{"id":"2b2a83a7-007d-4d37-9477-3950ad07a592","slug":"m2quant","kind":"skill","name":"M2Quant","summary":"Claude Code Skill for Global Liquidity Quantitative Analysis","owner":"Kampter","repoUrl":"https://github.com/Kampter/M2Quant","license":"MIT","licenseTier":"permissive","trust":"community","stars":1,"match":null,"fewStars":true,"format":"skill","createdAt":"2026-01-22T04:19:58.000Z","addedAt":"2026-10-21T22:27:15.008Z","logoUrl":"https://avatars.githubusercontent.com/u/44108557?u=3b9768f1a3b466897b1426355ed1cbf562902633&v=4","linkOnly":false,"collection":null,"skillPath":null,"categories":["developer-tools"],"tags":[],"runtime":"node","local":true,"cloudCapable":false,"cloudCapability":"unknown","cloudReadyOnPlatform":false,"needsApiKey":false,"reviewed":false,"syncedAt":"2026-10-27T23:35:09.886Z","sample":false,"url":"https://conduits.dev/tools/m2quant","language":"Python","transports":["stdio"],"install":{"npm":"@m2quant/claude-skill"},"requirements":{"env":[],"upstreamMayCharge":false},"tools":null,"readme":"# M2Quant\n\n[![Version](https://img.shields.io/badge/version-1.2.0-blue.svg)](https://github.com/Kampter/M2Quant/releases)\n[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\n[![Python](https://img.shields.io/badge/python-3.10+-yellow.svg)](https://python.org)\n\nA Claude Code Skill for Global Liquidity Quantitative Analysis.\n\n## Overview\n\nM2Quant 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.\n\n## Features\n\n- **Real-time Liquidity Analysis**: Fetches and processes Fed liquidity indicators from FRED\n- **Multi-Asset Coverage**: Tracks Gold, Nasdaq, and Bitcoin against liquidity\n- **Scissors Factor**: Proprietary metric comparing liquidity growth vs asset price growth\n- **Trading Signals**: Generates Buy/Hold/Sell recommendations based on quadrant analysis\n- **Professional Reports**: Clean, formatted output for analysis review\n- **Standalone Runner**: Execute analysis without Claude Code CLI\n\n## What's New in v1.2.0\n\n- **npm Distribution**: Install via `npm install @m2quant/claude-skill`\n- **skills.sh Integration**: One-click installation at [skills.sh](https://skills.sh)\n- **Cross-Platform Support**: Auto-installs to Claude Code, Cursor, and Codex agents\n\nSee [CHANGELOG.md](CHANGELOG.md) for full details.\n\n## Installation\n\n### Option 1: One-Click Install (Recommended)\n\nVisit [skills.sh](https://skills.sh) and search for `m2quant`.\n\n### Option 2: npm Install\n\n```bash\nnpm install @m2quant/claude-skill\n```\n\nThe skill will be automatically installed to your AI agent's skill directory.\n\n### Option 3: Manual Setup\n\n**Prerequisites:**\n- Python 3.10+\n- [Claude Code](https://github.com/anthropics/claude-code) CLI (optional, for skill usage)\n- FRED API key (free from [FRED](https://fred.stlouisfed.org/docs/api/api_key.html))\n\n**Steps:**\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/Kampter/M2Quant.git\ncd M2Quant\n```\n\n2. Create and activate virtual environment:\n```bash\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n```\n\n3. Install dependencies:\n```bash\npip install -r requirements.txt\n```\n\n4. Configure API key:\n```bash\ncp config/api_keys.example.json config/api_keys.json\n# Edit config/api_keys.json with your FRED API key\n```\n\n## Usage\n\n### Via Claude Code Skills\n\n```bash\n# Full analysis with detailed report\nclaude /m2quant\n\n# View liquidity conditions only\nclaude /m2quant liquidity\n\n# View trading signals only\nclaude /m2quant signal\n\n# Generate complete report\nclaude /m2quant report\n```\n\n### Via Standalone Script\n\nRun analysis directly without Claude Code:\n\n```bash\n# Full analysis (default)\npython scripts/run_analysis.py\n\n# Liquidity data only\npython scripts/run_analysis.py liquidity\n\n# Trading signals only\npython scripts/run_analysis.py signal\n```\n\n## Data Sources\n\n### Liquidity Indicators (FRED API)\n| Series | Description | Frequency |\n|--------|-------------|-----------|\n| WALCL | Federal Reserve Total Assets | Weekly |\n| WTREGEN | Treasury General Account | Weekly |\n| RRPONTSYD | Overnight Reverse Repo | Daily |\n| M2SL | M2 Money Supply | Monthly |\n\n### Asset Prices (Yahoo Finance)\n| Symbol | Description |\n|--------|-------------|\n| GC=F | Gold Futures |\n| ^IXIC | Nasdaq Composite |\n| BTC-USD | Bitcoin |\n\n## Core Methodology\n\n### Net Liquidity Formula\n```\nNet_Liquidity = WALCL - TGA - RRP\n```\n\n### Scissors Factor\n```\nScissors = Liquidity_YoY% - Asset_Price_YoY%\n```\n\nThe scissors factor measures the divergence between liquidity growth and asset price appreciation, helping identify over/undervalued conditions.\n\n### Signal Generation\n\nSignals are generated using a weighted composite:\n- **Regime Signal (50%)**: Based on liquidity regime and scissors factor\n- **Threshold Signal (30%)**: Based on deviation from liquidity-implied value\n- **Momentum Signal (20%)**: Based on liquidity vs price momentum divergence\n\n## Project Structure\n\n```\nM2Quant/\n├── .claude/              # Claude Code configuration\n├── skills/               # Claude Code Skills\n│   ├── m2quant/          # Main orchestration skill\n│   │   ├── SKILL.md      # Skill definition with frontmatter\n│   │   ├── reference.md  # Methodology documentation\n│   │   └── examples/     # Example outputs\n│   ├── fetch-liquidity/  # FRED data fetching\n│   ├── fetch-prices/     # Yahoo Finance fetching\n│   ├── calculate-factors/\n│   ├── generate-signals/\n│   └── generate-report/\n├── src/                  # Python modules\n│   ├── __init__.py       # Version: 1.2.0\n│   ├── data_fetcher.py\n│   ├── factor_engine.py\n│   ├── signal_generator.py\n│   └── report_generator.py\n├── scripts/              # Standalone scripts\n│   └── run_analysis.py   # Direct execution runner\n├── config/               # Configuration files\n├── data/                 # Data cache (gitignored)\n└── reports/              # Generated reports (gitignored)\n```\n\n## Configuration\n\n### API Keys (`config/api_keys.json`)\n```json\n{\n  \"fred_api_key\": \"YOUR_FRED_API_KEY\"\n}\n```\n\n### Signal Thresholds (`config/thresholds.json`)\n```json\n{\n  \"buy_threshold\": -1.5,\n  \"sell_threshold\": 1.5,\n  \"strong_buy_threshold\": -2.0,\n  \"strong_sell_threshold\": 2.0,\n  \"zscore_window\": 60,\n  \"momentum_window\": 20\n}\n```\n\n## Skill Context Costs\n\n| Command | Context Cost | Use When |\n|---------|--------------|----------|\n| `/m2quant` | High | Full analysis needed |\n| `/m2quant liquidity` | Low | Quick liquidity check |\n| `/m2quant signal` | Medium | Just need trading signals |\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nMIT License - see [LICENSE](LICENSE) for details.\n\n## Disclaimer\n\nThis analysis is for informational purposes only. Not financial advice. Past performance does not guarantee future results.\n\n## Author\n\nCreated as a Claude Code Skill for quantitative analysis.\n"}