c247d9cab0
Per pearl_cold_path_no_exception_to_gpu_drives.md and explicit user
direction ("remove the legacy paths!"), the two Plan-1-era host-side
DtoH+CPU-loop helpers are GONE — not just routed around. Replaced
with GPU kernel reductions writing to ISV slots.
Deleted (160 lines):
- GpuDqnTrainer::per_branch_vsn_mean() — DtoH params slice + host abs+mean loop
- GpuDqnTrainer::per_branch_target_drift() — DtoH (target+online) slices + host RMS loop
- FusedTrainingCtx::per_branch_vsn_mean() and per_branch_target_drift() wrappers
Added (GPU-only producers, ~150 lines):
- target_drift_kernel.cu: 2-block reduction RMS(target − online) for mag/dir
branches. 256-thread smem tree-reduce per block; thread 0 EMA-updates
ISV slot via pinned device-mapped (no DtoH).
- ISV slots [92] TARGET_DRIFT_MAG_EMA_INDEX, [93] TARGET_DRIFT_DIR_EMA_INDEX
- Fingerprint shifted [90,91] → [94,95]; ISV_TOTAL_DIM 92 → 96
- Trainer accessors: branch_param_slice_indices(), target_params_buf_device_ptr()
- Collector launcher launch_target_drift_ema_inplace()
- Constructor cold-start writes for the 2 new slots
HEALTH_DIAG site refactor:
- vsn_mag/vsn_dir read from ISV[VSN_MAG_EMA_INDEX=87], ISV[88] (Task 5
GPU-driven kernel produced these, replacing the legacy VSN scalars)
- drift_mag/drift_dir read from ISV[TARGET_DRIFT_MAG_EMA_INDEX=92], ISV[93]
- All 4 reads via FusedTrainingCtx::read_isv_signal_at — pinned device-mapped
so host reads are coherent with GPU kernel writes without explicit DtoH
isv-slots.md: rows for [87..89] updated to reflect GPU-only producers
(replaces stale text claiming host-DtoH); 2 new rows for [92, 93];
fingerprint shifted to [94, 95]; ISV_TOTAL_DIM bumped 92 → 96.
Smoke fold-2 best Sharpe 100.45 at ep5; per-fold best_val_metric
3.75/10.09/20.21 (avg 11.35) — within Plan 3 T5 baseline (95-117 range).
Pearl validation: this commit demonstrates the cold-path-no-exception
rule applied retroactively. The legacy methods existed for an entire
plan generation; "cold path is fine" was the rationale. New rule:
if a value is computed (reduction/EMA/RMS), the compute is in a kernel,
period — regardless of frequency.
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.