11979d7c08
SP5 implementation plan covering:
Layer A: 8 per-pearl commits (Tasks A0-A8)
A0: ISV slot constants foundation
A1: Pearl 1 per-branch atom span + Q-stats source
A2: Pearl 3 per-branch NoisyNet σ
A3: Pearl 2 per-branch loss budget (after Pearls 1+3)
A4: Pearl 4 per-group Adam β/β/ε
A5: Pearl 5 per-branch IQN τ schedule
A6: Pearl 6 cross-fold-persistent Kelly
A7: Pearl 8 per-direction trail distance
A8: Pearl 1-ext per-branch num_atoms
Layer B: 1 atomic commit (Task B1) — 11 consumer migrations
Layer C: validation + cleanup (Tasks C1-C6)
C1: Local GPU unit tests
C2: L40S 5-epoch smoke
C3: L40S 3-seed × 50-epoch full validation
C4: Pearl 7 investigation (post-validation)
C5: Audit doc + 8 memory pearls
C6: Layer C commit
Layer D: separate atomic commit (Tasks D1-D4)
D1: PnL aggregation kernel
D2: Health composition kernel
D3: Training metrics EMA kernel
D4: Layer D atomic commit
Total: 21 numbered tasks, ~110 ISV slots, 9-11 producer kernels,
11 consumer migrations.
Plan follows SP4's high-fidelity-for-Task-A1 + differential-pattern-
for-A2-A8 structure. Each task has concrete file paths, code blocks,
verification commands, exact commit messages.
Self-review:
- Spec coverage: all 9 pearls + 4 layers covered
- No TBD/TODO placeholders in step bodies
- Type/slot consistency verified across tasks
Refs: docs/superpowers/specs/2026-05-01-sp5-magnitude-differentiation-and-eval-collapse-design.md (HEAD 6e6e0fa11)
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.