removing deprecated pd.TimeGrouper and using the .xs multi level indexing versus []

pyfolio/capacity.py

@@ -18,10 +18,9 @@ def daily_txns_with_bar_data(transactions, market_data):
     transactions : pd.DataFrame
         Prices and amounts of executed trades. One row per trade.
         - See full explanation in tears.create_full_tear_sheet
-    market_data : pd.DataFrame
-        Daily market_data
-        - DataFrame has a multi-index index, one level is dates and another is
-        market_data contains volume & price, equities as columns
+    market_data : pd.Panel
+        Contains "volume" and "price" DataFrames for the tickers
+        in the passed positions DataFrames
 
     Returns
     -------
@@ -35,8 +34,9 @@ def daily_txns_with_bar_data(transactions, market_data):
     txn_daily = pd.DataFrame(transactions.assign(
         amount=abs(transactions.amount)).groupby(
         ['symbol', pd.Grouper(freq='D')]).sum()['amount'])
-    txn_daily['price'] = market_data.xs('price', level=1).unstack()
-    txn_daily['volume'] = market_data.xs('volume', level=1).unstack()
+    txn_daily['price'] = market_data.xs('price', level='market_data').unstack()
+    txn_daily['volume'] = market_data.xs('volume',
+                                         level='market_data').unstack()
 
     txn_daily = txn_daily.reset_index().set_index('date')
 
@@ -64,10 +64,10 @@ def days_to_liquidate_positions(positions, market_data,
     positions: pd.DataFrame
         Contains daily position values including cash
         - See full explanation in tears.create_full_tear_sheet
-    market_data : pd.DataFrame
-        Daily market_data
-        - DataFrame has a multi-index index, one level is dates and another is
-        market_data contains volume & price, equities as columns
+    market_data : pd.Panel
+        Panel with items axis of 'price' and 'volume' DataFrames.
+        The major and minor axes should match those of the
+        the passed positions DataFrame (same dates and symbols).
     max_bar_consumption : float
         Max proportion of a daily bar that can be consumed in the
         process of liquidating a position.
@@ -84,16 +84,18 @@ def days_to_liquidate_positions(positions, market_data,
         Datetime index, symbols as columns.
     """
 
-    DV = market_data.xs('volume', level=1) * market_data.xs('price', level=1)
-    roll_mean_dv = DV.rolling(window=mean_volume_window,
+    dv = market_data.xs('volume', level='market_data') * \
+         market_data.xs('price', level='market_data')
+
+    roll_mean_dv = dv.rolling(window=mean_volume_window,
                               center=False).mean().shift()
     roll_mean_dv = roll_mean_dv.replace(0, np.nan)
 
     positions_alloc = pos.get_percent_alloc(positions)
     positions_alloc = positions_alloc.drop('cash', axis=1)
 
     days_to_liquidate = (positions_alloc * capital_base) / \
-        (max_bar_consumption * roll_mean_dv)
+                        (max_bar_consumption * roll_mean_dv)
 
     return days_to_liquidate.iloc[mean_volume_window:]
 
@@ -112,10 +114,10 @@ def get_max_days_to_liquidate_by_ticker(positions, market_data,
     positions: pd.DataFrame
         Contains daily position values including cash
         - See full explanation in tears.create_full_tear_sheet
-    market_data : pd.DataFrame
-        Daily market_data
-        - DataFrame has a multi-index index, one level is dates and another is
-        market_data contains volume & price, equities as columns
+    market_data : pd.Panel
+        Panel with items axis of 'price' and 'volume' DataFrames.
+        The major and minor axes should match those of the
+        the passed positions DataFrame (same dates and symbols).
     max_bar_consumption : float
         Max proportion of a daily bar that can be consumed in the
         process of liquidating a position.
@@ -169,10 +171,10 @@ def get_low_liquidity_transactions(transactions, market_data,
     transactions : pd.DataFrame
         Prices and amounts of executed trades. One row per trade.
          - See full explanation in create_full_tear_sheet.
-    market_data : pd.DataFrame
-        Daily market_data
-        - DataFrame has a multi-index index, one level is dates and another is
-        market_data contains volume & price, equities as columns
+    market_data : pd.Panel
+        Panel with items axis of 'price' and 'volume' DataFrames.
+        The major and minor axes should match those of the
+        the passed positions DataFrame (same dates and symbols).
     last_n_days : integer
         Compute for only the last n days of the passed backtest data.
     """
@@ -186,8 +188,8 @@ def get_low_liquidity_transactions(transactions, market_data,
         txn_daily_w_bar = txn_daily_w_bar[txn_daily_w_bar.date > md]
 
     bar_consumption = txn_daily_w_bar.assign(
-        max_pct_bar_consumed=(
-            txn_daily_w_bar.amount/txn_daily_w_bar.volume)*100
+        max_pct_bar_consumed=(txn_daily_w_bar.amount /
+                              txn_daily_w_bar.volume) * 100
     ).sort_values('max_pct_bar_consumed', ascending=False)
     max_bar_consumption = bar_consumption.groupby('symbol').first()
 
@@ -228,8 +230,8 @@ def apply_slippage_penalty(returns, txn_daily, simulate_starting_capital,
     simulate_traded_dollars = txn_daily.price * simulate_traded_shares
     simulate_pct_volume_used = simulate_traded_shares / txn_daily.volume
 
-    penalties = simulate_pct_volume_used**2 \
-        * impact * simulate_traded_dollars
+    penalties = (simulate_pct_volume_used ** 2 *
+                 impact * simulate_traded_dollars)
 
     daily_penalty = penalties.resample('D').sum()
     daily_penalty = daily_penalty.reindex(returns.index).fillna(0)