jgrusewski 0426ce8887 fix(sp16-p1): MIN_HOLD_TEMPERATURE signal swap to hold-rate overrun + slot 330 non-bug closure
Per train-multi-seed-pfh9n post-mortem follow-up: slot 460 stuck at 50
in Fold 1 was NOT a launch-lifecycle bug. Producer fires per-epoch but
kernel had early-return guard on AUX_DIR_ACC_SHORT_EMA (slot 373) at
sentinel 0.5. Slot 373 reset on fold boundary; aux dir-acc EMA either
didn't fire or settled within ε of 0.5 → kernel kept early-returning.

Fix: drop slot 373 dependency entirely. Drive temperature from observed
hold-rate vs target overrun:

  overrun = max(0, observed_hold_rate - target_hold_rate)
  overrun_norm = clamp(overrun / max(target, 0.01), 0, 1)
  new_temp = TEMP_MIN + (TEMP_MAX - TEMP_MIN) × overrun_norm
  blended_temp = Welford EMA α=0.05 with Pearl-A bootstrap

When over-holding: temp HIGH → exit ramp permissive (matches design intent).
When at/under target: temp LOW → exit penalty strict.

Survives fold reset: hold-rate measurement starts fresh with real data
immediately, no chained-input-sentinel masking.

Slot 330 (KELLY_WARMUP_FLOOR) investigated and confirmed NON-BUG: producer
behaves correctly per pearl_kelly_cap_signal_driven_floors cross-fold-
persistence. floor=0 post-warmup is correct steady state.

Behavioral tests:
- sp16_phase1_min_hold_temp_climbs_with_hold_overrun
- sp16_phase1_min_hold_temp_strict_when_at_target
- sp16_phase1_min_hold_temp_strict_when_under_target
- sp16_phase1_min_hold_temp_no_longer_reads_slot_373

Instrumentation: HEALTH_DIAG[N]: min_hold_temp_diag obs/target/overrun/temp.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-08 15:24:12 +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
Languages
Rust 88.2%
Cuda 7.7%
Python 1.3%
Shell 1.1%
PLpgSQL 0.8%
Other 0.8%