f31a6b7ff0
Verifies that under uniform A (== 0 across all action × atom slots),
every direction has identical centered E[Q] regardless of V.
The plan's original wording probes this through Thompson selector
("uniform action distribution"), but the kernel's first-wins-strict-
`>` argmax over four i.i.d. Thompson samples produces a structurally
non-uniform distribution under symmetric A even with correct centering
(closed-form earlier-bias predicts ≈[44%, 26%, 18%, 11%] across
Short/Hold/Long/Flat from the tie statistics). The Thompson distribution
is V-dependent through tie statistics — NOT a centering regression.
Restated as the structural pre-Thompson property: with A=0 and
V arbitrary, centered logit = V + 0 is identical across all directions
⇒ per-direction E[Q] identical to ε=1e-5. The Thompson selector reads
these centered logits; if A=0 produced non-zero per-direction E[Q]
spread, *that* would be the centering regression — exactly what this
test catches.
Probed via compute_expected_q (reads back per-action E[Q] directly,
no Thompson noise as red herring). V-non-uniform sanity check confirms
the kernel reads V (non-zero E[Q] when V ≠ 0).
Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md
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.