advanced-algorithmic-trading

492
rolling_sharpe_s = np.sqrt(self.periods) * (
)
rolling.mean() / rolling.std()
..
..
statistics["rolling_sharpe"] = rolling_sharpe_s
# 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()
)
..
..
statistics["rolling_sharpe_b"] = rolling_sharpe_b
..
..
return statistics
To plot the annualised rolling Sharpe a new method _plot_rolling_sharpe has been created.
It is very similar to the _plot_equity method. It produces a similar plot to the equity
curve using identical colours to help distinguish the strategy performance from the benchmark
performance. The only minor addition is a dashed vertical line placed at k periods into the
strategy simulation to represent the first point at which the trailing ratio is calculated:
def _plot_rolling_sharpe(self, stats, ax=None, **kwargs):
..
..
sharpe = stats[’rolling_sharpe’]
..
..
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,