d710f9d50b
L40S 15-epoch repro #2 (train-multi-seed-kdkdv) revealed train_loss exploded 1000-2000× in fold 1 (3.1 → 318k-694k) while val Sharpe stayed healthy and CLIMBING (73 → 114). Train/val disconnect = pure measurement bug, not real instability. Mechanism: reset_for_fold() zeroed cached_iqr/cached_median at every fold boundary. The `if cached_iqr > 0.0` guard at fused_training.rs:1220 then forced fold N+1's first epoch to the Welford GPU path. Welford running stats inherited from fold N, combined with fold N+1's slightly different reward distribution post-S&P + adversarial regime, produced normalized rewards far outside C51 atom support [-50, 50] — categorical loss readings 10^5x inflated. On rare timing-sensitive paths the near-zero divide overflowed to Inf → NaN (the run-1 flagged=[2=on_b_logits, 3=mse_loss, 6=grad_buf, 7=save_current_lp, 8=save_projected] diagnostic — what we caught was downstream of THIS root cause). The diagnostic infrastructure from756b1ef31+32e5375acworked perfectly: its precise signal of "on_v_logits clean, on_b_logits NaN, but loss is also NaN, params clean pre-forward" surfaced the train/val disconnect that pointed at the reward normalization bug. Fix A (carry-forward, fused_training.rs:919-922): Stop resetting cached_iqr / cached_median at fold boundary. Carry fold N's final-epoch median/IQR forward as fold N+1's epoch-1 default. Same instrument, similar reward distribution between adjacent walk-forward folds, so the carry is safe and gets replaced by fresh stats at the end of fold N+1's epoch 1. prev_popart_var still resets (sole consumer is tau-change detection; a fresh fold counts as a change point regardless). Fix B (permanent floor, dqn_utility_kernels.cu:1657): Raise iqr fmaxf floor 1e-6 → 1e-4. With iqr=1e-6 a trade-exit reward of 5.0 normalizes to 5e6 (vs post-fix 5e4) — much harder to hit fp32 overflow. Defensive bound for genuinely-pathological iqr paths (e.g. genuinely degenerate data quantiles), not a tuned knob — Invariant 1 carve-out for numerical-stability bounds. Per pearl_blend_formulas_must_have_permanent_floor.md (the same recipe that resolved Kelly cap warmup + var_scale collapse before). Resolves task #84 ("Fold-boundary state reset gap causes fold 1 grad explosion").
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.