feat: Neural Trader — dynamic market graphs with MinCut coherence gating

Overview

Implement the Neural Trader system as described in ADR-085: a six-layer pipeline that treats the limit order book as a dynamic heterogeneous typed graph with proof-gated mutation.

Architecture

L1 Ingest → L2 Graph → L3 GNN+Attention → L4 Memory → L5 Coherence → L6 Policy

Implemented Capabilities

neural-trader-core

• MarketEvent envelope: event_id, nanosecond timestamps, venue/symbol, 7 event types, side, fixed-point price/qty, order/participant hashes, flags, sequence
• Graph Schema: 10 node kinds (Symbol, Venue, PriceLevel, Order, Trade, Event, Participant, TimeBucket, Regime, StrategyState) × 12 edge kinds × 17 property keys
• GraphDelta: diff type for graph projection (nodes added, edges added, properties updated)
• StateWindow: sliding window for embedding pipelines
• Traits: EventIngestor, GraphUpdater, Embedder

neural-trader-coherence

• ThresholdGate: regime-adaptive coherence gate with 4 tiered permissions (retrieve > write/act > learn)
• CUSUM drift detection: blocks mutations when cumulative sum exceeds threshold (default 4.5)
• Regime-adaptive floors: Calm=12, Normal=9, Volatile=6 — configurable per deployment
• Boundary stability: requires N consecutive stable windows before allowing writes (default 8)
• Drift margin: learning requires drift < 50% of max (stricter than write/act)
• VerifiedToken: proof token minted when coherence + policy approve
• WitnessReceipt: immutable audit record (timestamp, model ID, input hash, coherence hash, policy hash, action, token ID, state hash)
• WitnessLogger trait for pluggable audit backends

neural-trader-replay

• ReplaySegment: sealed window with events, embeddings, labels, coherence stats, lineage, witness hash
• 7 segment kinds: HighUncertainty, LargeImpact, RegimeTransition, StructuralAnomaly, RareQueuePattern, HeadDisagreement, Routine
• CoherenceStats + SegmentLineage: metadata for provenance tracking
• ReservoirStore: bounded VecDeque-backed memory with O(1) eviction, gated by CoherenceDecision.allow_write
• MemoryQuery: retrieve by symbol, embedding, regime, limit
• InMemoryReceiptLog: append-only witness logger for testing

neural-trader-wasm (@ruvector/neural-trader-wasm v0.1.1)

• 8 classes: MarketEventWasm, GraphDeltaWasm, GateConfigWasm, GateContextWasm, ThresholdGateWasm, CoherenceDecisionWasm, ReplaySegmentWasm, ReservoirStoreWasm
• 6 enums: EventTypeWasm(7), SideWasm(2), RegimeLabelWasm(3), SegmentKindWasm(7), NodeKindWasm(10), EdgeKindWasm(12), PropertyKeyWasm(17)
• BigInt-safe serialization via serialize_large_number_types_as_bigints (no u64 precision loss on nanosecond timestamps)
• Security hardened: non-ASCII hex rejection, 0x prefix support, zero-size store guard
• 172 KB WASM binary, 43 Node.js integration tests, Docker test runner
• ADR-086 documents type mapping and build workarounds

Key Design Decisions

• MinCut coherence gate: Tiered permissions (retrieve > write/act > learn) prevent the system from learning during volatile regimes
• CUSUM drift detection: Monitors parameter drift and blocks mutations when drift exceeds threshold
• Reservoir sampling: Bounded replay buffer with proof-gated writes — only stores experiences that pass the coherence gate
• Witness receipts: Every gated write produces a cryptographic receipt for audit trails
• BigInt timestamps: WASM bindings use BigInt-aware serialization to preserve nanosecond precision across the JS boundary

Test Summary

Current State

4 crates implemented with 65 passing tests (22 Rust + 43 Node.js). WASM published to npm as @ruvector/neural-trader-wasm@0.1.1.

Follow-up Work

• L2: Graph construction from order book snapshots
• L3: GNN embeddings with RuVector's existing attention crates
• L6: Policy actuation with Kelly criterion position sizing
• Backtest harness with historical LOB data
• Integration with ruvector-graph-transformer for graph attention
• WebSocket/FIX feed adapter for live market data
• PostgreSQL integration via ruvector-postgres SQL functions
• Streaming sketch memory (Count-Min, Top-K, delta histograms)
• Cognitum edge deployment (deterministic coherence gate kernel)

Read docs/adr/ADR-085-neural-trader-ruvector.md first, then choose a bounded item from the listed follow-up work such as L2 graph construction, L3 embeddings, or L6 policy actuation. Review the corresponding neural-trader crates and existing 65-test baseline; done means the selected capability is implemented with passing tests and its integration requirements addressed.