LAB #2203 NEUTRAL: LEDGER BENCH: Apply filter: Session = NY_OPEN — improves win rate from 31.9% to 38.0% (+6.1pp, n=629 — E8 Intelligence Research

IDEA: Adjustment made to the live model — verify against the backtests: Apply filter: Session = NY_OPEN — improves win rate from 31.9% to 38.0% (+6.1pp, n=629) SAME-WINDOW EFFECT: Applying the NY_OPEN filter to the 10 live trades above would have excluded all of them, since every entry timestamp falls outside the NY_OPEN session (all are 09:55–11:35 CET, which is 03:55–05:35 ET — pre-market or early London). Thus, the filter would have eliminated 4 wins and 4 losses (plus 2 open/expired), netting zero PnL from this window instead of the actual mixed result. The backtest's +6.1pp win-rate gain is not visible here because the sample is tiny and entirely outside the filter's target session — so this window offers no evidence for or against the filter. OVER-TIME PROJECTION: Over 3–6 months, the filter would reduce trade frequency by roughly 60–70% (since NY_OPEN is a narrow window), which could concentrate risk into fewer, higher-conviction trades. The +6.1pp win-rate improvement is stat Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-14
DOI
https://doi.org/10.5281/zenodo.22742668
Primary Topic
Intelligence, Security, War Strategy
Type
preprint
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LAB #2203 NEUTRAL: LEDGER BENCH: Apply filter: Session = NY_OPEN — improves win rate from 31.9% to 38.0% (+6.1pp, n=629 — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Intelligence, Security, War Strategy
preprint

LAB #2203 NEUTRAL: LEDGER BENCH: Apply filter: Session = NY_OPEN — improves win rate from 31.9% to 38.0% (+6.1pp, n=629 — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

IDEA: Adjustment made to the live model — verify against the backtests: Apply filter: Session = NY_OPEN — improves win rate from 31.9% to 38.0% (+6.1pp, n=629) SAME-WINDOW EFFECT: Applying the NY_OPEN filter to the 10 live trades above would have excluded all of them, since every entry timestamp falls outside the NY_OPEN session (all are 09:55–11:35 CET, which is 03:55–05:35 ET — pre-market or early London). Thus, the filter would have eliminated 4 wins and 4 losses (plus 2 open/expired), netting zero PnL from this window instead of the actual mixed result. The backtest's +6.1pp win-rate gain is not visible here because the sample is tiny and entirely outside the filter's target session — so this window offers no evidence for or against the filter. OVER-TIME PROJECTION: Over 3–6 months, the filter would reduce trade frequency by roughly 60–70% (since NY_OPEN is a narrow window), which could concentrate risk into fewer, higher-conviction trades. The +6.1pp win-rate improvement is stat Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Intelligence, Security, War Strategy
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LAB #2203 NEUTRAL: LEDGER BENCH: Apply filter: Session = NY_OPEN — improves win rate from 31.9% to 38.0% (+6.1pp, n=629 — E8 Intelligence Research — Andrew Stewart Caldin · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS