Detect and score classic candlestick patterns from OHLC time-series data.
npx skills add https://github.com/loanntc/Paave --skill candlestick-patterns
Please help me install this Agent Skill. Skill: candlestick-patterns Source: https://github.com/loanntc/Paave/tree/main/skills/candlestick Command: npx skills add https://github.com/loanntc/Paave --skill candlestick-patterns
Candlestick pattern analysts waste time converting OHLC data into consistent bullish/bearish/neutral signals for trading decisions.
Run the candlestick signal engine on your OHLCV DataFrame(s) (with open, high, low, close) to produce a per-bar signal series where 1 means long, -1 means short, and 0 means no trade.
High-intent search queries and answers about installing and using this skill.
To generate trading signals from OHLC data, this tool detects 15 classic candlestick patterns using vectorized pandas and NumPy logic, scoring bullish patterns as +1 and bearish as -1 to output per-bar long, short, or neutral signals.
The best way to detect candlestick patterns across multiple symbols is using a vectorized pandas and NumPy engine, which efficiently processes batch time-series data to identify single-, double-, and triple-candle formations concurrently.
Yes, you can use vectorized NumPy logic for candlestick pattern recognition. This approach identifies single, double, and triple-candle formations across OHLC time-series data without looping, enabling efficient batch processing.
Candlestick pattern recognition outputs long, short, or stand aside signals by calculating a composite score where bullish patterns add +1 and bearish patterns add -1. The final signal is determined by the sign of the total score.
To run technical analysis candlestick detection in Python, you need a pandas DataFrame containing open, high, low, and close (OHLC) columns, along with the numpy, pandas, and requests dependencies installed in your environment.
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