options_backtester/backtester/examples/selling_puts_monthly_example.ipynb

In [1]:

import sys

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

In [2]:

%config InlineBackend.figure_format="retina"
%matplotlib inline
import pyfolio as pf
import pandas as pd
import os
import matplotlib.pyplot as plt
import altair as alt

plt.style.use("seaborn")
plt.rcParams["figure.figsize"] = (14, 8)

/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]:

from backtester.datahandler import HistoricalOptionsData
from backtester.strategy import Strategy, StrategyLeg
from backtester.option import Type, Direction
from backtester import Backtest
from backtester.statistics import monthly_returns_heatmap, returns_histogram, returns_chart

In [4]:

# Cleaned up data
data = HistoricalOptionsData("options_data_clean_v2.h5", key="/SPX", where='quotedate >= "2006-12-06" & quotedate <= "2015-08-21"')
schema = data.schema

we test a (monhtly) strategy of selling puts at different OTM percentages

In [5]:

otm_percentages = [0,3,5,7,10,15,20,25,35]

In [15]:

bt = Backtest()
bt.data = data
bt.stop_if_broke = False
trade_logs = []
summaries = []
balances = []
pct_tolerance = 1
for otm_pct in otm_percentages:
    strat = Strategy(schema)
    leg = StrategyLeg(
            "leg_1",
            schema,
            option_type=Type.PUT,
            direction=Direction.SELL,
        )

    otm_lower_bound = (otm_pct - pct_tolerance) / 100
    otm_upper_bound = (otm_pct + pct_tolerance) / 100

    leg.entry_filter = (schema.underlying == "SPX") & (schema.dte >= 61) & (
            schema.dte <= 90) & (schema.strike <= schema.underlying_last *
                                                 (1 - otm_lower_bound)) & (schema.strike >= schema.underlying_last *
                                                                           (1 - otm_upper_bound))
    leg.exit_filter = (schema.dte <= 60)
    strat.add_leg(leg)
    bt.strategy = strat
    bt.run(monthly=True)
    trade_logs.append(bt.trade_log)
    summaries.append(bt.summary())
    balances.append(bt.balance)

0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:09
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:09
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:09
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:09
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:09
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:09
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:09
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:08
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:08

In [17]:

pf.create_returns_tear_sheet(returns = balances[0]['% change'].dropna())

/usr/local/anaconda3/lib/python3.7/site-packages/empyrical/stats.py:1492: RuntimeWarning: invalid value encountered in log1p
  cum_log_returns = np.log1p(returns).cumsum()

Start date	2006-12-06
End date	2015-08-03
Total months	5
	Backtest
Annual return	106785.8%
Cumulative returns	1728.3%
Annual volatility	793.2%
Sharpe ratio	2.03
Calmar ratio	567.72
Stability	NaN
Max drawdown	-188.1%
Omega ratio	1.77
Sortino ratio	3.56
Skew	1.75
Kurtosis	17.84
Tail ratio	1.27
Daily value at risk	-93.6%

/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	188.10	2007-06-01	2009-03-02	2009-12-01	653
1	74.69	2007-02-01	2007-03-01	2007-06-01	87
2	38.78	2011-05-02	2011-10-03	2013-01-02	438
3	17.03	2010-01-04	2010-02-01	2010-03-01	41
4	9.63	2014-01-02	2014-02-03	2014-05-01	86

In [ ]: