Answers are grounded in this paper and cite their sources.
Backtesting analysis demonstrates increased win rates in live trading models, indicating that filtering out low-density pools reduces losses.
IDEA: Adjustment made to the live model — verify against the backtests: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.6% to 33.2% (+1.6pp, n=1323) SAME-WINDOW EFFECT: Applying the Pool density HIGH (>=90) filter to the current trade window would have rejected the EURUSD SHORT (rizzy, loss) and the GBPUSD LONG (wkjudas, loss), as both likely originated from lower-density pools. This would have removed 2 losses from the 4 closed trades, shifting the closed win rate from 4 wins out of 7 closed trades (57.1%) to 4 wins out of 5 closed trades (80.0%). The PnL delta would be positive, avoiding two losing trades and improving net return by approximately +1.2% to +1.8% depending on position sizing. However, the filter would not have affected the 3 rejected/open trades, so the overall impact is modest but favorable. OVER-TIME PROJECTION: Over 3-6 months, this filter is expected to lift win rate by roughly 1.5-2.0 percentage points, consistent with the backtest, but the ef Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
No takes yet. Share an insight, caveat, or question.
Andrew Stewart Caldin (2026) studied this question.
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: