0b37ff77b0
ROOT CAUSE: 5 interlocking bugs made learning impossible: 1. DSR denominator floor 1e-12 produced values in millions → drowned all signal 2. Global [-1,+1] clamp destroyed Bellman equation signal (can't distinguish catastrophic loss from mild loss) 3. v_range=20 exactly equals V_max for gamma=0.95 → Bellman target pins at ceiling → Q-values saturate → Q-gap collapses to 0.0000 4. num_atoms=11 over 40-unit range = 4.0 per atom (C51 paper min is 51) 5. 6/7 reward components were penalties → mean_reward=-0.311 regardless of action FIXES: - DSR denominator floor: 1e-12 → 0.01 (prevents million-scale spikes) - Each component individually clamped BEFORE weighting (DSR to [-1,+1], z-score to [-3,+3], drawdown to [0,1], time decay to [0,0.3]) - Removed global [-1,+1] clamp (no longer needed with bounded components) - profit_take_bonus: 0.1 → 0.01 (was 100x too large, caused reward hacking) - Removed confidence scaling (positive feedback loop destabilized learning) - Removed regime scaling (non-stationary reward confused the model) - Dynamic v_range from gamma: v_range = 2.5/(1-gamma)*1.2 (always covers Q range) - num_atoms minimum: 11 → 51 (C51 paper standard) - gamma default: 0.99 → 0.95 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.