Curriculum: quantitative finance and systematic research

Local formal edition (2026-10-19): the complete English body and workbook PDFs for Chapters 1–45, Projects 1–6, and the Capstone have been independently accepted; the shared code, runner, and dependencies sit at this package root and were cold-started in an isolated directory with the commands on the entry page (scope in Validation scope). This is the authorized local formal edition; it has not been published externally, and real-market research has not been run.

The 45 rows preserve curriculum coverage, outcomes, examples and levels: 33 Must Know, 8 Should Know and 4 Advanced. Optional describes a route choice rather than permission to omit a core definition. Read the body for explanation and derivation, attempt Workbook recall and exercises before the complete same-chapter answers, then use each Notebook in its stated mode. Market-research completion is a separate question.

The quant landscape

Chapter	Coverage	Learning outcome	Checkable example	Level
Chapter 1 — Quantitative Finance and the Quant Industry	Quantitative finance; financial engineering; systematic and algorithmic trading; statistical arbitrage; research, trading, development, risk, derivatives, execution and HFT roles; fundamental quant; alpha, beta, factor, signal, position and exposure; idea-to-evaluation lifecycle	Separate research decisions, trading decisions and attribution; draw an auditable lifecycle	One momentum idea becomes different tasks for a researcher, trader and execution engineer	Must Know

Mathematics, statistics and programming

Chapter	Coverage	Learning outcome	Checkable example	Level
Chapter 2 — Python for Quantitative Research: Getting Returns and Time Indices Right	Python; NumPy; pandas; Polars; SciPy; matplotlib; Jupyter; Git; vectorization; time-series indexing; rolling and expanding windows; shift, rank, groupby and resample; environments, package management and floating-point error	Run the same analysis from a CLI and Notebook and verify return calculations by index alignment	Compare hand-table and vectorized price returns item by item	Must Know
Chapter 3 — Financial Data Engineering	OHLCV; adjusted prices; corporate actions, dividends and splits; delisting; missing data; time zones; trading calendars; PIT data; announcements; survivorship; symbol changes; data quality, revisions, licenses, permanent identifiers and bitemporal records	Define event_time, available_at, ingested_at and revision_time contracts	Put a split, dividend, after-close release, DST transition and delisting on one timeline	Must Know
Chapter 4: Calculus, Rates of Change, Curvature and Local Decisions	Derivatives; partial derivatives; gradients; Hessians; Taylor expansion; integration; multivariable calculus; optimization intuition; chain rule and numerical differences	Derive gradients and second-order risk from increments and check derivative units	Price sensitivity to yield and descent directions on a quadratic loss surface	Must Know
Chapter 5: Linear Algebra, Portfolio Objects and Risk	Vectors; matrices; multiplication; inverse; rank; basis; orthogonality; eigenvalues and eigenvectors; covariance; positive definiteness; SVD; PCA; condition numbers and linear solves	Read the dimensions of weights, return panels and covariance matrices	Covariance of two correlated assets and its degenerate cases	Must Know
Chapter 6 — Probability	Sample spaces; conditional probability; Bayes; random variables; expectation, variance and covariance; common distributions; LLN; CLT; random walks; Markov and Poisson processes; introduction to Brownian motion; conditional expectation and heavy tails	Update beliefs with conditional probability and distinguish path randomness from parameter uncertainty	Rare losses, random walks and simulated sample means	Must Know
Chapter 7 — Statistical Inference: uncertainty in research conclusions	Population and sample; estimators; bias, variance and consistency; maximum likelihood; confidence intervals; tests; p-values; Type I/II errors; bootstrap; Bayesian introduction; block bootstrap; effective sample size; power and multiple comparisons	Distinguish estimator, estimate and sampling distribution and explain an interval	Mean-return intervals under independence and autocorrelation	Must Know
Chapter 8 — Regression	OLS assumptions; R² and adjusted R²; t and F tests; heteroskedasticity; autocorrelation; multicollinearity; robust standard errors; Ridge, Lasso and Elastic Net; CAPM regression; HAC and introduction to panel-clustered standard errors	Derive normal equations and separate coefficient estimation, inference and prediction	Explain asset returns with market excess returns and compare ordinary and HAC errors	Must Know
Chapter 9 — Optimization	Objectives; constraints; convexity; Lagrangians; KKT intuition; least squares; linear and quadratic programming; portfolio optimization; CVXPY; feasibility, duality and solver status	Write budgets, weight bounds and risk as an explicit optimization problem	Geometry of least squares and two-asset minimum variance	Must Know

Financial foundations

Chapter	Coverage	Learning outcome	Checkable example	Level
Chapter 10 — Financial Markets	Stocks; ETFs; bonds; FX; futures; options; swaps; repo; exchanges, brokers, dealers and market makers; clearing and settlement; no-arbitrage; shorting; margin; collateral; contract multipliers; term structures; duration, convexity and futures rolls	Track distinct order, fill, clearing and cash-settlement times	Compare dividend, coupon and daily futures-marking cash flows	Must Know
Chapter 11 — Returns and Compounding	Simple and log returns; arithmetic and geometric means; CAGR; annualization; volatility; drawdown; wealth indices; excess and total returns; irregular intervals; benchmarks and cash accounts	Derive compounding, drawdown and return aggregation from wealth recursion	A 50% rise followed by a 50% fall, and returns including dividends	Must Know
Chapter 12 — Financial Statements for Quantitative Research	Income statements; balance sheets; cash-flow statements; revenue; EBITDA; EBIT; net income; CFO; CapEx; FCF; working capital; debt; cash; filing times; revisions; fiscal alignment and noncontrolling interests	Turn statement items into information available at a declared time and version	Factor changes between a period end, first release and restatement	Must Know
Chapter 13 — Valuation for Quants	Enterprise and equity value; EV-to-equity bridge; P/E; P/B; EV/EBITDA; EV/EBIT; FCF and earnings yields; DCF; WACC; terminal value; negative earnings; denominator policies; currency and unit matching	Express valuation bridges and ratios with explicit data conventions	Two companies with different cash, debt and noncontrolling interests	Should Know

Asset pricing, econometrics and portfolios

Chapter	Coverage	Learning outcome	Checkable example	Level
Chapter 14 — CAPM and Asset Pricing	No-arbitrage; risk premia; CAPM; systematic and idiosyncratic risk; beta; SML; CML; pricing versus prediction and empirical limits	Connect mean–variance logic, equilibrium relations and time-series estimates	Two assets with equal returns but different market sensitivities	Must Know
Chapter 15 — Factor Models	CAPM; Fama–French three- and five-factor models; SMB, HML, RMW and CMA; loadings, exposures, alpha and residual risk; Kenneth French historical data, frequency alignment and revisions	Explain excess-return regressions and exposures; distinguish constructed practice from the permission-gated historical French route	Monthly industry regressions on FF3 and FF5 as a historical case	Must Know
Chapter 16 — Time Series I: Dependence and ARIMA	Stationarity; white noise; autocorrelation; ACF and PACF; AR, MA, ARMA and ARIMA; lag operators; stationarity, invertibility and forecast intervals	Derive AR(1) moments and model price levels separately from returns	Series with equal mean and variance but different autocorrelation	Must Know
Chapter 17 — Time Series II: Unit Roots, Cointegration, and Prediction	Unit roots; ADF; VAR; Granger causality; cointegration; Engle–Granger; pairs trading; correlation versus cointegration; deterministic trends; residual-test critical values and multiple-pair screening	Distinguish high correlation, common stochastic trends and predictability	Correlated random walks versus a cointegrated pair	Must Know
Chapter 18 — Volatility Modeling and Forecasting	Realized volatility; EWMA; ARCH and GARCH; clustering; forecasts; persistence; sampling frequency and microstructure noise	Distinguish conditional variance from realized measures	Historical crisis-window rolling, EWMA and GARCH comparison, separately gated from constructed practice	Should Know
Chapter 19 — State-Space Models and the Kalman Filter	State-space representation; latent variables; Kalman filter; dynamic beta and hedge ratios; filtering, smoothing and innovations	Derive prediction and update steps and separate state from observation noise	Compare rolling beta and dynamic Kalman beta	Advanced
Chapter 20 — Modern Portfolio Theory	Expected return; covariance; portfolio variance; diversification; efficient frontiers; minimum variance; tangency; Sharpe ratio; risky and risk-free assets	Derive portfolio variance and the frontier from covariance expansion	How changing two-asset correlation changes the frontier	Must Know
Chapter 21 — Practical Portfolio Construction	Estimation error; covariance shrinkage; uncertain means; turnover; constraints; leverage; sector, beta and dollar neutrality; risk parity; ERC; Black–Litterman; Kelly; parameter uncertainty, risk budgets and factor-risk decomposition	Construct portfolios using stable estimation and explicit constraints	Compare equal weight, minimum variance, maximum Sharpe and ERC in rolling designs	Must Know

Systematic alpha research

Chapter	Coverage	Learning outcome	Checkable example	Level
Chapter 22 — Quantitative Research Methodology	Economic hypothesis, data, cleaning, feature, signal, portfolio, backtest, validation, costs and OOS; research logs; benchmarks; preregistration; negative results; ablations and reproduction	Freeze hypotheses, samples and evaluation before final-test inspection	A prospective value-factor plan versus retrospective mining	Must Know
Chapter 23 — Cross-Sectional Research	Ranks and percentiles; quantile and decile portfolios; long–short portfolios; IC and Rank IC; IC and signal decay; turnover; panel dependence; weighting and introduction to Fama–MacBeth	Form rankings at declared times and distinguish predictions from holdings	Month-end signals, next-month Rank IC and quantile portfolios	Must Know
Chapter 24 — Value Factors	Book-to-market; earnings and FCF yields; EV/EBITDA; industry normalization; value traps; filing PIT and value-risk interpretations	Define comparable value signals available at formation	Within-industry comparisons for financial and manufacturing firms	Must Know
Chapter 25 — Momentum	Cross-sectional and time-series momentum; 12–1; reversal; trends; skipped months; crashes and rebalancing	Derive exact window endpoints and position direction	Cross-sectional winner ranks versus a single asset’s signed trend	Must Know
Chapter 26 — Quality	ROE; ROA; gross and operating profitability; leverage; accruals; earnings stability; accounting comparability and financial-sector conventions	Derive quality measures from accounts and check economic meaning	ROE rising through better operations versus greater leverage	Must Know
Chapter 27 — Carry, Defensive Investing, and Low Volatility	FX, bond and commodity carry; defensive and low-volatility signals; forward premia; term structures; roll yield; beta leverage; crashes and funding risk	Explain carry through holding cash flows, price curves and financing	FX forwards and commodity-futures roll cash flows	Should Know
Chapter 28 — Multi-Factor Models: Preprocessing, Combination, and Constraints	z-scores; percentile ranks; winsorization; neutralization; standardization; orthogonalization; signal combination; factor correlations and crowding; missing factors, weights and ordering	Give preprocessing, combination and constraints separate responsibilities	Raw and neutralized exposures of value, momentum and quality	Must Know

Backtesting and risk

Chapter	Coverage	Learning outcome	Checkable example	Level
Chapter 29 — Building a Backtesting Engine	Data-to-metrics chain; target weights; execution lags; positions, costs and P&L; self-financing; cash ledgers; drift; orders, fills and rebalancing; vectorized versus event-driven boundaries	Implement a reconciled cash and holdings ledger	Month-end targets executed next day, with returns on actual filled holdings	Must Know
Chapter 30 — Backtesting Biases	Look-ahead; survivorship; selection; snooping; overfitting; corporate actions; parameter mining; incorrect costs; contemporaneous information; delisting and publication lags	Pair wrong and repaired code with same-input comparisons for each bias	Early use of after-close news, survivors, full-sample tuning and omitted dividends/costs	Must Know
Chapter 31 — Financial Model Validation	Chronological splits; rolling and expanding windows; walk-forward; TimeSeriesSplit; gap; embargo; purging; overlapping labels; nested validation and panel splits	Define purging by interval intersections and testing by trade times	Five-day forward labels overlapping a train/test boundary	Must Know
Chapter 32 — Performance Statistics & Inference	CAGR; volatility; Sharpe, Sortino and Calmar; drawdown; hit rate; turnover; Profit Factor; Information Ratio; Tracking Error; PSR; DSR; multiple testing; PBO; CSCV; correlated trials, nonnormality and dependence corrections	Report intervals, search counts and economic meaning alongside metrics	The best Sharpe among many random strategies and DSR/PBO diagnostics	Must Know
Chapter 33 — Risk Management	VaR; ES; drawdown; volatility; beta and factor exposures; stress and scenarios; liquidity; leverage; concentration; tails; correlation breakdown; margin, limits and model backtesting	Express risk as loss distributions, exposures and exit constraints	Gaps, liquidity contraction and margin calls despite ordinary volatility	Must Know

Microstructure and execution

Chapter	Coverage	Learning outcome	Checkable example	Level
Chapter 34 — Market Microstructure	Bid; ask; spread; mid; market and limit orders; books, depth and queues; liquidity; impact; adverse selection; tick size; fragmented venues and queue position	Distinguish quotes, fills and executable prices	An apparently profitable mid-price trade loses after bid/ask execution	Should Know
Chapter 35 — Transaction Costs	Commission; spread; slippage; impact; financing; borrow fees; capacity; gross versus net returns; temporary and permanent impact; calibration; currency versus return units	Connect each cost to a fill ledger and capacity sensitivity	One signal’s net result across turnover and participation levels	Must Know
Chapter 36 — Algorithmic Execution and Trade Scheduling	TWAP; VWAP; POV; IS; execution risk; impact; optimal-execution introduction; arrival prices; Almgren–Chriss and unfilled opportunity cost	Express benchmark tracking and risk–cost tradeoffs as execution objectives	IS decomposition for one parent order split through time	Should Know

Machine learning and advanced quant

Chapter	Coverage	Learning outcome	Checkable example	Level
Chapter 37. A Foundational Stance on Machine Learning for Finance	Nonstationarity; low signal-to-noise; time dependence; regime shifts; multiple testing; leakage; differences from Kaggle; baselines; prediction versus decision	Explain why higher accuracy need not improve net returns	A directionally accurate model fails on large-loss days or turns over excessively	Must Know
Chapter 38: Financial ML Pipeline	Features; labels; horizon; preprocessing; feature engineering; validation; tuning; evaluation; Pipeline; label availability; cross-sectional panels and calibration	Contract feature times, label intervals and fit times	A panel predicting five-day returns from closing features	Must Know
Chapter 39 Regularized Linear Models	Ridge; Lasso; Elastic Net; paths; scaling; bias–variance; nested tuning; sparsity, collinearity and baselines	Compare OLS and three penalties in a controlled design	OOS prediction with more noise factors than useful signals	Must Know
Chapter 40: Tree-Based Models	Decision trees; random forests; gradient boosting; depth; minimum leaf samples; learning rates; permutation importance and SHAP limits	Explain splits, bagging and boosting and assess stability	OOS ablations with nonlinear features	Should Know
Chapter 41: Unsupervised Learning	PCA; clustering; regime detection; explained variance; scaling; cluster stability; factor interpretation and retrospective state labels	Compress risk and test whether state identification is available before trading	Return-panel PCA and market-regime clustering	Should Know
Chapter 42: Deep Learning Foundations and Quantitative Applications	MLP; CNN; RNN and LSTM intuition; Transformers; financial-data limits; gradients; batching; attention; causal masks; early stopping; parameters versus sample size	Understand architectural bias and test complexity against simple baselines	A small MLP versus regularized linear models on the same split	Advanced
Chapter 43: Bayesian Inference, Causal Identification, and Alternative Text Data	Bayesian and hierarchical models; causal inference; text, news and calls; embeddings; sentiment; LLM-assisted research; DAGs; identification; confounding; release latency; corpus times; licenses and prompt versions	Separate posterior uncertainty, causal identification and text prediction into testable questions	An after-close call-text-to-next-day signal design	Advanced
Chapter 44 — Stochastic Calculus and Derivatives	Brownian motion; filtrations; martingales; Ito integral and lemma; SDEs; GBM; risk-neutral probability; Black–Scholes; Greeks; trees; Monte Carlo; PDE, Girsanov and Feynman–Kac intuition; replication, P/Q, smile and numerical error	Derive pricing from binomial replication to continuous time without dropping Ito’s second-order term	Compare European option tree, analytic and Monte Carlo prices	Advanced
Chapter 45 — Production Quant Pipelines	Architecture; src packages; Notebooks; tests; configuration; logging; reproducibility; SQL, DuckDB and Parquet; APIs; caching; Docker; scheduling; monitoring; tracking; lineage; idempotency; secrets; reconciliation; kill switches; paper trading; rollback and drift	Turn research code into a rerunnable, monitored and reconciled pipeline	A scheduled signal task fails, repeats or disagrees with its fill ledger	Should Know

Projects and completion

Project	Prerequisite chapters	Completion criterion
Project 1: Financial data analysis	2, 3, 10, 11	Return, wealth, volatility and drawdown calculations checked against hand examples; historical data retain provenance.
Project 2: CAPM and Fama–French	8, 11, 14, 15	Check excess returns, units, HAC and exposure interpretation.
Project 3: Portfolio optimization	9, 18, 20, 21	Estimate inside training windows and compare rolling OOS baselines and costs.
Project 4: Multi-factor equity strategy	12, 23–26, 28–31, 35–36	Require PIT signals, historical universes, net returns and robustness; constructed sublabs do not close market evidence.
Project 5: Pairs trading	16–17, 29–31, 35–36	Select within training, freeze rules and reconcile self-financing OOS ledgers and costs.
Project 6: Machine-learning alpha	31, 35, 37–40, 43	Split by date, fit transforms inside training and compare baselines, net returns and stability.
Capstone	Methods from all six projects, 33, 45	18 report items and reproducible workflow; negative results can pass. Market execution has separate gates.

Prepare a download plan and dictionary for P1, format parsing for P2, an equal-weight baseline for P3, a PIT dictionary for P4, alignment for P5, label calendars for P6, and hypothesis/data feasibility for the Capstone. Passing requires credible design, checks and reporting, not profitability.

Notebook modes · Required chapter prerequisites · Learning route