Identify 15 classic candlestick patterns in OHLCV data with pandas.
npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill candlestick-0xzknw
Please help me install this Agent Skill. Skill: candlestick Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent/src/skills/candlestick Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill candlestick-0xzknw
This Skill solves the challenge of manually identifying complex market trends by providing a high-performance, automated engine that detects 15 classic candlestick patterns across large datasets.
Use the candlestick skill to analyze the provided OHLCV data and generate trading signals for the specified assets.
High-intent search queries and answers about installing and using this skill.
You can detect candlestick patterns across large datasets using a vectorized pandas implementation. This engine identifies 15 classic patterns and generates bullish or bearish trading signals at high speed, scanning hundreds of assets simultaneously for trend reversals.
For technical analysis, you can identify 15 classic candlestick patterns including 5 single-candle, 5 double-candle, and 4 triple-candle configurations. These patterns generate automated bullish or bearish trading signals based on historical price action.
To generate trading signals from OHLCV data using pandas, apply a vectorized pattern recognition engine. It processes financial time-series data to output automated bullish or bearish signals, satisfying requirements for high-speed market trend detection.
Yes, you can scan hundreds of crypto pairs simultaneously for candlestick patterns. The vectorized pandas implementation enables rapid signal generation across large OHLCV datasets, making it suitable for high-speed automated market trend detection.
Yes, the candlestick pattern recognition engine requires numpy and requests, along with pandas. These dependencies support the vectorized calculations and data fetching needed to process financial time-series data for trading signal generation.
The best way to automate market trend detection with pandas is using a vectorized implementation. This approach identifies 15 classic candlestick patterns across financial time-series data, rapidly generating trading signals without manual chart analysis.
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