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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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Toc

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

Project N.1—recognizing an up session

Let’s start our first machine learning project. Based on Fisher’s experiment, we’re going to do the following:

  • Select a target
  • Select the features
  • Perform the machine learning pipeline

Selecting the target

The objective of Project N.1 is to automatically recognize an up session (Figure 11.4) for AAPL (Apple Inc.) using a machine learning process:

Figure 11.4 – An up session pattern

Figure 11.4 – An up session pattern

In Figure 11.4, you can see the target pattern for our first machine learning project. We chose this target for a reason; forecasting an up session allows a trader to plan a risk-calculated trade such as the following:

  • Buying at the bar close
  • Placing a stop loss under today’s low
  • Placing a first target on today’s high
  • Holding the position for one day

Let’s look at a practical example. We’ll assume that our model suggests a...

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