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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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EasyLanguage in AI – Bridging Traditional Trading and Advanced Analytics

There is a lot of enthusiasm surrounding the use of Artificial Intelligence (AI) in the financial sector. However, although AI offers amazing forecasting tools, it is easy to misuse them, as the complexity and large amount of data can lead to models that appear accurate but fail under real-world conditions. The use of AI models does not inherently exclude the pitfalls of overfitting.

In institutional settings, there is also a high level of skepticism about using AI innovative tools for the following reasons:

  • If AI models perfectly replicated the behavior of human traders, considering that 95% of human traders lose money in financial markets, 95% of AI models would also be destined to lose money in the markets
  • The mass adoption of AI models in financial markets could change the market structure in terms of prices and volumes, rendering the historical datasets on which the models are trained...
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