76ec68c1c9
v2 NaN instrumentation (S1.21) localized the bug to apply_fill_to_pos's open-from-flat branch writing pos.vwap_entry = avg_px where avg_px was 0 (194 cases) or > 21M finite (216 cases). The arithmetic was clean — root cause is walk_ask_for_buy/walk_bid_for_sell consuming size from deep levels (k=1..9) that have lvl_sz > 0 but lvl_px = 0 (or huge sentinel). MBP-10 fills empty depth slots beyond available levels with these sentinels. Existing per-level filter checked lvl_sz <= 0, !isfinite(lvl_sz), !isfinite(lvl_px) — but allowed lvl_px = 0 and lvl_px > max range. Fix: thread per-backtest min_reasonable_px / max_reasonable_px (already uploaded for the top-of-book skip in book_update_apply_snapshot via S1.19) through to walk_*, and reject any level whose price falls outside [min_px, max_px]. Same fix shape as Bug C-b (top-of-book skip), now applied at all 10 depths. Changes: resting_orders.cu (walk_* signatures + kernel param + 2 call sites), order_match.cu (walk_* signatures + kernel param + 1 call site), sim/mod.rs (submit_market + step_resting_orders launch args). 19 CUDA tests pass. 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.