🎓 Course Title: Quantitative Finance Mastery — From Math Foundations to Algorithmic Trading

🧠 Level: Beginner to Advanced

🕒 Duration: 3 Months

🎯 Goal: Become a job-ready Quant Analyst/Trader skilled in financial modeling, statistics, Python, machine learning for finance, derivatives, portfolio optimization & algorithmic trading.

🔹 Month 1: Foundations — Math, Markets & Python for Finance

📘 Week 1: Introduction to Financial Markets

• What is quantitative finance?

• Equity, Fixed Income, Derivatives, FX, Commodities

• Market participants (HFT firms, hedge funds, prop desks, banks)

• Market microstructure: order books, bid-ask spreads

• Risk factors: interest rates, inflation, volatility

• Trading strategies overview: stat-arb, mean reversion, momentum, options strategies

🧪 Exercise: Study market depth of a live order book (Binance/TradingView) & explain microstructure behavior.

📘 Week 2: Python for Finance

• Python basics: loops, functions, classes

• Pandas, NumPy, Matplotlib, Seaborn

• Reading financial APIs (Yahoo Finance, Alpha Vantage)

• Time-series data manipulation

• OHLCV datasets & resampling

• Vectorized operations and return calculations

🧪 Exercise: Import NIFTY/S&P 500 data & compute daily, monthly & log returns.

📘 Week 3: Core Mathematics for Quant Finance

• Linear algebra: vectors, matrices, operations

• Calculus: derivatives, integrals, optimization

• Probability & stats: distributions, expectation, variance

• Stochastic processes basics

• Brownian motion & random walks

• Monte Carlo simulations

🧪 Exercise: Simulate 1000 stock price paths using Geometric Brownian Motion (GBM).

📘 Week 4: Time-Series Analysis

• Stationarity, autocorrelation, AR, MA, ARIMA

• Seasonality & trends

• Volatility clustering

• GARCH & EGARCH

• Rolling windows

• Correlation & cointegration

🧪 Mini Project: Build a volatility model for a stock using GARCH & compare with historical volatility.

🔹 Month 2: Derivatives, Machine Learning & Portfolio Theory

📈 Week 5: Financial Derivatives & Pricing

• Options basics: call, put, payoff diagrams

• The Greeks: Delta, Gamma, Theta, Vega, Rho

• Options pricing: Binomial Tree, Black-Scholes Model

• Implied volatility (IV)

• Volatility smile & skew

• Futures pricing

• Swaps basics

🧪 Exercise: Implement Black-Scholes formula in Python & calculate Greeks.

🤖 Week 6: Machine Learning for Quant Finance

• Supervised learning: regression, classification

• ML algorithms: Random Forests, XGBoost, SVM

• Overfitting, cross-validation

• Feature engineering for financial time-series

• Walk-forward testing

• ML pitfalls in trading (look-ahead bias, data leakage)

🧪 Mini Project: Build an ML model to predict next-day returns for a stock index.

💼 Week 7: Portfolio Theory & Risk Management

• Portfolio returns, risk, covariance

• Modern Portfolio Theory (Markowitz)

• Efficient frontier & capital allocation line

• Sharpe, Sortino, Treynor ratios

• CAPM, beta, alpha

• Value at Risk (VaR): Parametric, Monte Carlo, Historical

• Stress testing

🧪 Exercise: Build the efficient frontier for a portfolio of 5 stocks.

🧠 Week 8: Algorithmic Trading Systems

• What is algorithmic trading?

• Strategy design & backtesting

• Order types & execution models

• Factor models (momentum, size, quality, value)

• Mean reversion & statistical arbitrage

• Pairs trading

• Backtesting with Backtrader / Zipline

🧪 Project: Create a full backtested momentum or mean-reversion trading strategy.

🔹 Month 3: Fixed Income, HFT, Advanced Models, Deployment & Capstone

💹 Week 9: Fixed Income & Interest Rate Models

• Bonds, yields, duration, convexity

• Term structure of interest rates

• Yield curve analysis

• Interest rate models: Vasicek, Cox-Ingersoll-Ross (CIR)

• Bond pricing & cash flow modeling

🧪 Exercise: Build a yield curve from real market data & calculate duration/convexity.

⚡ Week 10: High-Frequency Trading (HFT) Concepts

• Tick data vs bar data

• Market making basics

• Latency & co-location

• Quote stuffing & spoofing

• Execution algorithms: VWAP, TWAP, POV

• Microstructure alpha generation

• LOB (Limit Order Book) feature engineering

🧪 Exercise: Build a simple LOB predictor using short-window patterns.

☁️ Week 11: Quant Research Workflow & Deployment

• Data procurement & cleaning (WRDS, Quandl, Tick data)

• Research methodology for quant teams

• Scaling models on cloud (AWS/GCP/Azure)

• Using GPUs for ML in finance

• Real-time trading APIs (Interactive Brokers, Binance)

• Risk metrics for live deployment

• Logging, monitoring, error handling

🧪 Project: Deploy a live paper-trading bot using a Python API.

🏆 Week 12: Career Prep + Capstone

• Resume building for quant roles

• Projects portfolio for hedge funds & prop firms

• Interview prep: math, probability, brain teasers, ML, finance

• Case studies from Citadel, Jane Street, DE Shaw, Two Sigma

• Building GitHub notebooks for quant work

• Freelancing as a Quant Developer (rare but possible)

🧪 Capstone Project (Choose One):

• Options Greeks & pricing engine

• Full statistical arbitrage strategy

• Long-short factor portfolio

• Yield curve modeling system

• ML-based intraday prediction model

• Crypto market microstructure alpha strategy

• Real-time trading bot with risk management

📦 Tools & Technologies Covered:

Programming & Data: Python, NumPy, Pandas, SciPy, Matplotlib, Seaborn, Plotly, Backtrader/Zipline/Qlib, Jupyter Notebook, Bloomberg/Quandl/YFinance APIs

Machine Learning: Scikit-learn, XGBoost, TensorFlow/PyTorch (intro)

Math & Modeling: Stochastic Calculus, Time-Series Models, Black-Scholes, Monte Carlo Simulations, Portfolio Optimization Algorithms

Deployment & Trading: AWS/GCP/Azure, Docker (optional), Interactive Brokers API, Binance API

🏁 Final Outcome:

By the end of this course, you will be able to:

✔ Build trading models from scratch

✔ Analyze financial markets using quant techniques

✔ Price options & model volatility

✔ Perform portfolio optimization & risk analytics

✔ Backtest algorithmic strategies properly

✔ Deploy trading systems in the real world

✔ Break into roles such as: Quant Analyst, Quant Researcher, Algo Trader, Financial Data Scientist, Risk Analyst, Portfolio Analyst