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ISBN 10: 1835881203
ISBN 13: 9781835881200
Author: Domenico D Errico
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
Purchase of the print or Kindle book includes a free PDF eBook
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.
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
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