cb6a89714b
Fixes the SP7 controller dormancy discovered in smoke-test-8556k:
per-branch budgets stuck at exactly bootstrap constants because the
kernel's cold_start_basis numerically equaled the consumer's bootstrap
fallback, AND the Wiener-α was clamped at ALPHA_FLOOR=1e-4 from step 1.
Architectural change:
- 8 new ISV slots LB_{CQL,C51}_ACTIVE_BASE per (head × branch).
Activation flag is monotonic per fold, FoldReset on boundary.
- Kernel only sets active=1 when grads are populated AND on subsequent
active-state computation; cold-start branch leaves active=0 (fresh
branch) or holds prior budget steady (transient grad gate).
- Consumer dispatches on activation: bootstrap when active<0.5,
controller verbatim when active>=0.5. No more spurious bootstrap
when controller writes legitimate small values.
- Welford-α hybrid (max of 1/max(1,epoch_idx_in_fold) and Wiener-α)
gives full update on first active step, falls off as 1/N until
Wiener takes over with meaningful variance estimates. EPOCH_IDX_INDEX
is the existing per-fold-reset counter (no new tuned constants).
State reset registry: 2 new sp7_lb_*_active FoldReset entries +
matching dispatch arms in reset_named_state. Contract test
(every_fold_and_soft_reset_entry_has_dispatch_arm) gates compile.
GPU unit test sp7_loss_balance_controller_activation_flag_transitions
exercises 3 transitions (cold start → both flags 0; active → both
flags 1 with controller-computed budget != bootstrap; transient grad-
gate → flags hold at 1, prior budget held verbatim). Passes on local
RTX 3050 Ti.
Audit doc: Fix 31 sub-bullet describing the activation-flag fix.
Memory pearl out-of-tree (controller will dispatch separately).
Touched:
crates/ml/src/cuda_pipeline/sp5_isv_slots.rs
crates/ml/src/cuda_pipeline/loss_balance_controller_kernel.cu
crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
crates/ml/src/trainers/dqn/fused_training.rs
crates/ml/src/trainers/dqn/state_reset_registry.rs
crates/ml/src/trainers/dqn/trainer/training_loop.rs
crates/ml/tests/sp5_producer_unit_tests.rs
docs/dqn-wire-up-audit.md
Cargo check workspace clean. Cargo test ml --lib clean (incl. contract
test + 6 sp5_isv_slots tests). 16 pre-existing failures unchanged.
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.