Module #1 Introduction to Algorithmic Trading Overview of algorithmic trading, its benefits, and applications
Module #2 Quantitative Trading Strategies Exploring different quantitative trading strategies, including mean reversion, momentum, and statistical arbitrage
Module #3 Python for Algorithmic Trading Python basics, setting up a trading environment, and introduction to popular libraries (Pandas, NumPy, Matplotlib)
Module #4 Data Sources and Types Overview of different data sources (quandl, alpha vantage, etc.), data types, and formatting
Module #5 Data Preprocessing and Cleaning Handling missing values, data normalization, and feature engineering
Module #6 Time Series Analysis Introduction to time series analysis, stationary and non-stationary processes, and autocorrelation
Module #7 Statistical Arbitrage Theory and implementation of statistical arbitrage strategies
Module #8 Mean Reversion Strategies Theory and implementation of mean reversion strategies, including cointegration analysis
Module #9 Momentum Strategies Theory and implementation of momentum strategies, including trend following and momentum indicators
Module #10 Machine Learning in Trading Introduction to machine learning, supervised and unsupervised learning, and model evaluation metrics
Module #11 Linear Regression and Ridge Regression Theory and implementation of linear regression and ridge regression for trading
Module #12 Decision Trees and Random Forest Theory and implementation of decision trees and random forest for trading
Module #13 Support Vector Machines Theory and implementation of support vector machines for trading
Module #14 Risk Management and Portfolio Optimization Introduction to risk management, portfolio optimization, and performance metrics
Module #15 Backtesting and Walk-Forward Optimization Theory and implementation of backtesting and walk-forward optimization
Module #16 Walk-Forward Optimization in Python Implementing walk-forward optimization in Python using popular libraries (Zipline, Catalyst)
Module #17 Connecting to Brokers and Executing Trades Connecting to brokers, executing trades, and handling orders
Module #18 Live Trading and Monitoring Setting up a live trading environment, monitoring performance, and handling errors
Module #19 Advanced Topics in Algorithmic Trading Exploring advanced topics, including high-frequency trading, market making, and event-driven trading
Module #20 Case Study:Building a Trading Strategy Building a trading strategy from scratch, including idea generation, backtesting, and implementation
Module #21 Performance Metrics and Evaluation Evaluating trading strategy performance, including metrics and attribution analysis
Module #22 Common Mistakes and Biases in Trading Identifying and avoiding common mistakes and biases in trading
Module #23 Regulatory Environment and Compliance Overview of regulatory environment, compliance, and best practices
Module #24 Industry Applications and Trends Exploring industry applications, trends, and future of algorithmic trading
Module #25 Course Wrap-Up & Conclusion Planning next steps in Quantitative Methods in Algorithmic Trading career
