f62860ea82
SP6 sub-project 1 (Pearl 2): converts the 4 SAXPY launchers from a single
scalar budget (mean of 4 ISV branch slots) to per-branch differentiated
scaling via the correction-factor pattern.
Problem: SP5 Layer B read ISV[190..210) per-branch budget slots but
collapsed them to a scalar via sum/4.0, then passed the single scalar to
apply_c51_budget_scale / apply_cql_saxpy / apply_iqn_trunk_gradient.
Branch HEAD parameters received the same budget as trunk, defeating
per-branch differentiation.
Fix: compute_adaptive_budgets() now returns ([f32;4], [f32;4], [f32;4],
[f32;4], f32, f32, f32, f32) — four per-branch arrays + four trunk-mean
scalars. The trunk mean (D3 decision) is used for the full-buffer trunk/value
SAXPY call (preserving SP5 Layer B behavior for shared params). Branch HEAD
parameter slices receive a correction sub-launch:
correction = branch_budget[b] / trunk_mean (skip if |correction-1| <= 1e-6)
After both launches, branch HEAD slice is effectively scaled by
branch_budget[b], trunk/value is scaled by trunk_mean. No double-scaling.
New helpers added to GpuDqnTrainer:
apply_c51_budget_scale_branch(branch_idx, correction): scale_f32_ungraphed
on branch-slice [f32 elements], offset via padded_byte_offset.
apply_cql_saxpy_branch(branch_idx, correction): saxpy_f32_aux on
branch-slice of both grad_buf and cql_grad_scratch.
IQN trunk gradient: uses iqn_trunk (mean of 4 branch IQN budgets) — IQN
backward flows through trunk only; per-branch IQN routing is beyond SP6 scope.
HEALTH_DIAG: three new per-epoch info! lines emit per-branch c51/iqn/cql
budget arrays (dir/mag/ord/urg) after the intent_dist line.
State: per-branch budget arrays cached on GpuDqnTrainer
(last_*_budget_per_branch: [f32;4]) and on DqnTrainer
(last_*_budget_per_branch: Option<[f32;4]>) for diagnostics.
docs/isv-slots.md: updated Pearl 2 slot rows to reflect SP6 consumer wiring.
Verification: cargo check + release build clean (13 warnings, pre-existing).
13 sp5+sp4+state_reset_registry lib tests pass. sp5_producer_unit_tests
--no-run clean. Sanity grep for old sum/4.0 averaging pattern: empty.
Files changed (6): gpu_dqn_trainer.rs, fused_training.rs, constructor.rs,
mod.rs, training_loop.rs, docs/isv-slots.md. No Pearl 3 or Pearl 5 files touched.
Co-Authored-By: Claude Sonnet 4.6 <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.