ab94ce2a49
Stage 1+2 of #162 (CUDA Graph capture of training step). The AdamW kernels previously took the step counter as a host scalar arg, which gets baked into kernel args at CUDA Graph capture time — replays would freeze the counter and produce wrong bias-correction values. Both AdamW variants now read the step from a device pointer, advanced by a tiny 1-thread `increment_counter` kernel that goes inside the captured region. Each replay correctly increments and observes the new step value. Kernel changes: adamw_step.cu: - adamw_step: int step → const int* step_ptr - adamw_increment_counter: new, +=1 on step_ptr[0] mamba2_alpha_kernel.cu: - mamba2_alpha_adamw_step_devscale: int t → const int* step_ptr - mamba2_alpha_increment_step_counter: new Rust changes: trainer/optim.rs (AdamW): - host `step: i32` → device `step_count_d: CudaSlice<i32>` - step(): launch increment kernel BEFORE adamw kernel; both read device counter via pointer arg. - step_count(): test-only accessor, mapped-pinned readback (sync). mamba2_block.rs (Mamba2AdamW): - kept host `step_count: i32` for legacy paths (`step`, `step_from_buffers`) which aren't capture-compatible anyway (host grad-norm dtoh, host scalar grad_scale). - added device `step_count_d: CudaSlice<i32>` for the production gpu_clip path; advances via `kernel_increment_step` kernel inside the captured region. - adamw_apply_devscale: `t: i32` → `step_d: &CudaSlice<i32>`. Validation: - 4 adamw_invariants tests pass (step_count_increments specifically exercises the device counter). - 10 mamba2_block lib tests pass (training_loop_decreases_loss exercises legacy host-counter path). - Synthetic overfit smoke: initial=0.25 → final=0.0006 (matches pre-refactor trajectory bit-for-bit-equivalent). Stage 3+4 (capture brackets + first-call-capture / subsequent-replay state machine in step_batched) follows in the next commit. Co-Authored-By: Claude Opus 4.7 <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.