LAB #2233 NEUTRAL: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.8% to 33.5% (+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 31.8% to 33.5% (+1.6pp, n=1365) SAME-WINDOW EFFECT: Applying the pool-density filter (≥90) to the current live window would have excluded trades originating from low-density pools. In the 10 trades listed, the two XAUUSD SHORT losses (rizzy5m entries at 4048.74 and 4051.79) and the GBPUSD SHORT loss (rizzy5m) are prime candidates for exclusion if their pool density fell below 90 — likely removing 2–3 of the 4 losses. That would shift the window from 5W/4L/1O to roughly 5W/1L/1O, a win-rate improvement from ~55% to ~83% on closed trades, and a PnL delta of roughly +2R to +3R depending on position sizing. However, this is a small sample, and the filter's benefit is not yet proven to be causal rather than coincidental with market regime. OVER-TIME PROJECTION: Over 3–6 months, a +1.6pp win-rate gain (31.8%→33.5%) on n=1365 is statistically weak (p≈0.10–0 Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-15
DOI
https://doi.org/10.5281/zenodo.22762203
Primary Topic
Competitive and Knowledge Intelligence
Type
preprint
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LAB #2233 NEUTRAL: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.8% to 33.5% (+1.6pp — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Competitive and Knowledge Intelligence
preprint

LAB #2233 NEUTRAL: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.8% to 33.5% (+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 31.8% to 33.5% (+1.6pp, n=1365) SAME-WINDOW EFFECT: Applying the pool-density filter (≥90) to the current live window would have excluded trades originating from low-density pools. In the 10 trades listed, the two XAUUSD SHORT losses (rizzy5m entries at 4048.74 and 4051.79) and the GBPUSD SHORT loss (rizzy5m) are prime candidates for exclusion if their pool density fell below 90 — likely removing 2–3 of the 4 losses. That would shift the window from 5W/4L/1O to roughly 5W/1L/1O, a win-rate improvement from ~55% to ~83% on closed trades, and a PnL delta of roughly +2R to +3R depending on position sizing. However, this is a small sample, and the filter's benefit is not yet proven to be causal rather than coincidental with market regime. OVER-TIME PROJECTION: Over 3–6 months, a +1.6pp win-rate gain (31.8%→33.5%) on n=1365 is statistically weak (p≈0.10–0 Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Reduced inequalities
Competitive and Knowledge Intelligence
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LAB #2233 NEUTRAL: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.8% to 33.5% (+1.6pp — E8 Intelligence Research — Andrew Stewart Caldin · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS