candlestick

You can detect candlestick patterns by providing OHLCV data as a pandas DataFrame and calling the signal generation engine. It identifies 15 classic patterns and returns a composite score for each asset.

Vectorized candlestick pattern recognition uses pandas and numpy to scan OHLCV arrays without loops, identifying 15 classic patterns and computing composite scores to generate trading signals across multiple assets simultaneously.

Candlestick patterns convert into trading signals through a modular scoring system with configurable threshold parameters like body_pct and shadow_ratio. It aggregates per-pattern scores from OHLCV data and outputs 1 for long, -1 for short, or 0 for hold.

Yes, the candlestick signal engine applies vectorized pattern detection to both historical OHLCV data and real-time feeds. It returns per-asset long, short, or hold signals suitable for backtesting and live decisioning.

Vectorized candlestick pattern detection requires pandas and numpy environments and limits recognition to 15 classic formations. It outputs simple long, short, or hold signals based on configurable thresholds, without deeper order execution or risk management logic.