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Machine Learning For Algorithmic Trading - Regression Based

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Curriculum For This Course
  • 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
  • 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
  • 1. Pairs Trading Code Walk Through 17m 10s
  • 2. Backtest and Performance Analysis 15m 20s
  • 1. Rationale for Penalized Regression 3m 1s
  • 2. Application of Penalized Regression to Investing 11m 45s
  • 1. Kalman Filter Introduction 14m 24s
  • 2. Backtesting Kalman Filter Based Investing Strategy 13m 22s
  • 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

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