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smc

Analyze OHLCV market data to detect BOS, ChoCH, and FVG signals.

Updated Jun 30, 2026
Search Tags:#technical analysis#trading#crypto#institutional#market structure#smc
One-click install
npx skills add https://github.com/20YN04/vibe-trading-macos --skill smc-20yn04
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: smc
Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/smc
Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill smc-20yn04

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the difficulty of identifying institutional trading patterns like liquidity sweeps and structural shifts, which are often missed by standard technical indicators.

Core Features & Use Cases

  • Structural Analysis: Automatically detects Break of Structure (BOS) and Change of Character (ChoCH) to identify trend continuations and reversals.
  • Institutional Filtering: Uses Fair Value Gaps (FVG) to filter signals, ensuring trades align with market imbalances.
  • Use Case: A trader can use this to scan multiple crypto assets for high-probability entry points based on institutional order flow rather than lagging moving averages.

Quick Start

Use the smc skill to analyze the latest market data for BTC-USDT and identify current structural signals.

Frequently Asked Questions about smc

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

FAQPage Schema
How do I detect Break of Structure and Change of Character in crypto market data?▼

To detect Break of Structure (BOS) and Change of Character (ChoCH) in crypto market data, you can process OHLCV data streams to identify trend continuations and structural reversals using the smartmoneyconcepts library.

What are Fair Value Gaps and how do they filter institutional trading signals?▼

Fair Value Gaps (FVG) represent price imbalances used to filter institutional trading signals. By scanning OHLCV data for FVGs, you ensure structural breakouts align with market imbalances instead of lagging technical indicators.

Can I use pandas and numpy for institutional market structure analysis?▼

Yes, you can use pandas and numpy for institutional market structure analysis. Processing OHLCV data streams to identify structural breaks and price imbalances requires both libraries to handle the underlying quantitative data operations.

How do I scan multiple crypto assets for high-probability entry points based on order flow?▼

To scan multiple crypto assets for high-probability entry points based on order flow, analyze OHLCV market data to identify institutional patterns like liquidity sweeps, structural shifts, and Fair Value Gaps.

Does standard technical analysis miss liquidity sweeps and structural shifts?▼

Standard technical analysis often misses liquidity sweeps and structural shifts because it relies on lagging moving averages. Institutional market structure analysis overcomes this by directly detecting structural breaks and price imbalances in OHLCV data.

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