Spreadsheets alone no longer carry quantitative research. Modern quant work means data pipelines, feature engineering, backtesting systems, risk analytics, and increasingly AI support across all of it. This course shows how those pieces connect into one workflow, from raw market data through to portfolio decisions and performance reporting.

This Course Offers

• The complete quant research workflow, covering pipeline design, trading system architecture, and data to decision frameworks
• Python environment setup using Anaconda, Jupyter Notebook, APIs, and OpenAI integration tools
• Financial data engineering covering retrieval, organization, automation, cleaning, validation, missing value handling, and outlier detection
• Feature engineering for returns, volatility measures, moving averages, and trading signals, plus AI powered sentiment analysis on financial news
• Vectorized backtesting with performance metrics including CAGR, Sharpe ratio, volatility, win rate, and maximum drawdown, plus portfolio construction using equal weight and risk parity approaches

Why We Love This Course

1. It treats AI as part of the workflow rather than a standalone gimmick. ChatGPT is used to interpret backtest results, explain risk metrics, and suggest improvements.
2. The scope is realistic. Pipelines, features, backtesting, risk, and portfolio construction all get covered because that is what actual research involves.
3. Python familiarity is helpful but not required, so the course remains accessible if you are new to coding.
4. The curriculum was designed under Dheeraj Vaidya, a CFA and FRM charterholder with prior equity research experience at JPMorgan and CLSA, so the structure reflects professional practice.

Quant research roles reward people who can take an idea from data to evaluated strategy, and that full loop is what employers test. Do you want to keep learning indicators in isolation, or build the pipeline that supports real decisions? Enrollment is open, and the course contains AI tools used throughout.

• This course is perfect for trading researchers and aspiring quantitative analysts.
• It suits algorithmic trading enthusiasts and finance students exploring quantitative methods.
• Python learners interested in finance, financial analysts, and professionals exploring AI applications in trading will find the complete workflow covered.

• Basic familiarity with Python is helpful but not mandatory.
• A computer capable of running Python and Jupyter Notebook is needed to follow along.
• Access to ChatGPT is required, since AI tools are integrated throughout the workflow.

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.