stefan-jansen--machine-learning-for-trading/case_studies/nasdaq100_microstructure

Case Study: NASDAQ-100 Microstructure

This case study uses AlgoSeek TAQ-derived 15-minute bars for 114 NASDAQ-100 constituents to test whether microstructure signals -- order flow, quote staleness, relative spreads -- produce tradeable intraday alpha. This is the highest-frequency case in the book, and it is designed to show how a dominant cost floor makes a raw signal loss-making on the full universe -- and how two disciplined adjustments, a cost-feasible universe screen and ensemble model selection, recover it. The naive build fails; the disciplined build claws the holdout back from clearly-negative to marginally-positive. That iteration -- diagnose the cost problem, screen the universe, treat model selection as estimation under uncertainty -- is the lesson.

At a Glance

Property	Value
Asset Class	US equities (NASDAQ-100)
Frequency	15-minute bars
Universe	114 stocks
History	2020--2021
Primary Label	fwd_ret_15m
CV Folds	2 (6M train, 6M val)
Cost Model	per_share_plus_spread ($0.0035/share + measured half-spread; 5 bps friction floor)

Pipeline

Stage	Notebook	Chapter	Description	Writes
Feasibility	01_feasibility_analysis	Ch6	Measured per-symbol round-trip cost, breadth and move clearance by horizon, return persistence, walk-forward demo	liquidity_profile.parquet
Labels	02_labels	Ch7	15-minute, 5-minute, and 60-minute forward returns	One parquet per label in labels/ (fwd_ret_15m plus the fwd_ret_5m, fwd_ret_60m, fwd_dir_15m variants)
Features	03_financial_features	Ch8	Order-flow, spread, volatility, and microstructure features	features/financial.parquet
Temporal	04_model_based_features	Ch9	Walk-forward temporal features for intraday patterns	features/model_based.parquet
Evaluation	05_evaluation	Ch7--9	Feature-label IC diagnostics for 66 financial and temporal features	evaluation/triage_ledger.parquet, evaluation/ic_timeseries.parquet
Linear	06_linear	Ch11	Ridge baseline on the richest feature space in the book	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 on 13M+ training samples at 15-minute frequency	Training runs and prediction sets; boosters, learning_curves.parquet, and fold_metrics.parquet under run_log/training/{hash}/
NLinear	08_dl_nlinear	Ch13	Minimal temporal baseline for the intraday microstructure signal	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
LSTM	09_dl_lstm	Ch13	Recurrent memory for short-lived order-flow and spread dynamics	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
TCN	10_dl_tcn	Ch13	Dilated causal convolutions for intraday temporal patterns	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
PatchTST	11_dl_patchtst	Ch13	Multi-scale patch attention on minute-bar sequences	Training runs and prediction sets; checkpoints under run_log/training/deep_learning/
Causal DML	12_causal_dml	Ch15	Does signed volume share cause future 15-minute returns?	A row in the registry's causal_runs
Model Analysis	13_model_analysis	--	Cross-model IC comparison and fold stability diagnostics	Nothing - it reads the registry
Backtest	14_backtest	Ch16	Strategy simulation designed to demonstrate cost-driven failure	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	15_portfolio_management	Ch17	Allocation methods under dollar-neutral intraday constraints	One backtest run per allocation method, same artifact layout
Costs	16_costs	Ch18	Flagship cost analysis: spread, impact, and commission decomposition	One backtest run per cost level, same artifact layout
Risk	17_risk_management	Ch19	Intraday risk controls and position-level exit rules	One backtest run per overlay variant, same artifact layout
Strategy Analysis	18_strategy_analysis	Ch20	End-to-end strategy assessment with IC, Sharpe, and cost analysis	results/strategy_assessment.json, 20_strategy_synthesis/output/nasdaq100_microstructure/nasdaq100_microstructure_tearsheet.html

Key Results

The book's study in cost and selection discipline: a raw intraday cross-sectional signal that is friction-dominated on the full universe, and the two disciplined adjustments that recover it. Built the naive way — the full NASDAQ-100, no careful spread analysis — the strategy is loss-making across the sweep and the holdout Sharpe lands at −0.89. Careful cost analysis shows the edge is real but concentrated in names too expensive to trade at this cadence; screening to a cost-feasible universe and replacing the noisy single-best model with an ensemble walks the holdout from −0.89 → −0.21 → +0.53. The recovered number is modest and its confidence interval is wide — the lesson is how disciplined adjustments change the answer, not a claim of a deployable edge.

Signal quality: The highest-IC configuration on the primary fwd_ret_15m label is gbm/leaves_7_mae at +0.0060 (HAC CI [+0.0026, +0.0094], excludes zero); linear/ridge_a1000000.0 follows at +0.0049 (CI [+0.0022, +0.0076], excludes zero). IC strengthens monotonically toward the short end — the highest-IC GBM configuration runs +0.0104 at fwd_ret_5m. At fwd_ret_60m the linear/ridge_a1000000.0 IC is +0.0069 with HAC CI [-0.0021, +0.0159] (t=1.50, p=0.13) — point estimate faintly positive but the CI straddles zero. The most-shrunk regularizers achieve the highest IC at every horizon: small-leaf MAE GBM and ridge with \alpha \in \{10^6, 10^7\}.

The two adjustments, on the holdout. Each step is a single deliberate change, and each is one holdout consultation of the chosen configuration (all three reproducible from run_log/registry.db):

Step	Change	Holdout Sharpe	CI95
1. Naive baseline	full universe, single-best model	−0.89	[−3.59, +2.32]
2. Cost-feasible universe	screen to the cheapest-to-trade names, frozen per split	−0.21	[−2.37, +3.64]
3. Ensemble selection	average the 12-model set instead of the single-best pick	+0.53	[−1.94, +3.07]

Adjustment 1 is the microstructure lesson: the cost-expensive tail of the 114-name panel consumes the intraday edge, so the full universe collapses out of sample while the cost-feasible subset does not (see 16_costs.py for the full-vs-screened contrast). Adjustment 2 is the selection lesson: the per-model validation Sharpes have enormous, completely overlapping 95% CIs, so the single-best pick is noise — averaging the model set (an ensemble) is estimation under selection uncertainty. The ensemble is a robustness device against selection noise, not a return booster: it converts the single-best pick's out-of-sample loss (−0.21) into an honest modest positive rather than adding alpha. Every holdout CI above is wide and straddles zero (n≈128 days); the recovery is a point-estimate rescue from clearly-negative to marginally-positive, not a significance result. The naive-baseline row uses the direction label fwd_dir_15m while the screened steps use fwd_ret_60m; the ladder is directionally honest — the full universe is deeply negative regardless of label — but it is not one strategy tuned in place.

Holdout closure (the ensemble carrier): The deployed configuration is the cost-feasible ensemble at holdout Sharpe +0.53 [−1.94, +3.07] (n=128 trading days) — positive on the point estimate, where the naive full-universe baseline (−0.89) and the single-best cost-feasible pick (−0.21) are not. It remains below a passive equal-weight buy-and-hold of the same screened basket over the window: the recovery is to viability, not to out-performance — the active signal claws back to positive but does not beat simply holding the cost-feasible names. The strategy-vs-equal-weight paired-difference interval is not populated in the registry for this case study (backtest_paired_metrics has no producer here), so kill gate 2 reads no data rather than a spurious pass — the gate helpers treat the missing bootstrap as no-evidence, not as a green light. The honest headline: the ensemble recovers a positive point estimate, not statistical significance and not a market-beating edge; every interval above straddles zero.

Friction floor — why the naive build fails: The bps cost trajectory for the naive-lineage prediction (1c4327c80284, linear/ridge_a1000000.0/fwd_ret_60m) walks monotonically from Sharpe -0.78 at zero cost through -10 at the 50 bps tail; the CI upper bound clips positive only at the zero / 1 / 2 / 3 bps cells. The realistic NQ100 large-cap half-spread of 1–3 bps plus the per-share $0.0035 floor (~5 bps friction floor) sits inside the negative band. This is the diagnostic behind Adjustment 1: on the full universe the cost floor swamps the edge, which is why screening to the cheapest-to-trade names is load-bearing rather than cosmetic — the expensive tail is where the cost is, and dropping it is what moves the holdout off the floor. (Position-level overlays alone do not: the 20-row-per-label risk-overlay sweep — trailing_stop, stop_loss, time_exit — is uniformly negative across all three regression labels on the naive lineage.) Portfolio-level kill switches (max-drawdown breaker, daily-loss limit) are NOT swept for model selection — their permanent-halt semantics produced zero-std Sharpe artifacts in earlier passes; they remain available as Ch19 §19.8 governance instruments.

Running

# From repo root
uv run python case_studies/nasdaq100_microstructure/01_feasibility_analysis.py
uv run python case_studies/nasdaq100_microstructure/02_labels.py
uv run python case_studies/nasdaq100_microstructure/03_financial_features.py
uv run python case_studies/nasdaq100_microstructure/04_model_based_features.py
uv run python case_studies/nasdaq100_microstructure/05_evaluation.py
uv run python case_studies/nasdaq100_microstructure/06_linear.py
uv run python case_studies/nasdaq100_microstructure/07_gbm.py
uv run python case_studies/nasdaq100_microstructure/08_dl_nlinear.py
uv run python case_studies/nasdaq100_microstructure/09_dl_lstm.py
uv run python case_studies/nasdaq100_microstructure/10_dl_tcn.py
uv run python case_studies/nasdaq100_microstructure/11_dl_patchtst.py
uv run python case_studies/nasdaq100_microstructure/12_causal_dml.py
uv run python case_studies/nasdaq100_microstructure/13_model_analysis.py
uv run python case_studies/nasdaq100_microstructure/14_backtest.py
uv run python case_studies/nasdaq100_microstructure/15_portfolio_management.py
uv run python case_studies/nasdaq100_microstructure/16_costs.py
uv run python case_studies/nasdaq100_microstructure/17_risk_management.py
uv run python case_studies/nasdaq100_microstructure/18_strategy_analysis.py

Run Log

Model training runs, predictions, and backtest results are tracked in a content-addressed registry under run_log/registry.db.