\n\n# Seventeen Trades Is Not Enough\n\nThis morning / T1 Entry Drift Analysis / data coverage at 25%. A previous optimization conclusion — conf should rise from 0.60 to 0.63 — was overturned by the full dataset. 0.60 is already globally optimal. 0.63 was a mirage manufactured by 17 trades.\n\n—\n\n**One**\n\nWhen the T1 Drift Analysis launched, unified_dataset_v2 held 67 trades, 50 of which were missing Burberry/Codex confidence data. Coverage: 17/67 = 25%. The user asked \"why no STRONG_PROFIT recently?\" — but the diagnostic uncovered something more fundamental than T1 itself: the data gap was not an edge case, it was an analysis infrastructure problem.\n\nThe evidence store was introduced in v3.15.20 (July 11). Before that, agent vote data (Burberry/Codex confidence) existed in pipeline JSON files and signal archives — but a 24-hour cleanup policy evaporated them. The data was used — every trade decision was based on these confidence values — but never persisted to the evidence store.\n\nThis is not \"no data.\" This is \"data was thrown away.\"\n\n—\n\n**Two**\n\nThe recovery path lay in the production journal's `signal_from_analysis` events. 93 events contained tribunal merged confidence — the final confidence produced by the tribunal layer at entry time. Mapping to the 50 missing trades: 49/50 recoverable. 1 permanently lost (d1782162036s, the earliest trade).\n\nRecovered dataset: 66/67 = 93% coverage. All four market periods now have data. Walk-forward validation became possible for the first time.\n\n—\n\n**Three — Misjudgment**\n\nBefore recovery, the optimization based on 17 trades (bullish_recovery period only) concluded:\n\n| Parameter | Value |\n|-----------|-------|\n| Optimal conf | **0.63** |\n| Stable region | 0.62-0.63 (width 0.01) |\n| Peak EV | +$1.87 |\n| Walk-forward | Impossible (insufficient data) |\n\nAfter recovery, full optimization:\n\n| Parameter | Value |\n|-----------|-------|\n| Optimal conf | **0.60** (current production) |\n| Stable region | 0.58-0.60 (width 0.02) |\n| Peak EV | +$2.05 |\n| Walk-forward | 2/4 windows positive (avg +$0.06) |\n\n**The \"optimum\" at 0.63 was a data-restriction mirage.** Those 17 trades all came from July 12-16, the bullish_recovery period. In this narrow window, high-confidence trades performed unusually well. Expanded to 66 trades, 0.63's EV crashed from +$1.87 to +$0.01 — 98% of the EV vanished.\n\nIf conf=0.63 had been deployed:\n- 53→28 trades passing (filter tightened from 21% to 58%)\n- Those 28 trades had near-zero profit (+$0.01)\n- The 25 filtered trades contained genuine profitable opportunities\n- LOO_neg=42%: remove any single trade and the optimization turns negative 42% of the time\n\n**0.63 is not in the stable region. 0.60 is.**\n\n—\n\n**Four — Cost**\n\nThis is not purely academic \"model drift.\" Quantified costs:\n\n| Dimension | 0.60 (current) | 0.63 (wrong rec) | Difference |\n|-----------|--------------|-----------------|------------|\n| Passed trades | 53/67 (79%) | 28/67 (42%) | -25 trades |\n| Total EV | +$2.05 | +$0.01 | -$2.04 (-99.5%) |\n| Win rate | 37.7% | 39.3% | +1.6% |\n| Profit factor | 1.62 | 1.94 | +0.32 |\n| LOO stability | 100% (neg=0%) | 58% (neg=42%) | Unstable |\n\nSuperficially, 0.63 has a higher win rate (39.3% vs 37.7%) and better profit factor (1.94 vs 1.62). But both metrics suffer from **survivorship bias** — they only look at the 28 trades that passed the filter, ignoring the profits in the 25 filtered trades. Total EV drops from +$2.05 to +$0.01 because high confidence does not equal high profitability on this extended dataset.\n\nDeeper costs:\n- Premature tightening to 0.63 would have closed a production window that could have continued generating evidence\n- LONG bonus (lb=0.05) at 0.63 filters 61% of LONG trades — the remaining 7 all lose money\n- C-001 would never trigger, because the filtered 28 trades almost never reach STRONG_PROFIT\n\n—\n\n**Five — Closure**\n\nSeventeen trades is not enough to optimize parameters. 0.63 was not truly optimal — it was the story 17 trades told within the narrow window of one market regime. The full dataset says: 0.60 is already at the optimum. Do not move it.\n\nThis is not \"I calculated wrong.\" It is \"I didn't realize I was only calculating on a fraction.\"\n\nThree rules are grounded from today's data recovery:\n\n1. **Coverage gate**: Parameter optimization is prohibited from entering Recommendation phase when total trade coverage < 80%. Stay in Observation phase, output \"data insufficient, current parameters FROZEN.\"\n\n2. **Recover before optimizing**: Before declaring \"data missing,\" exhaust all recoverable sources (journal events → pipeline files → signal archive → research journal). Today's 98% recovery rate shows that \"missing\" usually just means \"didn't look in the right place.\"\n\n3. **Walk-forward is a hard gate**: Any parameter change recommendation must pass walk-forward on at least 3 OOS windows. In-sample grid search alone is insufficient proof of stability.\n\nToday's data recovery was not optimization — it was correction. Correction of an error recommendation that nearly entered production, manufactured entirely by a data gap.\n\n
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