Merge pull request mhallsmoore#163 from mhallsmoore/rollingsharpe

Added 'Rolling Sharpe Ratio' calculation and graph plot to the Tearsh…

Author: mhallsmoore

.travis.yml

@@ -3,9 +3,9 @@ sudo: false
 language: python
 
 env:
-  - PYTHON=2.7 PANDAS=0.17.1
-  - PYTHON=3.4 PANDAS=0.17.1
-  - PYTHON=3.5 PANDAS=0.17.1
+  - PYTHON=2.7 PANDAS=0.18.0
+  - PYTHON=3.4 PANDAS=0.18.0
+  - PYTHON=3.5 PANDAS=0.18.0
 
 #matrix:
 #    allow_failures:

qstrader/statistics/tearsheet.py

@@ -21,7 +21,8 @@ class TearsheetStatistics(AbstractStatistics):
     """
     def __init__(
         self, config, portfolio_handler,
-        title=None, benchmark=None, periods=252
+        title=None, benchmark=None, periods=252,
+        rolling_sharpe=False
     ):
         """
         Takes in a portfolio handler.
@@ -32,6 +33,7 @@ def __init__(
         self.title = '\n'.join(title)
         self.benchmark = benchmark
         self.periods = periods
+        self.rolling_sharpe = rolling_sharpe
         self.equity = {}
         self.equity_benchmark = {}
         self.log_scale = False
@@ -59,6 +61,12 @@ def get_results(self):
         # Returns
         returns_s = equity_s.pct_change().fillna(0.0)
 
+        # Rolling Annualised Sharpe
+        rolling = returns_s.rolling(window=self.periods)
+        rolling_sharpe_s = np.sqrt(self.periods) * (
+            rolling.mean() / rolling.std()
+        )
+
         # Cummulative Returns
         cum_returns_s = np.exp(np.log(1 + returns_s).cumsum())
 
@@ -78,13 +86,18 @@ def get_results(self):
         statistics["max_drawdown_duration"] = dd_dur
         statistics["equity"] = equity_s
         statistics["returns"] = returns_s
+        statistics["rolling_sharpe"] = rolling_sharpe_s
         statistics["cum_returns"] = cum_returns_s
         statistics["positions"] = self._get_positions()
 
         # Benchmark statistics if benchmark ticker specified
         if self.benchmark is not None:
             equity_b = pd.Series(self.equity_benchmark).sort_index()
             returns_b = equity_b.pct_change().fillna(0.0)
+            rolling_b = returns_b.rolling(window=self.periods)
+            rolling_sharpe_b = np.sqrt(self.periods) * (
+                rolling_b.mean() / rolling_b.std()
+            )
             cum_returns_b = np.exp(np.log(1 + returns_b).cumsum())
             dd_b, max_dd_b, dd_dur_b = perf.create_drawdowns(cum_returns_b)
             statistics["sharpe_b"] = perf.create_sharpe_ratio(returns_b)
@@ -93,6 +106,7 @@ def get_results(self):
             statistics["max_drawdown_duration_b"] = dd_dur_b
             statistics["equity_b"] = equity_b
             statistics["returns_b"] = returns_b
+            statistics["rolling_sharpe_b"] = rolling_sharpe_b
             statistics["cum_returns_b"] = cum_returns_b
 
         return statistics
@@ -173,6 +187,44 @@ def format_two_dec(x, pos):
 
         return ax
 
+    def _plot_rolling_sharpe(self, stats, ax=None, **kwargs):
+        """
+        Plots the curve of rolling Sharpe ratio.
+        """
+        def format_two_dec(x, pos):
+            return '%.2f' % x
+
+        sharpe = stats['rolling_sharpe']
+
+        if ax is None:
+            ax = plt.gca()
+
+        y_axis_formatter = FuncFormatter(format_two_dec)
+        ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
+        ax.xaxis.set_tick_params(reset=True)
+        ax.yaxis.grid(linestyle=':')
+        ax.xaxis.set_major_locator(mdates.YearLocator(1))
+        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
+        ax.xaxis.grid(linestyle=':')
+
+        if self.benchmark is not None:
+            benchmark = stats['rolling_sharpe_b']
+            benchmark.plot(
+                lw=2, color='gray', label=self.benchmark, alpha=0.60,
+                ax=ax, **kwargs
+            )
+
+        sharpe.plot(lw=2, color='green', alpha=0.6, x_compat=False,
+                    label='Backtest', ax=ax, **kwargs)
+
+        ax.axvline(sharpe.index[252], linestyle="dashed", c="gray", lw=2)
+        ax.set_ylabel('Rolling Annualised Sharpe')
+        ax.legend(loc='best')
+        ax.set_xlabel('')
+        plt.setp(ax.get_xticklabels(), visible=True, rotation=0, ha='center')
+
+        return ax
+
     def _plot_drawdown(self, stats, ax=None, **kwargs):
         """
         Plots the underwater curve
@@ -523,22 +575,29 @@ def plot_results(self, filename=None):
         sns.set_style("whitegrid")
         sns.set_palette("deep", desat=.6)
 
-        vertical_sections = 5
+        if self.rolling_sharpe:
+            offset_index = 1
+        else:
+            offset_index = 0
+        vertical_sections = 5 + offset_index
         fig = plt.figure(figsize=(10, vertical_sections * 3.5))
         fig.suptitle(self.title, y=0.94, weight='bold')
         gs = gridspec.GridSpec(vertical_sections, 3, wspace=0.25, hspace=0.5)
 
         stats = self.get_results()
-
         ax_equity = plt.subplot(gs[:2, :])
-        ax_drawdown = plt.subplot(gs[2, :])
-        ax_monthly_returns = plt.subplot(gs[3, :2])
-        ax_yearly_returns = plt.subplot(gs[3, 2])
-        ax_txt_curve = plt.subplot(gs[4, 0])
-        ax_txt_trade = plt.subplot(gs[4, 1])
-        ax_txt_time = plt.subplot(gs[4, 2])
+        if self.rolling_sharpe:
+            ax_sharpe = plt.subplot(gs[2, :])
+        ax_drawdown = plt.subplot(gs[2 + offset_index, :])
+        ax_monthly_returns = plt.subplot(gs[3 + offset_index, :2])
+        ax_yearly_returns = plt.subplot(gs[3 + offset_index, 2])
+        ax_txt_curve = plt.subplot(gs[4 + offset_index, 0])
+        ax_txt_trade = plt.subplot(gs[4 + offset_index, 1])
+        ax_txt_time = plt.subplot(gs[4 + offset_index, 2])
 
         self._plot_equity(stats, ax=ax_equity)
+        if self.rolling_sharpe:
+            self._plot_rolling_sharpe(stats, ax=ax_sharpe)
         self._plot_drawdown(stats, ax=ax_drawdown)
         self._plot_monthly_returns(stats, ax=ax_monthly_returns)
         self._plot_yearly_returns(stats, ax=ax_yearly_returns)