1708a028b3
The prior `test_eval_action_select_boltzmann_bounded` asserted Boltzmann theory (P(best)≈0.366 at tau=q_range) on the direction branch. Plan C T2 (commit52a2663a2) replaced the direction-branch Boltzmann path with single-distribution C51 Thompson + argmax-E[Q] eval; the T2 amendment (5de5e546a) added 4 args to the kernel ABI (b_logits_dir, per_sample_support, atom_positions, n_atoms). The old test was launching with an outdated arg count and asserting a distribution shape that no longer applies. Migration (per feedback_no_partial_refactor): - Rename to `test_eval_action_select_eval_argmax_picks_best` - Build per-direction C51 logits PEAKED at distinct E[Q] values: Short=-0.5, Hold=-0.1, Long=+0.5 (best), Flat=+0.1 - Linear adaptive support [v_min=-1, v_max=+1, delta_z=0.1] uniform across all (sample, direction) pairs - Run kernel in eval mode (eps_start=eps_end=0): assert P(Long)>=0.99 i.e. the kernel deterministically picks argmax(E[Q]) per sample This validates the T2 amended ABI runs end-to-end and exercises the production kernel's argmax-E[Q] eval branch with a controlled distinct-E[Q] setup. Companion to Phase 2 Tests 2.A-2.D in distributional_q_tests.rs which add training-mode assertions and production-vs-standalone parity checks. Verification: `SQLX_OFFLINE=true cargo check -p ml --lib --tests` clean. 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.