ea12c172e6
Promotes the Phase 2.5 follow-up from the plan to a load-bearing test.
Three #[ignore = "requires GPU"] tests exercise backtest_env_step_batch
directly with controlled inputs:
1. predicted_q_populated_on_close_with_real_q_values — open Long Full
at step 0 with q_values_per_window[w=0, a=Long_Full]=2.5, hold for
two bars, close Flat at step 3. Asserts the per-trade tape's
predicted_q[0] ≈ 2.5 (entry_q captured at open, persisted in
entry_q_state across holds, snapshotted before close emit).
2. predicted_q_stays_zero_when_q_values_is_null — same scenario with
q_values_per_window=NULL (single-step evaluate() semantics). The
kernel's open-time write is gated on the NULL guard, so
entry_q_state[w] stays at buffer-init 0.0 and the tape's
predicted_q is 0.0. Proves the NULL-tolerance contract is
kernel-enforced, not just launcher convention.
3. no_trades_no_predicted_q_emission — all-Flat action sequence
produces zero trades. Path-coverage check that entry_q's open
guard fires only on actual opens / reverses.
Test design:
- Kernel-direct (loads ENV_CUBIN via include_bytes!), no eval-pipeline
scaffolding (no QValueProvider, no cuBLAS forward, no chunked state
gather). Avoids the heavy mock infrastructure the plan flagged.
- Flat-market 4-bar single-window window: every OHLC=100, zero costs,
initial_capital=100k. Isolates the entry_q signal from P&L noise
so the assert-predicted_q is exact under IEEE-754 (kernel writes
the Q-value verbatim with no math).
- NULL fallback for isv_signals/conviction/exploration_scale matches
the existing single-step launcher pattern; FEATURE_DIM=4 (<41) ⇒
compute_regime_trail_scales takes the fixed-width fallback (no
feature deref needed).
Verification:
- SQLX_OFFLINE=true cargo test -p ml --test sp21_per_trade_predicted_q_test
--features cuda -- --ignored --nocapture
- 3/3 pass on RTX 3050 Ti (sm_86).
- All 4 prior SP20 GPU oracle suites still pass (25 tests).
- Total: 28 GPU oracle tests, 0 failures.
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.