Design and Implementation of Financial Computing Support Systems
- Introduction & Python Foundations - Financial systems overview, Python for data analysis.
Financial Data Handling - Time series, APIs (Yahoo Finance, FRED, Quandl).
Fixed Income Instruments - Bond pricing, yield curves, duration, convexity.
Derivatives & Option Pricing - Forwards, futures, swaps, Black-Scholes, binomial models.
Portfolio Optimization - Mean-variance optimization, risk-adjusted performance measures.
- Monte Carlo Simulation I - Pricing options and evaluating real investment projects.
Monte Carlo Simulation II - Portfolio risk, VaR estimation, variance reduction techniques.
Midterm Exam & Review - Assessment of theory and Python applications.
Risk Management - Market risk (VaR, CVaR) and credit risk (Merton model).
Volatility Modelling - Historical, implied, and GARCH volatility forecasting.
Algorithmic Trading - Basic (momentum, mean reversion) and advanced (pairs trading, stat arb) stra
- Reinforcement Learning I - Q-Learning and SARSA for trading and portfolio allocation.
Reinforcement Learning II - Actor-Critic, Deep RL, and their real-world limitations.
Integrated Financial Support Systems - Building risk dashboards, portfolio and decision-support tools.
Comprehensive Review & Career Connections - Linking course skills to CFA/FRM/CQF.
Final Exams - Written and coding evaluations.