079a06e485
Verifies that when one direction's centered advantage dominates
strongly enough, action selection picks it deterministically across
all N=1000 Philox-keyed runs.
Plan specifies "Thompson temp = 0.0 → pure argmax E[Q]", but the
kernel floors thompson_temp at MIN_TEMP=0.5 per
pearl_blend_formulas_must_have_permanent_floor — pure τ=0 isn't
ISV-accessible. With τ=0.5 the blend is q_eff = 0.5·E[Q] +
0.5·q_sample; deterministic argmax across all τ=0.5 draws requires
the E[Q] gap to exceed atom_span/2.
Construction: NA=3 atoms [-0.1, 0, +0.1] (atom_span=0.2). A_dir
puts +300 at z=2 for Long, -100 at z=2 for others (per-atom mean
zero ⇒ centering preserves shape). Long centered E[Q] ≈ +0.1, others
≈ -0.05 (gap 0.15 > 0.1). q_sample[Long] = +0.1 with prob ≈ 1
(softmax(300) ≈ delta at z=2); q_sample[other] ∈ {-0.1, 0} (zero
prob for +0.1). q_eff[Long] = 0.1; q_eff[other] ≤ -0.025. Long
strictly wins all draws.
If the centering breaks (Long no longer dominant under centered E[Q])
or τ blends a non-Long sample over Long, this test fires.
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.