Performance analytics
Learn how to generate and customize performance reports for your strategies.
A Primer notebook containing the code used in this page is available in the research environment. See Example notebooks for more information.
import sigtech.framework as sig
from sigtech.framework.analytics.performance.performance_report \
import PerformanceReport, View, CustomView
import uuid
import datetime as dtm
import pandas as pd
import numpy as np
import seaborn as sns
sns.set(rc={'figure.figsize': (18, 6)})
if not sig.config.is_initialised():
date = dtm.datetime(2020, 10, 30)
sig.config.init(data_date=date, env_date=date)
sig.config.set(sig.config.HISTORY_DATA_FILL_MISSING, True)
This step creates sample strategies for the performance report:
from sigtech.framework.default_strategy_objects.rolling_futures import *
from sigtech.framework.default_strategy_objects.rolling_futures_fx_hedged import *
us_basket = sig.BasketStrategy(
currency='USD',
start_date=dtm.date(2017, 1, 4),
end_date=dtm.date(2020, 1, 4),
constituent_names=[
es_index_front().name,
ty_comdty_front().name,
],
weights=[0.5, 0.5],
rebalance_frequency='EOM',
ticker='USD USA BASKET {}'.format(str(uuid.uuid4())[:8]),
)
ge_basket = sig.BasketStrategy(
currency='USD',
start_date=dtm.date(2017, 1, 4),
end_date=dtm.date(2020, 1, 4),
constituent_names=[
usd_gx_index_front().name,
usd_rx_comdty_front().name,
],
weights=[0.6, 0.4],
rebalance_frequency='EOM',
ticker='USD GERMANY BASKET {}'.format(str(uuid.uuid4())[:8]),
)
jp_basket = sig.BasketStrategy(
currency='USD',
start_date=dtm.date(2017, 1, 4),
end_date=dtm.date(2020, 1, 4),
constituent_names=[
usd_nk_index_front().name,
usd_jb_comdty_front().name,
],
weights=[0.3, 0.7],
rebalance_frequency='EOM',
ticker='USD JAPAN BASKET {}'.format(str(uuid.uuid4())[:8]),
)
strategy1 = sig.BasketStrategy(
currency='USD',
start_date=dtm.date(2017, 1, 4),
end_date=dtm.date(2020, 1, 4),
constituent_names=[us_basket.name, ge_basket.name, jp_basket.name],
weights=[0.6, 0.2, 0.2],
rebalance_frequency='EOM',
ticker='USD WORLD TOP_LEVEL BASKET {}'.format(str(uuid.uuid4())[:8])
)
strategy2 = sig.BasketStrategy(
currency='USD',
start_date=dtm.date(2017, 1, 4),
end_date=dtm.date(2020, 1, 4),
constituent_names=[ge_basket.name, jp_basket.name],
weights=[0.7, 0.3],
rebalance_frequency='EOM',
ticker='USD WORLD TOP_LEVEL BASKET {}'.format(str(uuid.uuid4())[:8])
)
strategy3 = sig.BasketStrategy(
currency='USD',
start_date=dtm.date(2017, 1, 4),
end_date=dtm.date(2020, 1, 4),
constituent_names=[ge_basket.name, jp_basket.name],
weights=[0.9, 0.1],
rebalance_frequency='EOM',
ticker='USD WORLD TOP_LEVEL BASKET {}'.format(str(uuid.uuid4())[:8])
)
A PerformanceReport can be created for a time series object. Method
default_views()
shows the report views enabled by default for this object:Python
Output
report0 = PerformanceReport(strategy1.history())
report0.default_views()
[View('SUMMARY_SINGLE'),
View('HISTORY'),
View('PRESENTATION_PERF_TABLE'),
View('SUMMARY_ROLLING_SERIES'),
View('ROLLING_RISK_VOL'),
View('ROLLING_RISK_SHARPE'),
View('ROLLING_PLOTS'),
View('MONTHLY_STATS'),
View('MONTHLY_STATS_HEATMAP'),
View('BENCHMARK_TABLE')]
A PerformanceReport can also be created for a Strategy object. Method
default_views()
shows an extended list of report views enabled by default:Input
Output
report1 = PerformanceReport(strategy1)
report1.default_views()
[View('SUMMARY_SINGLE'),
View('HISTORY'),
View('PRESENTATION_PERF_TABLE'),
View('SUMMARY_ROLLING_SERIES'),
View('ROLLING_RISK_VOL'),
View('ROLLING_RISK_SHARPE'),
View('POSITIONS_DF'),
View('TURNOVER'),
View('TURNOVER_STATS'),
View('ROLLING_PLOTS'),
View('MONTHLY_STATS'),
View('MONTHLY_STATS_HEATMAP'),
View('BENCHMARK_TABLE')]
All built-in views can be accessed using the following syntax:
Input
Output
View.TURNOVER
View('TURNOVER')
Customised views can be accessed using the following syntax:
Input
Output
View('Custom View')
View('Custom View')
Method
report()
will display the views enabled for the object used. Parameter dates
can be specified to reduce the output size for tables showing daily data.Input
Output
few_dates = pd.date_range(dtm.date(2019, 12, 2),
dtm.date(2020, 1, 1), freq='7d')
report1 = PerformanceReport(strategy1, name='My Strategy', dates=few_dates)
report1.report()








Parameter
views
can be used to select a subset of the default views to display:Input
Output
report2 = PerformanceReport(strategy1,
name='My Strategy',
views=[View.MONTHLY_STATS,
View.PRESENTATION_PERF_TABLE],
dates=few_dates)
report2.views()
[View('MONTHLY_STATS'), View('PRESENTATION_PERF_TABLE')]
Input
Output
report2.report()

Within the same report, the order of views can also be re-arranged:
Input
Output
report2.arrange_views([View.HISTORY, View.MONTHLY_STATS])
report2.report()

Parameter
benchmark
can be used to add a strategy or time series to the performance report. The following example uses one strategy as input object:Input
Output
s = es_index_front()
b = z_index_front()
report3 = PerformanceReport(s,
benchmark=b,
views=[View.POSITIONS_DF, View.TURNOVER_STATS],
dates=few_dates)
report3.report()




The same syntax is available for time series:
Input
Output
ts = s.history().rename('ES Rolling Future').loc[
dtm.date(2015, 1, 1):dtm.date(2020, 1, 1)]
ts_b = b.history()
report3 = PerformanceReport(ts,
benchmark=ts_b,
views=[View.HISTORY, View.MONTHLY_STATS,
View.BENCHMARK_TABLE])
report3.report()



Customized views can be added to PerformanceReport by simply defining a new function. The following example defines two functions handling a DataFrame and a Pyplot graph:
# Define custom functions for view
def custom_table(args):
# This function expects 2 parameters (Strategy, Integer)
print('Customising for: ', args[0].name)
df = pd.DataFrame(np.random.randint(0, args[1], size=(4, 4)),
columns=list('ABCD'))
return df
def custom_plot(args):
# This function expects 1 parameter (PerformanceReport)
import matplotlib.pyplot as plt
args.rolling_stat('excess_return').plot(
title="Rolling excess return", figsize=[15, 4])
plt.show()
return
A
CustomView
object helps wrapping the customised view and its parameters:custom_view1 = CustomView(custom_table, params=[strategy1, 99],
name='Custom Table 99')
custom_view2 = CustomView(custom_table, params=[strategy1, 25],
name='Custom Table 25')
custom_view3 = CustomView(custom_plot, params=report3,
name='Custom Plot')
The customized views can now be added to the report:
Input
Output
report3 = PerformanceReport(strategy1, name='Customised Strategy',
views=[View.SUMMARY_SINGLE])
report3.add_view(custom_view1)
report3.add_views([custom_view2, custom_view3])
report3.views()
[View('SUMMARY_SINGLE'),
View('Custom Table 99'),
View('Custom Table 25'),
View('Custom Plot')]
Input
Output
report3.report()



Reports for multiple strategies can be visualized by adding them to a list:
Input
Output
report1 = PerformanceReport([strategy1, strategy2],
benchmark=b, name=['S1', 'S2'],
views=[View.SUMMARY_SINGLE, View.HISTORY,
View.TURNOVER_STATS, View.BENCHMARK_TABLE])
report1.report()





Time series can be merged within a DataFrame object:
Input
Output
ts1 = es_index_front().history().rename('ES Rolling Future').loc[
dtm.date(2015, 1, 1):dtm.date(2020, 1, 1)]
ts2 = eo_index_front().history().rename('EO Rolling Future').loc[
dtm.date(2015, 1, 1):dtm.date(2020, 1, 1)]
b = z_index_front().history()
ts_df = pd.concat({
'ES Rolling Future': ts1,
'EO Rolling Future': ts2,
}, axis=1)
rep = PerformanceReport(ts_df, benchmark=b,
views=[View.ROLLING_PLOTS, View.BENCHMARK_TABLE])
rep.report()


You can include currency based metrics in
PerformanceReport
. The following code block demonstrates how to include these views.You can include currency based metrics in
PerformanceReport
. The following code block demonstrates how to include these views:rfs = sig.RollingFutureStrategy(
currency='USD',
start_date=dtm.date(2020,1,1),
contract_code='ES',
contract_sector='INDEX',
rolling_rule='front',
fixed_contracts=1
)
rfs.build()
dates = pd.date_range('2021-01-04', '2021-12-01', freq='1W-MON')
sig.PerformanceReport(rfs, views=[sig.View.SUMMARY_SINGLE_CCY, sig.View.SUMMARY_ROLLING_CCY_SERIES], dates=dates).report()
rfs = sig.RollingFutureStrategy(
currency='USD',
start_date=dtm.date(2020,1,1),
contract_code='ES',
contract_sector='INDEX',
rolling_rule='front',
fixed_contracts=1
)
rfs.build()
dates = pd.date_range('2021-01-04', '2021-12-01', freq='1W-MON')
sig.PerformanceReport(rfs, views=[sig.View.SUMMARY_SINGLE_CCY, sig.View.SUMMARY_ROLLING_CCY_SERIES], dates=dates).report()
The
aum
argument allows you to pass a representative AUM figure. This is useful in strategies where initial_cash=0
as it avoids dividing by zero. Following on from the above code block, the use of this new argument is demonstrated below.
The
aum
argument allows you to pass a representative AUM figure. This is useful in strategies where initial_cash=0
as it avoids dividing by zero. The use of this argument is demonstrated in the following code block:sig.PerformanceReport(rfs, dates=dates, views=[sig.View.SUMMARY_SINGLE]).report()
first_contract = sig.obj.get(rfs.rolling_table.rolled_in_contract.iloc[0])
first_contract_value = first_contract.history().asof(rfs.start_date.strftime("%Y-%m-%d")) * first_contract.contract_size
sig.PerformanceReport(
rfs,
dates=dates,
views=[sig.View.SUMMARY_SINGLE],
aum=first_contract_value
).report()
sig.PerformanceReport(rfs, dates=dates, views=[sig.View.SUMMARY_SINGLE]).report()
first_contract = sig.obj.get(rfs.rolling_table.rolled_in_contract.iloc[0])
first_contract_value = first_contract.history().asof(rfs.start_date.strftime("%Y-%m-%d")) * first_contract.contract_size
sig.PerformanceReport(
rfs,
dates=dates,
views=[sig.View.SUMMARY_SINGLE],
aum=first_contract_value
).report()
In addition, you can generate a
PerformanceReport
with compounded metrics by passing compound_metrics=True
. The below code block demonstrates this feature by building on the examples provided above.You can also generate a
PerformanceReport
with compounded metrics by passing compound_metrics=True
. The following code block demonstrates this feature by building on the examples provided above:\sig.PerformanceReport(
rfs,
dates=dates[-5:],
views=[sig.View.SUMMARY_ROLLING_SERIES],
aum=first_contract_value,
compound_metrics=True
).report()
sig.PerformanceReport(
rfs,
dates=dates[-5:],
views=[sig.View.SUMMARY_ROLLING_SERIES],
aum=first_contract_value,
compound_metrics=True
).report()
Last modified 6mo ago