c691bd381a
Step 4 of β migration: 3 SP14/SP13-EGF producers fire per-rollout-step
in collector using collector-owned kernel handles + collector stream.
Reads rollout-time q_values (post-expected-Q, pre-IQR/ensemble/noise)
+ exp_aux_nb_softmax. Writes to shared ISV.
Producer order preserved (matches trainer submit_aux_ops chain):
1. SP13 dir-acc reduce → 2 fixed-α EMAs → aux_pred to ISV[375]
2. SP14 q_disagreement_update (reads aux softmax + q_values)
3. SP14 alpha_grad_compute (pure ISV state machine)
Same kernel gate (commit 9d0c124ce) preserves EMAs across
no-contribution rollout steps.
q_logits semantic note: collector's q_values buffer is the
post-expected-Q output, BEFORE IQR/ensemble/noise SAXPY bonuses (those
run after this block). The kernel's argmax-over-K=4 finds Q's intended
direction; this matches the trainer's q_out_buf semantic exactly. If a
future audit shows noise-induced argmax flips matter, the launch site
is one indirection from the noise-free expected_q_kernel output.
Gated on isv_signals_dev_ptr != 0 && trainer_params_ptr != 0. No
seed_phase_active_cache gate — EGF Gate 1 needs to observe both
seed-phase scripted-policy and post-seed Q-policy actions across the
curriculum.
Compile clean; sp14_oracle_tests 2/2 non-GPU pass (7 GPU tests
ignored on RTX 3050 Ti host).
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.