7e63f83bf0
Per spec §7.3 + plan v2 Phase 2B differential table. Each test documents its behavioral contract and its enabling phase. Tests are #[ignore]'d so the pre-commit-hook-gated suite does not fail; per-test #[ignore] is removed by the commit that lands its enabling phase. Group 1 trading behavior (7 tests): 2.1 flat_holds, 2.2 trend_directional, 2.3 mean_revert_reverses, 2.4 cost_sensitivity, 2.6 regime_silences, 2.7 stop_discipline, 2.18 action_latency. Group 2 state/arch grounding (10 tests): 2.8 hold_vs_flat_semantics, 2.9 eval_fold_isolation, 2.13 eval_train_consistency, 2.14 magnitude_uses_ all_buckets, 2.15 fold_transition_stability, 2.16 q_value_bounded, 2.17 gradient_flow_to_all_heads, 2.19 kelly_calibration, 2.20 state_input_ grounding, 2.21 egf_gate_opens (re-export hook for sp14_oracle_tests.rs). Group 3 (Phase 2C — 5 tests) lands paired with Phase 3.5 commits. Harness invocations use trainer = () placeholder; Phase 2B.b extends BehavioralResult/harness with per-bar actions, position-state inspection, forced-action stepping, and per-head grad-norm snapshots. Each test's ignore reason references which Phase X enabling work + harness extension makes it green. 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.