0c57a5a31f
Phase 3.3 finalises the SP17 dueling-Q diagnostic chain. Canonical HEALTH_DIAG line (already emitted by Phase 3.2 commitb6b17d46b) matches the plan's exact format spec at line 1064-1066: HEALTH_DIAG[N]: dueling [v_share=(d=X m=Y o=Z u=W)] [a_var=(d=A m=B o=C u=D)] [clip=K] Reader parsing table added to audit doc, Phase 4 smoke gate criteria documented: - a_var > 0.01 per branch throughout (regression detector) - v_share ∈ [0.3, 0.7] per branch (balanced dueling) - clip ∈ [0.5, 5.0] (magnitude scale healthy) Memory pearl `pearl_sp4_histogram_warp_tile_undercount` filed (MEMORY.md updated separately as it's a user-private file outside the worktree). Documents the SP17 Phase 3.2 test-data trap discovered during the advantage_clip_bound oracle test: `sp4_histogram_p99`'s documented "1/(256×32) loss for typical signals" qualifies on signal distribution; lockstep-uniform synthetic patterns violate the assumption. Fix is per-element jitter in test data — NOT atomicAdd (which would violate `feedback_no_atomicadd`). Real |A_centered| in production is continuous, so the issue is test-only. Phase 3 commit chain (atomic per `feedback_no_partial_refactor`): -1e70cd5e5Phase 3.1: A_var_ema + 1 GPU oracle test -b6b17d46bPhase 3.2: V_share + advantage_clip_bound + 2 GPU oracles - this commit: closeout audit doc 13/13 SP17 GPU oracle tests pass on RTX 3050 Ti in 2.4s. Phase 3 is observability-only — NO consumer path is modified. The clip bound is producer-tracked but NOT yet wired as an actual clip on any kernel; that's Phase 5 follow-up. Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md Phase 3.3. 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.