9c1b70edec
Captures the ENTIRE per-step training pipeline into ONE cuGraphLaunch, eliminating ~150 individual kernel launches and ~143ms of host-side Rust overhead per step. Phase 1: Lobsim → raw_launch - apply_snapshot_from_device inlined as 4× raw_memcpy_dtod + raw_launch - step_fill_from_market_targets inlined as 2× raw_launch - LobSimRawPtrs trait method caches 30+ device pointers Phase 2: LR controller → GPU kernel - New rl_lr_from_mapped_pinned.cu reads losses from device pointers - New adamw_step_isv_lr kernel reads LR from ISV instead of scalar arg - All 12 Adam .step() calls use step_isv_lr() in mega-graph mode Phase 3: PER → main stream - mega_graph_single_stream flag routes PER to self.raw_stream - Cross-stream events skipped, K forced to 1 Phase 4: Mega-graph capture/replay - Three-state machine: warmup → capture → replay - enable_mega_graph() propagates to perception trainer - Perception sub-graph guards prevent sub-captures inside mega-capture - grad_h_accumulate_scaled_isv reads lambda from ISV on-device Validated: 500 steps, 68-76 sps sustained, no NaN, all losses finite. 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.