candlestick

To identify candlestick patterns in pandas, you need a DataFrame with open, high, low, and close columns. This engine applies vectorized operations to detect 15 classic patterns and generates a composite buy, sell, or neutral signal for your price series.

The signal engine recognizes 15 candlestick patterns for backtesting, comprising 5 single-candle, 5 double-candle, 4 triple-candle, and a trend-confirmation pattern. It scores bullish patterns as +1 and bearish patterns as -1 to calculate a total composite signal.

Yes, you can use this candlestick pattern recognition for real-time trading signals. The vectorized pandas implementation processes OHLCV data to generate composite bullish, bearish, or neutral scores, supporting both live automated trading decisions and historical strategy prototyping.

A composite candlestick signal score translates to trading decisions by summing pattern values: bullish patterns add +1 and bearish patterns subtract -1. The resulting total score determines long, short, or hold positions for automated short-term trading and backtesting.

Yes, you need OHLCV data to run candlestick pattern detection. The engine requires a pandas DataFrame containing open, high, low, and close columns to accurately identify single, double, and triple candlestick patterns and produce trading signals.

The best way to automate short-term trading decisions using technical analysis is scoring 15 candlestick patterns across OHLCV data. This vectorized pandas approach rapidly generates composite long, short, or hold signals for real-time execution or historical hypothesis testing.