Enrich M3 sim sub-specs with 11 discovered frameworks and 360:1 minimum speed

- M3 hub: add L3 invariant (360:1 minimum sim speed), six-horizon time table
- M3a: add Heston stochastic vol, rough volatility (fBM), HMM regime detection,
  DCC-GARCH copula, jump-diffusion; six-horizon mapping
- M3b: add Hegselmann-Krause bounded confidence, complex contagion, bandit-
  replicator hybrid, MFG (HJB+FP), pump-and-dump 3-type ABM; six-horizon mapping
- M3c: add six-horizon time table
- M3d: add Kolokoltsov adversarial (non-linear FP + WENO), DSMFG bilevel
  optimization, cross-chain adversarial arbitrage; six-horizon mapping
- M3e: add kinked lending rate model, DeXposure inter-protocol credit network,
  composable yield optimizer; six-horizon mapping
- M3f: add MFG for validator populations, six-horizon time table
- M3g: add Almgren-Chriss optimal execution, six-horizon mapping
- CLAUDE.md: add subagent productivity note

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

core/docs/plans/M3g-market-microstructure-sims.md

@@ -2,69 +2,100 @@
 
 ## 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.
+optimal execution, and cross-exchange arbitrage** at the fastest time scales. Pops here are
+**market makers, takers, and arbitrageurs** interacting across multiple venues. Includes the
+**Almgren-Chriss optimal execution framework** for minimizing market impact of large orders.
+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, across **six
+concurrent time horizons** at ≥ 360:1 speed.
 
 ## 2. Status / certainty
-DESIGN-FIRST · ABSENT. Order-book microstructure theory C4 (established academic field);
-DEX-specific microstructure C2 (emerging). Implementation C1.
+DESIGN-FIRST · ABSENT. Order-book microstructure theory C4 (established). Almgren-Chriss
+optimal execution C5 (industry standard since 2001; crypto adaptations validated 2023–2024,
+Kurz CMC thesis). 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.
+TBD · `src/economy/sims/microstructure/`. Needs high-frequency data handling, event-driven
+simulation, and Riccati equation solvers for optimal execution trajectories. 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.
+  sizes; solve **Almgren-Chriss optimal execution**:
+  $\min \int_0^T [\lambda \cdot x(t) \cdot \dot{x}(t) + \eta \cdot \dot{x}(t)^2] \, dt$
+  where $\lambda$ = permanent impact, $\eta$ = temporary impact, $x(t)$ = remaining order —
+  splits large orders across time to minimize market impact + timing risk; impact parameters
+  $\lambda, \eta$ re-estimated from order-flow streams every 10–30s; execution trajectory solved
+  via Riccati equations in <100ms; 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.
+- **Output bounds:** execution cost ranges, liquidity intervals, optimal trajectory envelopes.
+- **Time-horizon mapping** (all run concurrently, ≥ 360:1 speed):
+  | Horizon | Primary models | Update cadence |
+  |---------|---------------|----------------|
+  | Tick–hourly | Almgren-Chriss execution, slippage, spread, arb decay | Every tick |
+  | Daily | Liquidity regime, venue depth profiles | Hourly roll |
+  | Weekly | Cross-venue flow patterns, impact parameter drift | Daily roll |
+  | Monthly | Structural liquidity shifts, venue market share | Weekly roll |
+  | Annual | Microstructure regime (DEX vs CEX share evolution) | Monthly roll |
+  | 5-year | Venue topology evolution, structural impact trends | Quarterly roll |
+- Examples:
+  `{ value: 0.0034, lower_bound: 0.0018, upper_bound: 0.0052, confidence: 0.85,
+  time_horizon: "next_trade", sim_type: "market_microstructure" }` — slippage (%) for 10 ETH.
+  `{ value: 12400, lower_bound: 8200, upper_bound: 18600, confidence: 0.78,
+  time_horizon: "1h", sim_type: "market_microstructure" }` — depth (USD) within 50bps.
+  `{ value: [0.3, 0.3, 0.2, 0.1, 0.1], lower_bound: null, upper_bound: null, confidence: 0.80,
+  time_horizon: "30min", sim_type: "market_microstructure" }` — Almgren-Chriss optimal execution
+  schedule (fraction per 6-min bucket for 100 ETH sell).
 - **Prediction types:** `slippage_estimate`, `spread_forecast`, `depth_profile`,
-  `cross_venue_arb`, `optimal_execution_route`, `liquidity_score`.
+  `cross_venue_arb`, `optimal_execution_schedule`, `optimal_execution_route`,
+  `liquidity_score`, `impact_estimate`.
 - 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.
+- M3a Statistical — volatility estimates for Almgren-Chriss timing risk; *stub:* fixed vol.
 - 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
+- **L1 (C5):** 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
+- **L2 (C5):** 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
+- **L3 (C5):** Almgren-Chriss impact parameters $\lambda, \eta$ are **estimated from live data**,
+  never hardcoded — crypto impact dynamics differ by asset, venue, and time-of-day.
+- **L4 (C4):** cross-venue arbitrage opportunities **decay** — the sim models the time-to-close
   of an arb opportunity, not just its existence.
+- **L5 (C4):** optimal execution trajectories are **re-solved on every significant state change**
+  (vol spike, depth drop, regime transition) — a stale trajectory is worse than naive execution.
 
 ## 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.
+4. Implement Almgren-Chriss optimal execution (Riccati solver, impact estimation).
+5. Add cross-venue arb detection and decay modeling.
+6. Wire M2 tick data → calibration of impact parameters.
+7. Wire M3a vol estimates → Almgren-Chriss timing risk component.
 
 ## 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.
+Almgren-Chriss: optimal trajectory minimizes total cost vs. naive execution on backtest; impact
+parameters update when market conditions change. 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. Speed: sim advances ≥ 360:1.
 
 ## 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?).
+- Almgren-Chriss extensions for crypto (volume-dependent variant? discrete block propagation?).
 - Which venues to model initially.