Merge branch 'develop'

README.rst

@@ -17,9 +17,7 @@ insights regarding a particular strategy's performance. Catalyst also supports
 live-trading of crypto-assets starting with three exchanges (Bitfinex, Bittrex, 
 and Poloniex) with more being added over time. Catalyst empowers users to share 
 and curate data and build profitable, data-driven investment strategies. Please 
-visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst, or 
-refer to the `whitepaper <https://www.enigma.co/enigma_catalyst.pdf>`_ for 
-further technical details.
+visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst.
 
 Catalyst builds on top of the well-established 
 `Zipline <https://github.com/quantopian/zipline>`_ project. We did our best to 

catalyst/algorithm.py

@@ -939,7 +939,7 @@ class TradingAlgorithm(object):
             The field to query. The options have the following meanings:
               arena : str
                   The arena from the simulation parameters. This will normally
-                  be ``'backtest'`` but some systems may use this distinguish
+                  be ``backtest`` but some systems may use this distinguish
                   live trading from backtesting.
               data_frequency : {'daily', 'minute'}
                   data_frequency tells the algorithm if it is running with
@@ -954,7 +954,7 @@ class TradingAlgorithm(object):
                   The platform that the code is running on. By default this
                   will be the string 'catalyst'. This can allow algorithms to
                   know if they are running on the Quantopian platform instead.
-              * : dict[str -> any]
+              \* : dict[str -> any]
                   Returns all of the fields in a dictionary.
 
         Returns
@@ -1032,7 +1032,7 @@ class TradingAlgorithm(object):
             argument is the name of the column in the preprocessed dataframe
             containing the symbols. This will be used along with the date
             information to map the sids in the asset finder.
-        **kwargs
+        \*\*kwargs
             Forwarded to :func:`pandas.read_csv`.
 
         Returns
@@ -1156,7 +1156,7 @@ class TradingAlgorithm(object):
 
         Parameters
         ----------
-        **kwargs
+        \*\*kwargs
             The names and values to record.
 
         Notes
@@ -1273,7 +1273,7 @@ class TradingAlgorithm(object):
 
         Parameters
         ----------
-        *args : iterable[str]
+        \*args : iterable[str]
             The ticker symbols to lookup.
 
         Returns

catalyst/examples/buy_low_sell_high.py

@@ -60,7 +60,7 @@ def _handle_data(context, data):
         rsi=rsi,
     )
 
-    orders = get_open_orders(context.asset)
+    orders = context.blotter.open_orders
     if orders:
         log.info('skipping bar until all open orders execute')
         return
@@ -146,11 +146,11 @@ if __name__ == '__main__':
     live = True
     if live:
         run_algorithm(
-            capital_base=0.001,
+            capital_base=1000,
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='binance',
+            exchange_name='bittrex',
             live=True,
             algo_namespace=algo_namespace,
             base_currency='btc',

catalyst/examples/dual_moving_average.py

@@ -20,8 +20,8 @@ def initialize(context):
 
 def handle_data(context, data):
     # define the windows for the moving averages
-    short_window = 50
-    long_window = 200
+    short_window = 2
+    long_window = 2
 
     # Skip as many bars as long_window to properly compute the average
     context.i += 1
@@ -32,16 +32,18 @@ def handle_data(context, data):
     # moving average with the appropriate parameters. We choose to use
     # minute bars for this simulation -> freq="1m"
     # Returns a pandas dataframe.
-    short_mavg = data.history(context.asset,
+    short_data = data.history(context.asset,
                               'price',
                               bar_count=short_window,
-                              frequency="1m",
-                              ).mean()
-    long_mavg = data.history(context.asset,
+                              frequency="1T",
+                              )
+    short_mavg = short_data.mean()
+    long_data = data.history(context.asset,
                              'price',
                              bar_count=long_window,
-                             frequency="1m",
-                             ).mean()
+                             frequency="1T",
+                             )
+    long_mavg = long_data.mean()
 
     # Let's keep the price of our asset in a more handy variable
     price = data.current(context.asset, 'price')
@@ -82,7 +84,6 @@ def handle_data(context, data):
 
 
 def analyze(context, perf):
-
     # Get the base_currency that was passed as a parameter to the simulation
     exchange = list(context.exchanges.values())[0]
     base_currency = exchange.base_currency.upper()
@@ -93,7 +94,7 @@ def analyze(context, perf):
     ax1.legend_.remove()
     ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
     start, end = ax1.get_ylim()
-    ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+    ax1.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
     # Second chart: Plot asset price, moving averages and buys/sells
     ax2 = plt.subplot(412, sharex=ax1)
@@ -104,9 +105,9 @@ def analyze(context, perf):
     ax2.set_ylabel('{asset}\n({base})'.format(
         asset=context.asset.symbol,
         base=base_currency
-        ))
+    ))
     start, end = ax2.get_ylim()
-    ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+    ax2.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
     transaction_df = extract_transactions(perf)
     if not transaction_df.empty:
@@ -136,28 +137,40 @@ def analyze(context, perf):
     ax3.legend_.remove()
     ax3.set_ylabel('Percent Change')
     start, end = ax3.get_ylim()
-    ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+    ax3.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
     # Fourth chart: Plot our cash
     ax4 = plt.subplot(414, sharex=ax1)
     perf.cash.plot(ax=ax4)
     ax4.set_ylabel('Cash\n({})'.format(base_currency))
     start, end = ax4.get_ylim()
-    ax4.yaxis.set_ticks(np.arange(0, end, end/5))
+    ax4.yaxis.set_ticks(np.arange(0, end, end / 5))
 
     plt.show()
 
 
 if __name__ == '__main__':
     run_algorithm(
-            capital_base=1000,
-            data_frequency='minute',
-            initialize=initialize,
-            handle_data=handle_data,
-            analyze=analyze,
-            exchange_name='bitfinex',
-            algo_namespace=NAMESPACE,
-            base_currency='usd',
-            start=pd.to_datetime('2017-9-22', utc=True),
-            end=pd.to_datetime('2017-9-23', utc=True),
-        )
+        capital_base=1000,
+        data_frequency='minute',
+        initialize=initialize,
+        handle_data=handle_data,
+        analyze=analyze,
+        exchange_name='bitfinex',
+        algo_namespace=NAMESPACE,
+        base_currency='usd',
+        simulate_orders=True,
+        live=True,
+    )
+    # run_algorithm(
+    #         capital_base=1000,
+    #         data_frequency='minute',
+    #         initialize=initialize,
+    #         handle_data=handle_data,
+    #         analyze=analyze,
+    #         exchange_name='bitfinex',
+    #         algo_namespace=NAMESPACE,
+    #         base_currency='usd',
+    #         start=pd.to_datetime('2017-9-22', utc=True),
+    #         end=pd.to_datetime('2017-9-23', utc=True),
+    #     )

catalyst/exchange/ccxt/ccxt_exchange.py

@@ -425,15 +425,12 @@ class CCXT(Exchange):
                 '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']:
+        if start_dt is None:
+            # TODO: determine why binance is failing
+            if end_dt is None and self.name not in ['binance']:
+                end_dt = pd.Timestamp.utcnow()
+
+            if end_dt is not None:
                 dt_range = get_periods_range(
                     end_dt=end_dt,
                     periods=bar_count,
@@ -441,10 +438,13 @@ class CCXT(Exchange):
                 )
                 start_dt = dt_range[0]
 
-        since = None
         if start_dt is not None:
+            # Convert out start date to a UNIX timestamp, then translate to
+            # milliseconds
             delta = start_dt - get_epoch()
             since = int(delta.total_seconds()) * 1000
+        else:
+            since = None
 
         candles = dict()
         for index, asset in enumerate(assets):

catalyst/exchange/exchange_algorithm.py

@@ -391,8 +391,6 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             log.warn("Can't initialize signal handler inside another thread."
                      "Exit should be handled by the user.")
 
-        log.info('initialized trading algorithm in live mode')
-
     def interrupt_algorithm(self):
         self.is_running = False
 
@@ -874,6 +872,13 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         raise NotImplementedError()
 
     def _get_open_orders(self, asset=None):
+        if self.simulate_orders:
+            raise ValueError(
+                'The get_open_orders() method only works in live mode. '
+                'The purpose is to list open orders on the exchange '
+                'regardless who placed them. To list the open orders of '
+                'this algo, use `context.blotter.open_orders`.'
+            )
         if asset:
             exchange = self.exchanges[asset.exchange]
             return exchange.get_open_orders(asset)
@@ -907,6 +912,7 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
             If an asset is passed then this will return a list of the open
             orders for this asset.
         """
+        # TODO: should this be a shortcut to the open orders in the blotter?
         return retry(
             action=self._get_open_orders,
             attempts=self.attempts['get_open_orders_attempts'],

catalyst/exchange/exchange_bundle.py

@@ -458,7 +458,7 @@ class ExchangeBundle:
                 last_entry = None
 
         if start is None or \
-                (earliest_trade is not None and earliest_trade > start):
+            (earliest_trade is not None and earliest_trade > start):
             start = earliest_trade
 
         if last_entry is not None and (end is None or end > last_entry):
@@ -600,14 +600,14 @@ class ExchangeBundle:
         if show_breakdown:
             for asset in chunks:
                 with maybe_show_progress(
-                        chunks[asset],
-                        show_progress,
-                        label='Ingesting {frequency} price data for '
-                              '{symbol} on {exchange}'.format(
-                            exchange=self.exchange_name,
-                            frequency=data_frequency,
-                            symbol=asset.symbol
-                        )) as it:
+                    chunks[asset],
+                    show_progress,
+                    label='Ingesting {frequency} price data for '
+                          '{symbol} on {exchange}'.format(
+                        exchange=self.exchange_name,
+                        frequency=data_frequency,
+                        symbol=asset.symbol
+                    )) as it:
                     for chunk in it:
                         problems += self.ingest_ctable(
                             asset=chunk['asset'],
@@ -625,13 +625,13 @@ class ExchangeBundle:
                 key=lambda chunk: pd.to_datetime(chunk['period'])
             )
             with maybe_show_progress(
-                    all_chunks,
-                    show_progress,
-                    label='Ingesting {frequency} price data on '
-                          '{exchange}'.format(
-                        exchange=self.exchange_name,
-                        frequency=data_frequency,
-                    )) as it:
+                all_chunks,
+                show_progress,
+                label='Ingesting {frequency} price data on '
+                      '{exchange}'.format(
+                    exchange=self.exchange_name,
+                    frequency=data_frequency,
+                )) as it:
                 for chunk in it:
                     problems += self.ingest_ctable(
                         asset=chunk['asset'],
@@ -830,7 +830,6 @@ class ExchangeBundle:
                                            field,
                                            data_frequency,
                                            algo_end_dt=None,
-                                           trailing_bar_count=None,
                                            force_auto_ingest=False
                                            ):
         """
@@ -858,7 +857,6 @@ class ExchangeBundle:
                     bar_count=bar_count,
                     field=field,
                     data_frequency=data_frequency,
-                    trailing_bar_count=trailing_bar_count,
                 )
                 return pd.DataFrame(series)
 
@@ -887,7 +885,6 @@ class ExchangeBundle:
                     field=field,
                     data_frequency=data_frequency,
                     reset_reader=True,
-                    trailing_bar_count=trailing_bar_count,
                 )
                 return series
 
@@ -898,7 +895,6 @@ class ExchangeBundle:
                 bar_count=bar_count,
                 field=field,
                 data_frequency=data_frequency,
-                trailing_bar_count=trailing_bar_count,
             )
             return pd.DataFrame(series)
 
@@ -962,12 +958,7 @@ class ExchangeBundle:
                                   bar_count,
                                   field,
                                   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

catalyst/exchange/exchange_data_portal.py

@@ -298,7 +298,6 @@ class DataPortalExchangeBacktest(DataPortalExchangeBase):
             frequency, data_frequency
         )
         adj_bar_count = candle_size * bar_count
-        trailing_bar_count = candle_size - 1
 
         if data_frequency == 'minute' and adj_data_frequency == 'daily':
             end_dt = end_dt.floor('1D')
@@ -310,7 +309,6 @@ class DataPortalExchangeBacktest(DataPortalExchangeBase):
             field=field,
             data_frequency=adj_data_frequency,
             algo_end_dt=self._last_available_session,
-            trailing_bar_count=trailing_bar_count,
         )
 
         df = resample_history_df(pd.DataFrame(series), freq, field)

catalyst/exchange/utils/exchange_utils.py

@@ -540,7 +540,7 @@ def resample_history_df(df, freq, field):
     else:
         raise ValueError('Invalid field.')
 
-    resampled_df = df.resample(freq).agg(agg)
+    resampled_df = df.resample(freq, closed='left', label='left').agg(agg)
     return resampled_df
 
 

catalyst/support/issue_216.py

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

catalyst/utils/run_algo.py

@@ -55,6 +55,7 @@ class _RunAlgoError(click.ClickException, ValueError):
     ----------
     pyfunc_msg : str
         The message that will be shown when called as a python function.
+
     cmdline_msg : str
         The message that will be shown on the command line.
     """
@@ -416,7 +417,8 @@ def run_algorithm(initialize,
                   auth_aliases=None,
                   stats_output=None,
                   output=os.devnull):
-    """Run a trading algorithm.
+    """
+    Run a trading algorithm.
 
     Parameters
     ----------
@@ -458,7 +460,7 @@ def run_algorithm(initialize,
         This argument is mutually exclusive with ``data``.
     default_extension : bool, optional
         Should the default catalyst extension be loaded. This is found at
-        ``$ZIPLINE_ROOT/extension.py``
+        ``$CATALYST_ROOT/extension.py``
     extensions : iterable[str], optional
         The names of any other extensions to load. Each element may either be
         a dotted module path like ``a.b.c`` or a path to a python file ending
@@ -469,12 +471,8 @@ def run_algorithm(initialize,
     environ : mapping[str -> str], optional
         The os environment to use. Many extensions use this to get parameters.
         This defaults to ``os.environ``.
-    live: execute live trading
-    exchange_conn: The exchange connection parameters
-
-    Supported Exchanges
-    -------------------
-    bitfinex
+    live : bool, optional
+        Execute algorithm in live trading mode.
 
     Returns
     -------

docs/source/appendix.rst

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

docs/source/beginner-tutorial.rst

@@ -168,7 +168,7 @@ We'll start with the CLI, and introduce the ``run_algorithm()`` in the last
 example of this tutorial. Some of the :doc:`example algorithms <example-algos>` 
 provide instructions on how to run them both from the CLI, and using the 
 :func:`~catalyst.run_algorithm` function. For the third method, refer to the 
-corresponding section on :doc:`Catalyst & Jupyter Notebook <jupyter>` after you 
+corresponding section on :ref:`Catalyst & Jupyter Notebook <jupyter>` after you 
 have assimilated the contents of this tutorial.
 
 Command line interface
@@ -473,6 +473,7 @@ Which we execute by running:
     </div>
 
 |
+
 There is a row for each trading day, starting on the first day of our 
 simulation Jan 1st, 2016. In the columns you can find various
 information about the state of your algorithm. The column
@@ -518,7 +519,7 @@ alongside enigma-catalyst (with the exception of the ``Conda`` install, where it
 was included by default inside the conda environment we created). If for any 
 reason you don't have it installed, you can add it by running:
 
-.. code-block:: python
+.. code-block:: bash
 
   (catalyst)$ pip install matplotlib
 
@@ -806,6 +807,7 @@ the ``scikit-learn`` functions require ``numpy.ndarray``\ s rather than
 ``pandas.DataFrame``\ s, so you can simply pass the underlying
 ``ndarray`` of a ``DataFrame`` via ``.values``).
 
+.. _jupyter:
 
 Jupyter Notebook
 ~~~~~~~~~~~~~~~~
@@ -826,13 +828,13 @@ In order to use Jupyter Notebook, you first have to install it inside your
 environment. It's available as ``pip`` package, so regardless of how you 
 installed Catalyst, go inside your catalyst environemnt and run:
 
-.. code:: bash
+.. code-block:: bash
 
     (catalyst)$ pip install jupyter
 
 Once you have Jupyter Notebook installed, every time you want to use it run:
 
-.. code:: bash
+.. code-block:: bash
 
     (catalyst)$ jupyter notebook
 
@@ -846,7 +848,7 @@ Before running your algorithms inside the Jupyter Notebook, remember to ingest
 the data from the command line interface (CLI). In the example below, you would
 need to run first:
 
-.. code:: bash
+.. code-block:: bash
 
   catalyst ingest-exchange -x bitfinex -i btc_usd
 

docs/source/conf.py

@@ -27,8 +27,8 @@ extlinks = {
 
 # -- Docstrings ---------------------------------------------------------------
 
-#extensions += ['numpydoc']
-#numpydoc_show_class_members = False
+extensions += ['numpydoc']
+numpydoc_show_class_members = False
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['.templates']
@@ -97,3 +97,6 @@ intersphinx_mapping = {
 doctest_global_setup = "import catalyst"
 
 todo_include_todos = True
+
+suppress_warnings = ['image.nonlocal_uri']
+

docs/source/development-guidelines.rst

@@ -36,25 +36,15 @@ Finally, you can build the C extensions by running:
 
    $ python setup.py build_ext --inplace
 
-.. To finish, make sure `tests`__ pass.
+Development with Docker
+-----------------------
 
-.. __ #style-guide-running-tests
+If you want to work with zipline using a `Docker`__ container, you'll need to 
+build the ``Dockerfile`` in the Zipline root directory, and then build 
+``Dockerfile-dev``. Instructions for building both containers can be found in 
+``Dockerfile`` and ``Dockerfile-dev``, respectively.
 
-.. If you get an error running nosetests after setting up a fresh virtualenv, please try running
-
-.. code-block
-
-..   # where zipline is the name of your virtualenv
-..   $ deactivate zipline
-..   $ workon zipline
-
-
-.. Development with Docker
-.. -----------------------
-
-..If you want to work with zipline using a `Docker`__ container, you'll need to build the ``Dockerfile`` in the Zipline root directory, and then build ``Dockerfile-dev``. Instructions for building both containers can be found in ``Dockerfile`` and ``Dockerfile-dev``, respectively.
-
-.. __ https://docs.docker.com/get-started/
+__ https://docs.docker.com/get-started/
    
 Git Branching Structure
 -----------------------

docs/source/example-algos.rst

@@ -1,4 +1,5 @@
 |
+
 Example Algorithms
 ==================
 

docs/source/index.rst

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

docs/source/install.rst

@@ -298,7 +298,7 @@ Troubleshooting ``pip`` Install
 .. _pipenv:
 
 Installing with ``pipenv``
--------------------------
+--------------------------
 
 Installing Catalyst via ``pipenv`` is perhaps easier that installing it via
 ``pip`` itself but you need to install ``pipenv`` first via ``pip``.
@@ -476,6 +476,7 @@ mentioned above are as follows:
     default you get 0 as the Value Data)
 
 |
+
 - **The installer has encountered an unexpected error installing this package. 
   This may indicate a problem with this package. The error code is 2503.**
 

docs/source/live-trading.rst

@@ -113,7 +113,7 @@ Currency symbols (e.g. btc, eth, ltc) follow the Bittrex convention.
 
 Here are some examples:
 
-.. code-block:: json
+.. code:: python
 
   # With Bitfinex
   bitcoin_usd_asset = symbol('btc_usd')

docs/source/releases.rst

@@ -2,7 +2,23 @@
 Release Notes
 =============
 
-Version 0.5.0
+Version 0.5.3
+^^^^^^^^^^^^^
+**Release Date**: 2018-02-09
+
+Bug Fixes
+~~~~~~~~~
+- Fixed an issue with last candle in backtesting :issue:`219`
+
+Version 0.5.2
+^^^^^^^^^^^^^
+**Release Date**: 2018-02-08
+
+Bug Fixes
+~~~~~~~~~
+- Fixed an issue with live candle values :issue:`216` and :issue:`199`
+
+Version 0.5.1
 ^^^^^^^^^^^^^
 **Release Date**: 2018-02-07
 

docs/source/videos.rst

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

etc/requirements_dev.txt

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

etc/requirements_docs.txt

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

tests/exchange/test_ccxt.py

@@ -1,8 +1,7 @@
 import pandas as pd
 from logbook import Logger
 
-from catalyst.testing import ZiplineTestCase
-from catalyst.testing.fixtures import WithLogger
+from catalyst.exchange.utils.stats_utils import set_print_settings
 from .base import BaseExchangeTestCase
 from catalyst.exchange.ccxt.ccxt_exchange import CCXT
 from catalyst.exchange.exchange_execution import ExchangeLimitOrder
@@ -15,7 +14,7 @@ log = Logger('test_ccxt')
 class TestCCXT(BaseExchangeTestCase):
     @classmethod
     def setup(self):
-        exchange_name = 'binance'
+        exchange_name = 'bittrex'
         auth = get_exchange_auth(exchange_name)
         self.exchange = CCXT(
             exchange_name=exchange_name,
@@ -58,15 +57,20 @@ class TestCCXT(BaseExchangeTestCase):
     def test_get_candles(self):
         log.info('retrieving candles')
         candles = self.exchange.get_candles(
-            freq='30T',
+            freq='1T',
             assets=[self.exchange.get_asset('eth_btc')],
             bar_count=200,
-            start_dt=pd.to_datetime('2017-09-01', utc=True)
+            # start_dt=pd.to_datetime('2017-09-01', utc=True),
         )
 
         for asset in candles:
             df = pd.DataFrame(candles[asset])
             df.set_index('last_traded', drop=True, inplace=True)
+
+        set_print_settings()
+        print('got {} candles'.format(len(df)))
+        print(df.head(10))
+        print(df.tail(10))
         pass
 
     def test_tickers(self):

tests/exchange/test_suites/test_suite_bundle.py

@@ -2,6 +2,7 @@ import random
 
 import os
 import pandas as pd
+from datetime import timedelta
 from logbook import TestHandler
 from pandas.util.testing import assert_frame_equal
 
@@ -12,6 +13,7 @@ from catalyst.exchange.utils.exchange_utils import get_candles_df
 from catalyst.exchange.utils.factory import get_exchange
 from catalyst.exchange.utils.test_utils import output_df, \
     select_random_assets
+from catalyst.exchange.utils.stats_utils import set_print_settings
 
 pd.set_option('display.expand_frame_repr', False)
 pd.set_option('precision', 8)
@@ -58,6 +60,12 @@ class TestSuiteBundle:
 
         log_catcher = TestHandler()
         with log_catcher:
+            symbols = [asset.symbol for asset in assets]
+            print(
+                'comparing data for {}/{} with {} timeframe until {}'.format(
+                    exchange.name, symbols, freq, end_dt
+                )
+            )
             data['bundle'] = data_portal.get_history_window(
                 assets=assets,
                 end_dt=end_dt,
@@ -66,6 +74,12 @@ class TestSuiteBundle:
                 field='close',
                 data_frequency=data_frequency,
             )
+            set_print_settings()
+            print(
+                'the bundle first / last row:\n{}'.format(
+                    data['bundle'].iloc[[-1, 0]]
+                )
+            )
             candles = exchange.get_candles(
                 end_dt=end_dt,
                 freq=freq,
@@ -79,6 +93,11 @@ class TestSuiteBundle:
                 bar_count=bar_count,
                 end_dt=end_dt,
             )
+            print(
+                'the exchange first / last row:\n{}'.format(
+                    data['exchange'].iloc[[-1, 0]]
+                )
+            )
             for source in data:
                 df = data[source]
                 path, folder = output_df(
@@ -106,6 +125,65 @@ class TestSuiteBundle:
 
             pass
 
+    def compare_current_with_last_candle(self, exchange, assets, end_dt,
+                                         freq, data_frequency, data_portal):
+        """
+        Creates DataFrames from the bundle and exchange for the specified
+        data set.
+
+        Parameters
+        ----------
+        exchange: Exchange
+        assets
+        end_dt
+        bar_count
+        freq
+        data_frequency
+        data_portal
+
+        Returns
+        -------
+
+        """
+        data = dict()
+
+        assets = sorted(assets, key=lambda a: a.symbol)
+        log_catcher = TestHandler()
+        with log_catcher:
+            symbols = [asset.symbol for asset in assets]
+            print(
+                'comparing data for {}/{} with {} timeframe on {}'.format(
+                    exchange.name, symbols, freq, end_dt
+                )
+            )
+            data['candle'] = data_portal.get_history_window(
+                assets=assets,
+                end_dt=end_dt,
+                bar_count=1,
+                frequency=freq,
+                field='close',
+                data_frequency=data_frequency,
+            )
+            set_print_settings()
+            print(
+                'the bundle first / last row:\n{}'.format(
+                    data['candle'].iloc[[-1]]
+                )
+            )
+            current = data_portal.get_spot_value(
+                assets=assets,
+                field='close',
+                dt=end_dt,
+                data_frequency=data_frequency,
+            )
+            data['current'] = pd.Series(data=current, index=assets)
+            print(
+                'the current price:\n{}'.format(
+                    data['current']
+                )
+            )
+            pass
+
     def test_validate_bundles(self):
         # exchange_population = 3
         asset_population = 3
@@ -139,6 +217,7 @@ class TestSuiteBundle:
                 if end_dt is None or asset_end_dt < end_dt:
                     end_dt = asset_end_dt
 
+            end_dt = end_dt + timedelta(minutes=3)
             dt_range = pd.date_range(
                 end=end_dt, periods=bar_count, freq=freq
             )
@@ -152,3 +231,45 @@ class TestSuiteBundle:
                 data_portal=data_portal,
             )
         pass
+
+    def test_validate_last_candle(self):
+        # exchange_population = 3
+        asset_population = 3
+        data_frequency = random.choice(['minute'])
+
+        # bundle = 'dailyBundle' if data_frequency
+        #  == 'daily' else 'minuteBundle'
+        # exchanges = select_random_exchanges(
+        #     population=exchange_population,
+        #     features=[bundle],
+        # )  # Type: list[Exchange]
+        exchanges = [get_exchange('poloniex', skip_init=True)]
+
+        data_portal = TestSuiteBundle.get_data_portal(exchanges)
+        for exchange in exchanges:
+            exchange.init()
+
+            frequencies = exchange.get_candle_frequencies(data_frequency)
+            freq = random.sample(frequencies, 1)[0]
+
+            assets = select_random_assets(
+                exchange.assets, asset_population
+            )
+            end_dt = None
+            for asset in assets:
+                attribute = 'end_{}'.format(data_frequency)
+                asset_end_dt = getattr(asset, attribute)
+
+                if end_dt is None or asset_end_dt < end_dt:
+                    end_dt = asset_end_dt
+
+            end_dt = end_dt + timedelta(minutes=3)
+            self.compare_current_with_last_candle(
+                exchange=exchange,
+                assets=assets,
+                end_dt=end_dt,
+                freq=freq,
+                data_frequency=data_frequency,
+                data_portal=data_portal,
+            )
+        pass