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AdaptiveRsi

class backtide.strategies.AdaptiveRsi(min_period=8, max_period=28)

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

Dynamically adjusts its look-back period based on current market volatility and cycle length. In calm, trending markets the period lengthens for smoother signals; in volatile or choppy regimes it shortens for faster reaction. Useful when a fixed-period RSI produces too many whipsaws or lags behind regime changes.

Parameters

min_period : int, default=8

Minimum adaptive RSI period.

max_period : int, default=28
Maximum adaptive RSI period.
Attributes

name : str

Human-readable strategy name.

is_multi_asset : bool
Whether this is a multi-asset strategy.


See Also

AlphaRsiPro

Advanced Relative Strength Index with adaptive overbought/oversold levels.

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, 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.