Merge pull request #618 from huaiweicheng/master

add turnover_denom in create_txn_tear_sheet and fix some small issues

Author: twiecki

pyfolio/plotting.py

@@ -1323,7 +1323,7 @@ def plot_return_quantiles(returns, live_start_date=None, ax=None, **kwargs):
     return ax
 
 
-def plot_turnover(returns, transactions, positions,
+def plot_turnover(returns, transactions, positions, turnover_denom='AGB',
                   legend_loc='best', ax=None, **kwargs):
     """
     Plots turnover vs. date.
@@ -1345,6 +1345,9 @@ def plot_turnover(returns, transactions, positions,
     positions : pd.DataFrame
         Daily net position values.
          - See full explanation in tears.create_full_tear_sheet.
+    turnover_denom : str, optional
+        Either AGB or portfolio_value, default AGB.
+        - See full explanation in txn.get_turnover.
     legend_loc : matplotlib.loc, optional
         The location of the legend on the plot.
     ax : matplotlib.Axes, optional
@@ -1364,7 +1367,7 @@ def plot_turnover(returns, transactions, positions,
     y_axis_formatter = FuncFormatter(utils.two_dec_places)
     ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
 
-    df_turnover = txn.get_turnover(positions, transactions)
+    df_turnover = txn.get_turnover(positions, transactions, turnover_denom)
     df_turnover_by_month = df_turnover.resample("M").mean()
     df_turnover.plot(color='steelblue', alpha=1.0, lw=0.5, ax=ax, **kwargs)
     df_turnover_by_month.plot(
@@ -1517,7 +1520,7 @@ def plot_capacity_sweep(returns, transactions, market_data,
     return ax
 
 
-def plot_daily_turnover_hist(transactions, positions,
+def plot_daily_turnover_hist(transactions, positions, turnover_denom='AGB',
                              ax=None, **kwargs):
     """
     Plots a histogram of daily turnover rates.
@@ -1530,6 +1533,9 @@ def plot_daily_turnover_hist(transactions, positions,
     positions : pd.DataFrame
         Daily net position values.
          - See full explanation in tears.create_full_tear_sheet.
+    turnover_denom : str, optional
+        Either AGB or portfolio_value, default AGB.
+        - See full explanation in txn.get_turnover.
     ax : matplotlib.Axes, optional
         Axes upon which to plot.
     **kwargs, optional
@@ -1543,7 +1549,7 @@ def plot_daily_turnover_hist(transactions, positions,
 
     if ax is None:
         ax = plt.gca()
-    turnover = txn.get_turnover(positions, transactions)
+    turnover = txn.get_turnover(positions, transactions, turnover_denom)
     sns.distplot(turnover, ax=ax, **kwargs)
     ax.set_title('Distribution of daily turnover rates')
     ax.set_xlabel('Turnover rate')

pyfolio/tears.py

@@ -66,12 +66,6 @@ def create_full_tear_sheet(returns,
                            cone_std=(1.0, 1.5, 2.0),
                            bootstrap=False,
                            unadjusted_returns=None,
-                           style_factors=None,
-                           sectors=None,
-                           caps=None,
-                           shares_held=None,
-                           volumes=None,
-                           percentile=None,
                            turnover_denom='AGB',
                            set_context=True,
                            factor_returns=None,
@@ -402,6 +396,7 @@ def create_simple_tear_sheet(returns,
             plotting.plot_turnover(returns,
                                    transactions,
                                    positions,
+                                   turnover_denom=turnover_denom,
                                    ax=ax_turnover)
 
             plotting.plot_txn_time_hist(transactions, ax=ax_txn_timings)
@@ -603,8 +598,8 @@ def create_returns_tear_sheet(returns, positions=None,
 @plotting.customize
 def create_position_tear_sheet(returns, positions,
                                show_and_plot_top_pos=2, hide_positions=False,
-                               return_fig=False, sector_mappings=None,
-                               transactions=None, estimate_intraday='infer'):
+                               sector_mappings=None, transactions=None,
+                               estimate_intraday='infer', return_fig=False):
     """
     Generate a number of plots for analyzing a
     strategy's positions and holdings.
@@ -627,8 +622,6 @@ def create_position_tear_sheet(returns, positions,
     hide_positions : bool, optional
         If True, will not output any symbol names.
         Overrides show_and_plot_top_pos to 0 to suppress text output.
-    return_fig : boolean, optional
-        If True, returns the figure that was plotted on.
     sector_mappings : dict or pd.Series, optional
         Security identifier to sector mapping.
         Security ids as keys, sectors as values.
@@ -638,6 +631,8 @@ def create_position_tear_sheet(returns, positions,
     estimate_intraday: boolean or str, optional
         Approximate returns for intraday strategies.
         See description in create_full_tear_sheet.
+    return_fig : boolean, optional
+        If True, returns the figure that was plotted on.
     """
 
     positions = utils.check_intraday(estimate_intraday, returns,
@@ -697,8 +692,8 @@ def create_position_tear_sheet(returns, positions,
 
 @plotting.customize
 def create_txn_tear_sheet(returns, positions, transactions,
-                          unadjusted_returns=None, estimate_intraday='infer',
-                          return_fig=False):
+                          turnover_denom='AGB', unadjusted_returns=None,
+                          estimate_intraday='infer', return_fig=False):
     """
     Generate a number of plots for analyzing a strategy's transactions.
 
@@ -715,6 +710,9 @@ def create_txn_tear_sheet(returns, positions, transactions,
     transactions : pd.DataFrame
         Prices and amounts of executed trades. One row per trade.
          - See full explanation in create_full_tear_sheet.
+    turnover_denom : str, optional
+        Either AGB or portfolio_value, default AGB.
+        - See full explanation in txn.get_turnover.
     unadjusted_returns : pd.Series, optional
         Daily unadjusted returns of the strategy, noncumulative.
         Will plot additional swippage sweep analysis.
@@ -743,12 +741,15 @@ def create_txn_tear_sheet(returns, positions, transactions,
         returns,
         transactions,
         positions,
+        turnover_denom=turnover_denom,
         ax=ax_turnover)
 
     plotting.plot_daily_volume(returns, transactions, ax=ax_daily_volume)
 
     try:
-        plotting.plot_daily_turnover_hist(transactions, positions,
+        plotting.plot_daily_turnover_hist(transactions,
+                                          positions,
+                                          turnover_denom=turnover_denom,
                                           ax=ax_turnover_hist)
     except ValueError:
         warnings.warn('Unable to generate turnover plot.', UserWarning)
@@ -956,7 +957,8 @@ def create_capacity_tear_sheet(returns, positions, transactions,
                                trade_daily_vol_limit=0.05,
                                last_n_days=utils.APPROX_BDAYS_PER_MONTH * 6,
                                days_to_liquidate_limit=1,
-                               estimate_intraday='infer'):
+                               estimate_intraday='infer',
+                               return_fig=False):
     """
     Generates a report detailing portfolio size constraints set by
     least liquid tickers. Plots a "capacity sweep," a curve describing
@@ -993,6 +995,8 @@ def create_capacity_tear_sheet(returns, positions, transactions,
     estimate_intraday: boolean or str, optional
         Approximate returns for intraday strategies.
         See description in create_full_tear_sheet.
+    return_fig : boolean, optional
+        If True, returns the figure that was plotted on.
     """
 
     positions = utils.check_intraday(estimate_intraday, returns,
@@ -1046,14 +1050,17 @@ def create_capacity_tear_sheet(returns, positions, transactions,
         llt[llt['max_pct_bar_consumed'] > trade_daily_vol_limit * 100])
 
     bt_starting_capital = positions.iloc[0].sum() / (1 + returns.iloc[0])
-    _, ax_capacity_sweep = plt.subplots(figsize=(14, 6))
+    fig, ax_capacity_sweep = plt.subplots(figsize=(14, 6))
     plotting.plot_capacity_sweep(returns, transactions, market_data,
                                  bt_starting_capital,
                                  min_pv=100000,
                                  max_pv=300000000,
                                  step_size=1000000,
                                  ax=ax_capacity_sweep)
 
+    if return_fig:
+        return fig
+
 
 @plotting.customize
 def create_perf_attrib_tear_sheet(returns,
@@ -1062,8 +1069,8 @@ def create_perf_attrib_tear_sheet(returns,
                                   factor_loadings,
                                   transactions=None,
                                   pos_in_dollars=True,
-                                  return_fig=False,
-                                  factor_partitions=FACTOR_PARTITIONS):
+                                  factor_partitions=FACTOR_PARTITIONS,
+                                  return_fig=False):
     """
     Generate plots and tables for analyzing a strategy's performance.
 
@@ -1093,15 +1100,15 @@ def create_perf_attrib_tear_sheet(returns,
         Flag indicating whether `positions` are in dollars or percentages
         If True, positions are in dollars.
 
-    return_fig : boolean, optional
-        If True, returns the figure that was plotted on.
-
     factor_partitions : dict
         dict specifying how factors should be separated in factor returns
         and risk exposures plots
         - Example:
           {'style': ['momentum', 'size', 'value', ...],
            'sector': ['technology', 'materials', ... ]}
+
+    return_fig : boolean, optional
+        If True, returns the figure that was plotted on.
     """
     portfolio_exposures, perf_attrib_data = perf_attrib.perf_attrib(
         returns, positions, factor_returns, factor_loadings, transactions,