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Hi everyone,
I am a BBA student with a strong interest in quantitative finance, and I have put together a structured syllabus to self-learn topics covered in top Master’s in Financial Engineering (MFE) programs. My goal is to develop a solid foundation in mathematical finance, derivatives pricing, risk management, algorithmic trading, and DeFi applications while integrating programming tools like Python, C++, and SQL.
The syllabus is divided into six modules, here's a brief overview:
Phase 1 (Months 1-6): Core Foundations
I have also included a list of preparatory and advanced readings, as well as hands-on projects like backtesting strategies, option pricing engines, and trading bots.
I would love to hear feedback from experienced professionals and students in this field.
Thanks in advance.
I am a BBA student with a strong interest in quantitative finance, and I have put together a structured syllabus to self-learn topics covered in top Master’s in Financial Engineering (MFE) programs. My goal is to develop a solid foundation in mathematical finance, derivatives pricing, risk management, algorithmic trading, and DeFi applications while integrating programming tools like Python, C++, and SQL.
The syllabus is divided into six modules, here's a brief overview:
Phase 1 (Months 1-6): Core Foundations
- Mathematics (calculus, linear algebra, probability, stochastic processes)
- Programming (Python, C++, SQL)
- Financial Theory (portfolio optimization, derivatives basics)
- Projects: Brownian motion simulation, momentum back tester
Phase 2 (Months 7-9): Derivatives & Numerical Methods
- Stochastic calculus, Black-Scholes, exotic options
- Monte Carlo simulations, finite difference methods
- Projects: Asian option pricing, Heston model calibration
Phase 3 (Months 10-12): Fixed Income & Risk Management
- Interest rate modeling, VaR, credit risk, XVA
- Projects: VaR calculator, credit spread modeling
Phase 4 (Months 13-15): Machine Learning & Alternative Data
- Supervised learning, reinforcement learning, NLP for finance
- Projects: Earnings surprise prediction, multi-factor backtest
Phase 5 (Months 16-18): Algorithmic Trading & HFT
- Market microstructure, statistical arbitrage, execution strategies
- Projects: Limit order book simulator, crypto market-making bot
Phase 6 (Months 19-24): Blockchain, DeFi & Capstone
- Smart contracts, DeFi strategies, crypto derivatives
- Projects: DeFi lending optimizer, cross-DEX arbitrage bot
I have also included a list of preparatory and advanced readings, as well as hands-on projects like backtesting strategies, option pricing engines, and trading bots.
I would love to hear feedback from experienced professionals and students in this field.
- Does this syllabus provide a well-rounded learning experience for someone without an engineering or CS background?
- Are there any key topics or resources that I should add to make my learning more practical and industry-aligned?
- How can I improve the structure or sequence of learning to maximize understanding and skill application?
Thanks in advance.