Backtesting.py

Warnings

• breaking The `Backtest(commission=)` parameter in versions 0.6.0 and later now applies commission *twice* per trade (at both entry and exit). If you intend for costs to be applied only once at entry (e.g., for spread/slippage), use the new `Backtest(spread=)` parameter instead.
  Fix: Review existing strategies using `commission` and adjust if needed, or migrate to `spread` for one-time entry costs.
• breaking Version 0.2.0 introduced a completely new `Order / Trade / Position API`. Strategies written for versions prior to 0.2.0 are incompatible and require significant updates to use the new API.
  Fix: Consult the changelog and documentation for the 0.2.0 release to update strategy logic and order management to the new API.
• gotcha Bokeh versions 3.0.x and 3.2.x are known to have incompatibilities with `backtesting.py`'s interactive plotting feature, potentially leading to errors.
  Fix: Force a compatible Bokeh version during installation: `pip install "bokeh>=3.1,!=3.2.*"`.
• gotcha Trading decisions made in the `next()` method are typically executed on the *next* bar's open price (or current bar's close if `trade_on_close=True`). This simulation characteristic means immediate execution at the exact candle price that triggered a signal is not guaranteed, which can impact profitability, especially for high-frequency or tight stop-loss/take-profit strategies.
  Fix: Understand and account for this execution model. For more granular control or intra-candle trading, use higher-frequency data.
• gotcha When defining indicators within `Strategy.init()`, it's crucial to wrap indicator functions with `self.I()`. This ensures that indicator values are revealed gradually, bar-by-bar, simulating real-time data availability and preventing look-ahead bias. Failing to do so (e.g., directly computing indicators on `self.data.Close` for the full dataset outside `self.I()`) will lead to unrealistic, overly optimistic backtest results.
  Fix: Always use `self.I(indicator_func, data_series, *args)` for all technical indicators in your `init()` method.
• gotcha If a trading position is opened with `self.buy()` or `self.sell()` but never explicitly closed (e.g., with `self.position.close()`, `self.sell()`, or `self.buy()`), it will remain open until the end of the backtest. This can lead to misleading or `NaN`/`0` values in performance statistics because trades are not fully realized within the backtesting period.
  Fix: Ensure your strategy includes logic to close positions, either explicitly with an opposing order or `self.position.close()`, or by using `exclusive_orders=True` in `Backtest` constructor to automatically close previous positions on new opposing orders.

Install

• pip install backtesting Install stable version

Imports

• Backtest

  from backtesting import Backtest

• Strategy

  from backtesting import Strategy

• crossover

  from backtesting.lib import crossover

• SMA

  from backtesting.test import SMA

  SMA is a test utility; for real strategies, users typically import from other indicator libraries (e.g., pandas_ta, TA-Lib).
• GOOG

  from backtesting.test import GOOG

  GOOG is a built-in test dataset; users typically provide their own pandas DataFrames.

Quickstart

This quickstart defines a simple Moving Average Crossover strategy. It initializes two Simple Moving Averages (SMAs) in `init()` using `self.I()` to prevent look-ahead bias, and then places buy/sell orders in `next()` based on their crossover. The `Backtest` instance is run with historical Google stock data, and performance statistics are printed, followed by an interactive plot.

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()

# Prepare data (using built-in test data for quickstart)
# In a real scenario, you'd load your own pandas.DataFrame
# with columns 'Open', 'High', 'Low', 'Close', 'Volume' (optional)
# and a DatetimeIndex.
# For example: from pandas_datareader import data as yf
#              data = yf.DataReader('SPY', start='2000', end='2020')

bt = Backtest(GOOG, SmaCross, cash=10000, commission=.002, exclusive_orders=True)
stats = bt.run()
print(stats)
bt.plot(open_browser=False)