Rewrite M-series: crypto trading engine + market prediction sims

The initial M-series specs were wrong (text digestion pipeline). Replaced
with the actual economy organ architecture:

  M0  hub (independent system, scoped autonomy, multi-layered braking)
  M1  Marketplace (multi-trader harness, deterministic law script, veto)
  M2  Data Feeds (RSS + live market, bridges Marketplace ↔ Sims)
  M3  Sims hub + 7 sub-specs (always-running, bounded predictions):
      M3a statistical, M3b sociological, M3c AMM/liquidity,
      M3d MEV/adversarial, M3e tokenomics/macro, M3f consensus/staking,
      M3g market microstructure
  M4  Wallets (sovereign custody, our keys only, 1:1 trader binding)
  M5  Traders (AI actors, wallet-bound, all tool calls monitored)
  M6  Conductor (supervisory AI, veto, pause/investigate, SAE intake)
  M7  SAE monitor (trader surveillance, Brain-compatible message format)

Grounded in AMM invariant mechanics, MEV game theory, SDE tokenomics,
and evolutionary consensus games. Tax stub for Verschwörern Veregeister.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

core/docs/plans/M3a-statistical-sims.md

@@ -0,0 +1,59 @@
+# M3a — Statistical & quantitative sims
+
+## 1. Component
+Pure statistical simulation: **Monte Carlo methods, Bayesian inference, time-series forecasting,
+and volatility modeling**. The mathematical backbone — no game theory, no sociology, just the
+numbers. Operates across multiple time scales (tick to weekly). Pops in this sim represent
+**stochastic sample paths**, not behavioral agents.
+
+## 2. Status / certainty
+DESIGN-FIRST · ABSENT. Mathematical foundations C4 (standard quant methods); parameterization C1.
+
+## 3. Language & location
+TBD · `src/economy/sims/statistical/`. Python (NumPy/SciPy), Julia, or R for numerical
+computing. Needs efficient matrix operations and distribution sampling.
+
+## 4. Does / does-not
+- **Does:** run Monte Carlo price simulations (geometric Brownian motion, jump-diffusion);
+  Bayesian parameter estimation from live data (M2); time-series forecasting (ARIMA, GARCH for
+  volatility clustering); Value-at-Risk and Expected Shortfall calculations; produce bounded
+  predictions with confidence intervals as upper/lower bounds.
+- **Does-not:** model human behavior (M3b does); model protocol mechanics (M3c–M3f do);
+  trade or recommend (Traders do).
+
+## 5. Interface contract
+- Implements `query(PredictionQuery) -> BoundedPrediction` per M3 hub.
+- **Output bounds:** statistical confidence intervals.
+  Example: `{ value: 1847.30, lower_bound: 1790.15, upper_bound: 1905.60, confidence: 0.95,
+  time_horizon: "24h", sim_type: "statistical" }` — 95% CI on ETH price.
+- **Prediction types:** `price_forecast`, `volatility_estimate`, `var_calculation`,
+  `correlation_matrix`, `regime_detection`.
+- Calibration: ingests `price_tick` and `dex_pool_state` from M2 Data Feeds.
+
+## 6. Dependencies & stubs
+- M2 Data Feeds — price history for calibration; *stub:* canned price series.
+- M3 Sims hub — lifecycle management; *stub:* manual init.
+
+## 7. Invariants / laws
+- **L1 (C4):** bounds are **statistical confidence intervals** — derived from the model's
+  distribution, not hand-picked. The confidence level (e.g. 0.95) is explicit in the output.
+- **L2 (C4):** **multiple time scales run concurrently** — a tick-level volatility estimate and a
+  weekly price forecast coexist; neither blocks the other.
+- **L3 (C3):** model parameters are **re-estimated on each calibration** from live data — no
+  stale parameters carried across regime changes.
+
+## 8. Build steps
+1. Implement geometric Brownian motion Monte Carlo (simplest price sim).
+2. Add GARCH volatility estimation.
+3. Wire M2 price data → Bayesian parameter re-estimation.
+4. Implement the `BoundedPrediction` output with CIs.
+
+## 9. Tests
+Monte Carlo: N sample paths produce a distribution with correct mean/variance. CI: 95% interval
+contains true value ≥ 95% of the time on historical backtest. GARCH: volatility clusters detected
+in synthetic data. Calibration: new data shifts parameter estimates.
+
+## 10. Open items
+- Which distributions beyond GBM (heavy-tailed? Lévy?).
+- Regime-switching model complexity (hidden Markov? threshold?).
+- Computational budget (how many Monte Carlo paths per tick?).