LAB #2637 NEUTRAL: LEDGER BENCH: Apply filter: Session = NY_OPEN — improves win rate from 31.0% to 37.1% (+6.1pp, n=644 — E8 Intelligence Research

IDEA: Adjustment made to the live model — verify against the backtests: Apply filter: Session = NY_OPEN — improves win rate from 31.0% to 37.1% (+6.1pp, n=644) SAME-WINDOW EFFECT: Applying the NY_OPEN filter to the current 10-trade window would have excluded all trades, as every entry timestamp falls between 10:10 and 11:35 CET (08:10–09:35 ET), which is outside the NY_OPEN session (typically 14:30–21:00 CET / 08:30–15:00 ET). This would have eliminated 6 wins, 4 losses, and the 1 rejected trade — net PnL change is ambiguous because the filter removes both winners and losers. The backtested +6.1pp win-rate improvement is not visible here; in fact, this window shows a 60% win rate without the filter, so the filter would have reduced realized win rate to 0% (no trades). The filter appears to be a session-selection bias that may simply avoid low-volatility hours, but this sample is too small and time-shifted to validate. OVER-TIME PROJECTION: Over 3–6 months, the NY_OPEN filter could pl 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
2026-09-24
DOI
https://doi.org/10.5281/zenodo.22931273
Primary Topic
Intelligence, Security, War Strategy
Type
preprint
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LAB #2637 NEUTRAL: LEDGER BENCH: Apply filter: Session = NY_OPEN — improves win rate from 31.0% to 37.1% (+6.1pp, n=644 — E8 Intelligence Research

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

LAB #2637 NEUTRAL: LEDGER BENCH: Apply filter: Session = NY_OPEN — improves win rate from 31.0% to 37.1% (+6.1pp, n=644 — 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 31.0% to 37.1% (+6.1pp, n=644) SAME-WINDOW EFFECT: Applying the NY_OPEN filter to the current 10-trade window would have excluded all trades, as every entry timestamp falls between 10:10 and 11:35 CET (08:10–09:35 ET), which is outside the NY_OPEN session (typically 14:30–21:00 CET / 08:30–15:00 ET). This would have eliminated 6 wins, 4 losses, and the 1 rejected trade — net PnL change is ambiguous because the filter removes both winners and losers. The backtested +6.1pp win-rate improvement is not visible here; in fact, this window shows a 60% win rate without the filter, so the filter would have reduced realized win rate to 0% (no trades). The filter appears to be a session-selection bias that may simply avoid low-volatility hours, but this sample is too small and time-shifted to validate. OVER-TIME PROJECTION: Over 3–6 months, the NY_OPEN filter could pl Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Decent work and economic growth
Intelligence, Security, War Strategy
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