TradeStation EasyLanguage for Algorithmic Trading: Discover Real-World Institutional Applications of Equities, Futures, and Forex Markets

Gain professional insights into algorithmic trading with the help of practical cases and comprehensive trading tools to analyze, monitor, and trade in the main financial markets Key Features Learn how to use TradeStation EasyLanguage for algorithmic trading Explore real-life algorithmic trading tools on Equities, Futures, and Forex Enhance technical trading with a blended approach that includes machine learning Book Description 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. What you will learn Develop a scientific market mindset based on observations and statistics Set up the TradeStation EasyLanguage environment for algorithmic trading purposes Find out how to build Equity, Futures, and Forex market algorithmic tools Get to grips with programming risk management algorithms Discover how to program EasyLanguage for mechanical trading Enhance technical trading with the help of machine learning Who this book is for This book is for individual traders with over a year's experience in discretionary trading, with no programming skills, as well as for those who've grappled with market losses and the inundation of trading theories lacking statistical backing. TradeStation EasyLanguage for Algorithmic Trading Contributors About the author About the reviewer Preface Who this book is for What this book covers To get the most out of this book Download the example code files Conventions used Get in touch Share Your Thoughts Download a free PDF copy of this book 1 Introduction to Algorithmic Trading and the TradeStation Platform Introducing algorithmic trading Algorithmic trading definition Algorithmic trading in quantitative hedge funds Introducing the TradeStation platform The TradeStation story Download and installation procedure Introducing TradeStation apps Workspaces and desktops Summary 2 Getting Hands-On with EasyLanguage Understanding the basics of EasyLanguage What is EasyLanguage? How EasyLanguage works EasyLanguage words EasyLanguage expressions EasyLanguage statements EasyLanguage punctuation Writing indicators Exercise 01—Close Exercise 02—Close Open Exercise 03—Real Body Exercise 04—MidPrice Exercise 05—NetCh Exercise 06—NetCh with variables Exercise 07—Momentum Exercise 08—Moving Average Exercise 09—Crossover Exercise 10—Uptrend Exercise 11—Time Writing strategies How to program a basic strategy EasyLanguage order syntax MaxBarsBack How to program take profit and stop loss levels Summary 3 Writing a Trend Strategy Developing trading ideas Market rationale behind a trend strategy Information Market participants Prices and volumes Identifying a trend algorithmically Moving averages Higher highs Handling market noise Entry confirmations Volatility bands Summary 4 Strategy Backtesting and Validation Understanding backtesting and overfitting Strategy complexity Strategy robustness Sensitivity analysis One-way sensitivity analysis Double-way sensitivity analysis Multiple-way sensitivity analysis Backtesting across symbols TradeStation stock symbol universe ROA report in Excel Equity lines chart on Excel Backtesting versus buy-and-hold In-sample, out-of-sample analysis Modifying EasyLanguage scripts for in-sample, out-of-sample purposes An example of in-sample, out-of-sample validation Summary 5 Reversal Strategies The market rationale behind a reversal strategy Reversal up Reversal down Writing a reversal strategy Existing trend Volatility compression Final trend Backtesting long strategies Identifying stock to start with Running a multiple sensitivity analysis on HD (Home Depot Inc.) Exporting data into Excel and creating an ROA heatmap Selecting the best parameter set Backtesting the strategy on the full Dow Jones 30 index Backtesting short strategies Running multiple-sensitivity analysis on the SPY Exporting data into Excel and creating an ROA heatmap Selecting the best parameter set Summary 6 Trend Pullback Strategies The market rationale behind a trend pullback strategy Writing trend pullback components Existing trend Pullbacks Final impulse Assembling components Sensitivity analysis Out-of-sample analysis Summary 7 Risk Management Money management Price-based exits Percent-based exits Volatility-based exits Position sizing The P&L equation Equal dollar risk technique without technical exit levels Incorporating technical exits in equal dollar risk formulas Summary 8 Futures and Forex Algorithmic Trading Algorithmic trading for futures markets The basic concepts of futures How to write algorithms for time breakout strategies Forex algorithmic trading The basic concepts of forex How to write algorithms for the forex market Breakout versus fake breakout Summary 9 The Trading Operational Plan What is an operational plan? A fully automated trading plan for futures How to manage futures symbols for real trading How to size the four futures portfolio How to manage live fully automated strategies A semi-automated trading plan on large stock lists How to track entries with RadarScreen How to calculate volatility-based sizes How to monitor real positions Summary 10 EasyLanguage in AI – Bridging Traditional Trading and Advanced Analytics Python versus EasyLanguage Volatility Predictor on gold futures by the Gandalf Project Bridging Python and EasyLanguage Embedding AI predictions into EasyLanguage indicators Embedding AI predictions into EasyLanguage strategies Using the Volatility Predictor model as a filter Using the Volatility Predictor model for money management Using the Volatility Predictor model for position sizing Creating a volatility dashboard for multiple assets Using TradeStation to collect predictions from multiple AI models Summary 11 EasyLanguage for Machine Learning A definition of machine learning for pattern recognition The Iris dataset and Fisher’s project Labeling trading sessions Project N.1—recognizing an up session Selecting the target Selecting the features Building the pipeline The confusion matrix Project N.2—recognizing down sessions Selecting the target Selecting features Final results Summary Index Why subscribe? 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