smc

Name: smc
Availability: InStock
Author: hxhyyy

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

Updated Jul 8, 2026

Search Tags:#finance #technical analysis #trading #smart money concepts #market structure #signal engine

Ready to run

npx skills add https://github.com/hxhyyy/Vibe-Trading --skill smc-hxhyyy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: smc
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/smc
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill smc-hxhyyy

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 market behavior, such as liquidity zones and trend reversals, by automating the detection of complex price action patterns.

Core Features & Use Cases

Structural Analysis: Automatically identifies Break of Structure (BOS) and Change of Character (ChoCH) signals.
Liquidity & Imbalance Detection: Pinpoints Fair Value Gaps (FVG) and Order Blocks (OB) to filter high-probability trade setups.
Use Case: A trader can use this to scan multiple assets for institutional-grade entry signals, filtering out noise by requiring both structural confirmation and FVG alignment.

Quick Start

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

Frequently Asked Questions about smc

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

FAQPage Schema

How do I detect smart money concepts like BOS and ChoCH in market structure?▼

To detect smart money concepts like BOS and ChoCH, you can automate market structure analysis using OHLCV data streams to identify institutional trading patterns and trend reversals. This Skill filters price action signals based on structural shifts.

How do I find Fair Value Gaps and Order Blocks in OHLCV price data?▼

Finding Fair Value Gaps and Order Blocks in OHLCV price data requires liquidity and imbalance detection logic. This Skill pinpoints these zones to filter high-probability trade setups by requiring both structural confirmation and FVG alignment.

Can I use Python to scan multiple assets for institutional trading signals?▼

Yes, you can use Python to scan multiple assets for institutional trading signals by integrating pandas and numpy with market data. This enables filtering out market noise to identify institutional-grade entry signals across various financial assets.

Do I need the smartmoneyconcepts library to identify market structure shifts?▼

Yes, you need the smartmoneyconcepts library to identify market structure shifts. The Skill requires integration with this specific dependency alongside OHLCV data streams to execute its institutional signal generation logic accurately.

What is the best way to filter price action signals using institutional liquidity zones?▼

The best way to filter price action signals using institutional liquidity zones is to require structural confirmation from BOS or ChoCH alongside Fair Value Gap alignment. This approach removes noise by focusing on institutional liquidity and structural shifts.