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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

In this chapter, we learned how to evaluate an algorithmic strategy on equities. We emphasized that analyzing financial performance results is not sufficient, as the statistical aspect of algorithmic trading is just as important as the financial one. We highlighted the danger of overfitting and introduced several techniques to mitigate it, such as sensitivity analysis and backtesting on other similar assets. Additionally, we explored how to utilize the EquityEdge Buy&Hold Analyzer as an add-on for quickly evaluating strategies against the underlying asset’s buy-and-hold performance. Finally, we discussed the in-sample, out-of-sample process.

It’s important to note that the finance industry still lacks a shared and objective method for validating strategies, and research on this matter remains ongoing.

The backtesting and validation strategies are crucial concepts in algorithmic trading, so, re-read and review this chapter if needed. In the next chapter...

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TradeStation EasyLanguage for Algorithmic Trading
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