All Backtesting

Here is a description of the backtest function and all of its parameters. This is true for any kind of backtesting that you will be doing.

strategies.strategy.Strategy.backtest(*args, minutes_before_closing=5, minutes_before_opening=60, sleeptime=1, stats_file=None, risk_free_rate=None, logfile=None, config=None, auto_adjust=False, name=None, budget=None, benchmark_asset='SPY', plot_file_html=None, trades_file=None, settings_file=None, pandas_data=None, quote_asset=USD, starting_positions=None, show_plot=True, tearsheet_file=None, save_tearsheet=True, show_tearsheet=True, parameters={}, buy_trading_fees=[], sell_trading_fees=[], polygon_api_key=None, polygon_has_paid_subscription=False, indicators_file=None, **kwargs)

Backtest a strategy.

Parameters

  • datasource_class (class) – The datasource class to use. For example, if you want to use the yahoo finance datasource, then you would pass YahooDataBacktesting as the datasource_class.

  • backtesting_start (datetime) – The start date of the backtesting period.

  • backtesting_end (datetime) – The end date of the backtesting period.

  • minutes_before_closing (int) – The number of minutes before closing that the minutes_before_closing strategy method will be called.

  • minutes_before_opening (int) – The number of minutes before opening that the minutes_before_opening strategy method will be called.

  • sleeptime (int) – The number of seconds to sleep between each iteration of the backtest.

  • stats_file (str) – The file to write the stats to.

  • risk_free_rate (float) – The risk free rate to use.

  • logfile (str) – The file to write the log to.

  • config (dict) – The config to use to set up the brokers in live trading.

  • auto_adjust (bool) – Whether or not to automatically adjust the strategy.

  • name (str) – The name of the strategy.

  • budget (float) – The initial budget to use for the backtest.

  • benchmark_asset (str or Asset) – The benchmark asset to use for the backtest to compare to. If it is a string then it will be converted to a stock Asset object.

  • plot_file_html (str) – The file to write the plot html to.

  • trades_file (str) – The file to write the trades to.

  • pandas_data (list) – A list of Data objects that are used when the datasource_class object is set to PandasDataBacktesting. This contains all the data that will be used in backtesting.

  • quote_asset (Asset (crypto)) – An Asset object for the crypto currency that will get used as a valuation asset for measuring overall porfolio values. Usually USDT, USD, USDC.

  • starting_positions (dict) – A dictionary of starting positions for each asset. For example, if you want to start with $100 of SPY, and $200 of AAPL, then you would pass in starting_positions={‘SPY’: 100, ‘AAPL’: 200}.

  • show_plot (bool) – Whether or not to show the plot.

  • show_tearsheet (bool) – Whether or not to show the tearsheet.

  • save_tearsheet (bool) – Whether or not to save the tearsheet.

  • parameters (dict) – A dictionary of parameters to pass to the strategy. These parameters must be set up within the initialize() method.

  • buy_trading_fees (list of TradingFee objects) – A list of TradingFee objects to apply to the buy orders during backtests.

  • sell_trading_fees (list of TradingFee objects) – A list of TradingFee objects to apply to the sell orders during backtests.

  • polygon_api_key (str) – The polygon api key to use for polygon data. Only required if you are using PolygonDataBacktesting as the datasource_class.

  • polygon_has_paid_subscription (bool) – Whether or not you have a paid subscription to Polygon. Only required if you are using PolygonDataBacktesting as the datasource_class.

  • indicators_file (str) – The file to write the indicators to.

Returns

The backtest object.

Return type

Backtest

Examples

>>> from datetime import datetime
>>> from lumibot.backtesting import YahooDataBacktesting
>>> from lumibot.strategies import Strategy
>>>
>>> # A simple strategy that buys AAPL on the first day
>>> class MyStrategy(Strategy):
>>>    def on_trading_iteration(self):
>>>        if self.first_iteration:
>>>            order = self.create_order("AAPL", quantity=1, side="buy")
>>>            self.submit_order(order)
>>>
>>> # Create a backtest
>>> backtesting_start = datetime(2018, 1, 1)
>>> backtesting_end = datetime(2018, 1, 31)
>>>
>>> # The benchmark asset to use for the backtest to compare to
>>> benchmark_asset = Asset(symbol="QQQ", asset_type="stock")
>>>
>>> backtest = MyStrategy.backtest(
>>>     datasource_class=YahooDataBacktesting,
>>>     backtesting_start=backtesting_start,
>>>     backtesting_end=backtesting_end,
>>>     benchmark_asset=benchmark_asset,
>>> )