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sica-fondt/core/docs/plans/M3a-statistical-sims.md
Claude 98a6f9a0b1
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>
2026-07-13 21:10:15 +00:00

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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?).