4f13e2ca37
Single kernel per group reads (params, grads, adam_m, adam_v) once and fuses 5 passes (4 for non-trunk): Pass A: WEIGHT_BOUND[group] = p99(|params|) via sp4_histogram_p99 Pass B: ADAM_M_BOUND[group] = p99(|adam_m|) via sp4_histogram_p99 Pass C: ADAM_V_BOUND[group] = p99(|adam_v|) via sp4_histogram_p99 Pass D: WD_RATE[group] = |Σ w·g| / max(Σ w², ε_div); side: mean|g|, mean|w| Pass E (group 0 only): L1_LAMBDA[trunk] via gradient-direction entropy deficit × (mean|g|/mean|w|) magnitude scale Launcher loops over 8 groups (DQN trunk/value/branches, IQN, IQL hi/lo, attn, curiosity), one launch per group. Phase 2 applies Pearls A+D to each of the 4-5 outputs per group via host-side pearls_ad_update. Producer-scratch slots 5..38 reserved for this task's 33 outputs: [5..13)=WEIGHT, [13..21)=ADAM_M, [21..29)=ADAM_V, [29..37)=WD_RATE, 37=L1. Three of eight groups wired (DQN trunk/value/branches); five aux groups (IQN, IQL hi/lo, attn, curiosity) skip silently because their backing trainers live on FusedTrainingCtx, not GpuDqnTrainer. Wiring those requires either threading buffer pointers through the launcher signature or hoisting the launcher onto FusedTrainingCtx — both follow-up changes scoped beyond Task A7. Documented in launcher + param_group_buffers docstrings. Per-group GPU tests verify outputs within 5% rel_err (p99 quantization) or 2% rel_err (analytical formulas). Local RTX 3050 Ti: max WEIGHT rel_err=0.589%, max ADAM_M=0.589%, max ADAM_V=0.554%, max WD_RATE=0.001% across all 8 group shapes; group 0 L1 within tolerance. Pearl B 4× memory-bandwidth reduction vs naive 4 separate per-group producer kernels: each buffer read once for all 4-5 outputs. No consumer wired yet — Adam kernels still take hardcoded weight_decay + weight_clamp_max_abs config args. Behavior 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.