5b06484da7
Sharpe calculation: - Use per-trade returns (sum_returns/sum_sq_returns) instead of per-bar step_returns. Per-bar Sharpe collapsed variance → bogus 19.75 with PF=0.03. - Annualize by sqrt(trades_per_year) not sqrt(bars_per_year). Batch sizing: - Cap auto-computed batch_size at 8192 (VRAM ceiling of 2M is OOM limit, not optimal RL batch). - Add VRAM floor: batch_size < ceiling/4096 gets scaled UP (128 → 512 on H100). - Only let hyperopt override batch_size when explicitly non-zero — preserve profile's batch_size=0 auto-compute sentinel. Replay buffer: - Divide per_max_memory_bytes by 3.0 for regime heads (PER budget was 3x too large, causing OOM cascade 74M → 37M → 18M → 9M → 4.6M). - HER buffer uses original_buffer_size (pre-autosizer), not inflated 74M. Q-value clipping: - Wire hyperparams.q_clip_min/max to DQNConfig (was hardcoded ±500, production TOML has ±50). Prevents Q-value overestimation ratio of 94.6x at epoch 2. Training stability: - Anti-LR warmup: skip first 5 epochs (early Sharpe unreliable from random policy). Prevents bogus 3x LR boost at epoch 2. - min_replay_size from profile (1000), not hyperopt batch_size (128). 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.