jgrusewski 5b4cdec4ff feat(sp5): Task A6 — Pearl 6 cross-fold-persistent Kelly cap signals
Adds `pearl_6_kelly_kernel.cu` (single-block 6-thread kernel reading
portfolio_state directly) to populate ISV[280..286) with Bayesian-prior
Kelly fraction, conviction identity, trade variance, cumulative sample
count (max-semantics), win-rate and loss-rate EMAs. EWMA α=0.01 is an
Invariant 1 structural anchor for cross-fold inertia.

Key design departure from A1-A5: ISV[280..286) are intentionally EXEMPT
from the StateResetRegistry and from apply_pearls/Pearls A+D. portfolio_state
has WindowReset lifecycle (resets at EVERY window boundary, not just fold),
making cross-fold persistence essential. The max()-semantics for sample_count
(s==3) ensure the cumulative count never decreases across any boundary.
Wiener offsets [525..543) that naive formula would produce are intentionally
unused; in-kernel EWMA replaces external wiener bootstrap.

Verification: cargo check -p ml --offline clean (11 pre-existing warnings,
no new). sp5_producer_unit_tests --no-run clean. Two GPU tests (12, 13)
validate EWMA blend and cross-fold persistence.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-01 23:22:06 +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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