AlgoTrader: Quantitative Trading Skill for Claude Code

Comprehensive trading expert embodying real-world learnings from Indian equity markets

Overview

AlgoTrader is a Claude Code skill that provides expert guidance for building, optimizing, and running quantitative trading systems on Indian stock markets. It embodies 1,780 lines of real-world learnings from production trading, including:

• ✅ 65%+ win rate signal generation strategies
• ✅ 28x performance optimizations (Parquet caching, vectorization)
• ✅ Zero-regression code modifications with automated testing
• ✅ Production failure prevention (30+ gotchas documented)
• ✅ Backtest-live parity validation
• ✅ Risk-adjusted capital compounding

Installation

Quick Install (Recommended)

Install directly from the skills.sh directory:

npx skills add javajack/skill-algotrader

Manual Installation

Option 1: Clone to Claude Skills Directory

cd ~/.claude/skills
git clone https://github.com/javajack/skill-algotrader.git algotrader
cd algotrader
./start.sh wizard  # Start using the skill

Option 2: Custom Skills Path

export CLAUDE_SKILLS_PATH=~/work/skills
cd ~/work/skills
git clone https://github.com/javajack/skill-algotrader.git algotrader
cd algotrader
./start.sh wizard

Install Python Dependencies

After installation, set up the Python environment:

cd ~/.claude/skills/algotrader  # or your custom path
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Or use the convenience script (auto-creates venv)
./start.sh wizard

Features

🎯 Interactive Bot Generation Wizard

Launch /algotrader without parameters to enter the wizard:

$ /algotrader

╔══════════════════════════════════════════════════════════════╗
║              ALGOTRADER BOT GENERATION WIZARD                ║
╚══════════════════════════════════════════════════════════════╝

Scanning current directory for trading code...
✓ Found: backtest.py, signal_generator.py

What would you like to do?
  1. Generate new trading bot from scratch
  2. Enhance existing code (fix issues, optimize)
  3. Create universe JSON from live index data
  4. Run backtest comparison
  5. Analyze performance

> 1

Let's design your trading bot. I'll ask a few questions...

The wizard will:

• Scan your folder for existing trading code
• Ask strategic questions (trade type, universe, capital, risk tolerance)
• Generate a complete, working trading bot
• Create universe JSON files with latest index constituents
• Set up logging, analytics, and risk management

📊 Universe Fetcher (Live Index Data)

Automatically fetches latest index constituents from NSE:

/algotrader universe

Fetching live index data from NSE...
✓ Nifty 50: 50 stocks (updated: 2026-02-14)
✓ Nifty 100: 100 stocks
✓ Nifty Midcap 150: 150 stocks
✓ Nifty Smallcap 250: 250 stocks

Created:
  └─ universe/
     ├─ nifty50.json (50 stocks, ₹500Cr+ mcap)
     ├─ nifty100.json (100 stocks)
     ├─ midcap150.json (150 stocks, ₹50-500Cr mcap)
     └─ smallcap250.json (250 stocks, ₹10-50Cr mcap)

Each file includes:
  - Symbol, company name, ISIN
  - Market cap, sector
  - Liquidity metrics (avg volume, spread)
  - Last updated timestamp

🧠 16 Knowledge Domains