def corr_heatmap(returns, name, save=False):
''' Helper for correlation heatmap.'''
ax = heatmap(returns.corr())
name = name[0].upper() + name[1:].lower()
title = '{} Heatmap'.format(name)
plt.title(title)
if save:
save_plot(plt=plt, folder='index', name=name)
else:
plt.show()
def drawdown(cumulative, folder, save=False):
dd = drawdowns(cumulative=cumulative)
plt.plot(dd*100.0, label='Drawdown', lw=3)
plt.xlabel('Time, t')
plt.ylabel('Value, %')
plt.legend()
if save:
save_plot(plt=plt, folder=folder, name='drawdowns.png')
else:
plt.show()
def simple_plot(returns, folder):
plt.figure(figsize=(12,8))
ax = plt.gca()
returns = returns * 100.0
returns.cumsum().plot()
ax.set_ylabel('%')
ax.set_xlabel('Time')
ax.set_title('Cumulative Returns, %', fontweight='bold')
ax.axhline(0.0)
save_plot(plt=plt, folder=folder, name='returns_cumsum')
def rolling_sharpe_plot(returns, folder):
rs = rolling_sharpe(returns=returns, rf=0.0, per=63)
plt.figure(figsize=(12,8))
ax = plt.gca()
rs.plot()
ax.axhline(0.0)
ax.set_ylabel('Sharpe Ratio')
ax.set_xlabel('Time')
ax.set_title('Rolling Sharpe', fontweight='bold')
save_plot(plt=plt, folder=folder, name='rolling_sharpe_ratio')
def rolling_yearly_returns(returns, folder):
yr = returns.rolling(window=252).mean() * 252 * 100.0
plt.figure(figsize=(12,8))
ax = plt.gca()
yr.plot()
ax.axhline(0.0)
ax.set_ylabel('%')
ax.set_xlabel('Time')
ax.set_title('Rolling Yearly Returns, %', fontweight='bold')
save_plot(plt=plt, folder=folder, name='rolling_yearly_returns')
def drawdown_to_percentile(cumulative, folder, save=False):
d = drawdowns(cumulative=cumulative).dropna()
dd = d.loc[d != 0]
if len(dd) > 100:
x = percentiles(dd)
y = array([i for i in range(100)])
plt.plot(x, y/100)
plt.xlabel('Drawdown, *100%')
plt.ylabel('Probability')
if save:
save_plot(plt=plt, folder=folder, name='drawdown_percentile.png')
else:
plt.show()
def monthly_heatmap(returns, folder):
returns = returns.groupby([lambda x: x.year, lambda x: x.month]).apply(cumulate_returns)
returns = returns.to_frame().unstack()
returns = round(returns, 3)
returns.rename(columns={ 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr',
5: 'May', 6: 'Jun', 7: 'Jul', 8: 'Aug',
9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'},
inplace=True)
plt.figure(figsize=(12,8))
ax = plt.gca()
heatmap(returns*100.0, xticklabels=[i[1] for i in list(returns.columns)], annot=True, fmt='0.2f', annot_kws={'size': 8}, alpha=1.0, center=0.0, cbar=False, cmap=cm.RdYlGn, ax=ax)
ax.set_title('Monthly Returns, %', fontweight='bold')
save_plot(plt=plt, folder=folder, name='monthly_returns')
def save_yearly_returns(returns, folder):
plt.figure(figsize=(12,8))
ax = plt.gca()
despine()
ax.yaxis.grid(linestyle=':')
returns.index = returns.index.strftime('%Y')
plt.bar(x=returns.index, height=returns.values)
ax.set_title('Yearly Returns, %', fontweight='bold')
ax.set_ylabel('%')
plt.xticks(range(len(returns.index)), tuple(returns.index))
ax.set_xlabel('Year')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
ax.xaxis.grid(False)
save_plot(plt=plt, folder=folder, name='yearly_returns')
