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options_backtester/backtester/examples/merged_bt_example.ipynb
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In [2]:
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
/usr/local/anaconda3/lib/python3.7/site-packages/pyfolio/pos.py:27: UserWarning: Module "zipline.assets" not found; mutltipliers will not be applied to position notionals.
  'Module "zipline.assets" not found; mutltipliers will not be applied' +
In [3]:
options_data = HistoricalOptionsData('data/SPX_2017.csv')
options_data.quotedate = options_data.quotedate.dt.tz_localize(None)
schema = options_data.schema
In [4]:
stock_data = TiingoData('data/portfolio_data_2017.csv')
stock_data._data.date = stock_data._data.date.dt.tz_localize(None)
In [5]:
short_straddle = Strategy(schema)

leg1 = StrategyLeg("leg_1", schema, option_type=Type.CALL, direction=Direction.SELL)
leg1.entry_filter = (schema.underlying == "SPX") & (schema.dte >= 31) & (schema.dte <= 60) & (schema.strike >= schema.underlying_last * 0.95) & (schema.strike <= schema.underlying_last * 1.05)

leg1.exit_filter = (schema.dte <= 2)

leg2 = StrategyLeg("leg_2", schema, option_type=Type.PUT, direction=Direction.SELL)
leg2.entry_filter = (schema.underlying == "SPX") & (schema.dte >= 31) & (schema.dte <= 60) & (schema.strike >= schema.underlying_last * 0.95) & (schema.strike <= schema.underlying_last * 1.05)

leg2.exit_filter = (schema.dte <= 2)

short_straddle.add_legs([leg1, leg2]);
In [6]:
VOO = Stock('VOO', 0.1)
TUR = Stock('TUR', 0.05)
RSX = Stock('RSX', 0.05)
EWY = Stock('EWY', 0.05)
EWS = Stock('EWS', 0.05)
VTIP = Stock('VTIP', 0.10)
TLT = Stock('TLT', 0.20)
BWX = Stock('BWX', 0.10)
PDBC = Stock('PDBC', 0.05)
IAU = Stock('IAU', 0.15)
VNQI = Stock('VNQI', 0.10)
In [7]:
0.1 + 0.05 + 0.05 + 0.05 + 0.05 + 0.10 + 0.20 + 0.10 + 0.05 + 0.15 + 0.10
Out [7]:
1.0000000000000002
In [8]:
stocks = [
    VOO,
    TUR,
    RSX,
    EWY,
    EWS,
    VTIP,
    TLT,
    BWX,
    PDBC,
    IAU,
    VNQI
]
In [9]:
bt = Backtest({'stocks': 0.99, 'options': 0.01, 'cash': 0})
bt.stocks = stocks
bt._options_strategy = short_straddle
bt.options_data = options_data
bt.stocks_data = stock_data
In [10]:
bt.run(rebalance_freq=1)
Out [10]:
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:04
leg_1 leg_2 totals
contract underlying expiration type strike cost order contract underlying expiration type strike cost order cost qty date
0 SPX170217C02190000 SPX 2017-02-17 call 2190 -8110 Order.STO SPX170217P02190000 SPX 2017-02-17 put 2190 -1690 Order.STO -9800 1.0 2017-01-03
1 SPX170317C02170000 SPX 2017-03-17 call 2170 -11350 Order.STO SPX170317P02170000 SPX 2017-03-17 put 2170 -930 Order.STO -12280 1.0 2017-02-01
2 SPX170217C02190000 SPX 2017-02-17 call 2190 0 Order.BTC SPX170217P02190000 SPX 2017-02-17 put 2190 0 Order.BTC 0 1.0 2017-03-01
3 SPX170421C02280000 SPX 2017-04-21 call 2280 -12370 Order.STO SPX170421P02280000 SPX 2017-04-21 put 2280 -1030 Order.STO -13400 2.0 2017-03-01
4 SPX170317C02170000 SPX 2017-03-17 call 2170 0 Order.BTC SPX170317P02170000 SPX 2017-03-17 put 2170 0 Order.BTC 0 1.0 2017-04-03
5 SPX170519C02245000 SPX 2017-05-19 call 2245 -12060 Order.STO SPX170519P02245000 SPX 2017-05-19 put 2245 -940 Order.STO -13000 2.0 2017-04-03
6 SPX170421C02280000 SPX 2017-04-21 call 2280 0 Order.BTC SPX170421P02280000 SPX 2017-04-21 put 2280 0 Order.BTC 0 2.0 2017-05-01
7 SPX170616C02270000 SPX 2017-06-16 call 2270 -12120 Order.STO SPX170616P02270000 SPX 2017-06-16 put 2270 -750 Order.STO -12870 3.0 2017-05-01
8 SPX170519C02245000 SPX 2017-05-19 call 2245 0 Order.BTC SPX170519P02245000 SPX 2017-05-19 put 2245 0 Order.BTC 0 2.0 2017-06-01
9 SPX170721C02310000 SPX 2017-07-21 call 2310 -12510 Order.STO SPX170721P02310000 SPX 2017-07-21 put 2310 -810 Order.STO -13320 4.0 2017-06-01
10 SPX170616C02270000 SPX 2017-06-16 call 2270 0 Order.BTC SPX170616P02270000 SPX 2017-06-16 put 2270 0 Order.BTC 0 3.0 2017-07-03
11 SPX170818C02310000 SPX 2017-08-18 call 2310 -12370 Order.STO SPX170818P02310000 SPX 2017-08-18 put 2310 -960 Order.STO -13330 4.0 2017-07-03
12 SPX170721C02310000 SPX 2017-07-21 call 2310 0 Order.BTC SPX170721P02310000 SPX 2017-07-21 put 2310 0 Order.BTC 0 4.0 2017-08-01
13 SPX170915C02355000 SPX 2017-09-15 call 2355 -12270 Order.STO SPX170915P02355000 SPX 2017-09-15 put 2355 -740 Order.STO -13010 6.0 2017-08-01
14 SPX170818C02310000 SPX 2017-08-18 call 2310 0 Order.BTC SPX170818P02310000 SPX 2017-08-18 put 2310 0 Order.BTC 0 4.0 2017-09-01
15 SPX171020C02355000 SPX 2017-10-20 call 2355 -12840 Order.STO SPX171020P02355000 SPX 2017-10-20 put 2355 -970 Order.STO -13810 6.0 2017-09-01
16 SPX170915C02355000 SPX 2017-09-15 call 2355 0 Order.BTC SPX170915P02355000 SPX 2017-09-15 put 2355 0 Order.BTC 0 6.0 2017-10-02
17 SPX171117C02405000 SPX 2017-11-17 call 2405 -12740 Order.STO SPX171117P02405000 SPX 2017-11-17 put 2405 -730 Order.STO -13470 9.0 2017-10-02
18 SPX171020C02355000 SPX 2017-10-20 call 2355 0 Order.BTC SPX171020P02355000 SPX 2017-10-20 put 2355 0 Order.BTC 0 6.0 2017-11-01
19 SPX171215C02455000 SPX 2017-12-15 call 2455 -12650 Order.STO SPX171215P02455000 SPX 2017-12-15 put 2455 -820 Order.STO -13470 12.0 2017-11-01
20 SPX171117C02405000 SPX 2017-11-17 call 2405 0 Order.BTC SPX171117P02405000 SPX 2017-11-17 put 2405 0 Order.BTC 0 9.0 2017-12-01
21 SPX180119C02515000 SPX 2018-01-19 call 2515 -13990 Order.STO SPX180119P02515000 SPX 2018-01-19 put 2515 -1000 Order.STO -14990 16.0 2017-12-01
In [11]:
pf.create_returns_tear_sheet(returns =  bt.balance['% change'].dropna())
Start date2017-01-03
End date2017-12-28
Total months11
Backtest
Annual return 52.8%
Cumulative returns 52.3%
Annual volatility 17.0%
Sharpe ratio 2.58
Calmar ratio 9.80
Stability 0.96
Max drawdown -5.4%
Omega ratio 2.31
Sortino ratio 7.65
Skew 4.62
Kurtosis 31.31
Tail ratio 1.85
Daily value at risk -2.0% 
/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 5.39 2017-12-05 2017-12-12 NaT NaN
1 3.17 2017-09-07 2017-09-13 2017-10-02 18
2 2.90 2017-10-02 2017-10-20 2017-11-01 23
3 2.16 2017-11-15 2017-11-16 2017-12-01 13
4 1.63 2017-03-01 2017-03-09 2017-03-16 12
In [ ]:
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