BLD: tested stats with multiple assets

catalyst/examples/mean_reversion_simple.py

@@ -96,13 +96,13 @@ def handle_data(context, data):
     # 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=(context.market, current['volume']),
         price_change=(context.market, price_change),
         rsi=(context.market, rsi[-1]),
         cash=cash
     )
-
     # We are trying to avoid over-trading by limiting our trades to
     # one per day.
     if context.traded_today:

catalyst/exchange/exchange_algorithm.py

@@ -326,7 +326,7 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
         self.retry_order = 2
         self.retry_delay = 5
 
-        self.stats_minutes = 5
+        self.stats_minutes = 20
 
         super(ExchangeTradingAlgorithmLive, self).__init__(*args, **kwargs)
 

catalyst/exchange/exchange_blotter.py

@@ -176,6 +176,9 @@ class ExchangeBlotter(Blotter):
     @expect_types(asset=TradingPair)
     def order(self, asset, amount, style, order_id=None):
         log.debug('ordering {} {}'.format(amount, asset.symbol))
+        if amount == 0:
+            log.warn('skipping 0 amount orders')
+            return None
 
         if self.simulate_orders:
             return super(ExchangeBlotter, self).order(

catalyst/exchange/stats_utils.py

@@ -126,15 +126,15 @@ def vwap(df):
     return ret
 
 
-def set_position_row(row, position_index, recorded_cols=None):
+def set_position_row(row, asset, asset_values=list()):
     """
     Apply the position data as individual columns.
 
     Parameters
     ----------
     row: dict[str, Object]
-    position_index: int
+    asset: TradingPair
-    recorded_cols: list[str]
+    asset_values: list[str]
         If a recorded_col contains a tuple which first value is an asset
         matching a position, its value will be displayed with the
         position and not in the index.
@@ -143,32 +143,31 @@ def set_position_row(row, position_index, recorded_cols=None):
     -------
 
     """
-    position = row['positions'][position_index]
+    asset_cols = ['symbol']
-
-    asset = position['sid']
     row['symbol'] = asset.symbol
 
+    position = next((p for p in row['positions'] if p['sid'] == asset), None)
+
     columns = ['amount', 'cost_basis', 'last_sale_price']
     for column in columns:
-        row[column] = position[column]
+        if position is not None:
+            row[column] = position[column]
 
-    columns.insert(0, 'symbol')
+        else:
+            row[column] = 0
 
-    if recorded_cols is not None:
+        asset_cols.append(column)
-        for column in recorded_cols[:]:
-            value = row[column]
-            if type(value) in [list, tuple] and \
-                    isinstance(value[0], TradingPair) and asset == value[0]:
-                row[column] = value[1]
 
-                columns.append(column)
+    values = asset_values[asset] if asset in asset_values else list()
-                # Removing the asset specific entries
+    for column in values:
-                recorded_cols.remove(column)
+        row[column] = values[column]
 
-    return columns
+        asset_cols.append(column)
+
+    return asset_cols
 
 
-def prepare_stats(stats, recorded_cols=None):
+def prepare_stats(stats, recorded_cols=list()):
     """
     Prepare the stats DataFrame for user-friendly output.
 
@@ -181,30 +180,43 @@ def prepare_stats(stats, recorded_cols=None):
     -------
 
     """
-    position_cols = None
+    asset_cols = list()
 
     # Using a copy since we are adding rows inside the loop.
     for row_index, row_data in enumerate(list(stats)):
-        if len(row_data['positions']) == 1:
+        assets = [p['sid'] for p in row_data['positions']]
-            row = stats[row_index]
-            columns = set_position_row(row, 0, recorded_cols)
 
-        elif len(row_data['positions']) > 1:
+        asset_values = dict()
-            for pos_index, position in enumerate(row_data['positions']):
+        for column in recorded_cols[:]:
-                if pos_index > 0:
+            value = row_data[column]
-                    row = row_data
+            if type(value) is dict:
+                for asset in value:
+                    if not isinstance(asset, TradingPair):
+                        break
+
+                    if asset not in assets:
+                        assets.append(asset)
+
+                    if asset not in asset_values:
+                        asset_values[asset] = dict()
+
+                    asset_values[asset][column] = value[asset]
+
+        if len(assets) == 1:
+            row = stats[row_index]
+            asset_cols = set_position_row(row, assets[0], asset_values)
+
+        elif len(assets) > 1:
+            for asset_index, asset in enumerate(assets):
+                if asset_index > 0:
+                    row = copy.deepcopy(row_data)
                     stats.append(row)
 
                 else:
                     row = stats[row_index]
 
-                columns = set_position_row(row, pos_index, recorded_cols)
+                asset_cols = set_position_row(row, assets[asset_index],
-
+                                              asset_values)
-        else:
-            break
-
-        if position_cols is None:
-            position_cols = columns
 
     df = pd.DataFrame(list(stats))
 
@@ -212,6 +224,9 @@ def prepare_stats(stats, recorded_cols=None):
         'period_close', 'starting_cash', 'ending_cash', 'portfolio_value',
         'pnl', 'long_exposure', 'short_exposure', 'orders', 'transactions',
     ]
+
+    # Removing the asset specific entries
+    recorded_cols = [x for x in recorded_cols if x not in asset_cols]
     if recorded_cols is not None:
         for column in recorded_cols:
             index_cols.append(column)
@@ -223,8 +238,9 @@ def prepare_stats(stats, recorded_cols=None):
 
     df.set_index(index_cols, drop=True, inplace=True)
     df.dropna(axis=1, how='all', inplace=True)
+    df.sort_index(inplace=True)
 
-    return df, position_cols
+    return df, asset_cols
 
 
 def get_pretty_stats(stats, recorded_cols=None, num_rows=10):
@@ -245,7 +261,7 @@ def get_pretty_stats(stats, recorded_cols=None, num_rows=10):
     df, columns = prepare_stats(stats, recorded_cols=recorded_cols)
 
     pd.set_option('display.expand_frame_repr', False)
-    pd.set_option('precision', 3)
+    pd.set_option('precision', 8)
     pd.set_option('display.width', 1000)
     pd.set_option('display.max_colwidth', 1000)