cb69e410ea
build.rs::emit_feature_schema_hash only hashed
src/features/extraction.rs, src/fxcache.rs, and
../ml-core/src/state_layout.rs. The z-score normalization step lives
in examples/precompute_features.rs:625-631 (added 2026-04-03 in
9f7c14978f) and was NOT covered by the hash. A fxcache written by an
older precompute build (raw features, no normalization) silently
passed today's validate() because every other field matched.
Empirical impact (L40S Argo train-f8h6q, 2026-04-27):
- PVC fxcache: stale, written pre-normalization → feature column 0
contains RAW CLOSE PRICES (~$5180 ES futures) instead of z-
normalized log-returns
- aux head reads next_states[:, 0] as its next-bar regression
label (gpu_dqn_trainer.rs:7758-7789)
- EMA label_scale climbed to 5420 (vs smoke 0.05) → shared trunk
learned to predict next-bar prices → policy effectively traded
with future-bar information
- epoch-0 Sharpe = 141.99 with 0.32% max-drawdown over 214k bars
— physically impossible; clear future-leak signature
Fix adds examples/precompute_features.rs to schema_sources. New hash
invalidates the stale PVC cache. Argo's ensure-fxcache step has a
regenerate-on-failure branch (infra/k8s/argo/train-template.yaml:
372-383) that auto-regens with current normalized precompute.
Generalises beyond this incident: any future change to feature
normalization, target ordering, or precompute post-processing now
bumps the hash and forces fxcache regen.
Audit entry updated.
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.