Merge branch 'develop'

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 @@ def analyze(context, stats):
     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

@@ -425,26 +425,26 @@ def get_candles(self, freq, assets, bar_count=1, start_dt=None,
                 '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,
                     freq=freq,
                 )
                 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 @@ def __init__(self, *args, **kwargs):
             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 @@ def batch_market_order(self, share_counts):
         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 @@ def get_open_orders(self, asset=None):
             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'],