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smart-money-concepts

Convert OHLCV market data into BOS and ChoCH trading signals with FVG filters.

Updated Apr 14, 2026
Search Tags:#backtesting#trading signals#smart money concepts#institutional trading#bos choch fvg#ohlcv analysis
One-click install
npx skills add https://github.com/loanntc/Paave --skill smart-money-concepts
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: smart-money-concepts
Source: https://github.com/loanntc/Paave/tree/main/skills/smc
Command: npx skills add https://github.com/loanntc/Paave --skill smart-money-concepts

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you turn market structure concepts (BOS, ChoCH) and liquidity/imbalance ideas (FVG) into actionable long/short/stand-aside signals, reducing manual chart interpretation.

Core Features & Use Cases

  • BOS / ChoCH Structure Detection: Detects break of structure and change of character as trend continuation or reversal candidates.
  • FVG Filtering: Applies fair value gap directionality to confirm or invalidate structural signals.
  • Signal Output for Backtesting: Produces a per-timestamp signal series (1 long, -1 short, 0 stand aside) suitable for strategy evaluation.
  • Use Case: Feed daily OHLCV data for BTC/ETH/SOL and backtest whether ChoCH-driven structure shifts that align with same-direction FVG lead to better trade outcomes.

Quick Start

Use the smart-money-concepts skill to generate signals for a mapping of symbols to OHLCV DataFrames with open/high/low/close/volume columns.

Frequently Asked Questions about smart-money-concepts

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

FAQPage Schema
How do I generate trading signals from OHLCV data using smart money concepts?▼

Yes, you can use the generated per-timestamp signal series for backtesting multiple instruments. The processor applies smart money concepts to batch OHLCV data, outputting discrete long, short, or stand-aside signals suitable for evaluating your trading strategies.

How does BOS and ChoCH detection work for identifying market structure shifts?▼

To start generating signals, provide a mapping of symbols to OHLCV DataFrames containing open, high, low, close, and volume columns with standardized datetime indices. The processor computes swing highs and lows to determine BOS, ChoCH, and FVG patterns automatically.

Does this smart money concepts processor require specific data formats for institutional trading analysis?▼

FVG filtering applies fair value gap directionality to confirm or invalidate structural BOS and ChoCH signals. This mechanism ensures that only structure shifts aligning with same-direction fair value gaps produce actionable long or short trading signals.

What are the limitations of using smartmoneyconcepts for signal generation?▼

The smartmoneyconcepts processor is specifically tailored for smart money concepts, detecting BOS, ChoCH, and FVG patterns directly from OHLCV data. Unlike generic analytical libraries, it outputs a discrete per-bar signal series optimized for institutional trading structure analysis.

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