a454a26a5c
Three remaining GPU bottlenecks from the saturation backlog:
1. PPO rollout GPU sync stalls (3→1 per step):
- Merged sample_action to return (action_idx, probs_vec), eliminating
duplicate to_vec1 sync on action probabilities
- Batched critic forward after rollout loop — single GPU→CPU sync
replaces per-step critic.forward() calls (2048 syncs → 1)
- Safe indexing throughout (clippy deny rules)
2. VRAM-aware default network dimensions:
- Added detect_vram_mb() with GPU_MEMORY_MB env var override for K8s
- Added vram_scaled_hidden_dims() with 4 tiers (CPU/<8GB/16GB/40GB+)
- DQN: [256,256] → [2048,1024,512] on L40S/H100
- PPO: hidden_dim_base 128 → 1024 on L40S/H100
- Wired into train_baseline_rl.rs for non-hyperopt training runs
3. KAN B-spline GPU lookup table:
- Pre-compute basis values on 1024-point grid at layer construction
- GPU evaluation via gather + linear interpolation (replaces recursive
Cox-de Boor CPU bounce: 32K recursive calls → 2 GPU gathers)
- Fallback to CPU path when grid not pre-computed
5 files changed, +687/-43, 2476 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <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.