Fixed an issue with historical data in live mode

# Conflicts:
#	catalyst/exchange/exchange_bundle.py

catalyst/__main__.py

@@ -8,6 +8,8 @@ import pandas as pd
 from six import text_type
 
 from catalyst.data import bundles as bundles_module
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.init_utils import get_exchange
 from catalyst.utils.cli import Date, Timestamp
 from catalyst.utils.run_algo import _run, load_extensions
 
@@ -38,6 +40,7 @@ except NameError:
     default=True,
     help="Don't load the default catalyst extension.py file in $ZIPLINE_HOME.",
 )
+@click.version_option()
 def main(extension, strict_extensions, default_extension):
     """Top level catalyst entry point.
     """
@@ -126,7 +129,7 @@ def ipython_only(option):
 )
 @click.option(
     '--data-frequency',
-    type=click.Choice({'daily', '5-minute', 'minute'}),
+    type=click.Choice({'daily', 'minute'}),
     default='daily',
     show_default=True,
     help='The data frequency of the simulation.',
@@ -187,17 +190,11 @@ def ipython_only(option):
     default=None,
     help='Should the algorithm methods be resolved in the local namespace.'
 ))
-@click.option(
-    '--live/--no-live',
-    is_flag=True,
-    default=False,
-    help='Enable live trading.',
-)
 @click.option(
     '-x',
     '--exchange-name',
-    type=click.Choice({'bitfinex', 'bittrex'}),
+    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
-    help='The name of the targeted exchange (supported: bitfinex, bittrex).',
+    help='The name of the targeted exchange (supported: bitfinex, bittrex, poloniex).',
 )
 @click.option(
     '-n',
@@ -224,24 +221,18 @@ def run(ctx,
         output,
         print_algo,
         local_namespace,
-        live,
         exchange_name,
         algo_namespace,
         base_currency):
     """Run a backtest for the given algorithm.
     """
 
-    if live:
+    if (algotext is not None) == (algofile is not None):
-        if exchange_name is None:
+        ctx.fail(
-            ctx.fail("must specify an exchange name '-x' in live execution "
+            "must specify exactly one of '-f' / '--algofile' or"
-                     "mode '--live'")
+            " '-t' / '--algotext'",
-        if algo_namespace is None:
+        )
-            ctx.fail("must specify an algorithm name '-n' in live execution "
+
-                     "mode '--live'")
-        if base_currency is None:
-            ctx.fail("must specify a base currency '-c' in live "
-                     "execution mode '--live'")
-    else:
     # check that the start and end dates are passed correctly
     if start is None and end is None:
         # check both at the same time to avoid the case where a user
@@ -255,11 +246,8 @@ def run(ctx,
     if end is None:
         ctx.fail("must specify an end date with '-e' / '--end'")
 
-    if (algotext is not None) == (algofile is not None):
+    if exchange_name is None:
-        ctx.fail(
+        ctx.fail("must specify an exchange name '-x'")
-            "must specify exactly one of '-f' / '--algofile' or"
-            " '-t' / '--algotext'",
-        )
 
     perf = _run(
         initialize=None,
@@ -280,10 +268,11 @@ def run(ctx,
         print_algo=print_algo,
         local_namespace=local_namespace,
         environ=os.environ,
-        live=live,
+        live=False,
         exchange=exchange_name,
         algo_namespace=algo_namespace,
-        base_currency=base_currency
+        base_currency=base_currency,
+        live_graph=False
     )
 
     if output == '-':
@@ -327,15 +316,215 @@ def catalyst_magic(line, cell=None):
             raise ValueError('main returned non-zero status code: %d' % e.code)
 
 
+@main.command()
+@click.option(
+    '-f',
+    '--algofile',
+    default=None,
+    type=click.File('r'),
+    help='The file that contains the algorithm to run.',
+)
+@click.option(
+    '-t',
+    '--algotext',
+    help='The algorithm script to run.',
+)
+@click.option(
+    '-D',
+    '--define',
+    multiple=True,
+    help="Define a name to be bound in the namespace before executing"
+         " the algotext. For example '-Dname=value'. The value may be any python"
+         " expression. These are evaluated in order so they may refer to previously"
+         " defined names.",
+)
+@click.option(
+    '-o',
+    '--output',
+    default='-',
+    metavar='FILENAME',
+    show_default=True,
+    help="The location to write the perf data. If this is '-' the perf will"
+         " be written to stdout.",
+)
+@click.option(
+    '--print-algo/--no-print-algo',
+    is_flag=True,
+    default=False,
+    help='Print the algorithm to stdout.',
+)
+@ipython_only(click.option(
+    '--local-namespace/--no-local-namespace',
+    is_flag=True,
+    default=None,
+    help='Should the algorithm methods be resolved in the local namespace.'
+))
+@click.option(
+    '-x',
+    '--exchange-name',
+    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the targeted exchange (supported: bitfinex, bittrex, poloniex).',
+)
+@click.option(
+    '-n',
+    '--algo-namespace',
+    help='A label assigned to the algorithm for data storage purposes.'
+)
+@click.option(
+    '-c',
+    '--base-currency',
+    help='The base currency used to calculate statistics '
+         '(e.g. usd, btc, eth).',
+)
+@click.option(
+    '--live-graph/--no-live-graph',
+    is_flag=True,
+    default=False,
+    help='Display live graph.',
+)
+@click.pass_context
+def live(ctx,
+         algofile,
+         algotext,
+         define,
+         output,
+         print_algo,
+         local_namespace,
+         exchange_name,
+         algo_namespace,
+         base_currency,
+         live_graph):
+    """Trade live with the given algorithm.
+    """
+    if (algotext is not None) == (algofile is not None):
+        ctx.fail(
+            "must specify exactly one of '-f' / '--algofile' or"
+            " '-t' / '--algotext'",
+        )
+
+    if exchange_name is None:
+        ctx.fail("must specify an exchange name '-x'")
+    if algo_namespace is None:
+        ctx.fail("must specify an algorithm name '-n' in live execution mode")
+    if base_currency is None:
+        ctx.fail("must specify a base currency '-c' in live execution mode")
+
+    perf = _run(
+        initialize=None,
+        handle_data=None,
+        before_trading_start=None,
+        analyze=None,
+        algofile=algofile,
+        algotext=algotext,
+        defines=define,
+        data_frequency=None,
+        capital_base=None,
+        data=None,
+        bundle=None,
+        bundle_timestamp=None,
+        start=None,
+        end=None,
+        output=output,
+        print_algo=print_algo,
+        local_namespace=local_namespace,
+        environ=os.environ,
+        live=True,
+        exchange=exchange_name,
+        algo_namespace=algo_namespace,
+        base_currency=base_currency,
+        live_graph=live_graph
+    )
+
+    if output == '-':
+        click.echo(str(perf))
+    elif output != os.devnull:  # make the catalyst magic not write any data
+        perf.to_pickle(output)
+
+    return perf
+
+
+@main.command(name='ingest-exchange')
+@click.option(
+    '-x',
+    '--exchange-name',
+    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the exchange bundle to ingest (supported: bitfinex,'
+         ' bittrex, poloniex).',
+)
+@click.option(
+    '-f',
+    '--data-frequency',
+    type=click.Choice({'daily', 'minute', 'daily,minute', 'minute,daily'}),
+    default='daily',
+    show_default=True,
+    help='The data frequency of the desired OHLCV bars.',
+)
+@click.option(
+    '-s',
+    '--start',
+    default=None,
+    type=Date(tz='utc', as_timestamp=True),
+    help='The start date of the data range. (default: one year from end date)',
+)
+@click.option(
+    '-e',
+    '--end',
+    default=None,
+    type=Date(tz='utc', as_timestamp=True),
+    help='The end date of the data range. (default: today)',
+)
+@click.option(
+    '-i',
+    '--include-symbols',
+    default=None,
+    help='A list of symbols to ingest (optional comma separated list)',
+)
+@click.option(
+    '--exclude-symbols',
+    default=None,
+    help='A list of symbols to exclude from the ingestion '
+         '(optional comma separated list)',
+)
+@click.option(
+    '--show-progress/--no-show-progress',
+    default=True,
+    help='Print progress information to the terminal.'
+)
+def ingest_exchange(exchange_name, data_frequency, start, end,
+                    include_symbols, exclude_symbols, show_progress):
+    """
+    Ingest data for the given exchange.
+    """
+    exchange = get_exchange(exchange_name)
+    exchange_bundle = ExchangeBundle(exchange)
+
+    click.echo('Ingesting exchange bundle {}...'.format(exchange_name))
+    exchange_bundle.ingest(
+        data_frequency=data_frequency,
+        include_symbols=include_symbols,
+        exclude_symbols=exclude_symbols,
+        start=start,
+        end=end,
+        show_progress=show_progress
+    )
+
+
 @main.command()
 @click.option(
     '-b',
     '--bundle',
-    default='poloniex',
     metavar='BUNDLE-NAME',
-    show_default=True,
+    default=None,
+    show_default=False,
     help='The data bundle to ingest.',
 )
+@click.option(
+    '-x',
+    '--exchange-name',
+    type=click.Choice({'bitfinex', 'bittrex', 'poloniex'}),
+    help='The name of the exchange bundle to ingest (supported: bitfinex,'
+         ' bittrex, poloniex).',
+)
 @click.option(
     '-c',
     '--compile-locally',
@@ -354,9 +543,12 @@ def catalyst_magic(line, cell=None):
     default=True,
     help='Print progress information to the terminal.'
 )
-def ingest(bundle, compile_locally, assets_version, show_progress):
+@click.pass_context
+def ingest(ctx, bundle, exchange_name, compile_locally, assets_version,
+           show_progress):
     """Ingest the data for the given bundle.
     """
+
     bundles_module.ingest(
         bundle,
         os.environ,
@@ -376,6 +568,13 @@ def ingest(bundle, compile_locally, assets_version, show_progress):
     show_default=True,
     help='The data bundle to clean.',
 )
+@click.option(
+    '-x',
+    '--exchange_name',
+    metavar='EXCHANGE-NAME',
+    show_default=True,
+    help='The exchange bundle name to clean.',
+)
 @click.option(
     '-e',
     '--before',

catalyst/algorithm.py

@@ -125,6 +125,7 @@ from catalyst.utils.factory import create_simulation_parameters
 from catalyst.utils.math_utils import (
     tolerant_equals,
     round_if_near_integer,
+    round_nearest
 )
 from catalyst.utils.pandas_utils import clear_dataframe_indexer_caches
 from catalyst.utils.preprocess import preprocess
@@ -133,10 +134,7 @@ from catalyst.utils.security_list import SecurityList
 import catalyst.protocol
 from catalyst.sources.requests_csv import PandasRequestsCSV
 
-from catalyst.gens.sim_engine import (
+from catalyst.gens.sim_engine import MinuteSimulationClock
-    MinuteSimulationClock,
-    FiveMinuteSimulationClock,
-)
 from catalyst.sources.benchmark_source import BenchmarkSource
 from catalyst.catalyst_warnings import ZiplineDeprecationWarning
 
@@ -173,7 +171,7 @@ class TradingAlgorithm(object):
     algo_filename : str, optional
         The filename for the algoscript. This will be used in exception
         tracebacks. default: '<string>'.
-    data_frequency : {'daily', '5-minute', 'minute'}, optional
+    data_frequency : {'daily', 'minute'}, optional
         The duration of the bars.
     instant_fill : bool, optional
         Whether to fill orders immediately or on next bar. default: False
@@ -226,7 +224,7 @@ class TradingAlgorithm(object):
             script : str
                 Algoscript that contains initialize and
                 handle_data function definition.
-            data_frequency : {'daily', '5-minute', 'minute'}
+            data_frequency : {'daily', 'minute'}
                The duration of the bars.
             capital_base : float <default: 1.0e5>
                How much capital to start with.
@@ -434,8 +432,6 @@ class TradingAlgorithm(object):
         if get_loader is not None:
             if data_frequency == 'daily':
                 all_dates = self.trading_calendar.all_sessions
-            elif data_frequency == '5-minute':
-                all_dates = self.trading_calendar.all_five_minutes
             elif data_frequency == 'minute':
                 all_dates = self.trading_calendar.all_minutes
             else:
@@ -467,7 +463,7 @@ class TradingAlgorithm(object):
         self._in_before_trading_start = True
 
         with handle_non_market_minutes(data) if \
-                self.data_frequency in ('minute', '5-minute') else ExitStack():
+                self.data_frequency == 'minute' else ExitStack():
             self._before_trading_start(self, data)
 
         self._in_before_trading_start = False
@@ -523,11 +519,10 @@ class TradingAlgorithm(object):
         market_closes = trading_o_and_c['market_close']
         minutely_emission = False
 
-        if self.sim_params.data_frequency in set(('minute', '5-minute')):
+        if self.sim_params.data_frequency == 'minute':
             market_opens = trading_o_and_c['market_open']
 
-            minutely_emission = self.sim_params.emission_rate in \
+            minutely_emission = self.sim_params.emission_rate == 'minute'
-                set(('minute', '5-minute'))
         else:
             # in daily mode, we want to have one bar per session, timestamped
             # as the last minute of the session.
@@ -551,15 +546,6 @@ class TradingAlgorithm(object):
             'UTC',
         )
 
-        if self.sim_params.data_frequency == '5-minute':
-            return FiveMinuteSimulationClock(
-                self.sim_params.sessions,
-                execution_opens,
-                execution_closes,
-                before_trading_start_minutes,
-                minute_emission=minutely_emission,
-            )
-
         return MinuteSimulationClock(
             self.sim_params.sessions,
             execution_opens,
@@ -691,8 +677,6 @@ class TradingAlgorithm(object):
                     time_count = times.nunique()
                     if time_count == 1:
                         self.sim_params.data_frequency = 'daily'
-                    elif time_count == 288:
-                        self.sim_params.data_frequency = '5-minute'
                     else:
                         self.sim_params.data_frequency = 'minute'
 
@@ -714,8 +698,6 @@ class TradingAlgorithm(object):
 
                 if self.sim_params.data_frequency == 'daily':
                     equity_reader_arg = 'equity_daily_reader'
-                elif self.sim_params.data_frequency == '5-minute':
-                    equity_daily_reader = 'equity_5_minute_reader'
                 elif self.sim_params.data_frequency == 'minute':
                     equity_reader_arg = 'equity_minute_reader'
                 equity_reader = PanelBarReader(
@@ -959,9 +941,9 @@ class TradingAlgorithm(object):
                   The arena from the simulation parameters. This will normally
                   be ``'backtest'`` but some systems may use this distinguish
                   live trading from backtesting.
-              data_frequency : {'daily', '5-minute', 'minute'}
+              data_frequency : {'daily', 'minute'}
                   data_frequency tells the algorithm if it is running with
-                  daily, minute, or five-minute mode.
+                  daily or minute mode.
               start : datetime
                   The start date for the simulation.
               end : datetime
@@ -1136,17 +1118,10 @@ class TradingAlgorithm(object):
                           'time_rule= when calling schedule_function without '
                           'specifying a date_rule', stacklevel=3)
 
-        freq = self.sim_params.data_frequency
-
         date_rule = date_rule or date_rules.every_day()
-        if freq is 'daily':
+        time_rule = ((time_rule or time_rules.every_minute())
-            # ignore time rule in daily mode
+                     if self.sim_params.data_frequency == 'minute' else
-            time_rule = time_rules.every_minute()
+                     # If we are in daily mode the time_rule is ignored.
-        else:
-            # use provided time rule or default to every minute or 5 minutes
-            # based on desired data frequency.
-            time_rule = time_rule or (time_rules.every_5_minutes()
-                                      if freq is '5-minute' else
                      time_rules.every_minute())
 
         # Check the type of the algorithm's schedule before pulling calendar
@@ -1488,7 +1463,7 @@ class TradingAlgorithm(object):
 
     def _calculate_order(self, asset, amount,
                          limit_price=None, stop_price=None, style=None):
-        amount = self.round_order(amount)
+        amount = self.round_order(amount, asset)
 
         # Raises a ZiplineError if invalid parameters are detected.
         self.validate_order_params(asset,
@@ -1505,16 +1480,13 @@ class TradingAlgorithm(object):
         return amount, style
 
     @staticmethod
-    def round_order(amount):
+    def round_order(amount, asset):
         """
-        Convert number of shares to an integer.
+        Converts the number of shares to the smallest tradable lot size for
+        the asset being ordered.
         
-        By default, truncates to the integer share count that's either within
-        .0001 of amount or closer to zero.
-
-        E.g. 3.9999 -> 4.0; 5.5 -> 5.0; -5.5 -> -5.0
         """
-        return int(round_if_near_integer(amount))
+        return round_nearest(amount, asset.min_trade_size)
 
     def validate_order_params(self,
                               asset,
@@ -1550,7 +1522,6 @@ class TradingAlgorithm(object):
                              self.updated_portfolio(),
                              self.get_datetime(),
                              self.trading_client.current_data)
-
     @staticmethod
     def __convert_order_params_for_blotter(limit_price, stop_price, style):
         """
@@ -1822,7 +1793,7 @@ class TradingAlgorithm(object):
 
     @data_frequency.setter
     def data_frequency(self, value):
-        assert value in ('daily', '5-minute', 'minute')
+        assert value in ('daily', 'minute')
         self.sim_params.data_frequency = value
 
     @api_method

catalyst/assets/_assets.pyx

@@ -59,6 +59,7 @@ cdef class Asset:
 
     cdef readonly object exchange
     cdef readonly object exchange_full
+    cdef readonly object min_trade_size
 
     _kwargnames = frozenset({
         'sid',
@@ -70,6 +71,7 @@ cdef class Asset:
         'auto_close_date',
         'exchange',
         'exchange_full',
+        'min_trade_size',
     })
 
     def __init__(self,
@@ -81,7 +83,8 @@ cdef class Asset:
                  object end_date=None,
                  object first_traded=None,
                  object auto_close_date=None,
-                 object exchange_full=None):
+                 object exchange_full=None,
+                 object min_trade_size=None):
 
         self.sid = sid
         self.sid_hash = hash(sid)
@@ -94,6 +97,7 @@ cdef class Asset:
         self.end_date = end_date
         self.first_traded = first_traded
         self.auto_close_date = auto_close_date
+        self.min_trade_size = min_trade_size
 
     def __int__(self):
         return self.sid
@@ -148,7 +152,8 @@ cdef class Asset:
 
     def __repr__(self):
         attrs = ('symbol', 'asset_name', 'exchange',
-                 'start_date', 'end_date', 'first_traded', 'auto_close_date')
+                 'start_date', 'end_date', 'first_traded', 'auto_close_date',
+                 'min_trade_size')
         tuples = ((attr, repr(getattr(self, attr, None)))
                   for attr in attrs)
         strings = ('%s=%s' % (t[0], t[1]) for t in tuples)
@@ -170,7 +175,8 @@ cdef class Asset:
                                  self.end_date,
                                  self.first_traded,
                                  self.auto_close_date,
-                                 self.exchange_full))
+                                 self.exchange_full,
+                                 self.min_trade_size))
 
     cpdef to_dict(self):
         """
@@ -186,6 +192,7 @@ cdef class Asset:
             'auto_close_date': self.auto_close_date,
             'exchange': self.exchange,
             'exchange_full': self.exchange_full,
+            'min_trade_size': self.min_trade_size
         }
 
     @classmethod
@@ -234,7 +241,7 @@ cdef class Equity(Asset):
     def __repr__(self):
         attrs = ('symbol', 'asset_name', 'exchange',
                  'start_date', 'end_date', 'first_traded', 'auto_close_date',
-                 'exchange_full')
+                 'exchange_full', 'min_trade_size')
         tuples = ((attr, repr(getattr(self, attr, None)))
                   for attr in attrs)
         strings = ('%s=%s' % (t[0], t[1]) for t in tuples)
@@ -388,6 +395,9 @@ cdef class TradingPair(Asset):
     cdef readonly float leverage
     cdef readonly object market_currency
     cdef readonly object base_currency
+    cdef readonly object end_daily
+    cdef readonly object end_minute
+    cdef readonly object exchange_symbol
 
     _kwargnames = frozenset({
         'sid',
@@ -401,7 +411,11 @@ cdef class TradingPair(Asset):
         'exchange_full',
         'leverage',
         'market_currency',
-        'base_currency'
+        'base_currency',
+        'end_daily',
+        'end_minute',
+        'exchange_symbol',
+        'min_trade_size'
     })
     def __init__(self,
                  object symbol,
@@ -410,10 +424,14 @@ cdef class TradingPair(Asset):
                  object asset_name=None,
                  int sid=0,
                  float leverage=1.0,
+                 object end_daily=None,
+                 object end_minute=None,
                  object end_date=None,
+                 object exchange_symbol=None,
                  object first_traded=None,
                  object auto_close_date=None,
-                 object exchange_full=None):
+                 object exchange_full=None,
+                 object min_trade_size=None):
         """
         Replicates the Asset constructor with some built-in conventions
         and a new 'leverage' attribute.
@@ -465,10 +483,14 @@ cdef class TradingPair(Asset):
         :param asset_name:
         :param sid:
         :param leverage:
+        :param end_daily
+        :param end_minute
         :param end_date:
+        :param exchange_symbol:
         :param first_traded:
         :param auto_close_date:
         :param exchange_full:
+        :param min_trade_size:
         """
 
         symbol = symbol.lower()
@@ -502,23 +524,33 @@ cdef class TradingPair(Asset):
             first_traded=first_traded,
             auto_close_date=auto_close_date,
             exchange_full=exchange_full,
+            min_trade_size=min_trade_size
         )
 
         self.leverage = leverage
+        self.end_daily = end_daily
+        self.end_minute = end_minute
+        self.exchange_symbol = exchange_symbol
 
     def __repr__(self):
         return 'Trading Pair {symbol}({sid}) Exchange: {exchange}, ' \
                'Introduced On: {start_date}, ' \
                'Market Currency: {market_currency}, ' \
                'Base Currency: {base_currency}, ' \
-               'Exchange Leverage: {leverage}'.format(
+               'Exchange Leverage: {leverage}, ' \
+               'Minimum Trade Size: {min_trade_size} ' \
+               'Last daily ingestion: {end_daily} ' \
+               'Last minutely ingestion: {end_minute}'.format(
             symbol=self.symbol,
             sid=self.sid,
             exchange=self.exchange,
             start_date=self.start_date,
             market_currency=self.market_currency,
             base_currency=self.base_currency,
-            leverage=self.leverage
+            leverage=self.leverage,
+            min_trade_size=self.min_trade_size,
+            end_daily=self.end_daily,
+            end_minute=self.end_minute
         )
 
     cpdef __reduce__(self):
@@ -537,7 +569,8 @@ cdef class TradingPair(Asset):
                                  self.end_date,
                                  self.first_traded,
                                  self.auto_close_date,
-                                 self.exchange_full))
+                                 self.exchange_full,
+                                 self.min_trade_size))
 
 def make_asset_array(int size, Asset asset):
     cdef np.ndarray out = np.empty([size], dtype=object)

catalyst/assets/asset_db_schema.py

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

catalyst/assets/asset_writer.py

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

catalyst/curate/poloniex.py

@@ -1,22 +1,23 @@
 import json, time, csv
 from datetime import datetime
 import pandas as pd
-import os
+import os, time, shutil, requests, logbook
-import time
+from catalyst.exchange.exchange_utils import get_exchange_symbols_filename
-import requests
-import logbook
 
-DT_START        = time.mktime(datetime(2010, 1, 1, 0, 0).timetuple())
+
+DT_START        = int(time.mktime(datetime(2010, 1, 1, 0, 0).timetuple()))
+DT_END          = int(time.time())
 CSV_OUT_FOLDER  = '/var/tmp/catalyst/data/poloniex/'
+CSV_OUT_FOLDER  = '/Volumes/enigma/data/poloniex/'
 CONN_RETRIES    = 2
 
 logbook.StderrHandler().push_application()
 log = logbook.Logger(__name__)
 
 class PoloniexCurator(object):
-    """
+    '''
     OHLCV data feed generator for crypto data. Based on Poloniex market data
-    """
+    '''
 
     _api_path       = 'https://poloniex.com/public?'
     currency_pairs  = []
@@ -29,6 +30,9 @@ class PoloniexCurator(object):
                 log.error('Failed to create data folder: %s' % CSV_OUT_FOLDER)
                 log.exception(e)
 
+    '''
+        Retrieves and returns all currency pairs from the exchange 
+    '''
     def get_currency_pairs(self):
         url = self._api_path + 'command=returnTicker'
 
@@ -47,98 +51,244 @@ class PoloniexCurator(object):
 
         log.debug('Currency pairs retrieved successfully: %d' % (len(self.currency_pairs)))
 
-    def _get_start_date(self, csv_fn):
+
-        ''' Function returns latest appended date, if the file has been previously written
+    '''
-            the last line is an empty one, so we have to read the second to last line 
+        Helper function that reads tradeID and date fields from CSV readline
+    '''
+    def _retrieve_tradeID_date(self, row):
+        tId = int(row.split(',')[0])
+        d   = pd.to_datetime( row.split(',')[1], infer_datetime_format=True).value // 10 ** 9
+        return tId, d
+
+    '''
+        Retrieves TradeHistory from exchange for a given currencyPair between start and end dates.
+        If no start date is provided, uses a system-wide one (beginning of time for cryptotrading)
+        If no end date is provided, 'now' is used
+        Stores results in CSV file on disk.
+        This function is called recursively to work around the limitations imposed by the provider API.
+    '''
+    def retrieve_trade_history(self, currencyPair, start=DT_START, end=DT_END, temp=None):
+        csv_fn = CSV_OUT_FOLDER + 'crypto_trades-' + currencyPair + '.csv'
+
+        '''
+            Check what data we already have on disk, reading first and last lines from file.
+            Data is stored on file from NEWEST to OLDEST.
         '''
         try:
             with open(csv_fn, 'ab+') as f: 
-                f.seek(0, os.SEEK_END)              # First check file is not zero size
+                f.seek(0, os.SEEK_END)
-                if(f.tell() > 2):
+                if(f.tell() > 2):                           # First check file is not zero size
+                    f.seek(0)                               # Go to the beginning to read first line
+                    last_tradeID, end_file    = self._retrieve_tradeID_date(f.readline())
                     f.seek(-2, os.SEEK_END)                 # Jump to the second last byte.
                     while f.read(1) != b"\n":               # Until EOL is found...
                         f.seek(-2, os.SEEK_CUR)             # ...jump back the read byte plus one more.
-                    lastrow = f.readline() 
+                    first_tradeID, start_file = self._retrieve_tradeID_date(f.readline())
-                    return int(lastrow.split(',')[0]) + 300
+
+                    if( first_tradeID == 1 and end_file + 3600 > DT_END ):
+                        return
 
         except Exception as e:
             log.error('Error opening file: %s' % csv_fn)
             log.exception(e)
 
-        return DT_START
+        '''
+        Poloniex API limits querying TradeHistory to intervals smaller than 1 month,
+        so we make sure that start date is never more than 1 month apart from end date
+        '''
+        if( end - start > 2419200 ):       # 60 s/min * 60 min/hr * 24 hr/day * 28 days     
+            newstart = end - 2419200
+        else:
+            newstart = start
 
-    def get_data(self, currencyPair, start, end=9999999999, period=300):
+        log.debug(currencyPair+': Retrieving from '+str(newstart)+' to '+str(end) +'\t '
-        url = self._api_path + 'command=returnChartData&currencyPair=' + currencyPair + '&start=' + str(start) + '&end=' + str(end) + '&period=' + str(period)
+                    + time.ctime(newstart) + ' - '+ time.ctime(end))
+
+        url = self._api_path + 'command=returnTradeHistory&currencyPair=' + currencyPair + '&start=' + str(newstart) + '&end=' + str(end)
 
         try:
             response = requests.get(url)
         except Exception as e:
-            log.error('Failed to retrieve candlestick chart data for %s' % currencyPair)
+            log.error('Failed to retrieve trade history data for %s' % currencyPair)
             log.exception(e)
             return None
-
+        else:
-        return response.json()
+            if isinstance(response.json(), dict) and response.json()['error']:
+                log.error('Failed to to retrieve trade history data for %s: %s' % (currencyPair,response.json()['error']))
+                exit(1)
 
         '''
-    Pulls latest data for a single pair
+            If we get to transactionId == 1, and we already have that on disk, 
+            we got to the end of TradeHistory for this coin.
+        '''
+        if('first_tradeID' in locals() and response.json()[-1]['tradeID'] == first_tradeID): 
+            return
+
+        '''
+            There are primarily two scenarios:
+                a) There is newer data available that we need to add at the beginning
+                   of the file. We'll retrieve all what we need until we get to what 
+                   we already have, writing it to a temporary file; and we will write
+                   that at the beginning of our existing file.
+                b) We are going back in time, appending at the end of our existing
+                   TradeHistory until the first transaction for this currencyPair
         '''
-    def append_data_single_pair(self, currencyPair, repeat=0):
-        log.debug('Getting data for %s' % currencyPair)
-        csv_fn = CSV_OUT_FOLDER + 'crypto_prices-' + currencyPair + '.csv'
-        start  = self._get_start_date(csv_fn)
-        # Only fetch data if more than 5min have passed since last fetch
-        if (time.time() > start):
-            data   = self.get_data(currencyPair, start)
-            if data is not None:
         try: 
+            if( 'end_file' in locals() and end_file + 3600 < end):
+                if (temp is None):
+                    temp = os.tmpfile()
+                tempcsv = csv.writer(temp)
+                for item in response.json():
+                    if( item['tradeID'] <= last_tradeID ):
+                        continue
+                    tempcsv.writerow([
+                        item['tradeID'],
+                        item['date'],
+                        item['type'],
+                        item['rate'],
+                        item['amount'],
+                        item['total'],
+                        item['globalTradeID']        
+                    ])
+                if( response.json()[-1]['tradeID'] > last_tradeID ):
+                    end = pd.to_datetime( response.json()[-1]['date'], infer_datetime_format=True).value // 10 ** 9
+                    self.retrieve_trade_history(currencyPair, start, end, temp=temp) 
+                else:
+                    with open(csv_fn,'rb+') as f:
+                        shutil.copyfileobj(f,temp)
+                        f.seek(0)
+                        temp.seek(0)
+                        shutil.copyfileobj(temp,f)
+                    temp.close()
+                    end = start_file
+            else:
                 with open(csv_fn, 'ab') as csvfile:
                     csvwriter = csv.writer(csvfile)
-                        for item in data:
+                    for item in response.json():
-                            if item['date'] == 0:
+                        if( 'first_tradeID' in locals() and item['tradeID'] >= first_tradeID ):
                             continue
                         csvwriter.writerow([
+                            item['tradeID'],
                             item['date'],
-                                item['open'],
+                            item['type'],
-                                item['high'],
+                            item['rate'],
-                                item['low'],
+                            item['amount'],
-                                item['close'],
+                            item['total'],
-                                item['volume'],
+                            item['globalTradeID']
+                        ])
+                end = pd.to_datetime( response.json()[-1]['date'], infer_datetime_format=True).value // 10 ** 9
+
+        except Exception as e:
+            log.error('Error opening %s' % csv_fn)
+            log.exception(e)
+
+        '''
+            If we got here, we aren't done yet. Call recursively with 'end' times
+            that go sequentially back in time.
+        '''
+        self.retrieve_trade_history(currencyPair, start, end)
+
+
+    '''
+        Generates OHLCV dataframe from a dataframe containing all TradeHistory
+        by resampling with 1-minute period
+    '''
+    def generate_ohlcv(self, df):
+        df.set_index('date', inplace=True)                      # Index by date
+        vol = df['total'].to_frame('volume')                    # Will deal with vol separately, as ohlc() messes it up
+        df.drop('total', axis=1, inplace=True)                  # Drop volume data from dataframe
+        ohlc = df.resample('T').ohlc()                          # Resample OHLC in 1min bins
+        ohlc.columns = ohlc.columns.map(lambda t: t[1])         # Raname columns by dropping 'rate'
+        closes = ohlc['close'].fillna(method='pad')             # Pad forward missing 'close'
+        ohlc = ohlc.apply(lambda x: x.fillna(closes))           # Fill N/A with last close
+        vol = vol.resample('T').sum().fillna(0)                 # Add volumes by bin
+        ohlcv = pd.concat([ohlc,vol], axis=1)                   # Concatenate OHLC + Volume
+        return ohlcv
+
+
+    '''
+        Generates OHLCV data file with 1minute bars from TradeHistory on disk
+    '''
+    def write_ohlcv_file(self, currencyPair):        
+        csv_trades = CSV_OUT_FOLDER + 'crypto_trades-' + currencyPair + '.csv'
+        csv_1min   = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
+        if( os.path.isfile(csv_1min) ):
+            log.debug(currencyPair+': 1min data already present. Delete the file if you want to rebuild it.')
+        else:
+            df = pd.read_csv(csv_trades, names=['tradeID','date','type','rate','amount','total','globalTradeID'], 
+                    dtype = {'tradeID': int, 'date': str, 'type': str, 'rate': float, 'amount': float, 'total': float, 'globalTradeID': int } )
+            df.drop(['tradeID','type','amount','globalTradeID'], axis=1, inplace=True)
+            df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True) 
+            ohlcv = self.generate_ohlcv(df)
+            try: 
+                with open(csv_1min, 'ab') as csvfile:
+                    csvwriter = csv.writer(csvfile)
+                    for item in ohlcv.itertuples():
+                        if item.Index == 0:
+                            continue
+                        csvwriter.writerow([
+                            item.Index.value // 10 ** 9,
+                            item.open,
+                            item.high,
+                            item.low,
+                            item.close,
+                            item.volume,
                         ])
             except Exception as e:
                 log.error('Error opening %s' % csv_fn)
                 log.exception(e)
-            elif (repeat < CONN_RETRIES):
+            log.debug(currencyPair+': Generated 1min OHLCV data.')
-                    log.debug('Retrying: attemt %d' % (repeat+1) )
+
-                    self.append_data_single_pair(currencyPair, repeat + 1)    
 
     '''
-    Pulls latest data for all currency pairs
+    Returns a data frame for a given currencyPair from data on disk
     '''
-    def append_data(self):
+    def onemin_to_dataframe(self, currencyPair, start, end):
-        for currencyPair in self.currency_pairs:
+        csv_fn     = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
-            self.append_data_single_pair(currencyPair)
-            # Rate limit is 6 calls per second, sleep 1sec/6 to be safe
-            time.sleep(0.17)
-
-    '''
-    Returns a data frame for all pairs, or for the requests currency pair.
-    Makes sure data is up to date
-    '''
-    def to_dataframe(self, start, end, currencyPair=None):
-        csv_fn = CSV_OUT_FOLDER + 'crypto_prices-' + currencyPair + '.csv'
-        last_date = self._get_start_date(csv_fn)
-        if last_date + 300 < end or not os.path.exists(csv_fn):
-            # get latest data
-            self.append_data_single_pair(currencyPair)
-
-        # CSV holds the latest snapshot
         df         = pd.read_csv(csv_fn, names=['date', 'open', 'high', 'low', 'close', 'volume'])
         df['date'] = pd.to_datetime(df['date'],unit='s')
         df.set_index('date', inplace=True)
+        return df[start : end]
+
+    '''
+    Generates a symbols.json file with corresponding start_date for each currencyPair
+    '''
+    def generate_symbols_json(self, filename=None):
+        symbol_map = {}
+
+        if(filename is None):
+            filename = get_exchange_symbols_filename('poloniex')
+
+        with open(filename, 'w') as symbols:
+            for currencyPair in self.currency_pairs:
+                start = None
+                csv_fn     = CSV_OUT_FOLDER + 'crypto_trades-' + currencyPair + '.csv'
+                with open(csv_fn, 'r') as f: 
+                    f.seek(0, os.SEEK_END)
+                    if(f.tell() > 2):                           # First check file is not zero size
+                        f.seek(-2, os.SEEK_END)                 # Jump to the second last byte.
+                        while f.read(1) != b"\n":               # Until EOL is found...
+                            f.seek(-2, os.SEEK_CUR)             # ...jump back the read byte plus one more.
+                        start = pd.to_datetime( f.readline().split(',')[1], infer_datetime_format=True)
+
+                if(start is None):
+                    start = time.gmtime()
+                base, market = currencyPair.lower().split('_')
+                symbol = '{market}_{base}'.format( market=market, base=base )
+                symbol_map[currencyPair] = dict(
+                    symbol = symbol,
+                    start_date = start.strftime("%Y-%m-%d")
+                )
+            json.dump(symbol_map, symbols, sort_keys=True, indent=2, separators=(',',':'))    
 
-        return df[datetime.fromtimestamp(start):datetime.fromtimestamp(end-1)]
 
 if __name__ == '__main__':
     pc = PoloniexCurator()
     pc.get_currency_pairs()
-    pc.append_data()
+    #pc.generate_symbols_json()
+    
+    for currencyPair in pc.currency_pairs:
+        pc.retrieve_trade_history(currencyPair)
+        pc.write_ohlcv_file(currencyPair)
+
+    

catalyst/data/_equities.pyx

@@ -217,11 +217,11 @@ cpdef _read_bcolz_data(ctable_t table,
 
         if column_name in ['open', 'high', 'low', 'close']:
             where_nan = (outbuf == 0)
-            outbuf_as_float = outbuf.astype(float64) * .000001
+            outbuf_as_float = outbuf.astype(float64) * .000000001
             outbuf_as_float[where_nan] = NAN
             results.append(outbuf_as_float)
-        elif column_name != 'volume':
+        elif column_name in ['volume']:
-            results.append(outbuf.astype(uint32))
+            results.append(outbuf.astype(float64) * .000000001)
         else:
             results.append(outbuf)
     return results

catalyst/data/_minute_bar_internal.pyx

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

catalyst/data/bundles/base.py

@@ -60,10 +60,6 @@ class BaseBundle(object):
     def minutes_per_day(self):
         raise NotImplementedError()
 
-    @lazyval
-    def five_minutes_per_day(self):
-        raise NotImplementedError()
-
     @lazyval
     def frequencies(self):
         raise NotImplementedError()
@@ -115,7 +111,6 @@ class BaseBundle(object):
                environ,
                asset_db_writer,
                minute_bar_writer,
-               five_minute_bar_writer,
                daily_bar_writer,
                adjustment_writer,
                calendar,
@@ -162,7 +157,7 @@ class BaseBundle(object):
 
                 # Post-process metadata using cached symbol frames, and write to
                 # disk.  This metadata must be written before any attempt to write
-                # either minute or 5-minute data.
+                # minute data.
                 metadata = self._post_process_metadata(
                     raw_metadata,
                     cache,
@@ -170,26 +165,6 @@ class BaseBundle(object):
                 )
                 asset_db_writer.write(metadata)
 
-                # Compile 5-minute symbol data if bundle supports 5-minute mode and
-                # persist the dataset to disk.
-                '''
-                if '5-minute' in self.frequencies:
-                    five_minute_bar_writer.write(
-                        self._fetch_symbol_iter(
-                            api_key,
-                            cache,
-                            symbol_map,
-                            calendar,
-                            start_session,
-                            end_session,
-                            '5-minute',
-                            retries,
-                        ),
-                        length=len(symbol_map),
-                        show_progress=show_progress,
-                    )
-                '''
-
                 # Compile minute symbol data if bundle supports minute mode and
                 # persist the dataset to disk.
                 if 'minute' in self.frequencies:
@@ -491,7 +466,7 @@ class BaseBundle(object):
                     data_frequency,
                 )
                 raw_data.index = pd.to_datetime(raw_data.index, utc=True)
-                raw_data.index = raw_data.index.tz_localize('UTC')
+                #raw_data.index = raw_data.index.tz_localize('UTC')
 
                 # Filter incoming data to fit start and end sessions.
                 raw_data = raw_data[

catalyst/data/bundles/base_pricing.py

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

catalyst/data/bundles/core.py

@@ -17,10 +17,6 @@ from ..us_equity_pricing import (
     SQLiteAdjustmentReader,
     SQLiteAdjustmentWriter,
 )
-from ..five_minute_bars import (
-    BcolzFiveMinuteBarReader,
-    BcolzFiveMinuteBarWriter,
-)
 from ..minute_bars import (
     BcolzMinuteBarReader,
     BcolzMinuteBarWriter,
@@ -54,11 +50,6 @@ def minute_path(bundle_name, timestr, environ=None):
         environ=environ,
     )
 
-def five_minute_path(bundle_name, timestr, environ=None):
-    return pth.data_path(
-        five_minute_relative(bundle_name, timestr, environ),
-        environ=environ,
-    )
 
 def daily_path(bundle_name, timestr, environ=None):
     return pth.data_path(
@@ -92,8 +83,6 @@ def cache_relative(bundle_name, timestr, environ=None):
 def daily_relative(bundle_name, timestr, environ=None):
     return bundle_name, timestr, 'daily_equities.bcolz'
 
-def five_minute_relative(bundle_name, timestr, environ=None):
-    return bundle_name, timestr, 'five_minute.bcolz'
 
 def minute_relative(bundle_name, timestr, environ=None):
     return bundle_name, timestr, 'minute_equities.bcolz'
@@ -206,14 +195,13 @@ RegisteredBundle = namedtuple(
      'start_session',
      'end_session',
      'minutes_per_day',
-     'five_minutes_per_day',
      'ingest',
      'create_writers']
 )
 
 BundleData = namedtuple(
     'BundleData',
-    'asset_finder minute_bar_reader five_minute_bar_reader daily_bar_reader '
+    'asset_finder minute_bar_reader daily_bar_reader '
     'adjustment_reader',
 )
 
@@ -303,7 +291,6 @@ def _make_bundle_core():
             bundle.ingest,
             calendar_name=bundle.calendar_name,
             minutes_per_day=bundle.minutes_per_day,
-            five_minutes_per_day=bundle.five_minutes_per_day,
             start_session=start_session,
             end_session=end_session,
             create_writers=create_writers,
@@ -316,7 +303,6 @@ def _make_bundle_core():
                  start_session=None,
                  end_session=None,
                  minutes_per_day=1440,
-                 five_minutes_per_day=288,
                  create_writers=True):
         """Register a data bundle ingest function.
 
@@ -397,7 +383,6 @@ def _make_bundle_core():
             start_session=start_session,
             end_session=end_session,
             minutes_per_day=minutes_per_day,
-            five_minutes_per_day=five_minutes_per_day,
             ingest=f,
             create_writers=create_writers,
         )
@@ -496,16 +481,6 @@ def _make_bundle_core():
                 # that it can compute the adjustment ratios for the dividends.
                 daily_bar_writer.write(())
 
-                five_minute_bar_writer = BcolzFiveMinuteBarWriter(
-                    wd.ensure_dir(*five_minute_relative(
-                        name, timestr, environ=environ)
-                    ),
-                    calendar,
-                    start_session,
-                    end_session,
-                    five_minutes_per_day=bundle.five_minutes_per_day,
-                )
-
                 minute_bar_writer = BcolzMinuteBarWriter(
                     wd.ensure_dir(*minute_relative(
                         name, timestr, environ=environ)
@@ -532,7 +507,6 @@ def _make_bundle_core():
                 )
             else:
                 daily_bar_writer = None
-                five_minute_bar_writer = None
                 minute_bar_writer = None
                 asset_db_writer = None
                 adjustment_db_writer = None
@@ -544,7 +518,6 @@ def _make_bundle_core():
                 environ,
                 asset_db_writer,
                 minute_bar_writer,
-                five_minute_bar_writer,
                 daily_bar_writer,
                 adjustment_db_writer,
                 calendar,
@@ -631,9 +604,6 @@ def _make_bundle_core():
             minute_bar_reader=BcolzMinuteBarReader(
                 minute_path(name, timestr, environ=environ),
             ),
-            five_minute_bar_reader=BcolzFiveMinuteBarReader(
-                five_minute_path(name, timestr, environ=environ),
-            ),
             daily_bar_reader=BcolzDailyBarReader(
                 daily_path(name, timestr, environ=environ),
             ),

catalyst/data/bundles/poloniex.py

@@ -13,6 +13,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import sys
+
 from datetime import datetime
 
 import pandas as pd
@@ -23,6 +25,8 @@ from catalyst.data.bundles.core import register_bundle
 from catalyst.data.bundles.base_pricing import BaseCryptoPricingBundle
 from catalyst.utils.memoize import lazyval
 
+from catalyst.curate.poloniex import PoloniexCurator
+
 class PoloniexBundle(BaseCryptoPricingBundle):
     @lazyval
     def name(self):
@@ -36,7 +40,7 @@ class PoloniexBundle(BaseCryptoPricingBundle):
     def frequencies(self):
         return set((
             'daily',
-            #'5-minute',
+            'minute',
         ))
 
     @lazyval
@@ -75,12 +79,14 @@ class PoloniexBundle(BaseCryptoPricingBundle):
         start_date = sym_data.index[0]
         end_date = sym_data.index[-1]
         ac_date = end_date + pd.Timedelta(days=1)
+        min_trade_size = 0.00000001
 
         return (
             sym_md.symbol,
             start_date,
             end_date,
             ac_date,
+            min_trade_size,
         )
 
     def fetch_raw_symbol_frame(self,
@@ -90,6 +96,14 @@ class PoloniexBundle(BaseCryptoPricingBundle):
                                start_date,
                                end_date,
                                frequency):
+
+        # TODO: replace this with direct exchange call
+        # The end date and frequency should be used to calculate the number of bars
+        if(frequency == 'minute'):
+            pc = PoloniexCurator()
+            raw = pc.onemin_to_dataframe(symbol, start_date, end_date)
+
+        else:
             raw = pd.read_json(
                 self._format_data_url(
                     api_key,
@@ -105,7 +119,7 @@ class PoloniexBundle(BaseCryptoPricingBundle):
         # BcolzDailyBarReader introduces a 1/1000 factor in the way pricing is stored
         # on disk, which we compensate here to get the right pricing amounts
         # ref: data/us_equity_pricing.py
-        scale = 1000
+        scale = 1
         raw.loc[:, 'open'] /= scale
         raw.loc[:, 'high'] /= scale
         raw.loc[:, 'low'] /= scale
@@ -134,7 +148,6 @@ class PoloniexBundle(BaseCryptoPricingBundle):
                          data_frequency):
         period_map = {
             'daily': 86400,
-#            '5-minute': 300,
         }
 
         try:
@@ -153,6 +166,7 @@ class PoloniexBundle(BaseCryptoPricingBundle):
         return self._format_polo_query(query_params)
     
     def _format_polo_query(self, query_params):
+        # TODO: got against the exchange object
         return 'https://poloniex.com/public?{query}'.format(
             query=urlencode(query_params),
         )
@@ -166,4 +180,9 @@ register_bundle(PoloniexBundle, ['USDT_BTC',])
 For a production environment make sure to use (to bundle all pairs):
 register_bundle(PoloniexBundle)
 '''
+
+if 'ingest' in sys.argv and '-c' in sys.argv:
+    register_bundle(PoloniexBundle)
+else:
     register_bundle(PoloniexBundle, create_writers=False)
+

catalyst/data/data_portal.py

@@ -42,7 +42,6 @@ from catalyst.assets.roll_finder import (
 )
 from catalyst.data.dispatch_bar_reader import (
     AssetDispatchMinuteBarReader,
-    AssetDispatchFiveMinuteBarReader,
     AssetDispatchSessionBarReader
 )
 from catalyst.data.resample import (
@@ -120,10 +119,6 @@ class DataPortal(object):
         daily data backtests or daily history calls in a minute backetest.
         If a daily bar reader is not provided but a minute bar reader is,
         the minutes will be rolled up to serve the daily requests.
-    five_minute_reader : BcolzFiveMinuteBarReader, optional
-        The five minute bar reader for equities. This will be used to service
-        5-minute data backtests or five-minute history calls.  This can be used
-        to serve daily calls if no daily bar reader is provided.
     minute_reader : BcolzMinuteBarReader, optional
         The minute bar reader for equities. This will be used to service
         minute data backtests or minute history calls. This can be used
@@ -150,7 +145,6 @@ class DataPortal(object):
                  trading_calendar,
                  first_trading_day,
                  daily_reader=None,
-                 five_minute_reader=None,
                  minute_reader=None,
                  future_daily_reader=None,
                  future_minute_reader=None,
@@ -202,7 +196,6 @@ class DataPortal(object):
                 reader.last_available_dt
                 for reader in [
                     minute_reader,
-                    five_minute_reader,
                     future_minute_reader,
                 ]
                 if reader is not None
@@ -214,8 +207,6 @@ class DataPortal(object):
 
         aligned_minute_reader = self._ensure_reader_aligned(
             minute_reader)
-        aligned_five_minute_reader = self._ensure_reader_aligned(
-            five_minute_reader)
         aligned_session_reader = self._ensure_reader_aligned(
             daily_reader)
         aligned_future_minute_reader = self._ensure_reader_aligned(
@@ -229,13 +220,10 @@ class DataPortal(object):
         }
 
         aligned_minute_readers = {}
-        aligned_five_minute_readers = {}
         aligned_session_readers = {}
 
         if aligned_minute_reader is not None:
             aligned_minute_readers[Equity] = aligned_minute_reader
-        if aligned_five_minute_reader is not None:
-            aligned_five_minute_readers[Equity] = aligned_five_minute_reader
         if aligned_session_reader is not None:
             aligned_session_readers[Equity] = aligned_session_reader
 
@@ -267,13 +255,6 @@ class DataPortal(object):
             self._last_available_minute,
         )
 
-        _dispatch_five_minute_reader = AssetDispatchFiveMinuteBarReader(
-            self.trading_calendar,
-            self.asset_finder,
-            aligned_five_minute_readers,
-            self._last_available_minute,
-        )
-
         _dispatch_session_reader = AssetDispatchSessionBarReader(
             self.trading_calendar,
             self.asset_finder,
@@ -283,7 +264,6 @@ class DataPortal(object):
 
         self._pricing_readers = {
             'minute': _dispatch_minute_reader,
-            '5-minute': _dispatch_five_minute_reader,
             'daily': _dispatch_session_reader,
         }
 
@@ -719,17 +699,6 @@ class DataPortal(object):
             spot_value=result
         )
 
-
-    def _get_five_minute_spot_value(self, asset, column, dt, ffill=False):
-        return self._get_minutely_spot_value(
-            asset,
-            column,
-            dt,
-            ffill, 
-            '5-minute',
-        )
-        
-
     def _get_minute_spot_value(self, asset, column, dt, ffill=False):
         return self._get_minutely_spot_value(
             asset,

catalyst/data/dispatch_bar_reader.py

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

catalyst/data/history_loader.py

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

catalyst/data/loader.py

@@ -12,30 +12,25 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import datetime
 import os
 from collections import OrderedDict
 
 import logbook
 import pandas as pd
-import numpy as np
-from pandas_datareader.data import DataReader
-import datetime
-import time
 import pytz
+from pandas_datareader.data import DataReader
 from six import iteritems
 from six.moves.urllib_error import HTTPError
 
-from .benchmarks import get_benchmark_returns
+from catalyst.utils.calendars import get_calendar
 from . import treasuries, treasuries_can
+from .benchmarks import get_benchmark_returns
+from ..utils.deprecate import deprecated
 from ..utils.paths import (
     cache_root,
     data_root,
 )
-from ..utils.deprecate import deprecated
-
-from catalyst.data.bundles.poloniex import PoloniexBundle
-from catalyst.utils.calendars import get_calendar
-
 
 logger = logbook.Logger('Loader')
 
@@ -94,18 +89,27 @@ def has_data_for_dates(series_or_df, first_date, last_date):
     if not isinstance(dts, pd.DatetimeIndex):
         raise TypeError("Expected a DatetimeIndex, but got %s." % type(dts))
     first, last = dts[[0, -1]].tz_localize(None)
-    return (first <= first_date.tz_localize(None)) and (last >= last_date.tz_localize(None))
+    return (first <= first_date.tz_localize(None)) and (
+        last >= last_date.tz_localize(None))
 
-def load_crypto_market_data(trading_day=None, trading_days=None, bm_symbol='USDT_BTC',
-                            bundle=None, bundle_data=None, environ=None):
 
+def load_crypto_market_data(trading_day=None, trading_days=None,
+                            bm_symbol=None, bundle=None, bundle_data=None,
+                            environ=None, exchange=None, start_dt=None,
+                            end_dt=None):
     if trading_day is None:
         trading_day = get_calendar('OPEN').trading_day
-    if trading_days is None:
-        trading_days = get_calendar('OPEN').all_sessions
 
-    first_date = trading_days[1]
+    # TODO: consider making configurable
-    now = pd.Timestamp.utcnow()
+    bm_symbol = 'btc_usdt'
+    # if trading_days is None:
+    #    trading_days = get_calendar('OPEN').schedule
+
+    # if start_dt is None:
+    start_dt = get_calendar('OPEN').first_trading_session
+
+    if end_dt is None:
+        end_dt = pd.Timestamp.utcnow()
 
     # We expect to have benchmark and treasury data that's current up until
     # **two** full trading days prior to the most recently completed trading
@@ -121,6 +125,7 @@ def load_crypto_market_data(trading_day=None, trading_days=None, bm_symbol='USDT
 
     # We'll attempt to download new data if the latest entry in our cache is
     # before this date.
+    '''
     if(bundle_data):
         # If we are using the bundle to retrieve the cryptobenchmark, find the last
         # date for which there is trading data in the bundle
@@ -129,19 +134,31 @@ def load_crypto_market_data(trading_day=None, trading_days=None, bm_symbol='USDT
         last_date = pd.to_datetime(bundle_data.daily_bar_reader._spot_col('day')[ix],unit='s')
     else:
         last_date = trading_days[trading_days.get_loc(now, method='ffill') - 2]
+    '''
+    last_date = trading_days[trading_days.get_loc(end_dt, method='ffill') - 1]
+
+    if exchange is None:
+        # This is exceptional, since placing the import at the module scope
+        #  breaks things and it's only needed here
+        from catalyst.exchange.poloniex.poloniex import Poloniex
+        exchange = Poloniex('', '', '')
+
+    benchmark_asset = exchange.get_asset(bm_symbol)
+
+    # exchange.get_history_window() already ensures that we have the right data
+    # for the right dates
+    br = exchange.get_history_window(
+        assets=[benchmark_asset],
+        end_dt=last_date,
+        bar_count=pd.Timedelta(last_date - start_dt).days,
+        frequency='1d',
+        field='close',
+        data_frequency='daily')
+    br.columns = ['close']
+    br = br.pct_change(1).iloc[1:]
+    br.loc[start_dt] = 0
+    br = br.sort_index()
 
-    br = ensure_crypto_benchmark_data(
-        bm_symbol,
-        first_date,
-        last_date,
-        now,
-        # We need the trading_day to figure out the close prior to the first
-        # date so that we can compute returns for the first date.
-        trading_day,
-        bundle,
-        bundle_data,
-        environ,
-    )
     # Override first_date for treasury data since we have it for many more years
     # and is independent of crypto data
     first_date_treasury = pd.Timestamp('1990-01-02', tz='UTC')
@@ -149,11 +166,12 @@ def load_crypto_market_data(trading_day=None, trading_days=None, bm_symbol='USDT
         bm_symbol,
         first_date_treasury,
         last_date,
-        now,
+        end_dt,
         environ,
     )
-    benchmark_returns = br[br.index.slice_indexer(first_date, last_date)]
+    benchmark_returns = br[br.index.slice_indexer(start_dt, last_date)]
-    treasury_curves = tc[tc.index.slice_indexer(first_date_treasury, last_date)]
+    treasury_curves = tc[
+        tc.index.slice_indexer(first_date_treasury, last_date)]
     return benchmark_returns, treasury_curves
 
 
@@ -251,7 +269,6 @@ def ensure_crypto_benchmark_data(symbol,
                                  bundle,
                                  bundle_data,
                                  environ=None):
-
     filename = get_benchmark_filename(symbol)
 
     logger.info(
@@ -285,43 +302,51 @@ def ensure_crypto_benchmark_data(symbol,
         prevents users abroad from getting Catalyst to work
         '''
         logger.info(
-            ('Retrieving benchmark data from bundle for {symbol!r} from {first_date} to {last_date}'),
+            (
+                'Retrieving benchmark data from bundle for {symbol!r} from {first_date} to {last_date}'),
             symbol=symbol, first_date=first_date, last_date=last_date)
 
-        asset = bundle_data.asset_finder.lookup_symbol(symbol=symbol,as_of_date=None)
+        asset = bundle_data.asset_finder.lookup_symbol(symbol=symbol,
+                                                       as_of_date=None)
         fields = ['day', 'close']
         raw = bundle_data.daily_bar_reader.load_raw_arrays(
             columns=fields,
             start_date=first_date - trading_day,
             end_date=last_date,
             assets=[asset, ])
-        bench_raw = pd.concat([pd.DataFrame(raw[0], columns=['date']),pd.DataFrame(raw[1], columns=['close'])], axis=1)
+        bench_raw = pd.concat([pd.DataFrame(raw[0], columns=['date']),
+                               pd.DataFrame(raw[1], columns=['close'])],
+                              axis=1)
         bench_raw['date'] = pd.to_datetime(bench_raw['date'], unit='s')
         bench_raw.set_index('date', inplace=True)
         bench_raw.sort_index(inplace=True)
-        bench_raw = bench_raw[pd.to_datetime(first_date - trading_day):pd.to_datetime(last_date)]
+        bench_raw = bench_raw[
+                    pd.to_datetime(first_date - trading_day):pd.to_datetime(
+                        last_date)]
 
     else:
         # This is how it used to be: downloading the benchmark everytime. 
         # Leaving this code here to be repurposed in the future for other bundles.
         logger.info(
-            ('Downloading benchmark data for {symbol!r} from {first_date} to {last_date}'),
+            (
+                'Downloading benchmark data for {symbol!r} from {first_date} to {last_date}'),
             symbol=symbol, first_date=first_date, last_date=last_date)
 
+        raise DeprecationWarning('poloniex bundle deprecated')
         # Load benchmark symbol from Poloniex API
-        try:
+        # try:
-            bundle = PoloniexBundle()
+        #     bundle = PoloniexBundle()
-            bench_raw = bundle._fetch_symbol_frame(
+        #     bench_raw = bundle._fetch_symbol_frame(
-                None,
+        #         None,
-                symbol,
+        #         symbol,
-                get_calendar(bundle.calendar_name),
+        #         get_calendar(bundle.calendar_name),
-                first_date - trading_day,
+        #         first_date - trading_day,
-                last_date,
+        #         last_date,
-                'daily',
+        #         'daily',
-            )
+        #     )
-        except (OSError, IOError, HTTPError):
+        # except (OSError, IOError, HTTPError):
-            logger.exception('Failed to fetch new crypto benchmark returns')
+        #     logger.exception('Failed to fetch new crypto benchmark returns')
-            raise
+        #     raise
 
     # select close column and compute percent change between days
     daily_close = bench_raw[['close']]
@@ -525,7 +550,8 @@ def _load_cached_data(filename, first_date, last_date, now, resource_name,
             data = pd.DataFrame.from_csv(path)
             if data.empty:
                 raise ValueError("File is empty.")
-            data.index = pd.to_datetime(data.index, infer_datetime_format=True, errors='coerce' ).tz_localize('UTC')
+            data.index = pd.to_datetime(data.index, infer_datetime_format=True,
+                                        errors='coerce').tz_localize('UTC')
             if has_data_for_dates(data, first_date, last_date):
                 return data
 

catalyst/data/minute_bars.py

@@ -39,12 +39,11 @@ from catalyst.data._minute_bar_internal import (
 from catalyst.gens.sim_engine import NANOS_IN_MINUTE
 
 from catalyst.data.bar_reader import BarReader, NoDataOnDate
-from catalyst.data.us_equity_pricing import check_uint32_safe
+from catalyst.data.us_equity_pricing import check_uint64_safe
 from catalyst.utils.calendars import get_calendar
 from catalyst.utils.cli import maybe_show_progress
 from catalyst.utils.memoize import lazyval
 
-
 logger = logbook.Logger('MinuteBars')
 
 US_EQUITIES_MINUTES_PER_DAY = 390
@@ -52,7 +51,7 @@ FUTURES_MINUTES_PER_DAY = 1440
 
 DEFAULT_EXPECTEDLEN = US_EQUITIES_MINUTES_PER_DAY * 252 * 15
 
-OHLC_RATIO = 1000
+OHLC_RATIO = 100000000
 
 
 class BcolzMinuteOverlappingData(Exception):
@@ -114,15 +113,15 @@ def _sid_subdir_path(sid):
 
 
 def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
-    """Adapt OHLCV columns into uint32 columns.
+    """Adapt OHLCV columns into uint64 columns.
 
     Parameters
     ----------
     cols : dict
         A dict mapping each column name (open, high, low, close, volume)
-        to a float column to convert to uint32.
+        to a float column to convert to uint64.
     scale_factor : int
-        Factor to use to scale float values before converting to uint32.
+        Factor to use to scale float values before converting to uint64.
     sid : int
         Sid of the relevant asset, for logging.
     invalid_data_behavior : str
@@ -135,6 +134,7 @@ def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
     scaled_highs = np.nan_to_num(cols['high']) * scale_factor
     scaled_lows = np.nan_to_num(cols['low']) * scale_factor
     scaled_closes = np.nan_to_num(cols['close']) * scale_factor
+    scaled_volumes = np.nan_to_num(cols['volume']) * scale_factor
 
     exclude_mask = np.zeros_like(scaled_opens, dtype=bool)
 
@@ -143,11 +143,12 @@ def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
         ('high', scaled_highs),
         ('low', scaled_lows),
         ('close', scaled_closes),
+        ('volume', scaled_volumes),
     ]:
         max_val = scaled_col.max()
 
         try:
-            check_uint32_safe(max_val, col_name)
+            check_uint64_safe(max_val, col_name)
         except ValueError:
             if invalid_data_behavior == 'raise':
                 raise
@@ -155,20 +156,20 @@ def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
             if invalid_data_behavior == 'warn':
                 logger.warn(
                     'Values for sid={}, col={} contain some too large for '
-                    'uint32 (max={}), filtering them out',
+                    'uint64 (max={}), filtering them out',
                     sid, col_name, max_val,
                 )
 
             # We want to exclude all rows that have an unsafe value in
             # this column.
-            exclude_mask &= (scaled_col >= np.iinfo(np.uint32).max)
+            exclude_mask &= (scaled_col >= np.iinfo(np.uint64).max)
 
     # Convert all cols to uint32.
-    opens = scaled_opens.astype(np.uint32)
+    opens = scaled_opens.astype(np.uint64)
-    highs = scaled_highs.astype(np.uint32)
+    highs = scaled_highs.astype(np.uint64)
-    lows = scaled_lows.astype(np.uint32)
+    lows = scaled_lows.astype(np.uint64)
-    closes = scaled_closes.astype(np.uint32)
+    closes = scaled_closes.astype(np.uint64)
-    volumes = cols['volume'].astype(np.uint32)
+    volumes = scaled_volumes.astype(np.uint64)
 
     # Exclude rows with unsafe values by setting to zero.
     opens[exclude_mask] = 0
@@ -288,7 +289,7 @@ class BcolzMinuteBarMetadata(object):
         ohlc_ratio : int
             The default ratio by which to multiply the pricing data to
             convert the floats from floats to an integer to fit within
-            the np.uint32. If ohlc_ratios_per_sid is None or does not
+            the np.uint64. If ohlc_ratios_per_sid is None or does not
             contain a mapping for a given sid, this ratio is used.
         ohlc_ratios_per_sid : dict
              A dict mapping each sid in the output to the factor by
@@ -372,13 +373,13 @@ class BcolzMinuteBarWriter(object):
         The last trading session in the data set.
     default_ohlc_ratio : int, optional
         The default ratio by which to multiply the pricing data to
-        convert from floats to integers that fit within np.uint32. If
+        convert from floats to integers that fit within np.uint64. If
         ohlc_ratios_per_sid is None or does not contain a mapping for a
-        given sid, this ratio is used. Default is OHLC_RATIO (1000).
+        given sid, this ratio is used. Default is OHLC_RATIO (10^8).
     ohlc_ratios_per_sid : dict, optional
         A dict mapping each sid in the output to the ratio by which to
         multiply the pricing data to convert the floats from floats to
-        an integer to fit within the np.uint32.
+        an integer to fit within the np.uint64.
     expectedlen : int, optional
         The expected length of the dataset, used when creating the initial
         bcolz ctable.
@@ -401,11 +402,9 @@ class BcolzMinuteBarWriter(object):
     Each individual asset's data is stored as a bcolz table with a column for
     each pricing field: (open, high, low, close, volume)
 
-    The open, high, low, and close columns are integers which are 1000 times
+    The open, high, low, close and volume columns are integers which are 10^8 times
     the quoted price, so that the data can represented and stored as an
-    np.uint32, supporting market prices quoted up to the thousands place.
+    np.uint64, supporting market prices quoted up to the 1/10^8-th place.
-
-    volume is a np.uint32 with no mutation of the tens place.
 
     The 'index' for each individual asset are a repeating period of minutes of
     length `minutes_per_day` starting from each market open.
@@ -573,7 +572,7 @@ class BcolzMinuteBarWriter(object):
         if not os.path.exists(sid_containing_dirname):
             # Other sids may have already created the containing directory.
             os.makedirs(sid_containing_dirname)
-        initial_array = np.empty(0, np.uint32)
+        initial_array = np.empty(0, np.uint64)
         table = ctable(
             rootdir=path,
             columns=[
@@ -610,7 +609,7 @@ class BcolzMinuteBarWriter(object):
         minute_offset = len(table) % self._minutes_per_day
         num_to_prepend = numdays * self._minutes_per_day - minute_offset
 
-        prepend_array = np.zeros(num_to_prepend, np.uint32)
+        prepend_array = np.zeros(num_to_prepend, np.uint64)
         # Fill all OHLCV with zeros.
         table.append([prepend_array] * 5)
         table.flush()
@@ -815,11 +814,11 @@ class BcolzMinuteBarWriter(object):
 
         minutes_count = all_minutes_in_window.size
 
-        open_col = np.zeros(minutes_count, dtype=np.uint32)
+        open_col = np.zeros(minutes_count, dtype=np.uint64)
-        high_col = np.zeros(minutes_count, dtype=np.uint32)
+        high_col = np.zeros(minutes_count, dtype=np.uint64)
-        low_col = np.zeros(minutes_count, dtype=np.uint32)
+        low_col = np.zeros(minutes_count, dtype=np.uint64)
-        close_col = np.zeros(minutes_count, dtype=np.uint32)
+        close_col = np.zeros(minutes_count, dtype=np.uint64)
-        vol_col = np.zeros(minutes_count, dtype=np.uint32)
+        vol_col = np.zeros(minutes_count, dtype=np.uint64)
 
         dt_ixs = np.searchsorted(all_minutes_in_window.values,
                                  dts.astype('datetime64[ns]'))
@@ -1125,7 +1124,7 @@ class BcolzMinuteBarReader(MinuteBarReader):
             else:
                 return np.nan
 
-        if field != 'volume':
+        # if field != 'volume':
         value *= self._ohlc_ratio_inverse_for_sid(sid)
         return value
 
@@ -1248,7 +1247,7 @@ class BcolzMinuteBarReader(MinuteBarReader):
             if field != 'volume':
                 out = np.full(shape, np.nan)
             else:
-                out = np.zeros(shape, dtype=np.uint32)
+                out = np.zeros(shape, dtype=np.float64)
 
             for i, sid in enumerate(sids):
                 carray = self._open_minute_file(field, sid)
@@ -1262,11 +1261,11 @@ class BcolzMinuteBarReader(MinuteBarReader):
                 where = values != 0
                 # first slice down to len(where) because we might not have
                 # written data for all the minutes requested
-                if field != 'volume':
+                # if field != 'volume':
                 out[:len(where), i][where] = (
                     values[where] * self._ohlc_ratio_inverse_for_sid(sid))
-                else:
+                # else:
-                    out[:len(where), i][where] = values[where]
+                #    out[:len(where), i][where] = values[where]
 
             results.append(out)
         return results
@@ -1353,6 +1352,7 @@ class H5MinuteBarUpdateReader(MinuteBarUpdateReader):
     path : str
         The path of the HDF5 file from which to source data.
     """
+
     def __init__(self, path):
         self._panel = pd.read_hdf(path)
 

catalyst/data/resample.py

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

catalyst/data/us_equity_pricing.py

@@ -11,6 +11,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+from __future__ import division     # Python2 req to have division of ints yield float
+
 from errno import ENOENT
 from functools import partial
 from os import remove
@@ -80,7 +83,6 @@ from catalyst.utils.cli import (
 from ._equities import _compute_row_slices, _read_bcolz_data
 from ._adjustments import load_adjustments_from_sqlite
 
-
 logger = logbook.Logger('UsEquityPricing')
 
 OHLC = frozenset(['open', 'high', 'low', 'close'])
@@ -116,6 +118,8 @@ SQLITE_STOCK_DIVIDEND_PAYOUT_COLUMN_DTYPES = {
 UINT32_MAX = iinfo(uint32).max
 UINT64_MAX = iinfo(uint64).max
 
+PRICE_ADJUSTMENT_FACTOR = 1000000000    # Provides 9 decimals resolution. Also affects _equities.pyx L220
+
 
 def check_uint32_safe(value, colname):
     if value >= UINT32_MAX:
@@ -433,11 +437,11 @@ class BcolzDailyBarWriter(object):
             return raw_data
 
         winsorise_uint64(raw_data, invalid_data_behavior, 'volume', *OHLC)
-        processed = (raw_data[list(OHLC)] * 1000000).astype('uint64')
+        processed = (raw_data[list(OHLC)] * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
         dates = raw_data.index.values.astype('datetime64[s]')
         check_uint32_safe(dates.max().view(np.int64), 'day')
         processed['day'] = dates.astype('uint32')
-        processed['volume'] = raw_data.volume.astype('uint64')
+        processed['volume'] = (raw_data.volume * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
         return ctable.fromdataframe(processed)
 
 
@@ -490,9 +494,8 @@ class BcolzDailyBarReader(SessionBarReader):
 
     The data in these columns is interpreted as follows:
 
-    - Price columns ('open', 'high', 'low', 'close') are interpreted as 1000 *
+    - Price columns ('open', 'high', 'low', 'close') and Volume are interpreted 
-      as-traded dollar value.
+      as 10^9 * as-traded dollar value.
-    - Volume is interpreted as as-traded volume.
     - Day is interpreted as seconds since midnight UTC, Jan 1, 1970.
     - Id is the asset id of the row.
 
@@ -519,7 +522,6 @@ class BcolzDailyBarReader(SessionBarReader):
         # Need to test keeping the entire array in memory for the course of a
         # process first.
         self._spot_cols = {}
-        self.PRICE_ADJUSTMENT_FACTOR = 0.001
         self._read_all_threshold = read_all_threshold
 
     @lazyval
@@ -759,13 +761,10 @@ class BcolzDailyBarReader(SessionBarReader):
         """
         ix = self.sid_day_index(sid, dt)
         price = self._spot_col(field)[ix]
-        if field != 'volume':
+        if field != 'volume' and price == 0:
-            if price == 0:
             return nan
         else:
-                return price * 0.001
+            return price / PRICE_ADJUSTMENT_FACTOR
-        else:
-            return price
 
 
 class PanelBarReader(SessionBarReader):

catalyst/examples/arbitrage_with_interface.py

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

catalyst/examples/buy_btc_simple.py

@@ -0,0 +1,8 @@
+from catalyst.api import order, record, symbol
+
+def initialize(context):
+   context.asset = symbol('btc_usd')
+
+def handle_data(context, data):
+   order(asset, 1)
+   record(btc=data.current(context.asset, 'price'))

catalyst/examples/buy_low_sell_high.py

@@ -38,6 +38,8 @@ def initialize(context):
     context.retry_update_portfolio = 10
     context.retry_order = 5
 
+    context.swallow_errors = True
+
     context.errors = []
     pass
 
@@ -49,6 +51,7 @@ def _handle_data(context, data):
         bar_count=20,
         frequency='15m'
     )
+
     rsi = talib.RSI(prices.values, timeperiod=14)[-1]
     log.info('got rsi: {}'.format(rsi))
 
@@ -135,11 +138,11 @@ def _handle_data(context, data):
 
 def handle_data(context, data):
     log.info('handling bar {}'.format(data.current_dt))
-    # try:
+    try:
         _handle_data(context, data)
-    # except Exception as e:
+    except Exception as e:
-    #     log.warn('aborting the bar on error {}'.format(e))
+        log.warn('aborting the bar on error {}'.format(e))
-    #     context.errors.append(e)
+        context.errors.append(e)
 
     log.info('completed bar {}, total execution errors {}'.format(
         data.current_dt,

catalyst/examples/buy_low_sell_high_live.py

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

catalyst/examples/buy_low_sell_high_neo_with_interface.py

@@ -0,0 +1,173 @@
+import talib
+from logbook import Logger
+import pandas as pd
+
+from catalyst.api import (
+    order,
+    order_target_percent,
+    symbol,
+    record,
+    get_open_orders,
+)
+from catalyst.exchange.stats_utils import get_pretty_stats
+from catalyst.utils.run_algo import run_algorithm
+
+algo_namespace = 'buy_low_sell_high_neo'
+log = Logger(algo_namespace)
+
+
+def initialize(context):
+    log.info('initializing algo')
+    context.asset = symbol('neo_btc', 'bitfinex')
+
+    context.TARGET_POSITIONS = 50000
+    context.PROFIT_TARGET = 0.1
+    context.SLIPPAGE_ALLOWED = 0.02
+
+    context.retry_check_open_orders = 10
+    context.retry_update_portfolio = 10
+    context.retry_order = 5
+
+    context.errors = []
+    pass
+
+
+def _handle_data(context, data):
+    price = data.current(context.asset, 'close')
+    log.info('got price {price}'.format(price=price))
+
+    if price is None:
+        log.warn('no pricing data')
+        return
+
+    prices = data.history(
+        context.asset,
+        fields='price',
+        bar_count=1,
+        frequency='1m'
+    )
+    rsi = talib.RSI(prices.values, timeperiod=14)[-1]
+    log.info('got rsi: {}'.format(rsi))
+
+    # Buying more when RSI is low, this should lower our cost basis
+    if rsi <= 30:
+        buy_increment = 1
+    elif rsi <= 40:
+        buy_increment = 0.5
+    elif rsi <= 70:
+        buy_increment = 0.1
+    else:
+        buy_increment = None
+
+    cash = context.portfolio.cash
+    log.info('base currency available: {cash}'.format(cash=cash))
+
+    record(price=price)
+
+    orders = get_open_orders(context.asset)
+    if len(orders) > 0:
+        log.info('skipping bar until all open orders execute')
+        return
+
+    is_buy = False
+    cost_basis = None
+    if context.asset in context.portfolio.positions:
+        position = context.portfolio.positions[context.asset]
+
+        cost_basis = position.cost_basis
+        log.info(
+            'found {amount} positions with cost basis {cost_basis}'.format(
+                amount=position.amount,
+                cost_basis=cost_basis
+            )
+        )
+
+        if position.amount >= context.TARGET_POSITIONS:
+            log.info('reached positions target: {}'.format(position.amount))
+            return
+
+        if price < cost_basis:
+            is_buy = True
+        elif position.amount > 0 and \
+                        price > cost_basis * (1 + context.PROFIT_TARGET):
+            profit = (price * position.amount) - (cost_basis * position.amount)
+
+            log.info('closing position, taking profit: {}'.format(profit))
+            order_target_percent(
+                asset=context.asset,
+                target=0,
+                limit_price=price * (1 - context.SLIPPAGE_ALLOWED),
+            )
+        else:
+            log.info('no buy or sell opportunity found')
+    else:
+        is_buy = True
+
+    if is_buy:
+        if buy_increment is None:
+            return
+
+        if price * buy_increment > cash:
+            log.info('not enough base currency to consider buying')
+            return
+
+        log.info(
+            'buying position cheaper than cost basis {} < {}'.format(
+                price,
+                cost_basis
+            )
+        )
+        limit_price = price * (1 + context.SLIPPAGE_ALLOWED)
+        order(
+            asset=context.asset,
+            amount=buy_increment,
+            limit_price=limit_price
+        )
+        pass
+
+
+def handle_data(context, data):
+    log.info('handling bar {}'.format(data.current_dt))
+    # try:
+    _handle_data(context, data)
+    # except Exception as e:
+    #     log.warn('aborting the bar on error {}'.format(e))
+    #     context.errors.append(e)
+
+    log.info('completed bar {}, total execution errors {}'.format(
+        data.current_dt,
+        len(context.errors)
+    ))
+
+    if len(context.errors) > 0:
+        log.info('the errors:\n{}'.format(context.errors))
+
+
+def analyze(context, stats):
+    log.info('the daily stats:\n{}'.format(get_pretty_stats(stats)))
+
+    pass
+
+
+# run_algorithm(
+#     initialize=initialize,
+#     handle_data=handle_data,
+#     analyze=analyze,
+#     exchange_name='bitfinex',
+#     live=True,
+#     algo_namespace=algo_namespace,
+#     base_currency='btc',
+#     live_graph=False
+# )
+
+# Backtest
+run_algorithm(
+    capital_base=250,
+    data_frequency='minute',
+    initialize=initialize,
+    handle_data=handle_data,
+    analyze=analyze,
+    exchange_name='bitfinex',
+    algo_namespace=algo_namespace,
+    base_currency='btc'
+)

catalyst/examples/simple_loop.py

@@ -0,0 +1,51 @@
+import pandas as pd
+import talib
+
+from catalyst import run_algorithm
+from catalyst.api import symbol
+
+
+def initialize(context):
+    print('initializing')
+    context.asset = symbol('xrp_btc')
+
+
+def handle_data(context, data):
+    print('handling bar: {}'.format(data.current_dt))
+
+    price = data.current(context.asset, 'close')
+    print('got price {price}'.format(price=price))
+
+    prices = data.history(
+        context.asset,
+        fields='price',
+        bar_count=15,
+        frequency='1d'
+    )
+    rsi = talib.RSI(prices.values, timeperiod=14)[-1]
+    print('got rsi: {}'.format(rsi))
+    pass
+
+
+# run_algorithm(
+#     capital_base=250,
+#     start=pd.to_datetime('2015-08-01', utc=True),
+#     end=pd.to_datetime('2017-9-30', utc=True),
+#     data_frequency='daily',
+#     initialize=initialize,
+#     handle_data=handle_data,
+#     analyze=None,
+#     exchange_name='poloniex',
+#     algo_namespace='simple_loop',
+#     base_currency='eth'
+# )
+run_algorithm(
+    initialize=initialize,
+    handle_data=handle_data,
+    analyze=None,
+    exchange_name='bitfinex',
+    live=True,
+    algo_namespace='simple_loop',
+    base_currency='eth',
+    live_graph=False
+)

catalyst/exchange/asset_finder_exchange.py

@@ -4,8 +4,7 @@ log = Logger('AssetFinderExchange')
 
 
 class AssetFinderExchange(object):
-    def __init__(self, exchange):
+    def __init__(self):
-        self.exchange = exchange
         self._asset_cache = {}
 
     @property
@@ -47,7 +46,7 @@ class AssetFinderExchange(object):
                 log.info('fetching asset: {}'.format(sid))
         return list()
 
-    def lookup_symbol(self, symbol, as_of_date, fuzzy=False):
+    def lookup_symbol(self, symbol, exchange, as_of_date=None, fuzzy=False):
         """Lookup an asset by symbol.
 
         Parameters
@@ -81,11 +80,12 @@ class AssetFinderExchange(object):
             there are multiple candidates for the given ``symbol`` on the
             ``as_of_date``.
         """
-        log.debug('looking up symbol: {}'.format(symbol))
+        log.debug('looking up symbol: {} {}'.format(symbol, exchange.name))
 
-        if symbol in self._asset_cache:
+        key = ','.join([exchange.name, symbol])
-            return self._asset_cache[symbol]
+        if key in self._asset_cache:
+            return self._asset_cache[key]
         else:
-            asset = self.exchange.get_asset(symbol)
+            asset = exchange.get_asset(symbol)
-            self._asset_cache[symbol] = asset
+            self._asset_cache[key] = asset
             return asset

catalyst/exchange/bitfinex/bitfinex.py

@@ -4,6 +4,7 @@ import hmac
 import json
 import re
 import time
+import datetime
 
 import numpy as np
 import pandas as pd
@@ -13,8 +14,8 @@ import six
 from catalyst.assets._assets import TradingPair
 from logbook import Logger
 
-# from websocket import create_connection
 from catalyst.exchange.exchange import Exchange
+from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import (
     ExchangeRequestError,
     InvalidHistoryFrequencyError,
@@ -23,6 +24,8 @@ from catalyst.exchange.exchange_execution import ExchangeLimitOrder, \
     ExchangeStopLimitOrder, ExchangeStopOrder
 from catalyst.finance.order import Order, ORDER_STATUS
 from catalyst.protocol import Account
+from catalyst.exchange.exchange_utils import get_exchange_symbols_filename, \
+    download_exchange_symbols
 
 # Trying to account for REST api instability
 # https://stackoverflow.com/questions/15431044/can-i-set-max-retries-for-requests-request
@@ -40,6 +43,7 @@ class Bitfinex(Exchange):
         self.key = key
         self.secret = secret.encode('UTF-8')
         self.name = 'bitfinex'
+        self.color = 'green'
         self.assets = {}
         self.load_assets()
         self.base_currency = base_currency
@@ -47,6 +51,16 @@ class Bitfinex(Exchange):
         self.minute_writer = None
         self.minute_reader = None
 
+        # The candle limit for each request
+        self.num_candles_limit = 1000
+
+        # Max is 90 but playing it safe
+        # https://www.bitfinex.com/posts/188
+        self.max_requests_per_minute = 80
+        self.request_cpt = dict()
+
+        self.bundle = ExchangeBundle(self)
+
     def _request(self, operation, data, version='v1'):
         payload_object = {
             'request': '/{}/{}'.format(version, operation),
@@ -173,6 +187,7 @@ class Bitfinex(Exchange):
     def get_balances(self):
         log.debug('retrieving wallets balances')
         try:
+            self.ask_request()
             response = self._request('balances', None)
             balances = response.json()
         except Exception as e:
@@ -223,7 +238,8 @@ class Bitfinex(Exchange):
         # TODO: fetch account data and keep in cache
         return None
 
-    def get_candles(self, data_frequency, assets, bar_count=None):
+    def get_candles(self, data_frequency, assets, bar_count=None,
+                    start_dt=None, end_dt=None):
         """
         Retrieve OHLVC candles from Bitfinex
 
@@ -238,7 +254,6 @@ class Bitfinex(Exchange):
          '1M'
         """
 
-        # TODO: use BcolzMinuteBarReader to read from cache
         freq_match = re.match(r'([0-9].*)(m|h|d)', data_frequency, re.M | re.I)
         if freq_match:
             number = int(freq_match.group(1))
@@ -280,11 +295,27 @@ class Bitfinex(Exchange):
             if bar_count:
                 is_list = True
                 url += '/hist?limit={}'.format(int(bar_count))
+
+                def get_ms(date):
+                    epoch = datetime.datetime.utcfromtimestamp(0)
+                    epoch = epoch.replace(tzinfo=pytz.UTC)
+
+                    return (date - epoch).total_seconds() * 1000.0
+
+                if start_dt is not None:
+                    start_ms = get_ms(start_dt)
+                    url += '&start={0:f}'.format(start_ms)
+
+                if end_dt is not None:
+                    end_ms = get_ms(end_dt)
+                    url += '&end={0:f}'.format(end_ms)
+
             else:
                 is_list = False
                 url += '/last'
 
             try:
+                self.ask_request()
                 response = requests.get(url)
             except Exception as e:
                 raise ExchangeRequestError(error=e)
@@ -298,6 +329,9 @@ class Bitfinex(Exchange):
             candles = response.json()
 
             def ohlc_from_candle(candle):
+                last_traded = pd.Timestamp.utcfromtimestamp(
+                    candle[0] / 1000.0)
+                last_traded = last_traded.replace(tzinfo=pytz.UTC)
                 ohlc = dict(
                     open=np.float64(candle[1]),
                     high=np.float64(candle[3]),
@@ -305,8 +339,7 @@ class Bitfinex(Exchange):
                     close=np.float64(candle[2]),
                     volume=np.float64(candle[5]),
                     price=np.float64(candle[2]),
-                    last_traded=pd.Timestamp.utcfromtimestamp(
+                    last_traded=last_traded
-                        candle[0] / 1000.0)
                 )
                 return ohlc
 
@@ -367,6 +400,7 @@ class Bitfinex(Exchange):
 
         date = pd.Timestamp.utcnow()
         try:
+            self.ask_request()
             response = self._request('order/new', req)
             order_status = response.json()
         except Exception as e:
@@ -408,6 +442,7 @@ class Bitfinex(Exchange):
             orders for this asset.
         """
         try:
+            self.ask_request()
             response = self._request('orders', None)
             order_statuses = response.json()
         except Exception as e:
@@ -419,7 +454,7 @@ class Bitfinex(Exchange):
                     order_statuses['message'])
             )
 
-        orders = list()
+        orders = []
         for order_status in order_statuses:
             order, executed_price = self._create_order(order_status)
             if asset is None or asset == order.sid:
@@ -442,6 +477,7 @@ class Bitfinex(Exchange):
             The order object.
         """
         try:
+            self.ask_request()
             response = self._request(
                 'order/status', {'order_id': int(order_id)})
             order_status = response.json()
@@ -467,6 +503,7 @@ class Bitfinex(Exchange):
             if isinstance(order_param, Order) else order_param
 
         try:
+            self.ask_request()
             response = self._request('order/cancel', {'order_id': order_id})
             status = response.json()
         except Exception as e:
@@ -491,6 +528,7 @@ class Bitfinex(Exchange):
         log.debug('fetching tickers {}'.format(symbols))
 
         try:
+            self.ask_request()
             response = requests.get(
                 '{url}/v2/tickers?symbols={symbols}'.format(
                     url=self.url,
@@ -506,7 +544,10 @@ class Bitfinex(Exchange):
                     response.content)
             )
 
+        try:
             tickers = response.json()
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
 
         ticks = dict()
         for index, ticker in enumerate(tickers):
@@ -527,3 +568,124 @@ class Bitfinex(Exchange):
 
         log.debug('got tickers {}'.format(ticks))
         return ticks
+
+    def generate_symbols_json(self, filename=None, source_dates=False):
+        symbol_map = {}
+
+        if not source_dates:
+            fn, r = download_exchange_symbols(self.name)
+            with open(fn) as data_file:
+                cached_symbols = json.load(data_file)
+
+        response = self._request('symbols', None)
+
+        for symbol in response.json():
+            if (source_dates):
+                start_date = self.get_symbol_start_date(symbol)
+            else:
+                try:
+                    start_date = cached_symbols[symbol]['start_date']
+                except KeyError as e:
+                    start_date = time.strftime('%Y-%m-%d')
+
+            try:
+                end_daily = cached_symbols[symbol]['end_daily']
+            except KeyError as e:
+                end_daily = 'N/A'
+
+            try:
+                end_minute = cached_symbols[symbol]['end_minute']
+            except KeyError as e:
+                end_minute = 'N/A'
+
+            symbol_map[symbol] = dict(
+                symbol=symbol[:-3] + '_' + symbol[-3:],
+                start_date=start_date,
+                end_daily=end_daily,
+                end_minute=end_minute,
+            )
+
+        if (filename is None):
+            filename = get_exchange_symbols_filename(self.name)
+
+        with open(filename, 'w') as f:
+            json.dump(symbol_map, f, sort_keys=True, indent=2,
+                      separators=(',', ':'))
+
+    def get_symbol_start_date(self, symbol):
+
+        print(symbol)
+        symbol_v2 = 't' + symbol.upper()
+
+        """
+            For each symbol we retrieve candles with Monhtly resolution
+            We get the first month, and query again with daily resolution
+            around that date, and we get the first date
+        """
+        url = '{url}/v2/candles/trade:1M:{symbol}/hist'.format(
+            url=self.url,
+            symbol=symbol_v2
+        )
+
+        try:
+            self.ask_request()
+            response = requests.get(url)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        """
+            If we don't get any data back for our monthly-resolution query
+            it means that symbol started trading less than a month ago, so
+            arbitrarily set the ref. date to 15 days ago to be safe with
+            +/- 31 days
+        """
+        if (len(response.json())):
+            startmonth = response.json()[-1][0]
+        else:
+            startmonth = int((time.time() - 15 * 24 * 3600) * 1000)
+
+        """
+            Query again with daily resolution setting the start and end around
+            the startmonth we got above. Avoid end dates greater than now: time.time()
+        """
+        url = '{url}/v2/candles/trade:1D:{symbol}/hist?start={start}&end={end}'.format(
+            url=self.url,
+            symbol=symbol_v2,
+            start=startmonth - 3600 * 24 * 31 * 1000,
+            end=min(startmonth + 3600 * 24 * 31 * 1000,
+                    int(time.time() * 1000))
+        )
+
+        try:
+            self.ask_request()
+            response = requests.get(url)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        return time.strftime('%Y-%m-%d',
+                             time.gmtime(int(response.json()[-1][0] / 1000)))
+
+    def get_orderbook(self, asset, order_type='all', limit=100):
+        exchange_symbol = asset.exchange_symbol
+        try:
+            self.ask_request()
+            # TODO: implement limit
+            response = self._request(
+                'book/{}'.format(exchange_symbol), None)
+            data = response.json()
+
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        # TODO: filter by type
+        result = dict()
+        for order_type in data:
+            result[order_type] = []
+
+            for entry in data[order_type]:
+                result[order_type].append(dict(
+                    rate=float(entry['price']),
+                    quantity=float(entry['amount'])
+                ))
+
+        return result

catalyst/exchange/bitfinex/symbols.json

@@ -1,4 +1,17 @@
 {
+  "neobtc": {
+    "symbol": "neo_btc",
+    "start_date": "2017-09-07",
+    "precision": 5
+  },
+  "neousd": {
+    "symbol": "neo_usd",
+    "start_date": "2017-09-07"
+  },
+  "neoeth": {
+    "symbol": "neo_eth",
+    "start_date": "2017-09-07"
+  },
   "btcusd": {
     "symbol": "btc_usd",
     "start_date": "2010-01-01"
@@ -17,19 +30,19 @@
   },
   "ethusd": {
     "symbol": "eth_usd",
-    "start_date": "2010-01-01"
+    "start_date": "2017-01-01"
   },
   "ethbtc": {
     "symbol": "eth_btc",
-    "start_date": "2010-01-01"
+    "start_date": "2017-01-01"
   },
   "etcbtc": {
     "symbol": "etc_btc",
-    "start_date": "2010-01-01"
+    "start_date": "2017-01-01"
   },
   "etcusd": {
     "symbol": "etc_usd",
-    "start_date": "2010-01-01"
+    "start_date": "2017-01-01"
   },
   "rrtusd": {
     "symbol": "rrt_usd",

catalyst/exchange/bittrex/bittrex.py

@@ -7,11 +7,14 @@ from six.moves import urllib
 
 from catalyst.exchange.bittrex.bittrex_api import Bittrex_api
 from catalyst.exchange.exchange import Exchange
+from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import InvalidHistoryFrequencyError, \
     ExchangeRequestError, InvalidOrderStyle, OrderNotFound, OrderCancelError, \
     CreateOrderError
 from catalyst.finance.execution import LimitOrder, StopLimitOrder
 from catalyst.finance.order import Order, ORDER_STATUS
+from catalyst.exchange.exchange_utils import get_exchange_symbols_filename, \
+    download_exchange_symbols
 
 log = Logger('Bittrex')
 
@@ -22,15 +25,25 @@ class Bittrex(Exchange):
     def __init__(self, key, secret, base_currency, portfolio=None):
         self.api = Bittrex_api(key=key, secret=secret.encode('UTF-8'))
         self.name = 'bittrex'
+        self.color = 'blue'
         self.base_currency = base_currency
         self._portfolio = portfolio
 
+        self.num_candles_limit = 2000
+
+        # Not sure what the rate limit is but trying to play it safe
+        # https://bitcoin.stackexchange.com/questions/53778/bittrex-api-rate-limit
+        self.max_requests_per_minute = 60
+        self.request_cpt = dict()
+
         self.minute_writer = None
         self.minute_reader = None
 
         self.assets = dict()
         self.load_assets()
 
+        self.bundle = ExchangeBundle(self)
+
     @property
     def account(self):
         pass
@@ -50,42 +63,24 @@ class Bittrex(Exchange):
         """
         return exchange_symbol.lower()
 
-    def fetch_symbol_map(self):
-        """
-        Since Bittrex gives us a complete dictionary of symbols,
-        we can build the symbol map ad-hoc as opposed to maintaining
-        a static file. We must be careful with mapping any unconventional
-        symbol name as appropriate.
-
-        :return symbol_map:
-        """
-        symbol_map = dict()
-
-        markets = self.api.getmarkets()
-        for market in markets:
-            exchange_symbol = market['MarketName']
-            symbol = '{market}_{base}'.format(
-                market=self.sanitize_curency_symbol(market['MarketCurrency']),
-                base=self.sanitize_curency_symbol(market['BaseCurrency'])
-            )
-            symbol_map[exchange_symbol] = dict(
-                symbol=symbol,
-                start_date=pd.to_datetime(market['Created'], utc=True)
-            )
-
-        return symbol_map
-
     def get_balances(self):
         try:
             log.debug('retrieving wallet balances')
+            self.ask_request()
             balances = self.api.getbalances()
+
         except Exception as e:
             raise ExchangeRequestError(error=e)
 
         std_balances = dict()
+        try:
             for balance in balances:
                 currency = balance['Currency'].lower()
                 std_balances[currency] = balance['Available']
+
+        except TypeError:
+            raise ExchangeRequestError(error=balances)
+
         return std_balances
 
     def create_order(self, asset, amount, is_buy, style):
@@ -98,6 +93,7 @@ class Bittrex(Exchange):
 
             price = style.get_limit_price(is_buy)
             try:
+                self.ask_request()
                 if is_buy:
                     order_status = self.api.buylimit(exchange_symbol, amount,
                                                      price)
@@ -119,7 +115,18 @@ class Bittrex(Exchange):
                 )
                 return order
             else:
-                raise CreateOrderError(exchange=self.name, error=order_status)
+                if order_status == 'INSUFFICIENT_FUNDS':
+                    log.warn('not enough funds to create order')
+                    return None
+                elif order_status == 'DUST_TRADE_DISALLOWED_MIN_VALUE_50K_SAT':
+                    log.warn('Your order is too small, order at least 50K'
+                             ' Satoshi')
+                    return None
+                else:
+                    raise CreateOrderError(
+                        exchange=self.name,
+                        error=order_status
+                    )
         else:
             raise InvalidOrderStyle(exchange=self.name,
                                     style=style.__class__.__name__)
@@ -127,6 +134,7 @@ class Bittrex(Exchange):
     def get_open_orders(self, asset):
         symbol = self.get_symbol(asset)
         try:
+            self.ask_request()
             open_orders = self.api.getopenorders(symbol)
         except Exception as e:
             raise ExchangeRequestError(error=e)
@@ -170,6 +178,7 @@ class Bittrex(Exchange):
     def get_order(self, order_id):
         log.info('retrieving order {}'.format(order_id))
         try:
+            self.ask_request()
             order_status = self.api.getorder(order_id)
         except Exception as e:
             raise ExchangeRequestError(error=e)
@@ -185,6 +194,7 @@ class Bittrex(Exchange):
         log.info('cancelling order {}'.format(order_id))
 
         try:
+            self.ask_request()
             status = self.api.cancel(order_id)
         except Exception as e:
             raise ExchangeRequestError(error=e)
@@ -196,7 +206,8 @@ class Bittrex(Exchange):
                 error=status['message']
             )
 
-    def get_candles(self, data_frequency, assets, bar_count=None):
+    def get_candles(self, data_frequency, assets, bar_count=None,
+                    start_date=None):
         """
         Supported Intervals
         -------------------
@@ -287,6 +298,7 @@ class Bittrex(Exchange):
         for asset in assets:
             symbol = self.get_symbol(asset)
             try:
+                self.ask_request()
                 ticker = self.api.getticker(symbol)
             except Exception as e:
                 raise ExchangeRequestError(error=e)
@@ -305,3 +317,76 @@ class Bittrex(Exchange):
     def get_account(self):
         log.info('retrieving account data')
         pass
+
+    def generate_symbols_json(self, filename=None):
+        symbol_map = {}
+
+        fn, r = download_exchange_symbols(self.name)
+        with open(fn) as data_file:
+            cached_symbols = json.load(data_file)
+
+        markets = self.api.getmarkets()
+        for market in markets:
+            exchange_symbol = market['MarketName']
+            symbol = '{market}_{base}'.format(
+                market=self.sanitize_curency_symbol(market['MarketCurrency']),
+                base=self.sanitize_curency_symbol(market['BaseCurrency'])
+            )
+
+            try:
+                end_daily = cached_symbols[exchange_symbol]['end_daily']
+            except KeyError as e:
+                end_daily = 'N/A'
+
+            try:
+                end_minute = cached_symbols[exchange_symbol]['end_minute']
+            except KeyError as e:
+                end_minute = 'N/A'
+
+            symbol_map[exchange_symbol] = dict(
+                symbol=symbol,
+                start_date=pd.to_datetime(market['Created'],
+                                          utc=True).strftime("%Y-%m-%d"),
+                end_daily=end_daily,
+                end_minute=end_minute,
+            )
+
+        if (filename is None):
+            filename = get_exchange_symbols_filename(self.name)
+
+        with open(filename, 'w') as f:
+            json.dump(symbol_map, f, sort_keys=True, indent=2,
+                      separators=(',', ':'))
+
+    def get_orderbook(self, asset, order_type='all', limit=100):
+        if order_type == 'all':
+            order_type = 'both'
+        elif order_type == 'bid':
+            order_type = 'buy'
+        elif order_type == 'ask':
+            order_type = 'sell'
+        else:
+            raise ValueError('invalid type')
+
+        exchange_symbol = asset.exchange_symbol
+        data = self.api.getorderbook(
+            market=exchange_symbol,
+            type=order_type,
+            depth=100
+        )
+
+        result = dict()
+        for exchange_type in data:
+            if exchange_type == 'buy':
+                order_type = 'bids'
+            elif exchange_type == 'sell':
+                order_type = 'asks'
+
+            result[order_type] = []
+            for entry in data[exchange_type]:
+                result[order_type].append(dict(
+                    rate=entry['Rate'],
+                    quantity=entry['Quantity']
+                ))
+
+        return result

catalyst/exchange/bittrex/extensions-example.py

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

catalyst/exchange/bundle_utils.py

@@ -0,0 +1,254 @@
+import calendar
+import os
+import tarfile
+from datetime import timedelta, datetime, date
+
+import numpy as np
+import pandas as pd
+import pytz
+
+from catalyst.data.bundles import from_bundle_ingest_dirname
+from catalyst.data.bundles.core import download_without_progress
+from catalyst.exchange.exchange_errors import NoDataAvailableOnExchange
+from catalyst.exchange.exchange_utils import get_exchange_bundles_folder
+from catalyst.utils.deprecate import deprecated
+from catalyst.utils.paths import data_path
+
+EXCHANGE_NAMES = ['bitfinex', 'bittrex', 'poloniex']
+API_URL = 'http://data.enigma.co/api/v1'
+
+
+def get_date_from_ms(ms):
+    return datetime.fromtimestamp(ms / 1000.0)
+
+
+def get_seconds_from_date(date):
+    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.
+
+    :param exchange_name:
+    :param symbol:
+    :param data_frequency:
+    :param period:
+    :return:
+
+    Note:
+        Filename: bitfinex-daily-neo_eth-2017-10.tar.gz
+    """
+
+    root = get_exchange_bundles_folder(exchange_name)
+    name = '{exchange}-{frequency}-{symbol}-{period}'.format(
+        exchange=exchange_name,
+        frequency=data_frequency,
+        symbol=symbol,
+        period=period
+    )
+    path = os.path.join(root, name)
+
+    if not os.path.isdir(path):
+        url = 'https://s3.amazonaws.com/enigmaco/catalyst-bundles/' \
+              'exchange-{exchange}/{name}.tar.gz'.format(
+            exchange=exchange_name,
+            name=name
+        )
+
+        bytes = download_without_progress(url)
+        with tarfile.open('r', fileobj=bytes) as tar:
+            tar.extractall(path)
+
+    return path
+
+
+def get_delta(periods, data_frequency):
+    return timedelta(minutes=periods) \
+        if data_frequency == 'minute' else timedelta(days=periods)
+
+
+def get_periods_range(start_dt, end_dt, data_frequency):
+    freq = 'T' if data_frequency == 'minute' else 'D'
+
+    return pd.date_range(start_dt, end_dt, freq=freq)
+
+
+def get_periods(start_dt, end_dt, data_frequency):
+    delta = end_dt - start_dt
+
+    if data_frequency == 'minute':
+        delta_periods = delta.total_seconds() / 60
+
+    elif data_frequency == 'daily':
+        delta_periods = delta.total_seconds() / 60 / 60 / 24
+
+    else:
+        raise ValueError('frequency not supported')
+
+    return int(delta_periods)
+
+
+def get_start_dt(end_dt, bar_count, data_frequency):
+    periods = bar_count
+    if periods > 1:
+        delta = get_delta(periods, data_frequency)
+        start_dt = end_dt - delta
+    else:
+        start_dt = end_dt
+
+    return start_dt
+
+
+def get_adj_dates(start, end, assets, data_frequency):
+    """
+    Contains a date range to the trading availability of the specified pairs.
+
+    :param start:
+    :param end:
+    :param assets:
+    :param data_frequency:
+    :return:
+    """
+    earliest_trade = None
+    last_entry = None
+    for asset in assets:
+        if earliest_trade is None or earliest_trade > asset.start_date:
+            earliest_trade = asset.start_date
+
+        end_asset = asset.end_minute if data_frequency == 'minute' else \
+            asset.end_daily
+        if end_asset is not None and \
+                (last_entry is None or end_asset > last_entry):
+            last_entry = end_asset
+
+    if start is None or earliest_trade > start:
+        start = earliest_trade
+
+    if end is None or (last_entry is not None and end > last_entry):
+        end = last_entry
+
+    if end is None or start >= end:
+        raise NoDataAvailableOnExchange(
+            exchange=asset.exchange.title(),
+            symbol=[asset.symbol.encode('utf-8')],
+            data_frequency=data_frequency,
+        )
+
+    return start, end
+
+
+def get_month_start_end(dt):
+    """
+    Returns the first and last day of the month for the specified date.
+
+    :param dt:
+    :return:
+    """
+    month_range = calendar.monthrange(dt.year, dt.month)
+    month_start = pd.to_datetime(datetime(
+        dt.year, dt.month, 1, 0, 0, 0, 0
+    ), utc=True)
+
+    month_end = pd.to_datetime(datetime(
+        dt.year, dt.month, month_range[1], 23, 59, 0, 0
+    ), utc=True)
+
+    return month_start, month_end
+
+
+def get_year_start_end(dt):
+    """
+    Returns the first and last day of the year for the specified date.
+
+    :param dt:
+    :return:
+    """
+    year_start = pd.to_datetime(date(dt.year, 1, 1), utc=True)
+    year_end = pd.to_datetime(date(dt.year, 12, 31), utc=True)
+
+    return year_start, year_end
+
+
+def get_df_from_arrays(arrays, periods):
+    ohlcv = dict()
+    for index, field in enumerate(
+            ['open', 'high', 'low', 'close', 'volume']):
+        ohlcv[field] = arrays[index].flatten()
+
+    df = pd.DataFrame(
+        data=ohlcv,
+        index=periods
+    )
+    return df
+
+
+def range_in_bundle(asset, start_dt, end_dt, reader):
+    """
+    Evaluate whether price data of an asset is included has been ingested in
+    the exchange bundle for the given date range.
+
+    :param asset:
+    :param start_dt:
+    :param end_dt:
+    :param reader:
+    :return:
+    """
+    has_data = True
+    if has_data and reader is not None:
+        try:
+            start_close = \
+                reader.get_value(asset.sid, start_dt, 'close')
+
+            if np.isnan(start_close):
+                has_data = False
+
+            else:
+                end_close = reader.get_value(asset.sid, end_dt, 'close')
+
+                if np.isnan(end_close):
+                    has_data = False
+
+        except Exception as e:
+            has_data = False
+
+    else:
+        has_data = False
+
+    return has_data
+
+
+@deprecated
+def find_most_recent_time(bundle_name):
+    """
+    Find most recent "time folder" for a given bundle.
+
+    :param bundle_name:
+        The name of the targeted bundle.
+
+    :return folder:
+        The name of the time folder.
+    """
+    try:
+        bundle_folders = os.listdir(
+            data_path([bundle_name]),
+        )
+    except OSError:
+        return None
+
+    most_recent_bundle = dict()
+    for folder in bundle_folders:
+        date = from_bundle_ingest_dirname(folder)
+        if not most_recent_bundle or date > \
+                most_recent_bundle[most_recent_bundle.keys()[0]]:
+            most_recent_bundle = dict()
+            most_recent_bundle[folder] = date
+
+    if most_recent_bundle:
+        return most_recent_bundle.keys()[0]
+    else:
+        return None
+

catalyst/exchange/data_portal_exchange.py

@@ -11,29 +11,37 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import abc
 from time import sleep
 
+import numpy as np
+import pandas as pd
+from catalyst.assets._assets import TradingPair
 from logbook import Logger
 
 from catalyst.data.data_portal import DataPortal
+from catalyst.exchange.bundle_utils import get_start_dt
+from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import (
     ExchangeRequestError,
-    ExchangeBarDataError
+    ExchangeBarDataError,
-)
+    PricingDataBeforeTradingError,
+    PricingDataNotLoadedError, InvalidHistoryFrequencyError,
+    BundleNotFoundError)
 
 log = Logger('DataPortalExchange')
 
 
-class DataPortalExchange(DataPortal):
+class DataPortalExchangeBase(DataPortal):
-    def __init__(self, exchange, *args, **kwargs):
+    def __init__(self, *args, **kwargs):
-        self.exchange = exchange
 
+        self.exchanges = kwargs.pop('exchanges', None)
         # TODO: put somewhere accessible by each algo
         self.retry_get_history_window = 5
         self.retry_get_spot_value = 5
         self.retry_delay = 5
 
-        super(DataPortalExchange, self).__init__(*args, **kwargs)
+        super(DataPortalExchangeBase, self).__init__(*args, **kwargs)
 
     def _get_history_window(self,
                             assets,
@@ -45,7 +53,21 @@ class DataPortalExchange(DataPortal):
                             ffill=True,
                             attempt_index=0):
         try:
-            return self.exchange.get_history_window(
+            exchange_assets = dict()
+            for asset in assets:
+                if asset.exchange not in exchange_assets:
+                    exchange_assets[asset.exchange] = list()
+
+                exchange_assets[asset.exchange].append(asset)
+
+            if len(exchange_assets) > 1:
+                df_list = []
+                for exchange_name in exchange_assets:
+                    exchange = self.exchanges[exchange_name]
+                    assets = exchange_assets[exchange_name]
+
+                    df_exchange = self.get_exchange_history_window(
+                        exchange,
                         assets,
                         end_dt,
                         bar_count,
@@ -53,6 +75,24 @@ class DataPortalExchange(DataPortal):
                         field,
                         data_frequency,
                         ffill)
+
+                    df_list.append(df_exchange)
+
+                # Merging the values values of each exchange
+                return pd.concat(df_list)
+
+            else:
+                exchange = self.exchanges[exchange_assets.keys()[0]]
+                return self.get_exchange_history_window(
+                    exchange,
+                    assets,
+                    end_dt,
+                    bar_count,
+                    frequency,
+                    field,
+                    data_frequency,
+                    ffill)
+
         except ExchangeRequestError as e:
             log.warn(
                 'get history attempt {}: {}'.format(attempt_index, e)
@@ -80,8 +120,12 @@ class DataPortalExchange(DataPortal):
                            bar_count,
                            frequency,
                            field,
-                           data_frequency,
+                           data_frequency=None,
                            ffill=True):
+
+        if field == 'price':
+            field = 'close'
+
         return self._get_history_window(assets,
                                         end_dt,
                                         bar_count,
@@ -90,11 +134,63 @@ class DataPortalExchange(DataPortal):
                                         data_frequency,
                                         ffill)
 
+    @abc.abstractmethod
+    def get_exchange_history_window(self,
+                                    exchange,
+                                    assets,
+                                    end_dt,
+                                    bar_count,
+                                    frequency,
+                                    field,
+                                    data_frequency,
+                                    ffill=True):
+        pass
+
     def _get_spot_value(self, assets, field, dt, data_frequency,
                         attempt_index=0):
         try:
-            return self.exchange.get_spot_value(assets, field, dt,
+            if isinstance(assets, TradingPair):
-                                                data_frequency)
+                exchange = self.exchanges[assets.exchange]
+                spot_values = self.get_exchange_spot_value(
+                    exchange, [assets], field, dt, data_frequency)
+
+                if not spot_values:
+                    return np.nan
+
+                return spot_values[0]
+
+            else:
+                exchange_assets = dict()
+                for asset in assets:
+                    if asset.exchange not in exchange_assets:
+                        exchange_assets[asset.exchange] = list()
+
+                    exchange_assets[asset.exchange].append(asset)
+
+                if len(exchange_assets.keys()) == 1:
+                    exchange = self.exchanges[exchange_assets.keys()[0]]
+                    return self.get_exchange_spot_value(
+                        exchange, assets, field, dt, data_frequency)
+
+                else:
+                    spot_values = []
+                    for exchange_name in exchange_assets:
+                        exchange = self.exchanges[exchange_name]
+                        assets = exchange_assets[exchange_name]
+                        exchange_spot_values = self.get_exchange_spot_value(
+                            exchange,
+                            assets,
+                            field,
+                            dt,
+                            data_frequency
+                        )
+                        if len(assets) == 1:
+                            spot_values.append(exchange_spot_values)
+                        else:
+                            spot_values += exchange_spot_values
+
+                    return spot_values
+
         except ExchangeRequestError as e:
             log.warn(
                 'get spot value attempt {}: {}'.format(attempt_index, e)
@@ -111,11 +207,139 @@ class DataPortalExchange(DataPortal):
                 )
 
     def get_spot_value(self, assets, field, dt, data_frequency):
+        if field == 'price':
+            field = 'close'
+
         return self._get_spot_value(assets, field, dt, data_frequency)
 
+    @abc.abstractmethod
+    def get_exchange_spot_value(self, exchange, assets, field, dt,
+                                data_frequency):
+        return
+
     def get_adjusted_value(self, asset, field, dt,
                            perspective_dt,
                            data_frequency,
                            spot_value=None):
         # TODO: does this pertain to cryptocurrencies?
-        raise NotImplementedError("get_adjusted_value is not implemented yet!")
+        log.warn('get_adjusted_value is not implemented yet!')
+        return spot_value
+
+
+class DataPortalExchangeLive(DataPortalExchangeBase):
+    def __init__(self, *args, **kwargs):
+        super(DataPortalExchangeLive, self).__init__(*args, **kwargs)
+
+    def get_exchange_history_window(self,
+                                    exchange,
+                                    assets,
+                                    end_dt,
+                                    bar_count,
+                                    frequency,
+                                    field,
+                                    data_frequency,
+                                    ffill=True):
+        df = exchange.get_history_window(
+            assets,
+            end_dt,
+            bar_count,
+            frequency,
+            field,
+            data_frequency,
+            ffill)
+        return df
+
+    def get_exchange_spot_value(self, exchange, assets, field, dt,
+                                data_frequency):
+        exchange_spot_values = exchange.get_spot_value(
+            assets, field, dt, data_frequency)
+
+        return exchange_spot_values
+
+
+class DataPortalExchangeBacktest(DataPortalExchangeBase):
+    def __init__(self, *args, **kwargs):
+        super(DataPortalExchangeBacktest, self).__init__(*args, **kwargs)
+
+        self.exchange_bundles = dict()
+
+        self.history_loaders = dict()
+        self.minute_history_loaders = dict()
+
+        for exchange_name in self.exchanges:
+            exchange = self.exchanges[exchange_name]
+            self.exchange_bundles[exchange_name] = ExchangeBundle(exchange)
+
+    def _get_first_trading_day(self, assets):
+        first_date = None
+        for asset in assets:
+            if first_date is None or asset.start_date > first_date:
+                first_date = asset.start_date
+        return first_date
+
+    def get_exchange_history_window(self,
+                                    exchange,
+                                    assets,
+                                    end_dt,
+                                    bar_count,
+                                    frequency,
+                                    field,
+                                    data_frequency,
+                                    ffill=True):
+        """
+        Fetching price history window from the exchange bundle.
+
+        Using a try... except approach to minimize reads most of the time,
+        when the data exists.
+
+        :param exchange:
+        :param assets:
+        :param end_dt:
+        :param bar_count:
+        :param frequency:
+        :param field:
+        :param data_frequency:
+        :param ffill:
+        :return:
+        """
+
+        bundle = self.exchange_bundles[exchange.name]
+        series = bundle.get_history_window_series_and_load(
+            assets=assets,
+            end_dt=end_dt,
+            bar_count=bar_count,
+            field=field,
+            data_frequency=data_frequency
+        )
+        return pd.DataFrame(series)
+
+    def get_exchange_spot_value(self, exchange, assets, field, dt,
+                                data_frequency):
+        bundle = self.exchange_bundles[exchange.name]
+
+        if data_frequency == 'daily':
+            dt = dt.floor('1D')
+        else:
+            dt = dt.floor('1 min')
+
+        try:
+            return bundle.get_spot_values(assets, field, dt, data_frequency)
+
+        except PricingDataNotLoadedError:
+            log.info(
+                'pricing data for {symbol} not found on {dt}'
+                ', updating the bundles.'.format(
+                    symbol=[asset.symbol for asset in assets],
+                    dt=dt
+                )
+            )
+            bundle.ingest_assets(
+                assets=assets,
+                start_dt=self._first_trading_day,
+                end_dt=self._last_available_session,
+                data_frequency=data_frequency,
+                show_progress=True
+            )
+            return bundle.get_spot_values(
+                assets, field, dt, data_frequency, True
+            )

catalyst/exchange/exchange.py

@@ -1,7 +1,7 @@
 import abc
-import collections
+import re
-import random
 from abc import ABCMeta, abstractmethod, abstractproperty
+from datetime import timedelta
 from time import sleep
 
 import numpy as np
@@ -10,11 +10,13 @@ from catalyst.assets._assets import TradingPair
 from logbook import Logger
 
 from catalyst.data.data_portal import BASE_FIELDS
-from catalyst.errors import (
+from catalyst.exchange.bundle_utils import get_start_dt, \
-    SymbolNotFound,
+    get_delta, get_periods, get_adj_dates
-)
+from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import MismatchingBaseCurrencies, \
-    InvalidOrderStyle, BaseCurrencyNotFoundError
+    InvalidOrderStyle, BaseCurrencyNotFoundError, SymbolNotFoundOnExchange, \
+    InvalidHistoryFrequencyError, MismatchingFrequencyError, \
+    BundleNotFoundError, NoDataAvailableOnExchange, PricingDataNotLoadedError
 from catalyst.exchange.exchange_execution import ExchangeStopLimitOrder, \
     ExchangeLimitOrder, ExchangeStopOrder
 from catalyst.exchange.exchange_portfolio import ExchangePortfolio
@@ -30,13 +32,17 @@ class Exchange:
 
     def __init__(self):
         self.name = None
-        self.trading_pairs = None
         self.assets = {}
         self._portfolio = None
         self.minute_writer = None
         self.minute_reader = None
         self.base_currency = None
 
+        self.num_candles_limit = None
+        self.max_requests_per_minute = None
+        self.request_cpt = None
+        self.bundle = ExchangeBundle(self)
+
     @property
     def positions(self):
         return self.portfolio.positions
@@ -64,6 +70,44 @@ class Exchange:
     def time_skew(self):
         pass
 
+    def ask_request(self):
+        """
+        Asks permission to issue a request to the exchange.
+        The primary purpose is to avoid hitting rate limits.
+
+        The application will pause if the maximum requests per minute
+        permitted by the exchange is exceeded.
+
+        :return boolean:
+
+        """
+        now = pd.Timestamp.utcnow()
+        if not self.request_cpt:
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+
+        cpt_date = self.request_cpt.keys()[0]
+        cpt = self.request_cpt[cpt_date]
+
+        if now > cpt_date + timedelta(minutes=1):
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+
+        if cpt >= self.max_requests_per_minute:
+            delta = now - cpt_date
+
+            sleep_period = 60 - delta.total_seconds()
+            sleep(sleep_period)
+
+            now = pd.Timestamp.utcnow()
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+        else:
+            self.request_cpt[cpt_date] += 1
+
     def get_symbol(self, asset):
         """
         Get the exchange specific symbol of the given asset.
@@ -79,7 +123,7 @@ class Exchange:
 
         if not symbol:
             raise ValueError('Currency %s not supported by exchange %s' %
-                             (asset['symbol'], self.name))
+                             (asset['symbol'], self.name.title()))
 
         return symbol
 
@@ -97,6 +141,19 @@ class Exchange:
 
         return symbols
 
+    def get_assets(self, symbols=None):
+        assets = []
+
+        if symbols is not None:
+            for symbol in symbols:
+                asset = self.get_asset(symbol)
+                assets.append(asset)
+        else:
+            for key in self.assets:
+                assets.append(self.assets[key])
+
+        return assets
+
     def get_asset(self, symbol):
         """
         Find an Asset on the current exchange based on its Catalyst symbol
@@ -110,7 +167,13 @@ class Exchange:
                 asset = self.assets[key]
 
         if not asset:
-            raise SymbolNotFound(symbol=symbol)
+            supported_symbols = [pair.symbol.encode('utf-8') for pair in
+                                 self.assets.values()]
+            raise SymbolNotFoundOnExchange(
+                symbol=symbol,
+                exchange=self.name.title(),
+                supported_symbols=supported_symbols
+            )
 
         return asset
 
@@ -159,13 +222,32 @@ class Exchange:
             else:
                 asset_name = None
 
+            if 'min_trade_size' in asset:
+                min_trade_size = asset['min_trade_size']
+            else:
+                min_trade_size = 0.0000001
+
+            if 'end_daily' in asset and asset['end_daily'] != 'N/A':
+                end_daily = pd.to_datetime(asset['end_daily'], utc=True)
+            else:
+                end_daily = None
+
+            if 'end_minute' in asset and asset['end_minute'] != 'N/A':
+                end_minute = pd.to_datetime(asset['end_minute'], utc=True)
+            else:
+                end_minute = None
+
             trading_pair = TradingPair(
                 symbol=asset['symbol'],
                 exchange=self.name,
                 start_date=start_date,
                 end_date=end_date,
                 leverage=leverage,
-                asset_name=asset_name
+                asset_name=asset_name,
+                min_trade_size=min_trade_size,
+                end_daily=end_daily,
+                end_minute=end_minute,
+                exchange_symbol=exchange_symbol
             )
 
             self.assets[exchange_symbol] = trading_pair
@@ -247,19 +329,14 @@ class Exchange:
          '1D', '7D', '14D', '1M'
         """
         if field not in BASE_FIELDS:
-            raise KeyError('Invalid column: ' + str(field))
+            raise KeyError('Invalid column: {}'.format(field))
 
-        if isinstance(assets, collections.Iterable):
+        values = []
-            values = list()
         for asset in assets:
-                value = self.get_single_spot_value(
+            value = self.get_single_spot_value(asset, field, data_frequency)
-                    asset, field, data_frequency)
             values.append(value)
 
         return values
-        else:
-            return self.get_single_spot_value(
-                assets, field, data_frequency)
 
     def get_single_spot_value(self, asset, field, data_frequency):
         """
@@ -284,64 +361,45 @@ class Exchange:
             )
         )
 
-        if field == 'price':
-            field = 'close'
-
-        # Don't use a timezone here
-        dt = pd.Timestamp.utcnow().floor('1 min')
-        value = None
-        if self.minute_reader is not None:
-            try:
-                # Slight delay to minimize the chances that multiple algos
-                # might try to hit the cache at the exact same time.
-                sleep_time = random.uniform(0.5, 0.8)
-                sleep(sleep_time)
-                # TODO: This does not always! Why is that? Open an issue with zipline.
-                # See: https://github.com/zipline-live/zipline/issues/26
-                value = self.minute_reader.get_value(
-                    sid=asset.sid,
-                    dt=dt,
-                    field=field
-                )
-            except Exception as e:
-                log.warn('minute data not found: {}'.format(e))
-
-        if value is None or np.isnan(value):
         ohlc = self.get_candles(data_frequency, asset)
         if field not in ohlc:
             raise KeyError('Invalid column: %s' % field)
 
-            if self.minute_writer is not None:
-                df = pd.DataFrame(
-                    [ohlc],
-                    index=pd.DatetimeIndex([dt]),
-                    columns=['open', 'high', 'low', 'close', 'volume']
-                )
-
-                try:
-                    self.minute_writer.write_sid(
-                        sid=asset.sid,
-                        df=df
-                    )
-                    log.debug('wrote minute data: {}'.format(dt))
-                except Exception as e:
-                    log.warn(
-                        'unable to write minute data: {} {}'.format(dt, e))
-
         value = ohlc[field]
         log.debug('got spot value: {}'.format(value))
-        else:
-            log.debug('got spot value from cache: {}'.format(value))
 
         return value
 
+    def get_series_from_candles(self, candles, start_dt, end_dt,
+                                field, previous_value=None):
+        """
+        Get a series of field data for the specified candles.
+
+        :param candles:
+        :param start_dt:
+        :param end_dt:
+        :param field:
+        :param previous_value:
+        :return:
+        """
+
+        dates = [candle['last_traded'] for candle in candles]
+        values = [candle[field] for candle in candles]
+
+        periods = pd.date_range(start_dt, end_dt)
+        series = pd.Series(values, index=dates)
+
+        series.reindex(periods, method='ffill', fill_value=previous_value)
+
+        return series
+
     def get_history_window(self,
                            assets,
                            end_dt,
                            bar_count,
                            frequency,
                            field,
-                           data_frequency,
+                           data_frequency=None,
                            ffill=True):
 
         """
@@ -378,23 +436,93 @@ class Exchange:
         A dataframe containing the requested data.
         """
 
-        candles = self.get_candles(
+        freq_match = re.match(r'([0-9].*)(m|M|d|D)', frequency, re.M | re.I)
-            data_frequency=frequency,
+        if freq_match:
+            candle_size = int(freq_match.group(1))
+            unit = freq_match.group(2)
+
+        else:
+            raise InvalidHistoryFrequencyError(frequency)
+
+        if unit.lower() == 'd':
+            if data_frequency == 'minute':
+                data_frequency = 'daily'
+
+        elif unit.lower() == 'm':
+            if data_frequency == 'daily':
+                data_frequency = 'minute'
+
+        else:
+            raise InvalidHistoryFrequencyError(frequency)
+
+        adj_bar_count = candle_size * bar_count
+        try:
+            series = self.bundle.get_history_window_series_and_load(
                 assets=assets,
-            bar_count=bar_count,
+                end_dt=end_dt,
+                bar_count=adj_bar_count,
+                field=field,
+                data_frequency=data_frequency
+            )
+        except PricingDataNotLoadedError:
+            series = dict()
+
+        for asset in assets:
+            if asset not in series or series[asset].index[-1] < end_dt:
+                # Adding bars too recent to be contained in the consolidated
+                # exchanges bundles. We go directly against the exchange
+                # to retrieve the candles.
+                start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency)
+                trailing_dt = \
+                    series[asset].index[-1] + get_delta(1, data_frequency) \
+                        if asset in series else start_dt
+
+                trailing_bar_count = \
+                    get_periods(trailing_dt, end_dt, data_frequency)
+
+                # The get_history method supports multiple asset
+                candles = self.get_candles(
+                    data_frequency=data_frequency,
+                    assets=asset,
+                    bar_count=trailing_bar_count,
+                    end_dt=end_dt
                 )
 
-        series = dict()
+                last_value = series[asset].iloc(0) if asset in series \
-        for asset in assets:
+                    else np.nan
-            asset_candles = candles[asset]
 
-            values = map(lambda candle: candle[field], asset_candles)
+                candle_series = self.get_series_from_candles(
-            dates = map(lambda candle: candle['last_traded'], asset_candles)
+                    candles=candles,
+                    start_dt=trailing_dt,
+                    end_dt=end_dt,
+                    field=field,
+                    previous_value=last_value
+                )
 
-            value_series = pd.Series(values, index=dates)
+                if asset in series:
-            series[asset] = value_series
+                    series[asset].append(candle_series)
+
+                else:
+                    series[asset] = candle_series
+
+        df = pd.DataFrame(series)
+
+        if candle_size > 1:
+            if field == 'open':
+                agg = 'first'
+            elif field == 'high':
+                agg = 'max'
+            elif field == 'low':
+                agg = 'min'
+            elif field == 'close':
+                agg = 'last'
+            elif field == 'volume':
+                agg = 'sum'
+            else:
+                raise ValueError('Invalid field.')
+
+            df = df.resample('{}T'.format(candle_size)).agg(agg)
 
-        df = pd.concat(series)
         return df
 
     def synchronize_portfolio(self):
@@ -413,7 +541,7 @@ class Exchange:
         if base_position_available is None:
             raise BaseCurrencyNotFoundError(
                 base_currency=self.base_currency,
-                exchange=self.name
+                exchange=self.name.title()
             )
 
         portfolio = self._portfolio
@@ -440,18 +568,6 @@ class Exchange:
                 portfolio.portfolio_value = \
                     portfolio.positions_value + portfolio.cash
 
-    @abstractmethod
-    def get_balances(self):
-        """
-        Retrieve wallet balances for the exchange
-        :return balances: A dict of currency => available balance
-        """
-        pass
-
-    @abstractmethod
-    def create_order(self, asset, amount, is_buy, style):
-        pass
-
     def order(self, asset, amount, limit_price=None, stop_price=None,
               style=None):
         """Place an order.
@@ -515,7 +631,7 @@ class Exchange:
             style = ExchangeStopOrder(stop_price, exchange=self.name)
 
         elif style is not None:
-            raise InvalidOrderStyle(exchange=self.name,
+            raise InvalidOrderStyle(exchange=self.name.title(),
                                     style=style.__class__.__name__)
         else:
             raise ValueError('Incomplete order data.')
@@ -531,10 +647,39 @@ class Exchange:
             )
         )
         order = self.create_order(asset, amount, is_buy, style)
-
+        if order:
             self._portfolio.create_order(order)
-
             return order.id
+        else:
+            return None
+
+    # The methods below must be implemented for each exchange.
+    @abstractmethod
+    def get_balances(self):
+        """
+        Retrieve wallet balances for the exchange
+        :return balances: A dict of currency => available balance
+        """
+        pass
+
+    @abstractmethod
+    def create_order(self, asset, amount, is_buy, style):
+        """
+        Place an order on the exchange.
+
+        :param asset : Asset
+            The asset that this order is for.
+        :param amount : int
+            The amount of shares to order. If ``amount`` is positive, this is
+            the number of shares to buy or cover. If ``amount`` is negative,
+            this is the number of shares to sell or short.
+        :param style : ExecutionStyle
+            The execution style for the order.
+        :param is_buy: boolean
+            Is it a buy order?
+        :return:
+        """
+        pass
 
     @abstractmethod
     def get_open_orders(self, asset):
@@ -587,16 +732,34 @@ class Exchange:
         pass
 
     @abstractmethod
-    def get_candles(self, data_frequency, assets, bar_count=None):
+    def get_candles(self, data_frequency, assets, bar_count=None,
+                    start_dt=None, end_dt=None):
         """
         Retrieve OHLCV candles for the given assets
 
         :param data_frequency:
-        :param assets:
+            The candle frequency: minute or daily
-        :param end_dt:
+        :param assets: list[TradingPair]
+            The targeted assets.
         :param bar_count:
-        :param limit:
+            The number of bar desired. (default 1)
-        :return:
+        :param end_dt: datetime, optional
+            The last bar date.
+        :param start_dt: datetime, optional
+            The first bar date.
+
+        :return dict[TradingPair, dict[str, Object]]: OHLCV data
+            A dictionary of OHLCV candles. Each TradingPair instance is
+            mapped to a list of dictionaries with this structure:
+                open: float
+                high: float
+                low: float
+                close: float
+                volume: float
+                last_traded: datetime
+
+            See definition here:
+                http://www.investopedia.com/terms/o/ohlcchart.asp
         """
         pass
 
@@ -617,3 +780,16 @@ class Exchange:
         :return:
         """
         pass
+
+    @abc.abstractmethod
+    def get_orderbook(self, asset, order_type):
+        """
+        Retrieve the the orderbook for the given trading pair.
+
+        :param asset: TradingPair
+        :param order_type: str
+            The type of orders: bid, ask or all
+
+        :return:
+        """
+        pass

catalyst/exchange/exchange_algorithm.py

@@ -11,40 +11,50 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+import pickle
 import signal
 import sys
-import pickle
+from collections import deque
 from datetime import timedelta
-from time import sleep
 from os import listdir
 from os.path import isfile, join
-from collections import deque
+from time import sleep
 
 import logbook
 import pandas as pd
+from catalyst.assets._assets import TradingPair
 
 import catalyst.protocol as zp
 from catalyst.algorithm import TradingAlgorithm
 from catalyst.data.minute_bars import BcolzMinuteBarWriter, \
     BcolzMinuteBarReader
 from catalyst.errors import OrderInBeforeTradingStart
-from catalyst.exchange.exchange_clock import ExchangeClock
+from catalyst.exchange.exchange_blotter import ExchangeBlotter
 from catalyst.exchange.exchange_errors import (
     ExchangeRequestError,
     ExchangePortfolioDataError,
-    ExchangeTransactionError
+    ExchangeTransactionError,
-)
+    OrphanOrderError)
+from catalyst.exchange.exchange_execution import ExchangeStopLimitOrder, \
+    ExchangeLimitOrder, ExchangeStopOrder
 from catalyst.exchange.exchange_utils import get_exchange_minute_writer_root, \
-    save_algo_object, get_algo_object, get_algo_folder
+    save_algo_object, get_algo_object, get_algo_folder, get_algo_df, \
+    save_algo_df
+from catalyst.exchange.live_graph_clock import LiveGraphClock
+from catalyst.exchange.simple_clock import SimpleClock
 from catalyst.exchange.stats_utils import get_pretty_stats
+from catalyst.finance.execution import MarketOrder
 from catalyst.finance.performance.period import calc_period_stats
 from catalyst.gens.tradesimulation import AlgorithmSimulator
 from catalyst.utils.api_support import (
     api_method,
     disallowed_in_before_trading_start)
-from catalyst.utils.input_validation import error_keywords
+from catalyst.utils.input_validation import error_keywords, ensure_upper_case, \
+    expect_types
+from catalyst.utils.preprocess import preprocess
+from catalyst.utils.math_utils import round_nearest
 
-log = logbook.Logger("ExchangeTradingAlgorithm")
+log = logbook.Logger('exchange_algorithm')
 
 
 class ExchangeAlgorithmExecutor(AlgorithmSimulator):
@@ -52,174 +62,64 @@ class ExchangeAlgorithmExecutor(AlgorithmSimulator):
         super(self.__class__, self).__init__(*args, **kwargs)
 
 
-class ExchangeTradingAlgorithm(TradingAlgorithm):
+class ExchangeTradingAlgorithmBase(TradingAlgorithm):
     def __init__(self, *args, **kwargs):
-        self.exchange = kwargs.pop('exchange', None)
+        self.exchanges = kwargs.pop('exchanges', None)
-        self.algo_namespace = kwargs.pop('algo_namespace', None)
-        self.orders = {}
-        self.minute_stats = deque(maxlen=60)
-        self.is_running = True
 
-        self.retry_check_open_orders = 5
+        super(ExchangeTradingAlgorithmBase, self).__init__(*args, **kwargs)
-        self.retry_synchronize_portfolio = 5
-        self.retry_get_open_orders = 5
-        self.retry_order = 2
-        self.retry_delay = 5
 
-        self.stats_minutes = 5
+    def round_order(self, amount, asset):
-
-        super(self.__class__, self).__init__(*args, **kwargs)
-        # self._create_minute_writer()
-
-        signal.signal(signal.SIGINT, self.signal_handler)
-
-        log.info('exchange trading algorithm successfully initialized')
-
-    def _create_minute_writer(self):
-        root = get_exchange_minute_writer_root(self.exchange.name)
-        filename = os.path.join(root, 'metadata.json')
-
-        if os.path.isfile(filename):
-            writer = BcolzMinuteBarWriter.open(
-                root, self.sim_params.end_session)
-        else:
-            writer = BcolzMinuteBarWriter(
-                rootdir=root,
-                calendar=self.trading_calendar,
-                minutes_per_day=1440,
-                start_session=self.sim_params.start_session,
-                end_session=self.sim_params.end_session,
-                write_metadata=True
-            )
-
-        self.exchange.minute_writer = writer
-        self.exchange.minute_reader = BcolzMinuteBarReader(root)
-
-    def signal_handler(self, signal, frame):
-        self.is_running = False
-
-        if self._analyze is None:
-            log.info('Interruption signal detected {}, exiting the '
-                     'algorithm'.format(signal))
-
-        else:
-            log.info('Interruption signal detected {}, calling `analyze()` '
-                     'before exiting the algorithm'.format(signal))
-
-            algo_folder = get_algo_folder(self.algo_namespace)
-            folder = join(algo_folder, 'daily_perf')
-            files = [f for f in listdir(folder) if isfile(join(folder, f))]
-
-            daily_perf_list = []
-            for item in files:
-                filename = join(folder, item)
-                with open(filename, 'rb') as handle:
-                    daily_perf_list.append(pickle.load(handle))
-
-            stats = pd.DataFrame(daily_perf_list)
-
-            self.analyze(stats)
-
-        sys.exit(0)
-
-    def _create_clock(self):
-
-        # The calendar's execution times are the minutes over which we actually
-        # want to run the clock. Typically the execution times simply adhere to
-        # the market open and close times. In the case of the futures calendar,
-        # for example, we only want to simulate over a subset of the full 24
-        # hour calendar, so the execution times dictate a market open time of
-        # 6:31am US/Eastern and a close of 5:00pm US/Eastern.
-
-        # In our case, we are trading around the clock, so the market close
-        # corresponds to the last minute of the day.
-
-        # This method is taken from TradingAlgorithm.
-        # The clock has been replaced to use RealtimeClock
-        # TODO: should we apply a time skew? not sure to understand the utility.
-        return ExchangeClock(
-            self.sim_params.sessions,
-            time_skew=self.exchange.time_skew
-        )
-
-    def _create_generator(self, sim_params):
-        if self.perf_tracker is None:
-            self.perf_tracker = get_algo_object(
-                algo_name=self.algo_namespace,
-                key='perf_tracker'
-            )
-
-        # Call the simulation trading algorithm for side-effects:
-        # it creates the perf tracker
-        TradingAlgorithm._create_generator(self, sim_params)
-        self.trading_client = ExchangeAlgorithmExecutor(
-            self,
-            sim_params,
-            self.data_portal,
-            self._create_clock(),
-            self._create_benchmark_source(),
-            self.restrictions,
-            universe_func=self._calculate_universe
-        )
-
-        return self.trading_client.transform()
-
-    def updated_portfolio(self):
         """
-        We skip the entire performance tracker business and update the
+        We need fractions with cryptocurrencies
-        portfolio directly.
+
+        :param amount:
         :return:
         """
-        return self.exchange.portfolio
+        return round_nearest(amount, asset.min_trade_size)
 
-    def updated_account(self):
+    @api_method
-        return self.exchange.account
+    @preprocess(symbol_str=ensure_upper_case)
+    def symbol(self, symbol_str, exchange_name=None):
+        """Lookup an Equity by its ticker symbol.
 
-    def _synchronize_portfolio(self, attempt_index=0):
+        Parameters
-        try:
+        ----------
-            self.exchange.synchronize_portfolio()
+        symbol_str : str
+            The ticker symbol for the equity to lookup.
+        exchange_name: str
+            The name of the exchange containing the symbol
 
-            # Applying the updated last_sales_price to the positions
+        Returns
-            # in the performance tracker. This seems a bit redundant
+        -------
-            # but it will make sense when we have multiple exchange portfolios
+        equity : Equity
-            # feeding into the same performance tracker.
+            The equity that held the ticker symbol on the current
-            tracker = self.perf_tracker.todays_performance.position_tracker
+            symbol lookup date.
-            for asset in self.exchange.portfolio.positions:
+
-                position = self.exchange.portfolio.positions[asset]
+        Raises
-                tracker.update_position(
+        ------
-                    asset=asset,
+        SymbolNotFound
-                    last_sale_date=position.last_sale_date,
+            Raised when the symbols was not held on the current lookup date.
-                    last_sale_price=position.last_sale_price
+
-                )
+        See Also
-        except ExchangeRequestError as e:
+        --------
-            log.warn(
+        :func:`catalyst.api.set_symbol_lookup_date`
-                'update portfolio attempt {}: {}'.format(attempt_index, e)
+        """
-            )
+        # If the user has not set the symbol lookup date,
-            if attempt_index < self.retry_synchronize_portfolio:
+        # use the end_session as the date for sybmol->sid resolution.
-                sleep(self.retry_delay)
+
-                self._synchronize_portfolio(attempt_index + 1)
+        _lookup_date = self._symbol_lookup_date \
+            if self._symbol_lookup_date is not None \
+            else self.sim_params.end_session
+
+        if exchange_name is None:
+            exchange = self.exchanges.values()[0]
         else:
-                raise ExchangePortfolioDataError(
+            exchange = self.exchanges[exchange_name]
-                    data_type='update-portfolio',
-                    attempts=attempt_index,
-                    error=e
-                )
 
-    def _check_open_orders(self, attempt_index=0):
+        return self.asset_finder.lookup_symbol(
-        try:
+            symbol=symbol_str,
-            return self.exchange.check_open_orders()
+            exchange=exchange,
-        except ExchangeRequestError as e:
+            as_of_date=_lookup_date
-            log.warn(
-                'check open orders attempt {}: {}'.format(attempt_index, e)
-            )
-            if attempt_index < self.retry_check_open_orders:
-                sleep(self.retry_delay)
-                return self._check_open_orders(attempt_index + 1)
-            else:
-                raise ExchangePortfolioDataError(
-                    data_type='order-status',
-                    attempts=attempt_index,
-                    error=e
         )
 
     def prepare_period_stats(self, start_dt, end_dt):
@@ -289,6 +189,308 @@ class ExchangeTradingAlgorithm(TradingAlgorithm):
 
         return stats
 
+
+class ExchangeTradingAlgorithmBacktest(ExchangeTradingAlgorithmBase):
+    def __init__(self, *args, **kwargs):
+        super(ExchangeTradingAlgorithmBacktest, self).__init__(*args, **kwargs)
+
+        self.blotter = ExchangeBlotter(
+            data_frequency=self.data_frequency,
+            # Default to NeverCancel in catalyst
+            cancel_policy=self.cancel_policy,
+        )
+        log.info('initialized trading algorithm in backtest mode')
+
+    def _calculate_order(self, asset, amount,
+                         limit_price=None, stop_price=None, style=None):
+        # Raises a ZiplineError if invalid parameters are detected.
+        self.validate_order_params(asset,
+                                   amount,
+                                   limit_price,
+                                   stop_price,
+                                   style)
+
+        # Convert deprecated limit_price and stop_price parameters to use
+        # ExecutionStyle objects.
+        style = self.__convert_order_params_for_blotter(limit_price,
+                                                        stop_price,
+                                                        style)
+        return amount, style
+
+    @staticmethod
+    def __convert_order_params_for_blotter(limit_price, stop_price, style):
+        """
+        Helper method for converting deprecated limit_price and stop_price
+        arguments into ExecutionStyle instances.
+
+        This function assumes that either style == None or (limit_price,
+        stop_price) == (None, None).
+        """
+        if style:
+            assert (limit_price, stop_price) == (None, None)
+            return style
+        if limit_price and stop_price:
+            return ExchangeStopLimitOrder(limit_price, stop_price)
+        if limit_price:
+            return ExchangeLimitOrder(limit_price)
+        if stop_price:
+            return ExchangeStopOrder(stop_price)
+        else:
+            return MarketOrder()
+
+
+class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
+    def __init__(self, *args, **kwargs):
+        self.algo_namespace = kwargs.pop('algo_namespace', None)
+        self.live_graph = kwargs.pop('live_graph', None)
+
+        self._clock = None
+        self.minute_stats = deque(maxlen=60)
+
+        self.pnl_stats = get_algo_df(self.algo_namespace, 'pnl_stats')
+
+        self.custom_signals_stats = \
+            get_algo_df(self.algo_namespace, 'custom_signals_stats')
+
+        self.exposure_stats = \
+            get_algo_df(self.algo_namespace, 'exposure_stats')
+
+        self.is_running = True
+
+        self.retry_check_open_orders = 5
+        self.retry_synchronize_portfolio = 5
+        self.retry_get_open_orders = 5
+        self.retry_order = 2
+        self.retry_delay = 5
+
+        self.stats_minutes = 5
+
+        super(ExchangeTradingAlgorithmLive, self).__init__(*args, **kwargs)
+        # TODO: fix precision before re-enabling
+        # self._create_minute_writer()
+
+        signal.signal(signal.SIGINT, self.signal_handler)
+
+        log.info('initialized trading algorithm in live mode')
+
+    def _create_minute_writer(self):
+        root = get_exchange_minute_writer_root(self.exchange.name)
+        filename = os.path.join(root, 'metadata.json')
+
+        if os.path.isfile(filename):
+            writer = BcolzMinuteBarWriter.open(
+                root, self.sim_params.end_session)
+        else:
+            # TODO: need to be able to write more precise numbers
+            writer = BcolzMinuteBarWriter(
+                rootdir=root,
+                calendar=self.trading_calendar,
+                minutes_per_day=1440,
+                start_session=self.sim_params.start_session,
+                end_session=self.sim_params.end_session,
+                write_metadata=True
+            )
+
+        self.exchange.minute_writer = writer
+        self.exchange.minute_reader = BcolzMinuteBarReader(root)
+
+    def signal_handler(self, signal, frame):
+        self.is_running = False
+
+        if self._analyze is None:
+            log.info('Interruption signal detected {}, exiting the '
+                     'algorithm'.format(signal))
+
+        else:
+            log.info('Interruption signal detected {}, calling `analyze()` '
+                     'before exiting the algorithm'.format(signal))
+
+            algo_folder = get_algo_folder(self.algo_namespace)
+            folder = join(algo_folder, 'daily_perf')
+            files = [f for f in listdir(folder) if isfile(join(folder, f))]
+
+            daily_perf_list = []
+            for item in files:
+                filename = join(folder, item)
+                with open(filename, 'rb') as handle:
+                    daily_perf_list.append(pickle.load(handle))
+
+            stats = pd.DataFrame(daily_perf_list)
+
+            self.analyze(stats)
+
+        sys.exit(0)
+
+    @property
+    def clock(self):
+        if self._clock is None:
+            return self._create_clock()
+        else:
+            return self._clock
+
+    def _create_clock(self):
+
+        # The calendar's execution times are the minutes over which we actually
+        # want to run the clock. Typically the execution times simply adhere to
+        # the market open and close times. In the case of the futures calendar,
+        # for example, we only want to simulate over a subset of the full 24
+        # hour calendar, so the execution times dictate a market open time of
+        # 6:31am US/Eastern and a close of 5:00pm US/Eastern.
+
+        # In our case, we are trading around the clock, so the market close
+        # corresponds to the last minute of the day.
+
+        # This method is taken from TradingAlgorithm.
+        # The clock has been replaced to use RealtimeClock
+        # TODO: should we apply a time skew? not sure to understand the utility.
+
+        log.debug('creating clock')
+        if self.live_graph:
+            self._clock = LiveGraphClock(
+                self.sim_params.sessions,
+                context=self
+            )
+        else:
+            self._clock = SimpleClock(
+                self.sim_params.sessions,
+            )
+
+        return self._clock
+
+    def _create_generator(self, sim_params):
+        if self.perf_tracker is None:
+            self.perf_tracker = get_algo_object(
+                algo_name=self.algo_namespace,
+                key='perf_tracker'
+            )
+
+        # Call the simulation trading algorithm for side-effects:
+        # it creates the perf tracker
+        TradingAlgorithm._create_generator(self, sim_params)
+        self.trading_client = ExchangeAlgorithmExecutor(
+            self,
+            sim_params,
+            self.data_portal,
+            self.clock,
+            self._create_benchmark_source(),
+            self.restrictions,
+            universe_func=self._calculate_universe
+        )
+
+        return self.trading_client.transform()
+
+    def updated_portfolio(self):
+        """
+        We skip the entire performance tracker business and update the
+        portfolio directly.
+        :return:
+        """
+        # TODO: build cumulative portfolio
+        return self.perf_tracker.get_portfolio(False)
+
+    def updated_account(self):
+        return self.perf_tracker.get_account(False)
+
+    def _synchronize_portfolio(self, attempt_index=0):
+        try:
+            for exchange_name in self.exchanges:
+                exchange = self.exchanges[exchange_name]
+
+                exchange.synchronize_portfolio()
+
+                # Applying the updated last_sales_price to the positions
+                # in the performance tracker. This seems a bit redundant
+                # but it will make sense when we have multiple exchange portfolios
+                # feeding into the same performance tracker.
+                tracker = self.perf_tracker.todays_performance.position_tracker
+                for asset in exchange.portfolio.positions:
+                    position = exchange.portfolio.positions[asset]
+                    tracker.update_position(
+                        asset=asset,
+                        last_sale_date=position.last_sale_date,
+                        last_sale_price=position.last_sale_price
+                    )
+        except ExchangeRequestError as e:
+            log.warn(
+                'update portfolio attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_synchronize_portfolio:
+                sleep(self.retry_delay)
+                self._synchronize_portfolio(attempt_index + 1)
+            else:
+                raise ExchangePortfolioDataError(
+                    data_type='update-portfolio',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    def _check_open_orders(self, attempt_index=0):
+        try:
+            orders = list()
+            for exchange_name in self.exchanges:
+                exchange = self.exchanges[exchange_name]
+                exchange_orders = exchange.check_open_orders()
+
+                orders += exchange_orders
+
+            return orders
+        except ExchangeRequestError as e:
+            log.warn(
+                'check open orders attempt {}: {}'.format(attempt_index, e)
+            )
+            if attempt_index < self.retry_check_open_orders:
+                sleep(self.retry_delay)
+                return self._check_open_orders(attempt_index + 1)
+            else:
+                raise ExchangePortfolioDataError(
+                    data_type='order-status',
+                    attempts=attempt_index,
+                    error=e
+                )
+
+    def add_pnl_stats(self, period_stats):
+        starting = period_stats['starting_cash']
+        current = period_stats['portfolio_value']
+        appreciation = (current / starting) - 1
+        perc = (appreciation * 100) if current != 0 else 0
+
+        log.debug('adding pnl stats: {:6f}%'.format(perc))
+
+        df = pd.DataFrame(
+            data=[dict(performance=perc)],
+            index=[period_stats['period_close']]
+        )
+        self.pnl_stats = pd.concat([self.pnl_stats, df])
+
+        save_algo_df(self.algo_namespace, 'pnl_stats', self.pnl_stats)
+
+    def add_custom_signals_stats(self, period_stats):
+        log.debug('adding custom signals stats: {}'.format(self.recorded_vars))
+        df = pd.DataFrame(
+            data=[self.recorded_vars],
+            index=[period_stats['period_close']],
+        )
+        self.custom_signals_stats = pd.concat([self.custom_signals_stats, df])
+
+        save_algo_df(self.algo_namespace, 'custom_signals_stats',
+                     self.custom_signals_stats)
+
+    def add_exposure_stats(self, period_stats):
+        data = dict(
+            long_exposure=period_stats['long_exposure'],
+            base_currency=period_stats['ending_cash']
+        )
+        log.debug('adding exposure stats: {}'.format(data))
+
+        df = pd.DataFrame(
+            data=[data],
+            index=[period_stats['period_close']],
+        )
+        self.exposure_stats = pd.concat([self.exposure_stats, df])
+
+        save_algo_df(self.algo_namespace, 'exposure_stats',
+                     self.exposure_stats)
+
     def handle_data(self, data):
         if not self.is_running:
             return
@@ -314,14 +516,28 @@ class ExchangeTradingAlgorithm(TradingAlgorithm):
 
             minute_stats = self.prepare_period_stats(
                 data.current_dt, data.current_dt + timedelta(minutes=1))
+
             # Saving the last hour in memory
             self.minute_stats.append(minute_stats)
 
+            self.add_pnl_stats(minute_stats)
+            if self.recorded_vars:
+                self.add_custom_signals_stats(minute_stats)
+                recorded_cols = self.recorded_vars.keys()
+            else:
+                recorded_cols = None
+
+            self.add_exposure_stats(minute_stats)
+
             print_df = pd.DataFrame(list(self.minute_stats))
-            log.debug(
+            log.info(
                 'statistics for the last {stats_minutes} minutes:\n{stats}'.format(
                     stats_minutes=self.stats_minutes,
-                    stats=get_pretty_stats(print_df, self.stats_minutes)
+                    stats=get_pretty_stats(
+                        stats_df=print_df,
+                        recorded_cols=recorded_cols,
+                        num_rows=self.stats_minutes
+                    )
                 ))
 
             today = pd.to_datetime('today', utc=True)
@@ -349,10 +565,12 @@ class ExchangeTradingAlgorithm(TradingAlgorithm):
             log.warn('unable to save minute perfs to disk: {}'.format(e))
 
         try:
+            for exchange_name in self.exchanges:
+                exchange = self.exchanges[exchange_name]
                 save_algo_object(
                     algo_name=self.algo_namespace,
-                key='portfolio_{}'.format(self.exchange.name),
+                    key='portfolio_{}'.format(exchange_name),
-                obj=self.exchange.portfolio
+                    obj=exchange.portfolio
                 )
         except Exception as e:
             log.warn('unable to save portfolio to disk: {}'.format(e))
@@ -365,7 +583,8 @@ class ExchangeTradingAlgorithm(TradingAlgorithm):
                style=None,
                attempt_index=0):
         try:
-            return self.exchange.order(asset, amount, limit_price,
+            exchange = self.exchanges[asset.exchange]
+            return exchange.order(asset, amount, limit_price,
                                   stop_price,
                                   style)
         except ExchangeRequestError as e:
@@ -386,32 +605,51 @@ class ExchangeTradingAlgorithm(TradingAlgorithm):
 
     @api_method
     @disallowed_in_before_trading_start(OrderInBeforeTradingStart())
+    @expect_types(asset=TradingPair)
     def order(self,
               asset,
               amount,
               limit_price=None,
               stop_price=None,
               style=None):
+        """
+        We use the exchange specific portfolio to place orders.
+        The cumulative portfolio does not contain open orders but exchange
+        portfolios do.
+
+        :param asset: TradingPair
+        :param amount: float
+        :param limit_price: float
+        :param stop_price: float
+        :param style: Style
+        :return order: Order
+            The catalyst order object or None
+        """
+
         amount, style = self._calculate_order(asset, amount,
                                               limit_price, stop_price,
                                               style)
 
         order_id = self._order(asset, amount, limit_price, stop_price, style)
 
+        exchange = self.exchanges[asset.exchange]
+        exchange_portfolio = exchange.portfolio
         if order_id is not None:
-            order = self.portfolio.open_orders[order_id]
-            self.perf_tracker.process_order(order)
 
+            if order_id in exchange_portfolio.open_orders:
+                order = exchange_portfolio.open_orders[order_id]
+                self.perf_tracker.process_order(order)
                 return order
 
-    def round_order(self, amount):
+            else:
-        """
+                raise OrphanOrderError(
-        We need fractions with cryptocurrencies
+                    order_id=order_id,
-
+                    exchange=exchange.name
-        :param amount:
+                )
-        :return:
+        else:
-        """
+            log.warn('unable to order {} {} on exchange {}'.format(
-        return amount
+                amount, asset.symbol, asset.exchange))
+            return None
 
     @api_method
     def batch_market_order(self, share_counts):
@@ -419,7 +657,18 @@ class ExchangeTradingAlgorithm(TradingAlgorithm):
 
     def _get_open_orders(self, asset=None, attempt_index=0):
         try:
-            return self.exchange.get_open_orders(asset)
+            if asset:
+                exchange = self.exchanges[asset.exchange]
+                return exchange.get_open_orders(asset)
+
+            else:
+                open_orders = []
+                for exchange_name in self.exchanges:
+                    exchange = self.exchanges[exchange_name]
+                    exchange_orders = exchange.get_open_orders()
+                    open_orders.append(exchange_orders)
+
+                return open_orders
         except ExchangeRequestError as e:
             log.warn(
                 'open orders attempt {}: {}'.format(attempt_index, e)
@@ -441,12 +690,16 @@ class ExchangeTradingAlgorithm(TradingAlgorithm):
         return self._get_open_orders(asset)
 
     @api_method
-    def get_order(self, order_id):
+    def get_order(self, order_id, exchange_name):
-        return self.exchange.get_order(order_id)
+        exchange = self.exchanges[exchange_name]
+        return exchange.get_order(order_id)
 
     @api_method
-    def cancel_order(self, order_param):
+    def cancel_order(self, order_param, exchange_name):
+        exchange = self.exchanges[exchange_name]
+
         order_id = order_param
         if isinstance(order_param, zp.Order):
             order_id = order_param.id
-        self.exchange.cancel_order(order_id)
+
+        exchange.cancel_order(order_id)

catalyst/exchange/exchange_bcolz.py

@@ -0,0 +1,90 @@
+import numpy as np
+
+from catalyst import get_calendar
+from catalyst.data.minute_bars import BcolzMinuteBarReader, \
+    BcolzMinuteBarWriter
+from catalyst.exchange.bundle_utils import get_periods, get_periods_range
+
+
+class BcolzExchangeBarWriter(BcolzMinuteBarWriter):
+    def __init__(self, *args, **kwargs):
+        self._data_frequency = kwargs.pop('data_frequency', None)
+        kwargs.pop('minutes_per_day', None)
+        kwargs.pop('calendar', None)
+
+        end_session = kwargs.pop('end_session', None)
+        if end_session is not None:
+            end_session = end_session.floor('1d')
+
+        minutes_per_day = 1440 if self._data_frequency == 'minute' else 1
+        default_ohlc_ratio = kwargs.pop('default_ohlc_ratio', 1000000)
+        calendar = get_calendar('OPEN')
+
+        super(BcolzExchangeBarWriter, self) \
+            .__init__(*args, **dict(kwargs,
+                                    minutes_per_day=minutes_per_day,
+                                    default_ohlc_ratio=default_ohlc_ratio,
+                                    calendar=calendar,
+                                    end_session=end_session
+                                    ))
+
+
+class BcolzExchangeBarReader(BcolzMinuteBarReader):
+    def __init__(self, *args, **kwargs):
+        self._data_frequency = kwargs.pop('data_frequency', None)
+
+        super(BcolzExchangeBarReader, self).__init__(*args, **kwargs)
+
+    @property
+    def data_frequency(self):
+        return self._data_frequency
+
+    def load_raw_arrays(self, fields, start_dt, end_dt, sids):
+
+        # if self._data_frequency == 'minute':
+        #     return super(BcolzExchangeBarReader, self) \
+        #         .load_raw_arrays(fields, start_dt, end_dt, sids)
+        #
+        # else:
+        #     return self._load_daily_raw_arrays(fields, start_dt, end_dt, sids)
+
+        return self._load_raw_arrays(fields, start_dt, end_dt, sids)
+
+    def _load_raw_arrays(self, fields, start_dt, end_dt, sids):
+        start_idx = self._find_position_of_minute(start_dt)
+        end_idx = self._find_position_of_minute(end_dt)
+
+        periods = self.calendar.minutes_in_range(start_dt, end_dt) \
+            if self.data_frequency == 'minute' \
+            else self.calendar.sessions_in_range(start_dt, end_dt)
+
+        num_days = len(periods)
+        shape = num_days, len(sids)
+
+        all_fields = fields[:]
+        if len(all_fields) == 1 and all_fields[0] == 'volume':
+            all_fields.insert(0, 'close')
+
+        mask = None
+        data = []
+        for field in all_fields:
+            if field != 'volume':
+                out = np.full(shape, np.nan)
+            else:
+                out = np.zeros(shape, dtype=np.float64)
+
+            for i, sid in enumerate(sids):
+                carray = self._open_minute_file(field, sid)
+                a = carray[start_idx:end_idx + 1]
+
+                if mask is None:
+                    mask = a != 0
+
+                out[:len(mask), i][mask] = (
+                    a[mask] * self._ohlc_ratio_inverse_for_sid(sid)
+                )
+
+            if field in fields:
+                data.append(out)
+
+        return data

catalyst/exchange/exchange_blotter.py

@@ -0,0 +1,137 @@
+from catalyst.assets._assets import TradingPair
+from logbook import Logger
+
+from catalyst.finance.blotter import Blotter
+from catalyst.finance.commission import CommissionModel
+from catalyst.finance.slippage import SlippageModel
+from catalyst.finance.transaction import Transaction
+
+log = Logger('exchange_blotter')
+
+# It seems like we need to accept greater slippage risk in cryptos
+# Orders won't often close at Equity levels.
+# TODO: consider adjusting dynamically based on trading pair
+DEFAULT_SLIPPAGE_SPREAD = 0.02
+DEFAULT_MAKER_FEE = 0.001
+DEFAULT_TAKER_FEE = 0.002
+
+
+class TradingPairFeeSchedule(CommissionModel):
+    """
+    Calculates a commission for a transaction based on a per percentage fee.
+
+    Parameters
+    ----------
+    fee : float, optional
+        The percentage fee.
+    """
+
+    def __init__(self,
+                 maker_fee=DEFAULT_MAKER_FEE,
+                 taker_fee=DEFAULT_TAKER_FEE):
+        self.maker_fee = maker_fee
+        self.taker_fee = taker_fee
+
+    def __repr__(self):
+        return (
+            '{class_name}(maker_fee={maker_fee}, '
+            'taker_fee={taker_fee})'.format(
+                class_name=self.__class__.__name__,
+                maker_fee=self.maker_fee,
+                taker_fee=self.taker_fee,
+            )
+        )
+
+    def calculate(self, order, transaction):
+        """
+        Calculate the final fee based on the order parameters.
+
+        :param order:
+        :param transaction:
+
+        :return float:
+            The total commission.
+        """
+        cost = abs(transaction.amount) * transaction.price
+
+        # Assuming just the taker fee for now
+        fee = cost * self.taker_fee
+        return fee
+
+
+class TradingPairFixedSlippage(SlippageModel):
+    """
+    Model slippage as a fixed spread.
+
+    Parameters
+    ----------
+    spread : float, optional
+        spread / 2 will be added to buys and subtracted from sells.
+    """
+
+    def __init__(self, spread=DEFAULT_SLIPPAGE_SPREAD):
+        super(TradingPairFixedSlippage, self).__init__()
+        self.spread = spread
+
+    def __repr__(self):
+        return '{class_name}(spread={spread})'.format(
+            class_name=self.__class__.__name__, spread=self.spread,
+        )
+
+    def simulate(self, data, asset, orders_for_asset):
+        self._volume_for_bar = 0
+
+        price = data.current(asset, 'close')
+
+        dt = data.current_dt
+        for order in orders_for_asset:
+            if order.open_amount == 0:
+                continue
+
+            order.check_triggers(price, dt)
+            if not order.triggered:
+                log.debug('order has not reached the trigger at current '
+                          'price {}'.format(price))
+                continue
+
+            execution_price, execution_volume = self.process_order(data, order)
+
+            transaction = Transaction(
+                asset=order.asset,
+                amount=abs(execution_volume),
+                dt=dt,
+                price=execution_price,
+                order_id=order.id
+            )
+
+            self._volume_for_bar += abs(transaction.amount)
+            yield order, transaction
+
+    def process_order(self, data, order):
+        price = data.current(order.asset, 'close')
+
+        if order.amount > 0:
+            # Buy order
+            adj_price = price * (1 + self.spread)
+        else:
+            # Sell order
+            adj_price = price * (1 - self.spread)
+
+        log.debug('added slippage to price: {} => {}'.format(price, adj_price))
+
+        return adj_price, order.amount
+
+
+class ExchangeBlotter(Blotter):
+    def __init__(self, *args, **kwargs):
+        super(ExchangeBlotter, self).__init__(*args, **kwargs)
+
+        # Using the equity models for now
+        # We may be able to define more sophisticated models based on the fee
+        # structure of each exchange.
+        self.slippage_models = {
+            TradingPair: TradingPairFixedSlippage()
+        }
+        self.commission_models = {
+            TradingPair: TradingPairFeeSchedule()
+        }

catalyst/exchange/exchange_bundle.py

@@ -0,0 +1,603 @@
+import os
+import shutil
+from datetime import timedelta
+
+import pandas as pd
+from logbook import Logger, INFO
+
+from catalyst import get_calendar
+from catalyst.data.minute_bars import BcolzMinuteOverlappingData, \
+    BcolzMinuteBarMetadata
+from catalyst.exchange.bundle_utils import range_in_bundle, \
+    get_bcolz_chunk, get_delta, get_adj_dates, get_month_start_end, \
+    get_year_start_end, get_periods_range, get_df_from_arrays, get_start_dt
+from catalyst.exchange.exchange_bcolz import BcolzExchangeBarReader, \
+    BcolzExchangeBarWriter
+from catalyst.exchange.exchange_errors import EmptyValuesInBundleError, \
+    InvalidHistoryFrequencyError, PricingDataBeforeTradingError, \
+    TempBundleNotFoundError, NoDataAvailableOnExchange, \
+    PricingDataNotLoadedError
+from catalyst.exchange.exchange_utils import get_exchange_folder
+from catalyst.utils.cli import maybe_show_progress
+from catalyst.utils.paths import ensure_directory
+
+
+def _cachpath(symbol, type_):
+    return '-'.join([symbol, type_])
+
+
+BUNDLE_NAME_TEMPLATE = '{root}/{frequency}_bundle'
+log = Logger('exchange_bundle')
+log.level = INFO
+
+
+class ExchangeBundle:
+    def __init__(self, exchange):
+        self.exchange = exchange
+        self.minutes_per_day = 1440
+        self.default_ohlc_ratio = 1000000
+        self._writers = dict()
+        self._readers = dict()
+        self.calendar = get_calendar('OPEN')
+
+    def get_assets(self, include_symbols, exclude_symbols):
+        # TODO: filter exclude symbols assets
+        if include_symbols is not None:
+            include_symbols_list = include_symbols.split(',')
+
+            return self.exchange.get_assets(include_symbols_list)
+
+        else:
+            return self.exchange.get_assets()
+
+    def get_reader(self, data_frequency, path=None):
+        """
+        Get a data writer object, either a new object or from cache
+
+        :return: BcolzMinuteBarReader or BcolzDailyBarReader
+        """
+        if path is None:
+            root = get_exchange_folder(self.exchange.name)
+            path = BUNDLE_NAME_TEMPLATE.format(
+                root=root,
+                frequency=data_frequency
+            )
+
+        if path in self._readers and self._readers[path] is not None:
+            return self._readers[path]
+
+        try:
+            self._readers[path] = BcolzExchangeBarReader(
+                rootdir=path,
+                data_frequency=data_frequency
+            )
+        except IOError:
+            self._readers[path] = None
+
+        return self._readers[path]
+
+    def update_metadata(self, writer, start_dt, end_dt):
+        pass
+
+    def get_writer(self, start_dt, end_dt, data_frequency):
+        """
+        Get a data writer object, either a new object or from cache
+
+        :return: BcolzMinuteBarWriter or BcolzDailyBarWriter
+        """
+        root = get_exchange_folder(self.exchange.name)
+        path = BUNDLE_NAME_TEMPLATE.format(
+            root=root,
+            frequency=data_frequency
+        )
+
+        if path in self._writers:
+            return self._writers[path]
+
+        ensure_directory(path)
+
+        if len(os.listdir(path)) > 0:
+
+            metadata = BcolzMinuteBarMetadata.read(path)
+
+            write_metadata = False
+            if start_dt < metadata.start_session:
+                write_metadata = True
+                start_session = start_dt
+            else:
+                start_session = metadata.start_session
+
+            if end_dt > metadata.end_session:
+                write_metadata = True
+
+                end_session = end_dt
+            else:
+                end_session = metadata.end_session
+
+            self._writers[path] = \
+                BcolzExchangeBarWriter(
+                    rootdir=path,
+                    start_session=start_session,
+                    end_session=end_session,
+                    write_metadata=write_metadata,
+                    data_frequency=data_frequency
+                )
+        else:
+            self._writers[path] = BcolzExchangeBarWriter(
+                rootdir=path,
+                start_session=start_dt,
+                end_session=end_dt,
+                write_metadata=True,
+                data_frequency=data_frequency
+            )
+
+        return self._writers[path]
+
+    def filter_existing_assets(self, assets, start_dt, end_dt, data_frequency):
+        """
+        For each asset, get the close on the start and end dates of the chunk.
+            If the data exists, the chunk ingestion is complete.
+            If any data is missing we ingest the data.
+
+        :param assets: list[TradingPair]
+            The assets is scope.
+        :param start_dt:
+            The chunk start date.
+        :param end_dt:
+            The chunk end date.
+        :return: list[TradingPair]
+            The assets missing from the bundle
+        """
+        reader = self.get_reader(data_frequency)
+        missing_assets = []
+        for asset in assets:
+            has_data = range_in_bundle(asset, start_dt, end_dt, reader)
+
+            if not has_data:
+                missing_assets.append(asset)
+
+        return missing_assets
+
+    def _write(self, data, writer, data_frequency):
+        """
+        Write data to the writer
+
+        :param df:
+        :param writer:
+        :return:
+        """
+        try:
+            writer.write(
+                data=data,
+                show_progress=False,
+                invalid_data_behavior='raise'
+            )
+        except BcolzMinuteOverlappingData as e:
+            log.warn('chunk already exists: {}'.format(e))
+        except Exception as e:
+            log.warn('error when writing data: {}, trying again'.format(e))
+
+            # This is workaround, there is an issue with empty
+            # session_label when using a newly created writer
+            key = writer._rootdir if data_frequency == 'minute' \
+                else writer._filename
+
+            del self._writers[key]
+
+            writer = self.get_writer(writer._start_session,
+                                     writer._end_session, data_frequency)
+            writer.write(
+                data=data,
+                show_progress=False,
+                invalid_data_behavior='raise'
+            )
+
+    def get_calendar_periods_range(self, start_dt, end_dt, data_frequency):
+        return self.calendar.minutes_in_range(start_dt, end_dt) \
+            if data_frequency == 'minute' \
+            else self.calendar.sessions_in_range(start_dt, end_dt)
+
+    def ingest_ctable(self, asset, data_frequency, period, start_dt, end_dt,
+                      writer, empty_rows_behavior='strip', cleanup=False):
+        """
+        Merge a ctable bundle chunk into the main bundle for the exchange.
+
+        :param asset: TradingPair
+        :param data_frequency: str
+        :param period: str
+        :param writer:
+        :param empty_rows_behavior: str
+            Ensure that the bundle does not have any missing data.
+
+        :param cleanup: bool
+            Remove the temp bundle directory after ingestion.
+
+        :return:
+        """
+
+        path = get_bcolz_chunk(
+            exchange_name=self.exchange.name,
+            symbol=asset.symbol,
+            data_frequency=data_frequency,
+            period=period
+        )
+
+        reader = self.get_reader(data_frequency, path=path)
+        if reader is None:
+            raise TempBundleNotFoundError(path=path)
+
+        arrays = reader.load_raw_arrays(
+            sids=[asset.sid],
+            fields=['open', 'high', 'low', 'close', 'volume'],
+            start_dt=start_dt,
+            end_dt=end_dt
+        )
+
+        if not arrays:
+            return path
+
+        periods = self.get_calendar_periods_range(
+            start_dt, end_dt, data_frequency
+        )
+
+        df = get_df_from_arrays(arrays, periods)
+
+        if empty_rows_behavior is not 'ignore':
+            nan_rows = df[df.isnull().T.any().T].index
+
+            if len(nan_rows) > 0:
+                dates = []
+                previous_date = None
+                for row_date in nan_rows.values:
+                    row_date = pd.to_datetime(row_date)
+
+                    if previous_date is None:
+                        dates.append(row_date)
+
+                    else:
+                        seq_date = previous_date + get_delta(1, data_frequency)
+
+                        if row_date > seq_date:
+                            dates.append(previous_date)
+                            dates.append(row_date)
+
+                    previous_date = row_date
+
+                dates.append(pd.to_datetime(nan_rows.values[-1]))
+
+                name = path.split('/')[-1]
+                if empty_rows_behavior == 'warn':
+                    log.warn(
+                        '\n{name} with end minute {end_minute} has empty rows '
+                        'in ranges: {dates}'.format(
+                            name=name,
+                            end_minute=asset.end_minute,
+                            dates=dates
+                        )
+                    )
+
+                elif empty_rows_behavior == 'raise':
+                    raise EmptyValuesInBundleError(
+                        name=name,
+                        end_minute=asset.end_minute,
+                        dates=dates
+                    )
+                else:
+                    df.dropna(inplace=True)
+
+        data = []
+        if not df.empty:
+            df.sort_index(inplace=True)
+            data.append((asset.sid, df))
+        self._write(data, writer, data_frequency)
+
+        if cleanup:
+            log.debug('removing bundle folder following '
+                      'ingestion: {}'.format(path))
+            shutil.rmtree(path)
+
+        return path
+
+    def prepare_chunks(self, assets, data_frequency, start_dt, end_dt):
+        """
+        Split a price data request into chunks corresponding to individual
+        bundles.
+
+        :param assets:
+        :param data_frequency:
+        :param start_dt:
+        :param end_dt:
+        :return:
+        """
+        reader = self.get_reader(data_frequency)
+
+        chunks = []
+        for asset in assets:
+            try:
+                asset_start, asset_end = \
+                    get_adj_dates(start_dt, end_dt, [asset], data_frequency)
+
+            except NoDataAvailableOnExchange:
+                continue
+
+            # Aligning start / end dates with the daily calendar
+            sessions = get_periods_range(start_dt, end_dt, data_frequency) \
+                if data_frequency == 'minute' \
+                else self.calendar.sessions_in_range(start_dt, end_dt)
+
+            if asset_start < sessions[0]:
+                asset_start = sessions[0]
+
+            if asset_end > sessions[-1]:
+                asset_end = sessions[-1]
+
+            chunk_labels = []
+            dt = sessions[0]
+            while dt <= sessions[-1]:
+                label = '{}-{:02d}'.format(dt.year, dt.month) \
+                    if data_frequency == 'minute' else '{}'.format(dt.year)
+
+                if label not in chunk_labels:
+                    chunk_labels.append(label)
+
+                    # Adjusting the period dates to match the availability
+                    # of the trading pair
+                    if data_frequency == 'minute':
+                        period_start, period_end = get_month_start_end(dt)
+                        asset_start_month, _ = get_month_start_end(asset_start)
+
+                        if asset_start_month == period_start \
+                                and period_start < asset_start:
+                            period_start = asset_start
+
+                        _, asset_end_month = get_month_start_end(asset_end)
+                        if asset_end_month == period_end \
+                                and period_end > asset_end:
+                            period_end = asset_end
+
+                    elif data_frequency == 'daily':
+                        period_start, period_end = get_year_start_end(dt)
+                        asset_start_year, _ = get_year_start_end(asset_start)
+
+                        if asset_start_year == period_start \
+                                and period_start < asset_start:
+                            period_start = asset_start
+
+                        _, asset_end_year = get_year_start_end(asset_end)
+                        if asset_end_year == period_end \
+                                and period_end > asset_end:
+                            period_end = asset_end
+                    else:
+                        raise InvalidHistoryFrequencyError(
+                            frequency=data_frequency
+                        )
+
+                    # Currencies don't always start trading at midnight.
+                    # Checking the last minute of the day instead.
+                    range_start = period_start.replace(hour=23, minute=59) \
+                        if data_frequency == 'minute' else period_start
+                    has_data = range_in_bundle(
+                        asset, range_start, period_end, reader
+                    )
+
+                    if not has_data:
+                        log.debug('adding period: {}'.format(label))
+                        chunks.append(
+                            dict(
+                                asset=asset,
+                                period_start=period_start,
+                                period_end=period_end,
+                                period=label
+                            )
+                        )
+
+                dt += timedelta(days=1)
+
+        chunks.sort(key=lambda chunk: chunk['period_end'])
+
+        return chunks
+
+    def ingest_assets(self, assets, start_dt, end_dt, data_frequency,
+                      show_progress=False):
+        """
+        Determine if data is missing from the bundle and attempt to ingest it.
+
+        :param assets:
+        :param start_dt:
+        :param end_dt:
+        :return:
+        """
+        writer = self.get_writer(start_dt, end_dt, data_frequency)
+        chunks = self.prepare_chunks(
+            assets=assets,
+            data_frequency=data_frequency,
+            start_dt=start_dt,
+            end_dt=end_dt
+        )
+        with maybe_show_progress(
+                chunks,
+                show_progress,
+                label='Fetching {exchange} {frequency} candles: '.format(
+                    exchange=self.exchange.name,
+                    frequency=data_frequency
+                )) as it:
+            for chunk in it:
+                self.ingest_ctable(
+                    asset=chunk['asset'],
+                    data_frequency=data_frequency,
+                    period=chunk['period'],
+                    start_dt=chunk['period_start'],
+                    end_dt=chunk['period_end'],
+                    writer=writer,
+                    empty_rows_behavior='strip'
+                )
+
+    def ingest(self, data_frequency, include_symbols=None,
+               exclude_symbols=None, start=None, end=None,
+               show_progress=True, environ=os.environ):
+        """
+
+        :param data_frequency:
+        :param include_symbols:
+        :param exclude_symbols:
+        :param start:
+        :param end:
+        :param show_progress:
+        :param environ:
+        :return:
+        """
+        assets = self.get_assets(include_symbols, exclude_symbols)
+        start_dt, end_dt = get_adj_dates(start, end, assets, data_frequency)
+
+        for frequency in data_frequency.split(','):
+            self.ingest_assets(assets, start_dt, end_dt, frequency,
+                               show_progress)
+
+    def get_history_window_series_and_load(self,
+                                           assets,
+                                           end_dt,
+                                           bar_count,
+                                           field,
+                                           data_frequency):
+        try:
+            series = self.get_history_window_series(
+                assets=assets,
+                end_dt=end_dt,
+                bar_count=bar_count,
+                field=field,
+                data_frequency=data_frequency
+            )
+            return pd.DataFrame(series)
+
+        except PricingDataNotLoadedError:
+            start_dt = get_start_dt(end_dt, bar_count, data_frequency)
+            log.info(
+                'pricing data for {symbol} not found in range '
+                '{start} to {end}, updating the bundles.'.format(
+                    symbol=[asset.symbol for asset in assets],
+                    start=start_dt,
+                    end=end_dt
+                )
+            )
+            self.ingest_assets(
+                assets=assets,
+                start_dt=start_dt,
+                end_dt=end_dt,
+                data_frequency=data_frequency,
+                show_progress=True
+            )
+            series = self.get_history_window_series(
+                assets=assets,
+                end_dt=end_dt,
+                bar_count=bar_count,
+                field=field,
+                data_frequency=data_frequency,
+                reset_reader=True
+            )
+            return series
+
+    def get_spot_values(self, assets, field, dt, data_frequency,
+                        reset_reader=False):
+        values = []
+        try:
+            reader = self.get_reader(data_frequency)
+            if reset_reader:
+                del self._readers[reader._rootdir]
+                reader = self.get_reader(data_frequency)
+
+            for asset in assets:
+                value = reader.get_value(
+                    sid=asset.sid,
+                    dt=dt,
+                    field=field
+                )
+                values.append(value)
+
+            return values
+
+        except Exception:
+            symbols = [asset.symbol.encode('utf-8') for asset in assets]
+            raise PricingDataNotLoadedError(
+                field=field,
+                first_trading_day=min([asset.start_date for asset in assets]),
+                exchange=self.exchange.name,
+                symbols=symbols,
+                symbol_list=','.join(symbols),
+                data_frequency=data_frequency
+            )
+
+    def get_history_window_series(self,
+                                  assets,
+                                  end_dt,
+                                  bar_count,
+                                  field,
+                                  data_frequency,
+                                  reset_reader=False):
+        start_dt = get_start_dt(end_dt, bar_count, data_frequency)
+        start_dt, end_dt = \
+            get_adj_dates(start_dt, end_dt, assets, data_frequency)
+
+        reader = self.get_reader(data_frequency)
+        if reset_reader:
+            del self._readers[reader._rootdir]
+            reader = self.get_reader(data_frequency)
+
+        if reader is None:
+            symbols = [asset.symbol.encode('utf-8') for asset in assets]
+            raise PricingDataNotLoadedError(
+                field=field,
+                first_trading_day=min([asset.start_date for asset in assets]),
+                exchange=self.exchange.name,
+                symbols=symbols,
+                symbol_list=','.join(symbols),
+                data_frequency=data_frequency
+            )
+
+        for asset in assets:
+            asset_start_dt, asset_end_dt = \
+                get_adj_dates(start_dt, end_dt, assets, data_frequency)
+
+            in_bundle = range_in_bundle(
+                asset, asset_start_dt, asset_end_dt, reader
+            )
+            if not in_bundle:
+                raise PricingDataNotLoadedError(
+                    field=field,
+                    first_trading_day=asset.start_date,
+                    exchange=self.exchange.name,
+                    symbols=asset.symbol,
+                    symbol_list=asset.symbol,
+                    data_frequency=data_frequency
+                )
+
+        series = dict()
+        try:
+            arrays = reader.load_raw_arrays(
+                sids=[asset.sid for asset in assets],
+                fields=[field],
+                start_dt=start_dt,
+                end_dt=end_dt
+            )
+
+        except Exception:
+            symbols = [asset.symbol.encode('utf-8') for asset in assets]
+            raise PricingDataNotLoadedError(
+                field=field,
+                first_trading_day=min([asset.start_date for asset in assets]),
+                exchange=self.exchange.name,
+                symbols=symbols,
+                symbol_list=','.join(symbols),
+                data_frequency=data_frequency
+            )
+
+        periods = self.get_calendar_periods_range(
+            start_dt, end_dt, data_frequency
+        )
+
+        for asset_index, asset in enumerate(assets):
+            asset_values = arrays[asset_index]
+
+            value_series = pd.Series(asset_values.flatten(), index=periods)
+            series[asset] = value_series
+
+        return series

catalyst/exchange/exchange_errors.py

@@ -1,6 +1,21 @@
+import sys, traceback
 from catalyst.errors import ZiplineError
 
 
+def silent_except_hook(exctype, excvalue, exctraceback):
+    if exctype in [PricingDataBeforeTradingError, PricingDataNotLoadedError,
+                   SymbolNotFoundOnExchange, NoDataAvailableOnExchange, ]:
+        fn = traceback.extract_tb(exctraceback)[-1][0]
+        ln = traceback.extract_tb(exctraceback)[-1][1]
+        print "Error traceback: {1} (line {2})\n" \
+              "{0.__name__}:  {3}".format(exctype, fn, ln, excvalue)
+    else:
+        sys.__excepthook__(exctype, excvalue, exctraceback)
+
+
+sys.excepthook = silent_except_hook
+
+
 class ExchangeRequestError(ZiplineError):
     msg = (
         'Request failed: {error}'
@@ -34,6 +49,13 @@ class ExchangeTransactionError(ZiplineError):
     ).strip()
 
 
+class ExchangeNotFoundError(ZiplineError):
+    msg = (
+        'Exchange {exchange_name} not found. Please specify exchanges '
+        'supported by Catalyst and verify spelling for accuracy.'
+    ).strip()
+
+
 class ExchangeAuthNotFound(ZiplineError):
     msg = (
         'Please create an auth.json file containing the api token and key for '
@@ -56,7 +78,14 @@ class AlgoPickleNotFound(ZiplineError):
 
 class InvalidHistoryFrequencyError(ZiplineError):
     msg = (
-        'History frequency {frequency} not supported by the exchange.'
+        'Frequency {frequency} not supported by the exchange.'
+    ).strip()
+
+
+class MismatchingFrequencyError(ZiplineError):
+    msg = (
+        'Bar aggregate frequency {frequency} not compatible with '
+        'data frequency {data_frequency}.'
     ).strip()
 
 
@@ -87,6 +116,19 @@ class OrderNotFound(ZiplineError):
     ).strip()
 
 
+class OrphanOrderError(ZiplineError):
+    msg = (
+        'Order {order_id} found in exchange {exchange} but not tracked by '
+        'the algorithm.'
+    ).strip()
+
+
+class OrphanOrderReverseError(ZiplineError):
+    msg = (
+        'Order {order_id} tracked by algorithm, but not found in exchange {exchange}.'
+    ).strip()
+
+
 class OrderCancelError(ZiplineError):
     msg = (
         'Unable to cancel order {order_id} on exchange {exchange} {error}.'
@@ -111,3 +153,58 @@ class MismatchingBaseCurrencies(ZiplineError):
         'Unable to trade with base currency {base_currency} when the '
         'algorithm uses {algo_currency}.'
     ).strip()
+
+
+class MismatchingBaseCurrenciesExchanges(ZiplineError):
+    msg = (
+        'Unable to trade with base currency {base_currency} when the '
+        'exchange {exchange_name} users {exchange_currency}.'
+    ).strip()
+
+
+class SymbolNotFoundOnExchange(ZiplineError):
+    """
+    Raised when a symbol() call contains a non-existent symbol.
+    """
+    msg = ('Symbol {symbol} not found on exchange {exchange}. '
+           'Choose from: {supported_symbols}').strip()
+
+
+class BundleNotFoundError(ZiplineError):
+    msg = ('Unable to find bundle data for exchange {exchange} and '
+           'data frequency {data_frequency}.'
+           'Please ingest some price data.'
+           'See `catalyst ingest-exchange --help` for details.').strip()
+
+
+class TempBundleNotFoundError(ZiplineError):
+    msg = ('Temporary bundle not found in: {path}.').strip()
+
+
+class EmptyValuesInBundleError(ZiplineError):
+    msg = ('{name} with end minute {end_minute} has empty rows '
+           'in ranges: {dates}').strip()
+
+
+class PricingDataBeforeTradingError(ZiplineError):
+    msg = ('Pricing data for trading pairs {symbols} on exchange {exchange} '
+           'starts on {first_trading_day}, but you are either trying to trade or '
+           'retrieve pricing data on {dt}. Adjust your dates accordingly.').strip()
+
+
+class PricingDataNotLoadedError(ZiplineError):
+    msg = ('Pricing data {field} for trading pairs {symbols} trading on '
+           'exchange {exchange} since {first_trading_day} is unavailable. '
+           'The bundle data is either out-of-date or has not been loaded yet. '
+           'Please ingest data using the command '
+           '`catalyst ingest-exchange -x {exchange} -f {data_frequency} -i {symbol_list}`. '
+           'See catalyst documentation for details.').strip()
+
+
+class ApiCandlesError(ZiplineError):
+    msg = ('Unable to fetch candles from the remote API: {error}.').strip()
+
+class NoDataAvailableOnExchange(ZiplineError):
+    msg = ('Requested data for trading pair {symbol} is not available on exchange {exchange} '
+           'in `{data_frequency}` frequency at this time. '
+           'Check `http://enigma.co/catalyst/status` for market coverage.').strip()

catalyst/exchange/exchange_portfolio.py

@@ -70,6 +70,30 @@ class ExchangePortfolio(Portfolio):
 
         log.debug('updated portfolio with executed order')
 
+    def execute_transaction(self, transaction):
+        log.debug('executing transaction {}'.format(transaction.order_id))
+
+        order_position = self.positions[transaction.asset] \
+            if transaction.asset in self.positions else None
+
+        if order_position is None:
+            raise ValueError(
+                'Trying to execute transaction for a position not held: %s' % transaction.order_id
+            )
+
+        self.capital_used += transaction.amount * transaction.price
+
+        if transaction.amount > 0:
+            if order_position.cost_basis > 0:
+                order_position.cost_basis = np.average(
+                    [order_position.cost_basis, transaction.price],
+                    weights=[order_position.amount, transaction.amount]
+                )
+            else:
+                order_position.cost_basis = transaction.price
+
+        log.debug('updated portfolio with executed order')
+
     def remove_order(self, order):
         log.info('removing cancelled order {}'.format(order.id))
         del self.open_orders[order.id]

catalyst/exchange/exchange_utils.py

@@ -4,13 +4,14 @@ import pickle
 import urllib
 from datetime import date, datetime
 
+import pandas as pd
+
 from catalyst.exchange.exchange_errors import ExchangeAuthNotFound, \
     ExchangeSymbolsNotFound
-from catalyst.utils.paths import data_root, ensure_directory
+from catalyst.utils.paths import data_root, ensure_directory, last_modified_time
 
-# TODO: move to aws
+SYMBOLS_URL = 'https://s3.amazonaws.com/enigmaco/catalyst-exchanges/' \
-SYMBOLS_URL = 'https://raw.githubusercontent.com/enigmampc/catalyst/' \
+              '{exchange}/symbols.json'
-              'master/catalyst/exchange/{exchange}/symbols.json'
 
 
 def get_exchange_folder(exchange_name, environ=None):
@@ -24,20 +25,23 @@ def get_exchange_folder(exchange_name, environ=None):
     return exchange_folder
 
 
-def download_exchange_symbols(exchange_name, environ=None):
+def get_exchange_symbols_filename(exchange_name, environ=None):
     exchange_folder = get_exchange_folder(exchange_name, environ)
-    filename = os.path.join(exchange_folder, 'symbols.json')
+    return os.path.join(exchange_folder, 'symbols.json')
 
+
+def download_exchange_symbols(exchange_name, environ=None):
+    filename = get_exchange_symbols_filename(exchange_name)
     url = SYMBOLS_URL.format(exchange=exchange_name)
     response = urllib.urlretrieve(url=url, filename=filename)
     return response
 
 
 def get_exchange_symbols(exchange_name, environ=None):
-    exchange_folder = get_exchange_folder(exchange_name, environ)
+    filename = get_exchange_symbols_filename(exchange_name)
-    filename = os.path.join(exchange_folder, 'symbols.json')
 
-    if not os.path.isfile(filename):
+    if not os.path.isfile(filename) or \
+            pd.Timedelta(pd.Timestamp('now', tz='UTC') - last_modified_time(filename)).days > 1:
         download_exchange_symbols(exchange_name, environ)
 
     if os.path.isfile(filename):
@@ -78,6 +82,9 @@ def get_algo_folder(algo_name, environ=None):
 
 
 def get_algo_object(algo_name, key, environ=None, rel_path=None):
+    if algo_name is None:
+        return None
+
     folder = get_algo_folder(algo_name, environ)
 
     if rel_path is not None:
@@ -117,6 +124,37 @@ def append_algo_object(algo_name, key, obj, environ=None):
         pickle.dump(obj, handle, protocol=pickle.HIGHEST_PROTOCOL)
 
 
+def get_algo_df(algo_name, key, environ=None, rel_path=None):
+    folder = get_algo_folder(algo_name, environ)
+
+    if rel_path is not None:
+        folder = os.path.join(folder, rel_path)
+
+    filename = os.path.join(folder, key + '.csv')
+
+    if os.path.isfile(filename):
+        try:
+            with open(filename, 'rb') as handle:
+                return pd.read_csv(handle, index_col=0, parse_dates=True)
+        except IOError:
+            return pd.DataFrame()
+    else:
+        return pd.DataFrame()
+
+
+def save_algo_df(algo_name, key, df, environ=None, rel_path=None):
+    folder = get_algo_folder(algo_name, environ)
+
+    if rel_path is not None:
+        folder = os.path.join(folder, rel_path)
+        ensure_directory(folder)
+
+    filename = os.path.join(folder, key + '.csv')
+
+    with open(filename, 'wb') as handle:
+        df.to_csv(handle)
+
+
 def get_exchange_minute_writer_root(exchange_name, environ=None):
     exchange_folder = get_exchange_folder(exchange_name, environ)
 
@@ -125,6 +163,14 @@ def get_exchange_minute_writer_root(exchange_name, environ=None):
 
     return minute_data_folder
 
+def get_exchange_bundles_folder(exchange_name, environ=None):
+    exchange_folder = get_exchange_folder(exchange_name, environ)
+
+    temp_bundles = os.path.join(exchange_folder, 'temp_bundles')
+    ensure_directory(temp_bundles)
+
+    return temp_bundles
+
 
 def perf_serial(obj):
     """JSON serializer for objects not serializable by default json code"""

catalyst/exchange/init_utils.py

@@ -0,0 +1,32 @@
+from catalyst.exchange.bitfinex.bitfinex import Bitfinex
+from catalyst.exchange.bittrex.bittrex import Bittrex
+from catalyst.exchange.exchange_errors import ExchangeNotFoundError
+from catalyst.exchange.exchange_utils import get_exchange_auth
+from catalyst.exchange.poloniex.poloniex import Poloniex
+
+
+def get_exchange(exchange_name):
+    exchange_auth = get_exchange_auth(exchange_name)
+    if exchange_name == 'bitfinex':
+        return Bitfinex(
+            key=exchange_auth['key'],
+            secret=exchange_auth['secret'],
+            base_currency=None,  # TODO: make optional at the exchange
+            portfolio=None
+        )
+    elif exchange_name == 'bittrex':
+        return Bittrex(
+            key=exchange_auth['key'],
+            secret=exchange_auth['secret'],
+            base_currency=None,
+            portfolio=None
+        )
+    elif exchange_name == 'poloniex':
+        return Poloniex(
+            key=exchange_auth['key'],
+            secret=exchange_auth['secret'],
+            base_currency=None,
+            portfolio=None
+        )
+    else:
+        raise ExchangeNotFoundError(exchange_name=exchange_name)

catalyst/exchange/live_graph_clock.py

@@ -0,0 +1,226 @@
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from datetime import timedelta
+
+import pandas as pd
+from catalyst.gens.sim_engine import (
+    BAR,
+    SESSION_START
+)
+from logbook import Logger
+
+from catalyst.exchange.exchange_errors import \
+    MismatchingBaseCurrenciesExchanges
+
+
+log = Logger('LiveGraphClock')
+
+
+class LiveGraphClock(object):
+    """Realtime clock for live trading.
+
+    This class is a drop-in replacement for
+    :class:`zipline.gens.sim_engine.MinuteSimulationClock`.
+
+    This mixes the clock with a live graph.
+
+    Note
+    ----
+    This seemingly awkward approach allows us to run the program using a single
+    thread. This is important because Matplotlib does not play nice with
+    multi-threaded environments. Zipline probably does not either.
+
+
+    Matplotlib has a pause() method which is a wrapper around time.sleep()
+    used in the SimpleClock. The key difference is that users
+    can still interact with the chart during the pause cycles. This is
+    what enables us to keep a single thread. This is also why we are not using
+    the 'animate' callback of Matplotlib. We need to direct access to the
+    __iter__ method in order to yield events to Zipline.
+
+    The :param:`time_skew` parameter represents the time difference between
+    the exchange and the live trading machine's clock. It's not used currently.
+    """
+
+    def __init__(self, sessions, context, time_skew=pd.Timedelta('0s')):
+
+        global mdates, plt                  #TODO: Could be cleaner
+        import matplotlib.dates as mdates
+        from matplotlib import pyplot as plt
+        from matplotlib import style
+
+        self.sessions = sessions
+        self.time_skew = time_skew
+        self._last_emit = None
+        self._before_trading_start_bar_yielded = True
+        self.context = context
+        self.fmt = mdates.DateFormatter('%Y-%m-%d %H:%M')
+
+        style.use('dark_background')
+
+        fig = plt.figure()
+        fig.canvas.set_window_title('Enigma Catalyst: {}'.format(
+            self.context.algo_namespace))
+
+        self.ax_pnl = fig.add_subplot(311)
+
+        self.ax_custom_signals = fig.add_subplot(312, sharex=self.ax_pnl)
+
+        self.ax_exposure = fig.add_subplot(313, sharex=self.ax_pnl)
+
+        if len(context.minute_stats) > 0:
+            self.draw_pnl()
+            self.draw_custom_signals()
+            self.draw_exposure()
+
+        # rotates and right aligns the x labels, and moves the bottom of the
+        # axes up to make room for them
+        fig.autofmt_xdate()
+        fig.subplots_adjust(hspace=0.5)
+
+        plt.tight_layout()
+        plt.ion()
+        plt.show()
+
+    def format_ax(self, ax):
+        """
+        Trying to assign reasonable parameters to the time axis.
+
+        TODO: room for improvement
+
+        :param ax:
+        :return:
+        """
+        ax.xaxis.set_major_locator(mdates.DayLocator(interval=1))
+        ax.xaxis.set_major_formatter(self.fmt)
+
+        locator = mdates.HourLocator(interval=4)
+        locator.MAXTICKS = 5000
+        ax.xaxis.set_minor_locator(locator)
+
+        datemin = pd.Timestamp.utcnow()
+        ax.set_xlim(datemin)
+
+        ax.grid(True)
+
+    def set_legend(self, ax):
+        ax.legend(loc='upper left', ncol=1, fontsize=10, numpoints=1)
+
+    def draw_pnl(self):
+        ax = self.ax_pnl
+        df = self.context.pnl_stats
+
+        ax.clear()
+        ax.set_title('Performance')
+        ax.plot(df.index, df['performance'], '-',
+                color='green',
+                linewidth=1.0,
+                label='Performance'
+                )
+
+        def perc(val):
+            return '{:2f}'.format(val)
+
+        ax.format_ydata = perc
+
+        self.set_legend(ax)
+        self.format_ax(ax)
+
+    def draw_custom_signals(self):
+        ax = self.ax_custom_signals
+        df = self.context.custom_signals_stats
+
+        colors = ['blue', 'green', 'red', 'black', 'orange', 'yellow', 'pink']
+
+        ax.clear()
+        ax.set_title('Custom Signals')
+        for index, column in enumerate(df.columns.values.tolist()):
+            ax.plot(df.index, df[column], '-',
+                    color=colors[index],
+                    linewidth=1.0,
+                    label=column
+                    )
+
+        self.set_legend(ax)
+        self.format_ax(ax)
+
+    def draw_exposure(self):
+        ax = self.ax_exposure
+        context = self.context
+        df = context.exposure_stats
+
+        # TODO: list exchanges in graph
+        base_currency = None
+        positions = []
+        for exchange_name in context.exchanges:
+            exchange = context.exchanges[exchange_name]
+
+            if not base_currency:
+                base_currency = exchange.base_currency
+            elif base_currency != exchange.base_currency:
+                raise MismatchingBaseCurrenciesExchanges(
+                    base_currency=base_currency,
+                    exchange_name=exchange.name,
+                    exchange_currency=exchange.base_currency
+                )
+
+            positions += exchange.portfolio.positions
+
+        ax.clear()
+        ax.set_title('Exposure')
+        ax.plot(df.index, df['base_currency'], '-',
+                color='green',
+                linewidth=1.0,
+                label='Base Currency: {}'.format(base_currency.upper())
+                )
+
+        symbols = []
+        for position in positions:
+            symbols.append(position.symbol)
+
+        ax.plot(df.index, df['long_exposure'], '-',
+                color='blue',
+                linewidth=1.0,
+                label='Long Exposure: {}'.format(', '.join(symbols).upper()))
+
+        self.set_legend(ax)
+        self.format_ax(ax)
+
+    def __iter__(self):
+        yield pd.Timestamp.utcnow(), SESSION_START
+
+        while True:
+            current_time = pd.Timestamp.utcnow()
+            current_minute = current_time.floor('1 min')
+
+            if self._last_emit is None or current_minute > self._last_emit:
+                log.debug('emitting minutely bar: {}'.format(current_minute))
+
+                self._last_emit = current_minute
+                yield current_minute, BAR
+
+                try:
+                    self.draw_pnl()
+                    self.draw_custom_signals()
+                    self.draw_exposure()
+
+                    plt.draw()
+                except Exception as e:
+                    log.warn('Unable to update the graph: {}'.format(e))
+
+            else:
+                # I can't use the "animate" reactive approach here because
+                # I need to yield from the main loop.
+
+                # Workaround: https://stackoverflow.com/a/33050617/814633
+                plt.pause(1)

catalyst/exchange/poloniex/poloniex.py

@@ -0,0 +1,638 @@
+import base64
+import hashlib
+import hmac
+import json
+import re
+import time
+from collections import defaultdict
+
+import numpy as np
+import pandas as pd
+import pytz
+import requests
+# import six
+from six import iteritems
+from catalyst.assets._assets import TradingPair
+from logbook import Logger
+
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.poloniex.poloniex_api import Poloniex_api
+
+# from websocket import create_connection
+from catalyst.exchange.exchange import Exchange
+from catalyst.exchange.exchange_errors import (
+    ExchangeRequestError,
+    InvalidHistoryFrequencyError,
+    InvalidOrderStyle, OrderCancelError,
+    OrphanOrderReverseError)
+from catalyst.exchange.exchange_execution import ExchangeLimitOrder, \
+    ExchangeStopLimitOrder, ExchangeStopOrder
+from catalyst.finance.order import Order, ORDER_STATUS
+from catalyst.protocol import Account
+from catalyst.exchange.exchange_utils import get_exchange_symbols_filename, \
+    download_exchange_symbols
+from catalyst.finance.transaction import Transaction
+
+log = Logger('Poloniex')
+
+
+class Poloniex(Exchange):
+    def __init__(self, key, secret, base_currency, portfolio=None):
+        self.api = Poloniex_api(key=key, secret=secret.encode('UTF-8'))
+        self.name = 'poloniex'
+        self.assets = {}
+        self.load_assets()
+        self.base_currency = base_currency
+        self._portfolio = portfolio
+        self.minute_writer = None
+        self.minute_reader = None
+        self.transactions = defaultdict(list)
+
+        self.num_candles_limit = 2000
+        self.max_requests_per_minute = 60
+        self.request_cpt = dict()
+
+        self.bundle = ExchangeBundle(self)
+
+    def sanitize_curency_symbol(self, exchange_symbol):
+        """
+        Helper method used to build the universal pair.
+        Include any symbol mapping here if appropriate.
+
+        :param exchange_symbol:
+        :return universal_symbol:
+        """
+        return exchange_symbol.lower()
+
+    def _create_order(self, order_status):
+        """
+        Create a Catalyst order object from the Exchange order dictionary
+        :param order_status:
+        :return: Order
+        """
+        # if order_status['is_cancelled']:
+        #    status = ORDER_STATUS.CANCELLED
+        # elif not order_status['is_live']:
+        #    log.info('found executed order {}'.format(order_status))
+        #    status = ORDER_STATUS.FILLED
+        # else:
+        status = ORDER_STATUS.OPEN
+
+        amount = float(order_status['amount'])
+        # filled = float(order_status['executed_amount'])
+        filled = None
+
+        if order_status['type'] == 'sell':
+            amount = -amount
+            # filled = -filled
+
+        price = float(order_status['rate'])
+        order_type = order_status['type']
+
+        stop_price = None
+        limit_price = None
+
+        # TODO: is this comprehensive enough?
+        # if order_type.endswith('limit'):
+        #    limit_price = price
+        # elif order_type.endswith('stop'):
+        #    stop_price = price
+
+        # executed_price = float(order_status['avg_execution_price'])
+        executed_price = price
+
+        # TODO: bitfinex does not specify comission. I could calculate it but not sure if it's worth it.
+        commission = None
+
+        # date = pd.Timestamp.utcfromtimestamp(float(order_status['timestamp']))
+        # date = pytz.utc.localize(date)
+        date = None
+
+        order = Order(
+            dt=date,
+            asset=self.assets[order_status['symbol']],
+            # No such field in Poloniex
+            amount=amount,
+            stop=stop_price,
+            limit=limit_price,
+            filled=filled,
+            id=str(order_status['orderNumber']),
+            commission=commission
+        )
+        order.status = status
+
+        return order, executed_price
+
+    def get_balances(self):
+        log.debug('retrieving wallets balances')
+        try:
+            balances = self.api.returnbalances()
+        except Exception as e:
+            log.debug(e)
+            raise ExchangeRequestError(error=e)
+
+        if 'error' in balances:
+            raise ExchangeRequestError(
+                error='unable to fetch balance {}'.format(balances['error'])
+            )
+
+        std_balances = dict()
+        for (key, value) in iteritems(balances):
+            currency = key.lower()
+            std_balances[currency] = float(value)
+
+        return std_balances
+
+    @property
+    def account(self):
+        account = Account()
+
+        account.settled_cash = None
+        account.accrued_interest = None
+        account.buying_power = None
+        account.equity_with_loan = None
+        account.total_positions_value = None
+        account.total_positions_exposure = None
+        account.regt_equity = None
+        account.regt_margin = None
+        account.initial_margin_requirement = None
+        account.maintenance_margin_requirement = None
+        account.available_funds = None
+        account.excess_liquidity = None
+        account.cushion = None
+        account.day_trades_remaining = None
+        account.leverage = None
+        account.net_leverage = None
+        account.net_liquidation = None
+
+        return account
+
+    @property
+    def time_skew(self):
+        # TODO: research the time skew conditions
+        return pd.Timedelta('0s')
+
+    def get_account(self):
+        # TODO: fetch account data and keep in cache
+        return None
+
+    def get_candles(self, data_frequency, assets, bar_count=None,
+                    start_dt=None, end_dt=None):
+        """
+        Retrieve OHLVC candles from Poloniex
+
+        :param data_frequency:
+        :param assets:
+        :param bar_count:
+        :return:
+
+        Available Frequencies
+        ---------------------
+        '5m', '15m', '30m', '2h', '4h', '1D'
+        """
+
+        # TODO: implement end_dt and start_dt filters
+
+        if (
+                        data_frequency == '5m' or data_frequency == 'minute'):  # TODO: Polo does not have '1m'
+            frequency = 300
+        elif (data_frequency == '15m'):
+            frequency = 900
+        elif (data_frequency == '30m'):
+            frequency = 1800
+        elif (data_frequency == '2h'):
+            frequency = 7200
+        elif (data_frequency == '4h'):
+            frequency = 14400
+        elif (data_frequency == '1D' or data_frequency == 'daily'):
+            frequency = 86400
+        else:
+            raise InvalidHistoryFrequencyError(
+                frequency=data_frequency
+            )
+
+        # Making sure that assets are iterable
+        asset_list = [assets] if isinstance(assets, TradingPair) else assets
+        ohlc_map = dict()
+
+        for asset in asset_list:
+
+            end = int(time.time())
+            if (bar_count is None):
+                start = end - 2 * frequency
+            else:
+                start = end - bar_count * frequency
+
+            try:
+                response = self.api.returnchartdata(self.get_symbol(asset),
+                                                    frequency, start, end)
+            except Exception as e:
+                raise ExchangeRequestError(error=e)
+
+            if 'error' in response:
+                raise ExchangeRequestError(
+                    error='Unable to retrieve candles: {}'.format(
+                        response.content)
+                )
+
+            def ohlc_from_candle(candle):
+                last_traded = pd.Timestamp.utcfromtimestamp(candle['date'])
+                last_traded = last_traded.replace(tzinfo=pytz.UTC)
+
+                ohlc = dict(
+                    open=np.float64(candle['open']),
+                    high=np.float64(candle['high']),
+                    low=np.float64(candle['low']),
+                    close=np.float64(candle['close']),
+                    volume=np.float64(candle['volume']),
+                    price=np.float64(candle['close']),
+                    last_traded=last_traded
+                )
+
+                return ohlc
+
+            if bar_count is None:
+                ohlc_map[asset] = ohlc_from_candle(response[0])
+            else:
+                ohlc_bars = []
+                for candle in response:
+                    ohlc = ohlc_from_candle(candle)
+                    ohlc_bars.append(ohlc)
+                ohlc_map[asset] = ohlc_bars
+
+        return ohlc_map[assets] \
+            if isinstance(assets, TradingPair) else ohlc_map
+
+    def create_order(self, asset, amount, is_buy, style):
+        """
+        Creating order on the exchange.
+
+        :param asset:
+        :param amount:
+        :param is_buy:
+        :param style:
+        :return:
+        """
+        exchange_symbol = self.get_symbol(asset)
+
+        if isinstance(style, ExchangeLimitOrder) or isinstance(style,
+                                                               ExchangeStopLimitOrder):
+            if isinstance(style, ExchangeStopLimitOrder):
+                log.warn('{} will ignore the stop price'.format(self.name))
+
+            price = style.get_limit_price(is_buy)
+
+            try:
+                if (is_buy):
+                    response = self.api.buy(exchange_symbol, amount, price)
+                else:
+                    response = self.api.sell(exchange_symbol, -amount, price)
+            except Exception as e:
+                raise ExchangeRequestError(error=e)
+
+            date = pd.Timestamp.utcnow()
+
+            if ('orderNumber' in response):
+                order_id = str(response['orderNumber'])
+                order = Order(
+                    dt=date,
+                    asset=asset,
+                    amount=amount,
+                    stop=style.get_stop_price(is_buy),
+                    limit=style.get_limit_price(is_buy),
+                    id=order_id
+                )
+                return order
+            else:
+                log.warn(
+                    '{} order failed: {}'.format('buy' if is_buy else 'sell',
+                                                 response['error']))
+                return None
+        else:
+            raise InvalidOrderStyle(exchange=self.name,
+                                    style=style.__class__.__name__)
+
+    def get_open_orders(self, asset='all'):
+        """Retrieve all of the current open orders.
+
+        Parameters
+        ----------
+        asset : Asset
+            If passed and not 'all', return only the open orders for the given
+            asset instead of all open orders.
+
+        Returns
+        -------
+        open_orders : dict[list[Order]] or list[Order]
+            If 'all' is passed this will return a dict mapping Assets
+            to a list containing all the open orders for the asset.
+            If an asset is passed then this will return a list of the open
+            orders for this asset.
+        """
+
+        return self.portfolio.open_orders
+
+        """
+            TODO: Why going to the exchange if we already have this info locally?
+                  And why creating all these Orders if we later discard them?
+        """
+
+        try:
+            if (asset == 'all'):
+                response = self.api.returnopenorders('all')
+            else:
+                response = self.api.returnopenorders(self.get_symbol(asset))
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'error' in response:
+            raise ExchangeRequestError(
+                error='Unable to retrieve open orders: {}'.format(
+                    order_statuses['message'])
+            )
+
+        print(self.portfolio.open_orders)
+
+        # TODO: Need to handle openOrders for 'all'
+        orders = list()
+        for order_status in response:
+            order, executed_price = self._create_order(
+                order_status)  # will Throw error b/c Polo doesn't track order['symbol']
+            if asset is None or asset == order.sid:
+                orders.append(order)
+
+        return orders
+
+    def get_order(self, order_id):
+        """Lookup an order based on the order id returned from one of the
+        order functions.
+
+        Parameters
+        ----------
+        order_id : str
+            The unique identifier for the order.
+
+        Returns
+        -------
+        order : Order
+            The order object.
+        """
+
+        try:
+            order = self._portfolio.open_orders[order_id]
+        except Exception as e:
+            raise OrphanOrderError(order_id=order_id, exchange=self.name)
+
+        return order
+
+        # TODO: Need to decide whether we fetch orders locally or from exchnage
+        # The code below is ignored
+
+        try:
+            response = self.api.returnopenorders(self.get_symbol(order.sid))
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        for o in response:
+            if (int(o['orderNumber']) == int(order_id)):
+                return order
+
+        return None
+
+    def cancel_order(self, order_param):
+        """Cancel an open order.
+
+        Parameters
+        ----------
+        order_param : str or Order
+            The order_id or order object to cancel.
+        """
+
+        if (isinstance(order_param, Order)):
+            order = order_param
+        else:
+            order = self._portfolio.open_orders[order_param]
+
+        try:
+            response = self.api.cancelorder(order.id)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'error' in response:
+            log.info(
+                'Unable to cancel order {order_id} on exchange {exchange} {error}.'.format(
+                    order_id=order.id,
+                    exchange=self.name,
+                    error=response['error']
+                ))
+
+            # raise OrderCancelError(
+            #    order_id=order.id,
+            #    exchange=self.name,
+            #    error=response['error']
+            # )
+
+        self.portfolio.remove_order(order)
+
+    def tickers(self, assets):
+        """
+        Fetch ticket data for assets
+        https://docs.bitfinex.com/v2/reference#rest-public-tickers
+
+        :param assets:
+        :return:
+        """
+        symbols = self.get_symbols(assets)
+
+        log.debug('fetching tickers {}'.format(symbols))
+
+        try:
+            response = self.api.returnticker()
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        if 'error' in response:
+            raise ExchangeRequestError(
+                error='Unable to retrieve tickers: {}'.format(
+                    response['error'])
+            )
+
+        ticks = dict()
+
+        for index, symbol in enumerate(symbols):
+            ticks[assets[index]] = dict(
+                timestamp=pd.Timestamp.utcnow(),
+                bid=float(response[symbol]['highestBid']),
+                ask=float(response[symbol]['lowestAsk']),
+                last_price=float(response[symbol]['last']),
+                low=float(response[symbol]['lowestAsk']),
+                # TODO: Polo does not provide low
+                high=float(response[symbol]['highestBid']),
+                # TODO: Polo does not provide high
+                volume=float(response[symbol]['baseVolume']),
+            )
+
+        log.debug('got tickers {}'.format(ticks))
+        return ticks
+
+    def generate_symbols_json(self, filename=None, source_dates=False):
+        symbol_map = {}
+
+        if not source_dates:
+            fn, r = download_exchange_symbols(self.name)
+            with open(fn) as data_file:
+                cached_symbols = json.load(data_file)
+
+        response = self.api.returnticker()
+
+        for exchange_symbol in response:
+            base, market = self.sanitize_curency_symbol(exchange_symbol).split(
+                '_')
+            symbol = '{market}_{base}'.format(market=market, base=base)
+
+            if (source_dates):
+                start_date = self.get_symbol_start_date(exchange_symbol)
+            else:
+                try:
+                    start_date = cached_symbols[exchange_symbol]['start_date']
+                except KeyError as e:
+                    start_date = time.strftime('%Y-%m-%d')
+
+            try:
+                end_daily = cached_symbols[exchange_symbol]['end_daily']
+            except KeyError as e:
+                end_daily = 'N/A'
+
+            try:
+                end_minute = cached_symbols[exchange_symbol]['end_minute']
+            except KeyError as e:
+                end_minute = 'N/A'
+
+            symbol_map[exchange_symbol] = dict(
+                symbol=symbol,
+                start_date=start_date,
+                end_daily=end_daily,
+                end_minute=end_minute,
+            )
+
+        if (filename is None):
+            filename = get_exchange_symbols_filename(self.name)
+
+        with open(filename, 'w') as f:
+            json.dump(symbol_map, f, sort_keys=True, indent=2,
+                      separators=(',', ':'))
+
+    def get_symbol_start_date(self, symbol):
+        try:
+            r = self.api.returnchartdata(symbol, 86400, pd.to_datetime(
+                '2010-1-1').value // 10 ** 9)
+        except Exception as e:
+            raise ExchangeRequestError(error=e)
+
+        return time.strftime('%Y-%m-%d', time.gmtime(int(r[0]['date'])))
+
+    def check_open_orders(self):
+        """
+        Need to override this function for Poloniex:
+
+        Loop through the list of open orders in the Portfolio object.
+        Check if any transactions have been executed:
+            If so, create a transaction and apply to the Portfolio.
+        Check if the order is still open:
+            If not, remove it from open orders
+
+        :return:
+        transactions: Transaction[]
+        """
+        transactions = list()
+        if self.portfolio.open_orders:
+            for order_id in list(self.portfolio.open_orders):
+
+                order = self._portfolio.open_orders[order_id]
+                log.debug('found open order: {}'.format(order_id))
+
+                try:
+                    order_open = self.get_order(order_id)
+                except Exception as e:
+                    raise ExchangeRequestError(error=e)
+
+                if (order_open):
+                    delta = pd.Timestamp.utcnow() - order.dt
+                    log.info(
+                        'order {order_id} still open after {delta}'.format(
+                            order_id=order_id,
+                            delta=delta)
+                    )
+
+                try:
+                    response = self.api.returnordertrades(order_id)
+                except Exception as e:
+                    raise ExchangeRequestError(error=e)
+
+                if ('error' in response):
+                    if (not order_open):
+                        raise OrphanOrderReverseError(order_id=order_id,
+                                                      exchange=self.name)
+                else:
+                    for tx in response:
+                        """
+                            We maintain a list of dictionaries of transactions that correspond to
+                            partially filled orders, indexed by order_id. Every time we query 
+                            executed transactions from the exchange, we check if we had that 
+                            transaction for that order already. If not, we process it.
+
+                            When an order if fully filled, we flush the dict of transactions  
+                            associated with that order.
+                        """
+                        if (not filter(
+                                lambda item: item['order_id'] == tx['tradeID'],
+                                self.transactions[order_id])):
+                            log.debug(
+                                'Got new transaction for order {}: amount {}, price {}'.format(
+                                    order_id, tx['amount'], tx['rate']))
+                            tx['amount'] = float(tx['amount'])
+                            if (tx['type'] == 'sell'):
+                                tx['amount'] = -tx['amount']
+                            transaction = Transaction(
+                                asset=order.asset,
+                                amount=tx['amount'],
+                                dt=pd.to_datetime(tx['date'], utc=True),
+                                price=float(tx['rate']),
+                                order_id=tx['tradeID'],
+                                # it's a misnomer, but keeping it for compatibility
+                                commission=float(tx['fee'])
+                            )
+                            self.transactions[order_id].append(transaction)
+                            self.portfolio.execute_transaction(transaction)
+                            transactions.append(transaction)
+
+                    if (not order_open):
+                        """
+                            Since transactions have been executed individually
+                            the only thing left to do is remove them from list of open_orders
+                        """
+                        del self.portfolio.open_orders[order_id]
+                        del self.transactions[order_id]
+
+        return transactions
+
+    def get_orderbook(self, asset, order_type='all'):
+        exchange_symbol = asset.exchange_symbol
+        data = self.api.returnOrderBook(market=exchange_symbol)
+
+        result = dict()
+        for order_type in data:
+            # TODO: filter by type
+            if order_type != 'asks' and order_type != 'bids':
+                continue
+
+            result[order_type] = []
+            for entry in data[order_type]:
+                if len(entry) == 2:
+                    result[order_type].append(
+                        dict(
+                            rate=float(entry[0]),
+                            quantity=float(entry[1])
+                        )
+                    )
+        return result

catalyst/exchange/poloniex/poloniex_api.py

@@ -0,0 +1,183 @@
+#!/usr/bin/env python
+import json
+import time
+import hmac
+import hashlib
+
+from six.moves import urllib
+
+# Workaround for backwards compatibility
+# https://stackoverflow.com/questions/3745771/urllib-request-in-python-2-7
+urlopen = urllib.request.urlopen
+
+
+class Poloniex_api(object):
+    def __init__(self, key, secret):
+        self.key = key
+        self.secret = secret
+
+        self.max_requests_per_second = 6
+        self.request_cpt = dict()
+
+        self.public  = ['returnTicker', 'return24Volume', 'returnOrderBook',
+                        'returnTradeHistory', 'returnChartData',
+                        'returnCurrencies', 'returnLoanOrders']
+        self.trading = ['returnBalances','returnCompleteBalances','returnDepositAddresses',
+                        'generateNewAddress','returnDepositsWithdrawals','returnOpenOrders',
+                        'returnTradeHistory','returnOrderTrades',
+                        'buy', 'sell', 'cancelOrder', 'moveOrder',
+                        'withdraw', 'returnFeeInfo','returnAvailableAccountBalances',
+                        'returnTradableBalances', 'transferBalance',
+                        'returnMarginAccountSummary','marginBuy','marginSell',
+                        'getMarginPosition', 'closeMarginPosition','createLoanOffer',
+                        'cancelLoanOffer','returnOpenLoanOffers','returnActiveLoans',
+                        'returnLendingHistory','toggleAutoRenew']
+
+    def ask_request(self):
+        """
+        Asks permission to issue a request to the exchange.
+        The primary purpose is to avoid hitting rate limits.
+
+        The application will pause if the maximum requests per minute
+        permitted by the exchange is exceeded.
+
+        :return boolean:
+
+        """
+        now = time.time()
+        if not self.request_cpt:
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+
+        cpt_date = self.request_cpt.keys()[0]
+        cpt = self.request_cpt[cpt_date]
+
+        if now > cpt_date + 1:
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+
+        if cpt >= self.max_requests_per_second:
+            
+            log.debug('max requests 6 reached, sleeping for 1 seconds')
+            sleep(1)
+
+            now = time.time()
+            self.request_cpt = dict()
+            self.request_cpt[now] = 0
+            return True
+        else:
+            self.request_cpt[cpt_date] += 1
+
+    def query(self, method, req={}):
+
+        if method in self.public:
+            url = 'https://poloniex.com/public?command=' + method + '&' + urllib.parse.urlencode(req)
+            headers = {}
+            post_data = None
+        elif method in self.trading:
+            url = 'https://poloniex.com/tradingApi'
+            req['command'] = method
+            req['nonce']   = int(time.time()*1000)
+            post_data      = urllib.parse.urlencode(req)
+            signature      = hmac.new(self.secret, post_data, hashlib.sha512).hexdigest()
+            headers        = { 'Sign': signature, 'Key': self.key}
+        else:
+            raise ValueError('Method "' + method + '" not found in neither the Public API or Trading API endpoints')
+
+        self.ask_request()
+        req = urllib.request.Request(url, data=post_data, headers=headers)
+        return json.loads(urlopen(req).read())
+
+    def returnticker(self):
+        return self.query('returnTicker', {})
+
+    def return24volume(self):
+        return self.query('return24Volume', {})
+
+    def returnOrderBook(self, market='all'):
+        return self.query('returnOrderBook', {'currencyPair': market})
+
+    def returntradehistory(self, market, start=None, end=None):
+        if(start is not None and end is not None):
+            return self.query('returntradehistory', 
+                              {'currencyPair': market, 'start': start, 'end': end })
+        else:
+            return self.query('returntradehistory', {'currencyPair': market })
+
+    def returnchartdata(self, market, period, start, end=9999999999):
+        return self.query('returnChartData', {'currencyPair': market, 'period': period,
+                          'start': start, 'end': end})
+
+    def returncurrencies(self):
+        return self.query('returnCurrencies', {})
+
+    def returnloadorders(self, market):
+        return self.query('returnLoanOrders', {'currency': market})
+
+    def returnbalances(self):
+        return self.query('returnBalances')
+
+    def returncompletebalances(self, account):
+        if(account):
+            return self.query('returnCompleteBalances', {'account': account})
+        else:
+            return self.query('returnCompleteBalances')
+
+    def returndepositaddresses(self):
+        return self.query('returnDepositAddresses')
+
+    def generatenewaddress(self, currency):
+        return self.query('generateNewAddress', {'currency': currency})
+
+    def returnDepositsWithdrawals(self, start, end):
+        return self.query('returnDepositsWithdrawals', {'start': start, 'end': end})
+
+    def returnopenorders(self, market):
+        return self.query('returnOpenOrders', {'currencyPair': market})
+
+    def returntradehistory(self, market):
+        #TODO: optional start and/or end and limit
+        return self.query('returnTradeHistory', {'currencyPair': market})
+
+    def returnordertrades(self, ordernumber):
+        return self.query('returnOrderTrades', {'orderNumber': ordernumber})
+
+    def buy(self, market, amount, rate, fillorkill=0, immediateorcancel=0, postonly=0):
+        if(fillorkill):
+            return self.query('buy', {'currencyPair': market, 'rate':rate, 'amount': amount,
+                                      'fillOrKill': fillorkill, })
+        elif(immediateorcancel):
+            return self.query('buy', {'currencyPair': market, 'rate':rate, 'amount': amount,
+                                      'immediateOrCancel': immediateorcancel, })
+        elif(postonly):
+            return self.query('buy', {'currencyPair': market, 'rate':rate, 'amount': amount,
+                                      'postOnly': postonly, })
+        else:
+            return self.query('buy', {'currencyPair': market, 'rate':rate, 'amount': amount, })
+
+    def sell(self, market, amount, rate, fillorkill=0, immediateorcancel=0, postonly=0):
+        if(fillorkill):
+            return self.query('sell', {'currencyPair': market, 'rate':rate, 'amount': amount,
+                                      'fillOrKill': fillorkill, })
+        elif(immediateorcancel):
+            return self.query('sell', {'currencyPair': market, 'rate':rate, 'amount': amount,
+                                      'immediateOrCancel': immediateorcancel, })
+        elif(postonly):
+            return self.query('sell', {'currencyPair': market, 'rate':rate, 'amount': amount,
+                                      'postOnly': postonly, })
+        else:
+            return self.query('sell', {'currencyPair': market, 'rate':rate, 'amount': amount, })
+
+    def cancelorder(self, ordernumber):
+        return self.query('cancelOrder', {'orderNumber': ordernumber})
+
+    def withdraw(self, currency, quantity, address):
+        return self.query('withdraw',
+                          {'currency': currency, 'amount': quantity,
+                           'address': address})
+
+    def returnfeeinfo(self):
+        return self.query('returnFeeInfo')
+

catalyst/exchange/simple_clock.py

@@ -25,7 +25,7 @@ from logbook import Logger
 log = Logger('ExchangeClock')
 
 
-class ExchangeClock(object):
+class SimpleClock(object):
     """Realtime clock for live trading.
 
     This class is a drop-in replacement for

catalyst/exchange/stats_utils.py

@@ -1,7 +1,7 @@
 import pandas as pd
 
 
-def get_pretty_stats(stats_df, num_rows=10):
+def get_pretty_stats(stats_df, recorded_cols=None, num_rows=10):
     """
     Format and print the last few rows of a statistics DataFrame.
     See the pyfolio project for the data structure.
@@ -22,6 +22,10 @@ def get_pretty_stats(stats_df, num_rows=10):
                'pnl', 'long_exposure', 'short_exposure', 'orders',
                'transactions', 'positions']
 
+    if recorded_cols is not None:
+        for column in recorded_cols:
+            columns.append(column)
+
     def format_positions(positions):
         parts = []
         for position in positions:

catalyst/finance/performance/tracker.py

@@ -111,27 +111,11 @@ class PerformanceTracker(object):
                     self.treasury_curves,
                     self.trading_calendar
                 )
-        elif self.emission_rate == '5-minute':
-            self.all_benchmark_returns = pd.Series(
-                index=pd.date_range(
-                    self.sim_params.first_open,
-                    self.sim_params.last_close,
-                    freq='5min'
-                ),
-            )
-            self.cumulative_risk_metrics = \
-                risk.RiskMetricsCumulative(
-                    self.sim_params,
-                    self.treasury_curves,
-                    self.trading_calendar,
-                    create_first_day_stats=True,
-                )
         elif self.emission_rate == 'minute':
             self.all_benchmark_returns = pd.Series(index=pd.date_range(
                 self.sim_params.first_open, self.sim_params.last_close,
                 freq='Min')
             )
-
             self.cumulative_risk_metrics = \
                 risk.RiskMetricsCumulative(
                     self.sim_params,

catalyst/finance/slippage.py

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

catalyst/finance/transaction.py

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

catalyst/gens/sim_engine.pyx

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

catalyst/gens/tradesimulation.py

@@ -34,7 +34,6 @@ class AlgorithmSimulator(object):
 
     EMISSION_TO_PERF_KEY_MAP = {
         'minute': 'minute_perf',
-        '5-minute': '5_minute_perf',
         'daily': 'daily_perf'
     }
 
@@ -202,7 +201,7 @@ class AlgorithmSimulator(object):
             stack.enter_context(self.processor)
             stack.enter_context(ZiplineAPI(self.algo))
 
-            if algo.data_frequency in set(('minute', '5-minute')):
+            if algo.data_frequency == 'minute':
                 def execute_order_cancellation_policy():
                     algo.blotter.execute_cancel_policy(SESSION_END)
 

catalyst/pipeline/loaders/crypto_pricing_loader.py

@@ -41,10 +41,6 @@ class CryptoPricingLoader(PipelineLoader):
             reader = bundle.daily_bar_reader
             all_sessions = cal.all_sessions
 
-        elif data_frequency == '5-minute':
-            reader = bundle.five_minute_bar_reader
-            all_sessions = cal.all_five_minutes
-
         elif data_frequency == 'minute':
             reader = bundle.minute_bar_reader
             all_sessions = cal.all_minutes

catalyst/pipeline/loaders/equity_pricing_loader.py

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

catalyst/sources/benchmark_source.py

@@ -65,19 +65,6 @@ class BenchmarkSource(object):
                 )
 
                 self._precalculated_series = minute_series
-            elif self.emission_rate == '5-minute':
-                five_minutes = \
-                    trading_calendar.five_minutes_for_sessions_in_range(
-                        sessions[0],
-                        sessions[-1],
-                    )
-
-                five_minute_series = daily_series.reindex(
-                    index=five_minutes,
-                    method='ffill',
-                )
-                
-                self._precalculated_series = five_minute_series
             else:
                 self._precalculated_series = daily_series
         else:
@@ -85,7 +72,13 @@ class BenchmarkSource(object):
                             "benchmark_returns.")
 
     def get_value(self, dt):
-        return self._precalculated_series.loc[dt]
+        try:
+            series = self._precalculated_series
+            value = series.loc[dt]
+            return value
+        except Exception:
+            # TODO: workaround, find permanent fix
+            return 0
 
     def get_range(self, start_dt, end_dt):
         return self._precalculated_series.loc[start_dt:end_dt]
@@ -168,21 +161,6 @@ class BenchmarkSource(object):
                 ffill=True
             )[asset]
 
-            return benchmark_series.pct_change()[1:]
-        elif self.emission_rate == '5-minute':
-            five_minutes = trading_calendar.five_minutes_for_sessions_in_range(
-                self.sessions[0], self.sessions[-1]
-            )
-            benchmark_series = data_portal.get_history_window(
-                [asset],
-                five_minutes[-1],
-                bar_count=len(five_minutes) + 1,
-                frequency='5m',
-                field='price',
-                data_frequency=self.emission_rate,
-                ffill=True,
-            )[asset]
-
             return benchmark_series.pct_change()[1:]
         else:
             start_date = asset.start_date

catalyst/utils/calendars/exchange_calendar_open.py

@@ -31,4 +31,4 @@ class OpenExchangeCalendar(TradingCalendar):
         return DateOffset(days=1)
 
     def __init__(self, *args, **kwargs):
-        super(OpenExchangeCalendar, self).__init__(start=Timestamp('2015-03-01', tz='UTC'), **kwargs)
+        super(OpenExchangeCalendar, self).__init__(start=Timestamp('2015-3-1', tz='UTC'), **kwargs)

catalyst/utils/calendars/trading_calendar.py

@@ -118,9 +118,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
         self._trading_minutes_nanos = self.all_minutes.values.\
             astype(np.int64)
 
-        self._trading_five_minutes_nanos = self.all_five_minutes.values.\
-            astype(np.int64)
-
         self.first_trading_session = _all_days[0]
         self.last_trading_session = _all_days[-1]
 
@@ -182,18 +179,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
         """
         return int(self._minutes_per_session[start_session:end_session].sum())
 
-    @lazyval
-    def _five_minutes_per_session(self):
-        diff = self.schedule.market_close - self.schedule.market_open
-        diff = diff.astype('timedelta64[m]')
-        return (diff + 1) // 5
-
-    def five_minutes_count_for_sessions_in_range(self,
-                                                 start_session,
-                                                 end_session):
-        five_mins = self._five_minutes_per_session[start_session:end_session]
-        return int(five_mins.sum())
-
     @property
     def regular_holidays(self):
         """
@@ -386,10 +371,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
         idx = next_divider_idx(self._trading_minutes_nanos, dt.value)
         return self.all_minutes[idx]
 
-    def next_five_minute(self, dt):
-        idx = next_divider_idx(self._trading_five_minutes_nanos, dt.values)
-        return self.all_five_mintutes[idx]
-
     def previous_minute(self, dt):
         """
         Given a dt, return the previous exchange minute.
@@ -484,12 +465,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
             end_minute=self.schedule.at[session_label, 'market_close'],
         )
 
-    def five_minutes_for_session(self, session_label):
-        return self.five_minutes_in_range(
-            start_five_minute=self.schedule.at[session_label, 'market_open'],
-            end_five_minute=self.schedule.at[session_label, 'market_close'],
-        )
-
     def minutes_window(self, start_dt, count):
         start_dt_nanos = start_dt.value
         all_minutes_nanos = self._trading_minutes_nanos
@@ -591,20 +566,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
 
         return abs(end_idx - start_idx)
 
-    def five_minutes_in_range(self, start_five_minute, end_five_minute):
-        start_idx = searchsorted(self._trading_five_minutes_nanos,
-                                 start_five_minute.value)
-
-        end_idx = searchsorted(self._trading_five_minutes_nanos,
-                               end_five_minute.value)
-
-        if end_five_minute.value == self._trading_five_minutes_nanos[end_idx]:
-            # if the end minute is a market minute, increase by 1
-            end_idx += 1
-
-        return self.all_five_minutes[start_idx:end_idx]
-        
-
     def minutes_in_range(self, start_minute, end_minute):
         """
         Given start and end minutes, return all the calendar minutes
@@ -662,15 +623,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
 
         return self.minutes_in_range(first_minute, last_minute)
 
-    def five_minutes_for_sessions_in_range(self,
-                                           start_session_label,
-                                           end_session_label):
-
-        first_minute, _ = self.open_and_close_for_session(start_session_label)
-        _, last_minute = self.open_and_close_for_session(end_session_label)
-
-        return self.five_minutes_in_range(first_minute, last_minute)
-
     def open_and_close_for_session(self, session_label):
         """
         Returns a tuple of timestamps of the open and close of the session
@@ -777,13 +729,6 @@ class TradingCalendar(with_metaclass(ABCMeta)):
 
         return DatetimeIndex(all_minutes).tz_localize("UTC")
 
-    @lazyval
-    def all_five_minutes(self):
-        """
-        Returns a DatetimeIndex representing all the five minutes in this calendar.
-        """
-        return self._all_minutes_with_interval(5) 
-
     @lazyval
     def all_minutes(self):
         """

catalyst/utils/events.py

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

catalyst/utils/math_utils.py

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

catalyst/utils/run_algo.py

@@ -1,16 +1,16 @@
 import os
-import re
-from runpy import run_path
 import sys
 import warnings
-from time import sleep
 from datetime import timedelta
-
+from runpy import run_path
-import pandas as pd
+from time import sleep
 
 import click
+import pandas as pd
 
 from catalyst.exchange.bittrex.bittrex import Bittrex
+from catalyst.exchange.bitfinex.bitfinex import Bitfinex
+from catalyst.exchange.poloniex.poloniex import Poloniex
 
 try:
     from pygments import highlight
@@ -23,29 +23,22 @@ except:
 from toolz import valfilter, concatv
 from functools import partial
 
-from catalyst.algorithm import TradingAlgorithm
-from catalyst.data.bundles.core import load
-from catalyst.data.data_portal import DataPortal
-from catalyst.data.loader import load_crypto_market_data
 from catalyst.finance.trading import TradingEnvironment
-from catalyst.pipeline.data import USEquityPricing, CryptoPricing
-from catalyst.pipeline.loaders import (
-    USEquityPricingLoader,
-    CryptoPricingLoader,
-)
 from catalyst.utils.calendars import get_calendar
 from catalyst.utils.factory import create_simulation_parameters
+from catalyst.data.loader import load_crypto_market_data
 import catalyst.utils.paths as pth
 
-from catalyst.exchange.algorithm_exchange import ExchangeTradingAlgorithm
+from catalyst.exchange.exchange_algorithm import ExchangeTradingAlgorithmLive, \
-from catalyst.exchange.data_portal_exchange import DataPortalExchange
+    ExchangeTradingAlgorithmBacktest
-from catalyst.exchange.bitfinex.bitfinex import Bitfinex
+from catalyst.exchange.data_portal_exchange import DataPortalExchangeLive, \
+    DataPortalExchangeBacktest
 from catalyst.exchange.asset_finder_exchange import AssetFinderExchange
 from catalyst.exchange.exchange_portfolio import ExchangePortfolio
 from catalyst.exchange.exchange_errors import (
     ExchangeRequestError,
     ExchangeRequestErrorTooManyAttempts,
-    BaseCurrencyNotFoundError)
+    BaseCurrencyNotFoundError, ExchangeNotFoundError)
 from catalyst.exchange.exchange_utils import get_exchange_auth, \
     get_algo_object
 from logbook import Logger
@@ -95,7 +88,8 @@ def _run(handle_data,
          live,
          exchange,
          algo_namespace,
-         base_currency):
+         base_currency,
+         live_graph):
     """Run a backtest for the given algorithm.
 
     This is shared between the cli and :func:`catalyst.run_algo`.
@@ -147,72 +141,90 @@ def _run(handle_data,
     mode = 'live' if live else 'backtest'
     log.info('running algo in {mode} mode'.format(mode=mode))
 
-    if live and exchange is not None:
     exchange_name = exchange
-        start = pd.Timestamp.utcnow()
+    if exchange_name is None:
-        end = start + timedelta(minutes=1439)
+        raise ValueError('Please specify at least one exchange.')
 
+    exchange_list = [x.strip().lower() for x in exchange.split(',')]
+
+    exchanges = dict()
+    for exchange_name in exchange_list:
+
+        # Looking for the portfolio from the cache first
         portfolio = get_algo_object(
             algo_name=algo_namespace,
             key='portfolio_{}'.format(exchange_name),
             environ=environ
         )
+
         if portfolio is None:
             portfolio = ExchangePortfolio(
                 start_date=pd.Timestamp.utcnow()
             )
 
+        # This corresponds to the json file containing api token info
         exchange_auth = get_exchange_auth(exchange_name)
         if exchange_name == 'bitfinex':
-            exchange = Bitfinex(
+            exchanges[exchange_name] = Bitfinex(
                 key=exchange_auth['key'],
                 secret=exchange_auth['secret'],
                 base_currency=base_currency,
                 portfolio=portfolio
             )
         elif exchange_name == 'bittrex':
-            exchange = Bittrex(
+            exchanges[exchange_name] = Bittrex(
+                key=exchange_auth['key'],
+                secret=exchange_auth['secret'],
+                base_currency=base_currency,
+                portfolio=portfolio
+            )
+        elif exchange_name == 'poloniex':
+            exchanges[exchange_name] = Poloniex(
                 key=exchange_auth['key'],
                 secret=exchange_auth['secret'],
                 base_currency=base_currency,
                 portfolio=portfolio
             )
         else:
-            raise NotImplementedError(
+            raise ExchangeNotFoundError(exchange_name=exchange_name)
-                'exchange not supported: %s' % exchange_name)
 
     open_calendar = get_calendar('OPEN')
-    sim_params = create_simulation_parameters(
-        start=start,
-        end=end,
-        capital_base=capital_base,
-        data_frequency=data_frequency,
-        emission_rate=data_frequency,
-    )
 
-    if live and exchange is not None:
     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
+        asset_db_path=None  # We don't need an asset db, we have exchanges
     )
-        env.asset_finder = AssetFinderExchange(exchange)
+    env.asset_finder = AssetFinderExchange()
+    choose_loader = None  # TODO: use the DataPortal for in the algorithm class for this
 
-        data = DataPortalExchange(
+    if live:
-            exchange=exchange,
+        start = pd.Timestamp.utcnow()
+
+        # TODO: fix the end data.
+        end = start + timedelta(hours=8760)
+
+        data = DataPortalExchangeLive(
+            exchanges=exchanges,
             asset_finder=env.asset_finder,
             trading_calendar=open_calendar,
             first_trading_day=pd.to_datetime('today', utc=True)
         )
-        choose_loader = None
 
-        def fetch_capital_base(attempt_index=0):
+        def fetch_capital_base(exchange, attempt_index=0):
             """
             Fetch the base currency amount required to bootstrap
             the algorithm against the exchange.
 
             The algorithm cannot continue without this value.
 
+            :param exchange: the targeted exchange
             :param attempt_index:
             :return capital_base: the amount of base currency available for
             trading
@@ -223,8 +235,11 @@ def _run(handle_data,
                 balances = exchange.get_balances()
             except ExchangeRequestError as e:
                 if attempt_index < 20:
+                    log.warn('exchange error when retrieving balances, {} '
+                             'trying again in 5 seconds'.format(e))
                     sleep(5)
-                    return fetch_capital_base(attempt_index + 1)
+                    return fetch_capital_base(exchange, attempt_index + 1)
+
                 else:
                     raise ExchangeRequestErrorTooManyAttempts(
                         attempts=attempt_index,
@@ -239,109 +254,59 @@ def _run(handle_data,
                     exchange=exchange_name
                 )
 
+        capital_base = 0
+        for exchange_name in exchanges:
+            exchange = exchanges[exchange_name]
+            capital_base += fetch_capital_base(exchange)
+
         sim_params = create_simulation_parameters(
             start=start,
             end=end,
-            capital_base=fetch_capital_base(),
+            capital_base=capital_base,
             emission_rate='minute',
             data_frequency='minute'
         )
 
-    elif bundle is not None:
+        # TODO: use the constructor instead
-        bundles = bundle.split(',')
+        sim_params._arena = 'live'
 
-        def get_trading_env_and_data(bundles):
+        algorithm_class = partial(
-            env = data = None
+            ExchangeTradingAlgorithmLive,
-
+            exchanges=exchanges,
-            b = 'poloniex'
+            algo_namespace=algo_namespace,
-            if len(bundles) == 0:
+            live_graph=live_graph
-                return env, data
-            elif len(bundles) == 1:
-                b = bundles[0]
-
-            bundle_data = load(
-                b,
-                environ,
-                bundle_timestamp,
         )
-
-            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(
-                load=partial(load_crypto_market_data, bundle=b, bundle_data=bundle_data, environ=environ),
-                bm_symbol='USDT_BTC',
-                trading_calendar=open_calendar,
-                asset_db_path=connstr,
-                environ=environ,
-            )
-
-            first_trading_day = bundle_data.minute_bar_reader.first_trading_day
-
-            data = DataPortal(
-                env.asset_finder,
-                open_calendar,
-                first_trading_day=first_trading_day,
-                minute_reader=bundle_data.minute_bar_reader,
-                five_minute_reader=bundle_data.five_minute_bar_reader,
-                daily_reader=bundle_data.daily_bar_reader,
-                adjustment_reader=bundle_data.adjustment_reader,
-            )
-
-            return env, data
-
-        def get_loader_for_bundle(b):
-            bundle_data = load(
-                b,
-                environ,
-                bundle_timestamp,
-            )
-
-            if b == 'poloniex':
-                return CryptoPricingLoader(
-                    bundle_data,
-                    data_frequency,
-                    CryptoPricing,
-                )
-            elif b == 'quandl':
-                return USEquityPricingLoader(
-                    bundle_data,
-                    data_frequency,
-                    USEquityPricing,
-                )
-            raise ValueError(
-                "No PipelineLoader registered for bundle %s." % b
-            )
-
-        loaders = [get_loader_for_bundle(b) for b in bundles]
-        env, data = get_trading_env_and_data(bundles)
-
-        def choose_loader(column):
-            for loader in loaders:
-                if column in loader.columns:
-                    return loader
-            raise ValueError(
-                "No PipelineLoader registered for column %s." % column
-            )
-
     else:
-        env = TradingEnvironment(environ=environ)
+        # Removed the existing Poloniex fork to keep things simple
-        choose_loader = None
+        # We can add back the complexity if required.
 
-    TradingAlgorithmClass = (
+        # I don't think that we should have arbitrary price data bundles
-        partial(ExchangeTradingAlgorithm, exchange=exchange,
+        # Instead, we should center this data around exchanges.
-                algo_namespace=algo_namespace)
+        # We still need to support bundles for other misc data, but we
-        if live and exchange else TradingAlgorithm)
+        # can handle this later.
 
-    perf = TradingAlgorithmClass(
+        data = DataPortalExchangeBacktest(
+            exchanges=exchanges,
+            asset_finder=None,
+            trading_calendar=open_calendar,
+            first_trading_day=start,
+            last_available_session=end
+        )
+
+        sim_params = create_simulation_parameters(
+            start=start,
+            end=end,
+            capital_base=capital_base,
+            data_frequency=data_frequency,
+            emission_rate=data_frequency,
+        )
+
+        algorithm_class = partial(
+            ExchangeTradingAlgorithmBacktest,
+            exchanges=exchanges
+        )
+
+    perf = algorithm_class(
         namespace=namespace,
         env=env,
         get_pipeline_loader=choose_loader,
@@ -439,7 +404,8 @@ def run_algorithm(initialize,
                   live=False,
                   exchange_name=None,
                   base_currency=None,
-                  algo_namespace=None):
+                  algo_namespace=None,
+                  live_graph=False):
     """Run a trading algorithm.
 
     Parameters
@@ -511,6 +477,11 @@ def run_algorithm(initialize,
     """
     load_extensions(default_extension, extensions, strict_extensions, environ)
 
+    # I'm not sure that we need this since the modified DataPortal
+    # does not require extensions to be explicitly loaded.
+
+    # This will be useful for arbitrary non-pricing bundles but we may
+    # need to modify the logic.
     if not live:
         non_none_data = valfilter(bool, {
             'data': data is not None,
@@ -552,5 +523,6 @@ def run_algorithm(initialize,
         live=live,
         exchange=exchange_name,
         algo_namespace=algo_namespace,
-        base_currency=base_currency
+        base_currency=base_currency,
+        live_graph=live_graph
     )

docs/CNAME

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

docs/source/beginner-tutorial.rst

@@ -1,132 +1,178 @@
-Zipline Beginner Tutorial
+Catalyst Beginner Tutorial
--------------------------
+--------------------------
 
 Basics
 ~~~~~~
 
-Zipline is an open-source algorithmic trading simulator written in
+Catalyst is an open-source algorithmic trading simulator for crypto
-Python.
+assets written in Python.
 
-The source can be found at: https://github.com/quantopian/zipline
+The source can be found at: https://github.com/enigmampc/catalyst
 
 Some benefits include:
 
+-  Support for several of the top crypto-exchanges by trading volume.
 -  Realistic: slippage, transaction costs, order delays.
 -  Stream-based: Process each event individually, avoids look-ahead
    bias.
 -  Batteries included: Common transforms (moving average) as well as
    common risk calculations (Sharpe).
 -  Developed and continuously updated by
-   `Quantopian <https://www.quantopian.com>`__ which provides an
+   `Enigma MPC <https://www.enigma.co>`__ which is building the Enigma 
-   easy-to-use web-interface to Zipline, 10 years of minute-resolution
+   data marketplace protocol as well as Catalyst, the first application
-   historical US stock data, and live-trading capabilities. This
+   that will run on our protocol. Powered by our financial data 
-   tutorial is directed at users wishing to use Zipline without using
+   marketplace, Catalyst empowers users to share and curate data and 
-   Quantopian. If you instead want to get started on Quantopian, see
+   build profitable, data-driven investment strategies.
-   `here <https://www.quantopian.com/faq#get-started>`__.
 
-This tutorial assumes that you have zipline correctly installed, see the
+This tutorial assumes that you have Catalyst correctly installed, see the
-`installation
+:doc:`installation instructions <install>` if you haven't set up 
-instructions <https://github.com/quantopian/zipline#installation>`__ if
+Catalyst yet.
-you haven't set up zipline yet.
 
-Every ``zipline`` algorithm consists of two functions you have to
+Every ``catalyst`` algorithm consists of at least two functions you have to
 define:
 
 * ``initialize(context)``
 * ``handle_data(context, data)``
 
-Before the start of the algorithm, ``zipline`` calls the
+Before the start of the algorithm, ``catalyst`` calls the
 ``initialize()`` function and passes in a ``context`` variable.
 ``context`` is a persistent namespace for you to store variables you
 need to access from one algorithm iteration to the next.
 
-After the algorithm has been initialized, ``zipline`` calls the
+After the algorithm has been initialized, ``catalyst`` calls the
 ``handle_data()`` function once for each event. At every call, it passes
 the same ``context`` variable and an event-frame called ``data``
 containing the current trading bar with open, high, low, and close
-(OHLC) prices as well as volume for each stock in your universe. For
+(OHLC) prices as well as volume for each crypto asset in your universe. 
-more information on these functions, see the `relevant part of the
+
-Quantopian docs <https://www.quantopian.com/help#api-toplevel>`__.
+.. For more information on these functions, see the `relevant part of the
+.. Quantopian docs <https://www.quantopian.com/help#api-toplevel>`.
 
 My first algorithm
 ~~~~~~~~~~~~~~~~~~
 
 Lets take a look at a very simple algorithm from the ``examples``
-directory, ``buyapple.py``:
+directory, ``buy_btc.py``:
 
 .. code-block:: python
 
-   from zipline.examples import buyapple
+   from catalyst.api import order, record, symbol
-   buyapple??
-
-
-.. code-block:: python
-
-   from zipline.api import order, record, symbol
 
 
    def initialize(context):
-       pass
+       context.asset = symbol('btc_usd')
 
 
    def handle_data(context, data):
-       order(symbol('AAPL'), 10)
+       order(context.asset, 1)
-       record(AAPL=data.current(symbol('AAPL'), 'price'))
+       record(btc = data.current(context.asset, 'price'))
 
 
 As you can see, we first have to import some functions we would like to
 use. All functions commonly used in your algorithm can be found in
-``zipline.api``. Here we are using :func:`~zipline.api.order()` which takes two
+``catalyst.api``. Here we are using :func:`~catalyst.api.order()` which takes two
-arguments: a security object, and a number specifying how many stocks you would
+arguments: a cryptoasset object, and a number specifying how many assets you would
-like to order (if negative, :func:`~zipline.api.order()` will sell/short
+like to order (if negative, :func:`~catalyst.api.order()` will sell/short
-stocks). In this case we want to order 10 shares of Apple at each iteration. For
+assets). In this case we want to order 1 bitcoin at each iteration. 
-more documentation on ``order()``, see the `Quantopian docs
-<https://www.quantopian.com/help#api-order>`__.
 
-Finally, the :func:`~zipline.api.record` function allows you to save the value
+.. For more documentation on ``order()``, see the `Quantopian docs
+.. <https://www.quantopian.com/help#api-order>`__.
+
+Finally, the :func:`~catalyst.api.record` function allows you to save the value
 of a variable at each iteration. You provide it with a name for the variable
 together with the variable itself: ``varname=var``. After the algorithm
 finished running you will have access to each variable value you tracked
-with :func:`~zipline.api.record` under the name you provided (we will see this
+with :func:`~catalyst.api.record` under the name you provided (we will see this
-further below). You also see how we can access the current price data of the
+further below). You also see how we can access the current price data of
-AAPL stock in the ``data`` event frame (for more information see
+a bitcoin in the ``data`` event frame.
-`here <https://www.quantopian.com/help#api-event-properties>`__.
+
+.. (for more information see `here <https://www.quantopian.com/help#api-event-properties>`__.
 
 Running the algorithm
 ~~~~~~~~~~~~~~~~~~~~~
 
-To now test this algorithm on financial data, ``zipline`` provides three
+To can now test this algorithm on crypto data, ``catalyst`` provides three
-interfaces: A command-line interface, ``IPython Notebook`` magic, and
+interfaces: 
-:func:`~zipline.run_algorithm`.
 
-Ingesting Data
+-  A command-line interface,
+-  ``IPython Notebook`` magic, 
+-  and :func:`~catalyst.run_algorithm`.
+
+Ingesting data
 ^^^^^^^^^^^^^^
-If you haven't ingested the data, run:
 
-.. code-block:: bash
+In previous versions of Catalyst you needed to manually ingest data before running
+your algorithm to make it available at runtime. Starting with version 0.3, the
+algorithm will automagically ingest the data it needs the first time that encounters 
+a data request for data that it doesn't have.
 
-   $ zipline ingest [-b <bundle>]
+Still, we believe it is important for you to have a high-level understanding
+of how data is managed:
 
-where ``<bundle>`` is the name of the bundle to ingest, defaulting to
+-  Pricing data is split and packaged into ``bundles``: chunks of data organized 
-:ref:`quantopian-quandl <quantopian-quandl-mirror>`.
+   as time series that are kept up to date daily on Enigma's servers. Catalyst 
+   downloads the bundles that needs at any given time, and reconstructs the whole
+   dataset in your hard drive.
 
-you can check out the :ref:`ingesting data <ingesting-data>` section for
+-  Pricing data is provided in ``daily`` and ``minute`` resolution. Those are different
-more detail.
+   bundle datasets, and are managed separately.
+
+-  Bundles are exchange-specific, as the pricing data is specific to the trades that
+   happen in each exchange. You can optionally specify which exchange you want pricing
+   data from.
+
+-  Catalyst keeps track of all the downloaded bundles, so that it only has to download
+   them once, and will do incremental updates as needed.
+
+-  When running in ``live trading`` mode, Catalyst will first look for historical 
+   pricing data in the locally stored bundles. If there is anything missing, Catalyst will
+   hit the exchange for the most recent data, and merge it with the local bundle to make
+   it available for future iterations.
+
+If you want to learn more, check out the :ref:`ingesting data <ingesting-data>` section
+for more detail.
 
 Command line interface
 ^^^^^^^^^^^^^^^^^^^^^^
 
-After you installed zipline you should be able to execute the following
+After you installed Catalyst you should be able to execute the following
 from your command line (e.g. ``cmd.exe`` on Windows, or the Terminal app
-on OSX):
+on OSX). Displaying here a simplified output for eductional purposes:
 
 .. code-block:: bash
 
-   $ zipline run --help
+   $ catalyst --help
 
 .. parsed-literal::
 
-   Usage: zipline run [OPTIONS]
+     Usage: catalyst [OPTIONS] COMMAND [ARGS]...
+
+       Top level catalyst entry point.
+
+     Options:
+       --version               Show the version and exit.
+       --help                  Show this message and exit.
+
+     Commands:
+       ingest-exchange  Ingest data for the given exchange.
+       live             Trade live with the given algorithm.
+       run              Run a backtest for the given algorithm.
+
+There are three main modes you can run on Catalyst. The first being ``ingest-exchange`` 
+for data ingestion, which we have summarized in the previous section. The second 
+is ``live`` to use your algorithm to trade live against a given exchange, and the 
+third mode ``run`` is to backtest your algorithm before trading live with it.
+
+Let's start with backtesting, so run this other command to learn more about 
+the available options:
+
+.. code-block:: bash
+
+   $ catalyst run --help
+
+.. parsed-literal::
+
+      Usage: catalyst run [OPTIONS]
 
         Run a backtest for the given algorithm.
 
@@ -138,13 +184,13 @@ on OSX):
                                         '-Dname=value'. The value may be any python
                                         expression. These are evaluated in order so
                                         they may refer to previously defined names.
-     --data-frequency [minute|daily]
+        --data-frequency [daily|minute]
                                         The data frequency of the simulation.
                                         [default: daily]
         --capital-base FLOAT            The starting capital for the simulation.
                                         [default: 10000000.0]
         -b, --bundle BUNDLE-NAME        The data bundle to use for the simulation.
-                                     [default: quantopian-quandl]
+                                        [default: poloniex]
         --bundle-timestamp TIMESTAMP    The date to lookup data on or before.
                                         [default: <current-time>]
         -s, --start DATE                The start date of the simulation.
@@ -153,456 +199,83 @@ on OSX):
                                         is '-' the perf will be written to stdout.
                                         [default: -]
         --print-algo / --no-print-algo  Print the algorithm to stdout.
+        -x, --exchange-name [poloniex|bitfinex|bittrex]
+                                        The name of the targeted exchange
+                                        (supported: bitfinex, bittrex, poloniex).
+        -n, --algo-namespace TEXT       A label assigned to the algorithm for data
+                                        storage purposes.
+        -c, --base-currency TEXT        The base currency used to calculate
+                                        statistics (e.g. usd, btc, eth).
         --help                          Show this message and exit.
 
+
 As you can see there are a couple of flags that specify where to find your
-algorithm (``-f``) as well as parameters specifying which data to use,
+algorithm (``-f``) as well as a parameter to specify which exchange to use. 
-defaulting to the :ref:`quantopian-quandl-mirror`. There are also arguments for
+There are also arguments for the date range to run the algorithm over 
-the date range to run the algorithm over (``--start`` and ``--end``). Finally,
+(``--start`` and ``--end``). Finally, you'll want to save the performance 
-you'll want to save the performance metrics of your algorithm so that you can
+metrics of your algorithm so that you can analyze how it performed. This is 
-analyze how it performed. This is done via the ``--output`` flag and will cause
+done via the ``--output`` flag and will cause it to write the performance 
-it to write the performance ``DataFrame`` in the pickle Python file format.
+``DataFrame`` in the pickle Python file format. Note that you can also define 
-Note that you can also define a configuration file with these parameters that
+a configuration file with these parameters that you can then conveniently pass 
-you can then conveniently pass to the ``-c`` option so that you don't have to
+to the ``-c`` option so that you don't have to supply the command line args 
-supply the command line args all the time (see the .conf files in the examples
+all the time (see the .conf files in the examples directory).
-directory).
 
 Thus, to execute our algorithm from above and save the results to
-``buyapple_out.pickle`` we would call ``zipline run`` as follows:
+``buy_btc_simple_out.pickle`` we would call ``catalyst run`` as follows:
 
 .. code-block:: python
 
-    zipline run -f ../../zipline/examples/buyapple.py --start 2000-1-1 --end 2014-1-1 -o buyapple_out.pickle
+    catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2016-9-29 -o buy_simple_btc_out.pickle
 
 
-.. parsed-literal::
+.. 
+.. parsed-literal
 
-    AAPL
+..    AAPL
-    [2015-11-04 22:45:32.820166] INFO: Performance: Simulated 3521 trading days out of 3521.
+..    [2015-11-04 22:45:32.820166] INFO: Performance: Simulated 3521 trading days out of 3521.
-    [2015-11-04 22:45:32.820314] INFO: Performance: first open: 2000-01-03 14:31:00+00:00
+..    [2015-11-04 22:45:32.820314] INFO: Performance: first open: 2000-01-03 14:31:00+00:00
-    [2015-11-04 22:45:32.820401] INFO: Performance: last close: 2013-12-31 21:00:00+00:00
+..    [2015-11-04 22:45:32.820401] INFO: Performance: last close: 2013-12-31 21:00:00+00:00
 
 
 ``run`` first calls the ``initialize()`` function, and then
-streams the historical stock price day-by-day through ``handle_data()``.
+streams the historical asset price day-by-day through ``handle_data()``.
-After each call to ``handle_data()`` we instruct ``zipline`` to order 10
+After each call to ``handle_data()`` we instruct ``catalyst`` to order 1
-stocks of AAPL. After the call of the ``order()`` function, ``zipline``
+bitcoin. After the call of the ``order()`` function, ``catalyst``
 enters the ordered stock and amount in the order book. After the
-``handle_data()`` function has finished, ``zipline`` looks for any open
+``handle_data()`` function has finished, ``catalyst`` looks for any open
 orders and tries to fill them. If the trading volume is high enough for
-this stock, the order is executed after adding the commission and
+this asset, the order is executed after adding the commission and
 applying the slippage model which models the influence of your order on
 the stock price, so your algorithm will be charged more than just the
-stock price \* 10. (Note, that you can also change the commission and
+asset price. (Note, that you can also change the commission and
-slippage model that ``zipline`` uses, see the `Quantopian
+slippage model that ``catalyst`` uses).
-docs <https://www.quantopian.com/help#ide-slippage>`__ for more
-information).
 
-Lets take a quick look at the performance ``DataFrame``. For this, we
+.. see the `Quantopian docs <https://www.quantopian.com/help#ide-slippage>`__
+.. for more information).
+
+Let's take a quick look at the performance ``DataFrame``. For this, we
 use ``pandas`` from inside the IPython Notebook and print the first ten
-rows. Note that ``zipline`` makes heavy usage of ``pandas``, especially
+rows. Note that ``catalyst`` makes heavy usage of 
-for data input and outputting so it's worth spending some time to learn
+`pandas <http://pandas.pydata.org/>`_, especially for data input and 
-it.
+outputting so it's worth spending some time to learn it.
 
 .. code-block:: python
 
     import pandas as pd
-    perf = pd.read_pickle('buyapple_out.pickle') # read in perf DataFrame
+    perf = pd.read_pickle('buy_btc_simple_out.pickle') # read in perf DataFrame
     perf.head()
 
-.. raw:: html
+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
-    <div style="max-height:1000px;max-width:1500px;overflow:auto;">
-    <table border="1" class="dataframe">
-      <thead>
-        <tr style="text-align: right;">
-          <th></th>
-          <th>AAPL</th>
-          <th>algo_volatility</th>
-          <th>algorithm_period_return</th>
-          <th>alpha</th>
-          <th>benchmark_period_return</th>
-          <th>benchmark_volatility</th>
-          <th>beta</th>
-          <th>capital_used</th>
-          <th>ending_cash</th>
-          <th>ending_exposure</th>
-          <th>...</th>
-          <th>short_exposure</th>
-          <th>short_value</th>
-          <th>shorts_count</th>
-          <th>sortino</th>
-          <th>starting_cash</th>
-          <th>starting_exposure</th>
-          <th>starting_value</th>
-          <th>trading_days</th>
-          <th>transactions</th>
-          <th>treasury_period_return</th>
-        </tr>
-      </thead>
-      <tbody>
-        <tr>
-          <th>2000-01-03 21:00:00</th>
-          <td>3.738314</td>
-          <td>0.000000e+00</td>
-          <td>0.000000e+00</td>
-          <td>-0.065800</td>
-          <td>-0.009549</td>
-          <td>0.000000</td>
-          <td>0.000000</td>
-          <td>0.00000</td>
-          <td>10000000.00000</td>
-          <td>0.00000</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0.000000</td>
-          <td>10000000.00000</td>
-          <td>0.00000</td>
-          <td>0.00000</td>
-          <td>1</td>
-          <td>[]</td>
-          <td>0.0658</td>
-        </tr>
-        <tr>
-          <th>2000-01-04 21:00:00</th>
-          <td>3.423135</td>
-          <td>3.367492e-07</td>
-          <td>-3.000000e-08</td>
-          <td>-0.064897</td>
-          <td>-0.047528</td>
-          <td>0.323229</td>
-          <td>0.000001</td>
-          <td>-34.53135</td>
-          <td>9999965.46865</td>
-          <td>34.23135</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0.000000</td>
-          <td>10000000.00000</td>
-          <td>0.00000</td>
-          <td>0.00000</td>
-          <td>2</td>
-          <td>[{u'order_id': u'513357725cb64a539e3dd02b47da7...</td>
-          <td>0.0649</td>
-        </tr>
-        <tr>
-          <th>2000-01-05 21:00:00</th>
-          <td>3.473229</td>
-          <td>4.001918e-07</td>
-          <td>-9.906000e-09</td>
-          <td>-0.066196</td>
-          <td>-0.045697</td>
-          <td>0.329321</td>
-          <td>0.000001</td>
-          <td>-35.03229</td>
-          <td>9999930.43636</td>
-          <td>69.46458</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0.000000</td>
-          <td>9999965.46865</td>
-          <td>34.23135</td>
-          <td>34.23135</td>
-          <td>3</td>
-          <td>[{u'order_id': u'd7d4ad03cfec4d578c0d817dc3829...</td>
-          <td>0.0662</td>
-        </tr>
-        <tr>
-          <th>2000-01-06 21:00:00</th>
-          <td>3.172661</td>
-          <td>4.993979e-06</td>
-          <td>-6.410420e-07</td>
-          <td>-0.065758</td>
-          <td>-0.044785</td>
-          <td>0.298325</td>
-          <td>-0.000006</td>
-          <td>-32.02661</td>
-          <td>9999898.40975</td>
-          <td>95.17983</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>-12731.780516</td>
-          <td>9999930.43636</td>
-          <td>69.46458</td>
-          <td>69.46458</td>
-          <td>4</td>
-          <td>[{u'order_id': u'1fbf5e9bfd7c4d9cb2e8383e1085e...</td>
-          <td>0.0657</td>
-        </tr>
-        <tr>
-          <th>2000-01-07 21:00:00</th>
-          <td>3.322945</td>
-          <td>5.977002e-06</td>
-          <td>-2.201900e-07</td>
-          <td>-0.065206</td>
-          <td>-0.018908</td>
-          <td>0.375301</td>
-          <td>0.000005</td>
-          <td>-33.52945</td>
-          <td>9999864.88030</td>
-          <td>132.91780</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>-12629.274583</td>
-          <td>9999898.40975</td>
-          <td>95.17983</td>
-          <td>95.17983</td>
-          <td>5</td>
-          <td>[{u'order_id': u'9ea6b142ff09466b9113331a37437...</td>
-          <td>0.0652</td>
-        </tr>
-      </tbody>
-    </table>
-    <p>5 rows × 39 columns</p>
-    </div>
-
-
-
-As you can see, there is a row for each trading day, starting on the
-first business day of 2000. In the columns you can find various
 information about the state of your algorithm. The very first column
-``AAPL`` was placed there by the ``record()`` function mentioned earlier
+``btc`` was placed there by the ``record()`` function mentioned earlier
-and allows us to plot the price of apple. For example, we could easily
+and allows us to plot the price of bitcoin. For example, we could easily
 examine now how our portfolio value changed over time compared to the
-AAPL stock price.
+bitcoin price.
 
-.. code-block:: python
+Our algorithm performance as assessed by the
-
+``portfolio_value`` closely matches that of the bitcoin price. This
-    %pylab inline
+is not surprising as our algorithm only bought bitcoin every chance it got.
-    figsize(12, 12)
-    import matplotlib.pyplot as plt
-
-    ax1 = plt.subplot(211)
-    perf.portfolio_value.plot(ax=ax1)
-    ax1.set_ylabel('portfolio value')
-    ax2 = plt.subplot(212, sharex=ax1)
-    perf.AAPL.plot(ax=ax2)
-    ax2.set_ylabel('AAPL stock price')
-
-.. parsed-literal::
-
-    Populating the interactive namespace from numpy and matplotlib
-
-.. parsed-literal::
-
-    <matplotlib.text.Text at 0x7ff5c6147f90>
-
-.. image:: tutorial_files/tutorial_11_2.png
-
-
-As you can see, our algorithm performance as assessed by the
-``portfolio_value`` closely matches that of the AAPL stock price. This
-is not surprising as our algorithm only bought AAPL every chance it got.
-
-IPython Notebook
-~~~~~~~~~~~~~~~~
-
-The `IPython Notebook <http://ipython.org/notebook.html>`__ is a very
-powerful browser-based interface to a Python interpreter (this tutorial
-was written in it). As it is already the de-facto interface for most
-quantitative researchers ``zipline`` provides an easy way to run your
-algorithm inside the Notebook without requiring you to use the CLI.
-
-To use it you have to write your algorithm in a cell and let ``zipline``
-know that it is supposed to run this algorithm. This is done via the
-``%%zipline`` IPython magic command that is available after you
-``import zipline`` from within the IPython Notebook. This magic takes
-the same arguments as the command line interface described above. Thus
-to run the algorithm from above with the same parameters we just have to
-execute the following cell after importing ``zipline`` to register the
-magic.
-
-.. code-block:: python
-
-   %load_ext zipline
-
-.. code-block:: python
-
-   %%zipline --start 2000-1-1 --end 2014-1-1
-   from zipline.api import symbol, order, record
-
-   def initialize(context):
-       pass
-
-   def handle_data(context, data):
-       order(symbol('AAPL'), 10)
-       record(AAPL=data[symbol('AAPL')].price)
-
-Note that we did not have to specify an input file as above since the
-magic will use the contents of the cell and look for your algorithm
-functions there. Also, instead of defining an output file we are
-specifying a variable name with ``-o`` that will be created in the name
-space and contain the performance ``DataFrame`` we looked at above.
-
-.. code-block:: python
-
-   _.head()
-
-.. raw:: html
-
-    <div style="max-height:1000px;max-width:1500px;overflow:auto;">
-    <table border="1" class="dataframe">
-      <thead>
-        <tr style="text-align: right;">
-          <th></th>
-          <th>AAPL</th>
-          <th>algo_volatility</th>
-          <th>algorithm_period_return</th>
-          <th>alpha</th>
-          <th>benchmark_period_return</th>
-          <th>benchmark_volatility</th>
-          <th>beta</th>
-          <th>capital_used</th>
-          <th>ending_cash</th>
-          <th>ending_exposure</th>
-          <th>...</th>
-          <th>short_exposure</th>
-          <th>short_value</th>
-          <th>shorts_count</th>
-          <th>sortino</th>
-          <th>starting_cash</th>
-          <th>starting_exposure</th>
-          <th>starting_value</th>
-          <th>trading_days</th>
-          <th>transactions</th>
-          <th>treasury_period_return</th>
-        </tr>
-      </thead>
-      <tbody>
-        <tr>
-          <th>2000-01-03 21:00:00</th>
-          <td>3.738314</td>
-          <td>0.000000e+00</td>
-          <td>0.000000e+00</td>
-          <td>-0.065800</td>
-          <td>-0.009549</td>
-          <td>0.000000</td>
-          <td>0.000000</td>
-          <td>0.00000</td>
-          <td>10000000.00000</td>
-          <td>0.00000</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0.000000</td>
-          <td>10000000.00000</td>
-          <td>0.00000</td>
-          <td>0.00000</td>
-          <td>1</td>
-          <td>[]</td>
-          <td>0.0658</td>
-        </tr>
-        <tr>
-          <th>2000-01-04 21:00:00</th>
-          <td>3.423135</td>
-          <td>3.367492e-07</td>
-          <td>-3.000000e-08</td>
-          <td>-0.064897</td>
-          <td>-0.047528</td>
-          <td>0.323229</td>
-          <td>0.000001</td>
-          <td>-34.53135</td>
-          <td>9999965.46865</td>
-          <td>34.23135</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0.000000</td>
-          <td>10000000.00000</td>
-          <td>0.00000</td>
-          <td>0.00000</td>
-          <td>2</td>
-          <td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
-          <td>0.0649</td>
-        </tr>
-        <tr>
-          <th>2000-01-05 21:00:00</th>
-          <td>3.473229</td>
-          <td>4.001918e-07</td>
-          <td>-9.906000e-09</td>
-          <td>-0.066196</td>
-          <td>-0.045697</td>
-          <td>0.329321</td>
-          <td>0.000001</td>
-          <td>-35.03229</td>
-          <td>9999930.43636</td>
-          <td>69.46458</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0.000000</td>
-          <td>9999965.46865</td>
-          <td>34.23135</td>
-          <td>34.23135</td>
-          <td>3</td>
-          <td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
-          <td>0.0662</td>
-        </tr>
-        <tr>
-          <th>2000-01-06 21:00:00</th>
-          <td>3.172661</td>
-          <td>4.993979e-06</td>
-          <td>-6.410420e-07</td>
-          <td>-0.065758</td>
-          <td>-0.044785</td>
-          <td>0.298325</td>
-          <td>-0.000006</td>
-          <td>-32.02661</td>
-          <td>9999898.40975</td>
-          <td>95.17983</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>-12731.780516</td>
-          <td>9999930.43636</td>
-          <td>69.46458</td>
-          <td>69.46458</td>
-          <td>4</td>
-          <td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
-          <td>0.0657</td>
-        </tr>
-        <tr>
-          <th>2000-01-07 21:00:00</th>
-          <td>3.322945</td>
-          <td>5.977002e-06</td>
-          <td>-2.201900e-07</td>
-          <td>-0.065206</td>
-          <td>-0.018908</td>
-          <td>0.375301</td>
-          <td>0.000005</td>
-          <td>-33.52945</td>
-          <td>9999864.88030</td>
-          <td>132.91780</td>
-          <td>...</td>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-          <td>-12629.274583</td>
-          <td>9999898.40975</td>
-          <td>95.17983</td>
-          <td>95.17983</td>
-          <td>5</td>
-          <td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
-          <td>0.0652</td>
-        </tr>
-      </tbody>
-    </table>
-    <p>5 rows × 39 columns</p>
-    </div>
 
 
 Access to previous prices using ``history``
@@ -627,22 +300,16 @@ we need a new concept: History
 ``data.history()`` is a convenience function that keeps a rolling window of
 data for you. The first argument is the number of bars you want to
 collect, the second argument is the unit (either ``'1d'`` for ``'1m'``
-but note that you need to have minute-level data for using ``1m``). For
+but note that you need to have minute-level data for using ``1m``). This is
-a more detailed description ``history()``'s features, see the
+a function we use in the ``handle_data()`` section:
-`Quantopian docs <https://www.quantopian.com/help#ide-history>`__.
-Let's look at the strategy which should make this clear:
 
 .. code-block:: python
 
-   %%zipline --start 2000-1-1 --end 2012-1-1 -o dma.pickle
+    from catalyst.api import order, record, symbol
-
-
-   from zipline.api import order_target, record, symbol
 
     def initialize(context):
        context.i = 0
-       context.asset = symbol('AAPL')
+       context.asset = symbol('btc_usd')
-
 
    def handle_data(context, data):
        # Skip first 300 days to get full windows
@@ -665,67 +332,22 @@ Let's look at the strategy which should make this clear:
            order_target(context.asset, 0)
 
        # Save values for later inspection
-       record(AAPL=data.current(context.asset, 'price'),
+       record(btc=data.current(context.asset, 'price'),
               short_mavg=short_mavg,
               long_mavg=long_mavg)
 
 
-   def analyze(context, perf):
-       fig = plt.figure()
-       ax1 = fig.add_subplot(211)
-       perf.portfolio_value.plot(ax=ax1)
-       ax1.set_ylabel('portfolio value in $')
-
-       ax2 = fig.add_subplot(212)
-       perf['AAPL'].plot(ax=ax2)
-       perf[['short_mavg', 'long_mavg']].plot(ax=ax2)
-
-       perf_trans = perf.ix[[t != [] for t in perf.transactions]]
-       buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]]
-       sells = perf_trans.ix[
-           [t[0]['amount'] < 0 for t in perf_trans.transactions]]
-       ax2.plot(buys.index, perf.short_mavg.ix[buys.index],
-                '^', markersize=10, color='m')
-       ax2.plot(sells.index, perf.short_mavg.ix[sells.index],
-                'v', markersize=10, color='k')
-       ax2.set_ylabel('price in $')
-       plt.legend(loc=0)
-       plt.show()
-
-.. image:: tutorial_files/tutorial_22_1.png
-
-Here we are explicitly defining an ``analyze()`` function that gets
-automatically called once the backtest is done (this is not possible on
-Quantopian currently).
-
-Although it might not be directly apparent, the power of ``history()``
-(pun intended) can not be under-estimated as most algorithms make use of
-prior market developments in one form or another. You could easily
-devise a strategy that trains a classifier with
-`scikit-learn <http://scikit-learn.org/stable/>`__ which tries to
-predict future market movements based on past prices (note, that most of
-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``).
-
-We also used the ``order_target()`` function above. This and other
-functions like it can make order management and portfolio rebalancing
-much easier. See the `Quantopian documentation on order
-functions <https://www.quantopian.com/help#api-order-methods>`__ fore
-more details.
-
 Conclusions
 ~~~~~~~~~~~
 
 We hope that this tutorial gave you a little insight into the
-architecture, API, and features of ``zipline``. For next steps, check
+architecture, API, and features of ``catalyst``. For next steps, check
 out some of the
-`examples <https://github.com/quantopian/zipline/tree/master/zipline/examples>`__.
+`examples <https://github.com/enigmampc/catalyst/tree/master/catalyst/examples>`__.
+The natural next step would be too look into the 
+`buy_and_hodl <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_and_hodl.py>`_ 
+example, which is a more elaborated and realistic version of the ``buy_btc_simple`` example presented in this tutorial.
 
-Feel free to ask questions on `our mailing
+Feel free to ask questions on the ``#catalyst_dev`` channel of our 
-list <https://groups.google.com/forum/#!forum/zipline>`__, report
+`Discord group <https://discord.gg/SJK32GY>`__ and report
-problems on our `GitHub issue
+problems on our `GitHub issue tracker <https://github.com/enigmampc/catalyst/issues>`__.
-tracker <https://github.com/quantopian/zipline/issues?state=open>`__,
-`get
-involved <https://github.com/quantopian/zipline/wiki/Contribution-Requests>`__,
-and `checkout Quantopian <https://quantopian.com>`__.

docs/source/conf.py

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

docs/source/index.rst

@@ -1,12 +1,17 @@
-.. include:: ../../README.rst
+.. include:: welcome.rst
+|
+|
+Table of Contents
+-----------------
 
 .. toctree::
    :maxdepth: 1
 
    install
    beginner-tutorial
-   bundles
+   naming-convention
-   development-guidelines
+..   bundles
-   appendix
+..   development-guidelines
-   release-process
+..   appendix
-   releases
+..   release-process
+..   releases

docs/source/install.rst

@@ -4,16 +4,16 @@ Install
 Installing with ``pip``
 -----------------------
 
-Installing Zipline via ``pip`` is slightly more involved than the average
+Installing Catalyst via ``pip`` is slightly more involved than the average
 Python package.
 
 There are two reasons for the additional complexity:
 
-1. Zipline ships several C extensions that require access to the CPython C API.
+1. Catalyst ships several C extensions that require access to the CPython C API.
    In order to build the C extensions, ``pip`` needs access to the CPython
    header files for your Python installation.
 
-2. Zipline depends on `numpy <http://www.numpy.org/>`_, the core library for
+2. Catalyst depends on `numpy <http://www.numpy.org/>`_, the core library for
    numerical array computing in Python.  Numpy depends on having the `LAPACK
    <http://www.netlib.org/lapack>`_ linear algebra routines available.
 
@@ -28,13 +28,28 @@ your particular platform), you should be able to simply run
 
 .. code-block:: bash
 
-   $ pip install zipline
+   $ pip install enigma-catalyst
 
-If you use Python for anything other than Zipline, we **strongly** recommend
+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
 Python`_ provides an `excellent tutorial on virtualenv
-<http://docs.python-guide.org/en/latest/dev/virtualenvs/>`_.
+<http://docs.python-guide.org/en/latest/dev/virtualenvs/>`_. Here's a summarized
+version:
+
+.. code-block:: bash
+
+   $ virtualenv catalyst-venv
+   $ source ./catalyst-venv/bin/activate
+   $ pip install enigma-
+
+Though not required by Catalyst directly, our example algorithms use matplotlib 
+to visually display the results of the trading algorithms. If you wish to run 
+any examples or use matplotlib during development, it can be installed using:
+
+.. code-block:: bash
+
+    $ pip install matplotlib
 
 GNU/Linux
 ~~~~~~~~~
@@ -60,15 +75,17 @@ On `Arch Linux`_, you can acquire the additional dependencies via ``pacman``:
 
    $ pacman -S lapack gcc gcc-fortran pkg-config
 
-There are also AUR packages available for installing `Python 3.4
+.. Commenting it out until Catalyst fully supports Python 3.X
-<https://aur.archlinux.org/packages/python34/>`_ (Arch's default python is now
+..
-3.5, but Zipline only currently supports 3.4), and `ta-lib
+.. There are also AUR packages available for installing `Python 3.4
-<https://aur.archlinux.org/packages/ta-lib/>`_, an optional Zipline dependency.
+.. <https://aur.archlinux.org/packages/python34/>`_ (Arch's default python is now
-Python 2 is also installable via:
+.. 3.5, but Catalyst only currently supports 3.4), and `ta-lib
+.. <https://aur.archlinux.org/packages/ta-lib/>`_, an optional Catalyst dependency.
+.. Python 2 is also installable via:
 
-.. code-block:: bash
+.. 
 
-   $ pacman -S python2
+..   $ pacman -S python2
 
 OSX
 ~~~
@@ -87,36 +104,238 @@ following brew packages:
 
    $ brew install freetype pkg-config gcc openssl
 
+OSX + virtualenv + matplotlib
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+A note about using matplotlib in virtual enviroments on OSX: it may be necessary to run
+
+.. code-block:: bash
+
+   echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
+
+in order to override the default ``macosx`` backend for your system, which may not 
+be accessible from inside the virtual environment. This will allow Catalyst to open 
+matplotlib charts from within a virtual environment, which is useful for displaying 
+the performance of your backtests.  To learn more about matplotlib backends, please refer to the
+`matplotlib backend documentation <https://matplotlib.org/faq/usage_faq.html#what-is-a-backend>`_.
+
+
 Windows
 ~~~~~~~
 
-For windows, the easiest and best supported way to install zipline is to use
+In Windows, you will need 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.
+
+For windows, the easiest and best supported way to install Catalyst is to use
 :ref:`Conda <conda>`.
 
+Amazon Linux AMI
+~~~~~~~~~~~~~~~~
+
+The packages ``pip`` and ``setuptools`` that come shipped by default are very outdated. 
+Thus, you first need to run:
+
+.. code-block:: bash
+
+   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++
+
+Then you should follow the regular installation instructions outlined at the beginning 
+of this page.
+
+
+Troubleshooting ``pip`` Install
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+**Issue**: 
+   Package enigma-catalyst cannot be found 
+ 
+**Solution**: 
+   Make sure you have the most up-to-date version of pip installed, by running: 
+
+   .. code-block:: bash
+
+      pip install --upgrade pip
+
+   On Windows, the recommended command is:
+
+   .. code-block:: bash
+
+      python -m pip install --upgrade pip
+
+----
+
+**Issue**: 
+   Package enigma-catalyst cannot still be found, even after upgrading pip (see above), with an error similar to:
+
+   .. code-block:: bash
+
+      Downloading/unpacking enigma-catalyst
+      Could not find a version that satisfies the requirement enigma-catalyst (from versions: 0.1.dev9, 0.2.dev2, 0.1.dev4, 0.1.dev5, 0.1.dev3, 0.2.dev1, 0.1.dev8, 0.1.dev6)
+      Cleaning up...
+      No distributions matching the version for enigma-catalyst
+
+**Solution**:
+   In some systems (this error has been reported in Ubuntu), pip is configured to only find stable versions by default. Since Catalyst is in alpha version, pip cannot find a matching version that satisfies the installation requirements. The solution is to include the `--pre` flag to include pre-release and development versions:
+
+   .. code-block:: bash
+
+      pip install --pre enigma-catalyst
+
+----
+
+**Issue**: 
+   Package enigma-catalyst fails to install because of outdated setuptools
+
+**Solution**: 
+   Upgrade to the most up-to-date setuptools package by running: 
+
+   .. code-block:: bash
+
+      pip install --upgrade pip setuptools
+
+----
+
+**Issue**:
+   Missing required packages  
+
+**Solution**:
+   Download `requirements.txt 
+   <https://github.com/enigmampc/catalyst/blob/master/etc/requirements.txt>`_ 
+   (click on the *Raw* button and Right click -> Save As...) and use it to
+   install all the required dependencies by running:
+
+   .. code-block:: bash
+
+      pip install -r requirements.txt
+
+----
+
+**Issue**: 
+   Installation fails with error: ``fatal error: Python.h: No such file or directory``
+
+**Solution**: 
+   Some systems (this issue has been reported in Ubuntu) require `python-dev` for the proper build and installation of package dependencies. The solution is to install python-dev, which is independent of the virtual environment. In Ubuntu, you would need to run:
+
+   .. code-block:: bash
+
+      sudo apt-get install python-dev
+
+
 .. _conda:
 
 Installing with ``conda``
 -------------------------
 
-Another way to install Zipline is via the ``conda`` package manager, which
+Another way to install Catalyst is via the ``conda`` package manager, which
 comes as part of Continuum Analytics' `Anaconda
 <http://continuum.io/downloads>`_ distribution.
 
 The primary advantage of using Conda over ``pip`` is that conda natively
 understands the complex binary dependencies of packages like ``numpy`` and
-``scipy``.  This means that ``conda`` can install Zipline and its dependencies
+``scipy``.  This means that ``conda`` can install Catalyst and its dependencies
-without requiring the use of a second tool to acquire Zipline's non-Python
+without requiring the use of a second tool to acquire Catalyst's non-Python
 dependencies.
 
 For instructions on how to install ``conda``, see the `Conda Installation
-Documentation <http://conda.pydata.org/docs/download.html>`_
+Documentation <http://conda.pydata.org/docs/download.html>`_. Alternatively, 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:
 
-Once conda has been set up you can install Zipline from our ``Quantopian``
+1. Download `MiniConda <https://conda.io/miniconda.html>`_. Select Python 2.7 for 
-channel:
+   your Operating System.
+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 window,
+   which should print the list of packages installed with Conda.
+
+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>`_.
+2. Open a Terminal window and enter [``cd/dir``] into the directory where you saved
+   the above ``python2.7-environment.yml`` file.
+3. Install using this file. This step can take about 5-10 minutes to install.
 
    .. code-block:: bash
 
-    conda install -c Quantopian zipline
+      conda env create -f python2.7-environment.yml
+
+4. Activate the environment (which you need to do every time you start a new session
+   to run Catalyst):
+
+   **Linux or OSX:**
+
+   .. code-block:: bash
+
+      source activate catalyst
+
+   **Windows:**
+
+   .. code-block:: bash
+
+      activate catalyst
+
+Congratulations! You now have Catalyst installed.
+
+Troubleshooting ``conda`` Install
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If the command  ``conda env create -f python2.7-environment.yml`` in step 3 above failed 
+for any reason, you can try setting up the environment manually with the following steps:
+
+1. Create the environment:
+
+   .. code-block:: bash
+
+      conda create --name catalyst python=2.7 scipy
+
+2. Activate the environment:
+
+   **Linux or OSX:**
+
+   .. code-block:: bash
+
+      source activate catalyst
+
+   **Windows:**
+
+   .. code-block:: bash
+
+      activate catalyst
+
+3. Install the Catalyst inside the environment:
+
+   .. code-block:: bash
+
+      pip install enigma-catalyst matplotlib
+
+Getting Help
+------------
+
+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 `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 more descriptive and the more information you can provide, 
+  the easiest will be for others to help you out.
+
+- Report the problem you are experiencing on our 
+  `GitHub repository <https://github.com/enigmampc/catalyst/issues>`_ following the guidelines 
+  provided therein. Before you do so, take a moment to browse through all `previous reported issues
+  <https://github.com/enigmampc/catalyst/issues?utf8=%E2%9C%93&q=is%3Aissue>`_ in the likely case
+  that someone else experienced that same issue before, and you get a hint on how to solve it.
+
 
 .. _`Debian-derived`: https://www.debian.org/misc/children-distros
 .. _`RHEL-derived`: https://en.wikipedia.org/wiki/Red_Hat_Enterprise_Linux_derivatives

docs/source/naming-convention.rst

@@ -0,0 +1,66 @@
+Naming Convention
+=================
+
+Catalyst introduces a standardized naming convention for all asset pairs 
+trading on any exchange in the following form:
+
+
+    **{market_currency}_{base_currency}**
+
+Where {market_currency} is the asset to be traded using {base_currency} as 
+the reference, both written in lowercase and separated with an underscore.
+
+This standardization is needed to overcome the lack of consistency in the 
+naming of assets across different exchanges, and making it easier to the user
+to refer to the asset pairs that you want to trade.
+
+Catalyst maintains a `Market Coverage Overview <https://www.enigma.co/catalyst/status>`_ 
+where you can check the mapping between Catalyst naming pairs and that of each 
+exchange. Catalyst will always expect in all its functions that you will refer to 
+the asset pairs by using the Catalyst naming convention.
+
+If at any point, you input the wrong name for an asset pair, you will get an error 
+of that pair not found in the given exchange, and a list of pairs available on that exchange:
+
+.. code-block:: bash
+
+   $ catalyst ingest-exchange -x poloniex -i btc_usd
+
+.. parsed-literal::
+
+	Ingesting exchange bundle poloniex...
+	Error traceback: /Volumes/Data/Users/victoris/Desktop/Enigma/user-install/catalyst-dev/catalyst/exchange/exchange.py (line 175)
+	SymbolNotFoundOnExchange:  Symbol btc_usd not found on exchange Poloniex. 
+	Choose from: ['rep_usdt', 'gno_btc', 'xvc_btc', 'pink_btc', 'sys_btc', 
+	'emc2_btc', 'rads_btc', 'note_btc', 'maid_btc', 'bch_btc', 'gnt_btc', 
+	'bcn_btc', 'rep_btc', 'bcy_btc', 'cvc_btc', 'nxt_xmr', 'zec_usdt', 
+	'fct_btc', 'gas_btc', 'pot_btc', 'eth_usdt', 'btc_usdt', 'lbc_btc', 
+	'dcr_btc', 'etc_usdt', 'omg_eth', 'amp_btc', 'xpm_btc', 'nxt_btc', 
+	'vtc_btc', 'steem_eth', 'blk_xmr', 'pasc_btc', 'zec_xmr', 'grc_btc', 
+	'nxc_btc', 'btcd_btc', 'ltc_btc', 'dash_btc', 'naut_btc', 'zec_eth', 
+	'zec_btc', 'burst_btc', 'zrx_eth', 'bela_btc', 'steem_btc', 'etc_btc', 
+	'eth_btc', 'huc_btc', 'strat_btc', 'lsk_btc', 'exp_btc', 'clam_btc', 
+	'rep_eth', 'dash_xmr', 'cvc_eth', 'bch_usdt', 'zrx_btc', 'dash_usdt', 
+	'blk_btc', 'xrp_btc', 'nxt_usdt', 'neos_btc', 'omg_btc', 'bts_btc', 
+	'doge_btc', 'gnt_eth', 'sbd_btc', 'gno_eth', 'xcp_btc', 'ltc_usdt', 
+	'btm_btc', 'xmr_usdt', 'lsk_eth', 'omni_btc', 'nav_btc', 'fldc_btc', 
+	'ppc_btc', 'xbc_btc', 'dgb_btc', 'sc_btc', 'btcd_xmr', 'vrc_btc', 
+	'ric_btc', 'str_btc', 'maid_xmr', 'xmr_btc', 'sjcx_btc', 'via_btc', 
+	'xem_btc', 'nmc_btc', 'etc_eth', 'ltc_xmr', 'ardr_btc', 'gas_eth', 
+	'flo_btc', 'xrp_usdt', 'game_btc', 'bch_eth', 'bcn_xmr', 'str_usdt']
+
+In the example above, exchange Poloniex does not use USD, but uses instead the 
+USDT cryptocurrency asset that is issued on the Bitcoin blockchain via the Omni
+Layer Protocol. Each USDT unit is backed by a U.S Dollar held in the reserves of 
+Tether Limited. USDT can be transferred, stored, and spent, just like bitcoins 
+or any other cryptocurrency. Given its 1:1 mapping to the USD, is a viable alternative.
+
+.. code-block:: bash
+
+   $ catalyst ingest-exchange -x poloniex -i btc_usdt
+
+.. parsed-literal::
+
+	Ingesting exchange bundle poloniex...
+	    [====================================]  Fetching poloniex daily candles: :  100%
+

docs/source/welcome.rst

@@ -0,0 +1,28 @@
+.. image:: https://s3.amazonaws.com/enigmaco-docs/enigma-catalyst.jpg
+|
+Catalyst is a data-driven crypto investment platform. It supports both 
+backtesting and live-trading in a number of different crypto-exchanges. 
+Catalyst empowers users to share and curate data and build profitable, 
+data-driven investment strategies.
+
+Features
+========
+
+-  Ease of use: Catalyst tries to get out of your way so that you can 
+   focus on algorithm development. See 
+   `examples of trading strategies <https://github.com/enigmampc/catalyst/tree/master/catalyst/examples>`_ 
+   provided.
+-  Support for several of the top crypto-exchanges by trading volume:
+   `Bitfinex <https://www.bitfinex.com>`_, `Bittrex <http://www.bittrex.com>`_,
+   and `Poloniex <https://www.poloniex.com>`_. 
+-  Secure: You and only you have access to each exchange API keys for your accounts.
+-  Input of historical pricing data of all crypto-assets by exchange, 
+   with daily and minute resolution. See 
+   `Catalyst Market Coverage Overview <https://www.enigma.co/catalyst/status>`_.
+-  Backtesting and live-trading functionality, with a seamless transition
+   between the two modes.
+-  Output of performance statistics are based on Pandas DataFrames to 
+   integrate nicely into the existing PyData eco-system.
+-  Statistic and machine learning libraries like matplotlib, scipy, 
+   statsmodels, and sklearn support development, analysis, and 
+   visualization of state-of-the-art trading systems.

etc/python2.7-environment.yml

@@ -0,0 +1,84 @@
+name: catalyst
+channels:
+- statiskit
+- defaults
+dependencies:
+- certifi=2016.2.28=py27_0
+- coverage=4.4.1=py27_0
+- nose=1.3.7=py27_1
+- openssl=1.0.2l=0
+- path.py=10.3.1=py27_0
+- pip=9.0.1=py27_1
+- python=2.7.13=0
+- pyyaml=3.12=py27_0
+- readline=6.2=2
+- setuptools=36.4.0=py27_0
+- six=1.10.0=py27_0
+- sqlite=3.13.0=0
+- tk=8.5.18=0
+- wheel=0.29.0=py27_0
+- yaml=0.1.6=0
+- zlib=1.2.11=0
+- libdev=1.0.0=py27_0
+- python-dev=1.0.0=py27_0
+- python-scons=3.0.0=py27_0
+- pip:
+  - alembic==0.9.5
+  - backports.shutil-get-terminal-size==1.0.0
+  - bcolz==0.12.1
+  - bottleneck==1.2.1
+  - chardet==3.0.4
+  - click==6.7
+  - contextlib2==0.5.5
+  - cycler==0.10.0
+  - cyordereddict==1.0.0
+  - cython==0.26.1
+  - decorator==4.1.2
+  - empyrical==0.2.1
+  - enigma-catalyst>=0.2.dev2
+  - enum34==1.1.6
+  - functools32==3.2.3.post2
+  - idna==2.6
+  - intervaltree==2.1.0
+  - ipdb==0.10.3
+  - ipdbplugin==1.4.5
+  - ipython==5.5.0
+  - ipython-genutils==0.2.0
+  - logbook==1.1.0
+  - lru-dict==1.1.6
+  - mako==1.0.7
+  - markupsafe==1.0
+  - matplotlib==2.0.2
+  - multipledispatch==0.4.9
+  - networkx==1.11
+  - numexpr==2.6.4
+  - numpy==1.13.1
+  - pandas==0.19.2
+  - pandas-datareader==0.5.0
+  - pathlib2==2.3.0
+  - patsy==0.4.1
+  - pexpect==4.2.1
+  - pickleshare==0.7.4
+  - prompt-toolkit==1.0.15
+  - ptyprocess==0.5.2
+  - pygments==2.2.0
+  - pyparsing==2.2.0
+  - python-dateutil==2.6.1
+  - python-editor==1.0.3
+  - pytz==2017.2
+  - requests==2.18.4
+  - requests-file==1.4.2
+  - requests-ftp==0.3.1
+  - scandir==1.5
+  - scipy==0.19.1
+  - scons==3.0.0a20170821
+  - simplegeneric==0.8.1
+  - sortedcontainers==1.5.7
+  - sqlalchemy==1.1.14
+  - statsmodels==0.8.0
+  - subprocess32==3.2.7
+  - tables==3.4.2
+  - toolz==0.8.2
+  - traitlets==4.3.2
+  - urllib3==1.22
+  - wcwidth==0.1.7

setup.py

@@ -304,7 +304,7 @@ setup(
                   if '__pycache__' not in root},
     license='Apache 2.0',
     classifiers=[
-        'Development Status :: 2 - Pre-Alpha',
+        'Development Status :: 3 - Alpha',
         'License :: OSI Approved :: Apache Software License',
         'Natural Language :: English',
         'Programming Language :: Python',

tests/exchange/base.py

@@ -2,7 +2,7 @@ import unittest
 from abc import ABCMeta, abstractmethod
 
 
-class BaseExchangeTestCase():
+class BaseExchangeTestCase:
     __metaclass__ = ABCMeta
 
     @abstractmethod

tests/exchange/test_bitfinex.py

@@ -1,12 +1,9 @@
-from catalyst.exchange.bitfinex.bitfinex import Bitfinex
-from .base import BaseExchangeTestCase
 from logbook import Logger
-import pandas as pd
+
-from catalyst.finance.execution import (MarketOrder,
+from base import BaseExchangeTestCase
-                                        LimitOrder,
+from catalyst.exchange.bitfinex.bitfinex import Bitfinex
-                                        StopOrder,
-                                        StopLimitOrder)
 from catalyst.exchange.exchange_utils import get_exchange_auth
+from catalyst.finance.execution import (LimitOrder)
 
 log = Logger('test_bitfinex')
 
@@ -14,7 +11,7 @@ log = Logger('test_bitfinex')
 class BitfinexTestCase(BaseExchangeTestCase):
     @classmethod
     def setup(self):
-        print ('creating bitfinex object')
+        log.info('creating bitfinex object')
         auth = get_exchange_auth('bitfinex')
         self.exchange = Bitfinex(
             key=auth['key'],
@@ -50,13 +47,17 @@ class BitfinexTestCase(BaseExchangeTestCase):
 
     def test_get_candles(self):
         log.info('retrieving candles')
+        ohlcv_neo = self.exchange.get_candles(
+            data_frequency='1m',
+            assets=self.exchange.get_asset('neo_btc')
+        )
         pass
 
     def test_tickers(self):
         log.info('retrieving tickers')
         tickers = self.exchange.tickers([
-            self.exchange.get_asset('eth_usd'),
+            self.exchange.get_asset('eth_btc'),
-            self.exchange.get_asset('btc_usd')
+            self.exchange.get_asset('etc_btc')
         ])
         pass
 
@@ -68,3 +69,9 @@ class BitfinexTestCase(BaseExchangeTestCase):
         log.info('testing exchange balances')
         balances = self.exchange.get_balances()
         pass
+
+    def test_orderbook(self):
+        log.info('testing order book for bitfinex')
+        asset = self.exchange.get_asset('eth_btc')
+        orderbook = self.exchange.get_orderbook(asset)
+        pass

tests/exchange/test_bittrex.py

@@ -1,6 +1,6 @@
 from catalyst.exchange.bittrex.bittrex import Bittrex
 from catalyst.finance.order import Order
-from .base import BaseExchangeTestCase
+from base import BaseExchangeTestCase
 from logbook import Logger
 from catalyst.exchange.exchange_utils import get_exchange_auth
 
@@ -67,8 +67,8 @@ class BittrexTestCase(BaseExchangeTestCase):
     def test_tickers(self):
         log.info('retrieving tickers')
         tickers = self.exchange.tickers([
-            self.exchange.get_asset('ubq_btc'),
+            self.exchange.get_asset('eth_btc'),
-            self.exchange.get_asset('neo_btc')
+            self.exchange.get_asset('etc_btc')
         ])
         assert len(tickers) == 2
         pass
@@ -81,3 +81,9 @@ class BittrexTestCase(BaseExchangeTestCase):
     def test_get_account(self):
         log.info('testing account data')
         pass
+
+    def test_orderbook(self):
+        log.info('testing order book for bittrex')
+        asset = self.exchange.get_asset('eth_btc')
+        orderbook = self.exchange.get_orderbook(asset)
+        pass

tests/exchange/test_bundle.py

@@ -0,0 +1,286 @@
+from logging import Logger
+
+import pandas as pd
+
+from catalyst import get_calendar
+from catalyst.exchange.bundle_utils import get_bcolz_chunk, get_periods, \
+    get_periods_range
+from catalyst.exchange.exchange_bcolz import BcolzExchangeBarReader, \
+    BcolzExchangeBarWriter
+from catalyst.exchange.exchange_bundle import ExchangeBundle, \
+    BUNDLE_NAME_TEMPLATE
+from catalyst.exchange.exchange_utils import get_exchange_folder
+from catalyst.exchange.init_utils import get_exchange
+from catalyst.utils.paths import ensure_directory
+
+log = Logger('test_exchange_bundle')
+
+
+class ExchangeBundleTestCase:
+    def test_spot_value(self):
+        data_frequency = 'daily'
+        exchange_name = 'poloniex'
+
+        exchange = get_exchange(exchange_name)
+        exchange_bundle = ExchangeBundle(exchange)
+        assets = [
+            exchange.get_asset('btc_usdt')
+        ]
+        dt = pd.to_datetime('2017-10-14', utc=True)
+
+        values = exchange_bundle.get_spot_values(
+            assets=assets,
+            field='close',
+            dt=dt,
+            data_frequency=data_frequency
+        )
+        pass
+
+    def test_ingest_minute(self):
+        data_frequency = 'minute'
+        exchange_name = 'bitfinex'
+
+        exchange = get_exchange(exchange_name)
+        exchange_bundle = ExchangeBundle(exchange)
+        assets = [
+            exchange.get_asset('neo_eth')
+        ]
+
+        # start = pd.to_datetime('2017-09-01', utc=True)
+        start = pd.to_datetime('2017-9-15', utc=True)
+        end = pd.to_datetime('2017-9-30', utc=True)
+
+        log.info('ingesting exchange bundle {}'.format(exchange_name))
+        exchange_bundle.ingest(
+            data_frequency=data_frequency,
+            include_symbols=','.join([asset.symbol for asset in assets]),
+            # include_symbols=None,
+            exclude_symbols=None,
+            start=start,
+            end=end,
+            show_progress=True
+        )
+
+        reader = exchange_bundle.get_reader(data_frequency)
+        for asset in assets:
+            arrays = reader.load_raw_arrays(
+                sids=[asset.sid],
+                fields=['close'],
+                start_dt=start,
+                end_dt=end
+            )
+            print('found {} rows for {} ingestion\n{}'.format(
+                len(arrays[0]), asset.symbol, arrays[0])
+            )
+        pass
+
+    def test_ingest_minute_all(self):
+        exchange_name = 'bitfinex'
+
+        # start = pd.to_datetime('2017-09-01', utc=True)
+        start = pd.to_datetime('2017-10-01', utc=True)
+        end = pd.to_datetime('2017-10-05', utc=True)
+
+        exchange_bundle = ExchangeBundle(get_exchange(exchange_name))
+
+        log.info('ingesting exchange bundle {}'.format(exchange_name))
+        exchange_bundle.ingest(
+            data_frequency='minute',
+            exclude_symbols=None,
+            start=start,
+            end=end,
+            show_progress=True
+        )
+        pass
+
+    def test_ingest_daily(self):
+        # exchange_name = 'bitfinex'
+        # data_frequency = 'daily'
+        # include_symbols = 'neo_btc,bch_btc,eth_btc'
+
+        exchange_name = 'poloniex'
+        data_frequency = 'daily'
+        include_symbols = 'btc_usdt'
+
+        start = pd.to_datetime('2016-1-1', utc=True)
+        end = pd.to_datetime('2017-10-16', utc=True)
+        periods = get_periods_range(start, end, data_frequency)
+
+        exchange = get_exchange(exchange_name)
+        exchange_bundle = ExchangeBundle(exchange)
+
+        log.info('ingesting exchange bundle {}'.format(exchange_name))
+        exchange_bundle.ingest(
+            data_frequency=data_frequency,
+            include_symbols=include_symbols,
+            exclude_symbols=None,
+            start=start,
+            end=end,
+            show_progress=True
+        )
+
+        symbols = include_symbols.split(',')
+        assets = []
+        for pair_symbol in symbols:
+            assets.append(exchange.get_asset(pair_symbol))
+
+        reader = exchange_bundle.get_reader(data_frequency)
+        for asset in assets:
+            arrays = reader.load_raw_arrays(
+                sids=[asset.sid],
+                fields=['close'],
+                start_dt=start,
+                end_dt=end
+            )
+            print('found {} rows for {} ingestion\n{}'.format(
+                len(arrays[0]), asset.symbol, arrays[0])
+            )
+        pass
+
+    def test_merge_ctables(self):
+        exchange_name = 'bittrex'
+
+        # Switch between daily and minute for testing
+        # data_frequency = 'daily'
+        data_frequency = 'daily'
+
+        exchange = get_exchange(exchange_name)
+        assets = [
+            exchange.get_asset('eth_btc'),
+            exchange.get_asset('etc_btc'),
+            exchange.get_asset('wings_eth'),
+        ]
+
+        start = pd.to_datetime('2017-9-1', utc=True)
+        end = pd.to_datetime('2017-9-30', utc=True)
+
+        exchange_bundle = ExchangeBundle(exchange)
+
+        writer = exchange_bundle.get_writer(start, end, data_frequency)
+
+        # In the interest of avoiding abstractions, this is writing a chunk
+        # to the ctable. It does not include the logic which creates chunks.
+        for asset in assets:
+            exchange_bundle.ingest_ctable(
+                asset=asset,
+                data_frequency=data_frequency,
+                # period='2017-9',
+                period='2017',
+                # Dont't forget to update if you change your dates
+                start_dt=start,
+                end_dt=end,
+                writer=writer,
+                empty_rows_behavior='strip'
+            )
+
+        # In daily mode, this returns an error. It appears that writing
+        # a second asset in the same date range removed the first asset.
+
+        # In minute mode, the data is there too. This signals that the minute
+        # writer / reader is more powerful. This explains why I did not
+        # encounter these problems as I have been focusing on minute data.
+        reader = exchange_bundle.get_reader(data_frequency)
+        for asset in assets:
+            # Since this pair was loaded last. It should be there in daily mode.
+            arrays = reader.load_raw_arrays(
+                sids=[asset.sid],
+                fields=['close'],
+                start_dt=start,
+                end_dt=end
+            )
+            print('found {} rows for {} ingestion\n{}'.format(
+                len(arrays[0]), asset.symbol, arrays[0])
+            )
+        pass
+
+    def test_daily_data_to_minute_table(self):
+        exchange_name = 'poloniex'
+
+        # Switch between daily and minute for testing
+        data_frequency = 'daily'
+        # data_frequency = 'minute'
+
+        exchange = get_exchange(exchange_name)
+        assets = [
+            exchange.get_asset('eth_btc'),
+            exchange.get_asset('etc_btc'),
+        ]
+
+        start = pd.to_datetime('2017-9-1', utc=True)
+        end = pd.to_datetime('2017-9-30', utc=True)
+
+        # Preparing the bundle folder
+        root = get_exchange_folder(exchange.name)
+        path = BUNDLE_NAME_TEMPLATE.format(
+            root=root,
+            frequency=data_frequency
+        )
+        ensure_directory(path)
+
+        exchange_bundle = ExchangeBundle(exchange)
+        calendar = get_calendar('OPEN')
+
+        # We are using a BcolzMinuteBarWriter even though the data is daily
+        # Each day has a maximum of one bar
+
+        # I tried setting the minutes_per_day to 1 will not create
+        # unnecessary bars
+        writer = BcolzExchangeBarWriter(
+            rootdir=path,
+            data_frequency=data_frequency,
+            start_session=start,
+            end_session=end,
+            write_metadata=True
+        )
+
+        # This will read the daily data in a bundle created by
+        # the daily writer. It will write to the minute writer which
+        # we are passing.
+
+        # Ingesting a second asset to ensure that multiple chunks
+        # don't override each other
+        for asset in assets:
+            exchange_bundle.ingest_ctable(
+                asset=asset,
+                data_frequency=data_frequency,
+                period='2017',
+                start_dt=start,
+                end_dt=end,
+                writer=writer,
+                empty_rows_behavior='strip'
+            )
+
+        reader = BcolzExchangeBarReader(rootdir=path,
+                                        data_frequency=data_frequency)
+
+        # Reading the two assets to ensure that no data was lost
+        for asset in assets:
+            sid = asset.sid
+
+            daily_values = reader.load_raw_arrays(
+                fields=['open', 'high', 'low', 'close', 'volume'],
+                start_dt=start,
+                end_dt=end,
+                sids=[sid],
+            )
+
+            print('found {} rows for last ingestion'.format(
+                len(daily_values[0]))
+            )
+        pass
+
+    def test_minute_bundle(self):
+        exchange_name = 'poloniex'
+        data_frequency = 'minute'
+
+        exchange = get_exchange(exchange_name)
+        asset = exchange.get_asset('neo_btc')
+
+        path = get_bcolz_chunk(
+            exchange_name=exchange_name,
+            symbol=asset.symbol,
+            data_frequency=data_frequency,
+            period='2017-5',
+        )
+
+        pass

tests/exchange/test_clock.py

@@ -1,7 +1,7 @@
 from unittest import TestCase
 from logbook import Logger
 from mock import patch, sentinel
-from catalyst.exchange.exchange_clock import ExchangeClock
+from catalyst.exchange.simple_clock import SimpleClock
 from catalyst.utils.calendars.trading_calendar import days_at_time
 from datetime import time
 from collections import defaultdict
@@ -35,9 +35,9 @@ class ExchangeClockTestCase(TestCase):
         return self.internal_clock
 
     def test_clock(self):
-        with patch('catalyst.exchange.exchange_clock.pd.to_datetime') as to_dt, \
+        with patch('catalyst.exchange.simple_clock.pd.to_datetime') as to_dt, \
-                patch('catalyst.exchange.exchange_clock.sleep') as sleep:
+                patch('catalyst.exchange.simple_clock.sleep') as sleep:
-            clock = ExchangeClock(sessions=self.sessions)
+            clock = SimpleClock(sessions=self.sessions)
             to_dt.side_effect = self.get_clock
             sleep.side_effect = self.advance_clock
             start_time = pd.Timestamp.utcnow()

tests/exchange/test_data_portal.py

@@ -0,0 +1,108 @@
+import pandas as pd
+from logbook import Logger
+
+from catalyst import get_calendar
+from catalyst.exchange.asset_finder_exchange import AssetFinderExchange
+from catalyst.exchange.bitfinex.bitfinex import Bitfinex
+from catalyst.exchange.bittrex.bittrex import Bittrex
+from catalyst.exchange.data_portal_exchange import DataPortalExchangeBacktest, \
+    DataPortalExchangeLive
+from catalyst.exchange.exchange_utils import get_exchange_auth
+
+log = Logger('test_bitfinex')
+
+
+class ExchangeDataPortalTestCase:
+    @classmethod
+    def setup(self):
+        log.info('creating bitfinex exchange')
+        auth_bitfinex = get_exchange_auth('bitfinex')
+        self.bitfinex = Bitfinex(
+            key=auth_bitfinex['key'],
+            secret=auth_bitfinex['secret'],
+            base_currency='usd'
+        )
+
+        log.info('creating bittrex exchange')
+        auth_bitfinex = get_exchange_auth('bittrex')
+        self.bittrex = Bittrex(
+            key=auth_bitfinex['key'],
+            secret=auth_bitfinex['secret'],
+            base_currency='usd'
+        )
+
+        open_calendar = get_calendar('OPEN')
+        asset_finder = AssetFinderExchange()
+
+        self.data_portal_live = DataPortalExchangeLive(
+            exchanges=dict(bitfinex=self.bitfinex, bittrex=self.bittrex),
+            asset_finder=asset_finder,
+            trading_calendar=open_calendar,
+            first_trading_day=pd.to_datetime('today', utc=True)
+        )
+        self.data_portal_backtest = DataPortalExchangeBacktest(
+            exchanges=dict(bitfinex=self.bitfinex),
+            asset_finder=asset_finder,
+            trading_calendar=open_calendar,
+            first_trading_day=None  # will set dynamically based on assets
+        )
+
+    def test_get_history_window_live(self):
+        asset_finder = self.data_portal_live.asset_finder
+
+        assets = [
+            asset_finder.lookup_symbol('eth_btc', self.bitfinex),
+            asset_finder.lookup_symbol('eth_btc', self.bittrex)
+        ]
+        now = pd.Timestamp.utcnow()
+        data = self.data_portal_live.get_history_window(
+            assets,
+            now,
+            10,
+            '1m',
+            'price')
+        pass
+
+    def test_get_spot_value_live(self):
+        asset_finder = self.data_portal_live.asset_finder
+
+        assets = [
+            asset_finder.lookup_symbol('eth_btc', self.bitfinex),
+            asset_finder.lookup_symbol('eth_btc', self.bittrex)
+        ]
+        now = pd.Timestamp.utcnow()
+        value = self.data_portal_live.get_spot_value(
+            assets, 'price', now, '1m')
+        pass
+
+    def test_get_history_window_backtest(self):
+        asset_finder = self.data_portal_live.asset_finder
+
+        assets = [
+            asset_finder.lookup_symbol('neo_btc', self.bitfinex),
+        ]
+
+        date = pd.to_datetime('2017-09-10', utc=True)
+        data = self.data_portal_backtest.get_history_window(
+            assets,
+            date,
+            10,
+            '1m',
+            'close',
+            'minute')
+
+        log.info('found history window: {}'.format(data))
+        pass
+
+    def test_get_spot_value_backtest(self):
+        asset_finder = self.data_portal_backtest.asset_finder
+
+        assets = [
+            asset_finder.lookup_symbol('neo_btc', self.bitfinex),
+        ]
+
+        date = pd.to_datetime('2017-09-10', utc=True)
+        value = self.data_portal_backtest.get_spot_value(
+            assets, 'close', date, 'minute')
+        log.info('found spot value {}'.format(value))
+        pass

tests/exchange/test_poloniex.py

@@ -0,0 +1,91 @@
+from catalyst.exchange.bittrex.bittrex import Bittrex
+from catalyst.exchange.poloniex.poloniex import Poloniex
+from catalyst.finance.order import Order
+from base import BaseExchangeTestCase
+from logbook import Logger
+from catalyst.exchange.exchange_utils import get_exchange_auth
+
+log = Logger('test_poloniex')
+
+
+class PoloniexTestCase(BaseExchangeTestCase):
+    @classmethod
+    def setup(self):
+        print ('creating poloniex object')
+        auth = get_exchange_auth('poloniex')
+        self.exchange = Poloniex(
+            key=auth['key'],
+            secret=auth['secret'],
+            base_currency='btc'
+        )
+
+    def test_order(self):
+        log.info('creating order')
+        asset = self.exchange.get_asset('neo_btc')
+        order_id = self.exchange.order(
+            asset=asset,
+            limit_price=0.0005,
+            amount=1,
+        )
+        log.info('order created {}'.format(order_id))
+        assert order_id is not None
+        pass
+
+    def test_open_orders(self):
+        log.info('retrieving open orders')
+        asset = self.exchange.get_asset('neo_btc')
+        orders = self.exchange.get_open_orders(asset)
+        pass
+
+    def test_get_order(self):
+        log.info('retrieving order')
+        order = self.exchange.get_order(
+            u'2c584020-9caf-4af5-bde0-332c0bba17e2')
+        assert isinstance(order, Order)
+        pass
+
+    def test_cancel_order(self, ):
+        log.info('cancel order')
+        self.exchange.cancel_order(u'dc7bcca2-5219-4145-8848-8a593d2a72f9')
+        pass
+
+    def test_get_candles(self):
+        log.info('retrieving candles')
+        ohlcv_neo = self.exchange.get_candles(
+            data_frequency='5m',
+            assets=self.exchange.get_asset('neo_btc')
+        )
+        ohlcv_neo_ubq = self.exchange.get_candles(
+            data_frequency='5m',
+            assets=[
+                self.exchange.get_asset('neo_btc'),
+                self.exchange.get_asset('ubq_btc')
+            ],
+            bar_count=14
+        )
+        pass
+
+    def test_tickers(self):
+        log.info('retrieving tickers')
+        tickers = self.exchange.tickers([
+            self.exchange.get_asset('eth_btc'),
+            self.exchange.get_asset('etc_btc')
+        ])
+        assert len(tickers) == 2
+        pass
+
+    def test_get_balances(self):
+        log.info('testing wallet balances')
+        balances = self.exchange.get_balances()
+        pass
+
+    def test_get_account(self):
+        log.info('testing account data')
+        pass
+
+    def test_orderbook(self):
+        log.info('testing order book for poloniex')
+        asset = self.exchange.get_asset('eth_btc')
+
+        orderbook = self.exchange.get_orderbook(asset)
+        pass