Algo Trading Course

Algo Trading Course – Syllabus

1. Financial Markets & Algo Trading Basics

• Indian markets overview: NSE, BSE, MCX
• Asset classes: Equity, F&O, Commodity
• What is Algorithmic Trading? (types & history)
• SEBI regulations (latest rules)
• Order types: Market, Limit, SL, GTT
• Market microstructure (order book, bid-ask)
• Trade lifecycle (order → execution → settlement)
• Execution strategies: VWAP, TWAP
• Mini Project: Trade lifecycle mapping

2. Python for Trading (Beginner to Practical)

• Python setup: Anaconda, Jupyter Notebook
• Variables, data types, loops, functions
• NumPy for calculations
• Pandas for data handling
• Data visualization: Matplotlib, Plotly
• Live market data fetching (yfinance, NSEpy)
• Time series data handling
•  Mini Project: Nifty 50 chart plotting

3. Technical Analysis for Algorithms

• Moving averages: SMA, EMA, WMA
• Indicators: RSI, MACD, Bollinger Bands
• Advanced indicators: ATR, ADX, Stochastic
• Support & Resistance (auto detection)
• Candlestick pattern automation
• Volume indicators: OBV, VWAP
• Signal generation logic (buy/sell)
• TA-Lib introduction
• Mini Project: EMA crossover strategy

4. Strategy Building (Core Trading Logic)

• Strategy structure: Entry, Exit, Position sizing
• Trend-following strategies
• Mean reversion strategies
• Momentum trading strategies
• Intraday vs Swing vs Positional
• Risk-reward & trade management
• Data-driven trading rules
• Strategy coding in Python
•  Mini Project: RSI Mean Reversion Strategy

5. Backtesting & Performance Analysis

• Importance of backtesting
• Vectorized vs event-driven systems
• Building backtester in Pandas
• Slippage, transaction cost, market impact
• Performance metrics: CAGR, Sharpe Ratio, Drawdown
• Bias handling: look-ahead, survivorship
• Walk-forward & out-of-sample testing
• Tools: Backtrader, AlgoTest
•  Mini Project: MA crossover backtest

6. Risk Management & Position Sizing

• Capital protection strategies
• Position sizing: Fixed vs Kelly Criterion
• Stop-loss strategies (fixed, trailing, ATR-based)
• Portfolio diversification & correlation
• Value-at-Risk (VaR)
• Drawdown control techniques
• SEBI risk guidelines
• Hedging using Futures & Options
•  Mini Project: Risk-based position sizing tool

7. Broker API & Live Trading Integration

• Zerodha Kite Connect API
• Angel One SmartAPI
• Upstox API integration
• Real-time data using WebSockets
• Order execution via API
• Bracket, Cover & GTT orders
• Error handling & reconnection
• TradingView webhook integration
•  Live Project: Signal → Order automation

8. Options Trading Strategies (Algo Based)

• Options Greeks: Delta, Gamma, Theta, Vega
• Strategy building: Straddle, Strangle
• Advanced strategies: Iron Condor, Iron Butterfly
• Weekly options selling (Nifty/BankNifty)
• India VIX & volatility analysis
• Delta-neutral strategies
• Option chain analysis using Python
• Tools: Sensibull, Opstra
•  Mini Project: Automated Iron Condor

9. Machine Learning in Trading

• Feature engineering from market data
• Logistic Regression for signals
• Random Forest models
• Overfitting & cross-validation
• Introduction to LSTM (concept)
• Model evaluation for trading

10. Deployment & Automation

• Scheduling bots (Cron, APScheduler)
• VPS deployment (24/7 trading systems)
• AWS EC2 setup
• Telegram alerts integration
• Logging & monitoring systems

11. Compliance & Regulations

• SEBI algo trading rules
• Audit trail requirements
• Algo registration basics
• Risk compliance checklist

12. Capstone Project

• Design full trading strategy
• Backtest with real market data
• Optimize & validate strategy
• Deploy live trading system
• Monitor & improve performance