options_backtester/backtester/examples/backtester_example.ipynb

Backtester Examples

Data files

In [1]:

import os
import sys

BACKTESTER_DIR = os.path.realpath(os.path.join(os.getcwd(), '..', '..'))
TEST_DATA_DIR = os.path.join(BACKTESTER_DIR, 'backtester', 'test', 'test_data')
SAMPLE_STOCK_DATA = os.path.join(TEST_DATA_DIR, 'test_data_stocks.csv')
SAMPLE_OPTIONS_DATA = os.path.join(TEST_DATA_DIR, 'test_data_options.csv')

sys.path.append(BACKTESTER_DIR) # Add backtester base dir to $PYTHONPATH

Sample backtest

In [2]:

from backtester import Backtest, Stock, Type, Direction
from backtester.datahandler import HistoricalOptionsData, TiingoData
from backtester.strategy import Strategy, StrategyLeg

First we construct an options datahandler.

In [3]:

options_data = HistoricalOptionsData(SAMPLE_OPTIONS_DATA)
options_schema = options_data.schema

Next, we'll create a toy options strategy.

In [4]:

sample_strategy = Strategy(options_schema)

leg1 = StrategyLeg('leg_1', options_schema, option_type=Type.CALL, direction=Direction.BUY)
leg1.entry_filter = ((options_schema.contract == 'SPX170317C00300000') &
                     (options_schema.dte == 73)) | ((options_schema.contract == 'SPX170421C00500000') &
                                                    (options_schema.dte == 51))

leg1.exit_filter = (options_schema.dte == 44) | (options_schema.dte == 18)

leg2 = StrategyLeg('leg_2', options_schema, option_type=Type.PUT, direction=Direction.BUY)
leg2.entry_filter = ((options_schema.contract == 'SPX170317P00300000') &
                     (options_schema.dte == 73)) | ((options_schema.contract == 'SPX170421P01375000') &
                                                    (options_schema.dte == 51))

leg2.exit_filter = (options_schema.dte == 44) | (options_schema.dte == 18)

sample_strategy.add_legs([leg1, leg2])
sample_strategy.add_exit_thresholds(profit_pct=0.2, loss_pct=0.2)

We do the same for stocks: create a datahandler together with a list of the stocks we want in our inventory and their corresponding weights.

In [5]:

stocks_data = TiingoData(SAMPLE_STOCK_DATA)
stocks = [Stock('VOO', 0.4), Stock('TUR', 0.1), Stock('RSX', 0.5)]

We set our portfolio allocation, i.e. how much of our capital will be invested in stocks, options and cash.

In [6]:

allocation = {'stocks': 0.5, 'options': 0.5, 'cash': 0.0}

Finally, we create the Backtest object.

In [7]:

bt = Backtest(allocation, initial_capital=1_000_000)

bt.stocks = stocks
bt.stocks_data = stocks_data

bt.options_strategy = sample_strategy
bt.options_data = options_data

And run the backtest with a rebalancing period of one month.

In [8]:

bt.run(rebalance_freq=1)

Out [8]:

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

	leg_1							leg_2							totals
	contract	underlying	expiration	type	strike	cost	order	contract	underlying	expiration	type	strike	cost	order	cost	qty	date
0	SPX170317C00300000	SPX	2017-03-17	call	300	195010.0	Order.BTO	SPX170317P00300000	SPX	2017-03-17	put	300	5.0	Order.BTO	195015.0	2.0	2017-01-03
1	SPX170317C00300000	SPX	2017-03-17	call	300	-197060.0	Order.STC	SPX170317P00300000	SPX	2017-03-17	put	300	-0.0	Order.STC	-197060.0	2.0	2017-02-01
2	SPX170421C00500000	SPX	2017-04-21	call	500	189250.0	Order.BTO	SPX170421P01375000	SPX	2017-04-21	put	1375	40.0	Order.BTO	189290.0	2.0	2017-03-01
3	SPX170421C00500000	SPX	2017-04-21	call	500	-185650.0	Order.STC	SPX170421P01375000	SPX	2017-04-21	put	1375	-0.0	Order.STC	-185650.0	2.0	2017-04-03

The trade log (bt.trade_log) shows we executed 4 trades: we bought 2 calls and 2 puts on 2017-01-03 and 2017-03-01, and exited those positions on 2017-02-01 and 2017-04-03 respectively.

The balance data structure shows how our positions evolved in time:

• We started with $1000000 on 2017-01-02
• total capital is the sum of cash, stocks capital and options capital
• % change shows the inter day change in total capital
• accumulated return gives the compounded return in total capital since the start of the backtest

In [9]:

bt.balance

Out [9]:

	total capital	cash	VOO	TUR	RSX	options qty	calls capital	puts capital	stocks qty	options capital	stocks capital	% change	accumulated return
2017-01-02	1.000000e+06	1000000.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.000000	NaN	NaN
2017-01-03	9.990300e+05	110117.405920	199872.763320	49993.281167	249986.549593	2.0	389060.0	0.0	16186.0	389060.0	499852.594080	-0.000970	0.999030
2017-01-04	1.004228e+06	110117.405920	201052.238851	50072.862958	251605.333911	2.0	391380.0	0.0	16186.0	391380.0	502730.435720	0.005203	1.004228
2017-01-05	1.002706e+06	110117.405920	200897.553535	49865.950301	250564.686850	2.0	391260.0	0.0	16186.0	391260.0	501328.190686	-0.001516	1.002706
2017-01-06	1.003201e+06	110117.405920	201680.647945	49372.543196	248830.275081	2.0	393200.0	0.0	16186.0	393200.0	499883.466222	0.000494	1.003201
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2017-05-12	1.008599e+06	507298.036182	203126.836072	50621.271130	247552.567157	0.0	0.0	0.0	16415.0	0.0	501300.674359	-0.002805	1.008599
2017-05-15	1.015904e+06	507298.036182	204154.459189	51695.869759	252755.284844	0.0	0.0	0.0	16415.0	0.0	508605.613792	0.007243	1.015904
2017-05-16	1.014651e+06	507298.036182	204117.427725	51811.005327	251424.357064	0.0	0.0	0.0	16415.0	0.0	507352.790116	-0.001233	1.014651
2017-05-17	1.005524e+06	507298.036182	200497.602152	50659.649653	247068.593419	0.0	0.0	0.0	16415.0	0.0	498225.845224	-0.008995	1.005524
2017-05-18	1.001336e+06	507298.036182	201228.973560	49853.700681	242954.816643	0.0	0.0	0.0	16415.0	0.0	494037.490883	-0.004165	1.001336

96 rows × 13 columns

Statistics and Plots

In [10]:

from backtester.statistics import *

Summary table of our options strategy.

In [11]:

summary(bt.trade_log, bt.balance)

Out [11]:

	Strategy
Total trades	2
Number of wins	1
Number of losses	1
Win %	50.00%
Largest loss	$7280.00
Profit factor	1.00
Average profit	$-1595.00
Average P&L %	0.00%
Total P&L %	99.68%

Plot of the accumulated returns over time.

In [12]:

returns_chart(bt.balance)

Out [12]:

Daily returns histogram.

In [13]:

returns_histogram(bt.balance)

Out [13]:

In [14]:

monthly_returns_heatmap(bt.balance)

Out [14]: