691dec144e
Two orthogonal bugs that compounded to produce the exit_px=±21474836 sentinel in cluster smokes (315 trades baseline, 500 trades pearl): Bug A — NaN/Inf book propagation: - apply_snapshot_kernel passes through NaN/Inf prices from MBP-10 predecoded data (session boundaries, gap-fills). - walk_ask_for_buy / walk_bid_for_sell accumulate cost += take * NaN. - apply_fill_to_pos: avg_px = NaN; realized_pnl += NaN → permanent NaN. - Subsequent close: (int)(NaN-derived * 5000) → i32::MAX sentinel. - Fix: zero-out non-finite levels in apply_snapshot_kernel; add isfinite() guards in walk helpers as defense-in-depth; ATR update guards against non-finite mid. Bug B — pnl_track close branch doesn't reset scratch: - Scratch reset (full 24-byte memset to 0) was already present in the current code; no source change required for Bug B. Tests: - book_nan_inf_prices_dont_corrupt_realized_pnl: NaN at ask[3] + Inf at bid[5] survives the fill pipeline without making realized_pnl non-finite. - pnl_track_resets_scratch_on_close: open+close+5 flat events emits exactly 1 record; second open+close emits exactly 1 more (=2 total). 14/14 stop_controller tests pass; all other ml-backtesting test suites unaffected. 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.