d16cab18bb
Per spec §2.1 + §5.4 point 1 and Task 10 of the plan. Adds binding `cfc_step_per_branch_fwd_gpu` in cfc/step.rs that wraps the fused per-branch kernel with the spec-mandated launch config: grid=(B, N_HORIZONS=5, 1), block=(MAX_BUCKET_DIM=28, 1, 1) uniform predicate, cooperative staging of x_local + h_old_local (2 × HIDDEN_DIM floats). Also adds `cfc_step_per_branch_bwd_gpu` binding for Task 11. CfcTrunk loads two new cubins (cfc_step_per_branch + heads_block_diagonal) and caches three function handles. `heads_w_skip_compact_d` (from Task 9) remains allocated as Phase-2-prepared metadata; full heads consumption deferred to a follow-up task (Phase 2 heads dispatch needs GRN integration, which is out of Task 10 scope per `feedback_no_partial_refactor`). In `dispatch_train_step`, the CfC dispatch branches on TrainingPhase: - Phase1Warmup: existing `cfc_step_batched` (unchanged) - Phase2Routed: `cfc_step_per_branch_fwd` consuming bucket-routed `tau_all_d` + `bucket_channel_offset_d` + `bucket_dim_k_d` GRN heads dispatch stays unchanged for both phases per the Task 10 scope adjustment (Option B in the task brief). The `heads_w_skip` storage remains 640 floats; the compact 128-float `heads_w_skip_compact_d` is metadata-ready but not yet consumed. Workspace cargo check clean (ml-alpha lib + trainer compile; only pre-existing cupti/sp15/gpu_per_integration test errors remain). ml-alpha lib tests: 33 passed. Co-Authored-By: Claude Opus 4.7 (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.