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ENH Pyfolio integration #227

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merged 6 commits into from
Dec 19, 2017
Merged

ENH Pyfolio integration #227

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4121e6c
Moved utils.cumulative_returns to performance.cumulative_returns
luca-s Nov 28, 2017
5e8cfba
Make cumulative returns computation more robust
luca-s Nov 30, 2017
a07429a
Removed deprecated pd.TimeGrouper
luca-s Dec 2, 2017
00bc8e4
Added performance.create_pyfolio_input
luca-s Dec 11, 2017
bf7c1a8
Added example NB for pyfolio integration feature
luca-s Dec 18, 2017
8ca5f6d
Updated "tear sheet walk through" NB
luca-s Dec 19, 2017
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890 changes: 890 additions & 0 deletions alphalens/examples/pyfolio_integration.ipynb
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2,454 changes: 1,083 additions & 1,371 deletions alphalens/examples/tear_sheet_walk_through.ipynb
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342 changes: 319 additions & 23 deletions alphalens/performance.py
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44 changes: 14 additions & 30 deletions alphalens/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@
from functools import wraps

from . import utils
from . import performance as perf

DECIMAL_TO_BPS = 10000

Expand Down Expand Up @@ -706,7 +707,7 @@ def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):
return ax


def plot_cumulative_returns(factor_returns, period=1, overlap=True, ax=None):
def plot_cumulative_returns(factor_returns, period, ax=None):
"""
Plots the cumulative returns of the returns series passed in.

Expand All @@ -715,16 +716,10 @@ def plot_cumulative_returns(factor_returns, period=1, overlap=True, ax=None):
factor_returns : pd.Series
Period wise returns of dollar neutral portfolio weighted by factor
value.
period: int, optional
Period over which the daily returns are calculated
overlap: boolean, optional
Specify if subsequent returns overlaps.
If 'overlap' is True and 'period' N is greater than 1, the cumulative
returns plot is computed building and averaging the cumulative returns
of N interleaved portfolios (started at subsequent periods 1,2,3,...,N)
each one rebalancing every N periods. This results in trading the
factor at every value/signal computed by the factor and also the
cumulative returns don't dependent on a specific starting date.
period: pandas.Timedelta or string
Length of period for which the returns are computed (e.g. 1 day)
if 'period' is a string it must follow pandas.Timedelta constructor
format (e.g. '1 days', '1D', '30m', '3h', '1D1h', etc)
ax : matplotlib.Axes, optional
Axes upon which to plot.

Expand All @@ -736,9 +731,7 @@ def plot_cumulative_returns(factor_returns, period=1, overlap=True, ax=None):
if ax is None:
f, ax = plt.subplots(1, 1, figsize=(18, 6))

overlapping_period = period if overlap else 1
factor_returns = utils.cumulative_returns(factor_returns,
overlapping_period)
factor_returns = perf.cumulative_returns(factor_returns, period)

factor_returns.plot(ax=ax, lw=3, color='forestgreen', alpha=0.6)
ax.set(ylabel='Cumulative Returns',
Expand All @@ -751,26 +744,19 @@ def plot_cumulative_returns(factor_returns, period=1, overlap=True, ax=None):


def plot_cumulative_returns_by_quantile(quantile_returns,
period=1,
overlap=True,
period,
ax=None):
"""
Plots the cumulative returns of various factor quantiles.

Parameters
----------
quantile_returns : pd.DataFrame
Cumulative returns by factor quantile.
period: int, optional
Period over which the daily returns are calculated
overlap: boolean, optional
Specify if subsequent returns overlaps.
If 'overlap' is True and 'period' N is greater than 1, the cumulative
returns plot is computed building and averaging the cumulative returns
of N interleaved portfolios (started at subsequent periods 1,2,3,...,N)
each one rebalancing every N periods. This results in trading the
factor at every value/signal computed by the factor and also the
cumulative returns don't dependent on a specific starting date.
Returns by factor quantile
period: pandas.Timedelta or string
Length of period for which the returns are computed (e.g. 1 day)
if 'period' is a string it must follow pandas.Timedelta constructor
format (e.g. '1 days', '1D', '30m', '3h', '1D1h', etc)
ax : matplotlib.Axes, optional
Axes upon which to plot.

Expand All @@ -785,9 +771,7 @@ def plot_cumulative_returns_by_quantile(quantile_returns,
ret_wide = quantile_returns.reset_index()\
.pivot(index='date', columns='factor_quantile', values=period)

overlapping_period = period if overlap else 1
cum_ret = ret_wide.apply(utils.cumulative_returns,
args=(overlapping_period,))
cum_ret = ret_wide.apply(perf.cumulative_returns, period=period)
cum_ret = cum_ret.loc[:, ::-1]

cum_ret.plot(lw=2, ax=ax, cmap=cm.RdYlGn_r)
Expand Down
60 changes: 44 additions & 16 deletions alphalens/tears.py
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Original file line number Diff line number Diff line change
Expand Up @@ -205,7 +205,8 @@ def create_summary_tear_sheet(factor_data,
group_adjust=group_neutral)

mean_quant_rateret = \
mean_quant_ret.apply(utils.rate_of_return, axis=0)
mean_quant_ret.apply(utils.rate_of_return, axis=0,
base_period=mean_quant_ret.columns[0])

mean_quant_ret_bydate, std_quant_daily = \
perf.mean_return_by_quantile(factor_data,
Expand All @@ -214,10 +215,16 @@ def create_summary_tear_sheet(factor_data,
demeaned=long_short,
group_adjust=group_neutral)

mean_quant_rateret_bydate = \
mean_quant_ret_bydate.apply(utils.rate_of_return, axis=0)
mean_quant_rateret_bydate = mean_quant_ret_bydate.apply(
utils.rate_of_return,
axis=0,
base_period=mean_quant_ret_bydate.columns[0]
)

compstd_quant_daily = std_quant_daily.apply(utils.std_conversion, axis=0)
compstd_quant_daily = std_quant_daily.apply(
utils.std_conversion, axis=0,
base_period=std_quant_daily.columns[0]
)

alpha_beta = perf.factor_alpha_beta(factor_data,
long_short,
Expand Down Expand Up @@ -308,7 +315,8 @@ def create_returns_tear_sheet(factor_data,
group_adjust=group_neutral)

mean_quant_rateret = \
mean_quant_ret.apply(utils.rate_of_return, axis=0)
mean_quant_ret.apply(utils.rate_of_return, axis=0,
base_period=mean_quant_ret.columns[0])

mean_quant_ret_bydate, std_quant_daily = \
perf.mean_return_by_quantile(factor_data,
Expand All @@ -317,10 +325,14 @@ def create_returns_tear_sheet(factor_data,
demeaned=long_short,
group_adjust=group_neutral)

mean_quant_rateret_bydate = \
mean_quant_ret_bydate.apply(utils.rate_of_return, axis=0)
mean_quant_rateret_bydate = mean_quant_ret_bydate.apply(
utils.rate_of_return, axis=0,
base_period=mean_quant_ret_bydate.columns[0]
)

compstd_quant_daily = std_quant_daily.apply(utils.std_conversion, axis=0)
compstd_quant_daily = \
std_quant_daily.apply(utils.std_conversion, axis=0,
base_period=std_quant_daily.columns[0])

alpha_beta = perf.factor_alpha_beta(factor_data,
long_short,
Expand Down Expand Up @@ -375,8 +387,10 @@ def create_returns_tear_sheet(factor_data,
demeaned=long_short,
group_adjust=group_neutral)

mean_quant_rateret_group = \
mean_return_quantile_group.apply(utils.rate_of_return, axis=0)
mean_quant_rateret_group = mean_return_quantile_group.apply(
utils.rate_of_return, axis=0,
base_period=mean_return_quantile_group.columns[0]
)

num_groups = len(mean_quant_rateret_group.index
.get_level_values('group').unique())
Expand Down Expand Up @@ -452,7 +466,7 @@ def create_information_tear_sheet(factor_data,


@plotting.customize
def create_turnover_tear_sheet(factor_data):
def create_turnover_tear_sheet(factor_data, turnover_periods=None):
"""
Creates a tear sheet for analyzing the turnover properties of a factor.

Expand All @@ -464,9 +478,19 @@ def create_turnover_tear_sheet(factor_data):
each period, the factor quantile/bin that factor value belongs to, and
(optionally) the group the asset belongs to.
- See full explanation in utils.get_clean_factor_and_forward_returns
turnover_periods : sequence[string], optional
Periods to compute turnover analysis on. By default periods in
'factor_data' are used but custom periods can provided instead. This
can be useful when periods in 'factor_data' are not multiples of the
frequency at which factor values are computed i.e. the periods
are 2h and 4h and the factor is computed daily and so values like
['1D', '2D'] could be used instead
"""

turnover_periods = utils.get_forward_returns_columns(factor_data.columns)
if turnover_periods is None:
turnover_periods = utils.get_forward_returns_columns(
factor_data.columns)

quantile_factor = factor_data['factor_quantile']

quantile_turnover = \
Expand All @@ -487,7 +511,7 @@ def create_turnover_tear_sheet(factor_data):
vertical_sections = fr_cols + 3 * rows_when_wide + 2 * fr_cols
gf = GridFigure(rows=vertical_sections, cols=columns_wide)

for period in sorted(quantile_turnover.keys()):
for period in turnover_periods:
plotting.plot_top_bottom_quantile_turnover(quantile_turnover[period],
period=period,
ax=gf.next_row())
Expand Down Expand Up @@ -676,15 +700,19 @@ def create_event_study_tear_sheet(factor_data, prices, avgretplot=(5, 15)):
by_group=False,
demeaned=long_short)
mean_quant_ret = \
mean_quant_ret.apply(utils.rate_of_return, axis=0)
mean_quant_ret.apply(utils.rate_of_return, axis=0,
base_period=mean_quant_ret.columns[0])

mean_quant_ret_bydate, std_quant_daily = \
perf.mean_return_by_quantile(factor_data,
by_date=True,
by_group=False,
demeaned=long_short)
mean_quant_rateret_bydate = \
mean_quant_ret_bydate.apply(utils.rate_of_return, axis=0)

mean_quant_rateret_bydate = mean_quant_ret_bydate.apply(
utils.rate_of_return, axis=0,
base_period=mean_quant_ret_bydate.columns[0]
)

fr_cols = len(mean_quant_ret.columns)
vertical_sections = 2 + fr_cols * 3
Expand Down
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