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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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A definition of machine learning for pattern recognition

Machine learning is a broad field within AI that focuses on developing algorithms that allow computers to learn from data and make decisions or predictions, without being explicitly programmed for specific tasks. The primary goal is to enable machines to improve their performance on a given task through experience.

Pattern recognition is a specific application area within the broader field of machine learning. It involves the process of identifying patterns and regularities in data. Pattern recognition is essentially about categorizing or classifying data based on learned patterns.

In my experience, I’ve found that there are numerous similarities between the logic behind machine learning for pattern recognition and those of the traditional technical trading approach.

Every time traders analyze a stock chart, they search, sometimes unconsciously, for a price configuration that they have seen before. If you listen...

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