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Practical Python for Algorithmic Trading

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If you work in finance or have any interest in investing and trading, you know that there’s a treasure trove of financial data available to you at any moment. But how can you use all that information to your advantage? Algorithmic trading using machine learning techniques can help you make trading decisions based on data. In this course, Jesus Lopez teaches you about data preprocessing, feature engineering, and how to use advanced machine learning models to enhance your trading strategies. He shows you how to download stock market data from Yahoo Financeto be trained on machine learning models that predict the future, and how to create investment decisions based on the predictions. Learn how to optimize your strategies, understand backtesting techniques, and interpret performance reports with confidence.


01 - Introduction
  • 01 - Algorithmic trading using machine learning
  • 02 - Maximize your learning

  • 02 - 1. Working with Stock Market Data
  • 01 - Download and export data
  • 02 - Filter rows and create columns for trading strategies

  • 03 - 2. Backtesting with Classification Models
  • 01 - Compute machine learning classification model
  • 02 - First configurations of the strategy class
  • 03 - Simulate the investment strategy step by step
  • 04 - Run the backtest on the strategy
  • 05 - Challenge Backtest with other tickers

  • 04 - 3. Backtesting with Regression Models
  • 01 - Compute machine learning regression model
  • 02 - How to evaluate regression models
  • 03 - Configure and run the backtest with the regression model
  • 04 - How to interpret the backtesting dashboard

  • 05 - 4. Backtesting Optimization
  • 01 - Optimizing strategy parameters
  • 02 - Pandas reporting with heatmaps
  • 03 - Smart optimization to save computing time
  • 04 - Challenge Optimization with other datasets

  • 06 - 5. The Overfitting Problem in Backtesting
  • 01 - Why machine learning models overfit the data
  • 02 - How to train models within the backtest
  • 03 - Challenge Train test with other tickers
  • 04 - Walk forward validation in machine learning
  • 05 - Anchored walk forward validation in backtesting
  • 06 - Create library for backtesting strategies
  • 07 - Interpret reports from walk forward validation approaches
  • 08 - Challenge Walk forward with other tickers

  • 07 - Conclusion
  • 01 - Course summary
  • 02 - Whats next
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    تاریخ انتشار: 20 شهریور 1402
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