d7f60d4dd7
Phase 1.6 (CLI flags + dev_features/holdout_features stash) and Phase
1.7 (set_test_data_from_slices observer + test_features stash) landed
the data-flow scaffolding; both deferred the actual eval consumer.
This task wires both atomically as parallel evaluator instances:
- dev_evaluator: Option<GpuBacktestEvaluator> -- lazy-init after final
fold, fires once against Q8 dev_features (when dev_quarters > 0)
- test_evaluator: Option<GpuBacktestEvaluator> -- lazy-init per fold,
fires inside the fold loop against the WF test slice (when
fold.test_end > fold.test_start)
Architectural choice: parallel evaluator instances (NOT window-swap on
val_evaluator). Window-swap would require invalidating the CUDA graph
between val and dev/test runs -- fragile, and a direct violation of
pearl_no_host_branches_in_captured_graph. Parallel instances mirror
val_evaluator's lazy-init pattern (TLOB sync, ISV signal pointer,
training_mode = false toggle). Implementation lives behind a single
shared helper `launch_extra_eval` keyed on an `ExtraEvalKind` enum so
Dev / Test share TLOB / ISV / config setup verbatim.
HEALTH_DIAG additions:
HEALTH_DIAG[N]: dev_eval dev_sharpe_net=... dev_calmar=... dev_max_dd=... dev_trades=...
HEALTH_DIAG[N]: test_slice fold=K test_sharpe_net=... test_calmar=... test_max_dd=... test_trades=...
The *_sharpe_net key uses the fused-metrics-kernel cost-aware Sharpe
(post-Phase-1.1.b split -- already includes tx_cost_bps + spread_cost
via the env-step PnL feed); when Phase 1.2.b cost-net sharpe lands, the
key name is preserved so the aggregator-script contract holds.
Atomic per feedback_no_partial_refactor: both eval calls + both
evaluator fields + both HEALTH_DIAG lines + audit doc all in this
commit. No parallel paths, no feature flags. Dev_eval runs synchronously
via evaluate_dqn_graphed (one-shot, no async pipelining benefit since
it doesn't fire per-epoch); val path stays async.
Sealed Q9 holdout remains untouched -- Phase 4.3 will load Q9 via a
separate eval-only entry point (NOT train_walk_forward). The Phase 1.6
debug_assert sealed-slice guard catches accidental future refactors.
Verified: cargo check -p ml --features cuda clean; ml lib suite holds
946 pass / 13 fail baseline; the existing Phase 1.7 oracle test
set_test_data_from_slices_fires_observer_and_stashes still passes.
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.