smc liquidity sweep setup mapper
by nowrich
Maps price action to Smart Money Concepts (SMC) to generate structured trade setups and invalidation levels.
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Works with the AI tools you already use
See it in action
You say
Map the 4H EURUSD setup. Current price 1.1120. Last swing high 1.1150, swing low 1.1080. Bullish breakout just happened at 1.1150. Key levels at 1.1200 and 1.1100.
Your agent does
market_structure: trend: bullish bias: buy choch: {type: bullish, level: 1.1150} liquidity_zones: buy_side: [{level: 1.1200, type: equal_highs}] scenarios:
- name: Bullish OB Sweep entry_zone: [1.1085, 1.1110] stop_loss: 1.1080 invalidation: close below 1.1080
What you get
About this skill
The problem
Traders often struggle to objectively identify high-probability setups amidst messy price action, leading to inconsistent entries and poor risk management. Manual identification of market structure and liquidity zones is prone to emotional bias and missing critical invalidation points.
What it does
- Identifies Market Structure including Trends, Break of Structure (BOS), and Change of Character (CHoCH).
- Maps Buy-side and Sell-side Liquidity zones like equal highs, lows, and stop-hunt pools.
- Locates institutional Order Blocks based on impulse moves and liquidity sweeps.
- Generates structured trade scenarios with specific entry zones, targets, and hard invalidation levels.
- Outputs data in YAML format for easy integration into trading journals or execution bots.
Frameworks & tools
Price action analysis, Smart Money Concepts (SMC), OHLC data processing.
Why this beats prompting it yourself
Generic prompts often hallucinate support levels or fail to distinguish between a simple retracement and a CHoCH. This skill enforces a strict logical sequence—structure first, then liquidity, then order blocks—ensuring every trade scenario is anchored by a specific invalidation price.
Use cases
- Automating the technical analysis phase of a daily trading routine.
- Backtesting SMC strategies by generating consistent setup maps from historical data.
- Defining objective risk parameters for algorithmic execution.
- Filtering out low-probability trades that lack a clear liquidity sweep.
Known limitations
Requires clean OHLC or candlestick data as input. Performance depends on the quality and timeframe of the price data provided.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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