73e3ea87a4
Closes Fix 29 audit rows #14 and #15 (Bug-1 contract drift in val/HPO close-price extraction). Both sites read `target[0]` thinking it is raw_close, but post Bug-1 (commit `5a5dd0fed`) `target[0]` is preproc_close (z-normed log-return). The `fv[3]` fallback path is unreachable on every production code path because `set_val_data_from_slices` (in `dqn/trainer/mod.rs:1704`) always yields `Vec<f64>` of length 6 from `[f64; 6]` slices post `TARGET_DIM=6` bump (commit `063fd2716`), so `target.len() >= 2` is an always-true guard. Per `feedback_no_hiding` the dead fallback is removed in the same edit rather than left as a silent wrong-units path. Sites fixed: - crates/ml/src/trainers/dqn/trainer/metrics.rs:576 (val window_prices for GpuBacktestEvaluator) - crates/ml/src/hyperopt/adapters/dqn.rs:2492 (HPO adapter val_close_prices for window-aggregated backtest) Both now read `target[TARGET_RAW_CLOSE]` (col 2). Both import the named constant from `crate::fxcache` so a future column rename moves the call site with the writer (Fix 27 prevention pattern). Verification: - SQLX_OFFLINE=true cargo check -p ml --offline (8.03s) clean. - No GPU code changed; cubin not affected. Affects val Sharpe annualization + window equity curves on training metrics; affects HPO val score on every trial. Pre-fix would yield "prices" of magnitude ~stddev(log_return) (~7e-5 for ES 1-min) feeding into PnL math that expects dollar prices, producing degenerate backtest output. Post-fix prices are real raw_close values. References Fix 27 Bug B (host-side Welford) — same kind of bug surfaced at val/HPO consumer rather than training Welford. References feedback_no_hiding (delete unreachable fallbacks rather than leaving silent wrong-units fall-through), feedback_no_partial_refactor (both consumers of the same (fv, target) tuple convention migrate together), feedback_trust_code_not_docs (the `target.len() >= 2` guard read as defensive but was masking a contract drift). Co-Authored-By: Claude Opus 4.7 (1M context) <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.