697bb586d0
Adds the second of the two Phase E.1 kernel smoke tests in
alpha_kernels.rs (companion to the munchausen_target smoke from
91d1a52b9). End-to-end exercises the alpha_kill_criteria_compute_kernel
launcher with synthetic inputs covering all 4 outputs:
q_values = [[1, 2, 3], [5, 5, 5]] → q_spread ≈ 0.2041
action_counts = [10, 30, 60] → action_entropy ≈ 0.8980
rollout_R=100, isv[547]=-5185,
isv[548]=4953 → return_vs_random ≈ 1.0670
q_init=50, q_early=55 → early_movement = 0.1000
ISV buffer is sized to 552 floats with slots 547/548 populated using the
committed Task 7c baseline values — this exercises the production
slot-indexing path through the kernel's
`isv[random_baseline_mean_slot]` / `isv[random_baseline_std_slot]` reads,
not just isolated kernel arithmetic.
Does NOT chain apply_pearls_ad_kernel afterward — the smoothing path is
canonical SP4 applicator territory already covered elsewhere. This test
isolates the kill-criteria producer arithmetic.
Tolerance 0.01 on all four observations; passes on RTX 3050 Ti in <2s
including kernel JIT.
`cargo test -p ml --lib alpha_kernels`: 3 pass (compile witness + both
GPU smokes). Audit doc docs/isv-slots.md updated per Invariant 7.
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.