afb6d73396
Five CUDA kernels + their Rust caller migrated atomically to prevent
silent memory layout corruption between Rust-side bucket geometry
(MAX_BUCKET_DIM=96, BUCKET_DIM_K=[43,43,42], BUCKET_CHANNEL_OFFSET=
[0,43,86,128]) and kernel-side hardcoded constants.
Kernels:
- bucket_transition_kernels.cu: N_HORIZONS 5→3, MAX_BUCKET_DIM 28→96,
BUCKET_DIM_LAST 28→42, BUCKET_OFFSETS {0,25,50,75,100,128}→{0,43,86,128};
bucket_assign_kernel quintile→tercile rewire.
- cfc_step_per_branch.cu: defines + doc.
- heads_block_diagonal_fwd.cu: same; REDUCE_PAD 32→128 (next pow2 ≥ 96).
- multi_horizon_heads.cu: N_HORIZONS_H 5→3 covers fwd, bwd, batched, and
the GRN/2-layer variants via the single define + shared mem [N_HORIZONS_H]
+ loop bounds.
- output_smoothness.cu: OS_N_HORIZONS 5→3.
Rust caller (silent-corruption fix found during audit):
- crates/ml-alpha/src/cfc/step.rs:135,192 had local hardcoded
N_HORIZONS=5 / MAX_BUCKET_DIM=28 constants — replaced with
crate::heads::N_HORIZONS / crate::cfc::bucket_routing::MAX_BUCKET_DIM
(single source of truth via the Rust SoT constants).
cargo build -p ml-alpha: all 5 cubins rebuild PASS.
GPU oracle tests on RTX 3050 sm_86: 5/19 pass; 14 failures are test-side
fixtures hardcoding old 5×28 layout — owned by SDD Task 8 (not regression).
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.