English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 54m | 324 MB
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 Finance to be trained on machine learning models that can make predictions, and how to create investment decisions based on these predictions. Learn how to optimize your strategies, understand backtesting techniques, and interpret performance reports with confidence.
Table of Contents
Introduction
1 Algorithmic trading using machine learning
2 Maximize your learning
Working with Stock Market Data
3 Download and export data
4 Filter rows and create columns for trading strategies
Backtesting with Classification Models
5 Compute machine learning classification model
6 First configurations of the strategy class
7 Simulate the investment strategy step by step
8 Run the backtest on the strategy
9 Challenge Backtest with other tickers
Backtesting with Regression Models
10 Compute machine learning regression model
11 How to evaluate regression models
12 Configure and run the backtest with the regression model
13 How to interpret the backtesting dashboard
Backtesting Optimization
14 Optimizing strategy parameters
15 Pandas reporting with heatmaps
16 Smart optimization to save computing time
17 Challenge Optimization with other datasets
The Overfitting Problem in Backtesting
18 Why machine learning models overfit the data
19 How to train models within the backtest
20 Challenge Train test with other tickers
21 Walk forward validation in machine learning
22 Anchored walk forward validation in backtesting
23 Create library for backtesting strategies
24 Interpret reports from walk forward validation approaches
25 Challenge Walk forward with other tickers
Conclusion
26 Course summary
27 What’s next
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