Generate Smart Money Concepts trading signals from OHLCV data using BOS/ChoCH and FVG filtering.
npx skills add https://github.com/wudye/traderAssistHK --skill smc-wudye
Please help me install this Agent Skill. Skill: smc Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/smc Command: npx skills add https://github.com/wudye/traderAssistHK --skill smc-wudye
This Skill helps you turn Smart Money Concepts (ICT) market structure rules into consistent, machine-generated trading signals so you can spot likely long/short setups and avoid manual chart interpretation.
1 (long), -1 (short), 0 (stand aside) from standardized OHLCV data.Ask the system to run the SMC signal engine on your OHLCV DataFrame map (symbol to DataFrame) and return per-symbol signals labeled as long, short, or stand aside.
High-intent search queries and answers about installing and using this skill.
You can generate Smart Money Concepts trading signals by feeding standardized OHLCV time series into an automated signal engine that computes BOS, ChoCH, and FVG conditions to output directional biases of 1, -1, or 0 per timestamp.
BOS (Break of Structure) indicates trend continuation, while ChoCH (Change of Character) signals a potential reversal; both are detected using swing high and low logic to define market structure shifts in OHLCV data.
FVG (Fair Value Gap) filtering aligns trading signals directionally by validating that same-side Fair Value Gap conditions are present before confirming a BOS or ChoCH directional bias output.
Yes, you can batch analyze multiple instruments by mapping symbols to individual OHLCV DataFrames, allowing the engine to process daily candles and generate per-symbol signal timelines for backtesting or scanning.
The signal engine requires Python OHLCV inputs with standard open, high, low, close, and volume columns to accurately compute swing highs, swing lows, and FVG conditions for backtesting.
Signals return 0 to indicate standing aside when BOS or ChoCH structure detection does not align with same-side FVG conditions, filtering out low-probability trade setups in your OHLCV time series.
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