Ai Basics For Quant Research And Trading Automation
Published 3/2026
Created by Excel Mojo
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 13 Lectures ( 2h 6m ) | Size: 893 MB
What you'll learn
✓ Understand the complete quant research workflow from data to execution
✓ Build financial data pipelines using Python and real datasets
✓ Create features like returns, volatility, and moving averages
✓ Perform backtesting and evaluate trading strategies
✓ Calculate metrics like CAGR, Sharpe ratio, and drawdown
✓ Use ChatGPT to analyze strategies and automate insights
✓ Apply sentiment analysis for trading signals
✓ Combine AI with quantitative finance for smarter decision-making
Requirements
● A stable internet connection
● A laptop, desktop, or any other device that is capable of running Python and Jupyter Notebook
● Access to ChatGPT
Description
This course contains the use of artificial intelligence.
Quantitative research and trading are at the core of modern finance. From hedge funds to algorithmic trading desks, professionals rely on data, models, and automation to make informed investment decisions. With the rise of artificial intelligence, this process has become faster, smarter, and more scalable.
In this course, AI Basics for Quant Research and Trading Automation, you will learn how to combine Python, quantitative finance concepts, and AI tools like ChatGPT to build a complete research workflow.
We begin with a strong foundation by understanding the quant research pipeline-how raw data is transformed into trading decisions through features, models, backtesting, portfolio construction, and execution. You will then learn how to build data pipelines, import financial datasets, and prepare them for analysis.
The course moves into data cleaning and feature engineering, where you will create financial features such as returns, volatility, and moving averages. You will also explore sentiment analysis using AI, where ChatGPT helps convert textual data into actionable signals.
Next, you will build vectorized backtesting systems to evaluate trading strategies and compare them with benchmarks. You will learn how to calculate key performance metrics like CAGR, volatility, Sharpe ratio, and maximum drawdown, and understand what they mean in real-world trading.
Finally, the course integrates ChatGPT directly into the workflow, allowing you to automate analysis, generate explanations, and improve strategies like a professional quant analyst.
By the end of this course, you will understand how modern quant systems are designed, automated, and enhanced using AI.
Enroll now and start building AI-powered quant research and trading systems.
Who this course is for
■ Students interested in quantitative finance and algorithmic trading
■ Finance professionals looking to learn AI-driven research workflows
■ Python learners who want to apply coding in finance
■ Traders and analysts who want to automate strategy analysis
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