5099c1fdbb
Complete f32 refactor of the experience collection and replay storage: CUDA kernels: - experience_state_gather: output changed from __nv_bfloat16* to float* All portfolio features, multi-timeframe features, and zero-padding write f32 directly. Market features read bf16, convert to f32 in-kernel. - experience_env_step: batch_states and out_states changed to float* State copy to replay buffer is native f32 memcpy. - bn_tanh_concat_f32_kernel: f32 bottleneck tanh+concat variant - add_bias_relu_f32_kernel: f32 bias + ReLU for hidden layers - add_bias_f32_f32bias_kernel: f32 bias (no activation) for output/bottleneck - gather_f32_rows: f32 row gather for replay buffer sampling cuBLAS forward: - New sgemm_f32 and sgemm_f32_ldb methods for pure F32 SGEMM - New forward_online_f32 method: all-f32 forward pass (no bf16 GemmEx) - f32_weight_ptrs_from_base: f32 byte offset computation for weight pointers Experience collector: - batch_states: CudaSlice<half::bf16> → CudaSlice<f32> - states_out: CudaSlice<half::bf16> → CudaSlice<f32> - online_params_f32: new f32 master weight buffer - exp_h_s1_f32 through exp_h_b2_f32: f32 activation buffers - sync_weights_f32: DtoD from trainer's f32 master params - GpuExperienceBatch: states/next_states now CudaSlice<f32> Replay buffer (ml-dqn): - Internal storage: CudaSlice<u16> → CudaSlice<f32> for states - scatter_insert: uses scatter_insert_f32 (no bf16 cast) - gather: uses gather_f32_rows (no bf16 cast) - Sample output: f32→bf16 conversion at GpuBatch boundary (GpuTensor stores bf16 for tensor core training GemmEx) Deleted: insert_batch_tensors legacy dead code in config.rs Data flow: bf16 market data → f32 state_gather → f32 SGEMM → f32 Q-values → f32 action selection → f32 env_step → f32 replay insert → f32 replay storage → bf16 training batch (tensor core boundary) No bf16 truncation noise anywhere in experience collection or storage. The ONLY f32→bf16 conversion is at the training batch sample boundary where bf16 is required for H100 tensor core GemmEx throughput. 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.