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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

Project N.2—recognizing down sessions

After learning how to use a machine learning model to identify an up session, it is essential to analyze the other side of the coin, which is the more hidden and potentially riskier aspect – the short side. Our goal now will be to recognize a down session (see Figure 11.13).

In this section, we’re going to do the following:

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

Selecting the target

The objective of Project N.2 is to automatically recognize the down session (Figure 11.13) using the machine learning process we’ve explored so far:

Figure 11.13 – A down session

Figure 11.13 – A down session

In Figure 11.13, you can see the target pattern down session for Project N.2. Forecasting a down session allows the trader to plan a risk-calculated short trade such as the following:

  • Sell short at the bar close
  • Place a stop loss above today’s...
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