Generate ICT-style Smart Money Concepts signals from OHLCV data.
npx skills add https://github.com/GGwujun/SigmX --skill smc-ggwujun
Please help me install this Agent Skill. Skill: smc Source: https://github.com/GGwujun/SigmX/tree/main/agent/src/skills/smc Command: npx skills add https://github.com/GGwujun/SigmX --skill smc-ggwujun
The Smart Money Concepts signal engine provides a disciplined framework to detect institutional trading signals (BOS, ChoCH, FVG, and order blocks) from market data and translate them into actionable long/short signals.
Instantiate SignalEngine with your preferred swing_length and close_break parameters, then feed it a map of symbol to OHLCV DataFrames to generate signals.
High-intent search queries and answers about installing and using this skill.
To generate Smart Money Concepts signals from OHLCV data, instantiate SignalEngine with parameters like swing_length and close_break, then pass a map of symbols to OHLCV DataFrames to produce long/short signal series.
In ICT-style trading signals, ChoCH indicates a change in market character, BOS confirms a break of structure, and FVG represents a fair value gap used to filter entries for robust long/short signal generation.
Yes, you can apply SMC signals to live crypto and stock market signaling. The engine processes OHLCV data across various assets to compute institutional signals suitable for real-time strategy validation.
Yes, the smartmoneyconcepts library is required alongside pandas, numpy, and requests to compute order blocks, FVG, ChoCH, and BOS signals from your financial time-series data.
You configure SMC signal generation by setting swing_length and close_break parameters when initializing the SignalEngine, which controls how market structure shifts are detected before feeding it OHLCV DataFrames.
Smart Money Concepts signal generation depends entirely on OHLCV data quality and the smartmoneyconcepts library, meaning it cannot detect institutional order flow without volume data or real-time order book depth.
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