🎓 **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
Study the live order book of a major cryptocurrency exchange (Binance or Coinbase) and analyze bid-ask spreads, market depth, and order flow patterns. Document your findings in a Jupyter notebook with visualizations.
Download historical price data for NIFTY 50 or S&P 500 using yfinance, calculate daily returns, monthly returns, and log returns. Create visualizations showing price trends, return distributions, and volatility patterns.
Implement a Monte Carlo simulation to generate 1000 stock price paths using Geometric Brownian Motion (GBM). Visualize the paths, calculate expected value and variance, and compare with theoretical predictions.
Build a GARCH(1,1) model for a stock's volatility using historical price data. Compare the model's volatility forecasts with realized volatility and create visualizations showing volatility clustering effects.
Implement the Black-Scholes formula in Python to price European call and put options. Calculate all Greeks (Delta, Gamma, Theta, Vega, Rho) and create sensitivity analysis plots showing how option prices change with underlying parameters.
Build a machine learning model (Random Forest or XGBoost) to predict next-day stock returns. Use proper walk-forward validation to avoid look-ahead bias. Include feature engineering for technical indicators and evaluate model performance with appropriate metrics.
Select 5 stocks from different sectors and build the efficient frontier using Modern Portfolio Theory. Calculate optimal portfolio weights, Sharpe ratios, and create visualizations showing the risk-return trade-off. Include constraints like no short-selling.
Develop a complete momentum or mean-reversion trading strategy using Backtrader or Zipline. Include proper position sizing, transaction costs, and risk management. Generate performance metrics including Sharpe ratio, maximum drawdown, and win rate.
Download government bond yield data and construct a yield curve. Calculate duration and convexity for different bond maturities. Analyze the term structure and identify any anomalies or arbitrage opportunities.
Deploy a live paper-trading bot using Interactive Brokers API or Binance API. Implement proper error handling, logging, and risk management. Monitor the bot's performance over a week and document any issues or improvements needed.
Locked • 45 min
Locked • 50 min
Locked • 55 min
Locked • 60 min
Locked • 65 min
Locked • 70 min
Locked • 60 min
Locked • 75 min
Locked • 55 min
Locked • 50 min
Locked • 80 min
Locked • 90 min
PDF • 2.4 MB • Updated today
Link • usabilityhub.com
ZIP • 5.1 MB • Templates & Assets
Design System • Community File
New Session • June 17, 2025
Complete Chapter 2 to unlock Certificate
