75f83888ca
Mixed-precision loss/grad kernels: - MSE + C51 loss: float softmax/projection/TD-error (prevents bf16 exp overflow) - MSE + C51 grad: float arithmetic + bf16 range clamp before atomicAdd - Shared memory: float (4 bytes/elem) for numerically stable reductions - Bias kernels: float add+clamp ±500 (prevents bf16 Inf cascade between layers) - Noisy bias kernel: same float clamping fast_isnan/fast_isinf (ROOT CAUSE FIX): - nvcc --use_fast_math implies --no-nans → isnan()/isinf() compiled to false - ALL NaN guards across ALL kernels were dead code - Added bit-pattern IEEE 754 checks to common_device_functions.cuh - Replaced isnan/isinf in 7 kernel files (21 occurrences) - ml-dqn build.rs: all kernels now get common header (no more standalone) f32 PER IS-weights: - GpuBatchSlices.weights: CudaSlice<u16> → CudaSlice<f32> - GpuBatch.weights: GpuTensor → CudaSlice<f32> - Loss/grad kernel signatures: const __nv_bfloat16* → const float* - Upload path: separate f32 memcpy instead of bf16 staging - Eliminates bf16 overflow in IS-weight storage CUTLASS padding: - pad32() helper: round up to next multiple of 32 - 6 value-logit buffers: pad32(num_atoms) (51 → 64) - 6 branch-logit buffers: +32*3 padding per branch 895/895 unit tests, 8/9 smoke tests pass. 50-epoch convergence: NaN at step ~100-200 — backward pass produces NaN gradients within the CUDA graph replay (same atomic execution as Adam). Root cause: bf16 backward GemmEx inputs can overflow. Needs mixed-precision backward pass (same pattern as loss kernels) or f32 gradient output buffers. Co-Authored-By: Claude Opus 4.6 (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.