49cdf90ecc
Single-thread state-machine kernel that is the heart of the Earned Gradient Flow pearl. Reads driver signals from the global ISV bus, runs Schmitt-trigger Gate 1, computes adaptive k_aux/k_q/β, evaluates two sigmoids, multiplies with a host-supplied warmup gate, applies a β-rate-limiter, and writes 7 outputs back to ISV. Per-step pipeline: 1. Read aux_dir_acc (slot 373), q_disagreement (slot 383), Welford variance EMAs (388, 389, 390), persistent Schmitt state (391), alpha_smoothed_prev (393). 2. Compute adaptive k_aux = K_BASE_AUX/(1 + var_aux/VARIANCE_REF_AUX) and k_q analogously (B.2.5; floor at K_MIN = 1.0). 3. Run Schmitt-trigger Gate 1 state update (open at target+0.03, close at target-0.03; intentional discontinuity at transition is smoothed by the β rate-limiter downstream). 4. Evaluate Gate 1 sigmoid (aux competence, distance from threshold) and Gate 2 sigmoid (Q-aux disagreement vs analytic 0.5 baseline). 5. alpha_grad_raw = gate1 × gate2 × warmup_gate (structurally bounded to [0, 1] per pearl_bounded_modifier_outputs_require_structural_ activation; no runtime clamp). 6. Update Welford variance of alpha_grad_raw → adaptive β (B.2.8; floor BETA_BASE = 0.5, ceiling BETA_MAX = 0.95). 7. alpha_grad_smoothed = β × prev + (1-β) × raw (rate-limited). 8. Write back 7 outputs: k_aux (385), k_q (386), β (387), var_alpha (390), gate1_state (391), alpha_raw (392), alpha_smoothed (393). Sigmoid arguments clipped to [-30, 30] before __expf for fp32 overflow guard (precision-neutral; sigmoid saturates bit-equal at those bounds). Per pearl_bounded_modifier_outputs_require_structural_activation: sigmoid composition produces structurally-bounded [0, 1] output. KNOWN LIMITATION: as of B.4 landing, NO upstream kernel writes ISV[388] (AUX_DIR_ACC_VARIANCE_EMA). The grep at status-report time finds only the sp14_isv_slots.rs declaration. Effect: var_aux stays at sentinel 0.0 forever, so k_aux is degenerate-but-non-fatal at K_BASE_AUX (constant). Gate 1 still works, the sigmoid just doesn't soften under noisy aux_dir_acc. To be resolved in B.11 producer- chain orchestrator OR a separate fix-up task that adds a Welford- variance update next to the existing AUX_DIR_ACC_SHORT_EMA producer. var_q (389) IS written by q_disagreement_update_kernel (B.3), so adaptive k_q is fully functional from B.4 onward. Slot indices hardcoded inside the kernel via const int locals — must match crates/ml/src/cuda_pipeline/sp14_isv_slots.rs (and 372/373 from sp13_isv_slots.rs). The plan originally documented 381/383/ 384/385/386/387/388/389/390/391 for SP14 slots; the actual values are +2 because SP13 closeout added HOLD_RATE_TARGET=381 + HOLD_RATE_OBSERVED_EMA=382 after the plan was written. Tests (RTX 3050 Ti pass; B.3's 2 tests still pass — no regression): - alpha_grad_schmitt_hysteresis: 4-step trajectory verifies the closed→open→open→closed transition. Closed at aux=0.55 (below open=0.58); opens at aux=0.60; stays open at aux=0.54 (in hysteresis band [close=0.52, open=0.58]); finally closes at aux=0.50 (below close=0.52). - alpha_grad_adaptive_beta: 20-oscillation regime verifies β grows above β_base=0.5 and remains bounded by β_max=0.95. docs/dqn-wire-up-audit.md updated per Invariant 7 with full B.4 behaviour contract, per-step pipeline, single-thread launch convention, sigmoid clip rationale, Schmitt discontinuity note, and the var_aux Known Limitation. Co-Authored-By: Claude Opus 4.7 (1M context) <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.