ba83fcd1f5
P0a.T3 v2 implementer's audit revealed `DirectionAction` enum doesn't exist; the codebase uses an 8-variant fused `ExposureLevel` (ShortSmall/Half/Full, Hold, LongSmall/Half/Full, Flat) with cross-crate consumers across 77 files and 32+ test files pinning the 8-variant invariant. Atomic Hold elimination would cascade massively. User insight (2026-05-04): Hold being FREE is the bug, not Hold itself. MFT trading legitimately needs multi-bar holds; we want the model to use them deliberately, not as a CQL-bias lazy default. Holding isn't free in the real world — broker fees, margin interest, opportunity cost. v3 reframes as Hold-pricing: - 4-way action space stays; ExposureLevel::Hold stays; no cross-crate cascade - 3 new ISV slots (380-382): HOLD_COST_INDEX, HOLD_RATE_TARGET_INDEX, HOLD_RATE_OBSERVED_EMA_INDEX - Hold-rate observer: small GPU kernel + Pearls A+D smoothing - Hold-cost controller: 5-line deficit-driven formula (excess > target → cost rises 1×→5× base; observed ≤ target → relax) - Per-bar reward subtraction at action == DIR_HOLD site - 2 GPU oracle tests for the controller P0a.T3 cuts from ~250 LOC + 32-test cascade → ~120 LOC additive. T1+T2 already-staged work unchanged. T4/T5/Layer B/C/D structure preserved. Tension with pearl_event_driven_reward_density_alignment acknowledged in spec — per-bar Hold cost is exposure-NEGATIVE (away from Hold), models real economic carry, ISV-bounded by controller. Inverse of the pearl's failure mode. Faithful reward modeling, not artificial shaping. 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.