82fe6cea66
Detects suspected gradient hacking: when gate1 is open AND aux_dir_acc post-open-minimum drops > LOCKOUT_TRIGGER_DROP (0.05) below the Schmitt open-threshold (target + SCHMITT_BAND = target + 0.03) AND q_disagreement rises > LOCKOUT_TRIGGER_DIS_RISE (0.10) above the analytic random-alignment baseline 0.5, simultaneously. Action: force gate1_open_state = 0 (ISV[391]); set lockout_remaining = 2.0 epochs (LOCKOUT_EPOCHS). During lockout, gate1 stays force-closed regardless of alpha_grad_compute_kernel output. Tracks AUX_DIR_ACC_POST_OPEN_MIN (ISV[394]): running minimum of aux_dir_acc since gate1 last opened; resets to 1.0 sentinel when gate closes naturally or when circuit breaker fires. Slot indices shifted +2 from original plan (SP13 closeout added HOLD_RATE_TARGET=381 + HOLD_RATE_OBSERVED_EMA=382): Q_DIS_SHORT=383, GATE1=391, POST_OPEN_MIN=394, LOCKOUT=395. Matches sp14_isv_slots.rs. Single-thread state-machine kernel (threadIdx.x==0 guard); runs at end of each epoch after alpha_grad_compute_kernel. No atomicAdd per feedback_no_atomicadd.md. 1 oracle test: gradient_hack_circuit_breaker_fires verifies trigger conditions (aux_drop=0.08 > 0.05, q_rise=0.15 > 0.10) cause lockout=2.0 and gate1 force-close=0.0. B.3+B.4 regression: 4 GPU tests unchanged (5 total GPU pass, 1 host pass). Co-Authored-By: Claude Sonnet 4.6 <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.