advanced-algorithmic-trading

493
)
label=’Backtest’, ax=ax, **kwargs
ax.axvline(
sharpe.index[self.periods],
linestyle="dashed", c="gray", lw=2
)
ax.set_ylabel(’Rolling Annualised Sharpe’)
..
..
The plot_results code is modified to include the rolling Sharpe graph if the self.rolling_sharpe
flag is set to True. This is carried out using an offset_index. The index is used to let Matplotlib
know if there are five or six vertical sections in the chart, and if so, to adjust the plot
placement:
def plot_results(self, filename=None):
..
..
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, :])
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)
..
..