def plot_risk_exposures(exposures, ax=None):
"""
Parameters
----------
exposures : pd.DataFrame
df indexed by datetime, with factors as columns
- Example:
momentum reversal
dt
2017-01-01 -0.238655 0.077123
2017-01-02 0.821872 1.520515
ax : matplotlib.axes.Axes
axes on which plots are made. if None, current axes will be used
Returns
-------
ax : matplotlib.axes.Axes
"""
if ax is None:
ax = plt.gca()
for col in exposures:
ax.plot(exposures[col])
set_legend_location(ax)
ax.set_ylabel('Factor exposures')
ax.set_title('Risk factor exposures')
return ax
def plot_alpha_returns(alpha_returns, ax=None):
"""
Plot histogram of daily multi-factor alpha returns (specific returns).
Parameters
----------
alpha_returns : pd.Series
series of daily alpha returns indexed by datetime
ax : matplotlib.axes.Axes
axes on which plots are made. if None, current axes will be used
Returns
-------
ax : matplotlib.axes.Axes
"""
if ax is None:
ax = plt.gca()
ax.hist(alpha_returns, color='g', label='Multi-factor alpha')
ax.set_title('Histogram of alphas')
ax.axvline(0, color='k', linestyle='--', label='Zero')
avg = alpha_returns.mean()
ax.axvline(avg, color='b', label='Mean = {: 0.5f}'.format(avg))
set_legend_location(ax)
return ax
def plot_returns(perf_attrib_data, cost=None, ax=None):
"""
Plot total, specific, and common returns.
Parameters
----------
perf_attrib_data : pd.DataFrame
df with factors, common returns, and specific returns as columns,
and datetimes as index. Assumes the `total_returns` column is NOT
cost adjusted.
- Example:
momentum reversal common_returns specific_returns
dt
2017-01-01 0.249087 0.935925 1.185012 1.185012
2017-01-02 -0.003194 -0.400786 -0.403980 -0.403980
cost : pd.Series, optional
if present, gets subtracted from `perf_attrib_data['total_returns']`,
and gets plotted separately
ax : matplotlib.axes.Axes
axes on which plots are made. if None, current axes will be used
Returns
-------
ax : matplotlib.axes.Axes
"""
if ax is None:
ax = plt.gca()
returns = perf_attrib_data['total_returns']
total_returns_label = 'Total returns'
if cost is not None:
returns = returns - cost
total_returns_label += ' (adjusted)'
specific_returns = perf_attrib_data['specific_returns']
common_returns = perf_attrib_data['common_returns']
ax.plot(ep.cum_returns(returns), color='g', label=total_returns_label)
ax.plot(ep.cum_returns(specific_returns),
color='b',
label='Cumulative specific returns')
ax.plot(ep.cum_returns(common_returns),
color='r',
label='Cumulative common returns')
if cost is not None:
ax.plot(cost, color='p', label='Cost')
ax.set_title('Time series of cumulative returns')
ax.set_ylabel('Returns')
set_legend_location(ax)
return ax
def plot_factor_contribution_to_perf(exposures, perf_attrib_data, ax=None):
"""
Plot each factor's contribution to performance.
Parameters
----------
exposures : pd.DataFrame
df indexed by datetime, with factors as columns
- Example:
momentum reversal
dt
2017-01-01 -0.238655 0.077123
2017-01-02 0.821872 1.520515
perf_attrib_data : pd.DataFrame
df with factors, common returns, and specific returns as columns,
and datetimes as index
- Example:
momentum reversal common_returns specific_returns
dt
2017-01-01 0.249087 0.935925 1.185012 1.185012
2017-01-02 -0.003194 -0.400786 -0.403980 -0.403980
ax : matplotlib.axes.Axes
axes on which plots are made. if None, current axes will be used
Returns
-------
ax : matplotlib.axes.Axes
"""
if ax is None:
ax = plt.gca()
factors_and_specific = perf_attrib_data.drop(
['total_returns', 'common_returns'], axis='columns')
ax.stackplot(factors_and_specific.index,
[factors_and_specific[s] for s in factors_and_specific],
labels=factors_and_specific.columns,
colors=COLORS)
ax.axhline(0, color='k')
set_legend_location(ax)
ax.set_ylabel('Contribution to returns by factor')
ax.set_title('Returns attribution')
return ax
def plot_returns(perf_attrib_data, ax=None):
"""
Plot total, specific, and common returns.
Parameters
----------
perf_attrib_data : pd.DataFrame
df with factors, common returns, and specific returns as columns,
and datetimes as index
- Example:
momentum reversal common_returns specific_returns
dt
2017-01-01 0.249087 0.935925 1.185012 1.185012
2017-01-02 -0.003194 -0.400786 -0.403980 -0.403980
ax : matplotlib.axes.Axes
axes on which plots are made. if None, current axes will be used
Returns
-------
ax : matplotlib.axes.Axes
"""
if ax is None:
ax = plt.gca()
returns = perf_attrib_data['total_returns']
specific_returns = perf_attrib_data['specific_returns']
common_returns = perf_attrib_data['common_returns']
ax.plot(ep.cum_returns(returns), color='g', label='Total returns')
ax.plot(ep.cum_returns(specific_returns), color='b',
label='Cumulative specific returns')
ax.plot(ep.cum_returns(common_returns), color='r',
label='Cumulative common returns')
ax.set_title('Time series of cumulative returns')
ax.set_ylabel('Returns')
set_legend_location(ax)
return ax
def plot_returns(returns, specific_returns, common_returns, ax=None):
"""
Plot total, specific, and common returns.
Parameters
----------
returns : pd.Series
total returns, indexed by datetime
specific_returns : pd.Series
specific returns, indexed by datetime
commons_returns : pd.Series
common returns, indexed by datetime
ax : matplotlib.axes.Axes
axes on which plots are made. if None, current axes will be used
Returns
-------
ax : matplotlib.axes.Axes
"""
if ax is None:
ax = plt.gca()
ax.plot(ep.cum_returns(returns), color='g', label='Total returns')
ax.plot(ep.cum_returns(specific_returns),
color='b',
label='Cumulative specific returns')
ax.plot(ep.cum_returns(common_returns),
color='r',
label='Cumulative common returns')
ax.set_title('Time Series of cumulative returns')
ax.set_ylabel('Returns')
set_legend_location(ax)
return ax