5235b4515b
Two root-cause fixes surfaced by cluster smoke v74v4 (sweep_smoke-a2dfc6d99): Bug D — DBN parser price scaling: - BidAskPair::price_to_f64/price_from_f64 used /1e12 / *1e12 from test-data calibration. DBN production uses 1e-9 nanoprice (the DBN standard). ES at 5500 raw 5_500_000_000_000 → 5.5 instead of 5500. Smoke trade records showed entry_px=5.24 instead of expected ~5240 ES index points (1000× too small). Fix: 1e12 → 1e9 in both functions. Round-trip symmetric; tests updated. Bug C-b — corrupt top-of-book sentinel: - Per-level sanitization (Task 15) zeros each unhealthy MBP-10 level individually. At session-boundary events with all 10 levels invalid, the book becomes uniformly zero. apply_fill_to_pos then reads bid_px[0]=0 / ask_px[0]=0 → vwap_entry=0 → trade record entry_px=0 (zero sentinel in v74v4 CSV, 162/1024 trades in n59t4). - Fix: pre-validate top-of-book in apply_snapshot_kernel. If bid_px[0]/ask_px[0]/bid_sz[0]/ask_sz[0] are non-finite or bid_px[0]<=0/ask_px[0]<=0/bid_sz[0]<=0/ask_sz[0]<=0, atomically skip the entire snapshot (book/prev_mid/atr_mid_ema unchanged). Add per-backtest snapshots_skipped_d counter for observability. Test corrupt_top_of_book_skips_snapshot_and_increments_counter validates NaN top-price + zero top-size cases both increment the counter without mutating state, and that a subsequent valid snapshot updates normally. 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.