LAB #4091 PROMISING: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 32.6% to 34.1% (+1.5pp — E8 Intelligence Research

IDEA: Adjustment made to the live model — verify against the backtests: Apply filter: Pool density HIGH (>=90) — improves win rate from 32.6% to 34.1% (+1.5pp, n=1825) SAME-WINDOW EFFECT: Applying the pool density ≥90 filter would have excluded most of the wkjudas and rizzy5m entries above, as these were taken during low-density or fragmented liquidity windows (evidenced by the cluster of losses on 2026-08-27 and 2026-09-01). Specifically, the GBPUSD LONG, USDJPY SHORT, NZDUSD SHORT (wkjudas), and the three rizzy5m losses would likely have been filtered out — removing 5 of the 7 losing trades. The two wins (GBPUSD SHORT rizzy5m, EURUSD LONG rizzy7m) may or may not survive depending on their pool density at entry; if they survive, the window PnL improves from roughly −3.5R to near +1.5R, a swing of ~5R. The +1.5pp win-rate gain in backtest is directionally consistent with this live sample, though n is small and the effect is concentrated in loss avoidance rather than new wins. OVER-TI 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
2026-10-15
DOI
https://doi.org/10.5281/zenodo.23385276
Primary Topic
Financial Markets and Investment Strategies
Type
preprint
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LAB #4091 PROMISING: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 32.6% to 34.1% (+1.5pp — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Financial Markets and Investment Strategies
preprint

LAB #4091 PROMISING: LEDGER BENCH: Apply filter: Pool density HIGH (>=90) — improves win rate from 32.6% to 34.1% (+1.5pp — 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 32.6% to 34.1% (+1.5pp, n=1825) SAME-WINDOW EFFECT: Applying the pool density ≥90 filter would have excluded most of the wkjudas and rizzy5m entries above, as these were taken during low-density or fragmented liquidity windows (evidenced by the cluster of losses on 2026-08-27 and 2026-09-01). Specifically, the GBPUSD LONG, USDJPY SHORT, NZDUSD SHORT (wkjudas), and the three rizzy5m losses would likely have been filtered out — removing 5 of the 7 losing trades. The two wins (GBPUSD SHORT rizzy5m, EURUSD LONG rizzy7m) may or may not survive depending on their pool density at entry; if they survive, the window PnL improves from roughly −3.5R to near +1.5R, a swing of ~5R. The +1.5pp win-rate gain in backtest is directionally consistent with this live sample, though n is small and the effect is concentrated in loss avoidance rather than new wins. OVER-TI Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Financial Markets and Investment Strategies
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