# ⚡ RAID — Research Architecture for Intraday Data

UQO Working Paper No. 40 — A Metal-Accelerated C++ Framework for Reproducible High-Frequency Financial Econometrics

$Paper$

TL;DR — RAID asks whether heterogeneous high-frequency econometric workloads (realized measures, rolling/batched regressions, HAR, GARCH-family QML, block bootstrap) can be turned into batched, fused, streaming computation on Apple Silicon without losing estimator accuracy, inference validity or reproducibility. It is a C++20 library with a FP64 CPU reference backend, an optional FP32 Metal backend (Apple GPUs have no FP64), nanobind Python bindings, a CLI, a FinData OS client, and a benchmark protocol that reports break-even points and the cases where the CPU wins. Status: Phases 0–7 complete (2026-11-02); Phase 8 review in progress — see RESEARCH_STATUS.md.

# 📌 Status

Item	Value
Version	1.0 (November 2026) — manuscript v1.0, library 0.1.0
SSRN	not yet submitted
Journal	targets: J. Financial Econometrics, J. Financial Data Science, J. Computational Finance
Code & data	55 test cases pass on macOS 27 / Xcode 27.2 (CPU + Metal); synthetic fixtures only in git; real bars via FinData OS (private license, not redistributed); every table/figure generated from `experiments/results/`
Ledger	`RESEARCH_STATUS.md` · `docs/PROGRESS.md` · `docs/DECISIONS.md` · `docs/risks.md`

# 🎯 What this paper does

Three falsifiable questions. RQ1 At what data and batch sizes does Metal beat an optimized multicore CPU, once I/O, shader compilation, synchronization and transfers are counted? RQ2 Does batched estimation raise throughput while preserving statistical coverage and convergence? RQ3 Can an Arrow/Parquet-to-estimator pipeline materially reduce copies, memory pressure and end-to-end latency? Negative results are published. Tier A methods (returns, calendar-aware aggregation, RV/BPV/RSV/RQ/jumps/rCov, OLS/WLS/ridge with HC/HAC/cluster inference, HAR family, GARCH/GJR/EGARCH with Gaussian/Student-t QML, block bootstrap) are validated against R highfrequency/rugarch and Python statsmodels/arch; GPU results are validated against the FP64 CPU reference with per-method tolerances.

# 🏆 Headline results

Finding	Evidence
Metal (FP32 double-float) matches FP64 within 1e−6 on realized measures/regression statistics and 2e−5 on GARCH likelihoods; bitwise reproducible across runs and thread counts	`tests/unit/test_metal_backend.cpp`, `experiments/results/raw/e5_inference.jsonl`
Fused realized kernel: Metal wins from ≈ 4×10⁴ elements, 2.0–3.4× vs 18-thread CPU at 4×10⁶–4×10⁷ (kernel level); 1.07–1.41× at the estimator level on 40 M real returns	`bench_primitives_summary.json`, `raw/e1_batched_rv.jsonl`
Batched GARCH likelihood grids: 5.1× (888 series × 256 vectors) to 7.9×; single-vector recursions: CPU wins	`raw/e3_garch.jsonl`, `raw/bench_primitives.jsonl`
Rolling sums: CPU wins at every size (negative result)	`raw/bench_primitives.jsonl`
Parquet→estimates pipeline: 20 M bars/s (RAID CPU) vs 14 M (Polars, DuckDB) vs 0.9 M (pandas); column projection mattered more than any kernel	`raw/e6_pipeline.jsonl`, `raw/e6_baselines.jsonl`
11 Apple Silicon machines (M2→M6): all Metal tests pass; GPU speedup 4.0× (M2) → 1.1× (M5 Max) — largest where the CPU is weakest	`raw/e7/`, `e7_summary.json`
Tier A reproduces highfrequency / rugarch / statsmodels / arch to documented tolerances; Monte Carlo coverage 0.90–0.97	`tests/reference/`, `docs/methods/registry.md`

# 🗂 Repository layout

text

wp40_raid/
├── include/raid/{core,data,statistics,estimators,models,backend}/   # public C++20 headers
├── src/…  backends/cpu/  backends/metal/{host,shaders}/             # implementations (Metal: Phase 3)
├── bindings/python/ python/raid/ cli/                                # nanobind module, Python API, `raid` CLI
├── tests/{unit,property,reference,simulation,stress,integration}/   # Catch2 (reference = vs statsmodels/arch/highfrequency)
├── benchmarks/ experiments/{configs,scripts,results,manifests}/     # harness + tracked artifacts (Phase 6)
├── datasets/{synthetic,fixtures}/                                    # synthetic only; real data cached outside git
├── docs/ paper/ scripts/ .github/workflows/

# 🚀 Quick start

bash

brew install cmake ninja eigen apache-arrow catch2 nlohmann-json          # macOS
cmake --preset macos-release && cmake --build --preset macos-release      # CPU + Metal + Arrow
ctest --preset macos-release                                               # unit + reference tests
./build/macos-release/raid devices
./build/macos-release/raid validate datasets/fixtures/synthetic_bars_1m.csv
./build/macos-release/raid estimate realized datasets/fixtures/synthetic_bars_1m.csv --backend auto
uv venv --python 3.12 .venv && uv pip install --python .venv/bin/python -e ".[dev]"   # Python package (nanobind)

Linux / CPU-only: cmake --preset cpu-only && cmake --build --preset cpu-only && ctest --preset cpu-only.

# 🗃 Data

Market data come from the owner's FinData OS lakehouse (https://findata.spboucher.ai/api/v1, FirstRate Data under a private-use license): 1-minute OHLCV bars (bar-open timestamps, extended 04:00–20:00 ET, regular 09:30–16:00 with the 16:00 closing print, zero-volume minutes absent at the source), dataset versions pinned via X-Dataset-Version. Raw bars are never committed; datasets/fixtures/ contains synthetic bars generated by scripts/make_fixtures.py.

# 🔬 Methodology

See docs/methods/registry.md (one entry per method: equations, conventions, reference software and tolerance) and docs/architecture/overview.md.

# 🖼 Figures & tables inventory

Generated by experiments/scripts/make_paper_assets.py (hashes in experiments/manifests/paper_assets.json): paper/tables/tab_{primitives,e1,e2_accuracy,e2_runtime,e3_fits,e4,e5,e6,e7}.tex, paper/figures/fig_{primitives_throughput,e1,e7}.pdf.

# ⚠️ Limitations

Apple Metal has no FP64: GPU paths are FP32 with compensated reductions and FP64 combination on the CPU, and are only valid where the documented tolerance is met. No tick/quote data: microstructure-noise and order-flow methods are out of scope. Energy is reported only when measured.

# 📖 Citation

bibtex

@techreport{boucher2026raid,
  title  = {RAID: A Metal-Accelerated C++ Framework for Reproducible High-Frequency Financial Econometrics},
  author = {Boucher, Simon-Pierre},
  institution = {Université du Québec en Outaouais, Département des sciences administratives},
  type   = {UQO Working Paper}, number = {40}, year = {2026}
}

# 👤 Author & contact

Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca — Département des sciences administratives, UQO.

# 📝 Notes

Directory name wp40_raid (charter requirement) instead of the usual slug; git remote gitsrv:srv/git/wp40_uqo.git.

spb/wp40_uqo