candlestick

Candlestick pattern identification across OHLC time series detects 15 single-, double-, and triple-candle formations like hammers and engulfing patterns. It uses pandas DataFrames to vectorize recognition, applying configurable body and shadow thresholds to score bullish, bearish, and neutral setups.

Yes, you can generate trading signals from candlestick patterns using pandas by vectorizing the scoring process. The skill assigns +1 for bullish and -1 for bearish formations, aggregating these scores into a composite long, short, or neutral directional cue for each bar in your OHLC time series.

Vectorized candlestick pattern recognition is the process of scanning OHLC time series data to detect 15 classic formations without manual chart inspection. You need it when analyzing assets like equities or forex to quickly decide whether the latest market action supports a bullish, bearish, or wait-and-see posture.

Yes, candlestick pattern recognition works with crypto and forex OHLC data. As long as your market data is structured in a pandas DataFrame with open, high, low, and close columns, the vectorized scoring applies single-, double-, and triple-candle detection to emit composite directional signals for any asset.

Yes, candlestick pattern body and shadow thresholds are configurable. You can adjust these parameters within the pandas DataFrame processing logic to fine-tune the detection of 15 classic single, double, and triple candle formations, ensuring accurate vectorized scoring across various assets.

Candlestick signals are limited to the 15 classic single-, double-, and triple-candle formations detected in your OHLC time series. While vectorized scoring provides a composite long, short, or neutral cue, it focuses strictly on pattern recognition and does not incorporate volume, trend, or broader market context analysis.