jgrusewski b1ef312a40 feat(sp5): Task A5 — Pearl 5 per-branch IQN τ schedule GPU producer (Layer A)
Two new CUDA kernels land as SP5 Layer A additive producers feeding ISV[250..270)
with per-branch IQN quantile-τ schedules derived from Q-distribution skew.

q_skew_kurtosis_update: single-block 4-thread, reads save_q_online[B×13],
two-pass central moments → skew clamped [-3,+3] + ex_kurt clamped [-3,+30];
writes scratch[171..179). EPS_DIV=1e-12 (Invariant 1 anchor). No atomicAdd.

pearl_5_iqn_tau_update: single-block 4-thread, shifts symmetric default τ
{0.05,0.25,0.5,0.75,0.95} by skew×SKEW_SHIFT=0.05, clamps to [0.01,0.99];
writes scratch[179..199). SKEW_SHIFT and envelope are Invariant 1 anchors.

launch_sp5_pearl_5_iqn_tau fires both kernels + 20 apply_pearls_ad calls
(ALPHA_META=1e-3) → ISV[IQN_TAU_BASE=250..270). SP5_SCRATCH_TOTAL 171→199.

StateResetRegistry +1 FoldReset entry (sp5_iqn_tau, ISV[250..270)).
training_loop.rs wired after Pearl 4 with tracing::warn on error.
Two GPU-only unit tests (10+11): zero-skew symmetric default + left-skew
floor clamp. Module docstring updated A1-A5.

No consumer migration — Layer A additive only per spec.
cargo check -p ml --offline clean (11 pre-existing warnings, none new).
cargo test --no-run clean.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-01 22:50:06 +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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Readme 849 MiB
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Rust 88.2%
Cuda 7.7%
Python 1.3%
Shell 1.1%
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