Revert "Allow jupyter to run as root inside Docker image"

Allow jupyter to run as root inside Docker image

.gitignore

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

catalyst/__main__.py

@@ -3,6 +3,7 @@ import os
 from functools import wraps
 
 import click
+import sys
 import logbook
 import pandas as pd
 from six import text_type
@@ -257,7 +258,7 @@ def run(ctx,
     if capital_base is None:
         ctx.fail("must specify a capital base with '--capital-base'")
 
-    click.echo('Running in backtesting mode.')
+    click.echo('Running in backtesting mode.', sys.stdout)
 
     perf = _run(
         initialize=None,
@@ -282,13 +283,15 @@ def run(ctx,
         exchange=exchange_name,
         algo_namespace=algo_namespace,
         base_currency=base_currency,
+        analyze_live=None,
         live_graph=False,
         simulate_orders=True,
+        auth_aliases=None,
         stats_output=None,
     )
 
     if output == '-':
-        click.echo(str(perf))
+        click.echo(str(perf), sys.stdout)
     elif output != os.devnull:  # make the catalyst magic not write any data
         perf.to_pickle(output)
 
@@ -312,11 +315,11 @@ def catalyst_magic(line, cell=None):
                 '--algotext', cell,
                 '--output', os.devnull,  # don't write the results by default
             ] + ([
-                # these options are set when running in line magic mode
-                # set a non None algo text to use the ipython user_ns
-                '--algotext', '',
-                '--local-namespace',
-            ] if cell is None else []) + line.split(),
+                     # these options are set when running in line magic mode
+                     # set a non None algo text to use the ipython user_ns
+                     '--algotext', '',
+                     '--local-namespace',
+                 ] if cell is None else []) + line.split(),
             '%s%%catalyst' % ((cell or '') and '%'),
             # don't use system exit and propogate errors to the caller
             standalone_mode=False,
@@ -393,6 +396,12 @@ def catalyst_magic(line, cell=None):
     help='The base currency used to calculate statistics '
          '(e.g. usd, btc, eth).',
 )
+@click.option(
+    '-e',
+    '--end',
+    type=Date(tz='utc', as_timestamp=True),
+    help='An optional end date at which to stop the execution.',
+)
 @click.option(
     '--live-graph/--no-live-graph',
     is_flag=True,
@@ -406,6 +415,15 @@ def catalyst_magic(line, cell=None):
     help='Simulating orders enable the paper trading mode. No orders will be '
          'sent to the exchange unless set to false.',
 )
+@click.option(
+    '--auth-aliases',
+    default=None,
+    help='Authentication file aliases for the specified exchanges. By default,'
+         'each exchange uses the "auth.json" file in the exchange folder. '
+         'Specifying an "auth2" alias would use "auth2.json". It should be '
+         'specified like this: "[exchange_name],[alias],..." For example, '
+         '"binance,auth2" or "binance,auth2,bittrex,auth2".',
+)
 @click.pass_context
 def live(ctx,
          algofile,
@@ -418,7 +436,9 @@ def live(ctx,
          exchange_name,
          algo_namespace,
          base_currency,
+         end,
          live_graph,
+         auth_aliases,
          simulate_orders):
     """Trade live with the given algorithm.
     """
@@ -441,10 +461,10 @@ def live(ctx,
         ctx.fail("must specify a capital base with '--capital-base'")
 
     if simulate_orders:
-        click.echo('Running in paper trading mode.')
+        click.echo('Running in paper trading mode.', sys.stdout)
 
     else:
-        click.echo('Running in live trading mode.')
+        click.echo('Running in live trading mode.', sys.stdout)
 
     perf = _run(
         initialize=None,
@@ -460,7 +480,7 @@ def live(ctx,
         bundle=None,
         bundle_timestamp=None,
         start=None,
-        end=None,
+        end=end,
         output=output,
         print_algo=print_algo,
         local_namespace=local_namespace,
@@ -470,12 +490,14 @@ def live(ctx,
         algo_namespace=algo_namespace,
         base_currency=base_currency,
         live_graph=live_graph,
+        analyze_live=None,
         simulate_orders=simulate_orders,
+        auth_aliases=auth_aliases,
         stats_output=None,
     )
 
     if output == '-':
-        click.echo(str(perf))
+        click.echo(str(perf), sys.stdout)
     elif output != os.devnull:  # make the catalyst magic not write any data
         perf.to_pickle(output)
 
@@ -557,7 +579,7 @@ def ingest_exchange(ctx, exchange_name, data_frequency, start, end,
 
     exchange_bundle = ExchangeBundle(exchange_name)
 
-    click.echo('Ingesting exchange bundle {}...'.format(exchange_name))
+    click.echo('Ingesting exchange bundle {}...'.format(exchange_name), sys.stdout)
     exchange_bundle.ingest(
         data_frequency=data_frequency,
         include_symbols=include_symbols,
@@ -580,10 +602,11 @@ def ingest_exchange(ctx, exchange_name, data_frequency, start, end,
 @click.pass_context
 def clean_algo(ctx, algo_namespace):
     click.echo(
-        'Cleaning algo state: {}'.format(algo_namespace)
+        'Cleaning algo state: {}'.format(algo_namespace),
+        sys.stdout
     )
     delete_algo_folder(algo_namespace)
-    click.echo('Done')
+    click.echo('Done', sys.stdout)
 
 
 @main.command(name='clean-exchange')
@@ -610,11 +633,11 @@ def clean_exchange(ctx, exchange_name, data_frequency):
 
     exchange_bundle = ExchangeBundle(exchange_name)
 
-    click.echo('Cleaning exchange bundle {}...'.format(exchange_name))
+    click.echo('Cleaning exchange bundle {}...'.format(exchange_name), sys.stdout)
     exchange_bundle.clean(
         data_frequency=data_frequency,
     )
-    click.echo('Done')
+    click.echo('Done', sys.stdout)
 
 
 @main.command()
@@ -735,7 +758,7 @@ def bundles():
         # because there were no entries, print a single message indicating that
         # no ingestions have yet been made.
         for timestamp in ingestions or ["<no ingestions>"]:
-            click.echo("%s %s" % (bundle, timestamp))
+            click.echo("%s %s" % (bundle, timestamp), sys.stdout)
 
 
 if __name__ == '__main__':

catalyst/data/dispatch_bar_reader.py

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

catalyst/data/loader.py

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

catalyst/examples/buy_and_hodl.py

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

catalyst/examples/buy_btc_simple.py

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

catalyst/examples/buy_low_sell_high.py

@@ -143,7 +143,7 @@ def analyze(context, stats):
 
 
 if __name__ == '__main__':
-    live = False
+    live = True
     if live:
         run_algorithm(
             capital_base=0.001,

catalyst/examples/dual_moving_average.py

@@ -84,7 +84,8 @@ def handle_data(context, data):
 def analyze(context, perf):
 
     # Get the base_currency that was passed as a parameter to the simulation
-    base_currency = context.exchanges.values()[0].base_currency.upper()
+    exchange = list(context.exchanges.values())[0]
+    base_currency = exchange.base_currency.upper()
 
     # First chart: Plot portfolio value using base_currency
     ax1 = plt.subplot(411)

catalyst/examples/mean_reversion_simple.py

@@ -37,14 +37,14 @@ def initialize(context):
     context.base_price = None
     context.current_day = None
 
-    context.RSI_OVERSOLD = 50
-    context.RSI_OVERBOUGHT = 65
-    context.CANDLE_SIZE = '5T'
+    context.RSI_OVERSOLD = 55
+    context.RSI_OVERBOUGHT = 60
+    context.CANDLE_SIZE = '15T'
 
     context.start_time = time.time()
 
-    # context.set_commission(maker=0.1, taker=0.2)
-    context.set_slippage(spread=0.0001)
+    context.set_commission(maker=0.001, taker=0.002)
+    context.set_slippage(spread=0.001)
 
 
 def handle_data(context, data):
@@ -114,7 +114,7 @@ def handle_data(context, data):
     # TODO: retest with open orders
     # Since we are using limit orders, some orders may not execute immediately
     # we wait until all orders are executed before considering more trades.
-    orders = get_open_orders(context.market)
+    orders = context.blotter.open_orders
     if len(orders) > 0:
         log.info('exiting because orders are open: {}'.format(orders))
         return
@@ -161,7 +161,7 @@ def analyze(context=None, perf=None):
 
     import matplotlib.pyplot as plt
     # The base currency of the algo exchange
-    base_currency = context.exchanges.values()[0].base_currency.upper()
+    base_currency = list(context.exchanges.values())[0].base_currency.upper()
 
     # Plot the portfolio value over time.
     ax1 = plt.subplot(611)
@@ -244,11 +244,11 @@ def analyze(context=None, perf=None):
 
 if __name__ == '__main__':
     # The execution mode: backtest or live
-    live = False
+    live = True
 
     if live:
         run_algorithm(
-            capital_base=0.03,
+            capital_base=0.01,
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
@@ -259,6 +259,7 @@ if __name__ == '__main__':
             live_graph=False,
             simulate_orders=False,
             stats_output=None,
+            # auth_aliases=dict(poloniex='auth2')
         )
 
     else:
@@ -280,7 +281,7 @@ if __name__ == '__main__':
             analyze=analyze,
             exchange_name='bitfinex',
             algo_namespace=NAMESPACE,
-            base_currency='eth',
+            base_currency='btc',
             start=pd.to_datetime('2017-10-01', utc=True),
             end=pd.to_datetime('2017-11-10', utc=True),
             output=out

catalyst/examples/mean_reversion_simple_custom_fees.py

@@ -0,0 +1,288 @@
+# For this example, we're going to write a simple momentum script.  When the
+# stock goes up quickly, we're going to buy; when it goes down quickly, we're
+# going to sell.  Hopefully we'll ride the waves.
+import os
+import tempfile
+import time
+
+import numpy as np
+import pandas as pd
+import talib
+from logbook import Logger
+
+from catalyst import run_algorithm
+from catalyst.api import symbol, record, order_target_percent, get_open_orders
+from catalyst.exchange.utils.stats_utils import extract_transactions
+# We give a name to the algorithm which Catalyst will use to persist its state.
+# In this example, Catalyst will create the `.catalyst/data/live_algos`
+# directory. If we stop and start the algorithm, Catalyst will resume its
+# state using the files included in the folder.
+from catalyst.utils.paths import ensure_directory
+
+NAMESPACE = 'mean_reversion_simple'
+log = Logger(NAMESPACE)
+
+
+# To run an algorithm in Catalyst, you need two functions: initialize and
+# handle_data.
+
+def initialize(context):
+    # This initialize function sets any data or variables that you'll use in
+    # your algorithm.  For instance, you'll want to define the trading pair (or
+    # trading pairs) you want to backtest.  You'll also want to define any
+    # parameters or values you're going to use.
+
+    # In our example, we're looking at Neo in Ether.
+    context.market = symbol('eth_btc')
+    context.base_price = None
+    context.current_day = None
+
+    context.RSI_OVERSOLD = 50
+    context.RSI_OVERBOUGHT = 60
+    context.CANDLE_SIZE = '5T'
+
+    context.start_time = time.time()
+
+    context.set_commission(maker=0.001, taker=0.002)
+    # context.set_slippage(spread=0.001)
+
+
+def handle_data(context, data):
+    # This handle_data function is where the real work is done.  Our data is
+    # minute-level tick data, and each minute is called a frame.  This function
+    # runs on each frame of the data.
+
+    # We flag the first period of each day.
+    # Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
+    # would only execute once. This method works with minute and daily
+    # frequencies.
+    today = data.current_dt.floor('1D')
+    if today != context.current_day:
+        context.traded_today = False
+        context.current_day = today
+
+    # We're computing the volume-weighted-average-price of the security
+    # defined above, in the context.market variable.  For this example, we're
+    # using three bars on the 15 min bars.
+
+    # The frequency attribute determine the bar size. We use this convention
+    # for the frequency alias:
+    # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
+    prices = data.history(
+        context.market,
+        fields='close',
+        bar_count=50,
+        frequency=context.CANDLE_SIZE
+    )
+
+    # Ta-lib calculates various technical indicator based on price and
+    # volume arrays.
+
+    # In this example, we are comp
+    rsi = talib.RSI(prices.values, timeperiod=14)
+
+    # We need a variable for the current price of the security to compare to
+    # the average. Since we are requesting two fields, data.current()
+    # returns a DataFrame with
+    current = data.current(context.market, fields=['close', 'volume'])
+    price = current['close']
+
+    # If base_price is not set, we use the current value. This is the
+    # price at the first bar which we reference to calculate price_change.
+    if context.base_price is None:
+        context.base_price = price
+
+    price_change = (price - context.base_price) / context.base_price
+    cash = context.portfolio.cash
+
+    # Now that we've collected all current data for this frame, we use
+    # the record() method to save it. This data will be available as
+    # a parameter of the analyze() function for further analysis.
+
+    record(
+        volume=current['volume'],
+        price=price,
+        price_change=price_change,
+        rsi=rsi[-1],
+        cash=cash
+    )
+    # We are trying to avoid over-trading by limiting our trades to
+    # one per day.
+    if context.traded_today:
+        return
+
+    # TODO: retest with open orders
+    # Since we are using limit orders, some orders may not execute immediately
+    # we wait until all orders are executed before considering more trades.
+    orders = get_open_orders(context.market)
+    if len(orders) > 0:
+        log.info('exiting because orders are open: {}'.format(orders))
+        return
+
+    # Exit if we cannot trade
+    if not data.can_trade(context.market):
+        return
+
+    # Another powerful built-in feature of the Catalyst backtester is the
+    # portfolio object.  The portfolio object tracks your positions, cash,
+    # cost basis of specific holdings, and more.  In this line, we calculate
+    # how long or short our position is at this minute.
+    pos_amount = context.portfolio.positions[context.market].amount
+
+    if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
+        log.info(
+            '{}: buying - price: {}, rsi: {}'.format(
+                data.current_dt, price, rsi[-1]
+            )
+        )
+        # Set a style for limit orders,
+        limit_price = price * 1.005
+        order_target_percent(
+            context.market, 1, limit_price=limit_price
+        )
+        context.traded_today = True
+
+    elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
+        log.info(
+            '{}: selling - price: {}, rsi: {}'.format(
+                data.current_dt, price, rsi[-1]
+            )
+        )
+        limit_price = price * 0.995
+        order_target_percent(
+            context.market, 0, limit_price=limit_price
+        )
+        context.traded_today = True
+
+
+def analyze(context=None, perf=None):
+    end = time.time()
+    log.info('elapsed time: {}'.format(end - context.start_time))
+
+    import matplotlib.pyplot as plt
+    # The base currency of the algo exchange
+    base_currency = list(context.exchanges.values())[0].base_currency.upper()
+
+    # Plot the portfolio value over time.
+    ax1 = plt.subplot(611)
+    perf.loc[:, 'portfolio_value'].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio\nValue\n({})'.format(base_currency))
+
+    # Plot the price increase or decrease over time.
+    ax2 = plt.subplot(612, sharex=ax1)
+    perf.loc[:, 'price'].plot(ax=ax2, label='Price')
+
+    ax2.set_ylabel('{asset}\n({base})'.format(
+        asset=context.market.symbol, base=base_currency
+    ))
+
+    transaction_df = extract_transactions(perf)
+    if not transaction_df.empty:
+        buy_df = transaction_df[transaction_df['amount'] > 0]
+        sell_df = transaction_df[transaction_df['amount'] < 0]
+        ax2.scatter(
+            buy_df.index.to_pydatetime(),
+            perf.loc[buy_df.index.floor('1 min'), 'price'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax2.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index.floor('1 min'), 'price'],
+            marker='v',
+            s=100,
+            c='red',
+            label=''
+        )
+
+    ax4 = plt.subplot(613, sharex=ax1)
+    perf.loc[:, 'cash'].plot(
+        ax=ax4, label='Base Currency ({})'.format(base_currency)
+    )
+    ax4.set_ylabel('Cash\n({})'.format(base_currency))
+
+    perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
+
+    ax5 = plt.subplot(614, sharex=ax1)
+    perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
+    ax5.set_ylabel('Percent\nChange')
+
+    ax6 = plt.subplot(615, sharex=ax1)
+    perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
+    ax6.set_ylabel('RSI')
+    ax6.axhline(context.RSI_OVERBOUGHT, color='darkgoldenrod')
+    ax6.axhline(context.RSI_OVERSOLD, color='darkgoldenrod')
+
+    if not transaction_df.empty:
+        ax6.scatter(
+            buy_df.index.to_pydatetime(),
+            perf.loc[buy_df.index.floor('1 min'), 'rsi'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax6.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index.floor('1 min'), 'rsi'],
+            marker='v',
+            s=100,
+            c='red',
+            label=''
+        )
+    plt.legend(loc=3)
+    start, end = ax6.get_ylim()
+    ax6.yaxis.set_ticks(np.arange(0, end, end / 5))
+
+    # Show the plot.
+    plt.gcf().set_size_inches(18, 8)
+    plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    # The execution mode: backtest or live
+    live = False
+
+    if live:
+        run_algorithm(
+            capital_base=0.025,
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='poloniex',
+            live=True,
+            algo_namespace=NAMESPACE,
+            base_currency='btc',
+            live_graph=False,
+            simulate_orders=False,
+            stats_output=None,
+        )
+
+    else:
+        folder = os.path.join(
+            tempfile.gettempdir(), 'catalyst', NAMESPACE
+        )
+        ensure_directory(folder)
+
+        timestr = time.strftime('%Y%m%d-%H%M%S')
+        out = os.path.join(folder, '{}.p'.format(timestr))
+        # catalyst run -f catalyst/examples/mean_reversion_simple.py \
+        #    -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion \
+        #   --data-frequency minute --capital-base 10000
+        run_algorithm(
+            capital_base=0.1,
+            data_frequency='minute',
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='bitfinex',
+            algo_namespace=NAMESPACE,
+            base_currency='eth',
+            start=pd.to_datetime('2017-10-01', utc=True),
+            end=pd.to_datetime('2017-11-10', utc=True),
+            output=out
+        )
+        log.info('saved perf stats: {}'.format(out))

catalyst/examples/rsi_profit_target.py

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

catalyst/examples/simple_loop.py

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

catalyst/examples/simple_universe.py

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

catalyst/exchange/ccxt/ccxt_exchange.py

@@ -6,23 +6,29 @@ from collections import defaultdict
 import ccxt
 import pandas as pd
 import six
-from catalyst.assets._assets import TradingPair
-from ccxt import ExchangeNotAvailable, InvalidOrder
+from ccxt import InvalidOrder, NetworkError, \
+    ExchangeError
 from logbook import Logger
 from six import string_types
 
 from catalyst.algorithm import MarketOrder
+from catalyst.assets._assets import TradingPair
 from catalyst.constants import LOG_LEVEL
 from catalyst.exchange.exchange import Exchange
 from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import InvalidHistoryFrequencyError, \
     ExchangeSymbolsNotFound, ExchangeRequestError, InvalidOrderStyle, \
-    ExchangeNotFoundError, CreateOrderError, InvalidHistoryTimeframeError
+    ExchangeNotFoundError, CreateOrderError, InvalidHistoryTimeframeError, \
+    UnsupportedHistoryFrequencyError
 from catalyst.exchange.exchange_execution import ExchangeLimitOrder
 from catalyst.exchange.utils.exchange_utils import mixin_market_params, \
-    from_ms_timestamp, get_epoch, get_exchange_folder, get_catalyst_symbol, \
+    get_exchange_folder, get_catalyst_symbol, \
     get_exchange_auth
+from catalyst.exchange.utils.datetime_utils import from_ms_timestamp, \
+    get_epoch, \
+    get_periods_range
 from catalyst.finance.order import Order, ORDER_STATUS
+from catalyst.finance.transaction import Transaction
 
 log = Logger('CCXT', level=LOG_LEVEL)
 
@@ -55,6 +61,7 @@ class CCXT(Exchange):
                 'apiKey': key,
                 'secret': secret,
             })
+            self.api.enableRateLimit = True
 
         except Exception:
             raise ExchangeNotFoundError(exchange_name=exchange_name)
@@ -70,6 +77,7 @@ class CCXT(Exchange):
         self.max_requests_per_minute = 60
         self.low_balance_threshold = 0.1
         self.request_cpt = dict()
+        self._common_symbols = dict()
 
         self.bundle = ExchangeBundle(self.name)
         self.markets = None
@@ -105,7 +113,12 @@ class CCXT(Exchange):
                 with open(filename, 'w+') as f:
                     json.dump(self.markets, f, indent=4)
 
-            except ExchangeNotAvailable as e:
+            except (ExchangeError, NetworkError) as e:
+                log.warn(
+                    'unable to fetch markets {}: {}'.format(
+                        self.name, e
+                    )
+                )
                 raise ExchangeRequestError(error=e)
 
         self.load_assets()
@@ -210,6 +223,21 @@ class CCXT(Exchange):
         )
         return market
 
+    def substitute_currency_code(self, currency, source='catalyst'):
+        if source == 'catalyst':
+            currency = currency.upper()
+
+            key = self.api.common_currency_code(currency)
+            self._common_symbols[key] = currency.lower()
+            return key
+
+        else:
+            if currency in self._common_symbols:
+                return self._common_symbols[currency]
+
+            else:
+                return currency.lower()
+
     def get_symbol(self, asset_or_symbol, source='catalyst'):
         """
         The CCXT symbol.
@@ -217,6 +245,7 @@ class CCXT(Exchange):
         Parameters
         ----------
         asset_or_symbol
+        source
 
         Returns
         -------
@@ -226,7 +255,13 @@ class CCXT(Exchange):
         if source == 'ccxt':
             if isinstance(asset_or_symbol, string_types):
                 parts = asset_or_symbol.split('/')
-                return '{}_{}'.format(parts[0].lower(), parts[1].lower())
+                base_currency = self.substitute_currency_code(
+                    parts[0], source
+                )
+                quote_currency = self.substitute_currency_code(
+                    parts[1], source
+                )
+                return '{}_{}'.format(base_currency, quote_currency)
 
             else:
                 return asset_or_symbol.symbol
@@ -237,7 +272,13 @@ class CCXT(Exchange):
             ) else asset_or_symbol.symbol
 
             parts = symbol.split('_')
-            return '{}/{}'.format(parts[0].upper(), parts[1].upper())
+            base_currency = self.substitute_currency_code(
+                parts[0], source
+            )
+            quote_currency = self.substitute_currency_code(
+                parts[1], source
+            )
+            return '{}/{}'.format(base_currency, quote_currency)
 
     @staticmethod
     def map_frequency(value, source='ccxt', raise_error=True):
@@ -362,7 +403,7 @@ class CCXT(Exchange):
             timeframe, source='ccxt', raise_error=raise_error
         )
 
-    def get_candles(self, freq, assets, bar_count=None, start_dt=None,
+    def get_candles(self, freq, assets, bar_count=1, start_dt=None,
                     end_dt=None):
         is_single = (isinstance(assets, TradingPair))
         if is_single:
@@ -371,17 +412,46 @@ class CCXT(Exchange):
         symbols = self.get_symbols(assets)
         timeframe = CCXT.get_timeframe(freq)
 
-        ms = None
+        if timeframe not in self.api.timeframes:
+            freqs = [CCXT.get_frequency(t) for t in self.api.timeframes]
+            raise UnsupportedHistoryFrequencyError(
+                exchange=self.name,
+                freq=freq,
+                freqs=freqs,
+            )
+
+        if start_dt is not None and end_dt is not None:
+            raise ValueError(
+                'Please provide either start_dt or end_dt, not both.'
+            )
+
+        elif end_dt is not None:
+            # Make sure that end_dt really wants data in the past
+            # if it's close to now, we skip the 'since' parameters to
+            # lower the probability of error
+            bars_to_now = pd.date_range(
+                end_dt, pd.Timestamp.utcnow(), freq=freq
+            )
+            # See: https://github.com/ccxt/ccxt/issues/1360
+            if len(bars_to_now) > 1 or self.name in ['poloniex']:
+                dt_range = get_periods_range(
+                    end_dt=end_dt,
+                    periods=bar_count,
+                    freq=freq,
+                )
+                start_dt = dt_range[0]
+
+        since = None
         if start_dt is not None:
             delta = start_dt - get_epoch()
-            ms = int(delta.total_seconds()) * 1000
+            since = int(delta.total_seconds()) * 1000
 
         candles = dict()
-        for asset in assets:
+        for index, asset in enumerate(assets):
             ohlcvs = self.api.fetch_ohlcv(
-                symbol=symbols[0],
+                symbol=symbols[index],
                 timeframe=timeframe,
-                since=ms,
+                since=since,
                 limit=bar_count,
                 params={}
             )
@@ -398,6 +468,9 @@ class CCXT(Exchange):
                     close=ohlcv[4],
                     volume=ohlcv[5]
                 ))
+            candles[asset] = sorted(
+                candles[asset], key=lambda c: c['last_traded']
+            )
 
         if is_single:
             return six.next(six.itervalues(candles))
@@ -408,6 +481,7 @@ class CCXT(Exchange):
     def _fetch_symbol_map(self, is_local):
         try:
             return self.fetch_symbol_map(is_local)
+
         except ExchangeSymbolsNotFound:
             return None
 
@@ -559,8 +633,12 @@ class CCXT(Exchange):
             for key in balances:
                 balances_lower[key.lower()] = balances[key]
 
-        except Exception as e:
-            log.debug('error retrieving balances: {}', e)
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to fetch balance {}: {}'.format(
+                    self.name, e
+                )
+            )
             raise ExchangeRequestError(error=e)
 
         return balances_lower
@@ -695,6 +773,12 @@ class CCXT(Exchange):
         else:
             adj_amount = abs(amount)
 
+        if adj_amount == 0:
+            raise CreateOrderError(
+                exchange=self.name,
+                e='order amount lower than the smallest lot: {}'.format(amount)
+            )
+
         try:
             result = self.api.create_order(
                 symbol=symbol,
@@ -703,14 +787,18 @@ class CCXT(Exchange):
                 amount=adj_amount,
                 price=price
             )
-        except ExchangeNotAvailable as e:
-            log.debug('unable to create order: {}'.format(e))
-            raise ExchangeRequestError(error=e)
-
         except InvalidOrder as e:
             log.warn('the exchange rejected the order: {}'.format(e))
             raise CreateOrderError(exchange=self.name, error=e)
 
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to create order {} / {}: {}'.format(
+                    self.name, symbol, e
+                )
+            )
+            raise ExchangeRequestError(error=e)
+
         if 'info' not in result:
             raise ValueError('cannot use order without info attribute')
 
@@ -735,18 +823,121 @@ class CCXT(Exchange):
                 limit=None,
                 params=dict()
             )
-        except Exception as e:
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to fetch open orders {} / {}: {}'.format(
+                    self.name, asset.symbol, e
+                )
+            )
             raise ExchangeRequestError(error=e)
 
         orders = []
         for order_status in result:
-            order, executed_price = self._create_order(order_status)
+            order, _ = self._create_order(order_status)
             if asset is None or asset == order.sid:
                 orders.append(order)
 
         return orders
 
-    def get_order(self, order_id, asset_or_symbol=None):
+    def _process_order_fallback(self, order):
+        """
+        Fallback method for exchanges which do not play nice with
+        fetch-my-trades. Apparently, about 60% of exchanges will return
+        the correct executed values with this method. Others will support
+        fetch-my-trades.
+
+        Parameters
+        ----------
+        order: Order
+
+        Returns
+        -------
+        float
+
+        """
+        exc_order, price = self.get_order(
+            order.id, order.asset, return_price=True
+        )
+        order.status = exc_order.status
+
+        order.commission = exc_order.commission
+        if order.amount != exc_order.amount:
+            log.warn(
+                'executed order amount {} differs '
+                'from original'.format(
+                    exc_order.amount, order.amount
+                )
+            )
+            order.amount = exc_order.amount
+
+        if order.status == ORDER_STATUS.FILLED:
+            transaction = Transaction(
+                asset=order.asset,
+                amount=order.amount,
+                dt=pd.Timestamp.utcnow(),
+                price=price,
+                order_id=order.id,
+                commission=order.commission
+            )
+        return [transaction]
+
+    def process_order(self, order):
+        # TODO: move to parent class after tracking features in the parent
+        if not self.api.hasFetchMyTrades:
+            return self._process_order_fallback(order)
+
+        try:
+            all_trades = self.get_trades(order.asset)
+        except ExchangeRequestError as e:
+            log.warn(
+                'unable to fetch account trades, trying an alternate '
+                'method to find executed order {} / {}: {}'.format(
+                    order.id, order.asset.symbol, e
+                )
+            )
+            return self._process_order_fallback(order)
+
+        transactions = []
+        trades = [t for t in all_trades if t['order'] == order.id]
+        if not trades:
+            log.debug(
+                'order {} / {} not found in trades'.format(
+                    order.id, order.asset.symbol
+                )
+            )
+            return transactions
+
+        trades.sort(key=lambda t: t['timestamp'], reverse=False)
+        order.filled = 0
+        order.commission = 0
+        for trade in trades:
+            # status property will update automatically
+            filled = trade['amount'] * order.direction
+            order.filled += filled
+
+            commission = 0
+            if 'fee' in trade and 'cost' in trade['fee']:
+                commission = trade['fee']['cost']
+                order.commission += commission
+
+            order.check_triggers(
+                price=trade['price'],
+                dt=pd.to_datetime(trade['timestamp'], unit='ms', utc=True),
+            )
+            transaction = Transaction(
+                asset=order.asset,
+                amount=filled,
+                dt=pd.Timestamp.utcnow(),
+                price=trade['price'],
+                order_id=order.id,
+                commission=commission
+            )
+            transactions.append(transaction)
+
+        order.broker_order_id = ', '.join([t['id'] for t in trades])
+        return transactions
+
+    def get_order(self, order_id, asset_or_symbol=None, return_price=False):
         if asset_or_symbol is None:
             log.debug(
                 'order not found in memory, the request might fail '
@@ -758,10 +949,19 @@ class CCXT(Exchange):
             order_status = self.api.fetch_order(id=order_id, symbol=symbol)
             order, executed_price = self._create_order(order_status)
 
-        except Exception as e:
-            raise ExchangeRequestError(error=e)
+            if return_price:
+                return order, executed_price
 
-        return order, executed_price
+            else:
+                return order
+
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to fetch order {} / {}: {}'.format(
+                    self.name, order_id, e
+                )
+            )
+            raise ExchangeRequestError(error=e)
 
     def cancel_order(self, order_param, asset_or_symbol=None):
         order_id = order_param.id \
@@ -777,7 +977,12 @@ class CCXT(Exchange):
                 if asset_or_symbol is not None else None
             self.api.cancel_order(id=order_id, symbol=symbol)
 
-        except Exception as e:
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to cancel order {} / {}: {}'.format(
+                    self.name, order_id, e
+                )
+            )
             raise ExchangeRequestError(error=e)
 
     def tickers(self, assets):
@@ -793,48 +998,46 @@ class CCXT(Exchange):
         list[dict[str, float]
 
         """
-        tickers = dict()
-        try:
-            for asset in assets:
-                symbol = self.get_symbol(asset)
-                # TODO: use fetch_tickers() for efficiency
-                # I tried using fetch_tickers() but noticed some
-                # inconsistencies, see issue:
-                # https://github.com/ccxt/ccxt/issues/870
+        tickers = {}
+        for asset in assets:
+            symbol = self.get_symbol(asset)
+
+            # Test the CCXT throttling further to see if we need this
+            self.ask_request()
+
+            # TODO: use fetch_tickers() for efficiency
+            # I tried using fetch_tickers() but noticed some
+            # inconsistencies, see issue:
+            # https://github.com/ccxt/ccxt/issues/870
+            try:
                 ticker = self.api.fetch_ticker(symbol=symbol)
-                if not ticker:
-                    log.warn('ticker not found for {} {}'.format(
-                        self.name, symbol
-                    ))
-                    continue
-
-                ticker['last_traded'] = from_ms_timestamp(ticker['timestamp'])
-
-                if 'last_price' not in ticker:
-                    # TODO: any more exceptions?
-                    ticker['last_price'] = ticker['last']
-
-                if 'baseVolume' in ticker and ticker['baseVolume'] is not None:
-                    # Using the volume represented in the base currency
-                    ticker['volume'] = ticker['baseVolume']
-
-                elif 'info' in ticker and 'bidQty' in ticker['info'] \
-                        and 'askQty' in ticker['info']:
-                    ticker['volume'] = float(ticker['info']['bidQty']) + \
-                                       float(ticker['info']['askQty'])
-
-                else:
-                    ticker['volume'] = 0
-
-                tickers[asset] = ticker
-
-        except ExchangeNotAvailable as e:
-            log.warn(
-                'unable to fetch ticker: {} {}'.format(
-                    self.name, asset.symbol
+            except (ExchangeError, NetworkError) as e:
+                log.warn(
+                    'unable to fetch ticker {} / {}: {}'.format(
+                        self.name, asset.symbol, e
+                    )
                 )
-            )
-            raise ExchangeRequestError(error=e)
+                continue
+
+            ticker['last_traded'] = from_ms_timestamp(ticker['timestamp'])
+
+            if 'last_price' not in ticker:
+                # TODO: any more exceptions?
+                ticker['last_price'] = ticker['last']
+
+            if 'baseVolume' in ticker and ticker['baseVolume'] is not None:
+                # Using the volume represented in the base currency
+                ticker['volume'] = ticker['baseVolume']
+
+            elif 'info' in ticker and 'bidQty' in ticker['info'] \
+                    and 'askQty' in ticker['info']:
+                ticker['volume'] = float(ticker['info']['bidQty']) + \
+                                   float(ticker['info']['askQty'])
+
+            else:
+                ticker['volume'] = 0
+
+            tickers[asset] = ticker
 
         return tickers
 
@@ -864,3 +1067,27 @@ class CCXT(Exchange):
                 ))
 
         return result
+
+    def get_trades(self, asset, my_trades=True, start_dt=None, limit=None):
+        if not my_trades:
+            raise NotImplemented(
+                'get_trades only supports "my trades"'
+            )
+
+        # TODO: is it possible to sort this? Limit is useless otherwise.
+        ccxt_symbol = self.get_symbol(asset)
+        try:
+            trades = self.api.fetch_my_trades(
+                symbol=ccxt_symbol,
+                since=start_dt,
+                limit=limit,
+            )
+        except (ExchangeError, NetworkError) as e:
+            log.warn(
+                'unable to fetch trades {} / {}: {}'.format(
+                    self.name, asset.symbol, e
+                )
+            )
+            raise ExchangeRequestError(error=e)
+
+        return trades

catalyst/exchange/exchange.py

@@ -5,8 +5,6 @@ from time import sleep
 
 import numpy as np
 import pandas as pd
-from logbook import Logger
-
 from catalyst.constants import LOG_LEVEL
 from catalyst.data.data_portal import BASE_FIELDS
 from catalyst.exchange.exchange_bundle import ExchangeBundle
@@ -15,10 +13,12 @@ from catalyst.exchange.exchange_errors import MismatchingBaseCurrencies, \
     PricingDataNotLoadedError, \
     NoDataAvailableOnExchange, NoValueForField, LastCandleTooEarlyError, \
     TickerNotFoundError, NotEnoughCashError
-from catalyst.exchange.utils.bundle_utils import get_start_dt, \
-    get_delta, get_periods, get_periods_range
+from catalyst.exchange.utils.datetime_utils import get_delta, \
+    get_periods_range, \
+    get_periods, get_start_dt, get_frequency
 from catalyst.exchange.utils.exchange_utils import get_exchange_symbols, \
-    get_frequency, resample_history_df, has_bundle
+    resample_history_df, has_bundle
+from logbook import Logger
 
 log = Logger('Exchange', level=LOG_LEVEL)
 
@@ -234,11 +234,15 @@ class Exchange:
         """
         asset = None
 
+        # TODO: temp mapping, fix to use a single symbol convention
+        og_symbol = symbol
+        symbol = self.get_symbol(symbol) if not is_exchange_symbol else symbol
         log.debug(
             'searching assets for: {} {}'.format(
                 self.name, symbol
             )
         )
+        # TODO: simplify and loose the loop
         for a in self.assets:
             if asset is not None:
                 break
@@ -260,7 +264,8 @@ class Exchange:
 
             # The symbol provided may use the Catalyst or the exchange
             # convention
-            key = a.exchange_symbol if is_exchange_symbol else a.symbol
+            key = a.exchange_symbol if \
+                is_exchange_symbol else self.get_symbol(a)
             if not asset and key.lower() == symbol.lower():
                 if applies:
                     asset = a
@@ -276,7 +281,7 @@ class Exchange:
             supported_symbols = sorted([a.symbol for a in self.assets])
 
             raise SymbolNotFoundOnExchange(
-                symbol=symbol,
+                symbol=og_symbol,
                 exchange=self.name.title(),
                 supported_symbols=supported_symbols
             )
@@ -434,7 +439,7 @@ class Exchange:
         series = pd.Series(values, index=dates)
 
         periods = get_periods_range(
-            start_dt, end_dt, data_frequency
+            start_dt=start_dt, end_dt=end_dt, freq=data_frequency
         )
         # TODO: ensure that this working as expected, if not use fillna
         series = series.reindex(
@@ -498,39 +503,37 @@ class Exchange:
         freq, candle_size, unit, data_frequency = get_frequency(
             frequency, data_frequency
         )
-        adj_bar_count = candle_size * bar_count
-
-        start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency)
-
         # The get_history method supports multiple asset
         candles = self.get_candles(
             freq=freq,
             assets=assets,
             bar_count=bar_count,
-            start_dt=start_dt if not is_current else None,
             end_dt=end_dt if not is_current else None,
         )
 
         series = dict()
         for asset in candles:
+            first_candle = candles[asset][0]
             asset_series = self.get_series_from_candles(
                 candles=candles[asset],
-                start_dt=start_dt,
+                start_dt=first_candle['last_traded'],
                 end_dt=end_dt,
                 data_frequency=frequency,
                 field=field,
             )
-            if end_dt is not None:
-                delta = get_delta(candle_size, data_frequency)
-                adj_end_dt = end_dt - delta
-                last_traded = asset_series.index[-1]
 
-                if last_traded < adj_end_dt:
-                    raise LastCandleTooEarlyError(
-                        last_traded=last_traded,
-                        end_dt=adj_end_dt,
-                        exchange=self.name,
-                    )
+            # Checking to make sure that the dates match
+            delta = get_delta(candle_size, data_frequency)
+            adj_end_dt = end_dt - delta
+            last_traded = asset_series.index[-1]
+
+            if last_traded < adj_end_dt:
+                raise LastCandleTooEarlyError(
+                    last_traded=last_traded,
+                    end_dt=adj_end_dt,
+                    exchange=self.name,
+                )
+
             series[asset] = asset_series
 
         df = pd.DataFrame(series)
@@ -584,11 +587,11 @@ class Exchange:
             A dataframe containing the requested data.
 
         """
+        # TODO: this function needs some work, we're currently using it just for benchmark data
         freq, candle_size, unit, data_frequency = get_frequency(
             frequency, data_frequency
         )
         adj_bar_count = candle_size * bar_count
-
         try:
             series = self.bundle.get_history_window_series_and_load(
                 assets=assets,
@@ -615,15 +618,14 @@ class Exchange:
                 # The get_history method supports multiple asset
                 # Use the original frequency to let each api optimize
                 # the size of result sets
-                trailing_bar_count = get_periods(
+                trailing_bars = get_periods(
                     trailing_dt, end_dt, freq
                 )
                 candles = self.get_candles(
                     freq=freq,
                     assets=asset,
-                    bar_count=trailing_bar_count,
-                    start_dt=start_dt,
-                    end_dt=end_dt
+                    end_dt=end_dt,
+                    bar_count=trailing_bars if trailing_bars < 500 else 500,
                 )
 
                 last_value = series[asset].iloc(0) if asset in series \
@@ -900,6 +902,22 @@ class Exchange:
         """
         pass
 
+    @abstractmethod
+    def process_order(self, order):
+        """
+        Similar to get_order but looks only for executed orders.
+
+        Parameters
+        ----------
+        order: Order
+
+        Returns
+        -------
+        float
+            Avg execution price
+
+        """
+
     @abstractmethod
     def cancel_order(self, order_param, symbol_or_asset=None):
         """Cancel an open order.
@@ -914,8 +932,7 @@ class Exchange:
         pass
 
     @abstractmethod
-    def get_candles(self, freq, assets, bar_count=None,
-                    start_dt=None, end_dt=None):
+    def get_candles(self, freq, assets, bar_count, start_dt=None, end_dt=None):
         """
         Retrieve OHLCV candles for the given assets
 
@@ -980,7 +997,7 @@ class Exchange:
     @abc.abstractmethod
     def get_orderbook(self, asset, order_type, limit):
         """
-        Retrieve the the orderbook for the given trading pair.
+        Retrieve the orderbook for the given trading pair.
 
         Parameters
         ----------
@@ -994,3 +1011,20 @@ class Exchange:
         list[dict[str, float]
         """
         pass
+
+    @abc.abstractmethod
+    def get_trades(self, asset, my_trades, start_dt, limit):
+        """
+        Retrieve a list of trades.
+
+        Parameters
+        ----------
+        my_trades: bool
+            List only my trades.
+        start_dt
+        limit
+
+        Returns
+        -------
+
+        """

catalyst/exchange/exchange_algorithm.py

@@ -18,11 +18,9 @@ from datetime import timedelta
 from os import listdir
 from os.path import isfile, join
 
+import catalyst.protocol as zp
 import logbook
 import pandas as pd
-from redo import retry
-
-import catalyst.protocol as zp
 from catalyst.algorithm import TradingAlgorithm
 from catalyst.constants import LOG_LEVEL
 from catalyst.exchange.exchange_blotter import ExchangeBlotter
@@ -49,6 +47,7 @@ from catalyst.utils.api_support import api_method
 from catalyst.utils.input_validation import error_keywords, ensure_upper_case
 from catalyst.utils.math_utils import round_nearest
 from catalyst.utils.preprocess import preprocess
+from redo import retry
 
 log = logbook.Logger('exchange_algorithm', level=LOG_LEVEL)
 
@@ -130,7 +129,7 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
 
     @api_method
     def set_commission(self, maker=None, taker=None):
-        key = self.blotter.commission_models.keys()[0]
+        key = list(self.blotter.commission_models.keys())[0]
         if maker is not None:
             self.blotter.commission_models[key].maker = maker
 
@@ -139,7 +138,7 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
 
     @api_method
     def set_slippage(self, spread=None):
-        key = self.blotter.slippage_models.keys()[0]
+        key = list(self.blotter.slippage_models.keys())[0]
         if spread is not None:
             self.blotter.slippage_models[key].spread = spread
 
@@ -271,9 +270,9 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
         # Merging latest recorded variables
         stats.update(self.recorded_vars)
 
-        stats['positions'] = cum.position_tracker.get_positions_list()
-
         period = tracker.todays_performance
+        stats['positions'] = period.position_tracker.get_positions_list()
+
         # we want the key to be absent, not just empty
         # Only include transactions for given dt
         stats['transactions'] = []
@@ -304,6 +303,7 @@ class ExchangeTradingAlgorithmBacktest(ExchangeTradingAlgorithmBase):
         super(ExchangeTradingAlgorithmBacktest, self).__init__(*args, **kwargs)
 
         self.frame_stats = list()
+        self.state = {}
         log.info('initialized trading algorithm in backtest mode')
 
     def is_last_frame_of_day(self, data):
@@ -351,6 +351,7 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         self.live_graph = kwargs.pop('live_graph', None)
         self.stats_output = kwargs.pop('stats_output', None)
         self._analyze_live = kwargs.pop('analyze_live', None)
+        self.end = kwargs.pop('end', None)
 
         self._clock = None
         self.frame_stats = list()
@@ -391,16 +392,20 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
                      'before exiting the algorithm.')
 
             algo_folder = get_algo_folder(self.algo_namespace)
-            folder = join(algo_folder, 'daily_perf')
+            folder = join(algo_folder, 'daily_performance')
             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))
+                    perf_period = pickle.load(handle)
+                    perf_period_dict = perf_period.to_dict()
+                    daily_perf_list.append(perf_period_dict)
 
             stats = pd.DataFrame(daily_perf_list)
+            stats.set_index('period_close', drop=False, inplace=True)
 
             self.analyze(stats)
 
@@ -460,43 +465,69 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
 
         return self._clock
 
-    def get_generator(self):
-        if self.trading_client is not None:
-            return self.trading_client.transform()
+    def _init_trading_client(self):
+        """
+        This replaces Ziplines `_create_generator` method. The main difference
+        is that we are restoring performance tracker objects if available.
+        This allows us to stop/start algos without loosing their state.
+
+        """
+        self.state = get_algo_object(
+            algo_name=self.algo_namespace,
+            key='context.state',
+        )
+        if self.state is None:
+            self.state = {}
 
-        perf = None
         if self.perf_tracker is None:
+            # Note from the Zipline dev:
+            # HACK: When running with the `run` method, we set perf_tracker to
+            # None so that it will be overwritten here.
             tracker = self.perf_tracker = PerformanceTracker(
                 sim_params=self.sim_params,
                 trading_calendar=self.trading_calendar,
                 env=self.trading_environment,
             )
-
             # Set the dt initially to the period start by forcing it to change.
             self.on_dt_changed(self.sim_params.start_session)
 
+            new_position_tracker = tracker.position_tracker
+            tracker.position_tracker = None
+
             # Unpacking the perf_tracker and positions if available
-            perf = get_algo_object(
+            cum_perf = get_algo_object(
                 algo_name=self.algo_namespace,
                 key='cumulative_performance',
             )
+            if cum_perf is not None:
+                tracker.cumulative_performance = cum_perf
+                # Ensure single common position tracker
+                tracker.position_tracker = cum_perf.position_tracker
+
+            today = pd.Timestamp.utcnow().floor('1D')
+            todays_perf = get_algo_object(
+                algo_name=self.algo_namespace,
+                key=today.strftime('%Y-%m-%d'),
+                rel_path='daily_performance',
+            )
+            if todays_perf is not None:
+                # Ensure single common position tracker
+                if tracker.position_tracker is not None:
+                    todays_perf.position_tracker = tracker.position_tracker
+                else:
+                    tracker.position_tracker = todays_perf.position_tracker
+
+                tracker.todays_performance = todays_perf
+
+            if tracker.position_tracker is None:
+                # Use a new position_tracker if not is found in the state
+                tracker.position_tracker = new_position_tracker
 
         if not self.initialized:
+            # Calls the initialize function of the algorithm
             self.initialize(*self.initialize_args, **self.initialize_kwargs)
             self.initialized = True
 
-        # Call the simulation trading algorithm for side-effects:
-        # it creates the perf tracker
-        # TradingAlgorithm._create_generator(self, self.sim_params)
-        if perf is not None:
-            tracker.cumulative_performance = perf
-
-            period = self.perf_tracker.todays_performance
-            period.starting_cash = perf.ending_cash
-            period.starting_exposure = perf.ending_exposure
-            period.starting_value = perf.ending_value
-            period.position_tracker = perf.position_tracker
-
         self.trading_client = ExchangeAlgorithmExecutor(
             algo=self,
             sim_params=self.sim_params,
@@ -506,6 +537,11 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             restrictions=self.restrictions,
             universe_func=self._calculate_universe,
         )
+
+    def get_generator(self):
+        if self.trading_client is None:
+            self._init_trading_client()
+
         return self.trading_client.transform()
 
     def updated_portfolio(self):
@@ -523,10 +559,6 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         positions, returning the available cash, and raising error
         if the data goes out of sync.
 
-        Parameters
-        ----------
-        attempt_index: int
-
         Returns
         -------
         float
@@ -559,10 +591,19 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             if base_currency is None:
                 base_currency = exchange.base_currency
 
+            # Don't check the cash if there are open orders. This could
+            # results in false positives.
+            orders = []
+            for asset in self.blotter.open_orders:
+                asset_orders = self.blotter.open_orders[asset]
+                if asset_orders:
+                    orders += asset_orders
+
+            required_cash = self.portfolio.cash if not orders else None
             cash, positions_value = exchange.sync_positions(
                 positions=exchange_positions,
                 check_balances=check_balances,
-                cash=self.portfolio.cash,
+                cash=required_cash,
             )
             total_cash += cash
             total_positions_value += positions_value
@@ -670,17 +711,22 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         if not self.is_running:
             return
 
+        if self.end is not None and self.end < data.current_dt:
+            log.info('Algorithm has reached specified end time. Finishing...')
+            self.interrupt_algorithm()
+
         # Resetting the frame stats every day to minimize memory footprint
         today = data.current_dt.floor('1D')
         if self.current_day is not None and today > self.current_day:
             self.frame_stats = list()
 
         self.performance_needs_update = False
-        new_orders = self.perf_tracker.todays_performance.orders_by_id.keys()
-        if new_orders != self._last_orders:
+        orders = list(self.perf_tracker.todays_performance.orders_by_id.keys())
+        if orders != self._last_orders:
             self.performance_needs_update = True
 
-        self._last_orders = new_orders
+        # Saving current orders to detect changes in the next frame
+        self._last_orders = copy.deepcopy(orders)
 
         if self.performance_needs_update:
             self.perf_tracker.update_performance()
@@ -697,7 +743,7 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             self.portfolio_needs_update = False
 
         log.info(
-            'got totals from exchanges, cash: {} positions: {}'.format(
+            'portfolio balances, cash: {}, positions: {}'.format(
                 cash, positions_value
             )
         )
@@ -709,18 +755,34 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         # every bar no matter if the algorithm places an order or not.
         self.validate_account_controls()
 
+        self._save_algo_state(data)
+        self.current_day = data.current_dt.floor('1D')
+
+    def _save_algo_state(self, data):
+        today = data.current_dt.floor('1D')
         try:
             self._save_stats_csv(self._process_stats(data))
         except Exception as e:
             log.warn('unable to calculate performance: {}'.format(e))
 
+        log.debug('saving cumulative performance object')
         save_algo_object(
             algo_name=self.algo_namespace,
             key='cumulative_performance',
             obj=self.perf_tracker.cumulative_performance,
         )
-
-        self.current_day = data.current_dt.floor('1D')
+        log.debug('saving todays performance object')
+        save_algo_object(
+            algo_name=self.algo_namespace,
+            key=today.strftime('%Y-%m-%d'),
+            obj=self.perf_tracker.todays_performance,
+            rel_path='daily_performance'
+        )
+        log.debug('saving context.state object')
+        save_algo_object(
+            algo_name=self.algo_namespace,
+            key='context.state',
+            obj=self.state)
 
     def _process_stats(self, data):
         today = data.current_dt.floor('1D')
@@ -763,12 +825,6 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             start_dt=today,
             end_dt=data.current_dt
         )
-        save_algo_object(
-            algo_name=self.algo_namespace,
-            key=today.strftime('%Y-%m-%d'),
-            obj=daily_stats,
-            rel_path='daily_perf'
-        )
 
         return recorded_cols
 

catalyst/exchange/exchange_asset_finder.py

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

catalyst/exchange/exchange_blotter.py

@@ -1,8 +1,9 @@
+import numpy as np
 import pandas as pd
-from catalyst.assets._assets import TradingPair
 from logbook import Logger
 from redo import retry
 
+from catalyst.assets._assets import TradingPair
 from catalyst.constants import LOG_LEVEL
 from catalyst.exchange.exchange_errors import ExchangeRequestError
 from catalyst.finance.blotter import Blotter
@@ -42,6 +43,11 @@ class TradingPairFeeSchedule(CommissionModel):
             )
         )
 
+    def get_maker_taker(self, asset):
+        maker = self.maker if self.maker is not None else asset.maker
+        taker = self.taker if self.taker is not None else asset.taker
+        return maker, taker
+
     def calculate(self, order, transaction):
         """
         Calculate the final fee based on the order parameters.
@@ -55,8 +61,7 @@ class TradingPairFeeSchedule(CommissionModel):
         cost = abs(transaction.amount) * transaction.price
 
         asset = order.asset
-        maker = self.maker if self.maker is not None else asset.maker
-        taker = self.taker if self.taker is not None else asset.taker
+        maker, taker = self.get_maker_taker(asset)
 
         multiplier = taker
         if order.limit is not None:
@@ -90,7 +95,6 @@ class TradingPairFixedSlippage(SlippageModel):
 
     def simulate(self, data, asset, orders_for_asset):
         self._volume_for_bar = 0
-
         price = data.current(asset, 'close')
 
         dt = data.current_dt
@@ -100,18 +104,20 @@ class TradingPairFixedSlippage(SlippageModel):
 
             order.check_triggers(price, dt)
             if not order.triggered:
-                log.debug('order has not reached the trigger at current '
-                          'price {}'.format(price))
+                log.info(
+                    'order has not reached the trigger at current '
+                    'price {}'.format(price)
+                )
                 continue
 
             execution_price, execution_volume = self.process_order(data, order)
+            if execution_price is not None:
+                transaction = create_transaction(
+                    order, dt, execution_price, execution_volume
+                )
 
-            transaction = create_transaction(
-                order, dt, execution_price, execution_volume
-            )
-
-            self._volume_for_bar += abs(transaction.amount)
-            yield order, transaction
+                self._volume_for_bar += abs(transaction.amount)
+                yield order, transaction
 
     def process_order(self, data, order):
         price = data.current(order.asset, 'close')
@@ -202,34 +208,29 @@ class ExchangeBlotter(Blotter):
             for order in self.open_orders[asset]:
                 log.debug('found open order: {}'.format(order.id))
 
-                new_order, executed_price = exchange.get_order(order.id, asset)
-                log.debug(
-                    'got updated order {} {}'.format(
-                        new_order, executed_price
+                transactions = exchange.process_order(order)
+                # This is a temporary measure, we should really update all
+                # trades, not just when the order gets filled. I just think
+                # that this is safer until we have a robust way to track
+                # the trades already processed by the algo. We can't loose
+                # them if the algo shuts down.
+                if transactions and order.open_amount == 0:
+                    avg_price = np.average(
+                        a=[t.price for t in transactions],
+                        weights=[t.amount for t in transactions],
                     )
-                )
-                order.status = new_order.status
-
-                if order.status == ORDER_STATUS.FILLED:
-                    order.commission = new_order.commission
-                    if order.amount != new_order.amount:
-                        log.warn(
-                            'executed order amount {} differs '
-                            'from original'.format(
-                                new_order.amount, order.amount
-                            )
+                    ostatus = 'filled' if order.open_amount == 0 else 'partial'
+                    log.info(
+                        '{} order {} / {}: {}, avg price: {}'.format(
+                            ostatus,
+                            order.id,
+                            asset.symbol,
+                            order.filled,
+                            avg_price,
                         )
-                        order.amount = new_order.amount
-
-                    transaction = Transaction(
-                        asset=order.asset,
-                        amount=order.amount,
-                        dt=pd.Timestamp.utcnow(),
-                        price=executed_price,
-                        order_id=order.id,
-                        commission=order.commission
                     )
-                    yield order, transaction
+                    for transaction in transactions:
+                        yield order, transaction
 
                 elif order.status == ORDER_STATUS.CANCELLED:
                     yield order, None
@@ -250,7 +251,6 @@ class ExchangeBlotter(Blotter):
 
         for order, txn in self.check_open_orders():
             order.dt = txn.dt
-
             transactions.append(txn)
 
             if not order.open:

catalyst/exchange/exchange_bundle.py

@@ -8,12 +8,8 @@ from operator import is_not
 import numpy as np
 import pandas as pd
 import pytz
-from catalyst.assets._assets import TradingPair
-from logbook import Logger
-from pytz import UTC
-from six import itervalues
-
 from catalyst import get_calendar
+from catalyst.assets._assets import TradingPair
 from catalyst.constants import DATE_TIME_FORMAT, AUTO_INGEST
 from catalyst.constants import LOG_LEVEL
 from catalyst.data.minute_bars import BcolzMinuteOverlappingData, \
@@ -25,13 +21,16 @@ from catalyst.exchange.exchange_errors import EmptyValuesInBundleError, \
     NoDataAvailableOnExchange, \
     PricingDataNotLoadedError, DataCorruptionError, PricingDataValueError
 from catalyst.exchange.utils.bundle_utils import range_in_bundle, \
-    get_bcolz_chunk, get_month_start_end, \
-    get_year_start_end, get_df_from_arrays, get_start_dt, get_period_label, \
-    get_delta, get_assets
+    get_bcolz_chunk, get_df_from_arrays, get_assets
+from catalyst.exchange.utils.datetime_utils import get_delta, get_start_dt, \
+    get_period_label, get_month_start_end, get_year_start_end
 from catalyst.exchange.utils.exchange_utils import get_exchange_folder, \
     save_exchange_symbols, mixin_market_params, get_catalyst_symbol
 from catalyst.utils.cli import maybe_show_progress
 from catalyst.utils.paths import ensure_directory
+from logbook import Logger
+from pytz import UTC
+from six import itervalues
 
 log = Logger('exchange_bundle', level=LOG_LEVEL)
 
@@ -233,12 +232,12 @@ class ExchangeBundle:
 
                 problem = '{name} ({start_dt} to {end_dt}) has empty ' \
                           'periods: {dates}'.format(
-                            name=asset.symbol,
-                            start_dt=asset.start_date.strftime(
-                                DATE_TIME_FORMAT),
-                            end_dt=end_dt.strftime(DATE_TIME_FORMAT),
-                            dates=[date.strftime(
-                                DATE_TIME_FORMAT) for date in dates])
+                    name=asset.symbol,
+                    start_dt=asset.start_date.strftime(
+                        DATE_TIME_FORMAT),
+                    end_dt=end_dt.strftime(DATE_TIME_FORMAT),
+                    dates=[date.strftime(
+                        DATE_TIME_FORMAT) for date in dates])
 
                 if empty_rows_behavior == 'warn':
                     log.warn(problem)
@@ -287,12 +286,12 @@ class ExchangeBundle:
 
             problem = '{name} ({start_dt} to {end_dt}) has {threshold} ' \
                       'identical close values on: {dates}'.format(
-                        name=asset.symbol,
-                        start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
-                        end_dt=end_dt.strftime(DATE_TIME_FORMAT),
-                        threshold=threshold,
-                        dates=[pd.to_datetime(date).strftime(DATE_TIME_FORMAT)
-                               for date in dates])
+                name=asset.symbol,
+                start_dt=asset.start_date.strftime(DATE_TIME_FORMAT),
+                end_dt=end_dt.strftime(DATE_TIME_FORMAT),
+                threshold=threshold,
+                dates=[pd.to_datetime(date).strftime(DATE_TIME_FORMAT)
+                       for date in dates])
 
             problems.append(problem)
 
@@ -630,8 +629,8 @@ class ExchangeBundle:
                     show_progress,
                     label='Ingesting {frequency} price data on '
                           '{exchange}'.format(
-                            exchange=self.exchange_name,
-                            frequency=data_frequency,
+                        exchange=self.exchange_name,
+                        frequency=data_frequency,
                     )) as it:
                 for chunk in it:
                     problems += self.ingest_ctable(
@@ -965,15 +964,15 @@ class ExchangeBundle:
                                   data_frequency,
                                   trailing_bar_count=None,
                                   reset_reader=False):
+        if trailing_bar_count:
+            delta = get_delta(trailing_bar_count, data_frequency)
+            end_dt += delta
+
         start_dt = get_start_dt(end_dt, bar_count, data_frequency, False)
         start_dt, _ = self.get_adj_dates(
             start_dt, end_dt, assets, data_frequency
         )
 
-        if trailing_bar_count:
-            delta = get_delta(trailing_bar_count, data_frequency)
-            end_dt += delta
-
         # This is an attempt to resolve some caching with the reader
         # when auto-ingesting data.
         # TODO: needs more work

catalyst/exchange/exchange_data_portal.py

@@ -3,17 +3,16 @@ import abc
 import numpy as np
 import pandas as pd
 from catalyst.assets._assets import TradingPair
-from logbook import Logger
-from redo import retry
-
 from catalyst.constants import LOG_LEVEL, AUTO_INGEST
 from catalyst.data.data_portal import DataPortal
 from catalyst.exchange.exchange_bundle import ExchangeBundle
 from catalyst.exchange.exchange_errors import (
     ExchangeRequestError,
     PricingDataNotLoadedError)
-from catalyst.exchange.utils.exchange_utils import get_frequency, \
-    resample_history_df, group_assets_by_exchange
+from catalyst.exchange.utils.exchange_utils import resample_history_df, group_assets_by_exchange
+from catalyst.exchange.utils.datetime_utils import get_frequency
+from logbook import Logger
+from redo import retry
 
 log = Logger('DataPortalExchange', level=LOG_LEVEL)
 
@@ -292,6 +291,7 @@ class DataPortalExchangeBacktest(DataPortalExchangeBase):
         DataFrame
 
         """
+        # TODO: verify that the exchange supports the timeframe
         bundle = self.exchange_bundles[exchange_name]  # type: ExchangeBundle
 
         freq, candle_size, unit, adj_data_frequency = get_frequency(

catalyst/exchange/exchange_errors.py

@@ -100,6 +100,13 @@ class InvalidHistoryFrequencyError(ZiplineError):
     ).strip()
 
 
+class UnsupportedHistoryFrequencyError(ZiplineError):
+    msg = (
+        '{exchange} does not support candle frequency {freq}, please choose '
+        'from: {freqs}.'
+    ).strip()
+
+
 class InvalidHistoryTimeframeError(ZiplineError):
     msg = (
         'CCXT timeframe {timeframe} not supported by the exchange.'

catalyst/exchange/exchange_portfolio.py

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

catalyst/exchange/exchange_pricing_loader.py

@@ -11,12 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from logbook import Logger
-from numpy import (
-    iinfo,
-    uint32,
-)
-
 from catalyst.constants import LOG_LEVEL
 from catalyst.data.us_equity_pricing import BcolzDailyBarReader
 from catalyst.errors import NoFurtherDataError
@@ -26,6 +20,11 @@ from catalyst.pipeline.data import DataSet, Column
 from catalyst.pipeline.loaders.base import PipelineLoader
 from catalyst.utils.calendars import get_calendar
 from catalyst.utils.numpy_utils import float64_dtype
+from logbook import Logger
+from numpy import (
+    iinfo,
+    uint32,
+)
 
 UINT32_MAX = iinfo(uint32).max
 

catalyst/exchange/live_graph_clock.py

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

catalyst/exchange/simple_clock.py

@@ -14,14 +14,13 @@
 from time import sleep
 
 import pandas as pd
+from catalyst.constants import LOG_LEVEL
 from catalyst.gens.sim_engine import (
     BAR,
     SESSION_START
 )
 from logbook import Logger
 
-from catalyst.constants import LOG_LEVEL
-
 log = Logger('ExchangeClock', level=LOG_LEVEL)
 
 

catalyst/exchange/utils/bundle_utils.py

@@ -1,11 +1,18 @@
-import calendar
 import os
 import tarfile
-from datetime import timedelta, datetime, date
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+
+from catalyst.data.bundles.core import download_without_progress
+from catalyst.exchange.utils.exchange_utils import get_exchange_bundles_folder
+import os
+import tarfile
+from datetime import datetime
 
 import numpy as np
 import pandas as pd
-import pytz
 
 from catalyst.data.bundles.core import download_without_progress
 from catalyst.exchange.utils.exchange_utils import get_exchange_bundles_folder
@@ -14,41 +21,6 @@ EXCHANGE_NAMES = ['bitfinex', 'bittrex', 'poloniex']
 API_URL = 'http://data.enigma.co/api/v1'
 
 
-def get_date_from_ms(ms):
-    """
-    The date from the number of miliseconds from the epoch.
-
-    Parameters
-    ----------
-    ms: int
-
-    Returns
-    -------
-    datetime
-
-    """
-    return datetime.fromtimestamp(ms / 1000.0)
-
-
-def get_seconds_from_date(date):
-    """
-    The number of seconds from the epoch.
-
-    Parameters
-    ----------
-    date: datetime
-
-    Returns
-    -------
-    int
-
-    """
-    epoch = datetime.utcfromtimestamp(0)
-    epoch = epoch.replace(tzinfo=pytz.UTC)
-
-    return int((date - epoch).total_seconds())
-
-
 def get_bcolz_chunk(exchange_name, symbol, data_frequency, period):
     """
     Download and extract a bcolz bundle.
@@ -78,8 +50,8 @@ def get_bcolz_chunk(exchange_name, symbol, data_frequency, period):
     if not os.path.isdir(path):
         url = 'https://s3.amazonaws.com/enigmaco/catalyst-bundles/' \
               'exchange-{exchange}/{name}.tar.gz'.format(
-                exchange=exchange_name,
-                name=name)
+            exchange=exchange_name,
+            name=name)
 
         bytes = download_without_progress(url)
         with tarfile.open('r', fileobj=bytes) as tar:
@@ -88,178 +60,6 @@ def get_bcolz_chunk(exchange_name, symbol, data_frequency, period):
     return path
 
 
-def get_delta(periods, data_frequency):
-    """
-    Get a time delta based on the specified data frequency.
-
-    Parameters
-    ----------
-    periods: int
-    data_frequency: str
-
-    Returns
-    -------
-    timedelta
-
-    """
-    return timedelta(minutes=periods) \
-        if data_frequency == 'minute' else timedelta(days=periods)
-
-
-def get_periods_range(start_dt, end_dt, freq):
-    """
-    Get a date range for the specified parameters.
-
-    Parameters
-    ----------
-    start_dt: datetime
-    end_dt: datetime
-    freq: str
-
-    Returns
-    -------
-    DateTimeIndex
-
-    """
-    if freq == 'minute':
-        freq = 'T'
-
-    elif freq == 'daily':
-        freq = 'D'
-
-    return pd.date_range(start_dt, end_dt, freq=freq)
-
-
-def get_periods(start_dt, end_dt, freq):
-    """
-    The number of periods in the specified range.
-
-    Parameters
-    ----------
-    start_dt: datetime
-    end_dt: datetime
-    freq: str
-
-    Returns
-    -------
-    int
-
-    """
-    return len(get_periods_range(start_dt, end_dt, freq))
-
-
-def get_start_dt(end_dt, bar_count, data_frequency, include_first=True):
-    """
-    The start date based on specified end date and data frequency.
-
-    Parameters
-    ----------
-    end_dt: datetime
-    bar_count: int
-    data_frequency: str
-
-    Returns
-    -------
-    datetime
-
-    """
-    periods = bar_count
-    if periods > 1:
-        delta = get_delta(periods, data_frequency)
-        start_dt = end_dt - delta
-
-        if not include_first:
-            start_dt += get_delta(1, data_frequency)
-    else:
-        start_dt = end_dt
-
-    return start_dt
-
-
-def get_period_label(dt, data_frequency):
-    """
-    The period label for the specified date and frequency.
-
-    Parameters
-    ----------
-    dt: datetime
-    data_frequency: str
-
-    Returns
-    -------
-    str
-
-    """
-    if data_frequency == 'minute':
-        return '{}-{:02d}'.format(dt.year, dt.month)
-    else:
-        return '{}'.format(dt.year)
-
-
-def get_month_start_end(dt, first_day=None, last_day=None):
-    """
-    The first and last day of the month for the specified date.
-
-    Parameters
-    ----------
-    dt: datetime
-    first_day: datetime
-    last_day: datetime
-
-    Returns
-    -------
-    datetime, datetime
-
-    """
-    month_range = calendar.monthrange(dt.year, dt.month)
-
-    if first_day:
-        month_start = first_day
-    else:
-        month_start = pd.to_datetime(datetime(
-            dt.year, dt.month, 1, 0, 0, 0, 0
-        ), utc=True)
-
-    if last_day:
-        month_end = last_day
-    else:
-        month_end = pd.to_datetime(datetime(
-            dt.year, dt.month, month_range[1], 23, 59, 0, 0
-        ), utc=True)
-
-        if month_end > pd.Timestamp.utcnow():
-            month_end = pd.Timestamp.utcnow().floor('1D')
-
-    return month_start, month_end
-
-
-def get_year_start_end(dt, first_day=None, last_day=None):
-    """
-    The first and last day of the year for the specified date.
-
-    Parameters
-    ----------
-
-    dt: datetime
-    first_day: datetime
-    last_day: datetime
-
-    Returns
-    -------
-    datetime, datetime
-
-    """
-    year_start = first_day if first_day \
-        else pd.to_datetime(date(dt.year, 1, 1), utc=True)
-    year_end = last_day if last_day \
-        else pd.to_datetime(date(dt.year, 12, 31), utc=True)
-
-    if year_end > pd.Timestamp.utcnow():
-        year_end = pd.Timestamp.utcnow().floor('1D')
-
-    return year_start, year_end
-
-
 def get_df_from_arrays(arrays, periods):
     """
     A DataFrame from the specified OHCLV arrays.

catalyst/exchange/utils/datetime_utils.py

@@ -0,0 +1,327 @@
+import calendar
+import re
+from datetime import datetime, timedelta, date
+
+import pandas as pd
+import pytz
+
+from catalyst.exchange.exchange_errors import InvalidHistoryFrequencyError, \
+    InvalidHistoryFrequencyAlias
+
+
+def get_date_from_ms(ms):
+    """
+    The date from the number of miliseconds from the epoch.
+
+    Parameters
+    ----------
+    ms: int
+
+    Returns
+    -------
+    datetime
+
+    """
+    return datetime.fromtimestamp(ms / 1000.0)
+
+
+def get_seconds_from_date(date):
+    """
+    The number of seconds from the epoch.
+
+    Parameters
+    ----------
+    date: datetime
+
+    Returns
+    -------
+    int
+
+    """
+    epoch = datetime.utcfromtimestamp(0)
+    epoch = epoch.replace(tzinfo=pytz.UTC)
+
+    return int((date - epoch).total_seconds())
+
+
+def get_delta(periods, data_frequency):
+    """
+    Get a time delta based on the specified data frequency.
+
+    Parameters
+    ----------
+    periods: int
+    data_frequency: str
+
+    Returns
+    -------
+    timedelta
+
+    """
+    return timedelta(minutes=periods) \
+        if data_frequency == 'minute' else timedelta(days=periods)
+
+
+def get_periods_range(freq, start_dt=None, end_dt=None, periods=None):
+    """
+    Get a date range for the specified parameters.
+
+    Parameters
+    ----------
+    start_dt: datetime
+    end_dt: datetime
+    freq: str
+
+    Returns
+    -------
+    DateTimeIndex
+
+    """
+    if freq == 'minute':
+        freq = 'T'
+
+    elif freq == 'daily':
+        freq = 'D'
+
+    if start_dt is not None and end_dt is not None and periods is None:
+
+        return pd.date_range(start_dt, end_dt, freq=freq)
+
+    elif periods is not None and (start_dt is not None or end_dt is not None):
+        _, unit_periods, unit, _ = get_frequency(freq)
+        adj_periods = periods * unit_periods
+
+        # TODO: standardize time aliases to avoid any mapping
+        unit = 'd' if unit == 'D' else 'm'
+        delta = pd.Timedelta(adj_periods, unit)
+
+        if start_dt is not None:
+            return pd.date_range(
+                start=start_dt,
+                end=start_dt + delta,
+                freq=freq,
+                closed='left',
+            )
+
+        else:
+            return pd.date_range(
+                start=end_dt - delta,
+                end=end_dt,
+                freq=freq,
+            )
+
+    else:
+        raise ValueError(
+            'Choose only two parameters between start_dt, end_dt '
+            'and periods.'
+        )
+
+
+def get_periods(start_dt, end_dt, freq):
+    """
+    The number of periods in the specified range.
+
+    Parameters
+    ----------
+    start_dt: datetime
+    end_dt: datetime
+    freq: str
+
+    Returns
+    -------
+    int
+
+    """
+    return len(get_periods_range(start_dt=start_dt, end_dt=end_dt, freq=freq))
+
+
+def get_start_dt(end_dt, bar_count, data_frequency, include_first=True):
+    """
+    The start date based on specified end date and data frequency.
+
+    Parameters
+    ----------
+    end_dt: datetime
+    bar_count: int
+    data_frequency: str
+    include_first
+
+    Returns
+    -------
+    datetime
+
+    """
+    periods = bar_count
+    if periods > 1:
+        delta = get_delta(periods, data_frequency)
+        start_dt = end_dt - delta
+
+        if not include_first:
+            start_dt += get_delta(1, data_frequency)
+    else:
+        start_dt = end_dt
+
+    return start_dt
+
+
+def get_period_label(dt, data_frequency):
+    """
+    The period label for the specified date and frequency.
+
+    Parameters
+    ----------
+    dt: datetime
+    data_frequency: str
+
+    Returns
+    -------
+    str
+
+    """
+    if data_frequency == 'minute':
+        return '{}-{:02d}'.format(dt.year, dt.month)
+    else:
+        return '{}'.format(dt.year)
+
+
+def get_month_start_end(dt, first_day=None, last_day=None):
+    """
+    The first and last day of the month for the specified date.
+
+    Parameters
+    ----------
+    dt: datetime
+    first_day: datetime
+    last_day: datetime
+
+    Returns
+    -------
+    datetime, datetime
+
+    """
+    month_range = calendar.monthrange(dt.year, dt.month)
+
+    if first_day:
+        month_start = first_day
+    else:
+        month_start = pd.to_datetime(datetime(
+            dt.year, dt.month, 1, 0, 0, 0, 0
+        ), utc=True)
+
+    if last_day:
+        month_end = last_day
+    else:
+        month_end = pd.to_datetime(datetime(
+            dt.year, dt.month, month_range[1], 23, 59, 0, 0
+        ), utc=True)
+
+        if month_end > pd.Timestamp.utcnow():
+            month_end = pd.Timestamp.utcnow().floor('1D')
+
+    return month_start, month_end
+
+
+def get_year_start_end(dt, first_day=None, last_day=None):
+    """
+    The first and last day of the year for the specified date.
+
+    Parameters
+    ----------
+
+    dt: datetime
+    first_day: datetime
+    last_day: datetime
+
+    Returns
+    -------
+    datetime, datetime
+
+    """
+    year_start = first_day if first_day \
+        else pd.to_datetime(date(dt.year, 1, 1), utc=True)
+    year_end = last_day if last_day \
+        else pd.to_datetime(date(dt.year, 12, 31), utc=True)
+
+    if year_end > pd.Timestamp.utcnow():
+        year_end = pd.Timestamp.utcnow().floor('1D')
+
+    return year_start, year_end
+
+
+def get_frequency(freq, data_frequency=None):
+    """
+    Get the frequency parameters.
+
+    Notes
+    -----
+    We're trying to use Pandas convention for frequency aliases.
+
+    Parameters
+    ----------
+    freq: str
+    data_frequency: str
+
+    Returns
+    -------
+    str, int, str, str
+
+    """
+    if data_frequency is None:
+        data_frequency = 'daily' if freq.upper().endswith('D') else 'minute'
+
+    if freq == 'minute':
+        unit = 'T'
+        candle_size = 1
+
+    elif freq == 'daily':
+        unit = 'D'
+        candle_size = 1
+
+    else:
+        freq_match = re.match(r'([0-9].*)?(m|M|d|D|h|H|T)', freq, re.M | re.I)
+        if freq_match:
+            candle_size = int(freq_match.group(1)) if freq_match.group(1) \
+                else 1
+            unit = freq_match.group(2)
+
+        else:
+            raise InvalidHistoryFrequencyError(frequency=freq)
+
+    # TODO: some exchanges support H and W frequencies but not bundles
+    # Find a way to pass-through these parameters to exchanges
+    # but resample from minute or daily in backtest mode
+    # see catalyst/exchange/ccxt/ccxt_exchange.py:242 for mapping between
+    # Pandas offet aliases (used by Catalyst) and the CCXT timeframes
+    if unit.lower() == 'd':
+        unit = 'D'
+        alias = '{}D'.format(candle_size)
+
+        if data_frequency == 'minute':
+            data_frequency = 'daily'
+
+    elif unit.lower() == 'm' or unit == 'T':
+        unit = 'T'
+        alias = '{}T'.format(candle_size)
+
+        if data_frequency == 'daily':
+            data_frequency = 'minute'
+
+    # elif unit.lower() == 'h':
+    #     candle_size = candle_size * 60
+    #
+    #     alias = '{}T'.format(candle_size)
+    #     if data_frequency == 'daily':
+    #         data_frequency = 'minute'
+
+    else:
+        raise InvalidHistoryFrequencyAlias(freq=freq)
+
+    return alias, candle_size, unit, data_frequency
+
+
+def from_ms_timestamp(ms):
+    return pd.to_datetime(ms, unit='ms', utc=True)
+
+
+def get_epoch():
+    return pd.to_datetime('1970-1-1', utc=True)

catalyst/exchange/utils/exchange_utils.py

@@ -2,7 +2,6 @@ import hashlib
 import json
 import os
 import pickle
-import re
 import shutil
 from datetime import date, datetime
 
@@ -12,8 +11,7 @@ from six import string_types
 from six.moves.urllib import request
 
 from catalyst.constants import DATE_FORMAT, SYMBOLS_URL
-from catalyst.exchange.exchange_errors import ExchangeSymbolsNotFound, \
-    InvalidHistoryFrequencyError, InvalidHistoryFrequencyAlias
+from catalyst.exchange.exchange_errors import ExchangeSymbolsNotFound
 from catalyst.exchange.utils.serialization_utils import ExchangeJSONEncoder, \
     ExchangeJSONDecoder
 from catalyst.utils.paths import data_root, ensure_directory, \
@@ -130,7 +128,10 @@ def get_exchange_symbols(exchange_name, is_local=False, environ=None):
     if not is_local and (not os.path.isfile(filename) or pd.Timedelta(
                 pd.Timestamp('now', tz='UTC') - last_modified_time(
                 filename)).days > 1):
-        download_exchange_symbols(exchange_name, environ)
+        try:
+            download_exchange_symbols(exchange_name, environ)
+        except Exception as e:
+            pass
 
     if os.path.isfile(filename):
         with open(filename) as data_file:
@@ -190,7 +191,7 @@ def get_symbols_string(assets):
     return ', '.join([asset.symbol for asset in array])
 
 
-def get_exchange_auth(exchange_name, environ=None):
+def get_exchange_auth(exchange_name, alias=None, environ=None):
     """
     The de-serialized contend of the exchange's auth.json file.
 
@@ -205,7 +206,8 @@ def get_exchange_auth(exchange_name, environ=None):
 
     """
     exchange_folder = get_exchange_folder(exchange_name, environ)
-    filename = os.path.join(exchange_folder, 'auth.json')
+    name = 'auth' if alias is None else alias
+    filename = os.path.join(exchange_folder, '{}.json'.format(name))
 
     if os.path.isfile(filename):
         with open(filename) as data_file:
@@ -510,72 +512,6 @@ def get_common_assets(exchanges):
     return assets
 
 
-def get_frequency(freq, data_frequency):
-    """
-    Get the frequency parameters.
-
-    Notes
-    -----
-    We're trying to use Pandas convention for frequency aliases.
-
-    Parameters
-    ----------
-    freq: str
-    data_frequency: str
-
-    Returns
-    -------
-    str, int, str, str
-
-    """
-    if freq == 'minute':
-        unit = 'T'
-        candle_size = 1
-
-    elif freq == 'daily':
-        unit = 'D'
-        candle_size = 1
-
-    else:
-        freq_match = re.match(r'([0-9].*)?(m|M|d|D|h|H|T)', freq, re.M | re.I)
-        if freq_match:
-            candle_size = int(freq_match.group(1)) if freq_match.group(1) \
-                else 1
-            unit = freq_match.group(2)
-
-        else:
-            raise InvalidHistoryFrequencyError(frequency=freq)
-
-    # TODO: some exchanges support H and W frequencies but not bundles
-    # Find a way to pass-through these parameters to exchanges
-    # but resample from minute or daily in backtest mode
-    # see catalyst/exchange/ccxt/ccxt_exchange.py:242 for mapping between
-    # Pandas offet aliases (used by Catalyst) and the CCXT timeframes
-    if unit.lower() == 'd':
-        alias = '{}D'.format(candle_size)
-
-        if data_frequency == 'minute':
-            data_frequency = 'daily'
-
-    elif unit.lower() == 'm' or unit == 'T':
-        alias = '{}T'.format(candle_size)
-
-        if data_frequency == 'daily':
-            data_frequency = 'minute'
-
-    # elif unit.lower() == 'h':
-    #     candle_size = candle_size * 60
-    #
-    #     alias = '{}T'.format(candle_size)
-    #     if data_frequency == 'daily':
-    #         data_frequency = 'minute'
-
-    else:
-        raise InvalidHistoryFrequencyAlias(freq=freq)
-
-    return alias, candle_size, unit, data_frequency
-
-
 def resample_history_df(df, freq, field):
     """
     Resample the OHCLV DataFrame using the specified frequency.
@@ -649,14 +585,6 @@ def mixin_market_params(exchange_name, params, market):
                 params['lot'] = params['min_trade_size']
 
 
-def from_ms_timestamp(ms):
-    return pd.to_datetime(ms, unit='ms', utc=True)
-
-
-def get_epoch():
-    return pd.to_datetime('1970-1-1', utc=True)
-
-
 def group_assets_by_exchange(assets):
     exchange_assets = dict()
     for asset in assets:

catalyst/exchange/utils/factory.py

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

catalyst/exchange/utils/serialization_utils.py

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

catalyst/exchange/utils/stats_utils.py

@@ -8,9 +8,9 @@ import time
 import numpy as np
 import pandas as pd
 from catalyst.assets._assets import TradingPair
-
 from catalyst.exchange.utils.exchange_utils import get_algo_folder
 from catalyst.utils.paths import data_root, ensure_directory
+from operator import itemgetter
 
 s3_conn = []
 mailgun = []
@@ -261,7 +261,14 @@ def prepare_stats(stats, recorded_cols=list()):
     return df, columns
 
 
-def get_pretty_stats(stats, recorded_cols=None, num_rows=10):
+def set_print_settings():
+    pd.set_option('display.expand_frame_repr', False)
+    pd.set_option('precision', 8)
+    pd.set_option('display.width', 1000)
+    pd.set_option('display.max_colwidth', 1000)
+
+
+def get_pretty_stats(stats, recorded_cols=None, num_rows=10, show_tail=True):
     """
     Format and print the last few rows of a statistics DataFrame.
     See the pyfolio project for the data structure.
@@ -280,18 +287,18 @@ def get_pretty_stats(stats, recorded_cols=None, num_rows=10):
 
     """
     if isinstance(stats, pd.DataFrame):
-        stats = stats.T.to_dict().values()
+        stats = list(stats.T.to_dict().values())
+        stats.sort(key=itemgetter('period_close'))
+
+    if len(stats) > num_rows:
+        display_stats = stats[-num_rows:] if show_tail else stats[0:num_rows]
+    else:
+        display_stats = stats
 
-    display_stats = stats[-num_rows:] if len(stats) > num_rows else stats
     df, columns = prepare_stats(
         display_stats, recorded_cols=recorded_cols
     )
-
-    pd.set_option('display.expand_frame_repr', False)
-    pd.set_option('precision', 8)
-    pd.set_option('display.width', 1000)
-    pd.set_option('display.max_colwidth', 1000)
-
+    set_print_settings()
     return df.to_string(columns=columns)
 
 
@@ -352,9 +359,13 @@ def stats_to_s3(uri, stats, algo_namespace, recorded_cols=None,
     pid = os.getpid()
 
     parts = uri.split('//')
-    obj = s3.Object(parts[1], '{}/{}-{}-{}.csv'.format(
-        folder, timestr, algo_namespace, pid
-    ))
+    path = '{folder}/{algo}/{time}-{algo}-{pid}.csv'.format(
+        folder=folder,
+        algo=algo_namespace,
+        time=timestr,
+        pid=pid,
+    )
+    obj = s3.Object(parts[1], path)
     obj.put(Body=bytes_to_write)
 
 
@@ -439,6 +450,17 @@ def df_to_string(df):
     return df.to_string()
 
 
+def extract_orders(perf):
+    order_list = perf.orders.values
+    all_orders = [t for sublist in order_list for t in sublist]
+    all_orders.sort(key=lambda o: o['dt'])
+
+    orders = pd.DataFrame(all_orders)
+    if not orders.empty:
+        orders.set_index('dt', inplace=True, drop=True)
+    return orders
+
+
 def extract_transactions(perf):
     """
     Compute indexes for buy and sell transactions

catalyst/exchange/utils/test_utils.py

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

catalyst/pipeline/graph.py

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

catalyst/sources/test_source.py

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

catalyst/support/binance_history.py

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

catalyst/support/ccxt_issue_1358.py

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

catalyst/support/history_multiple_assets.py

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

catalyst/support/issue_111.py

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

catalyst/support/issue_112.py

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

catalyst/support/issue_169.py

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

catalyst/utils/run_algo.py

@@ -8,13 +8,14 @@ from time import sleep
 
 import click
 import pandas as pd
-from logbook import Logger
+from six import string_types
 
 from catalyst.data.bundles import load
 from catalyst.data.data_portal import DataPortal
 from catalyst.exchange.exchange_pricing_loader import ExchangePricingLoader, \
     TradingPairPricing
 from catalyst.exchange.utils.factory import get_exchange
+from logbook import Logger
 
 try:
     from pygments import highlight
@@ -40,9 +41,6 @@ from catalyst.exchange.exchange_algorithm import (
 from catalyst.exchange.exchange_data_portal import DataPortalExchangeLive, \
     DataPortalExchangeBacktest
 from catalyst.exchange.exchange_asset_finder import ExchangeAssetFinder
-from catalyst.exchange.exchange_errors import (
-    ExchangeRequestError, ExchangeRequestErrorTooManyAttempts,
-    BaseCurrencyNotFoundError, NotEnoughCapitalError)
 
 from catalyst.constants import LOG_LEVEL
 
@@ -70,7 +68,38 @@ class _RunAlgoError(click.ClickException, ValueError):
         return self.pyfunc_msg
 
 
-def _build_namespace(algotext, local_namespace, defines):
+def _run(handle_data,
+         initialize,
+         before_trading_start,
+         analyze,
+         algofile,
+         algotext,
+         defines,
+         data_frequency,
+         capital_base,
+         data,
+         bundle,
+         bundle_timestamp,
+         start,
+         end,
+         output,
+         print_algo,
+         local_namespace,
+         environ,
+         live,
+         exchange,
+         algo_namespace,
+         base_currency,
+         live_graph,
+         analyze_live,
+         simulate_orders,
+         auth_aliases,
+         stats_output):
+    """Run a backtest for the given algorithm.
+
+    This is shared between the cli and :func:`catalyst.run_algo`.
+    """
+    # TODO: refactor for more granularity
     if algotext is not None:
         if local_namespace:
             ip = get_ipython()  # noqa
@@ -84,197 +113,145 @@ def _build_namespace(algotext, local_namespace, defines):
             except ValueError:
                 raise ValueError(
                     'invalid define %r, should be of the form name=value' %
-                    assign)
+                    assign,
+                )
             try:
                 # evaluate in the same namespace so names may refer to
                 # eachother
                 namespace[name] = eval(value, namespace)
             except Exception as e:
                 raise ValueError(
-                    'failed to execute definition for name %r: %s' % (name, e))
+                    'failed to execute definition for name %r: %s' % (name, e),
+                )
     elif defines:
         raise _RunAlgoError(
             'cannot pass define without `algotext`',
-            "cannot pass '-D' / '--define' without '-t' / '--algotext'")
+            "cannot pass '-D' / '--define' without '-t' / '--algotext'",
+        )
     else:
         namespace = {}
+        if algofile is not None:
+            algotext = algofile.read()
 
-    return namespace
+    if print_algo:
+        if PYGMENTS:
+            highlight(
+                algotext,
+                PythonLexer(),
+                TerminalFormatter(),
+                outfile=sys.stdout,
+            )
+        else:
+            click.echo(algotext)
 
+    log.warn(
+        'Catalyst is currently in ALPHA. It is going through rapid '
+        'development and it is subject to errors. Please use carefully. '
+        'We encourage you to report any issue on GitHub: '
+        'https://github.com/enigmampc/catalyst/issues'
+    )
+    sleep(3)
 
-def _mode(simulate_orders, live):
-    if not live:
-        return 'backtest'
-    elif simulate_orders:
-        return 'paper-trading'
+    if live:
+        if simulate_orders:
+            mode = 'paper-trading'
+        else:
+            mode = 'live-trading'
     else:
-        return 'live-trading'
+        mode = 'backtest'
 
+    log.info('running algo in {mode} mode'.format(mode=mode))
 
-def _build_exchanges_dict(exchange, live, simulate_orders, base_currency):
     exchange_name = exchange
     if exchange_name is None:
         raise ValueError('Please specify at least one exchange.')
 
+    if isinstance(auth_aliases, string_types):
+        aliases = auth_aliases.split(',')
+        if len(aliases) < 2 or len(aliases) % 2 != 0:
+            raise ValueError(
+                'the `auth_aliases` parameter must contain an even list '
+                'of comma-delimited values. For example, '
+                '"binance,auth2" or "binance,auth2,bittrex,auth2".'
+            )
+
+        auth_aliases = dict(zip(aliases[::2], aliases[1::2]))
+
     exchange_list = [x.strip().lower() for x in exchange.split(',')]
-
-    exchanges = {exchange_name: get_exchange(
-                                    exchange_name=exchange_name,
-                                    base_currency=base_currency,
-                                    must_authenticate=(live and not simulate_orders))
-                 for exchange_name in exchange_list}
-
-    return exchanges
-
-
-def _pretty_print_code(algotext):
-    if PYGMENTS:
-        highlight(
-            algotext,
-            PythonLexer(),
-            TerminalFormatter(),
-            outfile=sys.stdout)
-    else:
-        click.echo(algotext)
-
-
-def _choose_loader(data_frequency, column):
-    bound_cols = TradingPairPricing.columns
-    if column in bound_cols:
-        return ExchangePricingLoader(data_frequency)
-    raise ValueError(
-        "No PipelineLoader registered for column %s." % column)
-
-
-def _get_live_time_range():
-    start = pd.Timestamp.utcnow()
-    # TODO: fix the end data.
-    end = start + timedelta(hours=8760)
-    return start, end
-
-
-def _data_for_live_trading(sim_params, exchanges, env, open_calendar):
-    data = DataPortalExchangeLive(
-        exchanges=exchanges,
-        asset_finder=env.asset_finder,
-        trading_calendar=open_calendar,
-        first_trading_day=pd.to_datetime('today', utc=True))
-
-    return data
-
-
-# TODO use proper retry here
-def _fetch_capital_base(base_currency, exchange_name, exchange,
-                        attempt_index=0):
-    """
-    Fetch the base currency amount required to bootstrap
-    the algorithm against the exchange.
-
-    The algorithm cannot continue without this value.
-
-    :param exchange: the targeted exchange
-    :param attempt_index:
-    :return capital_base: the amount of base currency available for
-    trading
-    """
-    try:
-        log.debug('retrieving capital base in {} to bootstrap '
-                  'exchange {}'.format(base_currency, exchange_name))
-        balances = exchange.get_balances()
-    except ExchangeRequestError as e:
-        if attempt_index < 20:
-            log.warn(
-                'could not retrieve balances on {}: {}'.format(
-                    exchange.name, e))
-            sleep(5)
-            return _fetch_capital_base(base_currency, exchange_name, exchange,
-                                       attempt_index + 1)
-
+    exchanges = dict()
+    for name in exchange_list:
+        if auth_aliases is not None and name in auth_aliases:
+            auth_alias = auth_aliases[name]
         else:
-            raise ExchangeRequestErrorTooManyAttempts(
-                attempts=attempt_index,
-                error=e)
+            auth_alias = None
 
-    if base_currency in balances:
-        base_currency_available = balances[base_currency]['free']
-        log.info(
-            'base currency available in the account: {} {}'.format(
-                base_currency_available, base_currency))
-
-        return base_currency_available
-    else:
-        raise BaseCurrencyNotFoundError(
+        exchanges[name] = get_exchange(
+            exchange_name=name,
             base_currency=base_currency,
-            exchange=exchange_name)
+            must_authenticate=(live and not simulate_orders),
+            skip_init=True,
+            auth_alias=auth_alias,
+        )
 
+    open_calendar = get_calendar('OPEN')
 
-def _algorithm_class_for_live(algo_namespace, live_graph, stats_output,
-                              analyze_live, base_currency, simulate_orders,
-                              exchanges, capital_base):
-    if not simulate_orders:
-        for exchange_name in exchanges:
-            exchange = exchanges[exchange_name]
-            balance = _fetch_capital_base(base_currency, exchange_name,
-                                          exchange)
+    env = TradingEnvironment(
+        load=partial(
+            load_crypto_market_data,
+            environ=environ,
+            start_dt=start,
+            end_dt=end
+        ),
+        environ=environ,
+        exchange_tz='UTC',
+        asset_db_path=None  # We don't need an asset db, we have exchanges
+    )
+    env.asset_finder = ExchangeAssetFinder(exchanges=exchanges)
 
-            if balance < capital_base:
-                raise NotEnoughCapitalError(
-                    exchange=exchange_name,
-                    base_currency=base_currency,
-                    balance=balance,
-                    capital_base=capital_base)
-
-    algorithm_class = partial(
-        ExchangeTradingAlgorithmLive,
-        exchanges=exchanges,
-        algo_namespace=algo_namespace,
-        live_graph=live_graph,
-        simulate_orders=simulate_orders,
-        stats_output=stats_output,
-        analyze_live=analyze_live,)
-
-    return algorithm_class
-
-
-def _bundle_trading_environment(bundle_data, environ):
-    prefix, connstr = re.split(
-        r'sqlite:///',
-        str(bundle_data.asset_finder.engine.url),
-        maxsplit=1)
-    if prefix:
+    def choose_loader(column):
+        bound_cols = TradingPairPricing.columns
+        if column in bound_cols:
+            return ExchangePricingLoader(data_frequency)
         raise ValueError(
-            "invalid url %r, must begin with 'sqlite:///'" %
-            str(bundle_data.asset_finder.engine.url))
+            "No PipelineLoader registered for column %s." % column
+        )
 
-    return TradingEnvironment(asset_db_path=connstr, environ=environ)
+    if live:
+        start = pd.Timestamp.utcnow()
 
+        # TODO: fix the end data.
+        if end is None:
+            end = start + timedelta(hours=8760)
 
-def _build_live_algo_and_data(sim_params, exchanges, env, open_calendar,
-                              simulate_orders, algo_namespace, capital_base,
-                              live_graph, stats_output, analyze_live,
-                              base_currency, namespace, choose_loader,
-                              algorithm_class_kwargs):
-    sim_params._arena = 'live'  # TODO: use the constructor instead
+        data = DataPortalExchangeLive(
+            exchanges=exchanges,
+            asset_finder=env.asset_finder,
+            trading_calendar=open_calendar,
+            first_trading_day=pd.to_datetime('today', utc=True)
+        )
 
-    data = _data_for_live_trading(sim_params, exchanges, env, open_calendar)
+        sim_params = create_simulation_parameters(
+            start=start,
+            end=end,
+            capital_base=capital_base,
+            emission_rate='minute',
+            data_frequency='minute'
+        )
 
-    algorithm_class = _algorithm_class_for_live(
-        algo_namespace, live_graph, stats_output, analyze_live,
-        base_currency, simulate_orders, exchanges, capital_base)
+        # TODO: use the constructor instead
+        sim_params._arena = 'live'
 
-    return data, algorithm_class(
-        namespace=namespace,
-        env=env,
-        get_pipeline_loader=choose_loader,
-        sim_params=sim_params,
-        **algorithm_class_kwargs)
-
-
-def _build_backtest_algo_and_data(
-        exchanges, bundle, env, environ, bundle_timestamp, open_calendar,
-        start, end, namespace, choose_loader, sim_params,
-        algorithm_class_kwargs):
-    if exchanges:
+        algorithm_class = partial(
+            ExchangeTradingAlgorithmLive,
+            exchanges=exchanges,
+            algo_namespace=algo_namespace,
+            live_graph=live_graph,
+            simulate_orders=simulate_orders,
+            stats_output=stats_output,
+            analyze_live=analyze_live,
+            end=end,
+        )
+    elif exchanges:
         # Removed the existing Poloniex fork to keep things simple
         # We can add back the complexity if required.
 
@@ -288,19 +265,41 @@ def _build_backtest_algo_and_data(
             asset_finder=None,
             trading_calendar=open_calendar,
             first_trading_day=start,
-            last_available_session=end)
+            last_available_session=end
+        )
+
+        sim_params = create_simulation_parameters(
+            start=start,
+            end=end,
+            capital_base=capital_base,
+            data_frequency=data_frequency,
+            emission_rate=data_frequency,
+        )
 
         algorithm_class = partial(
             ExchangeTradingAlgorithmBacktest,
-            exchanges=exchanges)
+            exchanges=exchanges
+        )
+
     elif bundle is not None:
-        # TODO This branch should probably be removed or fixed: it doesn't even
-        # build `algorithm_class`, so it will break when trying to instantiate
-        # it.
-        bundle_data = load(bundle, environ, bundle_timestamp)
+        bundle_data = load(
+            bundle,
+            environ,
+            bundle_timestamp,
+        )
 
-        env = _bundle_trading_environment(bundle_data, environ)
+        prefix, connstr = re.split(
+            r'sqlite:///',
+            str(bundle_data.asset_finder.engine.url),
+            maxsplit=1,
+        )
+        if prefix:
+            raise ValueError(
+                "invalid url %r, must begin with 'sqlite:///'" %
+                str(bundle_data.asset_finder.engine.url),
+            )
 
+        env = TradingEnvironment(asset_db_path=connstr, environ=environ)
         first_trading_day = \
             bundle_data.equity_minute_bar_reader.first_trading_day
 
@@ -309,103 +308,27 @@ def _build_backtest_algo_and_data(
             first_trading_day=first_trading_day,
             equity_minute_reader=bundle_data.equity_minute_bar_reader,
             equity_daily_reader=bundle_data.equity_daily_bar_reader,
-            adjustment_reader=bundle_data.adjustment_reader)
+            adjustment_reader=bundle_data.adjustment_reader,
+        )
 
-    return data, algorithm_class(
+    perf = algorithm_class(
         namespace=namespace,
         env=env,
         get_pipeline_loader=choose_loader,
         sim_params=sim_params,
-        **algorithm_class_kwargs)
-
-
-def _build_algo_and_data(handle_data, initialize, before_trading_start,
-                         analyze, algofile, algotext, defines, data_frequency,
-                         capital_base, data, bundle, bundle_timestamp, start,
-                         end, output, print_algo, local_namespace, environ,
-                         live, exchange, algo_namespace, base_currency,
-                         live_graph, analyze_live, simulate_orders,
-                         stats_output):
-    namespace = _build_namespace(algotext, local_namespace, defines)
-    if algotext is not None:
-        algotext = algofile.read()
-
-    if print_algo:
-        _pretty_print_code(algotext)
-
-    mode = _mode(simulate_orders, live)
-    log.info('running algo in {mode} mode'.format(mode=mode))
-
-    exchanges = _build_exchanges_dict(exchange, live, simulate_orders,
-                                      base_currency)
-
-    open_calendar = get_calendar('OPEN')
-
-    env = TradingEnvironment(
-        load=partial(load_crypto_market_data, environ=environ, start_dt=start,
-                     end_dt=end),
-        environ=environ,
-        exchange_tz='UTC',
-        asset_db_path=None)  # We don't need an asset db, we have exchanges
-
-    env.asset_finder = ExchangeAssetFinder(exchanges=exchanges)
-
-    choose_loader = partial(_choose_loader, data_frequency)
-
-    if live:
-        start, end = _get_live_time_range()
-        data_frequency = 'minute'  # TODO double check if this is the desired behavior
-
-    sim_params = create_simulation_parameters(
-        start=start,
-        end=end,
-        capital_base=capital_base,
-        emission_rate=data_frequency,
-        data_frequency=data_frequency)
-
-    if algotext is None:
-        algorithm_class_kwargs = {'initialize': initialize,
-                                  'handle_data': handle_data,
-                                  'before_trading_start': before_trading_start,
-                                  'analyze': analyze}
-    else:
-        algorithm_class_kwargs = {'algo_filename': getattr(algofile, 'name',
-                                                           '<algorithm>'),
-                                  'script': algotext}
-
-    if live:
-        return _build_live_algo_and_data(
-            sim_params, exchanges, env, open_calendar, simulate_orders,
-            algo_namespace, capital_base, live_graph, stats_output,
-            analyze_live, base_currency, namespace, choose_loader,
-            algorithm_class_kwargs)
-    else:
-        return _build_backtest_algo_and_data(
-            exchanges, bundle, env, environ, bundle_timestamp, open_calendar,
-            start, end, namespace, choose_loader, sim_params,
-            algorithm_class_kwargs)
-
-
-def _run(handle_data, initialize, before_trading_start, analyze, algofile,
-         algotext, defines, data_frequency, capital_base, data, bundle,
-         bundle_timestamp, start, end, output, print_algo, local_namespace,
-         environ, live, exchange, algo_namespace, base_currency, live_graph,
-         analyze_live, simulate_orders, stats_output):
-    """Run an algorithm in backtest,
-    paper-trading or live-trading mode.
-
-    This is shared between the cli and :func:`catalyst.run_algo`.
-    """
-
-    data, algorithm = _build_algo_and_data(
-        handle_data, initialize, before_trading_start, analyze, algofile,
-        algotext, defines, data_frequency, capital_base, data, bundle,
-        bundle_timestamp, start, end, output, print_algo, local_namespace,
-        environ, live, exchange, algo_namespace, base_currency, live_graph,
-        analyze_live, simulate_orders, stats_output)
-    perf = algorithm.run(
+        **{
+            'initialize': initialize,
+            'handle_data': handle_data,
+            'before_trading_start': before_trading_start,
+            'analyze': analyze,
+        } if algotext is None else {
+            'algo_filename': getattr(algofile, 'name', '<algorithm>'),
+            'script': algotext,
+        }
+    ).run(
         data,
-        overwrite_sim_params=False)
+        overwrite_sim_params=False,
+    )
 
     if output == '-':
         click.echo(str(perf))
@@ -462,7 +385,8 @@ def load_extensions(default, extensions, strict, environ, reload=False):
             # without `strict` we should just log the failure
             warnings.warn(
                 'Failed to load extension: %r\n%s' % (ext, e),
-                stacklevel=2)
+                stacklevel=2
+            )
         else:
             _loaded_extensions.add(ext)
 
@@ -489,6 +413,7 @@ def run_algorithm(initialize,
                   live_graph=False,
                   analyze_live=None,
                   simulate_orders=True,
+                  auth_aliases=None,
                   stats_output=None,
                   output=os.devnull):
     """Run a trading algorithm.
@@ -561,7 +486,8 @@ def run_algorithm(initialize,
     catalyst.data.bundles.bundles : The available data bundles.
     """
     load_extensions(
-        default_extension, extensions, strict_extensions, environ)
+        default_extension, extensions, strict_extensions, environ
+    )
 
     if capital_base is None:
         raise ValueError(
@@ -569,7 +495,8 @@ def run_algorithm(initialize,
             'amount of base currency available for trading. For example, '
             'if the `capital_base` is 5ETH, the '
             '`order_target_percent(asset, 1)` command will order 5ETH worth '
-            'of the specified asset.')
+            'of the specified asset.'
+        )
     # I'm not sure that we need this since the modified DataPortal
     # does not require extensions to be explicitly loaded.
 
@@ -587,11 +514,13 @@ def run_algorithm(initialize,
         elif len(non_none_data) != 1:
             raise ValueError(
                 'must specify one of `data`, `data_portal`, or `bundle`,'
-                ' got: %r' % non_none_data)
+                ' got: %r' % non_none_data,
+            )
 
         elif 'bundle' not in non_none_data and bundle_timestamp is not None:
             raise ValueError(
-                'cannot specify `bundle_timestamp` without passing `bundle`')
+                'cannot specify `bundle_timestamp` without passing `bundle`',
+            )
     return _run(
         handle_data=handle_data,
         initialize=initialize,
@@ -618,4 +547,6 @@ def run_algorithm(initialize,
         live_graph=live_graph,
         analyze_live=analyze_live,
         simulate_orders=simulate_orders,
-        stats_output=stats_output)
+        auth_aliases=auth_aliases,
+        stats_output=stats_output
+    )

docs/Makefile

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

docs/deploy.py

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

docs/make.bat

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

docs/source/beginner-tutorial.rst

@@ -483,7 +483,7 @@ bitcoin price.
 
 Now we will run the simulation again, but this time we extend our original 
 algorithm with the addition of the ``analyze()`` function. Somewhat analogously 
-as how ``initialize()`` gets called once before the start of the algorith, 
+as how ``initialize()`` gets called once before the start of the algorithm, 
 ``analyze()`` gets called once at the end of the algorithm, and receives two 
 variables: ``context``, which we discussed at the very beginning, and ``perf``, 
 which is the pandas dataframe containing the performance data for our algorithm 
@@ -589,7 +589,7 @@ the ``examples`` directory:
     from catalyst import run_algorithm
     from catalyst.api import (order, record, symbol, order_target_percent, 
             get_open_orders)
-    from catalyst.exchange.stats_utils import extract_transactions
+    from catalyst.exchange.utils.stats_utils import extract_transactions
 
     NAMESPACE = 'dual_moving_average'
     log = Logger(NAMESPACE)
@@ -660,7 +660,8 @@ the ``examples`` directory:
     def analyze(context, perf):
 
         # Get the base_currency that was passed as a parameter to the simulation
-        base_currency = context.exchanges.values()[0].base_currency.upper()
+        exchange = list(context.exchanges.values())[0]
+        base_currency = exchange.base_currency.upper()
 
         # First chart: Plot portfolio value using base_currency
         ax1 = plt.subplot(411)

docs/source/conf.py

@@ -41,11 +41,11 @@ master_doc = 'index'
 
 # General information about the project.
 project = u'Catalyst'
-copyright = u'2017, Enigma MPC, Inc.'
+copyright = u'2018, Enigma MPC, Inc.'
 
 # The full version, including alpha/beta/rc tags, but excluding the commit hash
 #release = version.split('+', 1)[0]
-release = '0.3'
+release = '0.4'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

docs/source/development-guidelines.rst

@@ -84,6 +84,25 @@ To build and view the docs locally, run:
    $ {BROWSER} build/html/index.html
 
 
+There is a `documented issue <https://github.com/sphinx-doc/sphinx/issues/3212>`_ 
+with ``sphinx`` and ``docutils`` that causes the error below when trying to build 
+the docs.
+
+.. code-block:: text
+
+   Exception occurred:
+     File "(...)/env-c/lib/python2.7/site-packages/docutils/writers/_html_base.py", line 671, in depart_document
+       assert not self.context, 'len(context) = %s' % len(self.context)
+   AssertionError: len(context) = 3
+
+If you get this error, you need to downgrade your version of ``docutils`` as 
+follows, and build the docs again:
+
+.. code-block:: bash
+
+   $ pip install docutils==0.12
+
+
 Commit messages
 ---------------
 

docs/source/features.rst

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

docs/source/install.rst

@@ -180,20 +180,6 @@ use a single tool to install Python and non-Python dependencies, or if you're
 already using `Anaconda <http://continuum.io/downloads>`_ as your Python 
 distribution, refer to the :ref:`Installing with Conda <conda>` section.
 
-Once you've installed the necessary additional dependencies for your system 
-(see below for your particular platform: :ref:`Linux`, :ref:`MacOS` or 
-:ref:`Windows`), you should be able to simply run
-
-.. code-block:: bash
-
-   $ pip install enigma-catalyst matplotlib
-
-Note that in the command above we install two different packages. The second 
-one, ``matplotlib`` is a visualization library. While it's not strictly 
-required to run catalyst simulations or live trading, it comes in very handy
-to visualize the performance of your algorithms, and for this reason we 
-recommend you install it, as well.
-
 If you use Python for anything other than Catalyst, we **strongly** recommend
 that you install in a `virtualenv
 <https://virtualenv.readthedocs.org/en/latest>`_.  The `Hitchhiker's Guide to
@@ -206,8 +192,21 @@ summarized version:
    $ pip install virtualenv
    $ virtualenv catalyst-venv
    $ source ./catalyst-venv/bin/activate
+
+Once you've installed the necessary additional dependencies for your system 
+(:ref:`Linux`, :ref:`MacOS` or :ref:`Windows`) **and have activated your virtualenv**, you should be able to simply run
+
+.. code-block:: bash
+
    $ pip install enigma-catalyst matplotlib
 
+Note that in the command above we install two different packages. The second 
+one, ``matplotlib`` is a visualization library. While it's not strictly 
+required to run catalyst simulations or live trading, it comes in very handy
+to visualize the performance of your algorithms, and for this reason we 
+recommend you install it, as well.
+
+
 Troubleshooting ``pip`` Install
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -219,13 +218,13 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      pip install --upgrade pip
+      $ pip install --upgrade pip
 
    On Windows, the recommended command is:
 
    .. code-block:: bash
 
-      python -m pip install --upgrade pip
+      $ python -m pip install --upgrade pip
 
 ----
 
@@ -251,7 +250,7 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      pip install --pre enigma-catalyst
+      $ pip install --pre enigma-catalyst
 
 ----
 
@@ -263,7 +262,7 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      pip install --upgrade pip setuptools
+      $ pip install --upgrade pip setuptools
 
 ----
 
@@ -278,7 +277,7 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      pip install -r requirements.txt
+      $ pip install -r requirements.txt
 
 ----
 
@@ -294,7 +293,7 @@ Troubleshooting ``pip`` Install
 
    .. code-block:: bash
 
-      sudo apt-get install python-dev
+      $ sudo apt-get install python-dev
 
 .. _pipenv:
 
@@ -376,14 +375,14 @@ outdated. Thus, you first need to run:
 
 .. code-block:: bash
 
-   pip install --upgrade pip setuptools
+   $ pip install --upgrade pip setuptools
 
 The default installation is also missing the C and C++ compilers, which you 
 install by:
 
 .. code-block:: bash
 
-   sudo yum install gcc gcc-c++
+   $ sudo yum install gcc gcc-c++
 
 Then you should follow the regular installation instructions outlined at the 
 beginning of this page.
@@ -408,20 +407,34 @@ following brew packages:
 
    $ brew install freetype pkg-config gcc openssl
 
-MacOS + virtualenv + matplotlib
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+MacOS + virtualenv/conda + matplotlib
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-A note about using matplotlib in virtual enviroments on MacOS: it may be 
-necessary to run
+The first time that you try to run an algorithm that loads the ``matplotlib`` 
+library, you may get the following error:
+
+.. code-block:: text
+
+  RuntimeError: Python is not installed as a framework. The Mac OS X backend 
+  will not be able to function correctly if Python is not installed as a 
+  framework. See the Python documentation for more information on installing 
+  Python as a framework on Mac OS X. Please either reinstall Python as a 
+  framework, or try one of the other backends. If you are using (Ana)Conda 
+  please install python.app and replace the use of 'python' with 'pythonw'. 
+  See 'Working with Matplotlib on OSX' in the Matplotlib FAQ for more 
+  information.
+
+This is a ``matplotlib``-specific error, that will go away once you run the 
+following command:
 
 .. code-block:: bash
 
-   echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
+   $ echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
 
 in order to override the default ``MacOS`` backend for your system, which 
-may not be accessible from inside the virtual environment. This will allow 
-Catalyst to open matplotlib charts from within a virtual environment, which 
-is useful for displaying the performance of your backtests.  To learn more 
+may not be accessible from inside the virtual or conda environment. This will 
+allow Catalyst to open matplotlib charts from within a virtual environment, 
+which is useful for displaying the performance of your backtests.  To learn more 
 about matplotlib backends, please refer to the
 `matplotlib backend documentation <https://matplotlib.org/faq/usage_faq.html#what-is-a-backend>`_.
 
@@ -475,6 +488,33 @@ mentioned above are as follows:
   - ``cd`` into the folder where you downloaded ``VCForPython27.msi``
   - Run ``msiexec /i VCForPython27.msi``
 
+Updating Catalyst
+-----------------
+
+Catalyst is currently in alpha and in under very active development. We release
+new minor versions every few days in response to the thorough battle testing 
+that our user community puts Catalyst in. As a result, you should expect to 
+update Catalyst frequently. Once installed, Catalyst can easily be updated as a 
+``pip`` package regardless of the environemnt used for installation. Make sure 
+you activate your environment first as you did in your first install, and then 
+execute:
+
+.. code-block:: bash
+
+   $ pip uninstall enigma-catalyst
+   $ pip install enigma-catalyst
+
+Alternatively, you could update Catalyst issuing the following command:
+
+.. code-block:: bash
+
+   $ pip install -U enigma-catalyst
+
+but this command will also upgrade all the Catalyst dependencies to the latest 
+versions available, and may have unexpected side effects if a newer version of a 
+dependency inadvertently breaks some functionality that Catalyst relies on. 
+Thus, the first method is the recommended one.
+
 Getting Help
 ------------
 

docs/source/live-trading.rst

@@ -4,11 +4,63 @@ This document explains how to get started with live trading.
 
 Supported Exchanges
 ^^^^^^^^^^^^^^^^^^^
-Catalyst can trade against these exchanges:
 
-- Bitfinex, id= ``bitfinex``
-- Bittrex, id= ``bittrex``
-- Poloniex, id= ``poloniex``
+Since version 0.4, Catalyst integrated with `CCXT <https://github.com/ccxt/ccxt>`_,
+a cryptocurrency trading library with support for more than 90 exchanges. The 
+range of CCXT and Catalyst support for each of those exchanges varies greatly. 
+The most supported exchanges are as follows:
+
+The exchanges available for backtesting are fully supported in live mode:
+
+- Bitfinex, id = ``bitfinex``
+- Bittrex, id = ``bittrex``
+- Poloniex, id = ``poloniex``
+
+Additionally, we have successfully tested the following exchanges:
+
+- Binance, id = ``binance``
+- Bitmex, id = ``bitmex``
+- GDAX, id = ``gdax``
+
+As Catalyst is currently in Alpha and in under active development, you are 
+encouraged to throughly test any exchange in *paper trading* mode before trading
+*live* with it.
+
+Paper Trading vs Live Trading modes
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Catalyst currently supports three different modes in which you can execute your
+trading algorithm. The first is backtesting, which is covered extensively in the 
+tutorial, and uses historical data to run your algorithm. There is no 
+interaction with the exchange in backtesting mode, and this is the first mode 
+that you should test any new algorithm.
+
+Once you are confident with the simulations that you have obtained with your
+algorithm in backtesting, you may switch to live trading, where you have two
+different modes:
+* *Paper Trading*: The simulated algorithm runs in real time, and fetches 
+pricing data in real time from the exchange, but the orders never reach the 
+exchange, and are instead kept within Catalyst and simulated. No real currency
+is bought or sold. Think of it as a `backtesting happening in real time`.
+* *Live Trading*: This is the proper live trading mode in which an algorithm
+runs in real time, fetching pricing data from live exchanges and placing orders 
+against the exchange. Real currency is transacted on the exchange driven by the 
+algorithm.
+
+These three modes are controlled by the following variables:
+
++---------------+-------------------------+
+|    Mode       |       Parameters        |
++               +-------+-----------------+
+|               | live  | simulate_orders |
++---------------+-------+-----------------+
+| backtesting   | False | True (default)  |
++---------------+-------+-----------------+
+| paper trading | True  | True            |
++---------------+-------+-----------------+
+| live trading  | True  | False           |
++---------------+-------+-----------------+
+
 
 Authentication
 ^^^^^^^^^^^^^^
@@ -75,7 +127,8 @@ Note that the trading pairs are always referenced in the same manner.
 However, not all trading pairs are available on all exchanges. An
 error will occur if the specified trading pair is not trading
 on the exchange. To check which currency pairs are available on each 
-of the supported exchanges, see `Catalyst Market Coverage <https://www.enigma.co/catalyst/status`_.
+of the supported exchanges, see 
+`Catalyst Market Coverage <https://www.enigma.co/catalyst/status>`_.
 
 Trading an Algorithm
 ^^^^^^^^^^^^^^^^^^^^
@@ -105,20 +158,22 @@ What differs are the arguments provided to the catalyst client or
 
 Here is the breakdown of the new arguments:
 
-- ``live``: Boolean flag which enables live trading.
+- ``live``: Boolean flag which enables live trading. It defaults to ``False``.
 - ``capital_base``: The amount of base_currency assigned to the strategy.
   It has to be lower or equal to the amount of base currency available for
   trading on the exchange. For illustration, order_target_percent(asset, 1)
   will order the capital_base amount specified here of the specified asset.
-- ``exchange_name``: The name of the targeted exchange
-  (supported values: *bitfinex*, *bittrex*).
+- ``exchange_name``: The name of the targeted exchange. See the 
+  `CCXT Supported Exchanges <https://github.com/ccxt/ccxt/wiki/Exchange-Markets>`_ 
+  for the full list.
 - ``algo_namespace``: A arbitrary label assigned to your algorithm for
   data storage purposes.
 - ``base_currency``: The base currency used to calculate the
   statistics of your algorithm. Currently, the base currency of all
   trading pairs of your algorithm must match this value.
 - ``simulate_orders``: Enables the paper trading mode, in which orders are
-  simulated in Catalyst instead of processed on the exchange.
+  simulated in Catalyst instead of processed on the exchange. It defaults to 
+  ``True``.
 
 Here is a complete algorithm for reference:
 `Buy Low and Sell High <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_low_sell_high_live.py>`_

docs/source/releases.rst

@@ -2,9 +2,70 @@
 Release Notes
 =============
 
-Version 0.4.1
+Version 0.4.7
 ^^^^^^^^^^^^^
-**Release Date**: 2017-01-03
+**Release Date**: 2018-01-19
+
+Bug Fixes
+~~~~~~~~~
+- Fixing issue :issue:`137` impacting the CLI
+
+Build
+~~~~~
+- Implemented authentication aliases (:issue:`60`)
+
+Version 0.4.6
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-18
+
+Bug Fixes
+~~~~~~~~~
+- Fixed some Python3 issues
+- Reading the trade log to get executed order prices on exchanges like Binance (:issue:`151`)
+- Fixed issue with market order executing price (:issue:`150` and :issue:`111`)
+- Implemented standardized symbol mapping (:issue:`157`)
+- Improved error handling for unsupported timeframes (:issue:`159`)
+- Using Bitfinex instead of Poloniex to fetch btc_usdt benchmark (:issue:`161`)
+
+
+Build
+~~~~~
+- Added a `context.state` dict to keep arbitrary state values between runs
+- Added ability to stop live algo at specified end date
+
+Version 0.4.5
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-12
+
+Bug Fixes
+~~~~~~~~~
+- Improved order execution for exchanges supporting trade lists (:issue:`151`)
+- Fixed an issue where requesting history of multiple assets repeats values
+- Raising an error for order amounts smaller than exchange lots
+- Handling multiple req errors with tickers more gracefully (:issue:`160`)
+
+Version 0.4.4
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-09
+
+Bug Fixes
+~~~~~~~~~
+- Removed redundant capital_base validation (:issue:`142`)
+- Fixed portfolio update issue with restored state (:issue:`111`)
+- Skipping cash validation where there are open orders (:issue:`144`)
+
+Version 0.4.3
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-05
+
+Bug Fixes
+~~~~~~~~~
+- Fixed CLI issue (:issue:`137`)
+- Upgraded CCXT
+
+Version 0.4.2
+^^^^^^^^^^^^^
+**Release Date**: 2018-01-03
 
 Bug Fixes
 ~~~~~~~~~
@@ -39,7 +100,7 @@ Build
 - Added market orders in live mode (:issue:`81`)
 
 Version 0.3.10
-^^^^^^^^^^^^^
+~~~~~~~~~~~~~~
 **Release Date**: 2017-11-28
 
 Bug Fixes

etc/python2.7-environment.yml

@@ -20,7 +20,7 @@ dependencies:
   - bcolz==0.12.1
   - bottleneck==1.2.1
   - chardet==3.0.4
-  - ccxt==1.10.283
+  - ccxt==1.10.774
   - click==6.7
   - contextlib2==0.5.5
   - cycler==0.10.0

etc/requirements.txt

@@ -81,6 +81,6 @@ empyrical==0.2.1
 tables==3.3.0
 
 #Catalyst dependencies
-ccxt==1.10.283
+ccxt==1.10.774
 boto3==1.4.8
 redo==1.6

etc/requirements_docs.txt

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

tests/exchange/test_bundle.py

@@ -10,7 +10,8 @@ from catalyst.exchange.exchange_bcolz import BcolzExchangeBarReader, \
 from catalyst.exchange.exchange_bundle import ExchangeBundle, \
     BUNDLE_NAME_TEMPLATE
 from catalyst.exchange.utils.bundle_utils import get_bcolz_chunk, \
-    get_start_dt, get_df_from_arrays
+    get_df_from_arrays
+from exchange.utils.datetime_utils import get_start_dt
 from catalyst.exchange.utils.exchange_utils import get_exchange_folder
 from catalyst.exchange.utils.factory import get_exchange
 from catalyst.exchange.utils.stats_utils import df_to_string

tests/exchange/test_ccxt.py

@@ -1,7 +1,9 @@
 import pandas as pd
 from logbook import Logger
 
-from base import BaseExchangeTestCase
+from catalyst.testing import ZiplineTestCase
+from catalyst.testing.fixtures import WithLogger
+from .base import BaseExchangeTestCase
 from catalyst.exchange.ccxt.ccxt_exchange import CCXT
 from catalyst.exchange.exchange_execution import ExchangeLimitOrder
 from catalyst.exchange.utils.exchange_utils import get_exchange_auth
@@ -13,22 +15,22 @@ log = Logger('test_ccxt')
 class TestCCXT(BaseExchangeTestCase):
     @classmethod
     def setup(self):
-        exchange_name = 'binance'
+        exchange_name = 'bitfinex'
         auth = get_exchange_auth(exchange_name)
         self.exchange = CCXT(
             exchange_name=exchange_name,
             key=auth['key'],
             secret=auth['secret'],
-            base_currency='eth',
+            base_currency='bnb',
         )
         self.exchange.init()
 
     def test_order(self):
         log.info('creating order')
-        asset = self.exchange.get_asset('neo_eth')
+        asset = self.exchange.get_asset('neo_bnb')
         order_id = self.exchange.order(
             asset=asset,
-            style=ExchangeLimitOrder(limit_price=0.7),
+            style=ExchangeLimitOrder(limit_price=10),
             amount=1,
         )
         log.info('order created {}'.format(order_id))
@@ -56,10 +58,10 @@ class TestCCXT(BaseExchangeTestCase):
     def test_get_candles(self):
         log.info('retrieving candles')
         candles = self.exchange.get_candles(
-            freq='5T',
+            freq='30T',
             assets=[self.exchange.get_asset('eth_btc')],
             bar_count=200,
-            start_dt=pd.to_datetime('2017-01-01', utc=True)
+            start_dt=pd.to_datetime('2017-09-01', utc=True)
         )
 
         for asset in candles:
@@ -70,12 +72,28 @@ class TestCCXT(BaseExchangeTestCase):
     def test_tickers(self):
         log.info('retrieving tickers')
         assets = [
-            self.exchange.get_asset('eng_eth'),
+            self.exchange.get_asset('iot_usd'),
         ]
         tickers = self.exchange.tickers(assets)
         assert len(tickers) == 1
         pass
 
+    def test_my_trades(self):
+        asset = self.exchange.get_asset('dsh_btc')
+
+        trades = self.exchange.get_trades(asset)
+        assert trades
+        pass
+
+    def test_get_executed_order(self):
+        log.info('retrieving executed order')
+        asset = self.exchange.get_asset('eng_eth')
+
+        order = self.exchange.get_order('165784', asset)
+        transactions = self.exchange.process_order(order)
+        assert transactions
+        pass
+
     def test_get_balances(self):
         log.info('testing wallet balances')
         # balances = self.exchange.get_balances()

tests/exchange/test_suites/test_suite_algo.py

@@ -0,0 +1,79 @@
+import importlib
+from os.path import join, isfile
+
+import pandas as pd
+import os
+
+from catalyst import run_algorithm
+from catalyst.exchange.utils.stats_utils import get_pretty_stats, \
+    extract_transactions, set_print_settings, extract_orders
+from catalyst.testing.fixtures import WithLogger, ZiplineTestCase
+from logbook import TestHandler, WARNING
+from pathtools.path import listdir
+
+filter_algos = [
+    'buy_and_hodl.py',
+    'buy_btc_simple.py',
+    'buy_low_sell_high.py',
+    'mean_reversion_simple.py',
+    'rsi_profit_target.py',
+    'simple_loop.py',
+    'simple_universe.py',
+]
+
+
+class TestSuiteAlgo(WithLogger, ZiplineTestCase):
+    @staticmethod
+    def analyze(context, perf):
+        set_print_settings()
+
+        transaction_df = extract_transactions(perf)
+        print('the transactions:\n{}'.format(transaction_df))
+
+        orders_df = extract_orders(perf)
+        print('the orders:\n{}'.format(orders_df))
+
+        stats = get_pretty_stats(perf, show_tail=False, num_rows=5)
+        print('the stats:\n{}'.format(stats))
+        pass
+
+    def test_run_examples(self):
+        folder = join('..', '..', '..', 'catalyst', 'examples')
+        files = [f for f in listdir(folder) if isfile(join(folder, f))]
+
+        algo_list = []
+        for filename in files:
+            name = os.path.basename(filename)
+            if filter_algos and name not in filter_algos:
+                continue
+
+            module_name = 'catalyst.examples.{}'.format(
+                name.replace('.py', '')
+            )
+            algo_list.append(module_name)
+
+        for module_name in algo_list:
+            algo = importlib.import_module(module_name)
+            namespace = module_name.replace('.', '_')
+
+            log_catcher = TestHandler()
+            with log_catcher:
+                run_algorithm(
+                    capital_base=0.1,
+                    data_frequency='minute',
+                    initialize=algo.initialize,
+                    handle_data=algo.handle_data,
+                    analyze=TestSuiteAlgo.analyze,
+                    exchange_name='poloniex',
+                    algo_namespace='test_{}'.format(namespace),
+                    base_currency='eth',
+                    start=pd.to_datetime('2017-10-01', utc=True),
+                    end=pd.to_datetime('2017-10-02', utc=True),
+                    # output=out
+                )
+                warnings = [record for record in log_catcher.records if
+                            record.level == WARNING]
+
+                if len(warnings) > 0:
+                    print('WARNINGS:\n{}'.format(warnings))
+            pass

tests/exchange/test_suites/test_suite_bundle.py

@@ -1,7 +1,8 @@
 import random
 
+import os
 import pandas as pd
-from logbook import Logger
+from logbook import TestHandler
 from pandas.util.testing import assert_frame_equal
 
 from catalyst import get_calendar
@@ -12,8 +13,6 @@ from catalyst.exchange.utils.factory import get_exchange
 from catalyst.exchange.utils.test_utils import output_df, \
     select_random_assets
 
-log = Logger('TestSuiteExchange')
-
 pd.set_option('display.expand_frame_repr', False)
 pd.set_option('precision', 8)
 pd.set_option('display.width', 1000)
@@ -22,10 +21,11 @@ pd.set_option('display.max_colwidth', 1000)
 
 class TestSuiteBundle:
     @staticmethod
-    def get_data_portal(exchange_names):
+    def get_data_portal(exchanges):
         open_calendar = get_calendar('OPEN')
-        asset_finder = ExchangeAssetFinder()
+        asset_finder = ExchangeAssetFinder(exchanges)
 
+        exchange_names = [exchange.name for exchange in exchanges]
         data_portal = DataPortalExchangeBacktest(
             exchange_names=exchange_names,
             asset_finder=asset_finder,
@@ -46,7 +46,9 @@ class TestSuiteBundle:
         assets
         end_dt
         bar_count
-        sample_minutes
+        freq
+        data_frequency
+        data_portal
 
         Returns
         -------
@@ -54,51 +56,60 @@ class TestSuiteBundle:
         """
         data = dict()
 
-        log.info('creating data sample from bundle')
-        data['bundle'] = data_portal.get_history_window(
-            assets=assets,
-            end_dt=end_dt,
-            bar_count=bar_count,
-            frequency=freq,
-            field='close',
-            data_frequency=data_frequency,
-        )
-        log.info('bundle data:\n{}'.format(
-            data['bundle'].tail(10))
-        )
+        log_catcher = TestHandler()
+        with log_catcher:
+            data['bundle'] = data_portal.get_history_window(
+                assets=assets,
+                end_dt=end_dt,
+                bar_count=bar_count,
+                frequency=freq,
+                field='close',
+                data_frequency=data_frequency,
+            )
+            candles = exchange.get_candles(
+                end_dt=end_dt,
+                freq=freq,
+                assets=assets,
+                bar_count=bar_count,
+            )
+            data['exchange'] = get_candles_df(
+                candles=candles,
+                field='close',
+                freq=freq,
+                bar_count=bar_count,
+                end_dt=end_dt,
+            )
+            for source in data:
+                df = data[source]
+                path, folder = output_df(
+                    df, assets, '{}_{}'.format(freq, source)
+                )
 
-        log.info('creating data sample from exchange api')
-        candles = exchange.get_candles(
-            end_dt=end_dt,
-            freq=freq,
-            assets=assets,
-            bar_count=bar_count,
-        )
-        data['exchange'] = get_candles_df(
-            candles=candles,
-            field='close',
-            freq=freq,
-            bar_count=bar_count,
-            end_dt=end_dt,
-        )
-        log.info('exchange data:\n{}'.format(
-            data['exchange'].tail(10))
-        )
-        for source in data:
-            df = data[source]
-            path = output_df(df, assets, '{}_{}'.format(freq, source))
-            log.info('saved {}:\n{}'.format(source, path))
+            print('saved {} test results: {}'.format(end_dt, folder))
 
-        assert_frame_equal(
-            right=data['bundle'],
-            left=data['exchange'],
-            check_less_precise=True,
-        )
+            assert_frame_equal(
+                right=data['bundle'],
+                left=data['exchange'],
+                check_less_precise=1,
+            )
+            try:
+                assert_frame_equal(
+                    right=data['bundle'],
+                    left=data['exchange'],
+                    check_less_precise=min([a.decimals for a in assets]),
+                )
+            except Exception as e:
+                print('Some differences were found within a 1 decimal point '
+                      'interval of confidence: {}'.format(e))
+                with open(os.path.join(folder, 'compare.txt'), 'w+') as handle:
+                    handle.write(e.args[0])
+
+            pass
 
     def test_validate_bundles(self):
         # exchange_population = 3
         asset_population = 3
-        data_frequency = random.choice(['minute', 'daily'])
+        data_frequency = random.choice(['minute'])
 
         # bundle = 'dailyBundle' if data_frequency
         #  == 'daily' else 'minuteBundle'
@@ -106,11 +117,9 @@ class TestSuiteBundle:
         #     population=exchange_population,
         #     features=[bundle],
         # )  # Type: list[Exchange]
-        exchanges = [get_exchange('bitfinex', skip_init=True)]
+        exchanges = [get_exchange('poloniex', skip_init=True)]
 
-        data_portal = TestSuiteBundle.get_data_portal(
-            [exchange.name for exchange in exchanges]
-        )
+        data_portal = TestSuiteBundle.get_data_portal(exchanges)
         for exchange in exchanges:
             exchange.init()
 

tests/exchange/test_suites/test_suite_exchange.py

@@ -1,21 +1,26 @@
 import json
 import os
 import random
-from logging import Logger
+from logging import Logger, WARNING
 from time import sleep
 
 import pandas as pd
+from catalyst.assets._assets import TradingPair
+from logbook import TestHandler
 
 from catalyst.exchange.exchange_errors import ExchangeRequestError
 from catalyst.exchange.exchange_execution import ExchangeLimitOrder
 from catalyst.exchange.utils.exchange_utils import get_exchange_folder
 from catalyst.exchange.utils.test_utils import select_random_exchanges, \
     handle_exchange_error, select_random_assets
+from catalyst.testing import ZiplineTestCase
+from catalyst.testing.fixtures import WithLogger
+from exchange.utils.factory import get_exchanges
 
 log = Logger('TestSuiteExchange')
 
 
-class TestSuiteExchange:
+class TestSuiteExchange(WithLogger, ZiplineTestCase):
     def _test_markets_exchange(self, exchange, attempts=0):
         assets = None
         try:
@@ -79,12 +84,13 @@ class TestSuiteExchange:
 
     def test_tickers(self):
         exchange_population = 3
-        asset_population = 3
+        asset_population = 15
 
-        exchanges = select_random_exchanges(
-            exchange_population,
-            features=['fetchTickers'],
-        )  # Type: list[Exchange]
+        # exchanges = select_random_exchanges(
+        #     exchange_population,
+        #     features=['fetchTickers'],
+        # )  # Type: list[Exchange]
+        exchanges = list(get_exchanges(['bitfinex']).values())
         for exchange in exchanges:
             exchange.init()
 
@@ -156,34 +162,47 @@ class TestSuiteExchange:
             base_currency=quote_currency,
         )  # Type: list[Exchange]
 
-        for exchange in exchanges:
-            exchange.init()
+        log_catcher = TestHandler()
+        with log_catcher:
+            for exchange in exchanges:
+                exchange.init()
 
-            assets = exchange.get_assets(quote_currency=quote_currency)
-            asset = select_random_assets(assets, 1)[0]
-            assert asset
+                assets = exchange.get_assets(quote_currency=quote_currency)
+                asset = select_random_assets(assets, 1)[0]
+                self.assertIsInstance(asset, TradingPair)
 
-            tickers = exchange.tickers([asset])
-            price = tickers[asset]['last_price']
+                tickers = exchange.tickers([asset])
+                price = tickers[asset]['last_price']
 
-            amount = order_amount / price
+                amount = order_amount / price
 
-            limit_price = price * 0.8
-            style = ExchangeLimitOrder(limit_price=limit_price)
+                limit_price = price * 0.8
+                style = ExchangeLimitOrder(limit_price=limit_price)
 
-            order = exchange.order(
-                asset=asset,
-                amount=amount,
-                style=style,
-            )
-            sleep(1)
+                order = exchange.order(
+                    asset=asset,
+                    amount=amount,
+                    style=style,
+                )
+                sleep(1)
 
-            open_order, _ = exchange.get_order(order.id, asset)
-            assert open_order.status == 0
+                open_order = exchange.get_order(order.id, asset)
+                self.assertEqual(0, open_order.status)
 
-            exchange.cancel_order(open_order, asset)
-            sleep(1)
+                exchange.cancel_order(open_order, asset)
+                sleep(1)
 
-            canceled_order, _ = exchange.get_order(open_order.id, asset)
-            assert canceled_order.status == 2
+                canceled_order = exchange.get_order(open_order.id, asset)
+                warnings = [record for record in log_catcher.records if
+                            record.level == WARNING]
+
+                self.assertEqual(0, len(warnings))
+                self.assertEqual(2, canceled_order.status)
+                print(
+                    'tested {exchange} / {symbol}, order: {order}'.format(
+                        exchange=exchange.name,
+                        symbol=asset.symbol,
+                        order=order.id,
+                    )
+                )
         pass