The Following is a course on Quant, that I developed and learned through I will keep adding more topics as I continue to learn.
Program Goal: To provide a self-contained, postgraduate-level education in quantitative finance. This program takes you from foundational mathematics through to the implementation of advanced quantitative models, with no prior expertise in advanced math assumed.
Core Philosophy: We learn what we need, when we need it. Every advanced topic is preceded by a focused primer on the required mathematical or statistical concepts, explained from the perspective of a financial analyst, not a pure mathematician. You learn the tool, then you immediately use it.
This program is structured to provide the knowledge base required to pursue the Certificate in Quantitative Finance (CQF). ● What is it? The CQF is a globally recognized, master's-level professional qualification in quantitative finance, highly respected by employers. Our Goal: By completing this program, you will have built the theoretical knowledge and practical coding skills that form the core of the CQF syllabus, positioning you for success should you choose to pursue the formal certification.
Module 1: The Language of Markets - Applied Calculus & Probability ● Objective: To build the absolute bedrock of quant finance: understanding continuous change and randomness. ● Topics: ○ Calculus Primer: The Physics of Price Change ■ Derivatives (Rates of Change) ■ Integrals (Summing Changes) ■ Taylor Series (Approximating Functions) ○ Probability Primer: Quantifying Uncertainty ■ Random Variables & The Normal Distribution ■ Expected Value, Variance, & Standard Deviation ○ Application: Modeling Asset Prices ■ Brownian Motion & Random Walks ■ Ito's Lemma (The Intuitive Way) ■ Geometric Brownian Motion (GBM) ● Project 1: Code a Monte Carlo simulator in Python using the GBM model to forecast a probability distribution of the Nifty 50's price one month from today.
Module 2: The World of Options - Multi-Variable Calculus in Action ● Objective: To price derivatives and understand their sensitivities by learning how functions change when they have multiple inputs. ● Topics: ○ Partial Derivatives Primer: The Greeks ■ Understanding how a value changes when multiple factors are at play (e.g., price, time). ■ Directly defining Delta, Gamma, Vega, Theta, and Rho. ○ Application: The Black-Scholes-Merton Universe ■ Understanding the BSM option pricing formula and its assumptions. ■ Building a BSM pricer in Python to calculate both the option price and its Greeks. ■ Discovering the "Volatility Smile" using real-world NSE options data. ● Project 2: Build a complete Black-Scholes-Merton option pricer class in Python. Use it to find and plot the implied volatility for a range of real Nifty 50 options.
Module 3: Portfolio & Risk - The Power of Linear Algebra ● Objective: To analyze entire portfolios of assets by learning the mathematics of vectors and matrices. ● Topics: ○ Linear Algebra Primer: Structuring Financial Data ■ Vectors & Matrices for portfolio representation. ■ The Covariance Matrix. ■ Eigenvectors & Eigenvalues (The hidden directions of risk). ○ Application: Advanced Risk & Portfolio Management ■ Principal Component Analysis (PCA) to uncover hidden risk factors. ■ Calculating Value at Risk (VaR) and Conditional VaR (CVaR). ● Project 3: Using a basket of Indian stocks, build a covariance matrix and perform a PCA to identify the main sources of portfolio risk.
Module 4: Learning from Data - Statistical Learning & Machine Learning ● Objective: To build models that learn from historical data by understanding the core principles of statistical learning. ● Topics: ○ Statistical Learning Primer: Beyond Correlation ■ Linear Regression Deep Dive. ■ The Bias-Variance Tradeoff (Overfitting vs. Underfitting). ■ Gradient Descent (The engine of machine learning). ○ Application: Machine Learning in Finance ■ Feature Engineering: The art of creating predictive signals. ■ Implementing powerful models like XGBoost for return prediction. ■ Backtesting fundamentals and avoiding common pitfalls. ● Project 4: Develop a machine learning model using XGBoost to predict the next day's direction for a specific stock, based on technical indicators and volume data.
Module 5: Capstone - Building & Testing an Algorithmic Strategy ● Objective: To integrate all your acquired knowledge to design, build, and rigorously test a complete quantitative trading strategy. ● The Capstone Project: 1. Choose a Strategy: For example, a Statistical Arbitrage (Pairs Trading) strategy on two highly correlated stocks (e.g., HDFC Bank vs. ICICI Bank). 2. Build the Model: Implement the cointegration tests and signal generation logic in Python. 3. Develop a Backtester: Code a simple, event-driven backtesting engine to simulate your strategy's performance on historical data. 4. Write a Professional Report: Document your methodology, results, performance metrics (Sharpe Ratio, Max Drawdown), and a critical analysis of your strategy's real-world viability.