b92bd72c72
S2.1 instrumentation (smoke cqpph @ 95a77c4ac) revealed the chain
worked end-to-end: max_hold check fired 1661 times, force-flat target
was written and seen by seed_inflight. Yet 86.5% of trades exceeded
the 60s cap with mean hold = 432s. Root cause: decision_policy's
stop_check_isv only runs every decision_stride events. At
decision_stride=200 on ES Q1 (2M events / ~91 days = ~3.9s sim-time
per event), decisions are ~13 min apart in sim-time, so the cap is
enforced 13 min late on average.
Fix: move the max_hold check to resting_orders_step (event-rate, runs
every iteration), same pattern as the session-gap force-close at
resting_orders.cu:282. Thread max_hold_ns_per_b and open_trade_state
into resting_orders_step. SL/trail stay in stop_check_isv because
they depend on ISV controller state at decision-rate.
Remove the dead decision-rate max_hold code and its 6 diagnostic
counter params from stop_check_isv per single-source-of-truth and
no-hiding rules. Keep mh_kernel_calls and mh_force_flat_seen_by_seed
counters as ongoing generic diagnostics. Update harness.rs log line
and stop_controller tests to exercise the event-rate path.
Co-Authored-By: Claude Opus 4.7 (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.