73925b15d3
Phase B commit 1 (cfc_step block-per-batch refactor) updated the TRAINING-path cfg_cfc to grid=(B,1,1) but missed the same change in evaluate_batched. The eval path silently kept the legacy grid=(1,1,1) launch config, which against the refactored kernel meant only block 0 ran — batches 1..B-1 got GARBAGE h_new (whatever was in scratch memory), which propagated through CfC + GRN to produce garbage probs, which BCE-eval'd to chance-level AUC. Caught by t6z89-vs-txftz cluster A/B at L40S: baseline (t6z89, pre-Phase-B): mean_auc=0.7428 / h6000=0.7211 @ E0 broken (txftz, Phase B): mean_auc=0.4973 / h6000=0.5136 @ E0 train_loss matched within noise (0.6232 vs 0.6258) — the smoking gun for "training works, eval is broken". Why the perception_overfit smokes didn't catch it: those tests check that loss converges on a synthetic up-ramp signal, exercising only the training path. Eval is exercised by `evaluate_works_after_*` smokes but those use n_batch=1, where grid=(1,1,1) and grid=(B,1,1)=(1,1,1) are bit-identical — the bug only manifests at B>1. Fix: eval cfg_cfc → grid=(b_sz, 1, 1), matching training. Plus an explanatory comment so future eyes don't repeat the mistake. Phase B perf gains are unchanged (training path was correct). Only eval's wall time may grow slightly because of the now-correct per-batch parallelism doing the work it should have done. Follow-up: re-submit cluster A/B vs t6z89 to confirm AUC trajectory recovers to ±0.005 of baseline. 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.