Bridging FinTech and Python: Data-Driven Trading Models

This tutorial aims to bridge the gap between FinTech and Python by providing a comprehensive guide to building data-driven trading models. We will explore advanced approaches to trading using Python, incorporating object-oriented programming concepts and leveraging data science techniques. By the end of this tutorial, you will have a solid understanding of how to develop and implement data-driven trading models using Python.

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Table of Contents

1. Introduction to Data-Driven Trading Models
2. Setting Up the Environment
3. Data Acquisition and Preprocessing
4. Exploratory Data Analysis
5. Feature Engineering
6. Building the Trading Model
7. Backtesting and Evaluation
8. Conclusion

1. Introduction to Data-Driven Trading Models

Data-driven trading models have gained significant popularity in recent years, as they leverage data science techniques to make informed trading decisions. These models analyze historical market data, identify patterns and generate trading signals based on these patterns. By incorporating machine learning algorithms, these models can adapt to changing market…