f691d754eb
Threads b_logits_dir / per_sample_support / atom_positions / n_atoms
through experience_action_select kernel launch in
gpu_backtest_evaluator.rs's chunked val pipeline (line ~1722, inside
submit_dqn_step_loop_cublas).
The evaluator sources Q-values via the QValueProvider trait
(delegates forward to the trainer's CUDA-Graphed cuBLAS), so this
change extends the trait surface rather than duplicating the
forward:
- New trait method compute_q_and_b_logits_to(states_ptr, batch,
q_out_ptr, b_logits_out_ptr) — DtoD-copies trainer's
on_b_logits_buf into caller's chunked buffer per sub-batch
iteration alongside the existing q_out copy.
- New trait accessors per_sample_support_ptr / atom_positions_ptr /
num_atoms / total_branch_atoms (stable trainer-owned buffers).
- FusedTrainingCtx implements all of the above; trainer gains
on_b_logits_buf_ptr / atom_positions_buf_ptr /
per_sample_support_ptr_get pub accessors.
- Evaluator gains chunked_b_logits_buf field (sized
[n_windows * CHUNK_SIZE, total_actions * num_atoms] + 32*3 tail
safety), allocated in ensure_action_select_ready.
Phase 6's last-step plan_params forward keeps using the original
compute_q_values_to (b_logits not consumed there).
Plan C Task 4 — evaluator companion to T3 collector wire-up. After
this commit, all production callers of experience_action_select use
the amended kernel ABI (T2 amendment 5de5e546a) end-to-end
(feedback_no_partial_refactor).
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.