The chapter gives the chapter its core editorial value: a disciplined way to move from a trading narrative to a feature specification. The three-step filter -- horizon alignment, driver hypothesis, and role separation -- turns feature design from indicator collecting into explicit hypothesis design, while the reference-frame, representation, and aggregation knobs make clear which choices actually change meaning and which only smooth noise.

Financial features differ by data requirement and role. Some are direct signals; others describe state, feasibility, or context.

Learning Objectives

Translate a trading hypothesis into a documented feature specification using horizon alignment, driver hypothesis, and role separation.
Choose a feature's reference frame, representation, and aggregation to match the economic claim and execution horizon, and distinguish hypothesis-changing choices from noise-control choices.
Distinguish signal features from state variables and identify when each should be used marginally, as an interaction, or as a conditioning variable.
Design representative feature specifications across price-derived, structural and cross-instrument, and contextual data families, with explicit timing assumptions and failure modes.
Combine signals with state variables using gating, scaling, and conditional variants, and evaluate whether the interaction adds incremental information.
Apply point-in-time discipline to slow-moving and revised data, including reporting lags, event timing, and vintage-aware availability rules.
Control feature-search degrees of freedom using one-knob-at-a-time exploration, within-family deduplication, and multiple-testing-aware triage.

Sections

8.1 Capturing and Configuring the Economic Drivers

This section gives the chapter its core editorial value: a disciplined way to move from a trading narrative to a feature specification. The three-step filter -- horizon alignment, driver hypothesis, and role separation -- turns feature design from indicator collecting into explicit hypothesis design, while the reference-frame, representation, and aggregation knobs make clear which choices actually change meaning and which only smooth noise.

8.2 Price-Derived Features

This section builds the reusable feature families available from the minimum market dataset: trend, reversal, volatility, liquidity, and microstructure. Its value is not just cataloging common signals, but showing how each family encodes a specific economic claim, operates at particular horizons, and fails in recognizable ways when costs, latency, or regime shifts are ignored.

01_price_volume_features — This notebook demonstrates the core feature families derived from a single asset's price and volume history. These are the workhorse features of most quantitative strategies — available for every tradeable instrument.
02_microstructure_features — Microstructure features capture market dynamics invisible in daily OHLCV data. They proxy for liquidity, information flow, and execution quality.

8.3 Structural and Cross-Instrument Features

Here the chapter moves beyond single-series transformations to information that only appears in relationships across contracts, assets, and derivative markets. Carry, relative value, lead-lag structure, and options-implied features all expand the feature space in economically meaningful ways, and the section usefully emphasizes that construction choices such as maturity alignment, peer-set definition, and surface policy are part of the hypothesis, not implementation detail.

03_structural_cross_instrument_features — This notebook demonstrates features that require data beyond a single asset's price series: term structures, cross-instrument relationships, and derivatives-implied quantities. These encode information invisible in any individual price history.

8.4 Contextual and Slow-Moving Features

This section shows how fundamentals, calendars, and macro variables enter ML systems mainly as state variables that condition faster signals. Its main practical contribution is to make point-in-time correctness the central constraint, reminding readers that slow data is often more dangerous than fast data because reporting lags, revisions, and repeated values can easily create fake evidence.

04_fundamentals_macro_calendar — Slow-moving features that condition faster signals: SEC XBRL fundamentals (value/quality factors with point-in-time ASOF alignment), FRED macro indicators (yield curve, VIX regimes, credit spreads with publication-lag handling), and calendar encodings (cyclical sin/cos, time-to-event proximity).

8.5 Cross-Cutting Features and the Limits of Aggregation

This section marks the conceptual boundary of the chapter. It explains when deterministic rolling transformations are enough and when hidden structure -- latent states, conditional dynamics, cycle strength, or path shape -- requires fitted models and learned representations, which sets up Chapter 9 cleanly without duplicating it.

8.6 Combining Features and Controlling Search

This is the chapter's second major contribution after the feature-design grammar. It shows that practical improvement often comes from signal-by-state interactions, but also that these interactions multiply degrees of freedom quickly, so gating, scaling, conditional variants, deduplication, and one-knob-at-a-time discipline are necessary to keep the search credible.

05_feature_selection — A feature engineering pipeline produces many candidates — different lookbacks, transforms, and interaction variants. This notebook demonstrates how to reduce that set to a focused, production-ready collection using systematic selection and deduplication.
06_robustness_sensitivity — A robust signal maintains performance across reasonable variations in parameters, regimes, and implementation choices. This notebook teaches how to assess robustness through parameter sweeps, regime conditioning, and signal × state interactions.
07_event_studies — Event studies measure abnormal returns around specific events (signal triggers, macro announcements, earnings) to assess their predictive power. This is a key validation technique for trading signals.
case_study_feature_summary — Cross-case-study feature inventory: feature counts per case study, family heatmap (momentum/volatility/return everywhere; carry on futures/FX; options-implied on the options case studies), and a breadth-vs-IC view that combines best-IC-per-case-study from the registry with universe-size metadata (Fundamental Law: IR ≈ IC × √BR).

Running the Notebooks

# From the repository root
uv run python 08_financial_features/<notebook>.py

# Test mode (reduced data via Papermill)
uv run pytest tests/test_notebooks.py -v -k "08_financial_features"

Memory: 03_structural_cross_instrument_features peaks at ~7.4 GB RSS scanning the AlgoSeek S&P-500 options surface — recommend ≥8 GB system RAM for §8.3.

References

Yakov Amihud (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets.
Andrew Ang and Allan Timmermann (2011). Regime Changes and Financial Markets.
Clifford S. Asness et al. (2013). Value and Momentum Everywhere. The Journal of Finance.
Peter Carr and Liuren Wu (2009). Variance Risk Premiums. The Review of Financial Studies.
Rama Cont et al. (2014). The Price Impact of Order Book Events. Journal of Financial Econometrics.
David Easley et al. (2021). Microstructure in the Machine Age. The Review of Financial Studies.
Eugene F. Fama and Kenneth R. French (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance.
Mark B. Garman and Michael J. Klass (1980). On the Estimation of Security Price Volatilities from Historical Data. The Journal of Business.
Campbell R. Harvey et al. (2016). ...and the Cross-Section of Expected Returns. Review of Financial Studies.
Zura Kakushadze et al. (2015). 101 Formulaic Alphas.
Albert S. Kyle (1985). Continuous Auctions and Insider Trading. Econometrica.
Ari Levine and Lasse Heje Pedersen (2016). Which Trend Is Your Friend?. Financial Analysts Journal.
Giuseppe A. Paleologo (2025). The Elements of Quantitative Investing. John Wiley & Sons.
Joseph D. Piotroski (2000). Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. Journal of Accounting Research.
Marcos Lopez de Prado (2018). Advances in Financial Machine Learning. John Wiley & Sons.
Dennis Yang and Qiang Zhang (2000). Drift‐Independent Volatility Estimation Based on High, Low, Open, and Close Prices. The Journal of Business.

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Chapter 8: Financial Feature Engineering