1c917e3cb0
The train-multi-seed-p526h repro (with MSE-clamp + PopArt-carry already
landed) reproduced a geometric Q-divergence in fold 1 that train_loss
can no longer hide:
F0 ep5 Q=+0.79 (healthy carryover)
F1 ep1 Q=+0.82 (boundary handoff fine)
F1 ep2 Q=+2.23 (2.7× jump — drift starts)
F1 ep3 Q=+4.05
F1 ep4 Q=+10.66
F1 ep5 NaN at step 5
A model with this Q dynamic is catastrophically unsafe for live trading:
Kelly cap floats with Q-confidence, so runaway Q drives oversized
positions and can blow up the strategy before any downstream safety
trips. This commit installs a production safety net — NOT a root-cause
fix — that hard-halts training the moment Q-drift is detected:
if |q_mean| > 2× prev AND |q_mean| > 1.5 production-unsafe floor
→ return Err, training halts, model rejected from deployment
Inserted in training_loop.rs immediately before the existing
`self.prev_epoch_q_mean = q_mean` update so the comparison uses the
same source-of-truth values that already feed downstream diagnostics.
The 2× ratio catches genuine geometric divergence (typical healthy
growth <30%/epoch); the 1.5 absolute floor prevents false positives
in early training where small Q magnitudes oscillate by large ratios
(verified: F0 ep4→ep5 ratio 5.3× would NOT trigger because |0.79|<1.5).
Both thresholds are numerical-stability bounds (Invariant 1 carve-
out), not tuned hyperparameters. Skips first epoch (no prev_q). Does
NOT skip fold-boundary epochs — those are exactly the failures
we're catching.
This is a safety net. The actual root cause is a fold-boundary reset
gap (which reset isn't firing correctly?). A systematic audit is
queued to identify it. Suspects: prev_grad_buf, PopArt GPU Welford
buffers (popart_count huge from fold 0 → new-fold stats track too
slowly), isv_q_abs_ref_* magnitude EMAs, spectral norm σ EMAs, TLOB
Adam state.
Per user's "can't happen at production" philosophy: production
deployments must have this safety net regardless of whether the
root-cause fix lands first.
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.