jgrusewski 2b91f587b7 plan(sp18 v2): 22 fold-reset registry entries + dispatch arms (PP.3)
Adds FoldReset registry coverage for all 22 SP18 ISV slots [483..505)
landed in PP.2 (10 D-leg + 12 B-leg), in lockstep with matching match
arms in `reset_named_state` per the existing
`every_fold_and_soft_reset_entry_has_dispatch_arm` contract test (which
catches the "add registry entry, forget dispatch arm → runtime panic
at fold boundary" pattern surfaced twice historically — SP5 #281, SP7
T7 commit 6e479c55c).

Sentinels match `sp14_isv_slots.rs` constants (single source of truth):
  D-leg POS/NEG caps  → 5.0 / -10.0    (SP14 P0-A REWARD_*_CAP_ADAPTIVE pattern)
  D-leg HRC Welford   → 0.0            (SENTINEL_WELFORD_ZERO)
  D-leg DIAG/FIRE     → 0.0            (Pearl-A direct-replace)
  B-leg HEALTH_DIAG   → 0.0            (Pearl-A)
  B-leg POPART_RESET  → 1.0            (one-shot per B-DD11 — see below)
  B-leg TDB Welford   → 0.0            (SENTINEL_WELFORD_ZERO)
  B-leg RESERVED      → 0.0

Slot 497 POPART_RESET_FLAG semantics: each fold start, FoldReset writes
1.0; on the first epoch of that fold the Phase 5 PopArt-reset consumer
reads 1.0, resets PopArt slot 63 EMA to identity normalization, and
writes 0.0 back — one-shot per fold. Cheap insurance against the 1+
epoch PopArt adaptation lag when switching `q_next` source from
rewards-distributed to Q-distributed in the Phase 5 atomic refactor.

New lock test `sp18_fold_reset_entries_present` asserts all 22 entries
exist with FoldReset category and a description containing "SP18 D-leg"
or "SP18 B-leg" prefix marker. Existing
`every_fold_and_soft_reset_entry_has_dispatch_arm` catches drift in
either direction (registry adds without dispatch arms, or dispatch arms
without registry entries).

Audit doc updated per Invariant 7 with PP.3 entry + full sentinel
mapping table.

Plan: docs/superpowers/plans/2026-05-08-sp18-reward-shape-hold-attractor.md

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-09 00:39:16 +02:00

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.

Description
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Python 1.3%
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