HybridAlphaRsi

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class backtide.strategies.HybridAlphaRsi(min_period=8, max_period=28, vol_window=20)

Full-featured Relative Strength Index combining adaptive period, levels, and trend filter.

The most sophisticated RSI variant, combining an adaptive look-back period (like AdaptiveRsi), adaptive overbought/oversold levels (like AlphaRsiPro), and trend confirmation via a moving-average filter. Designed to deliver the highest-quality RSI signals across different market regimes.

Parameters	min_period : int, default=8 Minimum adaptive RSI period. max_period : int, default=28 Maximum adaptive RSI period. vol_window : int, default=20 Window for the volatility-based level adjustment.
Attributes	name : str Human-readable strategy name. is_multi_asset : bool Whether this is a multi-asset strategy.

See Also

AdaptiveRsi

Relative Strength Index with a dynamically adaptive look-back period.

AlphaRsiPro

Advanced Relative Strength Index with adaptive overbought/oversold levels.

Rsi

Relative Strength Index combined with Bollinger Bands for dual confirmation.

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, np.array | pd.DataFrame | pl.DataFrame] Keys are the experiment's symbols and values are the historical OHLCV data available up to the current bar. portfolio : Portfolio Current portfolio holdings (cash, positions and open orders). state : State Current simulation state. indicators : dict[str, dict[str, np.array | pd.DataFrame | pl.DataFrame]] | None The first keys are the indicator names. The second keys are the experiment's symbols. The values are the pre-computed indicator values. None if 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.