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  • Book Overview & Buying TradeStation EasyLanguage for Algorithmic Trading
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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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Summary

This chapter explored the practical application of AI in financial trading, focusing on machine learning for pattern recognition. It highlighted effective techniques and using EasyLanguage within TradeStation to integrate advanced learning algorithms into trading strategies. Despite EasyLanguage’s limitations compared to Python, it remains a valuable tool for traders.

In this chapter, we learned that, unlike algorithmic trading, the primary goal of machine learning for pattern recognition is not to evaluate strategies from a financial perspective but, rather, to assess their predictive capabilities. Evaluating the financial performance of a strategy without verifying its predictive abilities first is a waste of time and often leads to overfitting.

This chapter drew inspiration from Sir R. Fisher’s pioneering pattern recognition experiments of the 1930s, which share many elements with trading. The key topics that were covered included defining machine learning...

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