🇪🇸 Leer en Español | 🇺🇸 English

🎓 Start Your Quant - Your First Steps into Quantitative Trading

From zero to professional quant trader with practical, progressive modules.

Learn quantitative trading in a structured way, at your own pace, with real examples and hands-on exercises. From basic concepts to institutional strategies.

🌟 Completely free and open source - Available as GitHub Pages

⚠️ Disclaimer

This repository reflects my personal learning journey in quantitative trading. The ideas and concepts come from my experience studying courses, attending seminars and talks, reading books, and practicing in the markets. For several academic and technical topics, I used AI tools (Perplexity, Claude, ChatGPT) to help me develop and articulate my thoughts more completely — but the core ideas, structure, and trading perspective are my own.

It's a starting point, not an absolute truth.

A note about language: My native language is Spanish, so you may find occasional grammatical errors or awkward phrasing in the English content. If you spot anything that could be improved — whether it's a translation issue, a technical correction, or an expansion on any topic — please don't hesitate to send a pull request. Question the content — the idea is for this to be a useful foundation that grows with community contributions.

If this repo helped you and you think your comment, observation, or contribution could help me or others, send it without hesitation. I'd be more than grateful.

🚀 Where Do I Start?

| Your Level | Start Here | Time | |----------|--------------|--------| | Complete beginner | 🎯 What Is Being a Quant? | 2-4 months | | I know some Python | 🐍 Basic Python Trading | 1-3 months | | I already trade manually | 📊 Technical Indicators | 1-2 months | | I want my first strategy | 🤖 First Strategy | Immediate | | Advanced developer | 🏗️ Infrastructure | Immediate |

Not sure where to start? → 📖 Getting Started Guide | 📚 Learning Path

📚 Table of Contents

Core Concepts

• Introduction - What is quantitative trading and why it works
• Risk Management - Systematic risk control frameworks
• Performance Metrics - Key metrics for strategy evaluation
• Financial Instruments - Types of instruments and their characteristics
• History of Algo Trading - Evolution of algorithmic trading
• Cognitive Biases in Algo Trading - How biases affect your decisions
• Compound Growth and Risk - The mathematics of sustainable growth
• Expected Growth Metrics - Hierarchy of performance metrics
• Trading Plan Framework - How to structure your trading plan
• Trading Systems Anatomy - Components of a trading system

Technical Practices

• Strategy Development - Systematic approach to strategy creation
• Backtesting - Three Levels - Backtesting methodologies by complexity level
• Common Backtesting Errors - Common pitfalls and how to avoid them
• Data Quality and Adjustments - Cleaning and preparing financial data
• Filters and Asset Selection - Practical filtering examples
• KISS Design Principles - Keeping strategies simple and effective
• Scientific Method in Trading - Applying the scientific method to system development
• Entry and Exit Structure - Designing entry and exit rules

Advanced Topics

• Alternative Data - Non-traditional data sources
• Portfolio Optimization - Applying modern portfolio theory
• Dynamic Position Sizing - Dynamic adjustment of position size
• Execution Algorithms - Order execution optimization
• Regime Detection - Identifying market regime changes

Detailed Documentation (docs/)

| Category | Content | |-----------|-----------| | Fundamentals | What is quant, types of strategies, discretionary vs quantitative | | Setup | Broker configuration, data providers, getting started | | Strategies | Gap & Go, VWAP Reclaim, Low Float Runners, First Green/Red Day, Short Selling | | Indicators | Moving Averages, VWAP, Bollinger Bands, Parabolic SAR, Gap/Float, Volume | | Backtesting | Simple engine, metrics, overfitting, walk-forward analysis | | Risk | Position sizing, stops, portfolio risk, asymmetric risk | | Data | Data sources, cleaning, real-time data, backtesting datasets | | Analysis | ML, sentiment, microstructure, fundamental analysis, transformers | | Automation | System architecture, robo-advisors, automation strategies | | Tools | Essential libraries, advanced infrastructure, production deployment | | Compliance | Regulatory frameworks, ethical AI in trading | | Validation | Strategy testing, model evaluation, institutional considerations | | Quick Reference | Key concepts cheat sheet |

Templates and Scripts

• Strategy Templates - Ready-to-use frameworks (momentum, mean-reversion)
• Metrics Calculators - Sharpe ratio, max drawdown, profit factor

Real-World Examples

• IBKR Premarket Trader - Gap trading system for small caps

Infrastructure

• Trading Stack - Docker, Kubernetes, monitoring, data pipelines

Reference Source Code (src/)

src/
├── indicators/       # MovingAverages (SMA/EMA/WMA), VWAP with bands
├── strategies/       # Gap and Go strategy
├── backtesting/      # Simple engine + CSV export (TraderVue/TradesViz)
├── risk/             # Position sizing (fixed, Kelly, ATR, risk parity)
├── data/             # Data interfaces (yfinance, Alpha Vantage)
└── examples/         # Complete integrated example

🛠️ Quick Start for Developers

# Clone the repository
git clone https://github.com/jefrnc/start-your-quant.git
cd start-your-quant
Install minimum dependencies for the src/ examples
pip install pandas numpy matplotlib requests
Run the integrated example
cd src/examples && python completestrategyexample.py
Metrics calculators
python scripts/strategy-metrics/sharpe-calculator/calculate_sharpe.py
python scripts/strategy-metrics/max-drawdown/calculate_drawdown.py
python scripts/strategy-metrics/profit-factor/calculateprofitfactor.py

Local Site Preview (Jekyll)

bundle install
bundle exec jekyll serve
Site at http://localhost:4000/start-your-quant/

🛠️ Tech Stack

• Python as the main language
• Pandas/NumPy for data manipulation
• Plotly/Matplotlib for visualization
• yfinance/Polygon for market data
• IBKR TWS for execution
• Jekyll for the GitHub Pages site
• Docker/Kubernetes for infrastructure

🤝 Contributing

Any improvement, correction, or observation is greatly appreciated:

• 🐛 Report bugs in implementations or documentation
• 💡 Suggest improvements to strategies or methodologies
• 📚 Propose new content based on your experience
• 🔧 Optimize existing code or add new features

📚 Recommended Additional Resources

Courses and Educational Material

• Quantitative Trading in Python - Complete course with 15+ practical modules (broker integration, applied ML, sentiment analysis). Material that inspired several implementations in this repository.

Tools and Platforms

• QuantConnect - Cloud backtesting with institutional data
• Backtrader - Python backtesting framework
• TradingView - Technical analysis and alerts

Essential Books

• "Quantitative Trading" - Ernest Chan
• "Algorithmic Trading" - Ernie Chan
• "A Man for All Markets" - Edward Thorp

Communities

• QuantStart - Technical articles on quantitative trading
• r/algotrading - Active community of algorithmic traders

APIs and Data Providers

• Alpha Vantage - Free API
• Polygon.io - Real-time and historical data

⚠️ Risk Disclaimer: This content is purely educational. Trading involves risk of loss. Always trade with proper risk management.

Support this project

Free, and maintained on my own time. Sponsorship covers the market data and broker API access behind these tools, and keeps them working when brokers change their export formats.