In this course, you will learn how to leverage TradeStation EasyLanguage and machine learning to develop robust algorithmic trading strategies. As financial markets continue to evolve, algorithmic trading has become a crucial tool for both individual and institutional traders. This course will help you combine human insight with AI-powered tools to navigate Equities, Futures, and Forex markets confidently.
TradeStation EasyLanguage for Algorithmic Trading
TradeStation EasyLanguage for Algorithmic Trading
Instructor: Packt - Course Instructors
Access provided by ExxonMobil
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Develop a scientific mindset by using statistical observations to analyze market behavior.
Set up and utilize the TradeStation EasyLanguage environment to design and implement trading algorithms.
Integrate machine learning techniques to enhance and refine algorithmic trading strategies.
Skills you'll gain
- Risk Management
- Machine Learning
- Performance Analysis
- Market Trend
- Machine Learning Methods
- Scripting Languages
- Data Validation
- Algorithms
- Finance
- Machine Learning Algorithms
- Artificial Intelligence
- Futures Exchange
- Market Data
- Trend Analysis
- Technical Analysis
- Model Evaluation
- Portfolio Risk
- Statistical Programming
- Securities Trading
- Financial Trading
- Skills section collapsed. Showing 10 of 20 skills.
Details to know
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Assessments
11 assignments
Taught in English
Recently updated!
February 2026
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There are 11 modules in this course
In this section, we introduce the fundamentals of algorithmic trading, demonstrate installation and setup of the TradeStation platform, and highlight its essential features for individual traders' practical workflows.
What's included
2 videos3 readings1 assignment
In this section, we introduce EasyLanguage fundamentals, demonstrate how to write custom indicators and basic trading strategies, and explain applying key syntax and logical operators for developing automated solutions on TradeStation.
What's included
1 video5 readings1 assignment
In this section, we develop algorithmic trend-following strategies using EasyLanguage, explore market rationale, identify trends with indicators, and address market noise to improve trading decisions for equities and Forex.
What's included
1 video4 readings1 assignment
In this section, we backtest trading strategies using EasyLanguage, perform sensitivity and overfitting analysis, and compare results against buy-and-hold benchmarks to validate robustness and predictive power.
What's included
1 video2 readings1 assignment
In this section, we explore the theory and implementation of reversal trading strategies, guiding you through designing, backtesting, and analyzing these strategies using TradeStation and Excel for multiple market assets.
What's included
1 video3 readings1 assignment
In this section, we design and assemble algorithmic components for trend pullback trading, conduct sensitivity and out-of-sample analysis in Excel, and implement strategies in TradeStation for robust market application.
What's included
1 video3 readings1 assignment
In this section, we will learn to manage trading risk by automating exit decisions and position sizing, strengthening your algorithmic strategies for more consistent results in unpredictable markets.
What's included
1 video3 readings1 assignment
In this section, we expand algorithmic trading concepts to futures and forex markets, learning to design, implement, and backtest long and short strategies in TradeStation for greater market versatility.
What's included
1 video5 readings1 assignment
In this section, we construct actionable trading operational plans, examine automated and semi-automated trading strategies, and emphasize validating algorithmic approaches using simulated trading environments for capital protection and effective real-world implementation.
What's included
1 video3 readings1 assignment
In this section, we examine the integration of AI with traditional trading methods, address overfitting and real-world pitfalls, and explore hybrid models to enhance adaptability and competitive advantage in financial markets.
What's included
1 video3 readings1 assignment
In this section, we introduce machine learning concepts for pattern recognition in trading, demonstrate implementing classification models using EasyLanguage on TradeStation, and show how to evaluate session classification with a confusion matrix.
What's included
1 video5 readings1 assignment
Instructor
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