bt_example.py

import bt
import pandas as pd
from bt.algos import WeighTarget

# data = pd.read_csv('histdata/stocks_psi_geral/son-pl_daily_01-03-2000_11-02-2018.csv', names = ["open","high","low","close","date"], parse_dates=['date'], index_col=4, date_parser=parse, skiprows=1)

def long_only_ew(tickers, start='2010-01-01', name='long_only_ew'):
    s = bt.Strategy(name, [bt.algos.RunOnce(),
                           bt.algos.SelectAll(),
                           bt.algos.WeighEqually(),
                           bt.algos.Rebalance()])
    data = bt.get(tickers, start=start)
    bt.data.csv()
    return bt.Backtest(s, data)

class SelectWhere(bt.Algo):

    """
    Selects securities based on an indicator DataFrame.

    Selects securities where the value is True on the current date (target.now).

    Args:
        * signal (DataFrame): DataFrame containing the signal (boolean DataFrame)

    Sets:
        * selected

    """
    def __init__(self, signal):
        self.signal = signal

    def __call__(self, target):
        # get signal on target.now
        if target.now in self.signal.index:
            sig = self.signal.ix[target.now]

            # get indices where true as list
            selected = list(sig.index[sig])

            # save in temp - this will be used by the weighing algo
            target.temp['selected'] = selected

        # return True because we want to keep on moving down the stack
        return True

def above_sma(tickers, sma_per=50, start='2010-01-01', name='above_sma'):
    """
    Long securities that are above their n period
    Simple Moving Averages with equal weights.
    """
    # download data
    data = bt.get(tickers, start=start)
    print(type(data))
    print(data.head())
    # calc sma
    sma = data.rolling(sma_per).mean()

    # create strategy
    s = bt.Strategy(name, [SelectWhere(data > sma),
                           bt.algos.WeighEqually(),
                           bt.algos.Rebalance()])

    # now we create the backtest
    return bt.Backtest(s, data)

def ma_cross(tickers, start='2010-01-01',
             short_ma=50, long_ma=200, name='ma_cross'):
    # these are all the same steps as above
    data = bt.get(tickers, start=start)
    short_sma = data.rolling(short_ma).mean()
    long_sma  = data.rolling(long_ma).mean()

    # target weights
    tw = long_sma.copy()
    tw[short_sma > long_sma] = 1.0
    tw[short_sma <= long_sma] = -1.0
    tw[long_sma.isnull()] = 0.0

    # here we specify the children (3rd) arguemnt to make sure the strategy
    # has the proper universe. This is necessary in strategies of strategies
    s = bt.Strategy(name, [WeighTarget(tw), bt.algos.Rebalance()], [tickers])

    return bt.Backtest(s, data)

# create the backtests
tickers = 'aapl,msft,c,gs,ge'
sma10 = above_sma(tickers, sma_per=10, name='sma10')
sma20 = above_sma(tickers, sma_per=20, name='sma20')
sma40 = above_sma(tickers, sma_per=40, name='sma40')
benchmark = long_only_ew('spy', name='spy')

# run all the backtests!
res = bt.run(sma10, sma20, sma40, benchmark)
print(res.plot(freq='m'))

res.display()
print(res.plot_correlation())
print(res.get_transactions())