872bd73927
Root cause from train-v8ztm 9-epoch HEALTH_DIAG aux next_bar_mse trajectory: - Ep 0: 0.352 (learnable signal — below random baseline ln(2)≈0.693) - Ep 9: 0.717 (above random baseline — aux is now WORSE than random) - aux_dir_acc_long stuck at 0.19 (anti-correlated with truth) Aux head's backward gradient was flowing back to shared trunk activation h_s2 via dh_s2_out write at aux_heads_kernel.cu:599-613. Q-loss gradient on h_s2 dominates (larger magnitude, structurally different objective: cumulative discounted reward vs next-bar direction). h_s2 evolves to support Q's task; aux's CE loss climbs as h_s2 features become anti-aligned with direction prediction. Fix: stop-gradient. Aux reads h_s2 via forward, trains its own w1/b1/w2/b2 from CE loss, but does NOT propagate to h_s2. Q-loss is the sole shaping force on h_s2. Aux must adapt to whatever h_s2 happens to be — if the representation has direction signal, aux's params will extract it; if not, aux can't learn (separate-trunk Option 2 deferred for that case). This was the SEVENTH fix in today's chain (after 6 SP14 EGF cadence/ gate/saturation fixes). The EGF was a scaffold over a broken aux head; fixing aux first is the architectural prerequisite for EGF to route useful signal. Verification: cargo check clean; sp14_oracle_tests 7/7 pass. Validation: aux next_bar_mse should now DECREASE during training in the next L40S smoke (vs the rising-from-0.35-to-0.72 pattern in v8ztm). Deferred follow-up: aux_regime_backward has the same architecture (propagates dh_s2 to trunk). Same fix is a candidate once next_bar result validates the approach. 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.