62b5a50e8b
Smoke v1 (train-grfcw) evaluate phase failed with "Failed to load DQN
checkpoint" for fold 0 and fold 1. MinIO log inspection confirmed
checkpoints WERE saved (1431144 bytes each) — the failure was
eval-side shape mismatch.
Root cause:
- Training uses STATE_DIM=128 (ml_core::state_layout), num_actions=108
(factored b0*b1*b2*b3=4*3*3*3), num_order_types=3,
num_urgency_levels=3.
- evaluate_baseline CLI defaults: --feature-dim=54, --num-actions=5
(legacy from pre-branching DQN era).
- Loading 128-state-dim 108-action checkpoint into 54-feature 5-action
net → tensor shape mismatch → `load_from_safetensors` returned
parse error → `with_context(...)` wrapped it as the generic "Failed
to load DQN checkpoint" message, hiding the actual shape error.
- Both GPU and CPU eval paths hit the same root cause.
Fix:
Both eval paths now call `DQNConfig::from_safetensors_file(&ckpt_path)`
to read architecture-critical fields from the checkpoint's embedded
metadata (state_dim, num_actions, hidden_dims, num_order_types,
num_urgency_levels, dueling_hidden_dim, num_atoms, gamma). Eval-time
fields (LR, epsilon, buffer caps) overridden; hyperopt-derived gamma/
v_min/v_max applied if present in hyperopt config.
Older checkpoints without embedded metadata fall back to CLI-args-built
config + warn! log. All production SP21+ checkpoints embed metadata
via the existing DQNConfig::checkpoint_metadata path.
Files changed:
- crates/ml/examples/evaluate_baseline.rs: shape-aware config for both
dqn_eval_gpu_path (line ~1238) and dqn_eval_cpu_path (line ~1029)
- docs/dqn-wire-up-audit.md: 2026-05-11 audit entry
Verification:
- cargo check -p ml --examples --features cuda: 0 errors
- cargo test -p ml --lib financials: 7/7 (unchanged)
- cargo test -p ml --lib sp21_isv_slots: 4/4 (unchanged)
Behavioral gate: smoke v3 (train-psf86, in-flight on 2937da889) won't
have this fix; smoke v4 dispatch on this commit will validate
evaluate phase succeeds for all folds. Look for
"[DQN GPU] Architecture from checkpoint: ..." log line per fold.
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.