Financial Engineering

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1Algorithmic Trading70 cards · Market microstructure, execution algorithms (VWAP, TWAP, implementation shortfall), market-making, signals, and transaction-cost analysis.2Asset Allocation76 cards · Strategic and tactical allocation across asset classes — mean-variance, Black–Litterman, risk parity, factor investing, and rebalancing.3Continuous-Time Models77 cards · Brownian motion, Itô calculus, stochastic differential equations, Black–Scholes–Merton, and Girsanov's theorem.4Corporate Finance & Investment Banking69 cards · Capital structure, valuation (DCF, comparables, precedent transactions), M&A, IPOs, leveraged buyouts, and capital budgeting.5Credit Risk & Credit Derivatives72 cards · Default modeling (structural and reduced-form), credit spreads, CDS pricing, CDOs and tranching, counterparty credit risk and CVA.6Deep Learning for Finance70 cards · Feedforward, recurrent, and transformer architectures applied to time-series forecasting, derivatives pricing, and trading — with practical training and regularization techniques.7Foundations of Financial Engineering78 cards · Core arbitrage pricing, risk-neutral valuation, binomial models, forwards/futures, swaps, and the first pass at option pricing.8Foundations of Financial Technology65 cards · Payments, lending and credit-scoring, blockchain and digital assets, market infrastructure, RegTech, and data-driven business models in finance.9FX & Related Derivatives68 cards · Foreign exchange markets, covered/uncovered interest parity, FX forwards and swaps, currency options, and quanto/multi-currency products.10Machine Learning for Finance75 cards · Supervised and unsupervised learning for return prediction, risk modeling, factor discovery, and signal generation — with attention to overfitting and finance-specific data issues.11Monte Carlo Simulation Methods68 cards · Random sampling techniques for pricing derivatives, estimating risk, and solving high-dimensional integrals — including variance reduction and quasi-Monte Carlo.12Optimization Models & Methods for Finance63 cards · Linear, quadratic, and convex optimization applied to portfolio construction, hedging, and capital allocation.13Programming for Financial Engineering75 cards · Python (NumPy, pandas), C++, and computational tooling for quantitative finance — numerical methods, performance, and production-quality code.14Quantitative Risk Management66 cards · Value at Risk, Expected Shortfall, copulas, extreme value theory, stress testing, and regulatory capital frameworks (Basel).15Reinforcement Learning for Finance75 cards · MDPs, dynamic programming, Q-learning, policy gradients, and actor-critic methods applied to optimal execution, market making, and portfolio control.16Stochastic Models for Financial Engineering73 cards · Probability, martingales, Markov chains, and discrete-time stochastic processes underpinning option pricing and risk modeling.17Structured & Hybrid Products63 cards · Engineering exotic payoffs from vanilla building blocks — barrier options, autocallables, principal-protected notes, and hybrid equity/rates/credit structures.18Statistical Analysis & Time Series79 cards · Stationarity, autocorrelation, ARMA/ARIMA, GARCH volatility models, cointegration, and state-space methods for financial data.19Implied Volatility & the Smile78 cards · Implied volatility surfaces, smile and skew, local volatility (Dupire), stochastic volatility (Heston, SABR), and calibration to market quotes.
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Graduate-level financial engineering flashcards — 1,360 cards in 19 decks: stochastic models, derivatives, risk, optimization, computation, and ML applied to financial markets. Built from publicly available sources such as university course materials and standard textbooks.