M2Quant


A Claude Code Skill for Global Liquidity Quantitative Analysis.

Overview

M2Quant is a quantitative analysis system that tracks global liquidity indicators and generates trading signals for core assets. It analyzes Federal Reserve balance sheet data, Treasury General Account (TGA), and Overnight Reverse Repo (RRP) to quantify money liquidity, then compares against Gold, Nasdaq, and Bitcoin using a "scissors factor" methodology.

Features

- Real-time Liquidity Analysis: Fetches and processes Fed liquidity indicators from FRED
- Multi-Asset Coverage: Tracks Gold, Nasdaq, and Bitcoin against liquidity
- Scissors Factor: Proprietary metric comparing liquidity growth vs asset price growth
- Trading Signals: Generates Buy/Hold/Sell recommendations based on quadrant analysis
- Professional Reports: Clean, formatted output for analysis review
- Standalone Runner: Execute analysis without Claude Code CLI

What's New in v1.2.0

- npm Distribution: Install via npm install @m2quant/claude-skill
- skills.sh Integration: One-click installation at skills.sh
- Cross-Platform Support: Auto-installs to Claude Code, Cursor, and Codex agents

See CHANGELOG.md for full details.

Installation

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Visit skills.sh and search for m2quant.

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``bash
npm install @m2quant/claude-skill
`

The skill will be automatically installed to your AI agent's skill directory.

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Prerequisites:
- Python 3.10+
- Claude Code CLI (optional, for skill usage)
- FRED API key (free from FRED)

Steps:

1. Clone the repository:
`bash
git clone https://github.com/Kampter/M2Quant.git
cd M2Quant
`

2. Create and activate virtual environment:
`bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
`

3. Install dependencies:
`bash
pip install -r requirements.txt
`

4. Configure API key:
`bash
cp config/api_keys.example.json config/api_keys.json


Edit config/api_keys.json with your FRED API key


`

Usage

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`bash


Full analysis with detailed report

claude /m2quant

View liquidity conditions only

claude /m2quant liquidity

View trading signals only

claude /m2quant signal

Generate complete report

claude /m2quant report
`

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Run analysis directly without Claude Code:

`bash


Full analysis (default)

python scripts/run_analysis.py

Liquidity data only

python scripts/run_analysis.py liquidity

Trading signals only

python scripts/run_analysis.py signal
`

Data Sources

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| Series | Description | Frequency |
|--------|-------------|-----------|
| WALCL | Federal Reserve Total Assets | Weekly |
| WTREGEN | Treasury General Account | Weekly |
| RRPONTSYD | Overnight Reverse Repo | Daily |
| M2SL | M2 Money Supply | Monthly |

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| Symbol | Description |
|--------|-------------|
| GC=F | Gold Futures |
| ^IXIC | Nasdaq Composite |
| BTC-USD | Bitcoin |

Core Methodology

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`
Net_Liquidity = WALCL - TGA - RRP
`

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`
Scissors = Liquidity_YoY% - Asset_Price_YoY%
`

The scissors factor measures the divergence between liquidity growth and asset price appreciation, helping identify over/undervalued conditions.

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Signals are generated using a weighted composite:
- Regime Signal (50%): Based on liquidity regime and scissors factor
- Threshold Signal (30%): Based on deviation from liquidity-implied value
- Momentum Signal (20%): Based on liquidity vs price momentum divergence

Project Structure

`
M2Quant/
├── .claude/ # Claude Code configuration
├── skills/ # Claude Code Skills
│ ├── m2quant/ # Main orchestration skill
│ │ ├── SKILL.md # Skill definition with frontmatter
│ │ ├── reference.md # Methodology documentation
│ │ └── examples/ # Example outputs
│ ├── fetch-liquidity/ # FRED data fetching
│ ├── fetch-prices/ # Yahoo Finance fetching
│ ├── calculate-factors/
│ ├── generate-signals/
│ └── generate-report/
├── src/ # Python modules
│ ├── __init__.py # Version: 1.2.0
│ ├── data_fetcher.py
│ ├── factor_engine.py
│ ├── signal_generator.py
│ └── report_generator.py
├── scripts/ # Standalone scripts
│ └── run_analysis.py # Direct execution runner
├── config/ # Configuration files
├── data/ # Data cache (gitignored)
└── reports/ # Generated reports (gitignored)
`

Configuration

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`json
{
"fred_api_key": "YOUR_FRED_API_KEY"
}
`

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`json
{
"buy_threshold": -1.5,
"sell_threshold": 1.5,
"strong_buy_threshold": -2.0,
"strong_sell_threshold": 2.0,
"zscore_window": 60,
"momentum_window": 20
}
`

Skill Context Costs

| Command | Context Cost | Use When |
|---------|--------------|----------|
| /m2quant | High | Full analysis needed |
| /m2quant liquidity | Low | Quick liquidity check |
| /m2quant signal` | Medium | Just need trading signals |

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE for details.

Disclaimer

This analysis is for informational purposes only. Not financial advice. Past performance does not guarantee future results.

Author

Created as a Claude Code Skill for quantitative analysis.