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دسته بندی دوره ها
13

Machine Learning In Algorithmic Trading

سرفصل های دوره

Blending Algorithmic Trading with Machine Learning For Forex and Stock Market Indicators


1. Introduction
  • 1. Introduction
  • 2. Course Content

  • 2. Machine Learning Introduction
  • 1. What Is Machine Learning
  • 2. Understanding The Basics
  • 3. Types Of Machine Learning Models
  • 4. Building Blocks And The Machine Learning Process
  • 5. About Applications Using Machine Learning

  • 3. Supervised Learning
  • 1. Supervised Learning
  • 2. Supervised Learning Key Concepts
  • 3. Supervised Learning Regression By Example
  • 4. Supervised Learning Classification
  • 5. Supervised Learning Training Process

  • 4. Unsupervised Learning
  • 1. Unsupervised Learning
  • 2. Unsupervised Learning Key Concepts
  • 3. Unsupervised Learning Process Pipeline
  • 4. Unsupervised Learning Challenges And Best Practices Discussion
  • 5. Unsupervised Learning Importance And Closing Notes

  • 5. Data Splitting Methods Overfitting And Underfitting
  • 1. Data Splitting For Machine Learning
  • 2. Data Splitting Techniques
  • 3. Overfitting Underfitting And Generalization
  • 4.1 03 data splitting.zip
  • 4. Splitting Python Examples

  • 6. Classification Algorithms
  • 1. Classifiers Introduction
  • 2. K Nearest Neighbors
  • 3. K Nearest Neighbors Python Example
  • 4. K means
  • 5. K means Python Example
  • 6. Decision Trees
  • 7. Decision Trees Python Example
  • 8. Random Forests
  • 9. Random Forests Python Example
  • 10. Logistic Regression
  • 11. Logistic Regression Explained Example
  • 12. Logistic Regression Python Example
  • 13. Naive Bayes Classifier
  • 14. Naive Bayes Gaussian Classifier
  • 15. Naive Bayes Python Example
  • 16. Support Vector Machines
  • 17. Support Vector Machines Kernels
  • 18. Support Vector Machines Python Example
  • Files.zip

  • 7. Evaluating Classifiers
  • 1. Accuracy, Confusion Matrix And Classification Report
  • 2. ROC-AUC And PR-AUC
  • 3. Confusion Matrix Python Example
  • 4. ROC-AUC Python Example
  • 5. PR-AUC Python Example
  • Files.zip

  • 8. Data Analysis And Labelling Financial Data
  • 1. About Data Sources And Labeling
  • 2. Downloading Historical Data
  • 3. Visualizing And Inspecting Indicators
  • 4. Visualization Examples
  • 5. Data Labeling
  • 6. Fixed Time Horizon Method
  • 7. Fixed Time Horizon Python Example
  • 8. Improved Time Horizon Method
  • 9. Improved Time Horizon Python Example
  • 10. Triple Barrier Method
  • 11. Triple Barrier Python Example
  • 12. Strategy Specific Dynamic Labeling
  • 13. Strategy Specific Dynamic Labeling Python Example
  • 14. Strategy Thresholds Optimization
  • 15. Strategy Example For Optimization
  • 16. Thresholds Optimization Python Example
  • Files.zip

  • 9. Feature Engineering
  • 1. Processing Technical Indicators
  • 2. Processing Technical Indicators Python Examples
  • 3. Features Enhancement And Dimensionality Reduction
  • 4. Features Enhancement And Dimensionality Reduction Python Examples
  • 5. Standardization Normalization And One Hot Encoding
  • 6. Standardization Normalization And One Hot Encoding Python Examples
  • Files.zip

  • 10. Training Machine Learning Models
  • 1. Fitting Classifiers
  • 2. Fitting Classifiers Without Data Leakage
  • 3. XGBoost
  • 4. Neural Networks Classifier
  • 5. XGBoost And Neural Networks Python Example
  • Files.zip

  • 11. Financial Backtesting Of Machine Learning Strategies In Python
  • 1.1 02 XGBooost Backtesting.zip
  • 1. Backtesting Machine Learning Indicators In Python
  • 2. Quick Recap And Final Thoughts
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    تاریخ انتشار: ۱۹ خرداد ۱۴۰۳
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