148aa1f464
Three compile errors in crates/ml/tests/smoke_test_real_data.rs at lines 568, 644, and 715 — all three were calls to `collect_experiences_gpu(&market_buf, &target_buf, ...)` where `market_buf` and `target_buf` were `CudaSlice<f32>` allocated via `stream.alloc_zeros + memcpy_htod`. The production API changed incba9f25ed(Bug-1 close-out): both parameters now require `&MappedF32Buffer` (mapped pinned DEVICEMAP, no HtoD copy). The test helper `real_market_data` was never updated. Migration applied to smoke_test_real_data.rs: - Added `use ml::cuda_pipeline::mapped_pinned::MappedF32Buffer` import - Removed now-unused `type CudaSlice<T>` alias - Changed `real_market_data` return type from `(CudaSlice<f32>, CudaSlice<f32>, usize)` to `(MappedF32Buffer, MappedF32Buffer, usize)` - Replaced `stream.alloc_zeros + memcpy_htod` with `unsafe { MappedF32Buffer::new(len) } + write_from_slice` at lines 511-517 - Parameter renamed `stream` → `_stream` since it is no longer used by the buffer construction (stream is still used by callers via `GpuExperienceCollector::new`) Pattern mirrors production caller in crates/ml/src/trainers/dqn/trainer/training_loop.rs:1322-1337. The two `unsafe` block warnings in the test are expected (same `warn(unsafe_code)` lint applied project-wide; production code carries the same warnings). mamba2_hyperopt_p0_p1_fixes.rs was also reported as failed but its failure was purely a cascading linker OOM kill from the smoke_test_real_data compile error, not an independent type error — confirmed by `cargo build --test mamba2_hyperopt_p0_p1_fixes` succeeding independently. NOTE: The SP7 T7 magnitude_distribution smoke does NOT pass — see report in commit message body. Root cause: four sp7_lb_* entries were registered in state_reset_registry.rs (commitaa2854017) but their dispatch arms in DQNTrainer::reset_named_state (training_loop.rs) were never wired. Error at runtime: "unknown name 'sp7_lb_diff_var_cql'". This is a T7 wire-up regression separate from the test-compile fixes in this commit. 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.