a1346dac1b
Fold 1 of the 50-epoch L40S smoke (train-92xbj) NaN'd at Epoch 12 even
with the config.v_range clamp from commit 679881fa5. Root cause: the
adaptive C51 v_range state (eval_q_mean_ema, eval_q_std_ema,
eval_ema_initialized, and the pinned eval_v_range_pinned buffer) was
initialised at construction but never reset at fold boundaries.
Reset checklist before this commit (fused_training::reset_for_fold):
- graph children destroyed ✓
- shrink-and-perturb applied to weights ✓
- Adam momentum reset ✓
- replay buffer cleared (via DQNTrainer::reset_for_fold) ✓
- PopArt running stats reset ✓
- eval_v_range EMA state ✗ ← the gap
Fold N+1 therefore inherited Fold N's final tight atom support. When
Fold 1's new data distribution produced Q-values that didn't fit Fold
0's narrow range, TD errors saturated the atom bins; gradient norm
blew up geometrically (170K → 2.3e15 → inf) and the NaN guard bailed
the trainer out at Epoch 12.
Fix: new GpuDqnTrainer::reset_eval_v_range_state() zeros the EMAs,
clears eval_ema_initialized (so the next update_eval_v_range call
reinitialises from observed statistics), and restores the pinned
range buffer to [config.v_min, config.v_max] so any kernel that reads
the range before the first update sees the wide safe support. Invoked
right after reset_adam_state() in fused.reset_for_fold().
Verified: multi-trial smoke 5/5 pass, median_q_gap=2.13, mean_sharpe_ema=11.03
(best observed on this branch). No regression on single-fold training.
50-epoch L40S retest will confirm Fold 1 converges without divergence.
Closes task #33.
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.