Quantitative Methods in Algorithmic Trading
( 25 Modules )

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

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Quantitative Methods in Algorithmic Trading ( 25 Modules )

Quantitative Methods in Algorithmic Trading
( 25 Modules )