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smc

Identify BOS, ChoCH, and FVG signals from OHLC data.

Updated Apr 14, 2026
Search Tags:#fvg#bos#choch#smc#smartmoneyconcepts#order blocks
One-click install
npx skills add https://github.com/Liangwei-zhang/six-stock --skill smc-liangwei-zhang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: smc
Source: https://github.com/Liangwei-zhang/six-stock/tree/main/Vibe-Trading/agent/src/skills/smc
Command: npx skills add https://github.com/Liangwei-zhang/six-stock --skill smc-liangwei-zhang

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires smartmoneyconcepts, pandas, numpy, requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This engine helps traders identify institutional-driven market moves by analyzing core ICT concepts: Break of Structure (BOS), Change of Character (ChoCH), Fair Value Gaps (FVG), and Order Blocks (OB). It converts raw OHLCV data into actionable signals, reducing manual analysis time and improving decision quality.

Core Features & Use Cases

  • Detection of BOS and ChoCH to indicate trend continuation or reversal
  • FVG-based filtering to validate or prune signals
  • OB / order block recognition with historical context for potential reaction zones
  • Configurable swing_length and close_break to adapt to different timeframes
  • End-to-end signal generation for crypto and equities with minimal setup

Quick Start

Create a SignalEngine with desired swing_length and run generate on OHLCV data to obtain position signals.

Frequently Asked Questions about smc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify BOS and ChoCH signals from OHLC data?▼

Filter FVG signals by applying configurable swing_length and close_break parameters to OHLCV data, validating or pruning institutional trading signals based on fair value gap detection.

What is the best way to detect order blocks in crypto and equities time series?▼

Use the smartmoneyconcepts library with pandas and numpy to process OHLCV data, requiring only a configured SignalEngine to generate actionable market structure signals.

Does this smart money concepts engine support configurable swing length for different timeframes?▼

Apply the engine to both crypto and traditional market time series, generating long, short, or neutral positions with optional closing-break filtering for institutional moves.

Can I use pandas and numpy to analyze Fair Value Gaps in OHLCV data?▼

Limitations include dependency on the smartmoneyconcepts library and raw OHLCV data input, meaning signals may require additional context for optimal execution in volatile markets.

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