Master Quantitative Skills with Quant Guild

Algebra and geometry, functions, polynomials, exponents and logs, coordinate geometry, basic proofs.

Model and solve deterministic problems.

Calculus I–IV, optimization, multivariable calculus, gradients, linear algebra, matrix decompositions (eigen, SVD, PCA).

Set up and solve optimization problems.

Stochastic calculus, ODEs and PDEs (Black–Scholes), Brownian motion, Itô's lemma, Monte Carlo, finite differences.

Price derivatives and model random dynamics.

Mean, variance, and standard deviation, set theory, sample spaces, conditional probability, combinatorics, independence and mutual exclusion.

Model a fixed distribution, a coin or a die.

Random variables, common distributions, the central limit theorem and the law of large numbers, hypothesis testing, confidence intervals, regression.

Know when distributions converge, and when they don't.

Stochastic processes, time series (ARMA, ARIMA, GARCH), Bayesian inference, non-parametric and robust stats, non-stationarity.

Handle dependent, non-stationary real-world data.

Micro and macro, supply and demand, stocks, bonds, and funds, time value of money, inflation and rates, GDP, monetary and fiscal policy.

Understand the news and its hidden assumptions.

Instruments, modern portfolio theory, derivatives (options, futures, swaps), fixed income, FX, CAPM, EMH, alpha, VaR, DCF.

Take every model with a grain of salt. Alpha exists.

Behavioral finance, advanced risk (VaR and CVaR), cognitive biases, market anomalies, tail risk and black-swan events.

Turn market intuition into modeled, calculated risk.

What learning from data means, features and labels, train and test splits, linear and logistic regression, loss functions, gradient descent, overfitting.

Fit a simple model and know when it fails.

The full model pipeline, from question to data, features, model, and deploy. Supervised and unsupervised learning, bias–variance, and cross-validation.

Understand the entire development pipeline.

Time-series ML, walk-forward validation, reinforcement learning, transformers, generative models, causal inference.

Build rigorous, bias-free trading models.