c70c5cdf21
Previously the per-step snap_feature path did B*K = 768 single-snapshot kernel launches (at B=8, K=96) + 768 DtoD copies into the window tensor. New `snap_feature_assemble_batched` processes all B*K snapshots in a SINGLE launch and writes outputs directly into the window tensor's storage. Per-step CPU work: pack 12 mapped-pinned staging buffers (~150 KB total host writes), then 10 DtoD copies of the staging → device buffers. Per-step GPU work: 1 batched kernel launch with B*K threads (each writes 32 floats to its output row). Mapped-pinned staging buffers cover the full B*K capacity at trainer init — no per-step allocation. New `MappedI32Buffer` and `MappedI64Buffer` types parallel `MappedF32Buffer` to stage `trade_count` (i32) and `ts_ns` / `prev_ts_ns` (i64) without violating the no-htod rule (`feedback_no_htod_htoh_only_mapped_pinned.md`). Dead per-snapshot scratch + helpers (`bid_px_d`, `snap_feat_d`, `stg_bid_px`, `snap_fn`, `upload_into`, etc.) removed per `feedback_no_legacy_aliases.md` — the only callers were the per-snapshot path, gone. Expected per-step savings: ~5-10 ms launch + DtoD overhead at B=8, K=96. Over 2000 steps/epoch = 10-20 sec/epoch. 77 ml-alpha tests pass. Synthetic overfit unchanged. Co-Authored-By: Claude Opus 4.7 <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.