7746e5294c
The max_hold force-close used event-timestamp nanoseconds, but MBP-10 events fire at variable rates. At dense periods (~100 events/sec), a 60s cap = 6000 events = 6000 training steps — effectively never fires. Observed: alpha-rl-final-f0 step 1500+ → dones=0 (model "hold forever" attractor), pnl growing from unrealized drift, not real trades. Fix: switch to step-based max_hold. - Added entry_step: u32 to PosFlat at offset 28 (struct 28 → 32 bytes) - order_match.cu + resting_orders.cu record entry_step on open/flip - Replaced ns-based check with step-based: current_step - entry_step - max_hold_steps_d replaces max_hold_ns_d in LobSimCuda - alpha_rl_train.rs sets max_hold_steps=100 (matches γ-derived min_hold) - New fill_u32.cu kernel; deleted fill_u64.cu (no remaining consumers) The mega-graph captures self.isv_dev_ptr + 548*4 as current_step pointer so rl_increment_step's in-graph ISV[548] mutation drives the lobsim clock live during fast-path replay. Local smoke 1000 steps (b=32, per=4096): - 750 trades, dones fire consistently every 100-step window - avg_hold stabilizes at 35 (well within 100-step cap) - No "never close" collapse - All architectural fixes preserved Co-Authored-By: Claude Opus 4.7 <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.