Backtest trading strategies with Python.
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$ 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):
price = self.data.Close
self.ma1 = self.I(SMA, price, 10)
self.ma2 = self.I(SMA, price, 20)
def next(self):
if crossover(self.ma1, self.ma2):
self.buy()
elif crossover(self.ma2, self.ma1):
self.sell()
bt = Backtest(GOOG, SmaCross, commission=.002,
exclusive_orders=True)
stats = 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 Time [%] 94.27
Equity Final [$] 68935.12
Equity Peak [$] 68991.22
Return [%] 589.35
Buy & Hold Return [%] 703.46
Return (Ann.) [%] 25.42
Volatility (Ann.) [%] 38.43
CAGR [%] 16.80
Sharpe Ratio 0.66
Sortino Ratio 1.30
Calmar Ratio 0.77
Alpha [%] 450.62
Beta 0.02
Max. Drawdown [%] -33.08
Avg. Drawdown [%] -5.58
Max. Drawdown Duration 688 days 00:00:00
Avg. Drawdown Duration 41 days 00:00:00
# Trades 93
Win Rate [%] 53.76
Best Trade [%] 57.12
Worst Trade [%] -16.63
Avg. Trade [%] 1.96
Max. Trade Duration 121 days 00:00:00
Avg. Trade Duration 32 days 00:00:00
Profit Factor 2.13
Expectancy [%] 6.91
SQN 1.78
Kelly Criterion 0.6134
_strategy SmaCross(n1=10, n2=20)
_equity_curve Equ...
_trades Size EntryB...
dtype: object
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
Before reporting bugs or posting to the
discussion board,
please read contributing guidelines, particularly the section
about crafting useful bug reports and ```-fencing your code. We thank you!
See alternatives.md for a list of alternative Python backtesting frameworks and related packages.