16100912c3
Adds combined SP18 D-leg Hold-reward adaptive-cap slots [483..493) + B-leg TD(λ) Q(s') bootstrap diagnostics + PopArt reset flag at [493..505) per spec DD6 (D-leg 10 slots) + B-DD13 (B-leg 12 slots). D-leg [483..493) — 10 slots: 483 HOLD_REWARD_POS_CAP (sentinel 5.0, bounds [0.5, 50]) 484 HOLD_REWARD_NEG_CAP (sentinel -10.0) 485 HOLD_REWARD_DECOMP_DIAG (sentinel 0.0) 486 HOLD_OPP_COST_FIRE_RATE_EMA (sentinel 0.0) 487..493 HRC_* Welford accumulators (mirrors SP16 T3 HCS_* pattern) B-leg [493..505) — 12 slots: 493 TD_ERROR_MAG_EMA (HEALTH_DIAG B-DD9 ratio gate input) 494 Q_NEXT_TARGET_P99 (target-Q bootstrap bound check) 495 Q_NEXT_MINUS_REWARD_P99 (sanity: should be O(1) post-fix) 496 V_SHARE_TREND_DIAG (B-leg synergy probe) 497 POPART_RESET_FLAG (sentinel 1.0 — one-shot, B-DD11) 498..504 TDB_* Welford accumulators (mirrors HRC_* pattern) 504 RESERVED (B-leg follow-up) Pearl-A first-observation bootstrap sentinels match position-side SP14 P0-A REWARD_POS_CAP_ADAPTIVE pattern (POS=5.0, NEG=-10.0). Slot 497 POPART_RESET_FLAG sentinel = 1.0 per B-DD11 — host writes 1.0 once at first SP18 epoch, kernel zeroes after consuming, gating the per-fold PopArt slot 63 EMA reset at the SP18 deployment boundary. ISV_TOTAL_DIM bumped 483 → 505; layout_fingerprint_seed updated with all 22 new slot names; state_layout.cuh C-side mirror in lockstep (continues SP14-P0A/P1/audit-fix-4A/4B mirror precedent — SP16/SP17 slots intentionally not mirrored per existing pattern, only SP18 gets fresh mirror entries). `feedback_no_partial_refactor`: both legs share an ISV section + a single fingerprint bump; SP13 [380..383) and SP16 [461..474) slots remain ALLOCATED but RETIRED in PP.4 (sentinel 0.0, no producer launch — RESERVED-gap pattern from SP14-C.1 preserves checkpoint compatibility). Audit doc updated per Invariant 7 with Pre-Phase PP.2 entry. Spec: docs/superpowers/specs/2026-05-08-sp18-reward-shape-hold-attractor-design.md Plan: docs/superpowers/plans/2026-05-08-sp18-reward-shape-hold-attractor.md Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.