us_equities_panel

Case Study: US Equities Panel

This case study is the broadest cross-sectional equity workflow in the book. It uses daily OHLCV data from NASDAQ Data Link for ~3,200 US stocks spanning 1990 through 2018-Q1 to test whether weak per-stock signals translate into a tradable strategy when scaled across thousands of names. The Fundamental Law of Active Management is the operating frame: the per-stock edge is small, but breadth across the cross-section is supposed to compensate. The role of this case study is to hold that claim against measured signal quality, paired-bootstrap confidence intervals, and an explicit holdout window.

The pipeline is unusually long because the universe is unusually large. Sixteen walk-forward folds (10y train, 1y validation), the most folds of any case study, are paired with multi-horizon labels and a feature panel that mixes momentum, mean-reversion, volatility, liquidity, value proxies, and walk-forward temporal models. The strategy is a daily long-short top-K cross-sectional ranker with dollar-neutral construction and material era-dependent costs (15-30 bps pre-decimalization, 5-15 bps after). The question the strategy-analysis notebook answers is whether the gross signal that survives this much testing also survives selection-adjusted resampling and the 2016-2018 holdout.

At a Glance

Property	Value
Asset Class	Broad US equities (NYSE/NASDAQ/AMEX)
Frequency	Daily
Universe	~3,200 stocks (price > $5, ADV > $1M, point-in-time)
History	1990-2018Q1
Primary Label	fwd_ret_1d
CV Folds	16 (10Y train, 1Y val)
Cost Model	Material (5-30 bps per leg, era-dependent + borrow)

Pipeline

Stage	Notebook	Chapter	Description	Writes
Feasibility	01_feasibility_analysis	Ch6	Universe breadth per decision date, cost regime, move-to-cost scale, walk-forward folds	Nothing - the evidence stays in the notebook
Labels	02_labels	Ch7	1-day, 5-day, and 21-day forward returns	labels/fwd_ret_1d.parquet, labels/fwd_ret_5d.parquet, labels/fwd_ret_21d.parquet, each with a .digest.json sidecar
Features	03_financial_features	Ch8	62 cross-sectional factors: momentum, volatility, liquidity, value	features/financial.parquet
Temporal	04_model_based_features	Ch9	Walk-forward Wasserstein regime distance, FFD, GARCH features	features/model_based.parquet
Evaluation	05_evaluation	Ch7--9	Feature-label IC diagnostics across the full panel	evaluation/triage_ledger.parquet, evaluation/ic_timeseries.parquet
Linear	06_linear	Ch11	Ridge, LASSO, ElasticNet on the full feature matrix	Training runs and prediction sets in run_log/registry.db; coefficients under run_log/training/{hash}/, scores under run_log/predictions/{hash}/
GBM	07_gbm	Ch12	LightGBM grid across leaf profiles and loss functions	Training runs and prediction sets; boosters, learning_curves.parquet, and fold_metrics.parquet under run_log/training/{hash}/
Tabular DL	08_tabular_dl	Ch12	TabM attention-style ensembling on the cross-section	Training runs and prediction sets; checkpoints under run_log/training/tabular_dl/
NLinear	09_dl_nlinear	Ch13	Minimal temporal baseline with last-value normalization	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
LSTM	10_dl_lstm	Ch13	Sequential memory across daily return windows	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
TSMixer	11_dl_tsmixer	Ch13	Time-mixing and feature-mixing across the 60-day lookback	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
Weekly DL	12_dl_weekly	Ch13	Weekly-cadence LSTM/NLinear comparison	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
Latent Factors	13_latent_factors	Ch14	Index notebook for PCA + IPCA on the broad equity panel	Nothing - it reads the registry
PCA	13a_pca	Ch14	Static factor extraction from the return covariance	Training runs and prediction sets
IPCA	13b_ipca	Ch14	Instrumented PCA with characteristic-conditioned loadings	Training runs and prediction sets
Causal DML	14_causal_dml	Ch15	Causal effect of 12-1 momentum on daily returns	A row in the registry's causal_runs
Model Analysis	15_model_analysis	--	Cross-model IC comparison and fold stability diagnostics	Nothing - it reads the registry
Backtest	16_backtest	Ch16	Daily long-short top-K strategy simulation	One backtest run per prediction set and entry scheme; daily_returns.parquet, weights.parquet, trades.parquet, fills.parquet, equity.parquet, portfolio_state.parquet, and spec.json under run_log/backtest/{hash}/
Portfolio	17_portfolio_management	Ch17	Allocation sweep on the highest-IC GBM signal	One backtest run per allocation method, same artifact layout
Risk	18_risk_management	Ch19	Position-level and portfolio-level risk overlays	One backtest run per overlay variant, same artifact layout
Costs	19_costs	Ch18	Cost-grid sweep on the strategies the three earlier stages produced	One backtest run per cost level, same artifact layout
Holdout Predictions	20_holdout_predictions	Ch20	Refit of the selected configuration on history ending before the holdout window	One training run and one prediction set at split='holdout'
Holdout Backtest	21_holdout_backtest	Ch20	The holdout predictions traded under the selected allocator, overlay and cost level	One backtest run at stage='holdout', same artifact layout
Strategy Analysis	22_strategy_analysis	Ch20	End-to-end strategy assessment: signal, lineage, holdout, attribution	results/strategy_assessment.json, 20_strategy_synthesis/output/us_equities_panel/us_equities_panel_tearsheet.html

Running

# From repo root
uv run python case_studies/us_equities_panel/01_feasibility_analysis.py
uv run python case_studies/us_equities_panel/02_labels.py
uv run python case_studies/us_equities_panel/03_financial_features.py
uv run python case_studies/us_equities_panel/04_model_based_features.py
uv run python case_studies/us_equities_panel/05_evaluation.py
uv run python case_studies/us_equities_panel/06_linear.py
uv run python case_studies/us_equities_panel/07_gbm.py
uv run python case_studies/us_equities_panel/08_tabular_dl.py
uv run python case_studies/us_equities_panel/09_dl_nlinear.py
uv run python case_studies/us_equities_panel/10_dl_lstm.py
uv run python case_studies/us_equities_panel/11_dl_tsmixer.py
uv run python case_studies/us_equities_panel/12_dl_weekly.py
uv run python case_studies/us_equities_panel/13_latent_factors.py
uv run python case_studies/us_equities_panel/13a_pca.py
uv run python case_studies/us_equities_panel/13b_ipca.py
uv run python case_studies/us_equities_panel/14_causal_dml.py
uv run python case_studies/us_equities_panel/15_model_analysis.py
uv run python case_studies/us_equities_panel/16_backtest.py
uv run python case_studies/us_equities_panel/17_portfolio_management.py
uv run python case_studies/us_equities_panel/18_risk_management.py
uv run python case_studies/us_equities_panel/19_costs.py
uv run python case_studies/us_equities_panel/20_holdout_predictions.py
uv run python case_studies/us_equities_panel/21_holdout_backtest.py
uv run python case_studies/us_equities_panel/22_strategy_analysis.py

The strategy-analysis notebook in 22_strategy_analysis.py writes a full diagnostic tear sheet (template="full") to the case study's gitignored output directory; readers regenerate it locally.

Results

This README describes how the case study is built, not what it found. Results are not restated here: the registry is rebuilt whenever the case study is re-derived, and a number copied into prose stays correct only until the next rebuild.

22_strategy_analysis reads the registry back and reports the selected configuration with its interval evidence. That notebook, and the registry it reads, are where a result comes from.

To read the results without training anything, download the published bundle, which carries the registry and the artifacts behind it:

uv run python scripts/download_artifacts.py --cs us_equities_panel

Two bundles are published, and they are separate generations rather than revisions of one another. v3.1.0-artifacts is current and is what the command above fetches. v3.0.0-artifacts holds the results as first published. The 3.1 rebuild re-keyed every content-addressed hash, so a hash taken from one bundle does not resolve in the other.

Run Log

run_log/registry.db records every training run, prediction set and backtest, each addressed by a hash of the specification that produced it. The artifacts sit beside it under run_log/training/, run_log/predictions/ and run_log/backtest/.