AI for Algorithmic Trading Full Syallabus

Module 1: Introduction to Financial Markets & Algo Trading

• What is algorithmic trading?
• Evolution of AI in finance
• How hedge funds & institutions use AI
• Market types: Stocks, Forex, Crypto, Derivatives
• Types of strategies: Arbitrage, Momentum, Mean Reversion, etc.

Module 2: Tools & Environment Setup

• Python for trading: Pandas, NumPy, Scikit-learn
• Trading platforms: MetaTrader, QuantConnect, Alpaca, Interactive Brokers
• APIs: Yahoo Finance, Alpha Vantage, Binance API
• Jupyter Notebook setup for backtesting

Module 3: Basics of Quantitative Trading

• Time series data basics (OHLCV)
• Returns, log returns, cumulative returns
• Risk vs reward, Sharpe ratio
• Technical indicators: SMA, RSI, MACD, Bollinger Bands

Module 4: Data Collection & Preprocessing

• Fetching live and historical market data
• Cleaning missing or noisy data
• Feature engineering: Lag features, rolling windows
• Labeling data for ML models (Buy, Hold, Sell)

Module 5: Machine Learning in Trading

• Classification models: SVM, Logistic Regression, Random Forest
• Regression models for price prediction
• Model evaluation: Accuracy, Precision, Recall, F1-score
• Cross-validation with time series split

Module 6: Deep Learning for Market Prediction

• LSTM for time series forecasting
• CNNs for pattern recognition in candlestick charts
• Autoencoders for anomaly detection
• Attention-based transformers in finance

Module 7: Sentiment Analysis with NLP

• News headlines & Twitter sentiment
• FinBERT, Vader, TextBlob for financial sentiment
• Predicting price movements using news
• Merging price + sentiment for hybrid models

Module 8: Backtesting Strategies

• Backtest trading signals with Backtrader / QuantConnect
• Avoiding lookahead bias and overfitting
• Walk-forward analysis
• Measuring drawdown and win rate

Module 9: Building Your Trading Bot

• Setting entry & exit rules based on AI predictions
• Position sizing & risk management
• Creating bots with Python or MetaTrader 5
• Deployment via cloud (AWS/GCP) or VPS

Module 10: Reinforcement Learning in Trading

• Intro to RL: Agent, Environment, Reward
• Q-learning, DQN, PPO for trading agents
• Train agents in simulated markets
• Use OpenAI Gym + FinRL environments

Module 11: Live Trading & Automation

• Connecting AI models to brokerage APIs
• Executing trades in real time
• Logging trades & performance metrics
• Setting up alerts (Telegram, Discord, Email)

Module 12: Ethics, Regulations & Risk

• Market manipulation & ethical AI
• SEBI, SEC, MiFID guidelines for algo trading
• Risk of overfitting & black-box models
• Responsible AI use in finance

Module 13: Datasets & Resources

• Datasets: Quandl, Kaggle, CryptoCompare, Yahoo Finance
• Books:
  • “Advances in Financial ML” – Marcos López de Prado
  • “AI in Finance” – Yves Hilpisch
• Communities: QuantInsti, Elite Trader, Quantocracy