fd1426a398
Per-direction trail-stop distance derived from the current bar's ATR. Short[270] and Long[272] receive 2× denormalized ATR (industry-standard 2×ATR trail); Hold[271] and Flat[273] receive EPS_CLAMP_FLOOR=1.0 (no trail-stop fires for those directions; floor prevents zero). 4 ISV slots [TRAIL_DIST_PER_DIR_BASE=270..274). ATR is read from features[bar_idx × market_dim + 9] (ATR_NORM column) and denormalized via the canonical fxcache scheme: atr_abs = max(0.01, exp(atr_norm × 16 − 7)) Constants 16, 7, 0.01 are Invariant 1 anchors from the existing fxcache normalization in experience_kernels.cu:2701. Layer A simplification: Short and Long share the same current-bar ATR value. The spec implied direction-conditional ATR via per-trade-event aggregation, but that requires infrastructure not yet in place. The 4-slot ISV layout preserves the consumer contract so a future Layer C extension can populate truly direction-specific values without breaking Layer B's check_trailing_stop reads. producer_step_scratch_buf grew 199 → 203 (1 new constant SCRATCH_PEARL_8_TRAIL=199). wiener_state_buf stays at 543 (sized at A1 for entire SP5 block). StateResetRegistry: 1 new FoldReset entry (sp5_trail_dist_per_dir). Pearl A sentinel 0 → bootstrap on fold boundary's first launch. check_trailing_stop consumer migration deferred to Layer B. Co-Authored-By: Claude Sonnet 4.6 <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.