AlphaRsiPro

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class backtide.strategies.AlphaRsiPro(period=14, vol_window=20)

Advanced Relative Strength Index with adaptive overbought/oversold levels.

An advanced RSI variant that computes adaptive overbought and oversold thresholds based on recent volatility, and adds a trend-bias filter to avoid counter-trend entries. In strong uptrends the oversold level is raised so buy signals fire earlier; in downtrends the overbought level is lowered so sells trigger sooner. Useful for reducing false signals in trending markets compared to a plain RSI strategy.

Parameters	period : int, default=14 RSI look-back 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.

HybridAlphaRsi

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

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.