jgrusewski 107bcc6648 fix(crypto): MANDATORY hedgeability filter — funding edge was partly phantom
Phase-2 setup (orders command) revealed 0/7 qualifying coins were hedgeable: all perp-only (no
spot leg = cannot build the delta-neutral hedge). Diagnostic: of 191 liquid crypto-native perps,
135 hedgeable / 56 perp-only. HEDGEABLE median funding -0.12bp/day, 0 of 134 clear 5bp (arbed flat
by existing cash-and-carry). PERP-ONLY median +2.59bp/day, all 8 qualifiers live there. The carry
survives ONLY where it can't be hedged. crypto_pit backtest never checked hedgeability -> the
~2-3.6 Sharpe was inflated by un-capturable perp-only funding. Fix: universe() now requires a spot
market (hedgeable only). Current regime: 0/135 hedgeable qualify -> nothing to harvest (deleverage
arbed flat). Real edge = classic basis trade on hedgeable majors: regime-dependent (rich in bull
leverage, ~zero now), more competed, lower true Sharpe than backtest. Phase-2 deploy correctly
BLOCKED by reality, not process. State reset (prior bookings were on un-hedgeable coins).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-07 11:03:41 +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
No description provided
Readme 849 MiB
Languages
Rust 88.2%
Cuda 7.7%
Python 1.3%
Shell 1.1%
PLpgSQL 0.8%
Other 0.8%