Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

ENH Ability to make cones with multiple shades stdev regions #168

Closed
wants to merge 11 commits into from
Closed

ENH Ability to make cones with multiple shades stdev regions #168

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
11 commits
Select commit Hold shift + click to select a range
aab448f
ENH add ability to plot multiple stdev regions for the OOS cone in pl…
Oct 12, 2015
b81e207
STY PEP8
Oct 12, 2015
3cffef4
STY Python3 compat
Oct 13, 2015
efcf5ae
ENH in plotting.plot_rolling_returns, factor out cone drawing into lo…
Oct 14, 2015
8da2192
DOC update docstring
Oct 14, 2015
8300be5
STY PEP8 and code cleanup from CR recommendations
Oct 14, 2015
b3c2215
FIX reverse hardcoding of turning off cone in volatility matched plot…
Oct 14, 2015
2a0c49c
Added TO-DO comment/question in plotting.plot_rolling_returns() for h…
Oct 14, 2015
cf83da2
STY delete stale comments leftover from debugging
Oct 14, 2015
50540ff
STY change input cone_std in plotting.plot_rolling_returns to be tuple
Oct 16, 2015
62958f5
FIX update all callers of plotting.plot_rolling_returns to pass tuple…
Oct 16, 2015
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
62 changes: 37 additions & 25 deletions pyfolio/plotting.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -552,8 +552,9 @@ def plot_rolling_returns(
live_start_date : datetime, optional
The point in time when the strategy began live trading, after
its backtest period.
cone_std : float, optional
The standard deviation to use for the cone plots.
cone_std : float, or tuple, optional
If float, The standard deviation to use for the cone plots.
If tuple, Tuple of standard deviation values to use for the cone plots
- The cone is a normal distribution with this standard deviation
centered around a linear regression.
legend_loc : matplotlib.loc, optional
Expand All @@ -573,6 +574,23 @@ def plot_rolling_returns(
The axes that were plotted on.

"""
def draw_cone(returns, num_stdev, live_start_date, ax):
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Any reason to prepend the variable with num_ here?

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Because I wanted to be consistent with the parameter name in timeseries.cone_rolling() which is the only place it gets passed. Is it worth changing for simplicity, or worth keeping because better to be consistent with where it is used downstream? thoughts?

cone_df = timeseries.cone_rolling(
returns,
num_stdev=num_stdev,
cone_fit_end_date=live_start_date)

cone_in_sample = cone_df[cone_df.index < live_start_date]
cone_out_of_sample = cone_df[cone_df.index > live_start_date]
cone_out_of_sample = cone_out_of_sample[
cone_out_of_sample.index < returns.index[-1]]

ax.fill_between(cone_out_of_sample.index,
cone_out_of_sample.sd_down,
cone_out_of_sample.sd_up,
color='steelblue', alpha=0.25)

return cone_in_sample, cone_out_of_sample

if ax is None:
ax = plt.gca()
Expand All @@ -582,12 +600,9 @@ def plot_rolling_returns(
'factor_returns.')
elif volatility_match and factor_returns is not None:
bmark_vol = factor_returns.loc[returns.index].std()
df_cum_rets = timeseries.cum_returns(
(returns / returns.std()) * bmark_vol,
1.0
)
else:
df_cum_rets = timeseries.cum_returns(returns, 1.0)
returns = (returns / returns.std()) * bmark_vol

df_cum_rets = timeseries.cum_returns(returns, 1.0)

y_axis_formatter = FuncFormatter(utils.one_dec_places)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
Expand All @@ -611,25 +626,27 @@ def plot_rolling_returns(
label='Live', ax=ax, **kwargs)

if cone_std is not None:
cone_df = timeseries.cone_rolling(
returns,
num_stdev=cone_std,
cone_fit_end_date=live_start_date)

cone_df_fit = cone_df[cone_df.index < live_start_date]

cone_df_live = cone_df[cone_df.index > live_start_date]
cone_df_live = cone_df_live[cone_df_live.index < returns.index[-1]]

cone_df_fit['line'].plot(
# check to see if cone_std was passed as a single value and,
# if so, just convert to list automatically
if isinstance(cone_std, float):
cone_std = [cone_std]

for cone_i in cone_std:
cone_in_sample, cone_out_of_sample = draw_cone(
returns,
cone_i,
live_start_date,
ax)

cone_in_sample['line'].plot(
ax=ax,
ls='--',
label='Backtest trend',
lw=2,
color='forestgreen',
alpha=0.7,
**kwargs)
cone_df_live['line'].plot(
cone_out_of_sample['line'].plot(
ax=ax,
ls='--',
label='Predicted trend',
Expand All @@ -638,11 +655,6 @@ def plot_rolling_returns(
alpha=0.7,
**kwargs)

ax.fill_between(cone_df_live.index,
cone_df_live.sd_down,
cone_df_live.sd_up,
color='red', alpha=0.30)

ax.axhline(1.0, linestyle='--', color='black', lw=2)
ax.set_ylabel('Cumulative returns')
ax.set_title('Cumulative Returns')
Expand Down
16 changes: 9 additions & 7 deletions pyfolio/tears.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ def create_full_tear_sheet(returns, positions=None, transactions=None,
benchmark_rets=None,
gross_lev=None,
live_start_date=None, bayesian=False,
cone_std=1.0, set_context=True):
cone_std=(1.0, 1.5, 2.0), set_context=True):
"""
Generate a number of tear sheets that are useful
for analyzing a strategy's performance.
Expand Down Expand Up @@ -94,8 +94,9 @@ def create_full_tear_sheet(returns, positions=None, transactions=None,
after its backtest period.
bayesian: boolean, optional
If True, causes the generation of a Bayesian tear sheet.
cone_std : float, optional
The standard deviation to use for the cone plots.
cone_std : float, or tuple, optional
If float, The standard deviation to use for the cone plots.
If tuple, Tuple of standard deviation values to use for the cone plots
- The cone is a normal distribution with this standard deviation
centered around a linear regression.
set_context : boolean, optional
Expand Down Expand Up @@ -140,7 +141,7 @@ def create_full_tear_sheet(returns, positions=None, transactions=None,

@plotting_context
def create_returns_tear_sheet(returns, live_start_date=None,
cone_std=1.0,
cone_std=(1.0, 1.5, 2.0),
benchmark_rets=None,
return_fig=False):
"""
Expand All @@ -162,8 +163,9 @@ def create_returns_tear_sheet(returns, live_start_date=None,
live_start_date : datetime, optional
The point in time when the strategy began live trading,
after its backtest period.
cone_std : float, optional
The standard deviation to use for the cone plots.
cone_std : float, or tuple, optional
If float, The standard deviation to use for the cone plots.
If tuple, Tuple of standard deviation values to use for the cone plots
- The cone is a normal distribution with this standard deviation
centered around a linear regression.
benchmark_rets : pd.Series, optional
Expand Down Expand Up @@ -230,7 +232,7 @@ def create_returns_tear_sheet(returns, live_start_date=None,
returns,
factor_returns=benchmark_rets,
live_start_date=live_start_date,
cone_std=cone_std,
cone_std=None,
volatility_match=True,
ax=ax_rolling_returns_vol_match)
ax_rolling_returns_vol_match.set_title(
Expand Down