options_backtester/backtester/examples/2legs_example.ipynb

In [4]:

import pyfolio as pf

from backtester import Backtest
from backtester.strategy import Strategy, StrategyLeg
from backtester.enums import Type, Direction, Stock
from backtester.datahandler import HistoricalOptionsData, TiingoData
# Cleaned up data
options_data = HistoricalOptionsData(
        "/Users/lambdaclass/options_backtester_copy/backtester/examples/options_data_clean_v2.h5",
        key="/SPX",
        where='quotedate >= "2012-01-01" & quotedate <= "2014-01-01"')


schema = options_data.schema


options_schema = options_data.schema
put_otm = Strategy(options_schema)

leg_1 = StrategyLeg("leg_1", options_schema, option_type=Type.PUT, direction=Direction.BUY)
leg_1.entry_filter = (options_schema.underlying == "SPX") & (options_schema.dte >= 60)
leg_1.exit_filter = (options_schema.dte <= 30)

leg_2 = StrategyLeg("leg_2", options_schema, option_type=Type.CALL, direction=Direction.BUY)
leg_2.entry_filter = (options_schema.underlying == "SPX") & (options_schema.dte >= 60)
leg_2.exit_filter = (options_schema.dte <= 30)
put_otm.add_legs([leg_1, leg_2])
options_data.columns = [
    'underlying', 'underlying_last', 'optionroot', 'type', 'expiration', 'quotedate', 'strike', 'last', 'bid', 'ask',
        'volume', 'openinterest', 'impliedvol', 'delta', 'gamma', 'theta', 'vega', 'optionalias', 'dte'
    ]
options_data.quotedate = options_data.quotedate.dt.tz_localize(None)
options_data._data.quotedate = options_data._data.quotedate.dt.tz_localize(None)

allocation = {'cash': 0, 'stocks': 97, 'options': 3}

bt = Backtest(allocation=allocation)

    #asset_data = HistoricalAssetData('/Users/lambdaclass/options_backtester_copy/data/ivy_5assets.csv')
asset_data = TiingoData('/Users/lambdaclass/options_backtester_copy/data/ivy_5assets.csv')
asset_data._data.date = asset_data._data.date.dt.tz_localize(None)

asset_data.start_date = min(asset_data._data.date)
asset_data.end_date = max(asset_data._data.date)
VTI = Stock("VTI", 0.2)
VEU = Stock("VEU", 0.2)
BND = Stock("BND", 0.2)
VNQ = Stock("VNQ", 0.2)
DBC = Stock("DBC", 0.2)
bt.stocks = [VTI, VEU, BND, VNQ, DBC]
asset_data._data = asset_data._data.query('date >= "2012-01-01" & date <= "2014-01-01"')
bt.options_data = options_data
bt._options_strategy = put_otm
bt.stocks_data = asset_data
bt.stocks_data.start_date = min(bt.stocks_data['date'])
bt.stocks_data.end_date = max(bt.stocks_data['date'])

bt.current_cash = 1_000_000
bt.run(rebalance_freq=1)
bt.balance

Out [4]:

0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:08

	total capital	cash	VTI	VEU	BND	VNQ	DBC	options qty	calls capital	puts capital	stocks qty	options capital	stocks capital	% change	accumulated return
2012-01-02	1.000000e+06	1000000.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.000000e+00	NaN	NaN
2012-01-03	9.978700e+05	2233.455073	193990.075633	193976.304225	193986.878802	193966.992283	193976.293985	6.0	25740.0	0.0	24257.0	25740.0	9.698965e+05	-0.002130	0.997870
2012-01-04	9.949656e+05	2233.455073	194079.321182	193451.915101	194080.052134	190650.750322	195450.064670	6.0	25020.0	0.0	24257.0	25020.0	9.677121e+05	-0.002911	0.994966
2012-01-05	9.927494e+05	2233.455073	194942.028158	191259.015126	194010.172135	192441.520981	192783.241526	6.0	25080.0	0.0	24257.0	25080.0	9.654360e+05	-0.002227	0.992749
2012-01-06	9.887005e+05	2233.455073	194495.800412	188827.756460	194126.638801	191811.435009	193625.396202	6.0	23580.0	0.0	24257.0	23580.0	9.628870e+05	-0.004079	0.988700
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2013-12-23	1.331694e+06	2059.477942	259726.002177	257396.446444	254976.455889	257136.218232	256859.352660	7.0	43540.0	0.0	28406.0	43540.0	1.286094e+06	0.005449	1.331694
2013-12-24	1.336095e+06	2059.477942	260437.655255	257861.901140	254442.393001	257363.391128	257660.161242	7.0	46270.0	0.0	28406.0	46270.0	1.287766e+06	0.003305	1.336095
2013-12-26	1.342131e+06	2059.477942	261669.362505	258947.962095	254061.063076	257562.280921	258060.565534	7.0	49770.0	0.0	28406.0	49770.0	1.290301e+06	0.004517	1.342131
2013-12-27	1.344328e+06	2059.477942	261505.134872	259982.305862	254061.063076	258119.172339	258761.273042	7.0	49840.0	0.0	28406.0	49840.0	1.292429e+06	0.001637	1.344328
2013-12-30	1.344886e+06	2059.477942	261587.248689	261326.952759	254474.170495	258198.728256	257259.756951	7.0	49980.0	0.0	28406.0	49980.0	1.292847e+06	0.000415	1.344886

502 rows × 15 columns

In [5]:

pf.create_returns_tear_sheet(returns =  bt.balance['% change'].dropna())

Start date	2012-01-03
End date	2013-12-30
Total months	23
	Backtest
Annual return	16.1%
Cumulative returns	34.5%
Annual volatility	14.5%
Sharpe ratio	1.10
Calmar ratio	1.49
Stability	0.86
Max drawdown	-10.8%
Omega ratio	1.22
Sortino ratio	1.62
Skew	-0.01
Kurtosis	6.54
Tail ratio	1.00
Daily value at risk	-1.8%

/usr/local/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:61: FutureWarning: 
The current behaviour of 'Series.argmin' is deprecated, use 'idxmin'
instead.
The behavior of 'argmin' will be corrected to return the positional
minimum in the future. For now, use 'series.values.argmin' or
'np.argmin(np.array(values))' to get the position of the minimum
row.
  return bound(*args, **kwds)

Worst drawdown periods	Net drawdown in %	Peak date	Valley date	Recovery date	Duration
0	10.79	2013-05-21	2013-06-24	2013-10-18	109
1	9.85	2012-04-02	2012-06-04	2012-08-09	94
2	8.15	2013-10-29	2013-12-13	NaT	NaN
3	7.68	2012-09-14	2012-11-15	2013-01-02	79
4	3.27	2013-04-11	2013-04-18	2013-04-29	13

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