912f33c6fc
GPU-oracle test (per `feedback_no_cpu_test_fallbacks`) validating: 1. Cold-start EMA values match spec defaults (avg_w=1, avg_l=1, wr_ema=0.5 from B-3 cold_start bootstrap) 2. ISV slot 721/722/723 defaults exposed in diag (α_slow_min=0.001, n_full_threshold=30000, cv_gain=1.0) 3. Asymmetric direction holds at cold-start: avg_loss EMA grows ~50× faster than avg_win EMA per equivalent observation (because α_fast/α_slow_min = 0.05/0.001 = 50). Verified locally: avg_l=$425 vs avg_w=$4.36 at train_end (100× empirical ratio — matches expected math under volatile batch=16 data) 4. Boundary reset: at eval[1], avg_w=avg_l=1.0 and wr_ema=0.5 (cold_start resumed via reset_session_state) Test result locally: cold-start: avg_w=1 avg_l=1 wr_ema=0.5 ISV slots: alpha_slow_min=0.001 trust_full=30000 cv_gain=1 train_end: avg_w=4.36 avg_l=425.39 dones=131 eval[1]: avg_w=1.00 avg_l=1.00 wr_ema=0.500 dones=0 TEST PASS 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.