mirror of https://github.com/SHOGGOTH-SECTOR/sica-fondt.git synced 2026-07-31 16:16:26 +00:00

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

3.6 KiB

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M3c — AMM & liquidity pool sims

1. Component

Automated Market Maker simulation: models constant-product invariant mechanics, impermanent loss, and the non-cooperative game between liquidity providers and arbitrageurs. Pops here are LP positions and arbitrage bots operating on the x \cdot y = k curve. Grounded in the DEX/AMM literature [1,2].

2. Status / certainty

DESIGN-FIRST · ABSENT. Mathematical foundations C5 (constant product invariant is proven); impermanent loss formula C5 (closed-form: \text{IL}(r) = \frac{2\sqrt{r}}{1+r} - 1); simulation parameterization C1.

3. Language & location

TBD · src/economy/sims/amm/. Needs precise fixed-point or arbitrary-precision arithmetic for invariant calculations (Solidity-equivalent precision). Python, Rust, or Julia.

4. Does / does-not

Does: simulate constant-product pools with fee parameter \gamma: (x + \gamma \Delta x)(y - \Delta y) = k; model impermanent loss as a function of price ratio shift r; simulate LP strategies (provide, withdraw, rebalance) against arbitrageur behavior; model non-linear price slippage from the curve geometry; produce bounded predictions on pool profitability, IL risk, and optimal LP ranges.
Does-not: model consensus mechanics (M3f); model social behavior (M3b); execute real swaps (Marketplace does).

5. Interface contract

Implements query(PredictionQuery) -> BoundedPrediction per M3 hub.
Output bounds: IL ranges and pool return intervals. Example: { value: -0.034, lower_bound: -0.058, upper_bound: -0.012, confidence: 0.90, time_horizon: "7d", sim_type: "amm_liquidity" } — projected impermanent loss for ETH/USDC pool. Example: { value: 0.082, lower_bound: 0.041, upper_bound: 0.127, confidence: 0.85, time_horizon: "30d", sim_type: "amm_liquidity" } — net LP return (fees − IL).
Prediction types: impermanent_loss, pool_return, optimal_range, slippage_estimate, lp_withdrawal_threshold.
Calibration: ingests dex_pool_state and price_tick from M2.

6. Dependencies & stubs

M2 Data Feeds — pool state and price data; stub: canned pool snapshots.
M3 Sims hub — lifecycle management; stub: manual init.

7. Invariants / laws

L1 (C5): the constant product invariant x \cdot y = k (adjusted for fees \gamma) is the ground truth — all pool state transitions must satisfy the invariant or the sim is wrong.
L2 (C5): impermanent loss follows the proven formula \text{IL}(r) = \frac{2\sqrt{r}}{1+r} - 1 — the sim must reproduce this exactly for the base case (no fees, no concentrated liquidity).
L3 (C4): LP withdrawal thresholds are derived from IL, not hardcoded — the sim calculates at what price ratio a rational LP withdraws, based on the IL formula and fee income.

8. Build steps

Implement the constant-product pool simulator with fee parameter.
Verify IL formula reproduction against known inputs.
Add LP pop strategies (passive hold, active rebalance, just-in-time liquidity).
Add arbitrageur pops (sandwich, backrun).
Wire M2 pool state data → calibration.

9. Tests

Invariant: every swap satisfies (x + \gamma \Delta x)(y - \Delta y) = k. IL formula: matches closed-form for known price ratios. Slippage: large swaps produce greater slippage than small. LP threshold: LP withdraws when IL exceeds fee income. Bounds: all outputs bounded.

10. Open items

Concentrated liquidity (Uniswap v3 style) — extends the base model significantly.
Multi-pool routing (split swaps across pools).
Which specific pools to simulate (ETH/USDC? stablecoin pairs?).

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