7ed4e5ca90
Five reward computation fixes in experience_env_step CUDA kernel: 1. Replace CUSUM vol proxy with ATR(14): CUSUM at feature[41] is a binary direction indicator [-1,1,0], NOT volatility. When CUSUM≈0, vol_proxy became 0.0001 causing 10000x reward amplification. ATR(14) at feature[9] is actual realized volatility — reverse the safe_normalize encoding (ln(atr)+7)/16 to recover atr_pct = exp(norm*16-7) / price. 2. Move loss aversion BEFORE squash: previously applied after hard clamp, creating asymmetric [-15, +10] range making expected reward negative even for fair strategies. Now applied pre-squash for smooth asymmetry. 3. Replace hard clamp with tanh soft squash: fmaxf(-10, fminf(10, reward)) destroyed tail information (1% and 5% wins both → 10.0). tanh preserves that larger wins produce proportionally larger rewards. 4. Remove turnover penalty: the 0.05*|delta|/max_position penalty double- counted transaction costs already deducted from cash via Almgren-Chriss impact model at line ~679, over-penalizing necessary rebalancing. 5. Clarify CUSUM spread_scale usage: CUSUM at feature[41] is correctly used as market-stress proxy for spread widening in tx cost computation — this is distinct from the (now-fixed) vol proxy for reward normalization. Also: annotate min_hold_bars=5 as hyperopt candidate. 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.