Backtest trading strategies with Python.
$ pip install backtesting
from backtesting import Backtest, Strategy
from backtesting.lib import crossover
from backtesting.test import SMA, GOOG
class SmaCross(Strategy):
def init(self):
Close = self.data.Close
self.ma1 = self.I(SMA, Close, 10)
self.ma2 = self.I(SMA, Close, 20)
def next(self):
if crossover(self.ma1, self.ma2):
self.buy()
elif crossover(self.ma2, self.ma1):
self.sell()
bt = Backtest(GOOG, SmaCross,
cash=10000, commission=.002)
bt.run()
bt.plot()Results in:
Start 2004-08-19 00:00:00
End 2013-03-01 00:00:00
Duration 3116 days 00:00:00
Exposure [%] 94.29
Equity Final [$] 69665.12
Equity Peak [$] 69722.15
Return [%] 596.65
Buy & Hold Return [%] 703.46
Max. Drawdown [%] -33.61
Avg. Drawdown [%] -5.68
Max. Drawdown Duration 689 days 00:00:00
Avg. Drawdown Duration 41 days 00:00:00
# Trades 93
Win Rate [%] 53.76
Best Trade [%] 56.98
Worst Trade [%] -17.03
Avg. Trade [%] 2.44
Max. Trade Duration 121 days 00:00:00
Avg. Trade Duration 32 days 00:00:00
Expectancy [%] 6.92
SQN 1.77
Sharpe Ratio 0.22
Sortino Ratio 0.54
Calmar Ratio 0.07
_strategy SmaCross
Find more usage examples in the documentation.
- Simple, well-documented API
- Blazing fast execution
- Built-in optimizer
- Library of composable base strategies and utilities
- Indicator-library-agnostic
- Supports any financial instrument with candlestick data
- Detailed results
- Interactive visualizations