19b008e1cb
Quality-review follow-ups to commit 97f1d25f5:
1. CRITICAL: preserve regression sentinel for slots 24, 25, 26, 32.
The post-clamps in apply_iqn_trunk_gradient + launch_cublas_backward_to
ran BEFORE run_nan_checks_post_backward, silently zeroing the buffers
the diagnostic was supposed to observe. Fix: inline check_nan_f32 calls
IMMEDIATELY BEFORE each launch_clamp_finite_f32 — same pattern as the
existing slot 35 check at line 6949. Now the diagnostic fires on NaN
entry; the clamp then sanitizes (preventing propagation but preserving
the flag — flags are sticky/OR'd in dqn_nan_check_f32, never cleared).
2. CRITICAL: correct F0 paper-review line reference + reasoning. The
commit body cited "5.0 norm-clip at line 16747" — that's a memset, not
a clip. Real norm-clip lives at lines 16827-16868. Also tightened the
L2-vs-per-element reasoning: L2 norm <= 5.0 worst-case bounds per-element
|x| <= 5.0 (single-element edge), still several orders of magnitude
below the 1e6 max_abs guard.
3. Inline comment at line 6951 mislabeled the clamp target as 'slot 27
input' — actually clamps the slot 35 buffer (bw_d_h_s2 post-DtoD). Fixed.
4. Articulated pre-clamp rationale: defense-in-depth regression protection
for input buffers (slots 24, 25, 35) against future pathologies that
could create extreme-but-finite inputs. The F1 ep2 NaN was outputs
overflowing finite inputs, but the same fix template covers both
failure modes per feedback_no_partial_refactor.
5. Renamed clamp_finite_f32_kernel -> dqn_clamp_finite_f32_kernel for
consistency with sibling utility kernels (dqn_nan_check_f32,
dqn_zero_kernel, dqn_grad_norm_kernel).
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.