

catalyst/gens/tradesimulation.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 from contextlib2 import ExitStack
 from logbook import Logger, Processor
-from pandas.tslib import normalize_date
 from catalyst.protocol import BarData
 from catalyst.utils.api_support import ZiplineAPI
 from six import viewkeys
@@ -229,7 +228,7 @@ class AlgorithmSimulator(object):
                 elif action == SESSION_END:
                     # End of the session.
                     if emission_rate == 'daily':
-                        handle_benchmark(normalize_date(dt))
+                        handle_benchmark(dt).normalize()
                     execute_order_cancellation_policy()
 
                     yield self._get_daily_message(dt, algo, algo.perf_tracker)

catalyst/marketplace/contract_enigma_abi.json

@@ -0,0 +1,302 @@
+[
+    {
+      "constant": true,
+      "inputs": [],
+      "name": "name",
+      "outputs": [
+        {
+          "name": "",
+          "type": "string"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "view",
+      "type": "function"
+    },
+    {
+      "constant": false,
+      "inputs": [
+        {
+          "name": "_spender",
+          "type": "address"
+        },
+        {
+          "name": "_value",
+          "type": "uint256"
+        }
+      ],
+      "name": "approve",
+      "outputs": [
+        {
+          "name": "",
+          "type": "bool"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "nonpayable",
+      "type": "function"
+    },
+    {
+      "constant": true,
+      "inputs": [],
+      "name": "totalSupply",
+      "outputs": [
+        {
+          "name": "",
+          "type": "uint256"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "view",
+      "type": "function"
+    },
+    {
+      "constant": false,
+      "inputs": [
+        {
+          "name": "_from",
+          "type": "address"
+        },
+        {
+          "name": "_to",
+          "type": "address"
+        },
+        {
+          "name": "_value",
+          "type": "uint256"
+        }
+      ],
+      "name": "transferFrom",
+      "outputs": [
+        {
+          "name": "",
+          "type": "bool"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "nonpayable",
+      "type": "function"
+    },
+    {
+      "constant": true,
+      "inputs": [],
+      "name": "INITIAL_SUPPLY",
+      "outputs": [
+        {
+          "name": "",
+          "type": "uint256"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "view",
+      "type": "function"
+    },
+    {
+      "constant": true,
+      "inputs": [],
+      "name": "decimals",
+      "outputs": [
+        {
+          "name": "",
+          "type": "uint8"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "view",
+      "type": "function"
+    },
+    {
+      "constant": false,
+      "inputs": [
+        {
+          "name": "_spender",
+          "type": "address"
+        },
+        {
+          "name": "_subtractedValue",
+          "type": "uint256"
+        }
+      ],
+      "name": "decreaseApproval",
+      "outputs": [
+        {
+          "name": "success",
+          "type": "bool"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "nonpayable",
+      "type": "function"
+    },
+    {
+      "constant": false,
+      "inputs": [],
+      "name": "getAfterApproveTest",
+      "outputs": [
+        {
+          "name": "",
+          "type": "uint256"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "nonpayable",
+      "type": "function"
+    },
+    {
+      "constant": true,
+      "inputs": [
+        {
+          "name": "_owner",
+          "type": "address"
+        }
+      ],
+      "name": "balanceOf",
+      "outputs": [
+        {
+          "name": "balance",
+          "type": "uint256"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "view",
+      "type": "function"
+    },
+    {
+      "constant": true,
+      "inputs": [],
+      "name": "symbol",
+      "outputs": [
+        {
+          "name": "",
+          "type": "string"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "view",
+      "type": "function"
+    },
+    {
+      "constant": false,
+      "inputs": [
+        {
+          "name": "_to",
+          "type": "address"
+        },
+        {
+          "name": "_value",
+          "type": "uint256"
+        }
+      ],
+      "name": "transfer",
+      "outputs": [
+        {
+          "name": "",
+          "type": "bool"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "nonpayable",
+      "type": "function"
+    },
+    {
+      "constant": false,
+      "inputs": [
+        {
+          "name": "_spender",
+          "type": "address"
+        },
+        {
+          "name": "_addedValue",
+          "type": "uint256"
+        }
+      ],
+      "name": "increaseApproval",
+      "outputs": [
+        {
+          "name": "success",
+          "type": "bool"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "nonpayable",
+      "type": "function"
+    },
+    {
+      "constant": true,
+      "inputs": [
+        {
+          "name": "_owner",
+          "type": "address"
+        },
+        {
+          "name": "_spender",
+          "type": "address"
+        }
+      ],
+      "name": "allowance",
+      "outputs": [
+        {
+          "name": "",
+          "type": "uint256"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "view",
+      "type": "function"
+    },
+    {
+      "inputs": [
+        {
+          "name": "testValue",
+          "type": "address"
+        }
+      ],
+      "payable": false,
+      "stateMutability": "nonpayable",
+      "type": "constructor"
+    },
+    {
+      "anonymous": false,
+      "inputs": [
+        {
+          "indexed": true,
+          "name": "owner",
+          "type": "address"
+        },
+        {
+          "indexed": true,
+          "name": "spender",
+          "type": "address"
+        },
+        {
+          "indexed": false,
+          "name": "value",
+          "type": "uint256"
+        }
+      ],
+      "name": "Approval",
+      "type": "event"
+    },
+    {
+      "anonymous": false,
+      "inputs": [
+        {
+          "indexed": true,
+          "name": "from",
+          "type": "address"
+        },
+        {
+          "indexed": true,
+          "name": "to",
+          "type": "address"
+        },
+        {
+          "indexed": false,
+          "name": "value",
+          "type": "uint256"
+        }
+      ],
+      "name": "Transfer",
+      "type": "event"
+    }
+  ]

catalyst/marketplace/contract_enigma_address.txt

@@ -0,0 +1 @@
+0xf0ee6b27b759c9893ce4f094b49ad28fd15a23e4

catalyst/marketplace/contract_marketplace_address.txt

@@ -0,0 +1 @@
+0xa64927358a82254be92eb1f1cb01de68d1787004

catalyst/marketplace/marketplace.py

@@ -0,0 +1,814 @@
+from __future__ import print_function
+
+import glob
+import json
+import os
+import re
+import shutil
+import sys
+import time
+import webbrowser
+
+import bcolz
+import logbook
+import pandas as pd
+import requests
+from requests_toolbelt import MultipartDecoder
+from requests_toolbelt.multipart.decoder import \
+    NonMultipartContentTypeException
+
+from catalyst.constants import (
+    LOG_LEVEL, AUTH_SERVER, ETH_REMOTE_NODE, MARKETPLACE_CONTRACT,
+    MARKETPLACE_CONTRACT_ABI, ENIGMA_CONTRACT, ENIGMA_CONTRACT_ABI)
+from catalyst.exchange.utils.stats_utils import set_print_settings
+from catalyst.marketplace.marketplace_errors import (
+    MarketplacePubAddressEmpty, MarketplaceDatasetNotFound,
+    MarketplaceNoAddressMatch, MarketplaceHTTPRequest,
+    MarketplaceNoCSVFiles, MarketplaceRequiresPython3)
+from catalyst.marketplace.utils.auth_utils import get_key_secret, \
+    get_signed_headers
+from catalyst.marketplace.utils.bundle_utils import merge_bundles
+from catalyst.marketplace.utils.eth_utils import bin_hex, from_grains, \
+    to_grains
+from catalyst.marketplace.utils.path_utils import get_bundle_folder, \
+    get_data_source_folder, get_marketplace_folder, \
+    get_user_pubaddr, get_temp_bundles_folder, extract_bundle
+from catalyst.utils.paths import ensure_directory
+
+if sys.version_info.major < 3:
+    import urllib
+else:
+    import urllib.request as urllib
+
+log = logbook.Logger('Marketplace', level=LOG_LEVEL)
+
+
+class Marketplace:
+    def __init__(self):
+        global Web3
+        try:
+            from web3 import Web3, HTTPProvider
+        except ImportError:
+            raise MarketplaceRequiresPython3()
+
+        self.addresses = get_user_pubaddr()
+
+        if self.addresses[0]['pubAddr'] == '':
+            raise MarketplacePubAddressEmpty(
+                filename=os.path.join(
+                    get_marketplace_folder(), 'addresses.json')
+            )
+        self.default_account = self.addresses[0]['pubAddr']
+
+        self.web3 = Web3(HTTPProvider(ETH_REMOTE_NODE))
+
+        contract_url = urllib.urlopen(MARKETPLACE_CONTRACT)
+
+        self.mkt_contract_address = Web3.toChecksumAddress(
+            contract_url.readline().decode(
+                contract_url.info().get_content_charset()).strip())
+
+        abi_url = urllib.urlopen(MARKETPLACE_CONTRACT_ABI)
+        abi_url = abi_url.read().decode(
+                abi_url.info().get_content_charset())
+
+        abi = json.loads(abi_url)
+
+        self.mkt_contract = self.web3.eth.contract(
+            self.mkt_contract_address,
+            abi=abi,
+        )
+
+        contract_url = urllib.urlopen(ENIGMA_CONTRACT)
+
+        self.eng_contract_address = Web3.toChecksumAddress(
+            contract_url.readline().decode(
+                contract_url.info().get_content_charset()).strip())
+
+        abi_url = urllib.urlopen(ENIGMA_CONTRACT_ABI)
+        abi_url = abi_url.read().decode(
+                abi_url.info().get_content_charset())
+
+        abi = json.loads(abi_url)
+
+        self.eng_contract = self.web3.eth.contract(
+            self.eng_contract_address,
+            abi=abi,
+        )
+
+    # def get_data_sources_map(self):
+    #     return [
+    #         dict(
+    #             name='Marketcap',
+    #             desc='The marketcap value in USD.',
+    #             start_date=pd.to_datetime('2017-01-01'),
+    #             end_date=pd.to_datetime('2018-01-15'),
+    #             data_frequencies=['daily'],
+    #         ),
+    #         dict(
+    #             name='GitHub',
+    #             desc='The rate of development activity on GitHub.',
+    #             start_date=pd.to_datetime('2017-01-01'),
+    #             end_date=pd.to_datetime('2018-01-15'),
+    #             data_frequencies=['daily', 'hour'],
+    #         ),
+    #         dict(
+    #             name='Influencers',
+    #             desc='Tweets & related sentiments by selected influencers.',
+    #             start_date=pd.to_datetime('2017-01-01'),
+    #             end_date=pd.to_datetime('2018-01-15'),
+    #             data_frequencies=['daily', 'hour', 'minute'],
+    #         ),
+    #     ]
+
+    def to_text(self, hex):
+        return Web3.toText(hex).rstrip('\0')
+
+    def choose_pubaddr(self):
+        if len(self.addresses) == 1:
+            address = self.addresses[0]['pubAddr']
+            address_i = 0
+            print('Using {} for this transaction.'.format(address))
+        else:
+            while True:
+                for i in range(0, len(self.addresses)):
+                    print('{}\t{}\t{}\t{}'.format(
+                        i,
+                        self.addresses[i]['pubAddr'],
+                        self.addresses[i]['wallet'].ljust(10),
+                        self.addresses[i]['desc'])
+                    )
+                address_i = int(input('Choose your address associated with '
+                                      'this transaction: [default: 0] ') or 0)
+                if not (0 <= address_i < len(self.addresses)):
+                    print('Please choose a number between 0 and {}\n'.format(
+                        len(self.addresses) - 1))
+                else:
+                    address = Web3.toChecksumAddress(
+                        self.addresses[address_i]['pubAddr'])
+                    break
+
+        return address, address_i
+
+    def sign_transaction(self, tx):
+
+        url = 'https://www.mycrypto.com/#offline-transaction'
+        print('\nVisit {url} and enter the following parameters:\n\n'
+              'From Address:\t\t{_from}\n'
+              '\n\tClick the "Generate Information" button\n\n'
+              'To Address:\t\t{to}\n'
+              'Value / Amount to Send:\t{value}\n'
+              'Gas Limit:\t\t{gas}\n'
+              'Gas Price:\t\t[Accept the default value]\n'
+              'Nonce:\t\t\t{nonce}\n'
+              'Data:\t\t\t{data}\n'.format(
+                url=url,
+                _from=tx['from'],
+                to=tx['to'],
+                value=tx['value'],
+                gas=tx['gas'],
+                nonce=tx['nonce'],
+                data=tx['data'], )
+              )
+
+        webbrowser.open_new(url)
+
+        signed_tx = input('Copy and Paste the "Signed Transaction" '
+                          'field here:\n')
+
+        if signed_tx.startswith('0x'):
+            signed_tx = signed_tx[2:]
+
+        return signed_tx
+
+    def check_transaction(self, tx_hash):
+
+        if 'ropsten' in ETH_REMOTE_NODE:
+            etherscan = 'https://ropsten.etherscan.io/tx/'
+        elif 'rinkeby' in ETH_REMOTE_NODE:
+            etherscan = 'https://rinkeby.etherscan.io/tx/'
+        else:
+            etherscan = 'https://etherscan.io/tx/'
+        etherscan = '{}{}'.format(etherscan, tx_hash)
+
+        print('\nYou can check the outcome of your transaction here:\n'
+              '{}\n\n'.format(etherscan))
+
+    def _list(self):
+        data_sources = self.mkt_contract.functions.getAllProviders().call()
+
+        data = []
+        for index, data_source in enumerate(data_sources):
+            if index > 0:
+                if 'test' not in Web3.toText(data_source).lower():
+                    data.append(
+                        dict(
+                            dataset=self.to_text(data_source)
+                        )
+                    )
+        return pd.DataFrame(data)
+
+    def list(self):
+        df = self._list()
+
+        set_print_settings()
+        if df.empty:
+            print('There are no datasets available yet.')
+        else:
+            print(df)
+
+    def subscribe(self, dataset=None):
+
+        if dataset is None:
+
+            df_sets = self._list()
+            if df_sets.empty:
+                print('There are no datasets available yet.')
+                return
+
+            set_print_settings()
+            while True:
+                print(df_sets)
+                dataset_num = input('Choose the dataset you want to '
+                                    'subscribe to [0..{}]: '.format(
+                                        df_sets.size - 1))
+                try:
+                    dataset_num = int(dataset_num)
+                except ValueError:
+                    print('Enter a number between 0 and {}'.format(
+                        df_sets.size - 1))
+                else:
+                    if dataset_num not in range(0, df_sets.size):
+                        print('Enter a number between 0 and {}'.format(
+                            df_sets.size - 1))
+                    else:
+                        dataset = df_sets.iloc[dataset_num]['dataset']
+                        break
+
+        dataset = dataset.lower()
+
+        address = self.choose_pubaddr()[0]
+        provider_info = self.mkt_contract.functions.getDataProviderInfo(
+            Web3.toHex(dataset)
+        ).call()
+
+        if not provider_info[4]:
+            print('The requested "{}" dataset is not registered in '
+                  'the Data Marketplace.'.format(dataset))
+            return
+
+        grains = provider_info[1]
+        price = from_grains(grains)
+
+        subscribed = self.mkt_contract.functions.checkAddressSubscription(
+            address, Web3.toHex(dataset)
+        ).call()
+
+        if subscribed[5]:
+            print(
+                '\nYou are already subscribed to the "{}" dataset.\n'
+                'Your subscription started on {} UTC, and is valid until '
+                '{} UTC.'.format(
+                    dataset,
+                    pd.to_datetime(subscribed[3], unit='s', utc=True),
+                    pd.to_datetime(subscribed[4], unit='s', utc=True)
+                )
+            )
+            return
+
+        print('\nThe price for a monthly subscription to this dataset is'
+              ' {} ENG'.format(price))
+
+        print(
+            'Checking that the ENG balance in {} is greater than {} '
+            'ENG... '.format(address, price), end=''
+        )
+
+        wallet_address = address[2:]
+        balance = self.web3.eth.call({
+            'from': address,
+            'to': self.eng_contract_address,
+            'data': '0x70a08231000000000000000000000000{}'.format(
+                wallet_address
+            )
+        })
+
+        try:
+            balance = Web3.toInt(balance)  # web3 >= 4.0.0b7
+        except TypeError:
+            balance = Web3.toInt(hexstr=balance)  # web3 <= 4.0.0b6
+
+        if balance > grains:
+            print('OK.')
+        else:
+            print('FAIL.\n\nAddress {} balance is {} ENG,\nwhich is lower '
+                  'than the price of the dataset that you are trying to\n'
+                  'buy: {} ENG. Get enough ENG to cover the costs of the '
+                  'monthly\nsubscription for what you are trying to buy, '
+                  'and try again.'.format(
+                    address, from_grains(balance), price))
+            return
+
+        while True:
+            agree_pay = input('Please confirm that you agree to pay {} ENG '
+                              'for a monthly subscription to the dataset "{}" '
+                              'starting today. [default: Y] '.format(
+                                price, dataset)) or 'y'
+            if agree_pay.lower() not in ('y', 'n'):
+                print("Please answer Y or N.")
+            else:
+                if agree_pay.lower() == 'y':
+                    break
+                else:
+                    return
+
+        print('Ready to subscribe to dataset {}.\n'.format(dataset))
+        print('In order to execute the subscription, you will need to sign '
+              'two different transactions:\n'
+              '1. First transaction is to authorize the Marketplace contract '
+              'to spend {} ENG on your behalf.\n'
+              '2. Second transaction is the actual subscription for the '
+              'desired dataset'.format(price))
+
+        tx = self.eng_contract.functions.approve(
+            self.mkt_contract_address,
+            grains,
+        ).buildTransaction(
+            {'from': address,
+             'nonce': self.web3.eth.getTransactionCount(address)}
+        )
+
+        signed_tx = self.sign_transaction(tx)
+        try:
+            tx_hash = '0x{}'.format(
+                bin_hex(self.web3.eth.sendRawTransaction(signed_tx))
+            )
+            print(
+                '\nThis is the TxHash for this transaction: {}'.format(tx_hash)
+            )
+
+        except Exception as e:
+            print('Unable to subscribe to data source: {}'.format(e))
+            return
+
+        self.check_transaction(tx_hash)
+
+        print('Waiting for the first transaction to succeed...')
+
+        while True:
+            try:
+                if self.web3.eth.getTransactionReceipt(tx_hash).status:
+                    break
+                else:
+                    print('\nTransaction failed. Aborting...')
+                    return
+            except AttributeError:
+                pass
+            for i in range(0, 10):
+                print('.', end='', flush=True)
+                time.sleep(1)
+
+        print('\nFirst transaction successful!\n'
+              'Now processing second transaction.')
+
+        tx = self.mkt_contract.functions.subscribe(
+            Web3.toHex(dataset),
+        ).buildTransaction({
+            'from': address,
+            'nonce': self.web3.eth.getTransactionCount(address)})
+
+        signed_tx = self.sign_transaction(tx)
+
+        try:
+            tx_hash = '0x{}'.format(bin_hex(
+                self.web3.eth.sendRawTransaction(signed_tx)))
+            print('\nThis is the TxHash for this transaction: '
+                  '{}'.format(tx_hash))
+
+        except Exception as e:
+            print('Unable to subscribe to data source: {}'.format(e))
+            return
+
+        self.check_transaction(tx_hash)
+
+        print('Waiting for the second transaction to succeed...')
+
+        while True:
+            try:
+                if self.web3.eth.getTransactionReceipt(tx_hash).status:
+                    break
+                else:
+                    print('\nTransaction failed. Aborting...')
+                    return
+            except AttributeError:
+                pass
+            for i in range(0, 10):
+                print('.', end='', flush=True)
+                time.sleep(1)
+
+        print('\nSecond transaction successful!\n'
+              'You have successfully subscribed to dataset {} with'
+              'address {}.\n'
+              'You can now ingest this dataset anytime during the '
+              'next month by running the following command:\n'
+              'catalyst marketplace ingest --dataset={}'.format(
+                dataset, address, dataset))
+
+    def process_temp_bundle(self, ds_name, path):
+        """
+        Merge the temp bundle into the main bundle for the specified
+        data source.
+
+        Parameters
+        ----------
+        ds_name
+        path
+
+        Returns
+        -------
+
+        """
+        tmp_bundle = extract_bundle(path)
+        bundle_folder = get_data_source_folder(ds_name)
+        ensure_directory(bundle_folder)
+        if os.listdir(bundle_folder):
+            zsource = bcolz.ctable(rootdir=tmp_bundle, mode='r')
+            ztarget = bcolz.ctable(rootdir=bundle_folder, mode='r')
+            merge_bundles(zsource, ztarget)
+
+        else:
+            shutil.rmtree(bundle_folder, ignore_errors=True)
+            os.rename(tmp_bundle, bundle_folder)
+
+    def ingest(self, ds_name=None, start=None, end=None, force_download=False):
+
+        if ds_name is None:
+
+            df_sets = self._list()
+            if df_sets.empty:
+                print('There are no datasets available yet.')
+                return
+
+            set_print_settings()
+            while True:
+                print(df_sets)
+                dataset_num = input('Choose the dataset you want to '
+                                    'ingest [0..{}]: '.format(
+                                        df_sets.size - 1))
+                try:
+                    dataset_num = int(dataset_num)
+                except ValueError:
+                    print('Enter a number between 0 and {}'.format(
+                        df_sets.size - 1))
+                else:
+                    if dataset_num not in range(0, df_sets.size):
+                        print('Enter a number between 0 and {}'.format(
+                            df_sets.size - 1))
+                    else:
+                        ds_name = df_sets.iloc[dataset_num]['dataset']
+                        break
+
+        # ds_name = ds_name.lower()
+
+        # TODO: catch error conditions
+        provider_info = self.mkt_contract.functions.getDataProviderInfo(
+            Web3.toHex(ds_name)
+        ).call()
+
+        if not provider_info[4]:
+            print('The requested "{}" dataset is not registered in '
+                  'the Data Marketplace.'.format(ds_name))
+            return
+
+        address, address_i = self.choose_pubaddr()
+        fns = self.mkt_contract.functions
+        check_sub = fns.checkAddressSubscription(
+            address, Web3.toHex(ds_name)
+        ).call()
+
+        if check_sub[0] != address or self.to_text(check_sub[1]) != ds_name:
+            print('You are not subscribed to dataset "{}" with address {}. '
+                  'Plese subscribe first.'.format(ds_name, address))
+            return
+
+        if not check_sub[5]:
+            print('Your subscription to dataset "{}" expired on {} UTC.'
+                  'Please renew your subscription by running:\n'
+                  'catalyst marketplace subscribe --dataset={}'.format(
+                    ds_name,
+                    pd.to_datetime(check_sub[4], unit='s', utc=True),
+                    ds_name)
+                  )
+
+        if 'key' in self.addresses[address_i]:
+            key = self.addresses[address_i]['key']
+            secret = self.addresses[address_i]['secret']
+        else:
+            key, secret = get_key_secret(address,
+                                         self.addresses[address_i]['wallet'])
+
+        headers = get_signed_headers(ds_name, key, secret)
+        log.info('Starting download of dataset for ingestion...')
+        r = requests.post(
+            '{}/marketplace/ingest'.format(AUTH_SERVER),
+            headers=headers,
+            stream=True,
+        )
+        if r.status_code == 200:
+            log.info('Dataset downloaded successfully. Processing dataset...')
+            target_path = get_temp_bundles_folder()
+            try:
+                decoder = MultipartDecoder.from_response(r)
+                # with maybe_show_progress(
+                #     iter(decoder.parts),
+                #     True,
+                #     label='Processing files') as part:
+                counter = 1
+                for part in decoder.parts:
+                    log.info("Processing file {} of {}".format(
+                        counter, len(decoder.parts)))
+                    h = part.headers[b'Content-Disposition'].decode('utf-8')
+                    # Extracting the filename from the header
+                    name = re.search(r'filename="(.*)"', h).group(1)
+
+                    filename = os.path.join(target_path, name)
+                    with open(filename, 'wb') as f:
+                        # for chunk in part.content.iter_content(
+                        #         chunk_size=1024):
+                        #     if chunk: # filter out keep-alive new chunks
+                        #         f.write(chunk)
+                        f.write(part.content)
+
+                    self.process_temp_bundle(ds_name, filename)
+                    counter += 1
+
+            except NonMultipartContentTypeException:
+                response = r.json()
+                raise MarketplaceHTTPRequest(
+                    request='ingest dataset',
+                    error=response,
+                )
+        else:
+            raise MarketplaceHTTPRequest(
+                request='ingest dataset',
+                error=r.status_code,
+            )
+
+        log.info('{} ingested successfully'.format(ds_name))
+
+    def get_dataset(self, ds_name, start=None, end=None):
+        ds_name = ds_name.lower()
+
+        # TODO: filter ctable by start and end date
+        bundle_folder = get_data_source_folder(ds_name)
+        z = bcolz.ctable(rootdir=bundle_folder, mode='r')
+
+        df = z.todataframe()  # type: pd.DataFrame
+        df.set_index(['date', 'symbol'], drop=True, inplace=True)
+
+        # TODO: implement the filter more carefully
+        # if start and end is None:
+        #     df = df.xs(start, level=0)
+
+        return df
+
+    def clean(self, ds_name=None, data_frequency=None):
+
+        if ds_name is None:
+            mktplace_root = get_marketplace_folder()
+            folders = [os.path.basename(f.rstrip('/'))
+                       for f in glob.glob('{}/*/'.format(mktplace_root))
+                       if 'temp_bundles' not in f]
+
+            while True:
+                for idx, f in enumerate(folders):
+                    print('{}\t{}'.format(idx, f))
+                dataset_num = input('Choose the dataset you want to '
+                                    'clean [0..{}]: '.format(
+                                        len(folders) - 1))
+                try:
+                    dataset_num = int(dataset_num)
+                except ValueError:
+                    print('Enter a number between 0 and {}'.format(
+                        len(folders) - 1))
+                else:
+                    if dataset_num not in range(0, len(folders)):
+                        print('Enter a number between 0 and {}'.format(
+                            len(folders) - 1))
+                    else:
+                        ds_name = folders[dataset_num]
+                        break
+
+        ds_name = ds_name.lower()
+
+        if data_frequency is None:
+            folder = get_data_source_folder(ds_name)
+
+        else:
+            folder = get_bundle_folder(ds_name, data_frequency)
+
+        shutil.rmtree(folder)
+
+    def create_metadata(self, key, secret, ds_name, data_frequency, desc,
+                        has_history=True, has_live=True):
+        """
+
+        Returns
+        -------
+
+        """
+        headers = get_signed_headers(ds_name, key, secret)
+        r = requests.post(
+            '{}/marketplace/register'.format(AUTH_SERVER),
+            json=dict(
+                ds_name=ds_name,
+                desc=desc,
+                data_frequency=data_frequency,
+                has_history=has_history,
+                has_live=has_live,
+            ),
+            headers=headers,
+        )
+
+        if r.status_code != 200:
+            raise MarketplaceHTTPRequest(
+                request='register', error=r.status_code
+            )
+
+        if 'error' in r.json():
+            raise MarketplaceHTTPRequest(
+                request='upload file', error=r.json()['error']
+            )
+
+    def register(self):
+        while True:
+            desc = input('Enter the name of the dataset to register: ')
+            dataset = desc.lower().strip()
+            provider_info = self.mkt_contract.functions.getDataProviderInfo(
+                Web3.toHex(dataset)
+            ).call()
+
+            if provider_info[4]:
+                print('There is already a dataset registered under '
+                      'the name "{}". Please choose a different '
+                      'name.'.format(dataset))
+            else:
+                break
+
+        price = int(
+            input(
+                'Enter the price for a monthly subscription to '
+                'this dataset in ENG: '
+            )
+        )
+        while True:
+            freq = input('Enter the data frequency [daily, hourly, minute]: ')
+            if freq.lower() not in ('daily', 'hourly', 'minute'):
+                print('Not a valid frequency.')
+            else:
+                break
+
+        while True:
+            reg_pub = input(
+                'Does it include historical data? [default: Y]: '
+            ) or 'y'
+            if reg_pub.lower() not in ('y', 'n'):
+                print('Please answer Y or N.')
+            else:
+                if reg_pub.lower() == 'y':
+                    has_history = True
+                else:
+                    has_history = False
+                break
+
+        while True:
+            reg_pub = input(
+                'Doest it include live data? [default: Y]: '
+            ) or 'y'
+            if reg_pub.lower() not in ('y', 'n'):
+                print('Please answer Y or N.')
+            else:
+                if reg_pub.lower() == 'y':
+                    has_live = True
+                else:
+                    has_live = False
+                break
+
+        address, address_i = self.choose_pubaddr()
+        if 'key' in self.addresses[address_i]:
+            key = self.addresses[address_i]['key']
+            secret = self.addresses[address_i]['secret']
+        else:
+            key, secret = get_key_secret(address,
+                                         self.addresses[address_i]['wallet'])
+
+        grains = to_grains(price)
+
+        tx = self.mkt_contract.functions.register(
+            Web3.toHex(dataset),
+            grains,
+            address,
+        ).buildTransaction(
+            {'from': address,
+             'nonce': self.web3.eth.getTransactionCount(address)}
+        )
+
+        signed_tx = self.sign_transaction(tx)
+
+        try:
+            tx_hash = '0x{}'.format(
+                bin_hex(self.web3.eth.sendRawTransaction(signed_tx))
+            )
+            print(
+                '\nThis is the TxHash for this transaction: {}'.format(tx_hash)
+            )
+
+        except Exception as e:
+            print('Unable to register the requested dataset: {}'.format(e))
+            return
+
+        self.check_transaction(tx_hash)
+
+        print('Waiting for the transaction to succeed...')
+
+        while True:
+            try:
+                if self.web3.eth.getTransactionReceipt(tx_hash).status:
+                    break
+                else:
+                    print('\nTransaction failed. Aborting...')
+                    return
+            except AttributeError:
+                pass
+            for i in range(0, 10):
+                print('.', end='', flush=True)
+                time.sleep(1)
+
+        print('\nWarming up the {} dataset'.format(dataset))
+        self.create_metadata(
+            key=key,
+            secret=secret,
+            ds_name=dataset,
+            data_frequency=freq,
+            desc=desc,
+            has_history=has_history,
+            has_live=has_live,
+        )
+        print('\n{} registered successfully'.format(dataset))
+
+    def publish(self, dataset, datadir, watch):
+        dataset = dataset.lower()
+        provider_info = self.mkt_contract.functions.getDataProviderInfo(
+            Web3.toHex(dataset)
+        ).call()
+
+        if not provider_info[4]:
+            raise MarketplaceDatasetNotFound(dataset=dataset)
+
+        match = next(
+            (l for l in self.addresses if l['pubAddr'] == provider_info[0]),
+            None
+        )
+        if not match:
+            raise MarketplaceNoAddressMatch(
+                dataset=dataset,
+                address=provider_info[0])
+
+        print('Using address: {} to publish this dataset.'.format(
+            provider_info[0]))
+
+        if 'key' in match:
+            key = match['key']
+            secret = match['secret']
+        else:
+            key, secret = get_key_secret(provider_info[0], match['wallet'])
+
+        filenames = glob.glob(os.path.join(datadir, '*.csv'))
+
+        if not filenames:
+            raise MarketplaceNoCSVFiles(datadir=datadir)
+
+        files = []
+        for idx, file in enumerate(filenames):
+            log.info('Uploading file {} of {}: {}'.format(
+                idx+1, len(filenames), file))
+            files = []
+            files.append(('file', open(file, 'rb')))
+
+            headers = get_signed_headers(dataset, key, secret)
+            r = requests.post('{}/marketplace/publish'.format(AUTH_SERVER),
+                              files=files,
+                              headers=headers)
+
+            if r.status_code != 200:
+                raise MarketplaceHTTPRequest(request='upload file',
+                                             error=r.status_code)
+
+            if 'error' in r.json():
+                raise MarketplaceHTTPRequest(request='upload file',
+                                             error=r.json()['error'])
+
+            log.info('File processed successfully.')
+
+        print('\nDataset {} uploaded and processed successfully.'.format(
+            dataset))

catalyst/marketplace/marketplace_errors.py

@@ -0,0 +1,97 @@
+import sys
+import traceback
+
+from catalyst.errors import ZiplineError
+
+
+def silent_except_hook(exctype, excvalue, exctraceback):
+    if exctype in [MarketplacePubAddressEmpty, MarketplaceDatasetNotFound,
+                   MarketplaceNoAddressMatch, MarketplaceHTTPRequest,
+                   MarketplaceNoCSVFiles, MarketplaceContractDataNoMatch,
+                   MarketplaceSubscriptionExpired, MarketplaceJSONError,
+                   MarketplaceWalletNotSupported, MarketplaceEmptySignature,
+                   MarketplaceRequiresPython3]:
+        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 MarketplacePubAddressEmpty(ZiplineError):
+    msg = (
+        'Please enter your public address to use in the Data Marketplace '
+        'in the following file: {filename}'
+    ).strip()
+
+
+class MarketplaceDatasetNotFound(ZiplineError):
+    msg = (
+        'The dataset "{dataset}" is not registered in the Data Marketplace.'
+    ).strip()
+
+
+class MarketplaceNoAddressMatch(ZiplineError):
+    msg = (
+        'The address registered with the dataset {dataset}: {address} '
+        'does not match any of your addresses.'
+    ).strip()
+
+
+class MarketplaceHTTPRequest(ZiplineError):
+    msg = (
+        'Request to remote server to {request} failed: {error}'
+    ).strip()
+
+
+class MarketplaceNoCSVFiles(ZiplineError):
+    msg = (
+        'No CSV files found on {datadir} to upload.'
+    )
+
+
+class MarketplaceContractDataNoMatch(ZiplineError):
+    msg = (
+        'The information found on the contract does not match the '
+        'requested data:\n{params}.'
+    )
+
+
+class MarketplaceSubscriptionExpired(ZiplineError):
+    msg = (
+        'Your subscription to dataset "{dataset}" expired on {date} '
+        'and is no longer active. You have to subscribe again running the '
+        'following command:\n'
+        'catalyst marketplace subscribe --dataset={dataset}'
+    )
+
+
+class MarketplaceWalletNotSupported(ZiplineError):
+    msg = (
+        'Wallet {wallet} is not supported.'
+    )
+
+
+class MarketplaceEmptySignature(ZiplineError):
+    msg = (
+        'Signature cannot be empty.'
+    )
+
+
+class MarketplaceJSONError(ZiplineError):
+    msg = (
+        'The configuration file {file} is malformed. Please correct '
+        'the following error:\n{error}'
+    )
+
+
+class MarketplaceRequiresPython3(ZiplineError):
+    msg = (
+        '\nCatalyst requires Python3 to access the Enigma Data Marketplace.\n'
+        'If you want to use the Data Marketplace, you need to reinstall '
+        'Catalyst\nwith Python3. See the documentation website for additional '
+        'information.')

catalyst/marketplace/utils/auth_utils.py

@@ -0,0 +1,141 @@
+import hashlib
+import hmac
+import webbrowser
+
+import requests
+import time
+
+from catalyst.marketplace.marketplace_errors import (
+    MarketplaceHTTPRequest, MarketplaceWalletNotSupported,
+    MarketplaceEmptySignature)
+from catalyst.marketplace.utils.path_utils import (
+    get_user_pubaddr, save_user_pubaddr)
+from catalyst.constants import AUTH_SERVER, SUPPORTED_WALLETS
+
+
+def get_key_secret(pubAddr, wallet):
+    """
+    Obtain a new key/secret pair from authentication server
+
+    Parameters
+    ----------
+    pubAddr: str
+    dataset: str
+
+    Returns
+    -------
+    key: str
+    secret: str
+
+    """
+    session = requests.Session()
+    response = session.get('{}/marketplace/getkeysecret'.format(AUTH_SERVER),
+                           headers={
+                            'Authorization': 'Digest username="{0}"'.format(
+                                pubAddr)})
+
+    if response.status_code != 401:
+        raise MarketplaceHTTPRequest(request=str('obtain key/secret'),
+                                     error='Unexpected response code: '
+                                           '{}'.format(response.status_code))
+
+    header = response.headers.get('WWW-Authenticate')
+    auth_type, auth_info = header.split(None, 1)
+    d = requests.utils.parse_dict_header(auth_info)
+
+    nonce = 'Catalyst nonce: 0x{}'.format(d['nonce'])
+
+    if wallet in SUPPORTED_WALLETS:
+        url = 'https://www.mycrypto.com/signmsg.html'
+
+        print('\nObtaining a key/secret pair to streamline all future '
+              'requests with the authentication server.\n'
+              'Visit {url} and sign the '
+              'following message (copy the entire line, without the '
+              'line break at the end):\n\n{nonce}'.format(
+                url=url,
+                nonce=nonce))
+
+        webbrowser.open_new(url)
+
+        signature = input('\nCopy and Paste the "sig" field from '
+                          'the signature here (without the double quotes, '
+                          'only the HEX value):\n')
+    else:
+        raise MarketplaceWalletNotSupported(wallet=wallet)
+
+    if signature is None:
+        raise MarketplaceEmptySignature()
+
+    signature = signature[2:]
+    r = int(signature[0:64], base=16)
+    s = int(signature[64:128], base=16)
+    v = int(signature[128:130], base=16)
+    vrs = [v, r, s]
+
+    response = session.get('{}/marketplace/getkeysecret'.format(AUTH_SERVER),
+                           headers={
+                'Authorization': 'Digest username="{0}",realm="{1}",'
+                'nonce="{2}",uri="/marketplace/getkeysecret",response="{3}",'
+                'opaque="{4}"'.format(pubAddr,
+                                      d['realm'],
+                                      d['nonce'],
+                                      ','.join(str(e) for e in vrs+[wallet]),
+                                      d['opaque'])})
+
+    if response.status_code == 200:
+
+        if 'error' in response.json():
+            raise MarketplaceHTTPRequest(request=str('obtain key/secret'),
+                                         error=str(response.json()['error']))
+        else:
+            addresses = get_user_pubaddr()
+
+            match = next((l for l in addresses if
+                          l['pubAddr'].lower() == pubAddr.lower()), None)
+
+            match['key'] = response.json()['key']
+            match['secret'] = response.json()['secret']
+
+            addresses[addresses.index(match)] = match
+
+            save_user_pubaddr(addresses)
+            print('Key/secret pair retrieved successfully from server.')
+
+            return match['key'], match['secret']
+
+    else:
+        raise MarketplaceHTTPRequest(request=str('obtain key/secret'),
+                                     error=response.status_code)
+
+
+def get_signed_headers(ds_name, key, secret):
+    """
+    Return a new request header including the key / secret signature
+
+    Parameters
+    ----------
+    ds_name
+    key
+    secret
+
+    Returns
+    -------
+
+    """
+    nonce = str(int(time.time() * 1000))
+
+    signature = hmac.new(
+        secret.encode('utf-8'),
+        '{}{}'.format(ds_name, nonce).encode('utf-8'),
+        hashlib.sha512
+    ).hexdigest()
+
+    headers = {
+        'Sign': signature,
+        'Key': key,
+        'Nonce': nonce,
+        'Dataset': ds_name,
+    }
+
+    return headers

catalyst/marketplace/utils/bundle_utils.py

@@ -0,0 +1,94 @@
+import os
+import random
+import re
+import shutil
+
+import bcolz
+import numpy as np
+import pandas as pd
+from six import string_types
+
+
+def merge_bundles(zsource, ztarget):
+    """
+    Merge
+    Parameters
+    ----------
+    zsource
+    ztarget
+
+    Returns
+    -------
+
+    """
+    # TODO: find a way to do this iteratively instead of in-memory
+    df_source = zsource.todataframe()
+    df_target = ztarget.todataframe()
+
+    df = pd.concat(
+        [df_source, df_target], ignore_index=True
+    )  # type: pd.DataFrame
+    df.drop_duplicates(inplace=True)
+    df.set_index(['date', 'symbol'], drop=False, inplace=True)
+
+    sanitize_df(df)
+
+    dirname = os.path.basename(ztarget.rootdir)
+    bak_dir = ztarget.rootdir.replace(dirname, '.{}'.format(dirname))
+    shutil.move(ztarget.rootdir, bak_dir)
+
+    z = bcolz.ctable.fromdataframe(df=df, rootdir=ztarget.rootdir)
+    shutil.rmtree(bak_dir)
+    return z
+
+
+def sanitize_df(df):
+    # Using a sampling method to identify dates for efficiency with
+    # large datasets
+    if len(df) > 100:
+        indexes = random.sample(range(0, len(df) - 1), 100)
+    elif len(df) > 1:
+        indexes = range(0, len(df) - 1)
+    else:
+        indexes = [0, ]
+
+    for column in df.columns:
+        is_date = False
+        for index in indexes:
+            value = df[column].iloc[index]
+            if not isinstance(value, string_types):
+                continue
+
+            # TODO: assuming that the date is at least daily
+            exp = re.compile(r'^\d{4}-\d{2}-\d{2}.*$')
+            matches = exp.findall(value)
+
+            if matches:
+                is_date = True
+                break
+
+        if is_date:
+            df[column] = pd.to_datetime(df[column])
+
+        else:
+            try:
+                ser = safely_reduce_dtype(df[column])
+                df[column] = ser
+            except Exception:
+                pass
+
+    return df
+
+
+def safely_reduce_dtype(ser):  # pandas.Series or numpy.array
+    orig_dtype = "".join(
+        [x for x in ser.dtype.name if x.isalpha()])  # float/int
+    mx = 1
+    for val in ser.values:
+        new_itemsize = np.min_scalar_type(val).itemsize
+        if mx < new_itemsize:
+            mx = new_itemsize
+        if orig_dtype == 'int':
+            mx = max(mx, 4)
+    new_dtype = orig_dtype + str(mx * 8)
+    return ser.astype(new_dtype)

catalyst/marketplace/utils/eth_utils.py

@@ -0,0 +1,82 @@
+import binascii
+
+
+# def bytes32(string):
+#     """
+#     Convert string to bytes32 data type for smart contract
+
+#     Parameters
+#     ----------
+#     string: str
+
+#     Returns
+#     -------
+#     list
+
+#     """
+#     return binascii.hexlify(string.encode('utf-8'))
+
+
+# def b32_str(bytes32):
+#     """
+#     Convert bytes32 to string
+
+#     Parameters
+#     ----------
+#     input: bytes object
+
+#     Returns
+#     -------
+#     str
+
+#     """
+#     return binascii.unhexlify(
+#             bytes32.decode('utf-8').rstrip('\0')).decode('ascii')
+
+
+def bin_hex(binary):
+    """
+    Convert bytes32 to string
+
+    Parameters
+    ----------
+    input: bytes object
+
+    Returns
+    -------
+    str
+
+    """
+    return binascii.hexlify(binary).decode('utf-8')
+
+
+def from_grains(amount):
+    """
+    Convert from grains to cryptocurrency
+
+    Parameters
+    ----------
+    input: amount
+
+    Returns
+    -------
+    int
+
+    """
+    return amount // 10 ** 8
+
+
+def to_grains(amount):
+    """
+    Convert from cryptocurrency to grains
+
+    Parameters
+    ----------
+    input: amount
+
+    Returns
+    -------
+    int
+
+    """
+    return amount * 10 ** 8

catalyst/marketplace/utils/path_utils.py

@@ -0,0 +1,213 @@
+import os
+import json
+import tarfile
+
+from catalyst.constants import SUPPORTED_WALLETS
+from catalyst.utils.deprecate import deprecated
+from catalyst.utils.paths import data_root, ensure_directory
+from catalyst.marketplace.marketplace_errors import MarketplaceJSONError
+
+
+def get_marketplace_folder(environ=None):
+    """
+    The root path of the marketplace folder.
+
+    Parameters
+    ----------
+    environ:
+
+    Returns
+    -------
+    str
+
+    """
+    if not environ:
+        environ = os.environ
+
+    root = data_root(environ)
+    marketplace_folder = os.path.join(root, 'marketplace')
+    ensure_directory(marketplace_folder)
+
+    return marketplace_folder
+
+
+def get_data_source_folder(data_source_name, environ=None):
+    """
+    The root path of an data_source folder.
+
+    Parameters
+    ----------
+    data_source_name: str
+    environ:
+
+    Returns
+    -------
+    str
+
+    """
+    if not environ:
+        environ = os.environ
+
+    root = data_root(environ)
+    data_source_folder = os.path.join(root, 'marketplace', data_source_name)
+    ensure_directory(data_source_folder)
+
+    return data_source_folder
+
+
+@deprecated
+def get_bundle_folder(data_source_name, data_frequency, environ=None):
+    data_source_folder = get_data_source_folder(data_source_name, environ)
+
+    bundle_folder = os.path.join(data_source_folder, data_frequency)
+
+    ensure_directory(bundle_folder)
+
+    return bundle_folder
+
+
+def get_temp_bundles_folder(environ=None):
+    """
+    The temp folder for bundle downloads by algo name.
+
+    Parameters
+    ----------
+    ds_name: str
+    environ:
+
+    Returns
+    -------
+    str
+
+    """
+    root = data_root(environ)
+    folder = os.path.join(root, 'marketplace', 'temp_bundles')
+    ensure_directory(folder)
+
+    return folder
+
+
+def extract_bundle(tar_filename):
+    """
+    Extract a bcolz bundle.
+
+    Parameters
+    ----------
+    ds_name
+
+    Returns
+    -------
+    str
+
+    """
+    target_path = tar_filename.replace('.tar.gz', '')
+    with tarfile.open(tar_filename, 'r') as tar:
+        tar.extractall(target_path)
+
+    return target_path
+
+
+def get_user_pubaddr(environ=None):
+    """
+    The de-serialized contend of the user's addresses.json file.
+
+    Parameters
+    ----------
+    environ:
+
+    Returns
+    -------
+    Object
+
+    """
+    marketplace_folder = get_marketplace_folder(environ)
+    filename = os.path.join(marketplace_folder, 'addresses.json')
+
+    if os.path.isfile(filename):
+        with open(filename) as data_file:
+            try:
+                data = json.load(data_file)
+            except json.decoder.JSONDecodeError as e:
+                raise MarketplaceJSONError(file=filename, error=e)
+            try:
+                d = data[0]['pubAddr']
+            except Exception as e:
+                data = [data, ]
+
+            changed = False
+
+            for idx, d in enumerate(data):
+                try:
+                    if d['wallet'] not in SUPPORTED_WALLETS:
+                        data[idx]['wallet'] = _choose_wallet(
+                            d['pubAddr'], False)
+                        changed = True
+                except KeyError:
+                    data[idx]['wallet'] = _choose_wallet(
+                        d['pubAddr'], True)
+                    changed = True
+
+            if changed:
+                save_user_pubaddr(data)
+
+            return data
+
+    else:
+        data = []
+        data.append(dict(pubAddr='', desc='', wallet=''))
+        with open(filename, 'w') as f:
+            json.dump(data, f, sort_keys=False, indent=2,
+                      separators=(',', ':'))
+            return data
+
+
+def _choose_wallet(pubAddr, missing):
+    while True:
+        if missing:
+            print('\nYou need to specify a wallet for address '
+                  '{}.'.format(pubAddr))
+        else:
+            print('\nThe wallet specified for address {} is not '
+                  'supported.'.format(pubAddr))
+
+        print('Please choose among the following options:')
+        for idx, wallet in enumerate(SUPPORTED_WALLETS):
+            print('{}\t{}'.format(idx, wallet))
+
+        lw = len(SUPPORTED_WALLETS)-1
+        w = input('Choose a number between 0 and {}: '.format(
+                    lw))
+        try:
+            w = int(w)
+        except ValueError:
+            print('Enter a number between 0 and {}'.format(lw))
+        else:
+            if w not in range(0, lw+1):
+                print('Enter a number between 0 and '
+                      '{}'.format(lw))
+            else:
+                return SUPPORTED_WALLETS[w]
+
+
+def save_user_pubaddr(data, environ=None):
+    """
+    Saves the user's public addresses and their related metadata in
+    the corresponding addresses.json file.
+
+    Parameters
+    ----------
+    data: dict
+
+    Returns
+    -------
+    True
+
+    """
+    marketplace_folder = get_marketplace_folder(environ)
+    filename = os.path.join(marketplace_folder, 'addresses.json')
+
+    with open(filename, 'w') as f:
+        json.dump(data, f, sort_keys=False, indent=2,
+                  separators=(',', ':'))
+
+    return True

catalyst/pipeline/graph.py

@@ -142,7 +142,7 @@ class TermGraph(object):
         at the end of execution.
         """
         refcounts = self.graph.out_degree()
-        for t in self.outputs.values():
+        for t in list(self.outputs.values()):
             refcounts[t] += 1
 
         for t in initial_terms:
@@ -238,7 +238,7 @@ class ExecutionPlan(TermGraph):
                  min_extra_rows=0):
         super(ExecutionPlan, self).__init__(terms)
 
-        for term in terms.values():
+        for term in list(terms.values()):
             self.set_extra_rows(
                 term,
                 all_dates,

catalyst/sources/test_source.py

@@ -144,7 +144,7 @@ class SpecificEquityTrades(object):
             for identifier in self.identifiers:
                 assets_by_identifier[identifier] = env.asset_finder.\
                     lookup_generic(identifier, datetime.now())[0]
-            self.sids = [asset.sid for asset in assets_by_identifier.values()]
+            self.sids = [asset.sid for asset in list(assets_by_identifier.values())]
             for event in self.event_list:
                 event.sid = assets_by_identifier[event.sid].sid
 
@@ -167,7 +167,7 @@ class SpecificEquityTrades(object):
             for identifier in self.identifiers:
                 assets_by_identifier[identifier] = env.asset_finder.\
                     lookup_generic(identifier, datetime.now())[0]
-            self.sids = [asset.sid for asset in assets_by_identifier.values()]
+            self.sids = [asset.sid for asset in list(assets_by_identifier.values())]
 
         # Hash_value for downstream sorting.
         self.arg_string = hash_args(*args, **kwargs)

catalyst/support/binance_history.py

@@ -0,0 +1,57 @@
+import pandas as pd
+from catalyst import run_algorithm
+
+
+def initialize(context):
+    context.i = -1  # counts the minutes
+    context.exchange = 'cryptopia'
+    context.base_currency = 'btc'
+    context.coins = context.exchanges[context.exchange].assets
+    context.coins = [c for c in context.coins if
+                     c.quote_currency == context.base_currency]
+
+
+def handle_data(context, data):
+    # current date formatted into a string
+    today = data.current_dt
+
+    # update universe everyday
+    new_day = 60 * 24  # assuming data_frequency='minute'
+    if not context.i % new_day:
+        context.coins = context.exchanges[context.exchange].assets
+        context.coins = [c for c in context.coins if
+                         c.quote_currency == context.base_currency]
+
+    # get data every 30 minutes
+    minutes = 1
+    if not context.i % minutes:
+        # we iterate for every pair in the current universe
+        for coin in context.coins:
+            pair = str(coin.symbol)
+
+            price = data.current(coin, 'price')
+            print(today, pair, price)
+
+
+def analyze(context=None, results=None):
+    pass
+
+
+if __name__ == '__main__':
+    start_date = pd.to_datetime('2018-01-17', utc=True)
+    end_date = pd.to_datetime('2018-01-18', utc=True)
+
+    performance = run_algorithm(
+        capital_base=1.0,
+        # amount of base_currency, not always in dollars unless usd
+        initialize=initialize,
+        handle_data=handle_data,
+        analyze=analyze,
+        exchange_name='cryptopia',
+        data_frequency='minute',
+        base_currency='btc',
+        live=True,
+        live_graph=False,
+        simulate_orders=True,
+        algo_namespace='simple_universe'
+    )

catalyst/support/ccxt_issue_1358.py

@@ -0,0 +1,8 @@
+import ccxt
+
+bitfinex = ccxt.bitfinex()
+bitfinex.verbose = True
+ohlcvs = bitfinex.fetch_ohlcv('ETH/BTC', '30m', 1504224000000)
+
+dt = bitfinex.iso8601(ohlcvs[0][0])
+print(dt)  # should print '2017-09-01T00:00:00.000Z'

catalyst/support/history_multiple_assets.py

@@ -0,0 +1,50 @@
+import pandas as pd
+
+from catalyst import run_algorithm
+from catalyst.api import symbol
+
+
+def initialize(context):
+    context.asset1 = symbol('fct_btc')
+    context.asset2 = symbol('btc_usdt')
+    context.coins = [context.asset1, context.asset2]
+
+
+def handle_data(context, data):
+    df = data.history(context.coins,
+                      'close',
+                      bar_count=10,
+                      frequency='5T',
+                      )
+    print(df)
+    print(data.current(context.asset1, 'close'))
+    print(data.current(context.asset2, 'close'))
+    exit(0)
+
+
+if __name__ == '__main__':
+    LIVE = True
+    if LIVE:
+        run_algorithm(
+            capital_base=1,
+            initialize=initialize,
+            handle_data=handle_data,
+            exchange_name='poloniex',
+            algo_namespace='test_multi_assets',
+            base_currency='usdt',
+            live=True,
+            simulate_orders=True,
+        )
+    else:
+        run_algorithm(
+            capital_base=1,
+            data_frequency='minute',
+            initialize=initialize,
+            handle_data=handle_data,
+            exchange_name='poloniex',
+            algo_namespace='test_multi_assets',
+            base_currency='usdt',
+            live=False,
+            start=pd.to_datetime('2017-12-1', utc=True),
+            end=pd.to_datetime('2017-12-1', utc=True),
+        )

catalyst/support/issue_111.py

@@ -0,0 +1,44 @@
+from logbook import Logger
+
+from catalyst import run_algorithm
+from catalyst.api import order_target_percent
+
+NAMESPACE = 'goose7'
+log = Logger(NAMESPACE)
+
+from catalyst.api import record, symbol
+
+
+def initialize(context):
+    context.asset = symbol('trx_btc')
+
+
+def handle_data(context, data):
+    price = data.current(context.asset, 'price')
+    record(btc=price)
+
+    # Only ordering if it does not have any position to avoid trying some
+    # tiny orders with the leftover btc
+    pos_amount = context.portfolio.positions[context.asset].amount
+    if pos_amount > 0:
+        return
+
+    # Adding a limit price to workaround an issue with performance
+    # calculations of market orders
+    order_target_percent(
+        context.asset, 1, limit_price=price * 1.01
+    )
+
+
+if __name__ == '__main__':
+    run_algorithm(
+        capital_base=0.003,
+        initialize=initialize,
+        handle_data=handle_data,
+        exchange_name='binance',
+        live=True,
+        algo_namespace=NAMESPACE,
+        base_currency='btc',
+        live_graph=False,
+        simulate_orders=False,
+    )

catalyst/support/issue_112.py

@@ -0,0 +1,44 @@
+import pandas as pd
+
+from catalyst import run_algorithm
+from catalyst.api import symbol
+
+
+def initialize(context):
+    context.asset = symbol('btc_usdt')
+
+
+def handle_data(context, data):
+    df = data.history(context.asset,
+                      'close',
+                      bar_count=10,
+                      frequency='5T',
+                      )
+
+
+if __name__ == '__main__':
+    LIVE = True
+    if LIVE:
+        run_algorithm(
+            capital_base=1,
+            initialize=initialize,
+            handle_data=handle_data,
+            exchange_name='poloniex',
+            algo_namespace='test_algo',
+            base_currency='usdt',
+            live=True,
+            simulate_orders=True,
+        )
+    else:
+        run_algorithm(
+            capital_base=1,
+            data_frequency='minute',
+            initialize=initialize,
+            handle_data=handle_data,
+            exchange_name='poloniex',
+            algo_namespace='test_algo',
+            base_currency='usdt',
+            live=False,
+            start=pd.to_datetime('2017-12-1', utc=True),
+            end=pd.to_datetime('2017-12-1', utc=True),
+        )

catalyst/support/issue_169.py

@@ -0,0 +1,44 @@
+import pandas as pd
+from catalyst.utils.run_algo import run_algorithm
+from catalyst.api import symbol
+from exchange.utils.stats_utils import set_print_settings
+
+
+def initialize(context):
+    context.i = 0
+    context.data = []
+
+
+def handle_data(context, data):
+    prices = data.history(
+        symbol('xlm_eth'),
+        fields=['open', 'high', 'low', 'close'],
+        bar_count=50,
+        frequency='1T'
+    )
+    set_print_settings()
+    print(prices.tail(10))
+    context.data.append(prices)
+
+    context.i = context.i + 1
+    if context.i == 3:
+        context.interrupt_algorithm()
+
+
+def analyze(context, prefs):
+    for dataset in context.data:
+        print(dataset[-2:])
+
+
+if __name__ == '__main__':
+    run_algorithm(
+        capital_base=0.1,
+        initialize=initialize,
+        handle_data=handle_data,
+        analyze=analyze,
+        exchange_name='binance',
+        algo_namespace='Test candles',
+        base_currency='eth',
+        data_frequency='minute',
+        live=True,
+        simulate_orders=True)

catalyst/support/issue_216.py

@@ -0,0 +1,376 @@
+# -*- coding: utf-8 -*-
+# !/usr/bin/env python2
+
+import sys
+import os
+import pandas as pd
+import signal
+# import talib
+
+from logbook import Logger
+
+from catalyst import run_algorithm
+from catalyst.api import (
+    symbol,
+    record,
+    order,
+    order_target,
+    order_target_percent,
+    get_open_orders
+)
+from catalyst.finance import commission
+
+
+# from base.telegrambot import TelegramBot
+
+
+class GracefulKiller:
+    # Source: https://stackoverflow.com/a/31464349
+    def __init__(self, context):
+        self.kill_now = False
+        self.signal = 0
+        self.context = context
+        signal.signal(signal.SIGINT, self.exit_gracefully)
+
+    def exit_gracefully(self, signum, frame):
+        self.kill_now = True
+        self.signal = signum
+        if hasattr(self.context,
+                   'telegram_bot') and self.context.telegram_bot is not None:
+            self.context.telegram_bot.updater.stop()
+        sys.exit(0)
+
+    def exit(self):
+        return self.kill_now
+
+
+class SimulationParameters:
+    MODE = 'paper'
+    CAPITAL_BASE = 1000
+    """
+    Capital base used on this simulation
+    """
+
+    DATA_FREQUECY = 'minute'
+
+    EXCHANGE_NAME = 'bitfinex'
+    # EXCHANGE_NAME = 'binance'
+    """
+    Exchange used on this simulation
+    """
+
+    DATA_DIR = '/home/av/Dropbox/simulations/data'
+    ALGO_NAMESPACE = os.path.basename(__file__).split('.')[0]
+    ALGO_NAMESPACE_IMAGE = '{}/{}/{}.png'.format(DATA_DIR, 'images',
+                                                 ALGO_NAMESPACE)
+    ALGO_NAMESPACE_RESULTS_TABLE = '{}/{}/{}.csv'.format(DATA_DIR, 'tables',
+                                                         ALGO_NAMESPACE + '_results')
+    ALGO_NAMESPACE_TRANSACTIONS_TABLE = '{}/{}/{}.csv'.format(DATA_DIR,
+                                                              'tables',
+                                                              ALGO_NAMESPACE + '_transactions')
+    BASE_CURRENCY = 'usd'
+    # BASE_CURRENCY = 'usdt'
+
+    # SHORT PERIOD
+    START_DATE = '2017-09-07'
+    """
+    Start date used on this simulation
+    """
+    END_DATE = '2017-12-12'
+    """
+    End date used on this simulation
+    """
+
+    SKIP_FIRST_CANDLES = 0
+
+    # CANDLES_SAMPLE_RATE = 60
+    # CANDLES_SAMPLE_RATE = 30
+    CANDLES_SAMPLE_RATE = 1
+    """
+    Candle interval used on this simulation (in minutes)
+    """
+
+    # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
+    # 30 minute interval ohlcv data (the standard data required for candlestick or
+    # indicators/signals)
+    # 30T means 30 minutes re-sampling of one minute data.
+    # CANDLES_FREQUENCY = '60T'
+    # CANDLES_FREQUENCY = '30T'
+    CANDLES_FREQUENCY = '1T'
+    CANDLES_BUFFER_SIZE = 48
+    COIN_PAIR = 'btc_usd'
+    # COIN_PAIR = 'btc_usdt'
+    """
+    Coin pair used on this simulation
+    """
+
+    # TRANSACTIONS
+    COMMISSION_FEE = 0.0030
+    BUY_MIN_AMOUNT = 5  # i.e: USD
+    SELL_MIN_AMOUNT = 0.001  # i.e: USD
+    BUY_SELL_PERCENTAGE = 1  # 0.50
+    BUY_PERCENTAGE = BUY_SELL_PERCENTAGE
+    SELL_PERCENTAGE = BUY_SELL_PERCENTAGE
+
+    BASE_PRICE = 'close'
+    """
+    Base price used (close / Heiken Ashi)
+    """
+
+
+log = None
+parameters = None
+
+
+def print_facts(context):
+    context.log.info("""
+Index: {}
+Date: {}
+Candle:
+O: {}
+H: {}
+L: {}
+C: {}
+V: {}
+Metrics:
+...
+Portfolio:
+Base price: {}
+Base coin (coin2/usd): {}
+Amount (coin1/btc): {}
+""".format(
+        # Facts
+        context.i,
+        context.curr_minute,
+        context.candles_open[-1],
+        context.candles_high[-1],
+        context.candles_low[-1],
+        context.candles_close[-1],
+        context.candles_volume[-1],
+        # Metrics
+        # ...
+        # Portfolio
+        context.curr_base_price,
+        context.portfolio.cash,
+        context.portfolio.positions[context.coin_pair].amount,
+    ))
+
+
+def print_facts_telegram(context):
+    price = context.curr_base_price
+    amount = context.portfolio.positions[context.coin_pair].amount
+    pnl = context.portfolio.pnl
+    capital_used = context.portfolio.capital_used
+    portfolio_value = context.portfolio.portfolio_value
+    portfolio_returns = context.portfolio.returns
+    starting_cash = context.portfolio.starting_cash
+    cash = context.portfolio.cash
+
+    msg = """
+Status...
+Price: {}
+Starting cash: {}
+Cash: {}
+Capital used: {}
+Amount: {}
+Portfolio value: {}
+Returns: {}
+PnL: {}
+    """.format(
+        price,
+        starting_cash,
+        cash,
+        capital_used,
+        amount,
+        portfolio_value,
+        portfolio_returns,
+        pnl,
+    )
+    if hasattr(context, 'telegram_bot') and context.telegram_bot is not None:
+        context.telegram_bot.msg(msg)
+
+
+def default_initialize(context):
+    # FIXME: set_benchmark
+    # set_benchmark(symbol(context.parameters.COIN_PAIR))
+
+    context.coin_pair = symbol(context.parameters.COIN_PAIR)
+    context.base_price = None
+    context.current_day = None
+    context.counter = -1
+    context.i = 0
+
+    context.candles_sample_rate = context.parameters.CANDLES_SAMPLE_RATE
+    context.candles_frequency = context.parameters.CANDLES_FREQUENCY
+    context.candles_buffer_size = context.parameters.CANDLES_BUFFER_SIZE
+    context.set_commission(
+        commission.PerShare(cost=context.parameters.COMMISSION_FEE))
+
+
+def default_handle_data(context, data):
+    context.curr_minute = data.current_dt
+    context.counter += 1
+
+    if context.candles_sample_rate == 1:
+        context.i += 1
+    elif context.counter % context.candles_sample_rate != 0:
+        context.i += 1
+        return
+
+    if context.i < context.parameters.SKIP_FIRST_CANDLES:
+        return
+
+    context.candles_open = data.history(
+        context.coin_pair,
+        'open',
+        bar_count=context.candles_buffer_size,
+        frequency=context.candles_frequency)
+    context.candles_high = data.history(
+        context.coin_pair,
+        'high',
+        bar_count=context.candles_buffer_size,
+        frequency=context.candles_frequency)
+    context.candles_low = data.history(
+        context.coin_pair,
+        'low',
+        bar_count=context.candles_buffer_size,
+        frequency=context.candles_frequency)
+    context.candles_close = data.history(
+        context.coin_pair,
+        'price',
+        bar_count=context.candles_buffer_size,
+        frequency=context.candles_frequency)
+    context.candles_volume = data.history(
+        context.coin_pair,
+        'volume',
+        bar_count=context.candles_buffer_size,
+        frequency=context.candles_frequency)
+
+    # FIXME: Here is the error!
+    # The candles_close frame shows more or less always a value of 94, while
+    # bitcoin price is very different from that
+    print(context.candles_close)
+
+    context.base_prices = context.candles_close
+    cash = context.portfolio.cash
+    amount = context.portfolio.positions[context.coin_pair].amount
+    price = data.current(context.coin_pair, 'price')
+    order_id = None
+    context.last_base_price = context.base_prices[-2]
+    context.curr_base_price = context.base_prices[-1]
+
+    # TA calculations
+    # ...
+
+    # Sanity checks
+    # assert cash >= 0
+    if cash < 0:
+        import ipdb;
+        ipdb.set_trace()  # BREAKPOINT
+
+    print_facts(context)
+    print_facts_telegram(context)
+
+    # Order management
+    net_shares = 0
+    if context.counter == 2:
+        brute_shares = (cash / price) * context.parameters.BUY_PERCENTAGE
+        share_commission_fee = brute_shares * context.parameters.COMMISSION_FEE
+        net_shares = brute_shares - share_commission_fee
+        buy_order_id = order(context.coin_pair, net_shares)
+
+    if context.counter == 3:
+        brute_shares = amount * context.parameters.SELL_PERCENTAGE
+        share_commission_fee = brute_shares * context.parameters.COMMISSION_FEE
+        net_shares = -(brute_shares - share_commission_fee)
+        sell_order_id = order(context.coin_pair, net_shares)
+
+    # Record
+    record(
+        price=price,
+        foo='bar',
+        # volume=current['volume'],
+        # price_change=price_change,
+        # Metrics
+        cash=cash,
+        # buy=context.buy,
+        # sell=context.sell
+    )
+
+
+def default_analyze(context=None, perf=None):
+    pass
+
+
+def initialize(context):
+    global log
+    context.parameters = parameters
+    context.log = Logger(context.parameters.ALGO_NAMESPACE)
+    log = context.log
+    default_initialize(context)
+    context.killer = GracefulKiller(context)
+    context.telegram_bot = None
+
+    # TELEGRAM_TOKEN='token'
+    # context.telegram_bot = TelegramBot()
+    # context.telegram_bot.initialize(TELEGRAM_TOKEN, context)
+
+
+if __name__ == '__main__':
+    # Parameters:
+    parameters = SimulationParameters()
+    start_date = pd.to_datetime(parameters.START_DATE, utc=True)
+    end_date = pd.to_datetime(parameters.END_DATE, utc=True)
+
+    if parameters.MODE == 'backtest':
+        results = run_algorithm(
+            capital_base=parameters.CAPITAL_BASE,
+            data_frequency=parameters.DATA_FREQUECY,
+            initialize=initialize,
+            handle_data=default_handle_data,
+            analyze=default_analyze,
+            exchange_name=parameters.EXCHANGE_NAME,
+            algo_namespace=parameters.ALGO_NAMESPACE,
+            base_currency=parameters.BASE_CURRENCY,
+            start=start_date,
+            end=end_date,
+            live=False,
+            live_graph=False
+        )
+
+        returns_daily = results
+        results.to_csv('{}'.format(parameters.ALGO_NAMESPACE_RESULTS_TABLE))
+
+        # returns_daily = returns_minutely.add(1).groupby(pd.TimeGrouper('24H')).prod().add(-1)
+
+        # FIXME: pyfolio integration
+        # pf_data = pyfolio.utils.extract_rets_pos_txn_from_zipline(results)
+        # pf_data = pyfolio.utils.extract_rets_pos_txn_from_zipline(results[:'2017-01-01'])
+        # pyfolio.create_full_tear_sheet(*pf_data)
+
+    elif parameters.MODE == 'paper':
+        results = run_algorithm(
+            capital_base=parameters.CAPITAL_BASE,
+            data_frequency=parameters.DATA_FREQUECY,
+            initialize=initialize,
+            handle_data=default_handle_data,
+            analyze=default_analyze,
+            exchange_name=parameters.EXCHANGE_NAME,
+            algo_namespace=parameters.ALGO_NAMESPACE,
+            base_currency=parameters.BASE_CURRENCY,
+            live=True,
+            simulate_orders=True,
+            live_graph=False
+        )
+
+    elif parameters.MODE == 'live':
+        results = run_algorithm(
+            initialize=initialize,
+            handle_data=default_handle_data,
+            analyze=default_analyze,
+            exchange_name=parameters.EXCHANGE_NAME,
+            algo_namespace=parameters.ALGO_NAMESPACE,
+            base_currency=parameters.BASE_CURRENCY,
+            live=True,
+            live_graph=True
+        )

catalyst/support/issue_227.py

@@ -0,0 +1,49 @@
+import pytz
+from datetime import datetime
+from catalyst.api import symbol
+from catalyst.utils.run_algo import run_algorithm
+
+coin = 'btc'
+base_currency = 'usd'
+n_candles = 5
+
+
+def initialize(context):
+    context.symbol = symbol('%s_%s' % (coin, base_currency))
+
+
+def handle_data_polo_partial_candles(context, data):
+    history = data.history(symbol('btc_usdt'), ['volume'],
+                           bar_count=10,
+                           frequency='4H')
+    print('\nnow: %s\n%s' % (data.current_dt, history))
+    if not hasattr(context, 'i'):
+        context.i = 0
+    context.i += 1
+    if context.i > 5:
+        raise Exception('stop')
+
+
+live = False
+
+if live:
+    run_algorithm(initialize=lambda ctx: True,
+                  handle_data=handle_data_polo_partial_candles,
+                  exchange_name='poloniex',
+                  base_currency='usdt',
+                  algo_namespace='ns',
+                  live=True,
+                  data_frequency='minute',
+                  capital_base=3000)
+else:
+    run_algorithm(initialize=lambda ctx: True,
+                  handle_data=handle_data_polo_partial_candles,
+                  exchange_name='poloniex',
+                  base_currency='usdt',
+                  algo_namespace='ns',
+                  live=False,
+                  data_frequency='minute',
+                  capital_base=3000,
+                  start=datetime(2018, 2, 2, 0, 0, 0, 0, pytz.utc),
+                  end=datetime(2018, 2, 20, 0, 0, 0, 0, pytz.utc)
+                  )

catalyst/support/issue_236.py

@@ -0,0 +1,32 @@
+from catalyst.api import symbol
+from catalyst.utils.run_algo import run_algorithm
+
+coins = ['dash', 'btc', 'dash', 'etc', 'eth', 'ltc', 'nxt', 'rep', 'str', 'xmr', 'xrp', 'zec']
+symbols = None
+
+
+def initialize(context):
+    pass
+
+
+def _handle_data(context, data):
+    global symbols
+    if symbols is None: symbols = [symbol(c + '_usdt') for c in coins]
+
+    print'getting history for: %s' % [s.symbol for s in symbols]
+    history = data.history(symbols,
+                    ['close', 'volume'],
+                    bar_count=1, # EXCEPTION, Change to 2
+                    frequency='5T')
+    #print 'history: %s' % history.shape
+
+run_algorithm(initialize=initialize,
+              handle_data=_handle_data,
+            analyze=lambda _, results: True,
+            exchange_name='poloniex',
+            base_currency='usdt',
+            algo_namespace='issue-236',
+            live=True,
+            data_frequency='minute',
+            capital_base=3000,
+            simulate_orders=True)

catalyst/support/issue_274.py

@@ -0,0 +1,35 @@
+import pytz
+from datetime import datetime
+from catalyst.api import symbol
+from catalyst.utils.run_algo import run_algorithm
+
+coin = 'btc'
+base_currency = 'usd'
+
+
+def initialize(context):
+    context.symbol = symbol('%s_%s' % (coin, base_currency))
+
+
+def handle_data_polo_partial_candles(context, data):
+    history = data.history(symbol('btc_usdt'), ['volume'],
+                           bar_count=10,
+                           frequency='1D')
+    print('\nnow: %s\n%s' % (data.current_dt, history))
+    if not hasattr(context, 'i'):
+        context.i = 0
+    context.i += 1
+    if context.i > 5:
+        raise Exception('stop')
+
+
+run_algorithm(initialize=lambda ctx: True,
+              handle_data=handle_data_polo_partial_candles,
+              exchange_name='poloniex',
+              base_currency='usdt',
+              algo_namespace='ns',
+              live=False,
+              data_frequency='minute',
+              capital_base=3000,
+              start=datetime(2018, 2, 2, 0, 0, 0, 0, pytz.utc),
+              end=datetime(2018, 2, 20, 0, 0, 0, 0, pytz.utc))

catalyst/utils/calendars/exchange_calendar_ice.py

@@ -7,7 +7,7 @@ from pandas.tseries.holiday import (
     USLaborDay,
     USThanksgivingDay
 )
-from pandas.tslib import Timestamp
+from pandas import Timestamp
 from pytz import timezone
 
 from catalyst.utils.calendars import TradingCalendar

catalyst/utils/calendars/trading_calendar.py

@@ -640,12 +640,9 @@ class TradingCalendar(with_metaclass(ABCMeta)):
         """
         sched = self.schedule
 
-        # `market_open` and `market_close` should be timezone aware, but pandas
-        # 0.16.1 does not appear to support this:
-        # http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#datetime-with-tz  # noqa
         return (
-            sched.at[session_label, 'market_open'].tz_localize('UTC'),
-            sched.at[session_label, 'market_close'].tz_localize('UTC'),
+            sched.at[session_label, 'market_open'],
+            sched.at[session_label, 'market_close'],
         )
 
     def session_open(self, session_label):

catalyst/utils/factory.py

@@ -117,9 +117,9 @@ def create_dividend(sid, payment, declared_date, ex_date, pay_date):
         'net_amount': payment,
         'payment_sid': None,
         'ratio': None,
-        'declared_date': pd.tslib.normalize_date(declared_date),
-        'ex_date': pd.tslib.normalize_date(ex_date),
-        'pay_date': pd.tslib.normalize_date(pay_date),
+        'declared_date': declared_date.normalize(),
+        'ex_date': ex_date.normalize(),
+        'pay_date': pay_date.normalize(),
         'type': DATASOURCE_TYPE.DIVIDEND,
         'source_id': 'MockDividendSource'
     })
@@ -134,9 +134,9 @@ def create_stock_dividend(sid, payment_sid, ratio, declared_date,
         'ratio': ratio,
         'net_amount': None,
         'gross_amount': None,
-        'dt': pd.tslib.normalize_date(declared_date),
-        'ex_date': pd.tslib.normalize_date(ex_date),
-        'pay_date': pd.tslib.normalize_date(pay_date),
+        'dt': declared_date.normalize(),
+        'ex_date': ex_date.normalize(),
+        'pay_date': pay_date.normalize(),
         'type': DATASOURCE_TYPE.DIVIDEND,
         'source_id': 'MockDividendSource'
     })

catalyst/utils/run_algo.py

@@ -8,13 +8,15 @@ from time import sleep
 
 import click
 import pandas as pd
-from logbook import Logger
+from six import string_types
 
+import catalyst
 from catalyst.data.bundles import load
 from catalyst.data.data_portal import DataPortal
 from catalyst.exchange.exchange_pricing_loader import ExchangePricingLoader, \
     TradingPairPricing
 from catalyst.exchange.utils.factory import get_exchange
+from logbook import Logger
 
 try:
     from pygments import highlight
@@ -22,7 +24,7 @@ try:
     from pygments.formatters import TerminalFormatter
 
     PYGMENTS = True
-except:
+except ImportError:
     PYGMENTS = False
 from toolz import valfilter, concatv
 from functools import partial
@@ -40,9 +42,6 @@ from catalyst.exchange.exchange_algorithm import (
 from catalyst.exchange.exchange_data_portal import DataPortalExchangeLive, \
     DataPortalExchangeBacktest
 from catalyst.exchange.exchange_asset_finder import ExchangeAssetFinder
-from catalyst.exchange.exchange_errors import (
-    ExchangeRequestError, ExchangeRequestErrorTooManyAttempts,
-    BaseCurrencyNotFoundError, NotEnoughCapitalError)
 
 from catalyst.constants import LOG_LEVEL
 
@@ -57,6 +56,7 @@ class _RunAlgoError(click.ClickException, ValueError):
     ----------
     pyfunc_msg : str
         The message that will be shown when called as a python function.
+
     cmdline_msg : str
         The message that will be shown on the command line.
     """
@@ -70,7 +70,38 @@ class _RunAlgoError(click.ClickException, ValueError):
         return self.pyfunc_msg
 
 
-def _build_namespace(algotext, local_namespace, defines):
+def _run(handle_data,
+         initialize,
+         before_trading_start,
+         analyze,
+         algofile,
+         algotext,
+         defines,
+         data_frequency,
+         capital_base,
+         data,
+         bundle,
+         bundle_timestamp,
+         start,
+         end,
+         output,
+         print_algo,
+         local_namespace,
+         environ,
+         live,
+         exchange,
+         algo_namespace,
+         base_currency,
+         live_graph,
+         analyze_live,
+         simulate_orders,
+         auth_aliases,
+         stats_output):
+    """Run a backtest for the given algorithm.
+
+    This is shared between the cli and :func:`catalyst.run_algo`.
+    """
+    # TODO: refactor for more granularity
     if algotext is not None:
         if local_namespace:
             ip = get_ipython()  # noqa
@@ -84,197 +115,146 @@ def _build_namespace(algotext, local_namespace, defines):
             except ValueError:
                 raise ValueError(
                     'invalid define %r, should be of the form name=value' %
-                    assign)
+                    assign,
+                )
             try:
                 # evaluate in the same namespace so names may refer to
                 # eachother
                 namespace[name] = eval(value, namespace)
             except Exception as e:
                 raise ValueError(
-                    'failed to execute definition for name %r: %s' % (name, e))
+                    'failed to execute definition for name %r: %s' % (name, e),
+                )
     elif defines:
         raise _RunAlgoError(
             'cannot pass define without `algotext`',
-            "cannot pass '-D' / '--define' without '-t' / '--algotext'")
+            "cannot pass '-D' / '--define' without '-t' / '--algotext'",
+        )
     else:
         namespace = {}
+        if algofile is not None:
+            algotext = algofile.read()
 
-    return namespace
+    if print_algo:
+        if PYGMENTS:
+            highlight(
+                algotext,
+                PythonLexer(),
+                TerminalFormatter(),
+                outfile=sys.stdout,
+            )
+        else:
+            click.echo(algotext)
 
+    log.warn(
+        'Catalyst is currently in ALPHA. It is going through rapid '
+        'development and it is subject to errors. Please use carefully. '
+        'We encourage you to report any issue on GitHub: '
+        'https://github.com/enigmampc/catalyst/issues'
+    )
+    log.info('Catalyst version {}'.format(catalyst.__version__))
+    sleep(3)
 
-def _mode(simulate_orders, live):
-    if not live:
-        return 'backtest'
-    elif simulate_orders:
-        return 'paper-trading'
+    if live:
+        if simulate_orders:
+            mode = 'paper-trading'
+        else:
+            mode = 'live-trading'
     else:
-        return 'live-trading'
+        mode = 'backtest'
 
+    log.info('running algo in {mode} mode'.format(mode=mode))
 
-def _build_exchanges_dict(exchange, live, simulate_orders, base_currency):
     exchange_name = exchange
     if exchange_name is None:
         raise ValueError('Please specify at least one exchange.')
 
+    if isinstance(auth_aliases, string_types):
+        aliases = auth_aliases.split(',')
+        if len(aliases) < 2 or len(aliases) % 2 != 0:
+            raise ValueError(
+                'the `auth_aliases` parameter must contain an even list '
+                'of comma-delimited values. For example, '
+                '"binance,auth2" or "binance,auth2,bittrex,auth2".'
+            )
+
+        auth_aliases = dict(zip(aliases[::2], aliases[1::2]))
+
     exchange_list = [x.strip().lower() for x in exchange.split(',')]
-
-    exchanges = {exchange_name: get_exchange(
-                                    exchange_name=exchange_name,
-                                    base_currency=base_currency,
-                                    must_authenticate=(live and not simulate_orders))
-                 for exchange_name in exchange_list}
-
-    return exchanges
-
-
-def _pretty_print_code(algotext):
-    if PYGMENTS:
-        highlight(
-            algotext,
-            PythonLexer(),
-            TerminalFormatter(),
-            outfile=sys.stdout)
-    else:
-        click.echo(algotext)
-
-
-def _choose_loader(data_frequency, column):
-    bound_cols = TradingPairPricing.columns
-    if column in bound_cols:
-        return ExchangePricingLoader(data_frequency)
-    raise ValueError(
-        "No PipelineLoader registered for column %s." % column)
-
-
-def _get_live_time_range():
-    start = pd.Timestamp.utcnow()
-    # TODO: fix the end data.
-    end = start + timedelta(hours=8760)
-    return start, end
-
-
-def _data_for_live_trading(sim_params, exchanges, env, open_calendar):
-    data = DataPortalExchangeLive(
-        exchanges=exchanges,
-        asset_finder=env.asset_finder,
-        trading_calendar=open_calendar,
-        first_trading_day=pd.to_datetime('today', utc=True))
-
-    return data
-
-
-# TODO use proper retry here
-def _fetch_capital_base(base_currency, exchange_name, 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(base_currency, exchange_name, exchange,
-                                       attempt_index + 1)
-
+    exchanges = dict()
+    for name in exchange_list:
+        if auth_aliases is not None and name in auth_aliases:
+            auth_alias = auth_aliases[name]
         else:
-            raise ExchangeRequestErrorTooManyAttempts(
-                attempts=attempt_index,
-                error=e)
+            auth_alias = None
 
-    if base_currency in balances:
-        base_currency_available = balances[base_currency]['free']
-        log.info(
-            'base currency available in the account: {} {}'.format(
-                base_currency_available, base_currency))
-
-        return base_currency_available
-    else:
-        raise BaseCurrencyNotFoundError(
+        exchanges[name] = get_exchange(
+            exchange_name=name,
             base_currency=base_currency,
-            exchange=exchange_name)
+            must_authenticate=(live and not simulate_orders),
+            skip_init=True,
+            auth_alias=auth_alias,
+        )
 
+    open_calendar = get_calendar('OPEN')
 
-def _algorithm_class_for_live(algo_namespace, live_graph, stats_output,
-                              analyze_live, base_currency, simulate_orders,
-                              exchanges, capital_base):
-    if not simulate_orders:
-        for exchange_name in exchanges:
-            exchange = exchanges[exchange_name]
-            balance = _fetch_capital_base(base_currency, exchange_name,
-                                          exchange)
+    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 = ExchangeAssetFinder(exchanges=exchanges)
 
-            if balance < capital_base:
-                raise NotEnoughCapitalError(
-                    exchange=exchange_name,
-                    base_currency=base_currency,
-                    balance=balance,
-                    capital_base=capital_base)
-
-    algorithm_class = partial(
-        ExchangeTradingAlgorithmLive,
-        exchanges=exchanges,
-        algo_namespace=algo_namespace,
-        live_graph=live_graph,
-        simulate_orders=simulate_orders,
-        stats_output=stats_output,
-        analyze_live=analyze_live,)
-
-    return algorithm_class
-
-
-def _bundle_trading_environment(bundle_data, environ):
-    prefix, connstr = re.split(
-        r'sqlite:///',
-        str(bundle_data.asset_finder.engine.url),
-        maxsplit=1)
-    if prefix:
+    def choose_loader(column):
+        bound_cols = TradingPairPricing.columns
+        if column in bound_cols:
+            return ExchangePricingLoader(data_frequency)
         raise ValueError(
-            "invalid url %r, must begin with 'sqlite:///'" %
-            str(bundle_data.asset_finder.engine.url))
+            "No PipelineLoader registered for column %s." % column
+        )
 
-    return TradingEnvironment(asset_db_path=connstr, environ=environ)
+    if live:
+        start = pd.Timestamp.utcnow()
 
+        # TODO: fix the end data.
+        if end is None:
+            end = start + timedelta(hours=8760)
 
-def _build_live_algo_and_data(sim_params, exchanges, env, open_calendar,
-                              simulate_orders, algo_namespace, capital_base,
-                              live_graph, stats_output, analyze_live,
-                              base_currency, namespace, choose_loader,
-                              algorithm_class_kwargs):
-    sim_params._arena = 'live'  # TODO: use the constructor instead
+        data = DataPortalExchangeLive(
+            exchanges=exchanges,
+            asset_finder=env.asset_finder,
+            trading_calendar=open_calendar,
+            first_trading_day=pd.to_datetime('today', utc=True)
+        )
 
-    data = _data_for_live_trading(sim_params, exchanges, env, open_calendar)
+        sim_params = create_simulation_parameters(
+            start=start,
+            end=end,
+            capital_base=capital_base,
+            emission_rate='minute',
+            data_frequency='minute'
+        )
 
-    algorithm_class = _algorithm_class_for_live(
-        algo_namespace, live_graph, stats_output, analyze_live,
-        base_currency, simulate_orders, exchanges, capital_base)
+        # TODO: use the constructor instead
+        sim_params._arena = 'live'
 
-    return data, algorithm_class(
-        namespace=namespace,
-        env=env,
-        get_pipeline_loader=choose_loader,
-        sim_params=sim_params,
-        **algorithm_class_kwargs)
-
-
-def _build_backtest_algo_and_data(
-        exchanges, bundle, env, environ, bundle_timestamp, open_calendar,
-        start, end, namespace, choose_loader, sim_params,
-        algorithm_class_kwargs):
-    if exchanges:
+        algorithm_class = partial(
+            ExchangeTradingAlgorithmLive,
+            exchanges=exchanges,
+            algo_namespace=algo_namespace,
+            live_graph=live_graph,
+            simulate_orders=simulate_orders,
+            stats_output=stats_output,
+            analyze_live=analyze_live,
+            end=end,
+        )
+    elif exchanges:
         # Removed the existing Poloniex fork to keep things simple
         # We can add back the complexity if required.
 
@@ -283,24 +263,55 @@ def _build_backtest_algo_and_data(
         # We still need to support bundles for other misc data, but we
         # can handle this later.
 
+        if start != pd.Timestamp(start).normalize() or \
+                        end != pd.Timestamp(end).normalize():
+            # todo: add to Sim_Params the option to start & end at specific times
+            log.warn(
+                "Catalyst currently starts and ends on the start and "
+                "end of the dates specified, respectively. We hope to "
+                "Modify this and support specific times in a future release."
+            )
+
         data = DataPortalExchangeBacktest(
             exchange_names=[exchange_name for exchange_name in exchanges],
             asset_finder=None,
             trading_calendar=open_calendar,
             first_trading_day=start,
-            last_available_session=end)
+            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)
+            exchanges=exchanges
+        )
+
     elif bundle is not None:
-        # TODO This branch should probably be removed or fixed: it doesn't even
-        # build `algorithm_class`, so it will break when trying to instantiate
-        # it.
-        bundle_data = load(bundle, environ, bundle_timestamp)
+        bundle_data = load(
+            bundle,
+            environ,
+            bundle_timestamp,
+        )
 
-        env = _bundle_trading_environment(bundle_data, environ)
+        prefix, connstr = re.split(
+            r'sqlite:///',
+            str(bundle_data.asset_finder.engine.url),
+            maxsplit=1,
+        )
+        if prefix:
+            raise ValueError(
+                "invalid url %r, must begin with 'sqlite:///'" %
+                str(bundle_data.asset_finder.engine.url),
+            )
 
+        env = TradingEnvironment(asset_db_path=connstr, environ=environ)
         first_trading_day = \
             bundle_data.equity_minute_bar_reader.first_trading_day
 
@@ -309,103 +320,27 @@ def _build_backtest_algo_and_data(
             first_trading_day=first_trading_day,
             equity_minute_reader=bundle_data.equity_minute_bar_reader,
             equity_daily_reader=bundle_data.equity_daily_bar_reader,
-            adjustment_reader=bundle_data.adjustment_reader)
+            adjustment_reader=bundle_data.adjustment_reader,
+        )
 
-    return data, algorithm_class(
+    perf = algorithm_class(
         namespace=namespace,
         env=env,
         get_pipeline_loader=choose_loader,
         sim_params=sim_params,
-        **algorithm_class_kwargs)
-
-
-def _build_algo_and_data(handle_data, initialize, before_trading_start,
-                         analyze, algofile, algotext, defines, data_frequency,
-                         capital_base, data, bundle, bundle_timestamp, start,
-                         end, output, print_algo, local_namespace, environ,
-                         live, exchange, algo_namespace, base_currency,
-                         live_graph, analyze_live, simulate_orders,
-                         stats_output):
-    namespace = _build_namespace(algotext, local_namespace, defines)
-    if algotext is not None:
-        algotext = algofile.read()
-
-    if print_algo:
-        _pretty_print_code(algotext)
-
-    mode = _mode(simulate_orders, live)
-    log.info('running algo in {mode} mode'.format(mode=mode))
-
-    exchanges = _build_exchanges_dict(exchange, live, simulate_orders,
-                                      base_currency)
-
-    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 = ExchangeAssetFinder(exchanges=exchanges)
-
-    choose_loader = partial(_choose_loader, data_frequency)
-
-    if live:
-        start, end = _get_live_time_range()
-        data_frequency = 'minute'  # TODO double check if this is the desired behavior
-
-    sim_params = create_simulation_parameters(
-        start=start,
-        end=end,
-        capital_base=capital_base,
-        emission_rate=data_frequency,
-        data_frequency=data_frequency)
-
-    if algotext is None:
-        algorithm_class_kwargs = {'initialize': initialize,
-                                  'handle_data': handle_data,
-                                  'before_trading_start': before_trading_start,
-                                  'analyze': analyze}
-    else:
-        algorithm_class_kwargs = {'algo_filename': getattr(algofile, 'name',
-                                                           '<algorithm>'),
-                                  'script': algotext}
-
-    if live:
-        return _build_live_algo_and_data(
-            sim_params, exchanges, env, open_calendar, simulate_orders,
-            algo_namespace, capital_base, live_graph, stats_output,
-            analyze_live, base_currency, namespace, choose_loader,
-            algorithm_class_kwargs)
-    else:
-        return _build_backtest_algo_and_data(
-            exchanges, bundle, env, environ, bundle_timestamp, open_calendar,
-            start, end, namespace, choose_loader, sim_params,
-            algorithm_class_kwargs)
-
-
-def _run(handle_data, initialize, before_trading_start, analyze, algofile,
-         algotext, defines, data_frequency, capital_base, data, bundle,
-         bundle_timestamp, start, end, output, print_algo, local_namespace,
-         environ, live, exchange, algo_namespace, base_currency, live_graph,
-         analyze_live, simulate_orders, stats_output):
-    """Run an algorithm in backtest,
-    paper-trading or live-trading mode.
-
-    This is shared between the cli and :func:`catalyst.run_algo`.
-    """
-
-    data, algorithm = _build_algo_and_data(
-        handle_data, initialize, before_trading_start, analyze, algofile,
-        algotext, defines, data_frequency, capital_base, data, bundle,
-        bundle_timestamp, start, end, output, print_algo, local_namespace,
-        environ, live, exchange, algo_namespace, base_currency, live_graph,
-        analyze_live, simulate_orders, stats_output)
-    perf = algorithm.run(
+        **{
+            'initialize': initialize,
+            'handle_data': handle_data,
+            'before_trading_start': before_trading_start,
+            'analyze': analyze,
+        } if algotext is None else {
+            'algo_filename': getattr(algofile, 'name', '<algorithm>'),
+            'script': algotext,
+        }
+    ).run(
         data,
-        overwrite_sim_params=False)
+        overwrite_sim_params=False,
+    )
 
     if output == '-':
         click.echo(str(perf))
@@ -462,7 +397,8 @@ def load_extensions(default, extensions, strict, environ, reload=False):
             # without `strict` we should just log the failure
             warnings.warn(
                 'Failed to load extension: %r\n%s' % (ext, e),
-                stacklevel=2)
+                stacklevel=2
+            )
         else:
             _loaded_extensions.add(ext)
 
@@ -489,9 +425,11 @@ def run_algorithm(initialize,
                   live_graph=False,
                   analyze_live=None,
                   simulate_orders=True,
+                  auth_aliases=None,
                   stats_output=None,
                   output=os.devnull):
-    """Run a trading algorithm.
+    """
+    Run a trading algorithm.
 
     Parameters
     ----------
@@ -533,7 +471,7 @@ def run_algorithm(initialize,
         This argument is mutually exclusive with ``data``.
     default_extension : bool, optional
         Should the default catalyst extension be loaded. This is found at
-        ``$ZIPLINE_ROOT/extension.py``
+        ``$CATALYST_ROOT/extension.py``
     extensions : iterable[str], optional
         The names of any other extensions to load. Each element may either be
         a dotted module path like ``a.b.c`` or a path to a python file ending
@@ -544,12 +482,8 @@ def run_algorithm(initialize,
     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
+    live : bool, optional
+        Execute algorithm in live trading mode.
 
     Returns
     -------
@@ -561,7 +495,8 @@ def run_algorithm(initialize,
     catalyst.data.bundles.bundles : The available data bundles.
     """
     load_extensions(
-        default_extension, extensions, strict_extensions, environ)
+        default_extension, extensions, strict_extensions, environ
+    )
 
     if capital_base is None:
         raise ValueError(
@@ -569,7 +504,8 @@ def run_algorithm(initialize,
             'amount of base currency available for trading. For example, '
             'if the `capital_base` is 5ETH, the '
             '`order_target_percent(asset, 1)` command will order 5ETH worth '
-            'of the specified asset.')
+            'of the specified asset.'
+        )
     # I'm not sure that we need this since the modified DataPortal
     # does not require extensions to be explicitly loaded.
 
@@ -587,11 +523,13 @@ def run_algorithm(initialize,
         elif len(non_none_data) != 1:
             raise ValueError(
                 'must specify one of `data`, `data_portal`, or `bundle`,'
-                ' got: %r' % non_none_data)
+                ' 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`')
+                'cannot specify `bundle_timestamp` without passing `bundle`',
+            )
     return _run(
         handle_data=handle_data,
         initialize=initialize,
@@ -618,4 +556,6 @@ def run_algorithm(initialize,
         live_graph=live_graph,
         analyze_live=analyze_live,
         simulate_orders=simulate_orders,
-        stats_output=stats_output)
+        auth_aliases=auth_aliases,
+        stats_output=stats_output
+    )

docs/Makefile

@@ -23,7 +23,7 @@ I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
 
 help:
 	@echo "Please use \`make <target>' where <target> is one of"
-	@echo "  build      to build the C and Cython extensions for zipline"
+	@echo "  build      to build the C and Cython extensions for catalyst"
 	@echo "  html       to make standalone HTML files"
 	@echo "  livehtml   to run a persistent process that rebuilds the docs"
 	@echo "  dirhtml    to make HTML files named index.html in directories"
@@ -96,9 +96,9 @@ qthelp: build
 	@echo
 	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
 	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
-	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/zipline.qhcp"
+	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/catalyst.qhcp"
 	@echo "To view the help file:"
-	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/zipline.qhc"
+	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/catalyst.qhc"
 
 applehelp: build
 	$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
@@ -113,8 +113,8 @@ devhelp: build
 	@echo
 	@echo "Build finished."
 	@echo "To view the help file:"
-	@echo "# mkdir -p $$HOME/.local/share/devhelp/zipline"
-	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/zipline"
+	@echo "# mkdir -p $$HOME/.local/share/devhelp/catalyst"
+	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/catalyst"
 	@echo "# devhelp"
 
 epub: build

docs/deploy.py

@@ -8,8 +8,8 @@ from shutil import move, rmtree
 from subprocess import check_call
 
 HERE = dirname(abspath(__file__))
-ZIPLINE_ROOT = dirname(HERE)
-TEMP_LOCATION = '/tmp/zipline-doc'
+CATALYST_ROOT = dirname(HERE)
+TEMP_LOCATION = '/tmp/catalyst-doc'
 TEMP_LOCATION_GLOB = TEMP_LOCATION + '/*'
 
 
@@ -46,8 +46,8 @@ def main():
             print("Copying built files to temp location.")
             move('build/html', TEMP_LOCATION)
 
-            print("Moving to '%s'" % ZIPLINE_ROOT)
-            os.chdir(ZIPLINE_ROOT)
+            print("Moving to '%s'" % CATALYST_ROOT)
+            os.chdir(CATALYST_ROOT)
 
             print("Checking out gh-pages branch.")
             check_call(
@@ -70,7 +70,7 @@ def main():
         os.chdir(old_dir)
 
     print()
-    print("Updated documentation branch in directory %s" % ZIPLINE_ROOT)
+    print("Updated documentation branch in directory %s" % CATALYST_ROOT)
     print("If you are happy with these changes, commit and push to gh-pages.")
 
 if __name__ == '__main__':

docs/make.bat

@@ -127,9 +127,9 @@ if "%1" == "qthelp" (
 	echo.
 	echo.Build finished; now you can run "qcollectiongenerator" with the ^
 .qhcp project file in %BUILDDIR%/qthelp, like this:
-	echo.^> qcollectiongenerator %BUILDDIR%\qthelp\zipline.qhcp
+	echo.^> qcollectiongenerator %BUILDDIR%\qthelp\catalyst.qhcp
 	echo.To view the help file:
-	echo.^> assistant -collectionFile %BUILDDIR%\qthelp\zipline.ghc
+	echo.^> assistant -collectionFile %BUILDDIR%\qthelp\catalyst.ghc
 	goto end
 )
 

docs/source/appendix.rst

@@ -4,7 +4,7 @@ API Reference
 Running a Backtest
 ~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: zipline.run_algorithm(...)
+.. autofunction:: catalyst.run_algorithm(...)
 
 Algorithm API
 ~~~~~~~~~~~~~
@@ -18,341 +18,335 @@ currently-executing :class:`~zipline.algorithm.TradingAlgorithm` instance.
 Data Object
 ```````````
 
-.. autoclass:: zipline.protocol.BarData
+.. autoclass:: catalyst.protocol.BarData
    :members:
 
 Scheduling Functions
 ````````````````````
 
-.. autofunction:: zipline.api.schedule_function
+.. autofunction:: catalyst.api.schedule_function
 
-.. autoclass:: zipline.api.date_rules
+.. autoclass:: catalyst.api.date_rules
    :members:
    :undoc-members:
 
-.. autoclass:: zipline.api.time_rules
+.. autoclass:: catalyst.api.time_rules
    :members:
 
 Orders
 ``````
 
-.. autofunction:: zipline.api.order
+.. autofunction:: catalyst.api.order
 
-.. autofunction:: zipline.api.order_value
+.. autofunction:: catalyst.api.order_value
 
-.. autofunction:: zipline.api.order_percent
+.. autofunction:: catalyst.api.order_percent
 
-.. autofunction:: zipline.api.order_target
+.. autofunction:: catalyst.api.order_target
 
-.. autofunction:: zipline.api.order_target_value
+.. autofunction:: catalyst.api.order_target_value
 
-.. autofunction:: zipline.api.order_target_percent
+.. autofunction:: catalyst.api.order_target_percent
 
-.. autoclass:: zipline.finance.execution.ExecutionStyle
+.. autoclass:: catalyst.finance.execution.ExecutionStyle
    :members:
 
-.. autoclass:: zipline.finance.execution.MarketOrder
+.. autoclass:: catalyst.finance.execution.MarketOrder
 
-.. autoclass:: zipline.finance.execution.LimitOrder
+.. autoclass:: catalyst.finance.execution.LimitOrder
 
-.. autoclass:: zipline.finance.execution.StopOrder
+.. autoclass:: catalyst.finance.execution.StopOrder
 
-.. autoclass:: zipline.finance.execution.StopLimitOrder
+.. autoclass:: catalyst.finance.execution.StopLimitOrder
 
-.. autofunction:: zipline.api.get_order
+.. autofunction:: catalyst.api.get_order
 
-.. autofunction:: zipline.api.get_open_orders
+.. autofunction:: catalyst.api.get_open_orders
 
-.. autofunction:: zipline.api.cancel_order
+.. autofunction:: catalyst.api.cancel_order
 
 Order Cancellation Policies
 '''''''''''''''''''''''''''
 
-.. autofunction:: zipline.api.set_cancel_policy
+.. autofunction:: catalyst.api.set_cancel_policy
 
-.. autoclass:: zipline.finance.cancel_policy.CancelPolicy
+.. autoclass:: catalyst.finance.cancel_policy.CancelPolicy
    :members:
 
-.. autofunction:: zipline.api.EODCancel
+.. autofunction:: catalyst.api.EODCancel
 
-.. autofunction:: zipline.api.NeverCancel
+.. autofunction:: catalyst.api.NeverCancel
 
 
 Assets
 ``````
 
-.. autofunction:: zipline.api.symbol
+.. autofunction:: catalyst.api.symbol
 
-.. autofunction:: zipline.api.symbols
+.. autofunction:: catalyst.api.symbols
 
-.. autofunction:: zipline.api.future_symbol
+.. autofunction:: catalyst.api.set_symbol_lookup_date
 
-.. autofunction:: zipline.api.set_symbol_lookup_date
-
-.. autofunction:: zipline.api.sid
+.. autofunction:: catalyst.api.sid
 
 
 Trading Controls
 ````````````````
 
-Zipline provides trading controls to help ensure that the algorithm is
+zipline provides trading controls to help ensure that the algorithm is
 performing as expected. The functions help protect the algorithm from certian
 bugs that could cause undesirable behavior when trading with real money.
 
-.. autofunction:: zipline.api.set_do_not_order_list
+.. autofunction:: catalyst.api.set_do_not_order_list
 
-.. autofunction:: zipline.api.set_long_only
+.. autofunction:: catalyst.api.set_long_only
 
-.. autofunction:: zipline.api.set_max_leverage
+.. autofunction:: catalyst.api.set_max_leverage
 
-.. autofunction:: zipline.api.set_max_order_count
+.. autofunction:: catalyst.api.set_max_order_count
 
-.. autofunction:: zipline.api.set_max_order_size
+.. autofunction:: catalyst.api.set_max_order_size
 
-.. autofunction:: zipline.api.set_max_position_size
+.. autofunction:: catalyst.api.set_max_position_size
 
 
 Simulation Parameters
 `````````````````````
 
-.. autofunction:: zipline.api.set_benchmark
+.. autofunction:: catalyst.api.set_benchmark
 
 Commission Models
 '''''''''''''''''
 
-.. autofunction:: zipline.api.set_commission
+.. autofunction:: catalyst.api.set_commission
 
-.. autoclass:: zipline.finance.commission.CommissionModel
+.. autoclass:: catalyst.finance.commission.CommissionModel
    :members:
 
-.. autoclass:: zipline.finance.commission.PerShare
+.. autoclass:: catalyst.finance.commission.PerShare
 
-.. autoclass:: zipline.finance.commission.PerTrade
+.. autoclass:: catalyst.finance.commission.PerTrade
 
-.. autoclass:: zipline.finance.commission.PerDollar
+.. autoclass:: catalyst.finance.commission.PerDollar
 
 Slippage Models
 '''''''''''''''
 
-.. autofunction:: zipline.api.set_slippage
+.. autofunction:: catalyst.api.set_slippage
 
-.. autoclass:: zipline.finance.slippage.SlippageModel
+.. autoclass:: catalyst.finance.slippage.SlippageModel
    :members:
 
-.. autoclass:: zipline.finance.slippage.FixedSlippage
+.. autoclass:: catalyst.finance.slippage.FixedSlippage
 
-.. autoclass:: zipline.finance.slippage.VolumeShareSlippage
+.. autoclass:: catalyst.finance.slippage.VolumeShareSlippage
 
 Pipeline
 ````````
 
-For more information, see :ref:`pipeline-api`
+Not supported yet.
 
-.. autofunction:: zipline.api.attach_pipeline
+.. For more information, see :ref:`pipeline-api`
 
-.. autofunction:: zipline.api.pipeline_output
+.. .. autofunction:: catalyst.api.attach_pipeline
+
+.. .. autofunction:: catalyst.api.pipeline_output
 
 
 Miscellaneous
 `````````````
 
-.. autofunction:: zipline.api.record
+.. autofunction:: catalyst.api.record
 
-.. autofunction:: zipline.api.get_environment
+.. autofunction:: catalyst.api.get_environment
 
-.. autofunction:: zipline.api.fetch_csv
+.. autofunction:: catalyst.api.fetch_csv
 
 
 .. _pipeline-api:
 
-Pipeline API
-~~~~~~~~~~~~
+.. Pipeline API
+.. ~~~~~~~~~~~~
 
-.. autoclass:: zipline.pipeline.Pipeline
-   :members:
-   :member-order: groupwise
+.. .. autoclass:: zipline.pipeline.Pipeline
+..    :members:
+..    :member-order: groupwise
 
-.. autoclass:: zipline.pipeline.CustomFactor
-   :members:
-   :member-order: groupwise
+.. .. autoclass:: zipline.pipeline.CustomFactor
+..    :members:
+..    :member-order: groupwise
 
-.. autoclass:: zipline.pipeline.filters.Filter
-   :members: __and__, __or__
-   :exclude-members: dtype
+.. .. autoclass:: zipline.pipeline.filters.Filter
+..    :members: __and__, __or__
+..    :exclude-members: dtype
 
-.. autoclass:: zipline.pipeline.factors.Factor
-   :members: bottom, deciles, demean, linear_regression, pearsonr,
-             percentile_between, quantiles, quartiles, quintiles, rank,
-             spearmanr, top, winsorize, zscore, isnan, notnan, isfinite, eq,
-             __add__, __sub__, __mul__, __div__, __mod__, __pow__, __lt__,
-             __le__, __ne__, __ge__, __gt__
-   :exclude-members: dtype
-   :member-order: bysource
+.. .. autoclass:: zipline.pipeline.factors.Factor
+..    :members: bottom, deciles, demean, linear_regression, pearsonr,
+..              percentile_between, quantiles, quartiles, quintiles, rank,
+..              spearmanr, top, winsorize, zscore, isnan, notnan, isfinite, eq,
+..              \__add__, \__sub__, \__mul__, \__div__, \__mod__, \__pow__, 
+..              \__lt__, \__le__, \__ne__, \__ge__, \__gt__
+..    :exclude-members: dtype
+..    :member-order: bysource
 
-.. autoclass:: zipline.pipeline.term.Term
-   :members:
-   :exclude-members: compute_extra_rows, dependencies, inputs, mask, windowed
+.. .. autoclass:: zipline.pipeline.term.Term
+..    :members:
+..    :exclude-members: compute_extra_rows, dependencies, inputs, mask, windowed
 
-.. autoclass:: zipline.pipeline.data.USEquityPricing
-   :members: open, high, low, close, volume
-   :undoc-members:
+.. .. autoclass:: zipline.pipeline.data.USEquityPricing
+..    :members: open, high, low, close, volume
+..    :undoc-members:
 
-Built-in Factors
-````````````````
+.. Built-in Factors
+.. ````````````````
 
-.. autoclass:: zipline.pipeline.factors.AverageDollarVolume
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.AverageDollarVolume
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.BollingerBands
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.BollingerBands
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.BusinessDaysSincePreviousEvent
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.BusinessDaysSincePreviousEvent
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.BusinessDaysUntilNextEvent
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.BusinessDaysUntilNextEvent
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingAverage
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingAverage
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingStdDev
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingStdDev
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.Latest
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.Latest
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.MaxDrawdown
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.MaxDrawdown
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.Returns
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.Returns
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.RollingLinearRegressionOfReturns
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.RollingLinearRegressionOfReturns
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.RollingPearsonOfReturns
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.RollingPearsonOfReturns
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.RollingSpearmanOfReturns
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.RollingSpearmanOfReturns
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.RSI
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.RSI
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.SimpleMovingAverage
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.SimpleMovingAverage
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.VWAP
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.VWAP
+..    :members:
 
-.. autoclass:: zipline.pipeline.factors.WeightedAverageValue
-   :members:
+.. .. autoclass:: zipline.pipeline.factors.WeightedAverageValue
+..    :members:
 
-Pipeline Engine
-```````````````
+.. Pipeline Engine
+.. ```````````````
 
-.. autoclass:: zipline.pipeline.engine.PipelineEngine
-   :members: run_pipeline, run_chunked_pipeline
-   :member-order: bysource
+.. .. autoclass:: zipline.pipeline.engine.PipelineEngine
+..    :members: run_pipeline, run_chunked_pipeline
+..    :member-order: bysource
 
-.. autoclass:: zipline.pipeline.engine.SimplePipelineEngine
-   :members: __init__, run_pipeline, run_chunked_pipeline
-   :member-order: bysource
+.. .. autoclass:: zipline.pipeline.engine.SimplePipelineEngine
+..    :members: __init__, run_pipeline, run_chunked_pipeline
+..    :member-order: bysource
 
-.. autofunction:: zipline.pipeline.engine.default_populate_initial_workspace
+.. .. autofunction:: zipline.pipeline.engine.default_populate_initial_workspace
 
-Data Loaders
-````````````
+.. Data Loaders
+.. ````````````
 
-.. autoclass:: zipline.pipeline.loaders.equity_pricing_loader.USEquityPricingLoader
-   :members: __init__, from_files, load_adjusted_array
-   :member-order: bysource
+.. .. autoclass:: zipline.pipeline.loaders.equity_pricing_loader.USEquityPricingLoader
+..    :members: __init__, from_files, load_adjusted_array
+..    :member-order: bysource
 
 Asset Metadata
 ~~~~~~~~~~~~~~
 
-.. autoclass:: zipline.assets.Asset
+.. autoclass:: catalyst.assets.Asset
    :members:
 
-.. autoclass:: zipline.assets.Equity
-   :members:
-
-.. autoclass:: zipline.assets.Future
-   :members:
-
-.. autoclass:: zipline.assets.AssetConvertible
+.. autoclass:: catalyst.assets.AssetConvertible
    :members:
 
 
 Trading Calendar API
 ~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: zipline.utils.calendars.get_calendar
+.. autofunction:: catalyst.utils.calendars.get_calendar
 
-.. autoclass:: zipline.utils.calendars.TradingCalendar
+.. autoclass:: catalyst.utils.calendars.TradingCalendar
    :members:
 
-.. autofunction:: zipline.utils.calendars.register_calendar
+.. autofunction:: catalyst.utils.calendars.register_calendar
 
-.. autofunction:: zipline.utils.calendars.register_calendar_type
+.. autofunction:: catalyst.utils.calendars.register_calendar_type
 
-.. autofunction:: zipline.utils.calendars.deregister_calendar
+.. autofunction:: catalyst.utils.calendars.deregister_calendar
 
-.. autofunction:: zipline.utils.calendars.clear_calendars
+.. autofunction:: catalyst.utils.calendars.clear_calendars
 
 
 Data API
 ~~~~~~~~
 
-Writers
-```````
-.. autoclass:: zipline.data.minute_bars.BcolzMinuteBarWriter
-   :members:
+.. Writers
+.. ```````
+.. .. autoclass:: zipline.data.minute_bars.BcolzMinuteBarWriter
+..    :members:
 
-.. autoclass:: zipline.data.us_equity_pricing.BcolzDailyBarWriter
-   :members:
+.. .. autoclass:: zipline.data.us_equity_pricing.BcolzDailyBarWriter
+..    :members:
 
-.. autoclass:: zipline.data.us_equity_pricing.SQLiteAdjustmentWriter
-   :members:
+.. .. autoclass:: zipline.data.us_equity_pricing.SQLiteAdjustmentWriter
+..    :members:
 
-.. autoclass:: zipline.assets.AssetDBWriter
-   :members:
+.. .. autoclass:: zipline.assets.AssetDBWriter
+..    :members:
 
-Readers
-```````
-.. autoclass:: zipline.data.minute_bars.BcolzMinuteBarReader
-   :members:
+.. Readers
+.. ```````
+.. .. autoclass:: zipline.data.minute_bars.BcolzMinuteBarReader
+..    :members:
 
-.. autoclass:: zipline.data.us_equity_pricing.BcolzDailyBarReader
-   :members:
+.. .. autoclass:: zipline.data.us_equity_pricing.BcolzDailyBarReader
+..    :members:
 
-.. autoclass:: zipline.data.us_equity_pricing.SQLiteAdjustmentReader
-   :members:
+.. .. autoclass:: zipline.data.us_equity_pricing.SQLiteAdjustmentReader
+..    :members:
 
-.. autoclass:: zipline.assets.AssetFinder
-   :members:
+.. .. autoclass:: zipline.assets.AssetFinder
+..    :members:
 
-.. autoclass:: zipline.data.data_portal.DataPortal
-   :members:
+.. .. autoclass:: zipline.data.data_portal.DataPortal
+..    :members:
 
-Bundles
-```````
-.. autofunction:: zipline.data.bundles.register
+.. Bundles
+.. ```````
+.. .. autofunction:: zipline.data.bundles.register
 
-.. autofunction:: zipline.data.bundles.ingest(name, environ=os.environ, date=None, show_progress=True)
+.. .. autofunction:: zipline.data.bundles.ingest(name, environ=os.environ, date=None, show_progress=True)
 
-.. autofunction:: zipline.data.bundles.load(name, environ=os.environ, date=None)
+.. .. autofunction:: zipline.data.bundles.load(name, environ=os.environ, date=None)
 
-.. autofunction:: zipline.data.bundles.unregister
+.. .. autofunction:: zipline.data.bundles.unregister
 
-.. data:: zipline.data.bundles.bundles
+.. .. data:: zipline.data.bundles.bundles
 
-   The bundles that have been registered as a mapping from bundle name to bundle
-   data. This mapping is immutable and should only be updated through
-   :func:`~zipline.data.bundles.register` or
-   :func:`~zipline.data.bundles.unregister`.
+..    The bundles that have been registered as a mapping from bundle name to bundle
+..    data. This mapping is immutable and should only be updated through
+..    :func:`~zipline.data.bundles.register` or
+..    :func:`~zipline.data.bundles.unregister`.
 
-.. autofunction:: zipline.data.bundles.yahoo_equities
+.. .. autofunction:: zipline.data.bundles.yahoo_equities
 
 
 
@@ -362,16 +356,16 @@ Utilities
 Caching
 ```````
 
-.. autoclass:: zipline.utils.cache.CachedObject
+.. autoclass:: catalyst.utils.cache.CachedObject
 
-.. autoclass:: zipline.utils.cache.ExpiringCache
+.. autoclass:: catalyst.utils.cache.ExpiringCache
 
-.. autoclass:: zipline.utils.cache.dataframe_cache
+.. autoclass:: catalyst.utils.cache.dataframe_cache
 
-.. autoclass:: zipline.utils.cache.working_file
+.. autoclass:: catalyst.utils.cache.working_file
 
-.. autoclass:: zipline.utils.cache.working_dir
+.. autoclass:: catalyst.utils.cache.working_dir
 
 Command Line
 ````````````
-.. autofunction:: zipline.utils.cli.maybe_show_progress
+.. autofunction:: catalyst.utils.cli.maybe_show_progress

docs/source/beginner-tutorial.rst

@@ -168,7 +168,7 @@ We'll start with the CLI, and introduce the ``run_algorithm()`` in the last
 example of this tutorial. Some of the :doc:`example algorithms <example-algos>` 
 provide instructions on how to run them both from the CLI, and using the 
 :func:`~catalyst.run_algorithm` function. For the third method, refer to the 
-corresponding section on :doc:`Catalyst & Jupyter Notebook <jupyter>` after you 
+corresponding section on :ref:`Catalyst & Jupyter Notebook <jupyter>` after you 
 have assimilated the contents of this tutorial.
 
 Command line interface
@@ -473,6 +473,7 @@ Which we execute by running:
     </div>
 
 |
+
 There is a row for each trading day, starting on the first day of our 
 simulation Jan 1st, 2016. In the columns you can find various
 information about the state of your algorithm. The column
@@ -483,7 +484,7 @@ bitcoin price.
 
 Now we will run the simulation again, but this time we extend our original 
 algorithm with the addition of the ``analyze()`` function. Somewhat analogously 
-as how ``initialize()`` gets called once before the start of the algorith, 
+as how ``initialize()`` gets called once before the start of the algorithm, 
 ``analyze()`` gets called once at the end of the algorithm, and receives two 
 variables: ``context``, which we discussed at the very beginning, and ``perf``, 
 which is the pandas dataframe containing the performance data for our algorithm 
@@ -518,7 +519,7 @@ alongside enigma-catalyst (with the exception of the ``Conda`` install, where it
 was included by default inside the conda environment we created). If for any 
 reason you don't have it installed, you can add it by running:
 
-.. code-block:: python
+.. code-block:: bash
 
   (catalyst)$ pip install matplotlib
 
@@ -579,161 +580,8 @@ which you can skim through for now. A copy of this algorithm is available in
 the ``examples`` directory:
 `dual_moving_average.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/dual_moving_average.py>`_.
 
-.. code-block:: python
-
-    import numpy as np
-    import pandas as pd
-    from logbook import Logger
-    import matplotlib.pyplot as plt 
-
-    from catalyst import run_algorithm
-    from catalyst.api import (order, record, symbol, order_target_percent, 
-            get_open_orders)
-    from catalyst.exchange.stats_utils import extract_transactions
-
-    NAMESPACE = 'dual_moving_average'
-    log = Logger(NAMESPACE)
-
-    def initialize(context):
-        context.i = 0
-        context.asset = symbol('ltc_usd')
-        context.base_price = None
-
-
-    def handle_data(context, data):
-        # define the windows for the moving averages 
-        short_window = 50
-        long_window = 200
-
-        # Skip as many bars as long_window to properly compute the average
-        context.i += 1
-        if context.i < long_window:
-           return
-
-        # Compute moving averages calling data.history() for each 
-        # moving average with the appropriate parameters. We choose to use
-        # minute bars for this simulation -> freq="1m"
-        # Returns a pandas dataframe.
-        short_mavg = data.history(context.asset, 'price', 
-                            bar_count=short_window, frequency="1m").mean()
-        long_mavg = data.history(context.asset, 'price',
-                            bar_count=long_window, frequency="1m").mean()
-
-        # Let's keep the price of our asset in a more handy variable
-        price = data.current(context.asset, 'price')
-
-        # 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
-
-        # Save values for later inspection
-        record(price=price,
-               cash=context.portfolio.cash,
-               price_change=price_change,
-               short_mavg=short_mavg,
-               long_mavg=long_mavg)
-
-        # 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.asset)
-        if len(orders) > 0:
-            return
-
-        # Exit if we cannot trade
-        if not data.can_trade(context.asset):
-            return
-
-        # We check what's our position on our portfolio and trade accordingly
-        pos_amount = context.portfolio.positions[context.asset].amount
-
-        # Trading logic
-        if short_mavg > long_mavg and pos_amount == 0:
-           # we buy 100% of our portfolio for this asset
-           order_target_percent(context.asset, 1)
-        elif short_mavg < long_mavg and pos_amount > 0:
-           # we sell all our positions for this asset
-           order_target_percent(context.asset, 0)
-
-
-    def analyze(context, perf):
-
-        # Get the base_currency that was passed as a parameter to the simulation
-        base_currency = context.exchanges.values()[0].base_currency.upper()
-
-        # First chart: Plot portfolio value using base_currency
-        ax1 = plt.subplot(411)
-        perf.loc[:, ['portfolio_value']].plot(ax=ax1)
-        ax1.legend_.remove()
-        ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
-        start, end = ax1.get_ylim()
-        ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
-
-        # Second chart: Plot asset price, moving averages and buys/sells
-        ax2 = plt.subplot(412, sharex=ax1)
-        perf.loc[:, ['price','short_mavg','long_mavg']].plot(ax=ax2, label='Price')
-        ax2.legend_.remove()
-        ax2.set_ylabel('{asset}\n({base})'.format(
-            asset = context.asset.symbol,
-            base = base_currency
-            ))
-        start, end = ax2.get_ylim()
-        ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
-
-        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=''
-            )
-
-        # Third chart: Compare percentage change between our portfolio
-        # and the price of the asset
-        ax3 = plt.subplot(413, sharex=ax1)
-        perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
-        ax3.legend_.remove()
-        ax3.set_ylabel('Percent Change')
-        start, end = ax3.get_ylim()
-        ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
-
-        # Fourth chart: Plot our cash
-        ax4 = plt.subplot(414, sharex=ax1)
-        perf.cash.plot(ax=ax4)
-        ax4.set_ylabel('Cash\n({})'.format(base_currency))
-        start, end = ax4.get_ylim()
-        ax4.yaxis.set_ticks(np.arange(0, end, end/5))
-
-        plt.show()
-
-
-    if __name__ == '__main__':
-        run_algorithm(
-                capital_base=1000,
-                data_frequency='minute',
-                initialize=initialize,
-                handle_data=handle_data,
-                analyze=analyze,
-                exchange_name='bitfinex',
-                algo_namespace=NAMESPACE,
-                base_currency='usd',
-                start=pd.to_datetime('2017-9-22', utc=True),
-                end=pd.to_datetime('2017-9-23', utc=True),
-            )
+.. literalinclude:: ../../catalyst/examples/dual_moving_average.py
+   :language: python
 
 In order to run the code above, you have to ingest the needed data first:
 
@@ -805,6 +653,7 @@ the ``scikit-learn`` functions require ``numpy.ndarray``\ s rather than
 ``pandas.DataFrame``\ s, so you can simply pass the underlying
 ``ndarray`` of a ``DataFrame`` via ``.values``).
 
+.. _jupyter:
 
 Jupyter Notebook
 ~~~~~~~~~~~~~~~~
@@ -825,13 +674,13 @@ In order to use Jupyter Notebook, you first have to install it inside your
 environment. It's available as ``pip`` package, so regardless of how you 
 installed Catalyst, go inside your catalyst environemnt and run:
 
-.. code:: bash
+.. code-block:: bash
 
     (catalyst)$ pip install jupyter
 
 Once you have Jupyter Notebook installed, every time you want to use it run:
 
-.. code:: bash
+.. code-block:: bash
 
     (catalyst)$ jupyter notebook
 
@@ -845,7 +694,7 @@ Before running your algorithms inside the Jupyter Notebook, remember to ingest
 the data from the command line interface (CLI). In the example below, you would
 need to run first:
 
-.. code:: bash
+.. code-block:: bash
 
   catalyst ingest-exchange -x bitfinex -i btc_usd
 
@@ -16606,7 +16455,49 @@ NaN
 
    </div>
 
+PyCharm IDE
+~~~~~~~~~~~
 
+PyCharm is an Integrated Development Environment (IDE) used in computer 
+programming, specifically for the Python language. It streamlines the continuos
+development of Python code, and among other things includes a debugger that 
+comes in handy to see the inner workings of Catalyst, and your trading 
+algorithms.
+
+Install
+^^^^^^^
+Install PyCharm from their `Website <https://www.jetbrains.com/pycharm/download/>`__.
+There is a free and open-source **Community** version.
+
+Setup
+^^^^^
+
+1. When creating a new project in PyCharm, right under you specify the Location,
+   click on **Project Interpreter** to display a drop down menu
+
+2. Select **Existing interpreter**, click the gear box right next to it and 
+   select 'add local'. Depending on your installation, select either 
+   "*Virtual Environemnt*" or "*Conda Environment" and click the '...' button to
+   navigate to your catalyst env and select the Python binary file: 
+   ``bin/python`` for Linux/MacOS installations or 'python.exe' for Windows 
+   installs (for example: 'C:\\Users\\user\\Anaconda2\\envs\\catalyst\\python.exe'). 
+   Select OK. You may want to click on *Make available to all projects* for your
+   future reference. Click OK again, and create your new environment using the
+   set up of your virtual environment.
+
+Alternatively, if you already have your project created, in Windows do:
+
+1. File -> Default Settings -> Project Interpreter. Click the gear box next to 
+   the project interpreter and select ‘add local’, and follow the steps from the
+   second step above.
+
+On MacOS:
+
+1. PyCharm -> Preferences -> Settings -> Project:’NAME_OF_PROJECT’ -> 
+   Project Interpreter. Click the gear box next to the project interpreter 
+   and select ‘add local’, and follow the steps from the second step above.
+
+You should now be able to run your project/scripts in PyCharm.
 
 Next steps
 ~~~~~~~~~~

docs/source/conf.py

@@ -27,8 +27,8 @@ extlinks = {
 
 # -- 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']
@@ -41,11 +41,11 @@ master_doc = 'index'
 
 # General information about the project.
 project = u'Catalyst'
-copyright = u'2017, Enigma MPC, Inc.'
+copyright = u'2018, Enigma MPC, Inc.'
 
 # The full version, including alpha/beta/rc tags, but excluding the commit hash
 #release = version.split('+', 1)[0]
-release = '0.3'
+release = '0.4'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -97,3 +97,6 @@ intersphinx_mapping = {
 doctest_global_setup = "import catalyst"
 
 todo_include_todos = True
+
+suppress_warnings = ['image.nonlocal_uri']
+

docs/source/development-guidelines.rst

@@ -36,25 +36,15 @@ Finally, you can build the C extensions by running:
 
    $ python setup.py build_ext --inplace
 
-.. To finish, make sure `tests`__ pass.
+Development with Docker
+-----------------------
 
-.. __ #style-guide-running-tests
+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 get an error running nosetests after setting up a fresh virtualenv, please try running
-
-.. code-block
-
-..   # where zipline is the name of your virtualenv
-..   $ deactivate zipline
-..   $ workon zipline
-
-
-.. 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/
+__ https://docs.docker.com/get-started/
    
 Git Branching Structure
 -----------------------
@@ -84,6 +74,25 @@ To build and view the docs locally, run:
    $ {BROWSER} build/html/index.html
 
 
+There is a `documented issue <https://github.com/sphinx-doc/sphinx/issues/3212>`_ 
+with ``sphinx`` and ``docutils`` that causes the error below when trying to build 
+the docs.
+
+.. code-block:: text
+
+   Exception occurred:
+     File "(...)/env-c/lib/python2.7/site-packages/docutils/writers/_html_base.py", line 671, in depart_document
+       assert not self.context, 'len(context) = %s' % len(self.context)
+   AssertionError: len(context) = 3
+
+If you get this error, you need to downgrade your version of ``docutils`` as 
+follows, and build the docs again:
+
+.. code-block:: bash
+
+   $ pip install docutils==0.12
+
+
 Commit messages
 ---------------
 

docs/source/example-algos.rst

@@ -1,4 +1,5 @@
 |
+
 Example Algorithms
 ==================
 
@@ -51,35 +52,8 @@ Buy BTC Simple Algorithm
 
 Source code: `examples/buy_btc_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_btc_simple.py>`_
 
-.. code-block:: python
-
-  '''
-    Run this example, by executing the following from your terminal:
-      catalyst ingest-exchange -x bitfinex -f daily -i btc_usdt
-      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 ingest-exchange -x poloniex -f daily -i btc_usdt
-    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'))
+.. literalinclude:: ../../catalyst/examples/buy_btc_simple.py
+   :language: python
 
 This simple algorithm does not produce any output nor displays any chart.
 
@@ -89,8 +63,6 @@ This simple algorithm does not produce any output nor displays any chart.
 Buy and Hodl Algorithm
 ~~~~~~~~~~~~~~~~~~~~~~
 
-Source code: `examples/buy_and_hodl.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_and_hodl.py>`_
-
 First ingest the historical pricing data needed to run this algorithm:
 
 .. code-block:: bash
@@ -118,157 +90,10 @@ that 2015-3-1 is the earliest date that Catalyst supports (if you choose an
 earlier date, you'll get an error), and the most recent date you can choose is 
 one day prior to the current date. 
 
+Source code: `examples/buy_and_hodl.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_and_hodl.py>`_
 
-.. code-block:: python
-
-  #!/usr/bin/env python
-  #
-  # Copyright 2017 Enigma MPC, Inc.
-  # Copyright 2015 Quantopian, Inc.
-  #
-  # 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 pandas as pd
-  import matplotlib.pyplot as plt
-
-  from catalyst import run_algorithm
-  from catalyst.api import (order_target_value, symbol, record,
-      cancel_order, get_open_orders, )
-
-
-  def initialize(context):
-      context.ASSET_NAME = 'btc_usd'
-      context.TARGET_HODL_RATIO = 0.8
-      context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
-
-      context.is_buying = True
-      context.asset = symbol(context.ASSET_NAME)
-
-      context.i = 0
-
-
-  def handle_data(context, data):
-      context.i += 1
-
-      starting_cash = context.portfolio.starting_cash
-      target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
-      reserve_value = context.RESERVE_RATIO * starting_cash
-
-      # Cancel any outstanding orders
-      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.current(context.asset, 'price')
-
-      # Check if still buying and could (approximately) afford another purchase
-      if context.is_buying and cash > price:
-          print('buying')
-          # Place order to make position in asset equal to target_hodl_value
-          order_target_value(
-              context.asset,
-              target_hodl_value,
-              limit_price=price * 1.1,
-          )
-
-      record(
-          price=price,
-          volume=data.current(context.asset, 'volume'),
-          cash=cash,
-          starting_cash=context.portfolio.starting_cash,
-          leverage=context.account.leverage,
-      )
-
-
-  def analyze(context=None, results=None):
-
-      # Plot the portfolio and asset data.
-      ax1 = plt.subplot(611)
-      results[['portfolio_value']].plot(ax=ax1)
-      ax1.set_ylabel('Portfolio Value (USD)')
-
-      ax2 = plt.subplot(612, sharex=ax1)
-      ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
-      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]
-      ]
-      ax2.scatter(
-          buys.index.to_pydatetime(),
-          results.price[buys.index],
-          marker='^',
-          s=100,
-          c='g',
-          label=''
-      )
-
-      ax3 = plt.subplot(613, sharex=ax1)
-      results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
-      ax3.set_ylabel('Leverage ')
-
-      ax4 = plt.subplot(614, sharex=ax1)
-      results[['starting_cash', 'cash']].plot(ax=ax4)
-      ax4.set_ylabel('Cash (USD)')
-
-      results[[
-          'treasury',
-          'algorithm',
-          'benchmark',
-      ]] = results[[
-          'treasury_period_return',
-          'algorithm_period_return',
-          'benchmark_period_return',
-      ]]
-
-      ax5 = plt.subplot(615, sharex=ax1)
-      results[[
-          'treasury',
-          'algorithm',
-          'benchmark',
-      ]].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()
-
-
-  if __name__ == '__main__':
-      run_algorithm(
-          capital_base=10000,
-          data_frequency='daily',
-          initialize=initialize,
-          handle_data=handle_data,
-          analyze=analyze,
-          exchange_name='bitfinex',
-          algo_namespace='buy_and_hodl',
-          base_currency='usd',
-          start=pd.to_datetime('2015-03-01', utc=True),
-          end=pd.to_datetime('2017-10-31', utc=True),
-      )
+.. literalinclude:: ../../catalyst/examples/buy_and_hodl.py
+   :language: python
 
 .. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/example_buy_and_hodl.png
 
@@ -277,166 +102,13 @@ one day prior to the current date.
 Dual Moving Average Crossover
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Source Code: `examples/dual_moving_average.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/dual_moving_average.py>`_
-
 This strategy is covered in detail in the last part of 
 `this tutorial <beginner-tutorial.html#history>`_.
 
-.. code-block:: python
+Source Code: `examples/dual_moving_average.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/dual_moving_average.py>`_
 
-  import numpy as np
-  import pandas as pd
-  from logbook import Logger
-  import matplotlib.pyplot as plt
-
-  from catalyst import run_algorithm
-  from catalyst.api import (order, record, symbol, order_target_percent,
-          get_open_orders)
-  from catalyst.exchange.stats_utils import extract_transactions
-
-  NAMESPACE = 'dual_moving_average'
-  log = Logger(NAMESPACE)
-
-  def initialize(context):
-      context.i = 0
-      context.asset = symbol('ltc_usd')
-      context.base_price = None
-
-
-  def handle_data(context, data):
-      # define the windows for the moving averages
-      short_window = 50
-      long_window = 200
-
-      # Skip as many bars as long_window to properly compute the average
-      context.i += 1
-      if context.i < long_window:
-         return
-
-      # Compute moving averages calling data.history() for each
-      # moving average with the appropriate parameters. We choose to use
-      # minute bars for this simulation -> freq="1m"
-      # Returns a pandas dataframe.
-      short_mavg = data.history(context.asset, 'price',
-                          bar_count=short_window, frequency="1m").mean()
-      long_mavg = data.history(context.asset, 'price',
-                          bar_count=long_window, frequency="1m").mean()
-
-      # Let's keep the price of our asset in a more handy variable
-      price = data.current(context.asset, 'price')
-
-      # 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
-
-      # Save values for later inspection
-      record(price=price,
-             cash=context.portfolio.cash,
-             price_change=price_change,
-             short_mavg=short_mavg,
-             long_mavg=long_mavg)
-
-      # 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.asset)
-      if len(orders) > 0:
-          return
-
-      # Exit if we cannot trade
-      if not data.can_trade(context.asset):
-          return
-
-      # We check what's our position on our portfolio and trade accordingly
-      pos_amount = context.portfolio.positions[context.asset].amount
-
-      # Trading logic
-      if short_mavg > long_mavg and pos_amount == 0:
-         # we buy 100% of our portfolio for this asset
-         order_target_percent(context.asset, 1)
-      elif short_mavg < long_mavg and pos_amount > 0:
-         # we sell all our positions for this asset
-         order_target_percent(context.asset, 0)
-
-
-  def analyze(context, perf):
-
-      # Get the base_currency that was passed as a parameter to the simulation
-      base_currency = context.exchanges.values()[0].base_currency.upper()
-
-      # First chart: Plot portfolio value using base_currency
-      ax1 = plt.subplot(411)
-      perf.loc[:, ['portfolio_value']].plot(ax=ax1)
-      ax1.legend_.remove()
-      ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
-      start, end = ax1.get_ylim()
-      ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
-
-      # Second chart: Plot asset price, moving averages and buys/sells
-      ax2 = plt.subplot(412, sharex=ax1)
-      perf.loc[:, ['price','short_mavg','long_mavg']].plot(ax=ax2, label='Price')
-      ax2.legend_.remove()
-      ax2.set_ylabel('{asset}\n({base})'.format(
-          asset = context.asset.symbol,
-          base = base_currency
-          ))
-      start, end = ax2.get_ylim()
-      ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
-
-      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=''
-          )
-
-      # Third chart: Compare percentage change between our portfolio
-      # and the price of the asset
-      ax3 = plt.subplot(413, sharex=ax1)
-      perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
-      ax3.legend_.remove()
-      ax3.set_ylabel('Percent Change')
-      start, end = ax3.get_ylim()
-      ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
-
-      # Fourth chart: Plot our cash
-      ax4 = plt.subplot(414, sharex=ax1)
-      perf.cash.plot(ax=ax4)
-      ax4.set_ylabel('Cash\n({})'.format(base_currency))
-      start, end = ax4.get_ylim()
-      ax4.yaxis.set_ticks(np.arange(0, end, end/5))
-
-      plt.show()
-
-
-  if __name__ == '__main__':
-      run_algorithm(
-              capital_base=1000,
-              data_frequency='minute',
-              initialize=initialize,
-              handle_data=handle_data,
-              analyze=analyze,
-              exchange_name='bitfinex',
-              algo_namespace=NAMESPACE,
-              base_currency='usd',
-              start=pd.to_datetime('2017-9-22', utc=True),
-              end=pd.to_datetime('2017-9-23', utc=True),
-          )
+.. literalinclude:: ../../catalyst/examples/dual_moving_average.py
+   :language: python
 
 .. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/tutorial_dual_moving_average.png
 
@@ -446,8 +118,6 @@ This strategy is covered in detail in the last part of
 Mean Reversion Algorithm
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-Source code: `examples/mean_reversion_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/mean_reversion_simple.py>`_
-
 This algorithm is based on a simple momentum strategy. When the cryptoasset goes
 up quickly, we're going to buy; when it goes down quickly, we're going to sell. 
 Hopefully, we'll ride the waves.
@@ -468,284 +138,10 @@ lines 218-245, so in order to run the algorithm we just type:
 
    python mean_reversion_simple.py
 
-.. code-block:: python
+Source code: `examples/mean_reversion_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/mean_reversion_simple.py>`_
 
-  import os
-  import tempfile
-  import time
-
-  import numpy as np
-  import pandas as pd
-  import talib
-  from logbook import Logger
-
-  from catalyst import run_algorithm
-  from catalyst.api import symbol, record, order_target_percent, get_open_orders
-  from catalyst.exchange.stats_utils import extract_transactions
-  # 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.utils.paths import ensure_directory
-
-  NAMESPACE = 'mean_reversion_simple'
-  log = Logger(NAMESPACE)
-
-
-  # To run an algorithm in Catalyst, you need two functions: initialize and
-  # handle_data.
-
-  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 Neo in USD.
-      context.neo_eth = symbol('neo_usd')
-      context.base_price = None
-      context.current_day = None
-
-      context.RSI_OVERSOLD = 30
-      context.RSI_OVERBOUGHT = 80
-      context.CANDLE_SIZE = '15T'
-
-      context.start_time = time.time()
-
-
-  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.neo_eth 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.neo_eth,
-          fields='close',
-          bar_count=50,
-          frequency=context.CANDLE_SIZE
-      )
-
-      # 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.neo_eth, 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.neo_eth)
-      if len(orders) > 0:
-          return
-
-      # Exit if we cannot trade
-      if not data.can_trade(context.neo_eth):
-          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.neo_eth].amount
-
-      if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
-          log.info(
-              '{}: buying - price: {}, rsi: {}'.format(
-                  data.current_dt, price, rsi[-1]
-              )
-          )
-          # Set a style for limit orders,
-          limit_price = price * 1.005
-          order_target_percent(
-              context.neo_eth, 1, limit_price=limit_price
-          )
-          context.traded_today = True
-
-      elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
-          log.info(
-              '{}: selling - price: {}, rsi: {}'.format(
-                  data.current_dt, price, rsi[-1]
-              )
-          )
-          limit_price = price * 0.995
-          order_target_percent(
-              context.neo_eth, 0, limit_price=limit_price
-          )
-          context.traded_today = True
-
-
-  def analyze(context=None, perf=None):
-      end = time.time()
-      log.info('elapsed time: {}'.format(end - context.start_time))
-
-      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\nValue\n({})'.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}\n({base})'.format(
-          asset=context.neo_eth.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.floor('1 min'), 'price'],
-              marker='^',
-              s=100,
-              c='green',
-              label=''
-          )
-          ax2.scatter(
-              sell_df.index.to_pydatetime(),
-              perf.loc[sell_df.index.floor('1 min'), '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\n({})'.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\nChange')
-
-      ax6 = plt.subplot(615, sharex=ax1)
-      perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
-      ax6.set_ylabel('RSI')
-      ax6.axhline(context.RSI_OVERBOUGHT, color='darkgoldenrod')
-      ax6.axhline(context.RSI_OVERSOLD, color='darkgoldenrod')
-
-      if not transaction_df.empty:
-          ax6.scatter(
-              buy_df.index.to_pydatetime(),
-              perf.loc[buy_df.index.floor('1 min'), 'rsi'],
-              marker='^',
-              s=100,
-              c='green',
-              label=''
-          )
-          ax6.scatter(
-              sell_df.index.to_pydatetime(),
-              perf.loc[sell_df.index.floor('1 min'), 'rsi'],
-              marker='v',
-              s=100,
-              c='red',
-              label=''
-          )
-      plt.legend(loc=3)
-      start, end = ax6.get_ylim()
-      ax6.yaxis.set_ticks(np.arange(0, end, end/5))
-
-      # 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':
-          folder = os.path.join(
-              tempfile.gettempdir(), 'catalyst', NAMESPACE
-          )
-          ensure_directory(folder)
-
-          timestr = time.strftime('%Y%m%d-%H%M%S')
-          out = os.path.join(folder, '{}.p'.format(timestr))
-          # catalyst run -f catalyst/examples/mean_reversion_simple.py -x bitfinex -s 2017-10-1 -e 2017-11-10 -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=NAMESPACE,
-              base_currency='usd',
-              start=pd.to_datetime('2017-10-01', utc=True),
-              end=pd.to_datetime('2017-11-10', utc=True),
-              output=out
-          )
-          log.info('saved perf stats: {}'.format(out))
-
-      elif MODE == 'live':
-          run_algorithm(
-              capital_base=0.5,
-              initialize=initialize,
-              handle_data=handle_data,
-              analyze=analyze,
-              exchange_name='bittrex',
-              live=True,
-              algo_namespace=NAMESPACE,
-              base_currency='usd',
-              live_graph=False
-          )
+.. literalinclude:: ../../catalyst/examples/mean_reversion_simple.py
+   :language: python
 
 .. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/example_mean_reversion_simple.png
 
@@ -762,8 +158,6 @@ strategy.
 Simple Universe
 ~~~~~~~~~~~~~~~
 
-Source code: `examples/simple_universe.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/simple_universe.py>`_
-
 This example aims to provide an easy way for users to learn how to 
 collect data from any given exchange and select a subset of the available
 currency pairs for trading. You simply need to specify the exchange and 
@@ -790,142 +184,10 @@ of the file:
   
   catalyst ingest-exchange -x bitfinex -f minute
 
-.. code-block:: bash
-
-  python simple_universe.py
-
-Credits: This code was originally submitted by `Abner Ayala-Acevedo 
-<https://github.com/abnera>`_. Thank you!
-
-.. code-block:: python
-
-  from datetime import timedelta
-
-  import numpy as np
-  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  # minute counter
-      context.exchange = context.exchanges.values()[0].name.lower()  
-      context.base_currency = context.exchanges.values()[0].base_currency.lower()  
-
-
-  def handle_data(context, data):
-      context.i += 1
-      lookback_days = 7  # 7 days
-
-      # current date & time in each iteration formatted into a string
-      now = data.current_dt
-      date, time = now.strftime('%Y-%m-%d %H:%M:%S').split(' ')
-      lookback_date = now - timedelta(days=lookback_days) 
-      # keep only the date as a string, discard the time
-      lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0]  
-
-      one_day_in_minutes = 1440  # 60 * 24 assumes data_frequency='minute'
-      # update universe everyday at midnight
-      if not context.i % one_day_in_minutes:
-          context.universe = universe(context, lookback_date, date)
-
-      # get data every 30 minutes
-      minutes = 30
-      # get lookback_days of history data: that is 'lookback' number of bins
-      lookback = one_day_in_minutes / minutes * lookback_days  
-      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)
-
-              # Get 30 minute interval OHLCV data. This is the standard data 
-              # required for candlestick or indicators/signals. Return Pandas
-              # DataFrames. 30T means 30-minute re-sampling of one minute data. 
-              # Adjust it to your desired time interval as needed.
-              opened = fill(data.history(coin, 'open', 
-                                    bar_count=lookback, frequency='30T')).values
-              high = fill(data.history(coin, 'high', 
-                                    bar_count=lookback, frequency='30T')).values
-              low = fill(data.history(coin, 'low', 
-                                    bar_count=lookback, frequency='30T')).values
-              close = fill(data.history(coin, 'price', 
-                                    bar_count=lookback, frequency='30T')).values
-              volume = fill(data.history(coin, 'volume', 
-                                    bar_count=lookback, frequency='30T')).values
-
-              # close[-1] is the last value in the set, which is the equivalent 
-              # to current price (as in the most recent value)
-              # displays the minute price for each pair every 30 minutes
-              print('{now}: {pair} -\tO:{o},\tH:{h},\tL:{c},\tC{c},\tV:{v}'.format(
-                      now=now, 
-                      pair=pair, 
-                      o=opened[-1], 
-                      h=high[-1], 
-                      l=low[-1],
-                      c=close[-1],
-                      v=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):
-      # get all the pairs for the given exchange
-      json_symbols = get_exchange_symbols(context.exchange)  
-      # convert into a DataFrame for easier processing
-      df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str) 
-      df['base_currency'] = df.apply(lambda row: row.symbol.split('_')[1],axis=1)
-      df['market_currency'] = df.apply(lambda row: row.symbol.split('_')[0],axis=1)
-
-      # Filter all the pairs to get only the ones for a given base_currency
-      df = df[df['base_currency'] == context.base_currency]
-
-      # Filter all the pairs to ensure that pair existed in the current date range
-      df = df[df.start_date < lookback_date]
-      df = df[df.end_daily >= current_date]
-      context.coins = symbols(*df.symbol)  # convert all the pairs to symbols
-
-      return 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('2017-11-10', utc=True)
-      end_date = pd.to_datetime('2017-11-13', utc=True)
-
-      performance = run_algorithm(start=start_date, end=end_date,
-                                  capital_base=100.0,  # amount of base_currency
-                                  initialize=initialize,
-                                  handle_data=handle_data,
-                                  analyze=analyze,
-                                  exchange_name='bitfinex',
-                                  data_frequency='minute',
-                                  base_currency='btc',
-                                  live=False,
-                                  live_graph=False,
-                                  algo_namespace='simple_universe')
+Source code: `examples/simple_universe.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/simple_universe.py>`_
 
+.. literalinclude:: ../../catalyst/examples/simple_universe.py
+   :language: python
 
 
 .. _portfolio_optimization:
@@ -939,135 +201,10 @@ use 180 days of historical data and rebalance every 30 days. This code was used
 in writting the following article: 
 `Markowitz Portfolio Optimization for Cryptocurrencies <https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556>`_.
 
-.. code-block:: python
+Source code: `examples/simple_universe.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/portfolio_optimization.py>`_
 
-  '''
-     You can run this code using the Python interpreter:
-
-     $ python portfolio_optimization.py
-  '''
-
-  from __future__ import division
-  import os
-  import pytz
-  import numpy as np
-  import pandas as pd
-  from scipy.optimize import minimize
-  import matplotlib.pyplot as plt
-  from datetime import datetime
-
-  from catalyst.api import record, symbol, symbols, order_target_percent
-  from catalyst.utils.run_algo import run_algorithm
-
-  np.set_printoptions(threshold='nan', suppress=True)
-
-
-  def initialize(context):
-     # Portfolio assets list
-     context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
-                              'xmr_usdt')
-     context.nassets = len(context.assets)
-     # Set the time window that will be used to compute expected return 
-     # and asset correlations
-     context.window = 180
-     # Set the number of days between each portfolio rebalancing
-     context.rebalance_period = 30                   
-     context.i = 0
-
-     
-  def handle_data(context, data):
-     # Only rebalance at the beggining of the algorithm execution and 
-     # every multiple of the rebalance period
-     if context.i == 0 or context.i%context.rebalance_period == 0:
-         n = context.window
-         prices = data.history(context.assets, fields='price', 
-                               bar_count=n+1, frequency='1d') 
-         pr = np.asmatrix(prices)
-         t_prices = prices.iloc[1:n+1]
-         t_val = t_prices.values
-         tminus_prices = prices.iloc[0:n]
-         tminus_val = tminus_prices.values
-         # Compute daily returns (r)
-         r = np.asmatrix(t_val/tminus_val-1)
-         # Compute the expected returns of each asset with the average 
-         # daily return for the selected time window
-         m = np.asmatrix(np.mean(r, axis=0))
-         # ###
-         stds = np.std(r, axis=0)
-         # Compute excess returns matrix (xr)
-         xr = r - m
-         # Matrix algebra to get variance-covariance matrix
-         cov_m = np.dot(np.transpose(xr),xr)/n
-         # Compute asset correlation matrix (informative only)
-         corr_m = cov_m/np.dot(np.transpose(stds),stds)
-         
-         # Define portfolio optimization parameters
-         n_portfolios = 50000
-         results_array = np.zeros((3+context.nassets,n_portfolios))
-         for p in xrange(n_portfolios):
-             weights = np.random.random(context.nassets)
-             weights /= np.sum(weights)
-             w = np.asmatrix(weights)
-             p_r = np.sum(np.dot(w,np.transpose(m)))*365
-             p_std = np.sqrt(np.dot(np.dot(w,cov_m),np.transpose(w)))*np.sqrt(365)
-             
-             #store results in results array
-             results_array[0,p] = p_r
-             results_array[1,p] = p_std
-             #store Sharpe Ratio (return / volatility) - risk free rate element 
-             #excluded for simplicity
-             results_array[2,p] = results_array[0,p] / results_array[1,p]
-             i = 0
-             for iw in weights:
-                 results_array[3+i,p] = weights[i]
-                 i += 1
-         
-         #convert results array to Pandas DataFrame
-         results_frame = pd.DataFrame(np.transpose(results_array),
-                            columns=['r','stdev','sharpe']+context.assets)
-         #locate position of portfolio with highest Sharpe Ratio
-         max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
-         #locate positon of portfolio with minimum standard deviation
-         min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
-         
-         #order optimal weights for each asset
-         for asset in context.assets:
-             if data.can_trade(asset):
-                 order_target_percent(asset, max_sharpe_port[asset])
-         
-         #create scatter plot coloured by Sharpe Ratio
-         plt.scatter(results_frame.stdev,results_frame.r,c=results_frame.sharpe,cmap='RdYlGn')
-         plt.xlabel('Volatility')
-         plt.ylabel('Returns')
-         plt.colorbar()
-         #plot red star to highlight position of portfolio with highest Sharpe Ratio
-         plt.scatter(max_sharpe_port[1],max_sharpe_port[0],marker='o',color='b',s=200)
-         #plot green star to highlight position of minimum variance portfolio
-         plt.show()
-         print(max_sharpe_port)
-         record(pr=pr,r=r, m=m, stds=stds ,max_sharpe_port=max_sharpe_port, corr_m=corr_m)
-     context.i += 1
-     
-         
-  def analyze(context=None, results=None):
-     # Form DataFrame with selected data
-     data = results[['pr','r','m','stds','max_sharpe_port','corr_m','portfolio_value']]
-     
-     # Save results in CSV file
-     filename = os.path.splitext(os.path.basename(__file__))[0]
-     data.to_csv(filename + '.csv')
-
-
-  # Bitcoin data is available from 2015-3-2. Dates vary for other tokens.    
-  start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
-  end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc) 
-  results = run_algorithm(initialize=initialize,
-                          handle_data=handle_data,
-                          analyze=analyze,
-                          start=start,
-                          end=end,
-                          exchange_name='poloniex',
-                          capital_base=100000, )
+.. literalinclude:: ../../catalyst/examples/portfolio_optimization.py
+   :language: python
 
 .. image:: https://cdn-images-1.medium.com/max/1600/0*EjjiKZHlYF3sn7yQ.
    :align: center

docs/source/features.rst

@@ -44,11 +44,11 @@ For additional details on the functionality added on recent releases, see the
 Upcoming features
 ~~~~~~~~~~~~~~~~~
 
-* Additional datasets beyond pricing data (Dec. 2017)
-* API documentation (Jan. 2017)
-* Support for decentralized exchanges (Jan. 2017)
-* Support for data ingestion of community-contributed data sets (Jan. 2017)
-* Pipeline support (Jan. 2018)
+* Additional datasets beyond pricing data (Q1 2018)
+* API documentation (Q1 2018)
+* Support for decentralized exchanges (Q1 2018)
+* Support for data ingestion of community-contributed data sets (Q1 2018)
+* Pipeline support (Q1 2018)
 * Web UI (Q2 2018)
 
 

docs/source/index.rst

@@ -1,6 +1,8 @@
 .. include:: ../../README.rst
+
 |
 |
+
 Table of Contents
 -----------------
 

docs/source/install.rst

@@ -47,8 +47,10 @@ you can install MiniConda, which is a smaller footprint (fewer packages and
 smaller size) than its big brother Anaconda, but it still contains all the 
 main packages needed. To install MiniConda, you can follow these steps:
 
-1. Download `MiniConda <https://conda.io/miniconda.html>`_. Select Python 2.7 
-   for your Operating System.
+1. Download `MiniConda <https://conda.io/miniconda.html>`_. Select either 
+   Python 3.6 (recommended) or Python 2.7 for your Operating System. The 
+   `Enigma Data Marketplace <https://enigmampc.github.io/marketplace/>`_ will 
+   require Python3, that's why we are recommending to opt for the newer version.
 2. Install MiniConda. See the `Installation Instructions 
    <https://conda.io/docs/user-guide/install/index.html>`_ if you need help.
 3. Ensure the correct installation by running ``conda list`` in a Terminal 
@@ -64,18 +66,27 @@ main packages needed. To install MiniConda, you can follow these steps:
 
 Once either Conda or MiniConda has been set up you can install Catalyst:
 
-1. Download the file `python2.7-environment.yml 
-   <https://github.com/enigmampc/catalyst/blob/master/etc/python2.7-environment.yml>`_.
+1. Download the file `python3.6-environment.yml 
+   <https://github.com/enigmampc/catalyst/blob/master/etc/python3.6-environment.yml>`_ 
+   (recommended) or `python2.7-environment.yml 
+   <https://github.com/enigmampc/catalyst/blob/master/etc/python2.7-environment.yml>`_ 
+   matching your Conda installation from step #1 above.
 
      To download, simply click on the 'Raw' button and save the file locally 
      to a folder you can remember. Make sure that the file gets saved with the
      ``.yml`` extension, and nothing like a ``.txt`` file or anything else.
 
 2. Open a Terminal window and enter [``cd/dir``] into the directory where you 
-   saved the above ``python2.7-environment.yml`` file.
+   saved the above ``.yml`` file.
 
 3. Install using this file. This step can take about 5-10 minutes to install.
 
+   .. code-block:: bash
+
+      conda env create -f python3.6-environment.yml
+
+  or
+
    .. code-block:: bash
 
       conda env create -f python2.7-environment.yml
@@ -122,10 +133,18 @@ with the following steps:
 
 2. Create the environment:
 
+   for python 2.7:
+
    .. code-block:: bash
 
       conda create --name catalyst python=2.7 scipy zlib
 
+  or for python 3.6:
+
+   .. code-block:: bash
+
+      conda create --name catalyst python=3.6 scipy zlib
+
 3. Activate the environment:
 
    **Linux or MacOS:**
@@ -180,20 +199,6 @@ use a single tool to install Python and non-Python dependencies, or if you're
 already using `Anaconda <http://continuum.io/downloads>`_ as your Python 
 distribution, refer to the :ref:`Installing with Conda <conda>` section.
 
-Once you've installed the necessary additional dependencies for your system 
-(see below for your particular platform: :ref:`Linux`, :ref:`MacOS` or 
-:ref:`Windows`), you should be able to simply run
-
-.. code-block:: bash
-
-   $ pip install enigma-catalyst matplotlib
-
-Note that in the command above we install two different packages. The second 
-one, ``matplotlib`` is a visualization library. While it's not strictly 
-required to run catalyst simulations or live trading, it comes in very handy
-to visualize the performance of your algorithms, and for this reason we 
-recommend you install it, as well.
-
 If you use Python for anything other than Catalyst, we **strongly** recommend
 that you install in a `virtualenv
 <https://virtualenv.readthedocs.org/en/latest>`_.  The `Hitchhiker's Guide to
@@ -206,8 +211,21 @@ summarized version:
    $ pip install virtualenv
    $ virtualenv catalyst-venv
    $ source ./catalyst-venv/bin/activate
+
+Once you've installed the necessary additional dependencies for your system 
+(:ref:`Linux`, :ref:`MacOS` or :ref:`Windows`) **and have activated your virtualenv**, you should be able to simply run
+
+.. code-block:: bash
+
    $ pip install enigma-catalyst matplotlib
 
+Note that in the command above we install two different packages. The second 
+one, ``matplotlib`` is a visualization library. While it's not strictly 
+required to run catalyst simulations or live trading, it comes in very handy
+to visualize the performance of your algorithms, and for this reason we 
+recommend you install it, as well.
+
+
 Troubleshooting ``pip`` Install
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -219,13 +237,13 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      pip install --upgrade pip
+      $ pip install --upgrade pip
 
    On Windows, the recommended command is:
 
    .. code-block:: bash
 
-      python -m pip install --upgrade pip
+      $ python -m pip install --upgrade pip
 
 ----
 
@@ -251,7 +269,7 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      pip install --pre enigma-catalyst
+      $ pip install --pre enigma-catalyst
 
 ----
 
@@ -263,7 +281,7 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      pip install --upgrade pip setuptools
+      $ pip install --upgrade pip setuptools
 
 ----
 
@@ -278,7 +296,7 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      pip install -r requirements.txt
+      $ pip install -r requirements.txt
 
 ----
 
@@ -294,12 +312,22 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      sudo apt-get install python-dev
+      $ sudo apt-get install python-dev
+
+----
+
+**Issue**:
+   Missing TA_Lib
+
+**Solution**:
+   Follow `these instructions
+   <https://mrjbq7.github.io/ta-lib/install.html>`_ to install the TA_Lib Python wrapper
+   (and if needed, its underlying C library as well).
 
 .. _pipenv:
 
 Installing with ``pipenv``
--------------------------
+--------------------------
 
 Installing Catalyst via ``pipenv`` is perhaps easier that installing it via
 ``pip`` itself but you need to install ``pipenv`` first via ``pip``.
@@ -376,14 +404,14 @@ outdated. Thus, you first need to run:
 
 .. code-block:: bash
 
-   pip install --upgrade pip setuptools
+   $ pip install --upgrade pip setuptools
 
 The default installation is also missing the C and C++ compilers, which you 
 install by:
 
 .. code-block:: bash
 
-   sudo yum install gcc gcc-c++
+   $ sudo yum install gcc gcc-c++
 
 Then you should follow the regular installation instructions outlined at the 
 beginning of this page.
@@ -408,20 +436,34 @@ following brew packages:
 
    $ brew install freetype pkg-config gcc openssl
 
-MacOS + virtualenv + matplotlib
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+MacOS + virtualenv/conda + matplotlib
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-A note about using matplotlib in virtual enviroments on MacOS: it may be 
-necessary to run
+The first time that you try to run an algorithm that loads the ``matplotlib`` 
+library, you may get the following error:
+
+.. code-block:: text
+
+  RuntimeError: Python is not installed as a framework. The Mac OS X backend 
+  will not be able to function correctly if Python is not installed as a 
+  framework. See the Python documentation for more information on installing 
+  Python as a framework on Mac OS X. Please either reinstall Python as a 
+  framework, or try one of the other backends. If you are using (Ana)Conda 
+  please install python.app and replace the use of 'python' with 'pythonw'. 
+  See 'Working with Matplotlib on OSX' in the Matplotlib FAQ for more 
+  information.
+
+This is a ``matplotlib``-specific error, that will go away once you run the 
+following command:
 
 .. code-block:: bash
 
-   echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
+   $ echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
 
 in order to override the default ``MacOS`` 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 
+may not be accessible from inside the virtual or conda 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>`_.
 
@@ -430,12 +472,22 @@ about matplotlib backends, please refer to the
 Windows Requirements
 --------------------
 
-In Windows, you will first need to install the `Microsoft Visual C++ Compiler 
-for Python 2.7 
-<https://www.microsoft.com/en-us/download/details.aspx?id=44266>`_. This 
-package contains the compiler and the set of system headers necessary for 
-producing binary wheels for Python 2.7 packages. If it's not already in your 
-system, download it and install it before proceeding to the next step.
+In Windows, you will first need to install the Microsoft Visual C++ Compiler, 
+which is different depending on the version of Python that you plan to use:
+
+* Python 3.5, 3.6: `Visual C++ 2015 Build Tools 
+  <http://landinghub.visualstudio.com/visual-cpp-build-tools>`_, 
+  which installs Visual C++ version 14.0. **This is the recommended version**
+
+* Python 2.7: `Microsoft Visual C++ Compiler for Python 2.7 
+  <https://www.microsoft.com/en-us/download/details.aspx?id=44266>`_, which 
+  installs version Visual C++ version 9.0
+
+This package contains the compiler and the set of system headers necessary for 
+producing binary wheels for Python packages. If it's not already in your 
+system, download it and install it before proceeding to the next step. If you 
+need additional help, or are looking for other versions of Visual C++ for 
+Windows (only advanced users), follow `this link <https://wiki.python.org/moin/WindowsCompilers>`_.
 
 Once you have the above compiler installed, the easiest and best supported way 
 to install Catalyst in Windows is to use :ref:`Conda <conda>`. If you didn't 
@@ -463,6 +515,7 @@ mentioned above are as follows:
     default you get 0 as the Value Data)
 
 |
+
 - **The installer has encountered an unexpected error installing this package. 
   This may indicate a problem with this package. The error code is 2503.**
 
@@ -475,6 +528,33 @@ mentioned above are as follows:
   - ``cd`` into the folder where you downloaded ``VCForPython27.msi``
   - Run ``msiexec /i VCForPython27.msi``
 
+Updating Catalyst
+-----------------
+
+Catalyst is currently in alpha and in under very active development. We release
+new minor versions every few days in response to the thorough battle testing 
+that our user community puts Catalyst in. As a result, you should expect to 
+update Catalyst frequently. Once installed, Catalyst can easily be updated as a 
+``pip`` package regardless of the environemnt used for installation. Make sure 
+you activate your environment first as you did in your first install, and then 
+execute:
+
+.. code-block:: bash
+
+   $ pip uninstall enigma-catalyst
+   $ pip install enigma-catalyst
+
+Alternatively, you could update Catalyst issuing the following command:
+
+.. code-block:: bash
+
+   $ pip install -U enigma-catalyst
+
+but this command will also upgrade all the Catalyst dependencies to the latest 
+versions available, and may have unexpected side effects if a newer version of a 
+dependency inadvertently breaks some functionality that Catalyst relies on. 
+Thus, the first method is the recommended one.
+
 Getting Help
 ------------
 
@@ -482,6 +562,10 @@ If after following the instructions above, and going through the
 *Troubleshooting* sections, you still experience problems installing Catalyst,
 you can seek additional help through the following channels:
 
+- Join our `Catalyst Forum <https://catalyst.enigma.co/>`_, and browse a variety
+  of topics and conversations around common issues that others face when using
+  Catalyst, and how to resolve them. And join the conversation!
+
 - Join our `Discord community <https://discord.gg/SJK32GY>`_, and head over 
   the #catalyst_dev channel where many other users (as well as the project 
   developers) hang out, and can assist you with your particular issue. The 

docs/source/live-trading.rst

@@ -4,11 +4,65 @@ This document explains how to get started with live trading.
 
 Supported Exchanges
 ^^^^^^^^^^^^^^^^^^^
-Catalyst can trade against these exchanges:
 
-- Bitfinex, id= ``bitfinex``
-- Bittrex, id= ``bittrex``
-- Poloniex, id= ``poloniex``
+Since version 0.4, Catalyst integrated with `CCXT <https://github.com/ccxt/ccxt>`_,
+a cryptocurrency trading library with support for more than 90 exchanges. The 
+range of CCXT and Catalyst support for each of those exchanges varies greatly. 
+The most supported exchanges are as follows:
+
+The exchanges available for backtesting are fully supported in live mode:
+
+- Bitfinex, id = ``bitfinex``
+- Bittrex, id = ``bittrex``
+- Poloniex, id = ``poloniex``
+
+Additionally, we have successfully tested the following exchanges:
+
+- Binance, id = ``binance``
+- Bitmex, id = ``bitmex``
+- GDAX, id = ``gdax``
+
+As Catalyst is currently in Alpha and in under active development, you are 
+encouraged to throughly test any exchange in *paper trading* mode before trading
+*live* with it.
+
+Paper Trading vs Live Trading modes
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Catalyst currently supports three different modes in which you can execute your
+trading algorithm. The first is **backtesting**, which is covered extensively in
+the tutorial, and uses historical data to run your algorithm. There is no 
+interaction with the exchange in backtesting mode, and this is the first mode 
+that you should test any new algorithm.
+
+Once you are confident with the simulations that you have obtained with your
+algorithm in backtesting, you may switch to live trading, where you have two
+different modes:
+
+* **Paper Trading**: The simulated algorithm runs in real time, and fetches 
+  pricing data in real time from the exchange, but the orders never reach the 
+  exchange, and are instead kept within Catalyst and simulated. No real currency
+  is bought or sold. Think of it as a `backtesting happening in real time`.
+
+* **Live Trading**: This is the proper live trading mode in which an algorithm
+  runs in real time, fetching pricing data from live exchanges and placing 
+  orders against the exchange. Real currency is transacted on the exchange 
+  driven by the algorithm.
+
+These three modes are controlled by the following variables:
+
++---------------+-------------------------+
+|    Mode       |       Parameters        |
++               +-------+-----------------+
+|               | live  | simulate_orders |
++---------------+-------+-----------------+
+| backtesting   | False | True (default)  |
++---------------+-------+-----------------+
+| paper trading | True  | True            |
++---------------+-------+-----------------+
+| live trading  | True  | False           |
++---------------+-------+-----------------+
+
 
 Authentication
 ^^^^^^^^^^^^^^
@@ -61,7 +115,7 @@ Currency symbols (e.g. btc, eth, ltc) follow the Bittrex convention.
 
 Here are some examples:
 
-.. code-block:: json
+.. code:: python
 
   # With Bitfinex
   bitcoin_usd_asset = symbol('btc_usd')
@@ -75,7 +129,8 @@ Note that the trading pairs are always referenced in the same manner.
 However, not all trading pairs are available on all exchanges. An
 error will occur if the specified trading pair is not trading
 on the exchange. To check which currency pairs are available on each 
-of the supported exchanges, see `Catalyst Market Coverage <https://www.enigma.co/catalyst/status`_.
+of the supported exchanges, see 
+`Catalyst Market Coverage <https://www.enigma.co/catalyst/status>`_.
 
 Trading an Algorithm
 ^^^^^^^^^^^^^^^^^^^^
@@ -105,20 +160,38 @@ What differs are the arguments provided to the catalyst client or
 
 Here is the breakdown of the new arguments:
 
-- ``live``: Boolean flag which enables live trading.
+- ``live``: Boolean flag which enables live trading. It defaults to ``False``.
 - ``capital_base``: The amount of base_currency assigned to the strategy.
   It has to be lower or equal to the amount of base currency available for
   trading on the exchange. For illustration, order_target_percent(asset, 1)
   will order the capital_base amount specified here of the specified asset.
-- ``exchange_name``: The name of the targeted exchange
-  (supported values: *bitfinex*, *bittrex*).
+- ``exchange_name``: The name of the targeted exchange. See the 
+  `CCXT Supported Exchanges <https://github.com/ccxt/ccxt/wiki/Exchange-Markets>`_ 
+  for the full list.
 - ``algo_namespace``: A arbitrary label assigned to your algorithm for
   data storage purposes.
 - ``base_currency``: The base currency used to calculate the
   statistics of your algorithm. Currently, the base currency of all
   trading pairs of your algorithm must match this value.
 - ``simulate_orders``: Enables the paper trading mode, in which orders are
-  simulated in Catalyst instead of processed on the exchange.
+  simulated in Catalyst instead of processed on the exchange. It defaults to 
+  ``True``.
+- ``end_date``: When setting the end_date to a time in the **future**,
+  it will schedule the live algo to finish gracefully at the specified date.
+- ``start_date``: (**Will be implemented in the future**)
+  The live algo starts by default in the present, as mentioned above.
+  by setting the start_date to a time in the future, the algorithm would
+  essentially sleep and when the predefined time comes, it would start executing.
+
+
+
+The `catalyst live` command offers additional parameters.
+You can learn more by running the following from the command line:
+
+.. code-block:: bash
+
+    catalyst live --help
+
 
 Here is a complete algorithm for reference:
 `Buy Low and Sell High <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_low_sell_high_live.py>`_

docs/source/releases.rst

@@ -2,9 +2,190 @@
 Release Notes
 =============
 
-Version 0.4.1
+Version 0.5.8
 ^^^^^^^^^^^^^
-**Release Date**: 2017-01-03
+**Release Date**: 2018-03-29
+
+Bug Fixes
+~~~~~~~~~
+- Fix proper release of Data Marketplace on mainnet.
+
+
+Version 0.5.7
+^^^^^^^^^^^^^
+**Release Date**: 2018-03-29
+
+Build
+~~~~~
+- Data Marketplace deployed on mainnet.
+- Added progress indicators for publishing data, and made the data publishing
+  synchronous to provide feedback to the publisher.
+
+Bug Fixes
+~~~~~~~~~
+- Added arguments to the ``reduce`` function in tha Asset class :issue:`214`, 
+  :issue:`287`
+
+Version 0.5.6
+^^^^^^^^^^^^^
+**Release Date**: 2018-03-22
+
+Build
+~~~~~
+- Data Marketplace: ensures compatibility across wallets, now fully supporting 
+  ``ledger``, ``trezor``, ``keystore``, ``private key``. Partial support for 
+  ``metamask`` (includes sign_msg, but not sign_tx). Current support for 
+  ``Digital Bitbox`` is unknown, but believed to be supported.
+- Data Marketplace: Switched online provider from MyEtherWallet to MyCrypto.
+- Data Marketplace: Added progress indicator for data ingestion.
+
+Bug Fixes
+~~~~~~~~~
+- Changed benchmark to be constant, so it doesn't ingest data at all. Temporary
+  fix for :issue:`271`, :issue:`285`
+
+Version 0.5.5
+^^^^^^^^^^^^^
+**Release Date**: 2018-03-19
+
+Bug Fixes
+~~~~~~~~~
+- Fixed an issue with the data history in daily frequency :issue:`274`
+- Fix hourly frequency issues :issue:`227` and :issue:`114`
+
+Version 0.5.4
+^^^^^^^^^^^^^
+**Release Date**: 2018-03-14
+
+Build
+~~~~~
+- Switched Data Marketplace from Ropstein testnet to Rinkeby testnet after 
+  incorporating changes resulting from the marketplace contract audit
+- Several usability improvements of the Data Marketplace that make the 
+  `--dataset` parameter optional. If it is not included in the command line, 
+  will list available datasets, and let you choose interactively.
+
+Bug Fixes
+~~~~~~~~~
+- Fix Binance requirement of symbol to be included in the cancelled order 
+  :issue:`204`
+- Fix `notenoughcasherror` when an open order is filled minutes later 
+  :issue:`237`
+- Properly handle of empty candles received from exchanges :issue:`236`
+- Added a function to reduce open orders amount from calculated target/amount 
+  for target orders :issue:`243`
+- Fix missing file in live trading mode on date change :issue:`252`, 
+  :issue:`253`
+- Upgraded Data Marketplace to Web3==4.0.0b11, which was breaking some 
+  functionality from prior version 4.0.0b7 :issue:`257`
+- Always request more data to avoid empty bars and always give the exact bar
+  number :issue:`260`
+
+Documentation
+~~~~~~~~~~~~~
+- PyCharm documentation :issue:`195`
+- Added TA-Lib troubleshooting instructions
+- Added instructions on how to create a Conda environment for Python 3.6, and
+  updated Visual C++ instructions for Windows and Python 3
+- Linking example algorithms in the documentation to their sources
+
+
+Version 0.5.3
+^^^^^^^^^^^^^
+**Release Date**: 2018-02-09
+
+Bug Fixes
+~~~~~~~~~
+- Fixed an issue with last candle in backtesting :issue:`219`
+
+Version 0.5.2
+^^^^^^^^^^^^^
+**Release Date**: 2018-02-08
+
+Bug Fixes
+~~~~~~~~~
+- Fixed an issue with live candle values :issue:`216` and :issue:`199`
+
+Version 0.5.1
+^^^^^^^^^^^^^
+**Release Date**: 2018-02-07
+
+Bug Fixes
+~~~~~~~~~
+- Fixed an issue with orders that stay open :issue:`211`
+- Fixed Jupyter issues :issue:`179`
+- Fetching multiple tickers in one call to minimize rate limit risks :issue:`174`
+- Improved live state presentation :issue:`171`
+
+
+Build
+~~~~~
+- Introducing the Enigma Marketplace
+
+Version 0.4.7
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-19
+
+Bug Fixes
+~~~~~~~~~
+- Fixing issue :issue:`137` impacting the CLI
+
+Build
+~~~~~
+- Implemented authentication aliases (:issue:`60`)
+
+Version 0.4.6
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-18
+
+Bug Fixes
+~~~~~~~~~
+- Fixed some Python3 issues
+- Reading the trade log to get executed order prices on exchanges like Binance (:issue:`151`)
+- Fixed issue with market order executing price (:issue:`150` and :issue:`111`)
+- Implemented standardized symbol mapping (:issue:`157`)
+- Improved error handling for unsupported timeframes (:issue:`159`)
+- Using Bitfinex instead of Poloniex to fetch btc_usdt benchmark (:issue:`161`)
+
+
+Build
+~~~~~
+- Added a `context.state` dict to keep arbitrary state values between runs
+- Added ability to stop live algo at specified end date
+
+Version 0.4.5
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-12
+
+Bug Fixes
+~~~~~~~~~
+- Improved order execution for exchanges supporting trade lists (:issue:`151`)
+- Fixed an issue where requesting history of multiple assets repeats values
+- Raising an error for order amounts smaller than exchange lots
+- Handling multiple req errors with tickers more gracefully (:issue:`160`)
+
+Version 0.4.4
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-09
+
+Bug Fixes
+~~~~~~~~~
+- Removed redundant capital_base validation (:issue:`142`)
+- Fixed portfolio update issue with restored state (:issue:`111`)
+- Skipping cash validation where there are open orders (:issue:`144`)
+
+Version 0.4.3
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-05
+
+Bug Fixes
+~~~~~~~~~
+- Fixed CLI issue (:issue:`137`)
+- Upgraded CCXT
+
+Version 0.4.2
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-03
 
 Bug Fixes
 ~~~~~~~~~
@@ -39,7 +220,7 @@ Build
 - Added market orders in live mode (:issue:`81`)
 
 Version 0.3.10
-^^^^^^^^^^^^^
+~~~~~~~~~~~~~~
 **Release Date**: 2017-11-28
 
 Bug Fixes

docs/source/videos.rst

@@ -11,6 +11,7 @@ Installation: MacOS
 
 |
 |
+
 Installation: Windows
 ---------------------
 
@@ -21,6 +22,7 @@ Where things go smoothly:
    <iframe width="560" height="315" src="https://www.youtube.com/embed/H8HqcEbZmkk" frameborder="0" allowfullscreen></iframe>
 
 |
+
 Where things don't:
 
 .. raw:: html
@@ -29,6 +31,7 @@ Where things don't:
 
 |
 |
+
 Backtesting a Strategy
 ----------------------
 
@@ -44,6 +47,7 @@ sell. Hopefully, we’ll ride the waves.
 
 |
 |
+
 Live Trading a Strategy
 -----------------------
 
@@ -54,5 +58,6 @@ in the previous video, we now take it to trade live against the Bittrex exchange
 .. raw:: html
 
 	<iframe width="560" height="315" src="https://www.youtube.com/embed/NupiE-Xuglw" frameborder="0" allowfullscreen></iframe>
+
+|
 |
-|

etc/docker_cmd.sh

@@ -16,4 +16,4 @@ fi
 
 jupyter notebook -y --no-browser --notebook-dir=${PROJECT_DIR} \
     --certfile=${SSL_CERT_PEM} --keyfile=${SSL_CERT_KEY} --ip='*' \
-    --config=${CONFIG_PATH}
+    --config=${CONFIG_PATH} --allow-root

etc/python2.7-environment.yml

@@ -1,6 +1,7 @@
 name: catalyst
 channels:
 - defaults
+- conda-forge
 dependencies:
 - certifi=2016.2.28=py27_0
 - mkl=2017.0.3
@@ -20,7 +21,11 @@ dependencies:
   - bcolz==0.12.1
   - bottleneck==1.2.1
   - chardet==3.0.4
-  - ccxt==1.10.283
+  - ccxt==1.10.1094
+# The Enigma Data Marketplace requires Python3 because it depends on
+# web3, which requires Python3, as building its dependencies breaks in Python2
+#  - web3==4.0.0b7
+  - requests-toolbelt==0.8.0
   - click==6.7
   - contextlib2==0.5.5
   - cycler==0.10.0
@@ -34,11 +39,11 @@ dependencies:
   - lru-dict==1.1.6
   - mako==1.0.7
   - markupsafe==1.0
-  - matplotlib==2.1.0
+  - matplotlib==2.1.2
   - multipledispatch==0.4.9
   - networkx==2.0
   - numexpr==2.6.4
-  - pandas==0.19.2
+  - pandas==0.22.0
   - pandas-datareader==0.5.0
   - patsy==0.4.1
   - pyparsing==2.2.0
@@ -57,4 +62,4 @@ dependencies:
   - tables==3.4.2
   - toolz==0.8.2
   - urllib3==1.22
-  - enigma-catalyst>=0.3
+  - enigma-catalyst>=0.5

etc/python3.6-environment.yml

@@ -0,0 +1,90 @@
+name: catalyst
+channels:
+- defaults
+- conda-forge
+dependencies:
+- ca-certificates=2017.08.26
+- certifi=2018.1.18
+- intel-openmp=2018.0.0
+- mkl=2018.0.1
+- numpy=1.14.0
+- openssl=1.0.2n
+- matplotlib=2.1.2=py36_0
+- pip=9.0.1
+- python=3.6.4
+- scipy=1.0.0
+- setuptools=38.4.0=py36_0
+- sqlite=3.22.0
+- tk=8.6.7
+- wheel=0.30.0
+- xz=5.2.3
+- zlib=1.2.11
+- pip:
+  - aiodns==1.1.1
+  - aiohttp==3.0.1
+  - alembic==0.9.7
+  - async-timeout==2.0.0
+  - attrdict==2.0.0
+  - attrs==17.4.0
+  - bcolz==0.12.1
+  - boto3==1.5.27
+  - botocore==1.8.41
+  - bottleneck==1.2.1
+  - cchardet==2.1.1
+  - ccxt==1.10.1102
+  - chardet==3.0.4
+  - click==6.7
+  - contextlib2==0.5.5
+  - cyordereddict==1.0.0
+  - cython==0.27.3
+  - cytoolz==0.9.0
+  - decorator==4.2.1
+  - docutils==0.14
+  - empyrical==0.2.1
+  - enigma-catalyst>=0.5.3
+  - eth-abi==1.0.0b0
+  - eth-account==0.1.0a2
+  - eth-keyfile==0.5.1
+  - eth-keys==0.2.0b1
+  - eth-rlp==0.1.0a2
+  - eth-utils==1.0.0b1
+  - hexbytes==0.1.0b0
+  - idna==2.6
+  - idna-ssl==1.0.0
+  - intervaltree==2.1.0
+  - jmespath==0.9.3
+  - logbook==1.2.1
+  - lru-dict==1.1.6
+  - lxml==4.1.1
+  - mako==1.0.7
+  - markupsafe==1.0
+  - multidict==4.1.0
+  - multipledispatch==0.4.9
+  - networkx==2.1
+  - numexpr==2.6.4
+  - pandas==0.22.0
+  - pandas-datareader==0.6.0
+  - patsy==0.5.0
+  - pycares==2.3.0
+  - pycryptodome==3.4.11
+  - pysha3==1.0.2
+  - python-dateutil==2.6.1
+  - python-editor==1.0.3
+  - pytz==2018.3
+  - redo==1.6
+  - requests==2.18.4
+  - requests-file==1.4.3
+  - requests-ftp==0.3.1
+  - requests-toolbelt==0.8.0
+  - rlp==0.6.0
+  - s3transfer==0.1.12
+  - six==1.11.0
+  - sortedcontainers==1.5.9
+  - sqlalchemy==1.2.2
+  - statsmodels==0.8.0
+  - tables==3.4.2
+  - toolz==0.9.0
+  - urllib3==1.22
+  - web3==4.0.0b9
+  - wrapt==1.10.11
+  - yarl==1.1.0

etc/requirements.txt

@@ -19,7 +19,7 @@ requests-file==1.4.1
 # scipy and pandas are required for statsmodels,
 # statsmodels in turn is required for some pandas packages
 scipy==0.17.1
-pandas==0.19.2
+pandas==0.22.0
 pandas-datareader==0.2.1
 # Needed for parts of pandas.stats
 patsy==0.4.0
@@ -81,6 +81,8 @@ empyrical==0.2.1
 tables==3.3.0
 
 #Catalyst dependencies
-ccxt==1.10.283
+ccxt==1.10.1094
 boto3==1.4.8
 redo==1.6
+web3==4.0.0b11; python_version > '3.4'
+requests-toolbelt==0.8.0

etc/requirements_dev.txt

@@ -16,7 +16,7 @@ babel==1.3
 docutils==0.12
 snowballstemmer==1.2.0
 sphinx-rtd-theme==0.1.8
-sphinx==1.3.4
+sphinx==1.6.7
 pbr==1.10.0
 
 mock==2.0.0

etc/requirements_docs.txt

@@ -1,3 +1,4 @@
-Sphinx>=1.3.2
+Sphinx==1.6.7
 numpydoc>=0.5.0
 sphinx-autobuild==0.6.0
+docutils==0.12

setup.py

@@ -165,7 +165,7 @@ def _filter_requirements(lines_iter, filter_names=None,
 
 REQ_UPPER_BOUNDS = {
     'bcolz': '<1',
-    'pandas': '<0.20',
+    'pandas': '>=0.22',
     'empyrical': '<0.2.2',
 }