BUG: fixed #75, adjusted the ruturn value of run_algorithm to support minute stats.

catalyst/examples/simple_loop.py

@@ -2,12 +2,15 @@ import talib
 import pandas as pd
 
 from catalyst import run_algorithm
-from catalyst.api import symbol
+from catalyst.api import symbol, record
+from catalyst.exchange.stats_utils import get_pretty_stats, \
+    extract_transactions
 
 
 def initialize(context):
     print('initializing')
-    context.asset = symbol('swift_btc')
+    context.asset = symbol('neo_usd')
+    context.base_price = None
 
 
 def handle_data(context, data):
@@ -20,26 +23,104 @@ def handle_data(context, data):
         prices = data.history(
             context.asset,
             fields='price',
-            bar_count=15,
-            frequency='1D'
+            bar_count=14,
+            frequency='15T'
         )
         rsi = talib.RSI(prices.values, timeperiod=14)[-1]
         print('got rsi: {}'.format(rsi))
     except Exception as e:
         print(e)
 
+    # If base_price is not set, we use the current value. This is the
+    # price at the first bar which we reference to calculate price_change.
+    if context.base_price is None:
+        context.base_price = price
+
+    price_change = (price - context.base_price) / context.base_price
+    cash = context.portfolio.cash
+
+    # Now that we've collected all current data for this frame, we use
+    # the record() method to save it. This data will be available as
+    # a parameter of the analyze() function for further analysis.
+    record(
+        price=price,
+        price_change=price_change,
+        cash=cash
+    )
+
+
+def analyze(context, perf):
+    import matplotlib.pyplot as plt
+    print('the stats: {}'.format(get_pretty_stats(perf)))
+
+    # The base currency of the algo exchange
+    base_currency = context.exchanges.values()[0].base_currency.upper()
+
+    # Plot the portfolio value over time.
+    ax1 = plt.subplot(611)
+    perf.loc[:, 'portfolio_value'].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio Value ({})'.format(base_currency))
+
+    # Plot the price increase or decrease over time.
+    ax2 = plt.subplot(612, sharex=ax1)
+    perf.loc[:, 'price'].plot(ax=ax2, label='Price')
+
+    ax2.set_ylabel('{asset} ({base})'.format(
+        asset=context.asset.symbol, base=base_currency
+    ))
+
+    transaction_df = extract_transactions(perf)
+    if not transaction_df.empty:
+        buy_df = transaction_df[transaction_df['amount'] > 0]
+        sell_df = transaction_df[transaction_df['amount'] < 0]
+        ax2.scatter(
+            buy_df.index.to_pydatetime(),
+            perf.loc[buy_df.index, 'price'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax2.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index, 'price'],
+            marker='v',
+            s=100,
+            c='red',
+            label=''
+        )
+
+    ax4 = plt.subplot(613, sharex=ax1)
+    perf.loc[:, 'cash'].plot(
+        ax=ax4, label='Base Currency ({})'.format(base_currency)
+    )
+    ax4.set_ylabel('Cash ({})'.format(base_currency))
+
+    perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
+
+    ax5 = plt.subplot(614, sharex=ax1)
+    perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
+    ax5.set_ylabel('Percent Change')
+
+    plt.legend(loc=3)
+
+    # Show the plot.
+    plt.gcf().set_size_inches(18, 8)
+    plt.show()
+    pass
+
 
 run_algorithm(
     capital_base=250,
-    start=pd.to_datetime('2015-4-1', utc=True),
-    end=pd.to_datetime('2017-11-1', utc=True),
+    start=pd.to_datetime('2017-11-1 0:00', utc=True),
+    end=pd.to_datetime('2017-11-10 23:59', utc=True),
     data_frequency='daily',
     initialize=initialize,
     handle_data=handle_data,
-    analyze=None,
-    exchange_name='bittrex',
+    analyze=analyze,
+    exchange_name='bitfinex',
     algo_namespace='simple_loop',
-    base_currency='btc'
+    base_currency='usd'
 )
 # run_algorithm(
 #     initialize=initialize,

catalyst/exchange/exchange_algorithm.py

@@ -245,19 +245,42 @@ class ExchangeTradingAlgorithmBacktest(ExchangeTradingAlgorithmBase):
         else:
             return MarketOrder()
 
+    def is_last_frame_of_day(self, data):
+        # TODO: adjust here to support more intervals
+        next_frame_dt = data.current_dt + timedelta(minutes=1)
+        if next_frame_dt.date() > data.current_dt.date():
+            return True
+        else:
+            return False
+
     def handle_data(self, data):
         super(ExchangeTradingAlgorithmBacktest, self).handle_data(data)
 
-        minute_stats = self.prepare_period_stats(
-            data.current_dt, data.current_dt + timedelta(minutes=1))
-        self.frame_stats.append(minute_stats)
+        if self.data_frequency == 'minute':
+            frame_stats = self.prepare_period_stats(
+                data.current_dt, data.current_dt + timedelta(minutes=1)
+            )
+            self.frame_stats.append(frame_stats)
 
-    def analyze(self, perf):
+    def _create_stats_df(self):
         stats = pd.DataFrame(self.frame_stats)
         stats.set_index('period_close', inplace=True, drop=False)
+        return stats
 
+    def analyze(self, perf):
+        stats = self._create_stats_df() if self.data_frequency == 'minute' \
+            else perf
         super(ExchangeTradingAlgorithmBacktest, self).analyze(stats)
 
+    def run(self, data=None, overwrite_sim_params=True):
+        perf = super(ExchangeTradingAlgorithmBacktest, self).run(
+            data, overwrite_sim_params
+        )
+        # Rebuilding the stats to support minute data
+        stats = self._create_stats_df() if self.data_frequency == 'minute' \
+            else perf
+        return stats
+
 
 class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
     def __init__(self, *args, **kwargs):

catalyst/exchange/exchange_bundle.py

@@ -26,7 +26,7 @@ from catalyst.exchange.exchange_bcolz import BcolzExchangeBarReader, \
 from catalyst.exchange.exchange_errors import EmptyValuesInBundleError, \
     TempBundleNotFoundError, \
     NoDataAvailableOnExchange, \
-    PricingDataNotLoadedError
+    PricingDataNotLoadedError, DataCorruptionError
 from catalyst.exchange.exchange_utils import get_exchange_folder
 from catalyst.utils.cli import maybe_show_progress
 from catalyst.utils.paths import ensure_directory
@@ -881,6 +881,14 @@ class ExchangeBundle:
                 start_dt=start_dt,
                 end_dt=end_dt
             )
+            if len(arrays) == 0:
+                raise DataCorruptionError(
+                    exchange=self.exchange.name,
+                    symbols=asset.symbol,
+                    start_dt=asset_start_dt,
+                    end_dt=asset_end_dt
+                )
+
             field_values = arrays[0][:, 0]
 
             value_series = pd.Series(field_values, index=periods)

catalyst/exchange/exchange_errors.py

@@ -218,6 +218,15 @@ class PricingDataNotLoadedError(ZiplineError):
            'for details.').strip()
 
 
+class DataCorruptionError(ZiplineError):
+    msg = ('Unable to validate data for {exchange} {symbols} in date range '
+           '[{start_dt} - {end_dt}]. The data is either corrupted or '
+           'unavailable. Please try deleting this bundle:'
+           '\n`catalyst clean-exchange -x {exchange}\n'
+           'Then, ingest the data again. Please contact the Catalyst team if '
+           'the issue persists.').strip()
+
+
 class ApiCandlesError(ZiplineError):
     msg = ('Unable to fetch candles from the remote API: {error}.').strip()