0e9d69787d
Replaces the kernel's hardcoded `ema_alpha` with the shared `pearls_ad_update`
host-side helper. 6 ISV slots retrofit (one per VSN feature group).
- Kernel `vsn_mask_ema_update` signature: drops `(isv, isv_first_index,
ema_alpha)` for `(scratch_buf, scratch_first_index=53)`. Per-group mean
writes to `scratch_buf[53..59)`. Single `__threadfence_system()` after
all 6 groups write.
- 6 ISV slots wired with Pearls A+D: ISV[105..111) (Wiener offsets
159..177). Wiener offset for group g: (53+g)*3.
- Wrapper `GpuDqnTrainer::launch_vsn_mask_ema(_ema_alpha_unused)`: sync
+ Pearls A+D loop over the 6 groups.
- `debug_assert_eq!(SL_NUM_FEATURE_GROUPS, 6)` guards against group-count
changes silently breaking the contiguous scratch layout.
Behavior: stationary signals converge to the same value at adaptive rate.
The 6 slots stay semantically identical (per-group mean of VSN softmax
mask); HEALTH_DIAG mirror + VSN focus monitor consume them unchanged.
Tests: `sp4_vsn_mask_ema_writes_step_obs_via_pearl_a_then_converges_pearl_d`
drives kernel with B=128 num_groups=6 mask `mask[b,g]=(g+1)/21` (rows sum
to 1, per-group mean = (g+1)/21). Asserts each slot ∈ ±1e-5, non-target
slots remain 0; verifies Pearl A bootstrap + Pearl D convergence.
Per `feedback_no_atomicadd.md`,
`feedback_no_htod_htoh_only_mapped_pinned.md`. Build: `cargo check -p ml
--lib --tests --offline` clean (11 pre-existing warnings, no new warnings).
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.