MultiBollingerRotation

class backtide.strategies.MultiBollingerRotation(period=20, std_dev=2.0, top_k=5, rebalance_interval=20)

Multi-asset Bollinger Bands breakout rotation strategy.

A breakout rotation strategy that periodically ranks all assets by how far their price exceeds the upper Bollinger Band and rotates into the top K positions. Assets that have broken out above their bands are considered to be in strong uptrends. Useful for momentum-driven portfolio rotation across a basket of assets.

Parameters	period : int, default=20 Bollinger Band moving average period. std_dev : float, default=2.0 Number of standard deviations for the bands. top_k : int, default=5 Number of top-ranked assets to hold. rebalance_interval : int, default=20 Number of bars between rebalancing.
Attributes	name : str Human-readable strategy name. is_multi_asset : bool Whether this is a multi-asset strategy.

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.