pit-release-gate

Completeness-aware release control for staggered-arrival cross-sectional data.

When the entities of a cross-section report on staggered dates — companies filing financial statements are the canonical case — any same-period cross-sectional signal computed before the last filer arrives is estimated from an incomplete, and possibly selectively incomplete, cross-section. If filing timing depends on the very disturbance the signal measures, releasing early produces a systematic bias (incomplete-cross-section leakage), while a blanket wait-for-the-deadline rule removes the bias at a timeliness cost paid by every signal, biased or not. pit-release-gate measures each signal's susceptibility to this bias — a disturbance-conditional partial correlation fitted honestly on prior completed periods — and grades the required completeness per signal, so benign signals release early and susceptible signals are withheld until enough of the cross-section has arrived to suppress the bias.

Install

pip install pit-release-gate        # once published to PyPI
# or, from a source checkout:
pip install .

Requires Python 3.10+ (numpy, pandas, scipy).

Quickstart (30 seconds)

import numpy as np
from pit_release_gate import SusceptibilityGate, ReleaseController, make_group

rng = np.random.default_rng(0)

# 1. Fit the susceptibility gate on prior COMPLETED periods (honest estimation:
#    never on the period being gated — its cross-section is still incomplete).
train = [make_group(c_a=0.3, c_x=0.7, rng=rng) for _ in range(10)]
gate = SusceptibilityGate(threshold=0.10)
rho = gate.fit_trailing(train)

# 2. Gate a fresh, live period with the frozen estimate.
controller = ReleaseController(gate=gate)
live = make_group(c_a=0.3, c_x=0.7, rng=rng)
decision = controller.run_until_release(live, policy="gated")

print(f"rho_hat={rho:+.3f}  ->  {decision.action} "
      f"at completeness {decision.completeness:.0%} ({decision.policy})")

To gate your own data, build an AsOfDataStore from your design matrix, signal values, and per-entity filing-arrival times, then call ReleaseController.decide(store, t) at each evaluation time — it returns WITHHOLD, REWEIGHT_RELEASE, or RELEASE plus the released values.

The known-ground-truth demo

The package ships a self-contained worked example with a planted leakage strength, so the right answer is known exactly and no licensed data is needed:

pit-release-gate            # or: python -m pit_release_gate

It compares five release policies (naive, threshold, reweight, deadline, gated) on four signal types. Headline behavior:

A sensitivity sweep of the policy slope κ shows the timeliness–bias dial: κ = 0.5 → release at 59% completeness (bias −0.229); κ = 1.0 → 83% (−0.118); κ = 2.0 → 100% (bias exactly 0). The demo is deterministic (fixed seed), and tests/test_reproduces_paper.py asserts these numbers.

Papers

The method and its evaluation are developed in three public papers:

1. Correct-by-Construction Factor Computation: A Verifiably Point-in-Time Engine for Tradeable Signals — doi:10.6084/m9.figshare.32952482
2. Measuring Incomplete-Cross-Section Leakage: A Matched Placebo, a Susceptibility Screen, and Evidence from Taiwan and US As-Filed Data — doi:10.6084/m9.figshare.33061955
3. Susceptibility-Graded Release Control: Preventing Incomplete-Cross-Section Leakage in Financial Machine-Learning Pipelines without a Blanket Timeliness Penalty — doi:10.6084/m9.figshare.33158615

This package is the reference implementation of paper 3's release controller; its demo reproduces the paper's controlled experiment.

Cite this

See CITATION.cff. If you use this software, please cite paper 3:

@article{wu2026releasecontrol,
  title  = {Susceptibility-Graded Release Control: Preventing Incomplete-Cross-Section
            Leakage in Financial Machine-Learning Pipelines without a Blanket
            Timeliness Penalty},
  author = {Wu, Kuan-Ta and Wu, Kuan-I},
  year   = {2026},
  doi    = {10.6084/m9.figshare.33158615}
}

License

MIT — see LICENSE.

Patent pending: this software implements techniques described in pending U.S. patent applications. The MIT license above governs use of this code.

Max Well Apex LLC — maxwellapexlab@proton.me