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/M3g-market-microstructure-sims.md

@@ -0,0 +1,70 @@
+# M3g — Market microstructure sims
+
+## 1. Component
+Market microstructure simulation: models **order flow, liquidity depth, slippage, spread dynamics,
+and cross-exchange arbitrage** at the fastest time scales (tick-level to hourly). Pops here are
+**market makers, takers, and arbitrageurs** interacting across multiple venues. The sim that
+operates at the highest temporal resolution — where M3a provides statistical forecasts and M3c
+models pool mechanics, M3g models the *plumbing* of how orders actually execute.
+
+## 2. Status / certainty
+DESIGN-FIRST · ABSENT. Order-book microstructure theory C4 (established academic field);
+DEX-specific microstructure C2 (emerging). Implementation C1.
+
+## 3. Language & location
+TBD · `src/economy/sims/microstructure/`. Needs high-frequency data handling and event-driven
+simulation. Rust, C++, or Python with optimized event loop.
+
+## 4. Does / does-not
+- **Does:** simulate order flow across venues (DEXs and CEXs); model bid-ask spread dynamics as a
+  function of inventory risk and adverse selection; simulate slippage curves for various order
+  sizes; model cross-exchange arbitrage opportunities and their decay; operate at **tick-level
+  resolution** (sub-second to minute); produce bounded predictions on execution quality, optimal
+  routing, and liquidity conditions.
+- **Does-not:** model protocol consensus (M3f); model macro token supply (M3e); model social
+  behavior (M3b); execute trades (Marketplace does).
+
+## 5. Interface contract
+- Implements `query(PredictionQuery) -> BoundedPrediction` per M3 hub.
+- **Output bounds:** execution cost ranges and liquidity intervals.
+  Example: `{ value: 0.0034, lower_bound: 0.0018, upper_bound: 0.0052, confidence: 0.85,
+  time_horizon: "next_trade", sim_type: "market_microstructure" }` — expected slippage (%) for a
+  10 ETH market sell.
+  Example: `{ value: 12400, lower_bound: 8200, upper_bound: 18600, confidence: 0.78,
+  time_horizon: "1h", sim_type: "market_microstructure" }` — available depth (USD) within 50bps
+  of mid.
+- **Prediction types:** `slippage_estimate`, `spread_forecast`, `depth_profile`,
+  `cross_venue_arb`, `optimal_execution_route`, `liquidity_score`.
+- Calibration: ingests `price_tick`, `dex_pool_state`, and `execution_fill` from M2.
+
+## 6. Dependencies & stubs
+- M2 Data Feeds — tick data and pool state; *stub:* canned order book snapshots.
+- M3c AMM sims — pool mechanics for DEX venues; *stub:* fixed pool state.
+- M3 Sims hub — lifecycle management; *stub:* manual init.
+
+## 7. Invariants / laws
+- **L1 (C4):** microstructure operates at the **highest temporal resolution** — predictions are
+  valid for seconds to hours, not days. Stale microstructure data is worse than no data.
+- **L2 (C4):** slippage is a **function of order size and current depth** — not a fixed
+  percentage. The sim must model the non-linear relationship.
+- **L3 (C3):** cross-venue arbitrage opportunities **decay** — the sim models the time-to-close
+  of an arb opportunity, not just its existence.
+
+## 8. Build steps
+1. Implement a simplified order-book simulator (limit orders, market orders, cancels).
+2. Add spread dynamics (inventory-based market maker model).
+3. Add slippage curves (order size → execution cost).
+4. Add cross-venue arb detection and decay modeling.
+5. Wire M2 tick data → calibration.
+
+## 9. Tests
+Slippage: larger orders produce greater slippage. Spread: spread widens under adverse selection.
+Arb decay: detected arb opportunity closes over time. Depth: depth profile matches order book
+state. Bounds: all outputs bounded. Resolution: predictions update at tick frequency.
+
+## 10. Open items
+- CEX order book data access (API limitations, costs).
+- DEX-specific microstructure (AMM pools don't have order books — translate pool state to
+  equivalent depth/spread).
+- Latency modeling (how fast can our traders actually reach an arb?).
+- Which venues to model initially.