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layout: default title: “Quantitative Trading Academy” description: “Structured learning paths to become a Quant Trader” —

Quantitative Trading Academy

Learning Paths

Fundamentals (Beginner)

For those starting from scratch in quantitative trading

Module 1: Introduction to Quantitative Trading

  • What Is Being a Quant? - Basic concepts and philosophy
  • Essential mathematics - Basic statistics and probability
  • Python for traders - Setup and first steps
  • Market data - How to obtain and handle data

Module 2: Technical Analysis with Python

  • Technical indicators - RSI, MACD, Moving Averages
  • Price patterns - Automatic detection
  • Data visualization - Professional charts
  • First strategy - Simple crossover system

Strategies (Intermediate)

Developing profitable strategies

Module 3: Classic Strategies

  • Momentum Trading - Trend following
  • Mean Reversion - Range trading
  • Pairs Trading - Statistical arbitrage
  • Market Making - Liquidity provision

Module 4: Professional Backtesting

  • Backtesting framework - Robust construction
  • Performance metrics - Sharpe, Sortino, Calmar
  • Walk-forward analysis - Temporal validation
  • Monte Carlo simulation - Robustness analysis

Advanced Analysis (Advanced)

Professional techniques and machine learning

Module 5: Machine Learning for Trading

  • Feature engineering - Creating predictive variables
  • Supervised models - Random Forest, XGBoost
  • Deep Learning - LSTM for time series
  • Reinforcement Learning - Trading agents

Module 6: Risk Management

  • Value at Risk (VaR) - Risk measurement
  • Kelly Criterion - Optimal position sizing
  • Portfolio optimization - Markowitz and Black-Litterman
  • Stress testing - Scenario analysis

Professional Trading (Expert)

Production implementation

Module 7: Trading Infrastructure

  • System architecture - Scalable design
  • Broker connections - IBKR, Alpaca APIs
  • Order execution - Smart order routing
  • Real-time monitoring - Dashboards and alerts

Module 8: Institutional Trading

  • Market microstructure - Market mechanics
  • High-frequency trading - Concepts and strategies
  • Regulatory compliance - Regulations and reporting
  • Fund management - Professional capital management

Practical Projects

Projects by Level

Beginner

  1. Stock Scanner - Find opportunities automatically
  2. Alert Bot - Trading signal notifications
  3. Portfolio Dashboard - Visualize your performance

Intermediate

  1. Complete Trading System - End-to-end with backtesting
  2. Parameter Optimizer - Automatic strategy tuning
  3. Paper Trading Bot - Test strategies risk-free

Advanced

  1. Crypto Arbitrage Bot - Trading between exchanges
  2. Options Market Maker - Options liquidity provision
  3. Multi-Strategy System - Strategy portfolio

Expert

  1. HFT Simulator - Simulate high-frequency trading
  2. Risk Management System - Professional risk system
  3. Full Trading Platform - Complete trading platform

Additional Resources

  • “Quantitative Trading” - Ernest Chan
  • “Advances in Financial Machine Learning” - Marcos Lopez de Prado
  • “Trading Systems” - Emilio Tomasini
  • “Inside the Black Box” - Rishi Narang

Free Datasets

  • Yahoo Finance - Historical data
  • Alpha Vantage - Free API
  • Quandl - Economic data
  • Polygon.io - Real-time data

Essential Tools

  • Python: pandas, numpy, matplotlib
  • Backtesting: backtrader, zipline, vectorbt
  • Machine Learning: scikit-learn, tensorflow
  • Broker APIs: ib_insync, alpaca-trade-api

Certification and Assessment

Self-Assessment by Level

  • Beginner: Basic concepts quiz
  • Intermediate: Implement a profitable strategy
  • Advanced: Develop a complete system
  • Expert: Manage a portfolio in paper trading

Next Step

Ready to begin? Select your level and start your journey:

“The stock market is a device for transferring money from the impatient to the patient.” - Warren Buffett

Start Your Quant - Your path to professional algorithmic trading.