Learn With Us

Curriculum & Education

QUANTT's 10-week educational program bridges the gap between theoretical finance and production-level quantitative software development. Our members learn core market concepts and advance into specialized tracks.

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Trading Track

Strategy & Markets

Focuses on market inefficiencies, hypothesis generation, quantitative strategy design, options theory, volatility models, post-earnings announcement drift, and portfolio risk management.

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Development Track

Data & Engineering

Focuses on Python and pandas engineering, data cleansing, regression modeling, time-series stationarity, building robust backtesting engines, computational numerical methods, and connecting with market APIs.

10-Week Roadmap

Autumn Term

Foundational Topics

→Stocks, bonds, futures, options, swaps, and ETFs
→Long and short positions, leverage, and margin
→Bid, ask, spreads, liquidity, and order types
→From hypothesis to signal, execution, profit and loss, transaction costs, and risk

Foundational Topics

→Random variables and common probability distributions
→Expected value, variance, and standard deviation
→Conditional probability, independence, covariance, and correlation
→Introductory Monte Carlo simulation and interpreting uncertainty

Foundational Topics

→Repositories, branches, commits, and pull requests
→GitHub issues, code review, and collaborative workflows
→Reproducible project structure, documentation, and secure credential management
→Responsible use, verification, and disclosure of AI-assisted work

Trading Track

→Economic hypotheses and potential market inefficiencies
→Signals, entry and exit rules, and holding periods
→Benchmarks, transaction costs, and basic backtest interpretation
→Defining the evidence that would support or invalidate a strategy

Development Track

→Python and pandas fundamentals
→Loading, cleaning, and organizing financial data
→Timestamps, missing values, duplicate records, and return calculations
→Basic rolling statistics, visualizations, and data-quality checks

Trading Track

→Calls, puts, strike prices, expiry, and moneyness
→Implied versus realized volatility
→Delta, gamma, vega, and theta
→Long- and short-volatility positions, volatility risk premium, and tail risk

Development Track

→Linear and logistic regression
→Features, targets, and baseline models
→Time-ordered training, testing, and leakage prevention
→Overfitting, regularization, and predictive versus trading performance

Trading Track

→Earnings surprises and market expectations
→Post-earnings announcement drift
→Pre-earnings volatility and implied-volatility crush
→Event-study interpretation and directional versus volatility-based trades

Development Track

→Lags, autocorrelation, and basic stationarity
→Correlation, prediction, causality, and confounding
→Causal graphs and introductory Granger causality or PCMCI
→Synthetic-data validation and false-discovery risks

Trading Track

→Present value, discounting, bonds, and yields
→Yield curves, duration, and DV01
→Interest-rate swaps and credit spreads
→Funding, liquidity, and hidden risks in apparent arbitrage opportunities

Development Track

→Backtest architecture and execution timing
→Look-ahead bias, survivorship bias, and data leakage
→Transaction costs, slippage, and train-test separation
→Sharpe ratio, drawdown, turnover, and benchmark comparison

Trading Track

→Time-series and cross-sectional momentum
→Trend, reversal, and regime dependence
→Underreaction, herding, crowding, and market microstructure
→Transaction costs, market impact, capacity, and edge decay

Development Track

→Project-specific option-pricing or fixed-income module
→Black–Scholes and Greeks, or bond pricing and DV01
→Basic numerical methods and sensitivity calculations
→Testing financial functions and important boundary cases

Trading Track

→Leading and lagging economic indicators
→Data-release timing, revisions, and look-ahead concerns
→Correlation, prediction, causality, and economic mechanisms
→Assessing whether a macroeconomic signal is stable and tradable

Development Track

→Research code versus production code
→Broker and market-data APIs
→Paper trading, order simulation, logging, and error handling
→Risk controls, monitoring, and kill switches

Trading Track

→Position sizing and volatility targeting
→Correlation, diversification, concentration, and exposure
→Drawdowns, stress testing, and kill conditions
→Final strategy defense and development handoff

Development Track

→Project-specific implementation-support sprint
→Pull requests, peer review, and unit tests
→Documentation, reproducibility, and known limitations
→Final demonstration and project-team handoff