ef373c34d7
Per spec §6.7. The walk-forward generator emits `test_start..test_end` per fold but the trainer at `mod.rs:1294` only consumed train+val — the 12.5% test slice was silently dropped, the model was never measured on held-out data the train/val pipeline didn't see. This commit lands the foundation: `set_test_data_from_slices` stashes the per-fold range immediately after `set_val_data_from_slices`, gated on `fold.test_end > fold.test_start` for defensively-empty slices. A `set_test_data_observer` hook lets unit tests verify the wiring without spinning up a full GPU eval pipeline. The actual `evaluate_dqn_graphed` invocation against the stashed slice plus the per-fold `HEALTH_DIAG test_slice fold=K test_sharpe_net=...` emit is deferred to a follow-up commit per `feedback_no_partial_refactor`. Wiring it through requires either standing up a second `GpuBacktestEvaluator` instance (parallel to the val one at `metrics.rs:550`) or refactoring the existing val evaluator to swap window data between val and test eval — the val evaluator's lazy-init path is fundamentally tied to the window passed at construction. Plus TLOB weight sync, ISV signal wiring, and a `training_mode` toggle (no such field exists yet on `DQNTrainer`). This deferral matches the Phase 1.5 (kernel + launcher first, trunk consumer follow-up) and Phase 1.6 (stash dev/holdout slices, eval consumer follow-up) precedents on this branch. The stash + observer surface is the analogous foundation; the L40S smoke once Task 1.7.b lands will surface the per-fold `test_sharpe_net` HEALTH_DIAG line as the canonical end-to-end verifier. New oracle test `set_test_data_from_slices_fires_observer_and_stashes` in `sp15_phase1_oracle_tests.rs` constructs a real trainer (sync init, no GPU forward), registers an observer, exercises the API with a synthetic [5000..6000) range, asserts the observer fires once with the right bounds. Passes locally on RTX 3050 Ti. `docs/dqn-wire-up-audit.md` extended with a Phase 1.7 entry documenting what landed, what's deferred, the wire-up locations, and the rationale. 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.