29e54a5bb9
Val-Flat-collapse fix #4 (task #94, 2026-04-24). Research-agent audit of train-bv2n5 identified a positive-feedback loop driving atom utilisation to 1%: util < 0.4 → γ -= 0.01 (toward 0.90 floor) γ × delta_z shrinks → TD targets concentrate on fewer bins → more collapse → util lower → γ lower ... The original controller had the right structure but the wrong direction on the low-util branch. Flipping the sign (`-=` → `+=`) breaks the loop: when atoms collapse, raising γ widens the effective TD-target support `γ × atom_support`, pushing targets across more bins of the C51 grid and giving the network more distributional signal to learn from. Evidence from train-bv2n5 (pre-fix): atom_util stuck at 0.01 for 16 epochs; γ slowly drifted toward 0.90. No mechanism recovered. Change localised to a single `else if util < 0.4` branch at `training_loop.rs:660-675`. Same step size (0.005) as the healthy branch avoids overshoot on the recovery path. Clamps retained [0.90, 0.95] at this layer; the fused trainer re-clamps to [0.90, 0.995] after regime adjustment downstream. Other pending atom-util fixes from the audit (deferred pending validation): - Atom-entropy regularisation term in C51 loss (medium risk) - Absolute v_half floor in update_eval_v_range (low risk) - TD-target dithering before Bellman projection (low risk) This single-line fix addresses the dominant mechanism — research agent labelled it "lowest risk, highest leverage". If util remains low after this, the others stack additively. 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.