Identify 15 classic candlestick patterns in OHLCV dataframes and generate trading signals.
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill candlestick-hxhyyy
Please help me install this Agent Skill. Skill: candlestick Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/candlestick Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill candlestick-hxhyyy
This skill automates the identification of complex technical trading patterns, removing the need for manual chart analysis and enabling rapid, data-driven decision-making.
Use the candlestick skill to analyze the provided OHLCV data and generate trading signals for the specified asset list.
High-intent search queries and answers about installing and using this skill.
To identify candlestick patterns in a pandas dataframe, you can use a vectorized pattern recognition engine that processes OHLCV data to detect 15 classic single, double, and triple-candle formations. It outputs aggregated bullish and bearish scores for directional trading signals.
Vectorized candlestick pattern recognition signals are aggregated bullish and bearish scores derived from 15 classic candlestick formations. Processing OHLCV dataframes without manual loops, the mechanism outputs clear long, short, or neutral directional indicators for market analysis.
Yes, you can scan large datasets of historical price action for multiple assets simultaneously. The vectorized pandas implementation processes bulk OHLCV dataframes efficiently, enabling rapid, data-driven identification of trend reversals or confirmations across specified asset lists.
Yes, the candlestick pattern recognition tool requires numpy and requests alongside pandas. These dependencies support the pure pandas vectorized calculations and potential data fetching operations needed to process OHLCV data and generate composite trading signals.
The best way to generate technical trading signals from OHLCV data is using a vectorized pandas implementation that identifies 15 classic candlestick patterns. This approach automates complex pattern detection, outputting aggregated bullish and bearish scores for rapid decision-making.
While pandas vectorized operations offer high-performance technical analysis for 15 candlestick patterns, limitations include the strict requirement for properly formatted OHLCV dataframes. Complex or non-standard market data structures may require preprocessing before pattern recognition can execute successfully.
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