BollingerMeanReversion

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class backtide.strategies.BollingerMeanReversion(period=20, std_dev=2.0)

Mean-reversion strategy using Bollinger Band boundaries.

A mean-reversion strategy that enters long when the price touches or crosses below the lower Bollinger Band and exits when it reaches the upper band. The assumption is that price will revert to its moving average after an extreme excursion. Useful in range-bound or mean-reverting markets.

Parameters	period : int, default=20 Number of bars for the Bollinger Band moving average. std_dev : float, default=2.0 Number of standard deviations for the band width.
Attributes	name : str Human-readable strategy name. is_multi_asset : bool Whether this is a multi-asset strategy.

See Also

MultiBollingerRotation

Multi-asset Bollinger Bands breakout rotation strategy.

Rsi

Relative Strength Index combined with Bollinger Bands for dual confirmation.

SmaCrossover

Simple Moving Average crossover strategy using fast and slow periods.

Methods

description	Short explanation of what the strategy does.
evaluate	Evaluate the strategy and return orders.
required_indicators	Indicators that must be computed up-front for this strategy.

method description()

Short explanation of what the strategy does.

Returns

str The description.

method evaluate(data, portfolio, state, indicators=None)

Evaluate the strategy and return orders.

Parameters

data : dict[str, numpy.ndarray | pandas.DataFrame | polars.DataFrame] Keys are the experiment's symbols and values are the historical OHLCV data available up to the current bar. For example, data["AAPL"]["close"] selects AAPL's visible close-price history. portfolio : backtide.backtest.Portfolio Current portfolio holdings (cash, positions and open orders). For example, portfolio.positions.get("AAPL", 0.0) returns the current signed quantity, while portfolio.orders contains pending orders. state : backtide.backtest.State Current simulation state. For example, use state.is_warmup to suppress orders during warmup and state.datetime to read the current bar's timezone-aware timestamp. indicators : dict[str, dict[str, numpy.ndarray | pandas.DataFrame | polars.DataFrame]] | None The first keys are the indicator names. The second keys are the experiment's symbols. The values are the pre-computed indicator histories available up to the current bar. For example, indicators["SMA_20"]["AAPL"] selects AAPL's visible 20-bar SMA history. None is permitted when no indicators were selected.

Returns

list[Order] Orders to place this tick.

method required_indicators()

Indicators that must be computed up-front for this strategy.

Returns a list of indicator instances, already parameterized with this strategy's current settings, that the engine will auto-include before the backtest starts.

Returns

list[BaseIndicator] The required indicator instances.