fb5f2394d0
Design spec for SP15 — addresses the train-dd4xl downward spiral post-mortem (8 epochs, trades 131k→64k, sharpe 79→44 with monotone descent across active_frac, dir_entropy) and the walk-forward audit finding (test_start..test_end slice generated but never consumed, val IS the selection set, no sealed test). Six phases over ~13-21 days: - Phase 0: SP14 EGF ISV-driven retune (gate1=closed forever fix) - Phase 1: honest numbers — unified sharpe kernel, cost-net (commission + spread + OFI-impact, dev/prod parity), drawdown reporting, 8 counterfactual baselines, dd_pct as foundational state input, --holdout-quarters/--dev-quarters CLI, consume abandoned test slice - Phase 2: 21 behavioral tests on dev RTX 3050 Ti, pre-commit hook gates argo-train.sh - Phase 3: 5 trader teachings (r_quality/r_discipline split, explicit cost, quadratic DD, regret, confidence-aware Hold floor) - Phase 3.5: 4 recovery mechanisms (asymmetric reward under DD, cooldown gate, plasticity injection, recovery curriculum in PER) - Phase 4: L40S walk-forward Q1-Q7, Q8 final dev, sealed Q9 OOS Cross-cutting discipline rule: no new pearl/controller/kernel/ISV slot/reward term ships without a Phase 2 behavioral test that proves the intended trader behavior. Hard rule, no exceptions. Decisions captured from 7 clarifying questions answered 2026-05-06. 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.