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TradeStation EasyLanguage for Algorithmic Trading

You're reading from   TradeStation EasyLanguage for Algorithmic Trading Discover real-world institutional applications of Equities, Futures, and Forex markets

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835881200
Length 282 pages
Edition 1st Edition
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Author (1):
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Domenico D'Errico Domenico D'Errico
Author Profile Icon Domenico D'Errico
Domenico D'Errico
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introduction to Algorithmic Trading and the TradeStation Platform FREE CHAPTER 2. Chapter 2: Getting Hands-On with EasyLanguage 3. Chapter 3: Writing a Trend Strategy 4. Chapter 4: Strategy Backtesting and Validation 5. Chapter 5: Reversal Strategies 6. Chapter 6: Trend Pullback Strategies 7. Chapter 7: Risk Management 8. Chapter 8: Futures and Forex Algorithmic Trading 9. Chapter 9: The Trading Operational Plan 10. Chapter 10: EasyLanguage in AI – Bridging Traditional Trading and Advanced Analytics 11. Chapter 11: EasyLanguage for Machine Learning 12. Index

The Iris dataset and Fisher’s project

While studying Python and its numerous libraries for machine learning, I came across the Iris dataset and the pattern recognition project that Sir R. A. Fisher, a scientist from the 1930s, undertook in the field of biology. The more I delved into Fisher’s project, the more I realized how many aspects of machine learning for pattern recognition align with trading. This intrigued me greatly, and I began to delve deeper into the subject on my own.

The Iris dataset (Figure 11.1) is a classic example of machine learning. It contains 150 samples of Iris flowers, with three species – Iris setosa, Iris versicolor, and Iris virginica. Each sample has four features – sepal length, sepal width, petal length, and petal width – measured in centimeters. The dataset is used to demonstrate classification algorithms as each species can be predicted based on the flower’s measurements. Fisher’s experiment aimed...

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