237b3dbfb0
SP7 T1 added 24 ISV slots (LB_DIFF_VAR_CQL_BASE=297 ... LB_C51_ACTIVE through 321) without bumping ISV_TOTAL_DIM, leaving SP7's wiener-state and activation slots out-of-bounds of the allocated pinned buffer (294 * 4 = 1176 bytes; SP7 slots write to bytes 1188..1284). GPU direct-pointer writes silently corrupted memory in the next page; CPU read_isv_signal_at returned garbage in release builds. This explains the SP7 smoke's confusing zero-budget-everywhere observation: the activation flag was reading OOB memory, the consumer saw inconsistent values, and the controller's actual ISV state was never visible. Bumped ISV_TOTAL_DIM to 321 (max valid index 320, covering SP5_SLOT_END-1). Made pub(crate) so the new contract test can reference it from sp5_isv_slots.rs. Updated layout_fingerprint_seed()'s slot entries to include the previously missing D3 and SP7 slots (TRAINING_SHARPE_EMA=294 through LB_C51_ACTIVE_BASE=317) and updated ISV_TOTAL_DIM= literal to 321 in lockstep per feedback_no_partial_refactor. Added contract test all_sp5_slots_fit_within_isv_total_dim to permanently gate this class of bug at cargo test time. The test would have caught SP7 T1 instantly; future slot allocations cannot regress this. Files: gpu_dqn_trainer.rs (ISV_TOTAL_DIM + comment + fingerprint), sp5_isv_slots.rs (test), docs/dqn-wire-up-audit.md (Fix 31 sub-bullet). Cargo check workspace clean. Cargo test ml --lib 935 passed (934 baseline + 1 new contract test); 16 pre-existing GPU-hardware failures unchanged. Co-Authored-By: Claude Sonnet 4.6 <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.