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TradeStation EasyLanguage for Algorithmic Trading

TradeStation EasyLanguage for Algorithmic Trading

By : Domenico D'Errico
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TradeStation EasyLanguage for Algorithmic Trading

TradeStation EasyLanguage for Algorithmic Trading

3 (2)
By: Domenico D'Errico

Overview of this book

With AI revolutionizing financial markets, every trader will soon get easy access to AI models through free Python libraries and datasets, with all of them making the same trades! This behavior will modify prices and trading volumes, potentially altering future datasets, leading to major corporations investing heavily in technology, big data, and expert teams. However, individual traders need not be intimidated because this dynamic has been seen before whenever new technologies have entered the trading market. Written by a quantitative algorithmic trading developer with over 15 years of experience in the finance industry, this book will ground you by taking a rational approach to algorithmic trading, where EasyLanguage, datasets, charts, and AI are tools for your journey toward mastering the markets. Your unique human intelligence remains invaluable in navigating and understanding market complexities as you explore the realm of institutional insights, satisfying your hunger to learn real-world algorithmic trading applications from the institutional perspective. By the end of this book, you’ll be able to confidently apply TradeStation EasyLanguage to algorithmic trading, integrate machine learning to refine your strategies, and craft a personalized approach to confidently navigate the financial markets.
Table of Contents (13 chapters)
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Trend Pullback Strategies

In the previous chapters, we explained how the financial market moves in trends and that every trend eventually ends, leading to a market reversal. We then wrote the algorithms for these two aspects of the market separately: trend in Chapters 3 and 4 and reversal in Chapter 5. However, in reality, these two phenomena often occur simultaneously, giving rise to the typical market pattern in waves.

The objective of this chapter is to understand the trend pullback dynamics, how to build some algorithm components suitable for these market phases, and how to use TradeStation and Excel to assemble such components into diversified approaches to trade pullbacks.

In this chapter, we are going to delve into the following:

  • The market rationale behind a trend pullback strategy
  • Writing trend pullback components
  • Assembling trend pullback components
  • Conducting sensitivity analysis
  • Conducting out-of-sample analysis

Note

For educational...

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