97f1d25f54
Patches two cuBLAS GEMM backward operations identified by SP1 Phase B
smoke smoke-test-xvzgk (commit f139a63ee) as the F1 ep2 NaN sources:
1. apply_iqn_trunk_gradient (gpu_dqn_trainer.rs:6885+):
- Pre-call: clamp bw_d_h_s2 (the IQN DtoD-overwrite target that feeds
the GRN trunk encoder's cuBLAS Linear_a/Linear_b dW/dX/dB GEMMs;
symmetric with slot 27 source-side check) to ±1e6×ISV[96].
- Post-call: sanitize iqn_trunk_m (slot 26 output) — isfinite-or-zero
+ magnitude clamp; defence-in-depth.
2. launch_cublas_backward_to main backward path:
- Pre-call: clamp d_value_logits_buf + d_adv_logits_buf (slots 24/25
inputs that feed bw.backward_full's dueling/branch backward GEMMs)
to ±1e6×ISV[21].
- Post-call: sanitize bn_d_concat_buf (slot 32 output, gated on
bottleneck_dim > 0).
Both fixes use the new clamp_finite_f32_kernel utility (CUDA — replaces
NaN/Inf with 0 + clamps finite values to ±max_abs) + launch_clamp_finite_f32
(Rust wrapper). Kernel lives in dqn_utility_kernels.cu (already in
build.rs); loaded into the same module as the NaN check kernels in
compile_training_kernels — both call sites land inside captured children
(forward_child for slot 32, aux_child for slot 26) and use only stream-
bound launch_builder, so the kernel is graph-safe.
ISV-driven max_abs bound = 1e6 × isv[<slot>] with 1e3 ε floor for
uninitialized state (Invariant 1 carve-out per
feedback_isv_for_adaptive_bounds). Wide guard band — F0 inputs sit
several orders of magnitude below the threshold (F0 ISV[96] ≈ 1.0
LayerNorm RMS, F0 ISV[21] ≈ 1.0 Q-value scale; F0 |iqn_d_h_s2| ≤ ~10²,
F0 |d_value_logits| ≤ ~10³ post the existing 5.0 norm-clip at
line 16747), so guards are no-ops on F0; F1 overflows trigger clamp.
F0 paper-review pre-smoke confirmed.
Combined RELATED commit per feedback_no_partial_refactor — both kernels
share the same unsafe-pattern (cuBLAS GEMM extreme-intermediate-product
overflow) + the same fix template, so they ship together.
Phase B slots 24-35 remain as permanent diagnostic infrastructure;
will catch any future regression of this NaN class.
SP1 Phase D validation smoke pending.
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.