1ce99efc53
Consolidates Phase 1 triage findings from Tracks 1-4 into an 11-task
Phase 2 plan at docs/superpowers/plans/2026-04-21-policy-quality-phase2.md.
Task inventory:
- 2.0 Per-component gradient decomposition (H4 keystone diagnostic)
- 2.1 H4 fix — magnitude-head gradient starvation (decision tree from 2.0)
- 2.2 H10 fix — stable argmax tie-break at eval
- 2.3 DELETE R5 micro-reward
- 2.4 DELETE R6 loss-aversion; relocate neg-tail to C51 target smoothing
- 2.5 Wiring-bug sweep (7 bugs: C1 fire, epsilon gen_range, if !true,
sigma_mean scale, fold-boundary reset, stale docstring, C5 ISV null)
- 2.6 E4 entropy-reg DELETE-or-KEEP (data-driven, post-2.0)
- 2.7 C4 adaptive grad-clip ablation + DELETE-or-KEEP
- 2.8 L40S validation run — all 4 tracks re-measured
- 2.9 Mandatory-gate verification + phase3-results.md
- 2.10 Tag policy-quality-phase2-complete (and policy-quality-v1 if soft pass)
Matches Phase 0/1 plan formatting (checkbox steps, concrete file paths,
code snippets, bash commands, per-task commit templates). References
project standing rules (no quickfixes, no stubs, no atomic-adds on hot
paths, no feature flags, no hiding errors) and the pinned-readback
pattern from Task 0.4 (commit bb42c9963).
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.