In [4]:
%matplotlib inline

In [5]:
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)


import argparse
import datetime
import random

import backtrader as bt


class St(bt.Strategy):
    params = (
        ('printout', False),
        ('stake', 1000),
    )

    def __init__(self):
        pass

    def start(self):
        if self.p.printout:
            txtfields = list()
            txtfields.append('Len')
            txtfields.append('Datetime')
            txtfields.append('Open')
            txtfields.append('High')
            txtfields.append('Low')
            txtfields.append('Close')
            txtfields.append('Volume')
            txtfields.append('OpenInterest')
            print(','.join(txtfields))

    def next(self):
        if self.p.printout:
            # Print only 1st data ... is just a check that things are running
            txtfields = list()
            txtfields.append('%04d' % len(self))
            txtfields.append(self.data.datetime.datetime(0).isoformat())
            txtfields.append('%.2f' % self.data0.open[0])
            txtfields.append('%.2f' % self.data0.high[0])
            txtfields.append('%.2f' % self.data0.low[0])
            txtfields.append('%.2f' % self.data0.close[0])
            txtfields.append('%.2f' % self.data0.volume[0])
            txtfields.append('%.2f' % self.data0.openinterest[0])
            print(','.join(txtfields))

        # Data 0
        for data in self.datas:
            toss = random.randint(1, 10)
            curpos = self.getposition(data)
            if curpos.size:
                if toss > 5:
                    size = curpos.size // 2
                    self.sell(data=data, size=size)
                    if self.p.printout:
                        print('SELL {} @%{}'.format(size, data.close[0]))

            elif toss < 5:
                self.buy(data=data, size=self.p.stake)
                if self.p.printout:
                    print('BUY  {} @%{}'.format(self.p.stake, data.close[0]))


def runstrat(args=None):
    args = parse_args(args)

    cerebro = bt.Cerebro()
    cerebro.broker.set_cash(args.cash)

    dkwargs = dict()
    if args.fromdate:
        fromdate = datetime.datetime.strptime(args.fromdate, '%Y-%m-%d')
        dkwargs['fromdate'] = fromdate

    if args.todate:
        todate = datetime.datetime.strptime(args.todate, '%Y-%m-%d')
        dkwargs['todate'] = todate

    data0 = bt.feeds.BacktraderCSVData(dataname=args.data0, **dkwargs)
    cerebro.adddata(data0, name='Data0')

    data1 = bt.feeds.BacktraderCSVData(dataname=args.data1, **dkwargs)
    cerebro.adddata(data1, name='Data1')

    data2 = bt.feeds.BacktraderCSVData(dataname=args.data2, **dkwargs)
    cerebro.adddata(data2, name='Data2')

    cerebro.addstrategy(St, printout=args.printout)
    if not args.no_pyfolio:
        cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')

    results = cerebro.run()
    if not args.no_pyfolio:
        strat = results[0]
        pyfoliozer = strat.analyzers.getbyname('pyfolio')

        returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()
        if args.printout:
            print('-- RETURNS')
            print(returns)
            print('-- POSITIONS')
            print(positions)
            print('-- TRANSACTIONS')
            print(transactions)
            print('-- GROSS LEVERAGE')
            print(gross_lev)

        import pyfolio as pf
        pf.create_full_tear_sheet(
            returns,
            positions=positions,
            transactions=transactions,
            gross_lev=gross_lev,
            live_start_date='2005-05-01',
            round_trips=True)

    if args.plot:
        cerebro.plot(style=args.plot_style)


def parse_args(args=None):

    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
        description='Sample for pivot point and cross plotting')

    parser.add_argument('--data0', required=False,
                        default='../../datas/yhoo-1996-2015.txt',
                        help='Data to be read in')

    parser.add_argument('--data1', required=False,
                        default='../../datas/orcl-1995-2014.txt',
                        help='Data to be read in')

    parser.add_argument('--data2', required=False,
                        default='../../datas/nvda-1999-2014.txt',
                        help='Data to be read in')

    parser.add_argument('--fromdate', required=False,
                        default='2005-01-01',
                        help='Starting date in YYYY-MM-DD format')

    parser.add_argument('--todate', required=False,
                        default='2006-12-31',
                        help='Ending date in YYYY-MM-DD format')

    parser.add_argument('--printout', required=False, action='store_true',
                        help=('Print data lines'))

    parser.add_argument('--cash', required=False, action='store',
                        type=float, default=50000,
                        help=('Cash to start with'))

    parser.add_argument('--plot', required=False, action='store_true',
                        help=('Plot the result'))

    parser.add_argument('--plot-style', required=False, action='store',
                        default='bar', choices=['bar', 'candle', 'line'],
                        help=('Plot style'))

    parser.add_argument('--no-pyfolio', required=False, action='store_true',
                        help=('Do not do pyfolio things'))

    import sys
    aargs = args if args is not None else sys.argv[1:]
    return parser.parse_args(aargs)

In [6]:
runstrat([])


Entire data start date: 2005-01-03
Entire data end date: 2006-12-29


Out-of-Sample Months: 20
Backtest Months: 3
Performance statistics All history Backtest Out of sample
annual_return 0.13 0.01 0.15
annual_volatility 0.13 0.13 0.13
sharpe_ratio 1.02 0.17 1.19
calmar_ratio 1.19 0.29 1.42
stability_of_timeseries 0.94 0.18 0.95
max_drawdown -0.11 -0.05 -0.11
omega_ratio 1.29 1.04 1.35
sortino_ratio 1.63 0.26 1.91
skew 0.53 0.57 0.53
kurtosis 13.97 5.91 15.41
tail_ratio 1.28 1.23 1.43
common_sense_ratio 1.45 1.25 1.65
information_ratio 0.01 0.05 0.00
alpha 0.09 0.04 0.09
beta 0.39 0.18 0.45
Worst Drawdown Periods net drawdown in % peak date valley date recovery date duration
0 10.90 2006-04-06 2006-06-13 2006-10-26 146
1 7.28 2005-04-21 2005-06-27 2005-10-31 138
2 6.75 2005-11-25 2005-12-30 2006-01-11 34
3 4.62 2005-01-18 2005-02-09 2005-02-18 24
4 3.12 2006-11-30 2006-12-26 NaT NaN

[-0.016 -0.036]
Stress Events mean min max
Low Volatility Bull Market 0.05% -6.05% 4.91%
Top 10 long positions of all time max
Data2 91.56%
Data0 77.49%
Data1 30.45%
Top 10 short positions of all time max
Top 10 positions of all time max
Data2 91.56%
Data0 77.49%
Data1 30.45%
All positions ever held max
Data2 91.56%
Data0 77.49%
Data1 30.45%
Summary stats All trades Long trades
Total number of round_trips 668.00 668.00
Percent profitable 0.55 0.55
Winning round_trips 365.00 365.00
Losing round_trips 301.00 301.00
Even round_trips 2.00 2.00
PnL stats All trades Long trades
Total profit $13825.01 $13825.01
Gross profit $33970.19 $33970.19
Gross loss $-20145.17 $-20145.17
Profit factor $1.69 $1.69
Avg. trade net profit $20.70 $20.70
Avg. winning trade $93.07 $93.07
Avg. losing trade $-66.93 $-66.93
Ratio Avg. Win:Avg. Loss $1.39 $1.39
Largest winning trade $2425.01 $2425.01
Largest losing trade $-1403.06 $-1403.06
Duration stats All trades Long trades
Avg duration 17 days 06:28:01.440119 17 days 06:28:01.440119
Median duration 17 days 00:00:00 17 days 00:00:00
Avg # round_trips per day 13.63 13.63
Avg # round_trips per month 286.29 286.29
Return stats All trades Long trades
Avg returns all round_trips 0.04% 0.04%
Avg returns winning 0.17% 0.17%
Avg returns losing -0.12% -0.12%
Median returns all round_trips 0.00% 0.00%
Median returns winning 0.03% 0.03%
Median returns losing -0.03% -0.03%
Largest winning trade 4.36% 4.36%
Largest losing trade -2.21% -2.21%
Symbol stats Data0 Data1 Data2
Avg returns all round_trips 0.01% 0.01% 0.10%
Avg returns winning 0.19% 0.04% 0.24%
Avg returns losing -0.15% -0.03% -0.20%
Median returns all round_trips -0.00% 0.00% 0.01%
Median returns winning 0.03% 0.01% 0.04%
Median returns losing -0.03% -0.01% -0.06%
Largest winning trade 2.02% 0.34% 4.36%
Largest losing trade -1.95% -0.27% -2.21%
Profitability (PnL / PnL total) per name pnl
symbol
Data2 0.89%
Data0 0.06%
Data1 0.06%
<matplotlib.figure.Figure at 0x261e9f60>