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HybridAlphaRsi

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, 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.