LAB #1842 NEUTRAL: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 32.0% to 33.6% (+1.6pp — E8 Intelligence Research

IDEA: Adjustment made to the live model — verify against the backtests: Apply filter: Pool density HIGH (>=90) — improves win rate from 32.0% to 33.6% (+1.6pp, n=1348) SAME-WINDOW EFFECT: Applying the pool-density filter (≥90) would have excluded the three rizzy5m XAUUSD shorts and the AUDUSD short, as those likely occurred in lower-density pools. That removes 4 trades from the 10 listed, all of which were losses or open/rejected — eliminating at least 2 confirmed losses and likely improving the window's net PnL. The remaining rizzy5m EURJPY short (win) and the other signals (wkjudas, sqz, rizzy) would stay, so the win rate in this sample would rise from ~50% (5/10) to ~83% (5/6) if we count only closed trades. Expected PnL delta: positive, roughly +2R to +3R over this window, though sample size is tiny. OVER-TIME PROJECTION: Over 3–6 months, a +1.6pp win-rate gain at n=1348 is statistically marginal (p ≈ 0.08–0.12), so it may be real but weak. The filter likely reduces trade frequen 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
2026-09-05
DOI
https://doi.org/10.5281/zenodo.22324702
Primary Topic
Intelligence, Security, War Strategy
Type
preprint
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LAB #1842 NEUTRAL: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 32.0% to 33.6% (+1.6pp — E8 Intelligence Research

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

LAB #1842 NEUTRAL: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 32.0% to 33.6% (+1.6pp — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

IDEA: Adjustment made to the live model — verify against the backtests: Apply filter: Pool density HIGH (>=90) — improves win rate from 32.0% to 33.6% (+1.6pp, n=1348) SAME-WINDOW EFFECT: Applying the pool-density filter (≥90) would have excluded the three rizzy5m XAUUSD shorts and the AUDUSD short, as those likely occurred in lower-density pools. That removes 4 trades from the 10 listed, all of which were losses or open/rejected — eliminating at least 2 confirmed losses and likely improving the window's net PnL. The remaining rizzy5m EURJPY short (win) and the other signals (wkjudas, sqz, rizzy) would stay, so the win rate in this sample would rise from ~50% (5/10) to ~83% (5/6) if we count only closed trades. Expected PnL delta: positive, roughly +2R to +3R over this window, though sample size is tiny. OVER-TIME PROJECTION: Over 3–6 months, a +1.6pp win-rate gain at n=1348 is statistically marginal (p ≈ 0.08–0.12), so it may be real but weak. The filter likely reduces trade frequen 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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