**This course contains the use of artificial intelligence**

Modern quantitative finance is no longer driven by spreadsheets alone.

Today's quant analysts, trading researchers, and quantitative finance professionals rely on financial data pipelines, feature engineering, backtesting systems, risk analytics, portfolio construction, and increasingly, artificial intelligence to support research and decision-making.

This course is designed to help you understand how these pieces fit together within a practical quantitative research workflow.

Rather than focusing only on coding or only on AI, you'll learn how modern quant systems transform raw financial data into research insights, trading signals, portfolio decisions, and performance analysis.

The course begins by introducing the complete quantitative research workflow. You'll understand how financial data moves through a structured process that includes data collection, feature generation, modeling, backtesting, portfolio construction, performance evaluation, and reporting.

You'll learn:

• Quant research workflow
• Research pipeline design
• Trading system architecture
• Data-to-decision frameworks
• Quantitative research fundamentals
• AI integration in quant workflows

Next, you'll set up a Python-based research environment using Anaconda, Jupyter Notebook, APIs, and OpenAI integration tools.

Here’s what we cover:

• Python environment setup
• Jupyter Notebook configuration
• OpenAI API setup
• Research notebook workflows
• Quant development environment setup

The course then moves into financial data engineering and market data pipelines, where you'll learn how to retrieve, organize, automate, and manage financial market datasets using APIs and Python-based workflows.

As the course progresses, you'll prepare financial data for quantitative analysis through data cleaning, validation, missing value handling, outlier detection, and time-series preparation.

You'll then create quantitative features such as returns, volatility measures, moving averages, and trading signals through feature engineering workflows.

The course also explores AI-powered sentiment analysis using financial news and ChatGPT-based scoring techniques. You'll learn how textual information can be transformed into quantitative inputs for research and trading systems.

From there, you'll build vectorized backtesting systems and evaluate strategies using professional performance and risk analytics metrics such as CAGR, Sharpe Ratio, volatility, win rate, and maximum drawdown. You'll also learn how ChatGPT can automatically interpret backtest results, generate performance reports, explain risk metrics, and suggest strategy improvements.

Finally, you'll explore portfolio construction concepts including equal-weight portfolios, risk parity approaches, portfolio allocation techniques, risk measurement, and AI-assisted portfolio analysis.

Throughout the course, you'll use Python, financial datasets, quantitative finance concepts, and ChatGPT workflows to understand how modern AI-enhanced quant research systems are built, evaluated, and improved.

By the End of This Course, You'll Be Able To

• Understand the complete quantitative research workflow from data collection to strategy evaluation.

• Set up a Python-based research environment using Jupyter Notebook, APIs, and OpenAI tools.

• Build and automate financial market data pipelines for quantitative analysis.

• Clean, validate, and prepare financial datasets for research and trading applications.

• Create quantitative features such as returns, volatility measures, and moving averages.

• Transform raw market data into actionable trading signals through feature engineering.

• Apply AI-powered sentiment analysis to financial news and textual datasets.

• Use ChatGPT to generate sentiment scores and enhance quantitative research workflows.

• Build vectorized backtesting systems to evaluate trading strategies efficiently.

• Measure strategy performance using metrics such as CAGR, Sharpe Ratio, volatility, win rate, and maximum drawdown.

• Analyze risk and return characteristics using professional quantitative finance techniques.

• Use AI to interpret backtest results, generate research insights, and identify potential strategy improvements.

• Understand portfolio construction, allocation techniques, and risk measurement concepts.

• Explore equal-weight and risk parity portfolio approaches.

• Apply AI-assisted analysis to support portfolio evaluation and decision-making.

• Understand how modern quantitative research and trading systems integrate data, analytics, automation, and artificial intelligence.

Why This Course Is Different

Most AI trading courses focus only on indicators, predictions, or automated trading systems.

This course focuses on the complete quantitative research workflow used by modern analysts and trading researchers.

You'll learn how data pipelines, feature engineering, sentiment analysis, backtesting, risk analytics, portfolio construction, and AI-assisted research fit together within a structured quant framework.

Rather than treating AI as a standalone topic, you'll learn how ChatGPT can support real quantitative research workflows across data analysis, signal development, strategy evaluation, and performance reporting.

About the Course Director

Dheeraj Vaidya is a CFA Charterholder and FRM with prior experience as an Equity Research Analyst at JPMorgan and CLSA. He is the Co-Founder of WallStreetMojo and ExcelMojo, educational platforms that have trained more than 100,000 learners globally. As Course Director, he oversees curriculum design and learning quality, ensuring that complex concepts are taught through practical, structured, and beginner-friendly learning experiences focused on real-world application.