Introduction

The world of quantitative finance relies heavily on advanced mathematical models, statistical analysis, and computational tools to make decisions about investments, risk management, and market predictions. For data scientists, a strong understanding of the intersection between data science and financial markets is crucial. This course is designed to equip data scientists with the tools, techniques, and knowledge required to tackle real-world financial problems using data science methods. Topics covered will include time series analysis, portfolio optimization, risk management, derivatives pricing, and algorithmic trading. By the end of this course, participants will be able to apply machine learning and data science techniques to the domain of quantitative finance.

Objectives

By the end of this course, participants will:

• Understand the core principles of financial markets, instruments, and markets’ behavior.
• Learn the fundamentals of financial mathematics and statistical modeling in finance.
• Apply data science techniques to time-series analysis, forecasting, and financial prediction.
• Build and optimize portfolios using various asset allocation methods and risk management techniques.
• Understand derivatives pricing using both traditional models and data-driven approaches.
• Gain hands-on experience with algorithmic trading strategies and their backtesting.
• Use tools such as Python, pandas, NumPy, Matplotlib, and scikit-learn to analyze financial data.

Who Should Attend?

This course is ideal for:

• Data scientists and analysts looking to apply their skills in the field of quantitative finance.
• Financial analysts and professionals who want to integrate data science methods into their work.
• Quantitative researchers and engineers interested in using machine learning in finance.
• Anyone interested in learning how to use data science techniques to solve real-world financial problems.

Day 1: Introduction to Quantitative Finance and Financial Markets

Morning Session: Overview of Financial Markets

• Understanding the financial markets: Stocks, bonds, commodities, derivatives, and foreign exchange.
• Key financial instruments and their characteristics.
• Market participants: Investors, traders, institutions, and hedge funds.
• Introduction to financial market behavior: Efficient market hypothesis, market microstructure.
• Hands-on: Exploring financial market data with Python and pandas.

Afternoon Session: Introduction to Financial Mathematics

• Financial time series analysis: Historical prices, returns, volatility, and risk.
• Understanding risk: Measuring risk and return using Sharpe Ratio, VaR, and CAPM.
• Introduction to financial models: Black-Scholes model, geometric Brownian motion.
• Discounted cash flows (DCF), and Net Present Value (NPV).
• Hands-on: Importing and manipulating financial time series data in Python and visualizing returns and volatility.

Day 2: Time Series Analysis and Forecasting

Morning Session: Time Series Analysis in Finance

• Understanding time series data: Stock prices, exchange rates, and commodity prices.
• Statistical tools for time series analysis: Autocorrelation, stationarity, and trend analysis.
• Introduction to ARIMA, GARCH, and other time series models.
• Forecasting techniques and tools for financial time series data.
• Hands-on: Applying ARIMA models for stock price forecasting and volatility prediction using Python.

Afternoon Session: Advanced Time Series and Forecasting Models

• Implementing machine learning models for financial forecasting: Random Forest, Gradient Boosting, and LSTM.
• Comparing classical time series models with machine learning models in financial forecasting.
• Model evaluation: Cross-validation, prediction accuracy, and error metrics.
• Hands-on: Build a financial forecasting model with machine learning algorithms (e.g., predicting stock prices with scikit-learn).

Day 3: Portfolio Management and Optimization

Morning Session: Portfolio Theory and Asset Allocation

• Introduction to Modern Portfolio Theory (MPT): Expected return, variance, and covariance.
• Markowitz efficient frontier and asset correlation.
• Risk-return trade-off and portfolio diversification.
• Hands-on: Constructing a portfolio with different assets and optimizing it using Python libraries (NumPy, pandas).

Afternoon Session: Advanced Portfolio Optimization Techniques

• Introduction to the Capital Asset Pricing Model (CAPM) and Factor Models.
• Black-Litterman model and its applications in portfolio management.
• Machine learning techniques for portfolio optimization: Reinforcement learning and genetic algorithms.
• Hands-on: Build a portfolio optimization model using Python and backtest with real financial data.

Day 4: Derivatives Pricing and Risk Management

Morning Session: Introduction to Derivatives

• Understanding derivatives: Options, futures, swaps, and forwards.
• Key pricing models: Black-Scholes model for options pricing.
• The Greeks: Delta, Gamma, Vega, Theta, and Rho and their importance in risk management.
• Hands-on: Use Python to calculate the price of a European call and put option using the Black-Scholes model.

Afternoon Session: Advanced Derivatives and Risk Management

• Pricing complex derivatives: Interest rate derivatives, exotic options, and structured products.
• Risk management techniques: Value-at-Risk (VaR), stress testing, and Monte Carlo simulations.
• Credit risk and counterparty risk management in derivatives.
• Hands-on: Implement a Monte Carlo simulation for option pricing and risk assessment using Python.

Day 5: Algorithmic Trading and Backtesting

Morning Session: Introduction to Algorithmic Trading

• What is algorithmic trading? Key concepts and strategies.
• Market-making, statistical arbitrage, and trend-following strategies.
• Introduction to trading algorithms: Moving averages, mean reversion, and momentum.
• Hands-on: Implement a simple moving average crossover trading strategy in Python using pandas and NumPy.

Afternoon Session: Backtesting and Optimization of Trading Strategies

• Backtesting: How to test the performance of trading strategies on historical data.
• Performance metrics: Sharpe ratio, drawdown, and profit factor.
• Optimizing trading strategies using machine learning algorithms.
• Hands-on: Backtest and optimize a trading strategy using real market data and libraries like Zipline or Backtrader.

Materials and Tools:

• Required tools: Python, pandas, NumPy, Matplotlib, scikit-learn, Statsmodels, Zipline, Backtrader
• Sample datasets: Historical stock prices, bond prices, futures, and options data
• Access to sample code, financial data, and trading platforms for backtesting
• Recommended resources: Financial textbooks, online resources for portfolio management and trading algorithms