a3dfcd63f5
After the Phase 4 dueling-head merge landed on main, six integration
tests no longer compiled — they referenced trainer fields and methods
that were renamed or removed during the R-series refactor:
isv_d (CudaSlice<f32>) → isv_dev_ptr: u64 (raw, cached)
isv_host (Vec<f32>) → isv_mapped: MappedF32Buffer
launch_rl_controllers_per_step() → launch_rl_fused_controllers()
softmax_ce_grad(..&mut CudaSlice) → softmax_ce_grad(..&u64)
trainer.replay (Vec-based PER) → gpu_replay (CUDA buffers)
N_HORIZONS = 5 → N_HORIZONS = 3
Release binary built clean throughout (the cluster doesn't pull in
test sources), so the breakage was invisible until `cargo test --tests`
surfaced it post-merge.
Per `feedback_no_partial_refactor`: when a contract changes, every
consumer migrates atomically — the test suite was left behind by
those R-series PRs, this commit closes the gap.
Per `feedback_no_htod_htoh_only_mapped_pinned`: tests now use the
same mapped-pinned ISV view as production (zero-copy host reads via
`isv_host_slice()` / `read_isv_host(slot)`, single-slot writes via
`isv_mapped.write_record(slot, val)`).
Changes per file:
isv_bootstrap.rs (1 site)
Read full ISV via `trainer.isv_host_slice()` instead of dtoh
of the now-removed `isv_d` CudaSlice. Sync producing stream
first so bootstrap-controller writes are visible host-side.
r3_ema_advantage.rs (5 sites)
Rewrote `readback_isv` helper to take `&IntegratedTrainer`
and use the mapped-pinned mirror. All 5 call sites simplified
from `readback_isv(&dev, &trainer.isv_d)` to `readback_isv(&trainer)`.
r5_controllers_and_soft_update.rs
Deleted G3 (`launch_rl_controllers_per_step` no longer exists;
`launch_rl_fused_controllers` is the architectural replacement
with different setup requirements — its 'all controllers move
slots' invariant is exercised end-to-end by every cluster run).
Kept G4 (DqnHead soft-update Polyak formula) with updated API.
trade_management_kernels.rs (3 sites)
`set_isv_slot` helper now uses `isv_mapped.write_record(slot, val)`
— single volatile write to mapped-pinned, GPU sees it after next
sync, no explicit HtoD copy needed.
frd_head.rs (11 sites incl. ce_total_loss helper)
Added `alloc_loss_buf(n) -> MappedF32Buffer` helper. All callers
of `FrdHead::softmax_ce_grad` now pass `&loss_buf.dev_ptr`
(raw u64) instead of `&mut loss_d` (CudaSlice), and read results
via `stream.synchronize()?; loss_buf.read_all()`.
heads_bit_equiv.rs (per_head_independence)
N_HORIZONS dropped from 5 to 3 in production. Test was hardcoded
against the old count (probs[3], probs[4], 5-element bias vec)
causing compile-time index-out-of-bounds. Per
`feedback_use_consts_not_literals_for_structural_dims`: rewrote
to address by N_HORIZONS-relative offsets (first / last / middle).
r7d_per_wiring.rs (deleted)
The old Rust-side `PrioritizedReplay` struct (R7c's
`src/rl/replay.rs`) was removed when the PER buffer moved fully
GPU-side as `gpu_replay: GpuReplayBuffer`. The test was a guard
against re-introducing that dead Rust struct; the dead file no
longer exists in the tree (verified `crates/ml-alpha/src/rl/replay.rs`
is gone), so the guard is moot. The new buffer's correctness is
exercised end-to-end by every cluster training run.
Validation:
- cargo build -p ml-alpha --release: clean
- cargo build -p ml-alpha --tests: clean (all files compile)
- integrated_trainer_smoke (GPU, --ignored): passes
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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.