3e9cefdbd9
`train_walk_forward` called `reset_for_fold().await?` directly at the fold boundary, which applies `shrink_and_perturb(α=0.8, σ=0.01)` to whatever `params_flat` holds. At end-of-fold, `params_flat` carries the LATEST-EPOCH decayed weights, NOT the best-Sharpe snapshot saved mid-fold. Fold N+1 then started from `0.8 × decayed + noise` and carried the within-fold edge-decay forward. Insert `restore_best_gpu_params()` BEFORE `reset_for_fold` so S&P operates on the best-Sharpe snapshot. Cold-start guard swallows the "no snapshot saved" error on the first fold (or any fold without a Sharpe improvement) — matches pre-fix behavior on cold start, no regression. State interaction with `reset_for_fold`: restore-best writes `params_flat` ONLY (online weights); reset_for_fold then runs S&P on the restored online, hard-syncs target ← restored online, and resets Adam state on every optimizer. Non-conflicting. `best_params_snapshot` is constructor-init `None` on FusedTrainingCtx and is NEVER cleared at fold boundary. The trainer's `self.best_sharpe` IS reset to NEG_INFINITY in `reset_for_fold` so the first improvement in fold N+1 will overwrite the snapshot. Until then, the snapshot holds whichever fold most recently saved a peak — intended training-scoped behavior. Strict within-fold semantics flagged as a follow-up consideration. Behavioral test (CPU oracle) pins the math contract: - shrink_and_perturb(α, σ) on X = α × X + (1−α) × N(0, σ) - with-fix vs without-fix differ by α × (W_best − W_curr) - cold-start (best == current) is bit-identical between orderings GPU-level kernel coverage stays in compile_training_kernels smoke and the L40S 30-epoch validation gating SP18 Phase 1. Pre-commit Invariant 7: docs/dqn-wire-up-audit.md updated with full rationale, state-interaction analysis, lifecycle notes, and the preserved cross-pearl invariants. Per: - feedback_no_partial_refactor (call-ordering + test + audit-doc atomic) - feedback_no_legacy_aliases (reuses existing API unchanged) - feedback_wire_everything_up (restore_best_gpu_params gains second cold-path consumer) - pearl_no_host_branches_in_captured_graph (runs outside graph capture)
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.