Machine Learning For Algorithmic Trading - Regression Based

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Introduction

1. Introduction
5m 20s
2. How to Succeed in This Course
3m 47s

Introduction to Machine Learning for Algorithmic Trading and Investing

1. Introduction and Classification of Machine Learning
10m 31s
2. Introduction to Machine Learning development Work Flow using Linear Regression
9m 39s
3. Characteristic of Financial Time Series and Linear Regression Assumptions
10m 27s
4. Effects of Outliers on Machine Learning Model
5m 20s
5. Model Selection and Quant Workflow
8m 25s

Machine Learning and Pairs Trading

1. Pairs Trading and Machine Learning
6m 38s
2. Understanding the Data (Data Exploration)
14m 10s
3. Python statsmodel Library
9m 7s
4. Python scikit-learn Library
6m 46s
5. Cointegration Test
3m 56s
6. Trading Logic
13m 9s

Backtesting Pairs Trading

1. Pairs Trading Code Walk Through
17m 10s
2. Backtest and Performance Analysis
15m 20s

Penalized Regression for Investing

1. Rationale for Penalized Regression
3m 1s
2. Application of Penalized Regression to Investing
11m 45s

Kalman Filter

1. Kalman Filter Introduction
14m 24s
2. Backtesting Kalman Filter Based Investing Strategy
13m 22s

Machine Learning and Multi-Assets Trend Following Strategies

1. Introduction to Multi-Assets Trend Following Strategies
10m 49s
2. Machine Learning and Multi-Assets Trend Following Strategies
14m 36s
3. Backtesting Multi-Assets Trend Following Machine Learning Strategies
13m 46s