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Backtest analysis demonstrates improved win rates using high pool density filters in algorithmic trading models, suggesting enhanced net profitability despite risks of overfitting.
IDEA: Adjustment made to the live model — verify against the backtests: Apply filter: Pool density HIGH (>=90) — improves win rate from 31.9% to 33.6% (+1.6pp, n=1347) SAME-WINDOW EFFECT: Applying the pool-density ≥90 filter over the current 10-trade window would have removed trades with low pool density. Based on the backtest delta (+1.6pp win rate), roughly 1–2 of the 10 trades would likely be filtered out — most plausibly the XAUUSD SHORT losses (which show weak confluence) and possibly the EURUSD SHORT. Expected PnL delta: small positive, likely +0.5 to +1.0R, since the filter removes low-quality entries without eliminating any of the four confirmed winners. However, the sample is too small to attribute the improvement to the filter versus noise. OVER-TIME PROJECTION: Over 3–6 months, a +1.6pp win-rate improvement on ~1,300 trades translates to roughly +20–25 additional winning trades, which at average risk/reward could add +5–8% to net PnL. Risk: the filter may overfit to recent Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
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Andrew Stewart Caldin (2026) studied this question.
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