57bae2cb68
Replace 8 unbounded Vec accumulation patterns with bounded VecDeque across ensemble, PPO, DQN, Mamba2, and data pipeline code to prevent OOM on RTX 3050 Ti (4GB VRAM) during live trading and extended training. Key OOM fixes: - Ensemble price/volatility history: Vec → VecDeque with O(1) eviction - Data pipeline: MAX_FEATURES=500K cap (~512MB) prevents unbounded loading - DQN replay buffer: full-array shuffle → HashSet random sampling (8MB → 256B) - PPO loss histories: bounded VecDeque (cap 1K), eliminated batch.clone() - Mamba2 scan: pre-allocated Vecs, explicit drop() after Tensor::cat - Mamba2 training history: capped at 100, Tensor::randn replaces Vec→Tensor - Mamba2 SSM reset: 2 unwrap() violations replaced with proper error handling Battle-testing (19 new integration tests): - KAN: 5 tests (forward, 50-epoch training 89.9% loss reduction, checkpoint) - xLSTM: 7 tests (2D+3D forward, 30-epoch training 82% reduction, checkpoint) - Diffusion: 7 tests (2D+3D forward, 20-epoch pipeline, checkpoint, validation) Bonus: fix pre-existing cache test failure (match .dbn.zst files, graceful skip) All 2390 lib tests pass, 0 new clippy errors. 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.