Without bootstrap, the brain learns purely from going-forward live ticks — meaning ~10 resolved trades per symbol per day. It takes WEEKS for BSS to converge, weeks for calibration to be honest, weeks for per-symbol blends to mean anything.

The bootstrap fixes this:

1. On first visit, pulls 250 days (~1 trading year) of daily OHLC bars from Stooq for 47 tracked symbols (broad indices, mega-tech, semis, financials, sector ETFs, bonds, vol, international, crypto) — free, no key
2. For each (symbol, day) pair, reconstructs the same 22-feature vector the brain uses live: RSI, ATR%, vol ratio, distance from 50/200 MA, SPY change, setup flags from price action, etc.
3. Outcome label = next day's close direction (LONG win if next close > today's by 0.3%+, SHORT win if -0.3%-, dropped if within noise)
4. Feeds (features, outcome) to every learning module: ModelStore (Adam SGD), Calibrator (Platt), IsotonicCalibrator (PAV), RegimeCalibrator, BrierSkill, SharpeTracker, SymbolSkill, SymbolSharpe, SectorPerf, ReliabilityDiagram, PredictionHistogram, SymbolBias
5. Fits all calibrators on the populated pools so probabilities become honest immediately

Result: ~11,000 training examples (47 symbols × ~235 valid days/symbol after dropping flat outcomes and warmup bars) → BSS converges in minutes, per-symbol stats become meaningful, the brain is warm-started instead of cold.

Gated by localStorage flag (key bumped to v2 in pass 69) so existing browsers re-run on next visit with the deeper window. Each run takes ~17 seconds (47 symbols × 350ms delay).