5d5a2c1f3d
Root cause: C51 distributional softmax structurally favors zero/low-variance actions (Flat for direction, Small for magnitude). This created irrecoverable feedback loops through FOUR paths: gradient, Bellman target argmax, action selection, and the Q-gap conviction filter. Direction branch (new): - Zero C51 gradient for direction (d==0) — same treatment as magnitude - Boltzmann softmax replaces argmax for direction selection (tau=2×Q_range) - 2× MSE gradient amplification for direction branch head - Mean advantage (not C51 softmax) for Bellman target argmax at d==0 - Q-gap conviction filter REMOVED from training path (redundant with Boltzmann, harmful after high-Sharpe epochs where Bellman max bootstrap raises Q(Flat) towards Q(directional), shrinking the gap) Magnitude branch (Bellman target fix): - Mean advantage for Bellman target argmax at d==1 in both MSE + C51 kernels - Proper softmax retained for online_eq and target_eq (learning objective) Metric fixes: - Diversity denominator: 9 → 7 (Flat forces mag=Half, max reachable is 7) - Threshold: 0.5% of total → 1% of directional (Flat-dominant policies mechanically killed diversity under the old metric) Result: 7/7 dir×mag diversity sustained epochs 7-10, Flat stable at 7-9% (was 60-87% growing). 19/19 smoke tests pass. Co-Authored-By: Claude Opus 4.6 (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.