LAB #2387 PROMISING: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.2% to 32.8% (+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 31.2% to 32.8% (+1.6pp, n=1401) SAME-WINDOW EFFECT: Applying a pool-density filter (≥90) would have excluded trades originating from low-density pools. In the current 10-trade window, 4 trades are losses (XAUUSD SHORT via rizzy5m, USDJPY SHORT via wkjudas, GBPUSD SHORT via rizzy5m, XAUUSD SHORT via rizzy5m, XAUUSD SHORT via rizzy5m) — if any of these came from pools with density <90, they'd be removed. Without knowing per-trade pool density, the expected win-rate delta is roughly +1.6pp, meaning ~1–2 of those losses might be filtered, shifting the window from 5W/5L to ~6W/4L or 7W/3L. PnL impact is modest but positive, likely +0.5R to +1.5R over this small sample. OVER-TIME PROJECTION: Over 3–6 months, a +1.6pp win-rate improvement on ~1,400 trades translates to ~22 additional wins, but the edge is thin and may be regime-dependent. Risk: pool density 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
2026-09-18
DOI
https://doi.org/10.5281/zenodo.22824373
Primary Topic
Competitive and Knowledge Intelligence
Type
preprint
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LAB #2387 PROMISING: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.2% to 32.8% (+1.6pp — E8 Intelligence Research

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

LAB #2387 PROMISING: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.2% to 32.8% (+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 31.2% to 32.8% (+1.6pp, n=1401) SAME-WINDOW EFFECT: Applying a pool-density filter (≥90) would have excluded trades originating from low-density pools. In the current 10-trade window, 4 trades are losses (XAUUSD SHORT via rizzy5m, USDJPY SHORT via wkjudas, GBPUSD SHORT via rizzy5m, XAUUSD SHORT via rizzy5m, XAUUSD SHORT via rizzy5m) — if any of these came from pools with density <90, they'd be removed. Without knowing per-trade pool density, the expected win-rate delta is roughly +1.6pp, meaning ~1–2 of those losses might be filtered, shifting the window from 5W/5L to ~6W/4L or 7W/3L. PnL impact is modest but positive, likely +0.5R to +1.5R over this small sample. OVER-TIME PROJECTION: Over 3–6 months, a +1.6pp win-rate improvement on ~1,400 trades translates to ~22 additional wins, but the edge is thin and may be regime-dependent. Risk: pool density Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
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Competitive and Knowledge Intelligence
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LAB #2387 PROMISING: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.2% to 32.8% (+1.6pp — E8 Intelligence Research — Andrew Stewart Caldin · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS