Probability & statistics
Distributions, conditional probability, expectation, variance, sampling, confidence intervals, hypothesis testing, correlation, and the mathematical foundations needed to interpret trading data.
Futures Trading Lab ยท Quant Finance
Build quantitative depth around real market questions.
The Quant Finance track is an applied learning program inside the Futures Trading Lab. The goal is not to collect formulas or certificates; it is to build enough statistical, market, and programming depth to challenge trading assumptions with evidence.
Current public state: the learning roadmap and project architecture are defined. Notes, notebooks, and project writeups will be published here as they are actually completed.
Empirical finance & data
Returns, volatility, rolling statistics, stationarity, autocorrelation, resampling, futures contract handling, data quality, and common sources of research bias.
Time-series thinking
AR/MA/ARIMA concepts, volatility clustering, GARCH concepts, regime behavior, feature engineering, forecast evaluation, and the limits of prediction.
Market microstructure
Limit-order books, spread, liquidity, market impact, volume/trade flow, auction mechanics, order types, and how futures sessions actually trade.
Derivatives & risk
Futures mechanics, margin, basis, term structure, options/Greeks, volatility concepts, scenario analysis, and portfolio/risk measures.
Systematic research
Event studies, time-series cross-validation, walk-forward analysis, overfitting controls, bootstrap/Monte Carlo concepts, risk-adjusted performance, and applied projects.
Use the Trading OS as a source of research questions.
Potential projects include market-state classification, EMA authority and spread strength, balance-to-expansion transition analysis, pullback-quality classification, session/regime expectancy, execution-cost modeling, and drawdown/risk simulation.
Implemented understanding beats passive study.
The preferred outputs are Python notebooks, small datasets, reusable code, research notes, interpretation writeups, and projects that can explain what was tested and what the result means. A numerical model is useful only when its assumptions and failure modes are understood.
Notes and notebooks will accumulate here.
This page is the public index for Quant Finance learning milestones. Material will be added when there is a completed exercise, notebook, study, or explanation worth preserving.