552d91bf45
Closes Phase 4-A's open loop by making the no-transaction band LEARN its width. Two coupled mechanisms wired in: 1. Adaptive turnover-target controller — single-thread CUDA kernel (rl_band_turnover_controller.cu) reads per-step `frac_not_masked` and adjusts the turnover target via asymmetric Schulman-bounded adapter: tighten (×0.97/step) above 0.60 threshold, loosen (×1.05/step) below 0.20. Asymmetric LOOSEN rate is faster per `pearl_dead_signal_resurrection_discipline`. First-observation bootstrap on slot 809 with dedicated boot-done sentinel slot 810. 2. Backward chain wiring — rl_band_head_backward.cu propagates `grad_band_per_b [B × 2]` through the asymmetric ±|tanh|·N_max activation and linear projection into per-batch weight/bias scratch plus `grad_h_t [B × HIDDEN_DIM]` (OVERWRITE). Trainer reduces via `reduce_axis0` and folds into the encoder grad combiner alongside π/V/FRD/outcome heads using the existing `grad_h_accumulate_scaled` pattern. Band-weight Adam steps share LR with π (RL_LR_PI_INDEX) — same plateau dynamics as policy. Plus a GPU reducer rl_band_frac_aggregate.cu (single-block tree-reduce over batch → writes to slot 812) and a host-side launch sequence in step_with_lobsim_gpu_body that runs frac_aggregate → controller → turnover_loss → backward outside graph capture, gated by RL_BAND_ENABLED_INDEX > 0.5 so Phase 3D bit-equality is preserved when the master gate is OFF. ISV slots added (809-812): turnover_ema, controller_boot_done, turnover_target_adaptive, frac_not_masked_observed. The turnover loss kernel now reads slot 811 (controller output) instead of the static slot 803. Bootstrap target 0.05 matches the Phase 4-A static default so first-step behavior is identical. Tests: 4 new band_invariants tests cover the controller bootstrap, the escalate-on-over-trading path, the loosen-on-saturation path, and the backward chain producing non-zero `grad_h_t` from a turnover/target mismatch. All 9 band tests + 18 multi_head_policy regression tests pass. Determinism: `determinism-check.sh --quick` exits 0 for the three relevant configurations (band off, band on, band + multi-head on). The Schulman controllers are deterministic by construction (no PRNG); backward kernels are sole-writer per (batch, slot) so reduce_axis0 delivers bit-equal weight grads across replays. Spec: docs/superpowers/specs/2026-06-03-no-transaction-band-architecture.md §3.1 Option (c), §3.3 (backward chain), §3.4 (loss weight controller), §5 (diag emission), §9.1 Mitigation 3 (resurrection discipline). Pearls applied: * pearl_bootstrap_must_respect_clamp_range — bootstrap 0.05 ∈ [0.01, 0.20] * pearl_dead_signal_resurrection_discipline — asymmetric tighten/loosen rates * pearl_welford_trade_count_is_step_not_trade — bootstrap-done sentinel slot * pearl_determinism_achieved — single-thread controller, deterministic reducers Single-seed local smoke (b=128, 2000+500 steps, FOXHUNT_BAND_ENABLED=1) runs end-to-end without NaN. The band saturates to `frac_masked = 1.0` because position state stays at 0 throughout — the sigmoid surrogate gradient is zero deep inside the band, so the band's upper boundary cannot be pulled toward 0 even though the controller drives the adaptive target to its MAX (0.20). Spec §9.1 Mitigation 2 (`RL_BAND_MAX_MASK_FRAC_INDEX` exploration-slot bypass in rl_band_mask.cu) was declared but not shipped in Phase 4-A; it is the structural fix for this chicken-and-egg trap. Phase 4-B mechanism itself is verified correct via the four new tests; the saturation is an architectural-grafting gap surfaced for Phase 4-A2 follow-up rather than a Phase 4-B implementation defect. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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.