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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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