b45c2fb108
Canonical Page-Hinkley as cited in spec/pearl uses m = Σ(x − μ̄ − δ), which detects mean INCREASE not decrease. First cluster smoke alpha-rl-n5x87 exposed the sign error: at train step 1277 the detector showed 77% fleet alerted (ph_mean=237.8 vs λ=5) — but training-time improvement shouldn't trigger the decay detector. The current formula was firing on "recent x > long-run μ̄" (improvement) rather than "x < μ̄" (decay). Fix: m = Σ(μ̄_pre − x_t − δ). Now grows when x_t < μ̄ − δ (signal below mean by more than tolerance = degradation). M still tracks min(m) and ph_stat = m − M still triggers when cumulative deviation rises above recent minimum. Local b=16 smoke (100+50) post-fix: train[99] ph_mean=40, alert=20%, warmup=0.69 — detector firing on early-training noise. Eval ph_mean=0 (too few closes for warmup). The cluster scale will give the real test. Also renamed kernel param `ph_M_per_batch` → `ph_mmin_per_batch` to match Rust snake_case field naming. (Was a name mismatch between Rust struct and CUDA kernel param that snuck through the v1 build because both sides were edited consistently within their own languages but the cross-language contract wasn't.) 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.