32e5375ac8
Previous L40S 15-epoch repro fired the wired NaN diagnostic with
flagged=[2=on_b_logits, 3=mse_loss_scalar, 6=grad_buf, 7=save_current_lp,
8=save_projected] while value stream (flag 1) and pre-forward params
(flags 4-5) stayed clean. That isolates the corruption to the branch
advantage GEMM/activation but leaves one open question: is the GEMM
input (h_s2, shared between value and branch heads) finite or already
NaN?
Flag 12 = save_h_s2 covers the post-trunk activation. Outcomes:
- flag 12 clean + flag 2 NaN → branch GEMM overflowed (advantage-side
fp32 overflow under post-S&P + adversarial reward stress).
- flag 12 NaN → trunk encoder corrupted upstream of both heads.
Mechanically: one new check_nan_f32 call against save_h_s2 in
run_nan_checks_post_forward, names array slot 12 updated rsv12 →
save_h_s2 in training_loop.rs error message. Same ~5µs cost as the
other 12 checks. Slots 13-15 still reserved.
Per `feedback_no_partial_refactor.md` (extending the NaN diagnostic
contract atomically) and `feedback_no_stubs.md` (rsv12 was a real
reservation; now consumed).
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.