What this book covers
Chapter 1, Introduction to Algorithmic Trading and the TradeStation Platform, aims to define what is meant by algorithmic trading in this book and to introduce the TradeStation platform features.
Chapter 2, Getting Hands-On with EasyLanguage, provides a basic EasyLanguage training course.
Chapter 3, Writing a Trend Strategy, shows you how to develop a trend-following strategy in the stock market, providing the rationale behind trends, a strategy development method, and the programming code.
Chapter 4, Strategy Backtesting and Validation, provides the methodology for backtesting and validating algorithmic strategies using EasyLanguage and TradeStation.
Chapter 5, Reversal Strategies, demonstrates how to develop a reversal strategy in the stock market, providing the rationale behind reversals, a strategy development method, and the programming code.
Chapter 6, Trend Pullback Strategies, explains how to use the techniques learned in Chapters 3 and 5 to merge the two methodologies in order to create Trend Pullback trading strategies.
Chapter 7, Risk Management, addresses the critical aspect of managing financial risk in trading. While previous chapters have focused on identifying optimal entry points for trades, this chapter emphasizes the importance of managing risk through effective exits and position sizing. Given the inherent unpredictability of financial markets, controlling how much one is willing to risk is paramount.
Chapter 8, Futures and Forex Algorithmic Trading, explains how Futures and Forex offer numerous trading opportunities on uncorrelated instruments, allowing for both long and short positions; this chapter demonstrates how to use TradeStation and EasyLanguage for algorithmic trading in this vast market.
Chapter 9, The Trading Operational Plan, provides a step-by-step guide on how to set up TradeStation to create and execute an operational trading plan.
Chapter 10, EasyLanguage in AI – Bridging Traditional Trading and Advanced Analytics, explains how to integrate TradeStation with various AI models, creating dashboards that combine proven market monitoring and trading techniques with advanced AI tools.
Chapter 11, EasyLanguage for Machine Learning, explains how to program EasyLanguage for machine learning in pattern recognition, by exploring one of the earliest supervised machine learning experiments conducted by the British scientist Sir R. Fisher in the 1930s.
