1e70cd5e59
Phase 3 of SP17 dueling-Q identifiability — first of three diagnostic
producers landed atomically with kernel + launcher + Rust wrapper +
HEALTH_DIAG emit + GPU oracle test per `feedback_wire_everything_up`.
Per branch d ∈ {dir, mag, ord, urg}:
Var_d = (1/(B × n_d × NA)) Σ_{i, a, z} (A[i, a, z] − mean_a A[*, z])²
Block tree-reduce (no atomicAdd, `feedback_no_atomicadd`); 4 blocks ×
256 threads. Pearl-A first-observation bootstrap (sentinel 0.0 →
REPLACE on first launch); steady-state α = WELFORD_ALPHA_MIN=0.4 per
`pearl_wiener_alpha_floor_for_nonstationary` — the structural-control
floor preserves catch-up bandwidth without storing 24 Welford
accumulator slots for a cold-path-cadence diagnostic.
Cold-path emit: single launch per HEALTH_DIAG cadence (epoch boundary)
right after `v_a_means`. New line:
HEALTH_DIAG[N]: dueling [a_var=(d=X m=Y o=Z u=W)]
The line will be extended with V_share + advantage_clip_bound in
Phase 3.2, then finalised in Phase 3.3.
GPU oracle test on RTX 3050 Ti: synthetic A constructed so each branch
d has a closed-form Var(A_centered); kernel readback matches expected
value within ε=1e-4 (f32 rounding budget for ~8×n×51 accumulator
length).
Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md
Phase 3.1.
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.