RiskAverse

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class backtide.strategies.RiskAverse(vol_period=14, breakout_period=20)

Low-volatility breakout strategy for risk-conscious investors.

Targets low-volatility stocks making new highs on above-average volume. Combines a volatility filter (e.g., ATR below a threshold) with a breakout condition and volume confirmation to find "quiet" stocks that are about to move. Designed for risk-conscious investors who want trend exposure with lower drawdowns.

Parameters	vol_period : int, default=14 ATR look-back period for the volatility filter. breakout_period : int, default=20 Number of bars for the new-high breakout condition.
Attributes	name : str Human-readable strategy name. is_multi_asset : bool Whether this is a multi-asset strategy.

See Also

BuyAndHold

Passive baseline that buys once and holds indefinitely.

TurtleTrading

Classic channel-breakout trend-following system with ATR-based position sizing.

Vcp

Volatility Contraction Pattern breakout 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.