84caa99b65
Adds two new public methods to LobSimCuda + bumps TRADE_LOG_CAP to prevent eval-phase ring-buffer wrap at cluster scale. - read_per_backtest_trade_counts() -> Vec<u32>: cumulative trade counters per backtest (length n_backtests). Replaces the broken pattern in alpha_rl_train.rs where head_before_eval = aggregate across batch was compared against all_records = single-account ring. - read_trade_records_all() -> Vec<Vec<TradeRecord>>: all backtests' rings in one call. Mapped-pinned staging per feedback_no_htod_htoh_only_mapped_pinned: allocate MappedRecordBuffer<u8> for the payload + MappedRecordBuffer<u32> for heads, DtoD copy from device buffers into mapped-pinned dev_ptrs, sync, read host_ptrs. - TRADE_LOG_CAP 1024 → 4096: cluster v11 (alpha-rl-8ll7j) showed ~342 eval trades/account mean with peaks toward 1000. 4096 gives 4× headroom; memory cost b=1024 × cap × 40 B = 167 MB (was 41 MB), comfortable on L40S 48GB / H100 80GB. Both methods synchronize after DtoD so host reads see the data. E.4 (alpha_rl_train.rs aggregation block replacement) lands separately after Phase A cluster validates to avoid alpha_rl_train.rs conflict. See spec docs/superpowers/specs/2026-05-31-eval-summary-trade-aggregation-design.md and plan docs/superpowers/plans/2026-05-31-eval-summary-trade-aggregation.md. 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.