18261c85a3
Tuning pass on the adaptive mechanisms. Changes: 1. F5 barrier weight raised 0.05 → 0.20 base, amplified 1×..2× by meta-Q collapse prediction (proactive, not reactive). Old 0.05 couldn't escape the Q-uniform attractor locally. 2. last_meta_q_pred field added on GpuDqnTrainer with set/get accessors, wired from DQNTrainer's meta_q.predict() each epoch boundary. Aux-op kernels now have per-step access to temporal collapse prediction. 3. DISTILL_HEALTH_THRESHOLD raised 0.4 → 0.55 (fire earlier). Additional temporal trigger: distill also fires when meta_q_pred > 0.5. 4. SNAPSHOT_HEALTH_THRESHOLD lowered 0.7 → 0.65. 5. Snapshot-on-winrate fallback: when last_epoch_win_rate >= 0.45, inflate effective health to 0.75 so a snapshot IS taken even if the health EMA is stuck in the 0.48 trough. Without this, the good moments (WinRate 56%, 49%) are never captured → distillation has nothing to pull toward. Result on local E1: distill=on every epoch (was permanently off), D6 fires only on bad-outcome epochs (was firing on convergence). Q-gap still collapsed — tuning alone won't fix the underlying attractor; root cause investigation next. 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.