43e10c8887
Add dqn_forward_only_kernel to dqn_training_kernel.cu — runs a single branching forward pass (shared layers -> value head -> 3 branch heads -> C51 distributional -> expected Q) and writes per-branch Q-values to q_out[B, 11]. No loss computation, no activation saves, no backward. ~3x faster than forward_loss for pure inference. Wire the kernel into GpuDqnTrainer via forward_only_q() which takes CudaSlice<f32> states directly and returns &CudaSlice<f32> Q-values, replacing the Candle BranchingDuelingQNetwork::forward_branches() call chain (~160 Candle kernel dispatches -> 1 fused CUDA launch). Mark all 6 ml-dqn Candle forward methods as #[cold] with documentation that hot-path compute is handled by fused CUDA kernels: - noisy_layers.rs: NoisyLinear::forward() -> dqn_experience_kernel.cu - quantile_regression.rs: QuantileNetwork::forward() -> iqn_dual_head_kernel.cu - distributional.rs: CategoricalDistribution::to_scalar() -> dqn_forward_only_kernel - curiosity.rs: ForwardDynamicsModel::predict() -> curiosity_training_kernel.cu - residual.rs: ResidualBlock::forward() -> dqn_forward_only_kernel - rmsnorm.rs: RMSNorm::forward() / LayerNorm::forward() -> dqn_experience_kernel.cu 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.