Content from the Quantitative Finance book, covering pricing models, portfolio construction, execution strategies, model calibration, backtesting, and deployment of quantitative trading systems.
Master derivatives, gradients, and optimization techniques essential for quantitative finance. Learn Greeks, portfolio optimization, and Lagrange multipliers.
Master vectors, matrices, and decompositions for portfolio optimization, risk analysis, and factor models. Essential math foundations for quant finance.
Master moments of returns, hypothesis testing, and confidence intervals. Essential statistical techniques for analyzing financial data and quantifying risk.
Master probability distributions essential for quantitative finance: normal, lognormal, binomial, Poisson, and fat-tailed distributions with Python examples.
Master probability distributions, expected values, Bayes' theorem, and risk measures. Essential foundations for portfolio theory and derivatives pricing.
Master time value of money concepts: compounding, discounting, present value, annuities, and interest rate conventions essential for quantitative finance.
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