SmaCrossover

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class backtide.strategies.SmaCrossover(fast_period=20, slow_period=50)

Simple Moving Average crossover strategy using fast and slow periods.

Generates buy and sell signals based on moving-average crossovers. A golden cross (fast MA crosses above slow MA) triggers a buy; a death cross (fast MA crosses below slow MA) triggers a sell. More robust than the naive SMA strategy because it requires confirmation from two different time horizons.

Parameters	fast_period : int, default=20 Fast moving average period. slow_period : int, default=50 Slow moving average period.
Attributes	name : str Human-readable strategy name. is_multi_asset : bool Whether this is a multi-asset strategy.

See Also

Macd

Moving Average Convergence Divergence crossover strategy.

Momentum

Trend-following strategy driven by short-term price momentum.

SmaNaive

Naive single Simple Moving Average trend-following 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, 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.