c1dc84a345
Initial B-10 commit placed `launch_q_distribution_stats` after the `dqn_head.forward(h_t)` at line 6615 — but that's inside `step_with_lobsim`, the legacy non-GPU path. The actual GPU path exercised by `alpha_rl_train` is `step_with_lobsim_gpu` (line 8402), which has its own h_t-based Q forward at line 8632. The G1 diag fields (q_dist_entropy_mean, q_value_range_mean, q_value_abs_max) returned 0 locally because the launch never ran in the GPU path. Added the same launch_q_distribution_stats call after line 8632. The non-GPU launch at 6615 is left in place — `step_with_lobsim` is still callable; per `feedback_no_partial_refactor.md` both paths are instrumented consistently. Validated locally (RTX 3050 Ti, 200+50 b=16 fold-1): q_dist_entropy_mean = 2.6482 (was 0) q_value_range_mean = 4.9257 (was 0) q_value_abs_max = 3.8715 (was 0) The entropy value 2.65 is consistent with a near-uniform softmax over Q_N_ATOMS=21 atoms with some learned concentration (ln(21)=3.04 = full uniform, 0 = one-hot delta). Range and abs_max in 4-5 unit scale match the locally-adapted atom support. Co-Authored-By: Claude Opus 4.7 <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.