730337375f
Combines SP19 (multi-horizon labels, already landed) + SP20 (WR-first reward) into one spec per pearl_no_deferrals_for_complementary_fixes. Goal: WR ≥ 55%, textbook PF ≥ 2.0, walk-forward stable, per-regime stable. Six components, atomic ship per feedback_no_partial_refactor: 1. Reward kernel (event-driven, 4-quadrant, asymmetric clamp ramped from wr_ema, multi-horizon directional ground-truth check) 2. Hold opportunity-cost (per-bar, dual emission for real reward + Hold baseline buffer) 3. n-step credit distributor (uniform over trade duration, fixes SP18 B-leg self-bootstrap bug at gpu_experience_collector.rs:4154) 4. Aux→Q confidence gate (sigmoid threshold, mean_a Q baseline avoids Hold-everywhere punishment) 5. 3 fused producer kernels (sp20_emas, sp20_controllers, sp20_stats) driving 9 ISV slots in [510..520) 6. 7 behavioral tests on RTX 3050 Ti, gating L40S deployment ~1,550 LoC total. Spec includes data flow, error handling philosophy, 4-tier test gate, success criteria with explicit failure modes. Cross-references: - pearl_event_driven_reward_density_alignment (design principle) - pearl_audit_unboundedness_for_implicit_asymmetry (asymmetric clamp) - pearl_separate_aux_trunk_when_shared_starves (aux gate leverages SP14-C) - project_metric_pipeline_inflation_audit (WR honest, Sharpe honest, goal grounded) - project_goal_wr_55_pf_2 (the goal this spec implements)
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.