0af691cae5
- grad_buf: CudaSlice<half::bf16> → CudaSlice<f32> (eliminates bf16 truncation in backward weight gradients) - cql_grad_scratch: same change (CQL gradient isolation buffer) - iqn_trunk_m, iqn_trunk_grad_norm: same change - Backward dW GemmEx: new gemmex_bf16_acc_f32 (C matrix CUDA_R_32F) - Backward dX GemmEx: unchanged (stays bf16, upstream gradient) - bias_grad_reduce_kernel: float db output + native atomicAdd - dqn_utility kernels: grad_norm, adam, clip, clipped_saxpy all read float* grads directly (no bf16→float conversion overhead) - Adam: gradient clipping computed entirely in float - GOFF byte offsets: sizeof::<f32> for grad_buf positions - IQN d_h_s2 DtoD: keep bf16 byte size (IQN head output is bf16) - Test gradient_budget: f32 test data for grad_buf/cql_scratch - fast_isnan/fast_isinf: bit-pattern IEEE 754 checks in common header (survives --use_fast_math which makes isnan() return false) - All standalone kernels in ml-dqn get common header (no more standalone) 895/895 unit tests pass. Smoke tests: intermittent NaN from forward-pass bf16 arithmetic (not from gradients). The remaining NaN source is cuBLAS GemmEx bf16 output overflow → Inf logits that survive bias clamping in edge cases. Needs investigation of which specific layer/sample triggers it. 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.