spb/wp40_uqo

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

added .github/workflows/ci.yml +22 −0

				@@ -0,0 +1,22 @@
		1	+name: ci
		2	+on: [push, pull_request]
		3	+jobs:
		4	+ cpu-linux:
		5	+ runs-on: ubuntu-24.04
		6	+ steps:
		7	+ - uses: actions/checkout@v4
		8	+ - run: sudo apt-get update && sudo apt-get install -y cmake ninja-build libeigen3-dev nlohmann-json3-dev catch2 libcurl4-openssl-dev
		9	+ - run: cmake --preset cpu-only -DRAID_WERROR=OFF
		10	+ - run: cmake --build --preset cpu-only
		11	+ - run: ctest --preset cpu-only
		12	+ - run: bash scripts/check_no_secrets.sh
		13	+ macos-metal:
		14	+ runs-on: macos-15
		15	+ steps:
		16	+ - uses: actions/checkout@v4
		17	+ - run: brew install cmake ninja eigen apache-arrow catch2 nlohmann-json
		18	+ - run: cmake --preset macos-release
		19	+ - run: cmake --build --preset macos-release
		20	+ - run: ctest --preset macos-release
		21	+ - run: ./build/macos-release/raid devices
		22	+ - run: ./build/macos-release/raid validate datasets/fixtures/synthetic_bars_1m.csv

modified .gitignore +8 −21

				@@ -1,5 +1,4 @@
1	−#	1	+# Build
2	−Build
3	build/	2	build/
4	build-*/	3	build-*/
5	cmake-build-*/	4	cmake-build-*/
				@@ -13,38 +12,27 @@ wheelhouse/
13	*.so	12	*.so
14	*.air	13	*.air
15	*.metallib	14	*.metallib
16	−#	15	+# Python
17	−Python
18	__pycache__/	16	__pycache__/
19	*.pyc	17	*.pyc
20	.pytest_cache/	18	.pytest_cache/
21	.hypothesis/	19	.hypothesis/
22	.venv/	20	.venv/
23	.ruff_cache/	21	.ruff_cache/
24	−#	22	+# Data — FirstRate private license: never commit real bars
25	−Data
26	−—
27	−FirstRate
28	−private
29	−license:
30	−never
31	−commit
32	−real
33	−bars
34	datasets/cache/	23	datasets/cache/
35	datasets/real/	24	datasets/real/
36	data/	25	data/
		26	+experiments/results/raw/
37	*.parquet	27	*.parquet
		28	+*.arrow
38	!datasets/fixtures/*.parquet	29	!datasets/fixtures/*.parquet
39	!datasets/synthetic/*.parquet	30	!datasets/synthetic/*.parquet
40	−experiments/results/raw/*.arrow	31	+# Secrets
41	−#
42	−Secrets
43	.env	32	.env
44	*.key	33	*.key
45	api-key*.txt	34	api-key*.txt
46	−#	35	+# LaTeX
47	−LaTeX
48	paper/*.aux	36	paper/*.aux
49	paper/*.log	37	paper/*.log
50	paper/*.out	38	paper/*.out
				@@ -58,8 +46,7 @@ paper/*.blg
58	paper/*.synctex.gz	46	paper/*.synctex.gz
59	paper/*.run.xml	47	paper/*.run.xml
60	paper/*.bcf	48	paper/*.bcf
61	−#	49	+# macOS
62	−macOS
63	.DS_Store	50	.DS_Store
64	Icon?	51	Icon?
65	logs/	52	logs/

added CITATION.cff +23 −0

				@@ -0,0 +1,23 @@
		1	+cff-version: 1.2.0
		2	+message: "If you use RAID, please cite the working paper."
		3	+title: "RAID: Research Architecture for Intraday Data"
		4	+version: 0.1.0
		5	+date-released: 2026-11-02
		6	+authors:
		7	+ - family-names: Boucher
		8	+ given-names: Simon-Pierre
		9	+ email: simon-pierre.boucher@uqo.ca
		10	+ affiliation: "Université du Québec en Outaouais, Département des sciences administratives"
		11	+license: MIT
		12	+repository-code: "https://git.spboucher.ai/wp40_uqo"
		13	+keywords: [high-frequency econometrics, realized volatility, GARCH, HAR, Apple Metal, GPU, C++20, reproducibility]
		14	+preferred-citation:
		15	+ type: report
		16	+ title: "RAID: A Metal-Accelerated C++ Framework for Reproducible High-Frequency Financial Econometrics"
		17	+ authors:
		18	+ - family-names: Boucher
		19	+ given-names: Simon-Pierre
		20	+ institution:
		21	+ name: "Université du Québec en Outaouais"
		22	+ number: "40"
		23	+ year: 2026

added CMakeLists.txt +189 −0

				@@ -0,0 +1,189 @@
		1	+cmake_minimum_required(VERSION 3.28)
		2	+project(raid VERSION 0.1.0 LANGUAGES CXX)
		3	+
		4	+# ---------------------------------------------------------------------------
		5	+# RAID — Research Architecture for Intraday Data (UQO WP 40)
		6	+# C++20 core + CPU backend (portable) + optional Metal backend (macOS arm64).
		7	+# ---------------------------------------------------------------------------
		8	+set(CMAKE_CXX_STANDARD 20)
		9	+set(CMAKE_CXX_STANDARD_REQUIRED ON)
		10	+set(CMAKE_CXX_EXTENSIONS OFF)
		11	+set(CMAKE_POSITION_INDEPENDENT_CODE ON)
		12	+set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
		13	+if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
		14	+ set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
		15	+endif()
		16	+
		17	+option(RAID_ENABLE_METAL "Build the Metal backend (macOS only)" ${APPLE})
		18	+option(RAID_WITH_ARROW "Build the Arrow/Parquet reader (requires Apache Arrow C++)" ON)
		19	+option(RAID_WITH_ACCELERATE "Use Apple Accelerate BLAS/LAPACK through Eigen (macOS only)" ${APPLE})
		20	+option(RAID_BUILD_TESTS "Build Catch2 tests" ON)
		21	+option(RAID_BUILD_BENCH "Build benchmark executables" ON)
		22	+option(RAID_BUILD_CLI "Build the raid command-line tool" ON)
		23	+option(RAID_BUILD_PYTHON "Build the nanobind Python extension" OFF)
		24	+option(RAID_NATIVE_ARCH "Tune for the host CPU (-mcpu=native)" ON)
		25	+option(RAID_WERROR "Treat warnings as errors" OFF)
		26	+
		27	+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake")
		28	+include(GNUInstallDirs)
		29	+
		30	+# ---- dependencies ----------------------------------------------------------
		31	+find_package(Eigen3 REQUIRED CONFIG)
		32	+find_package(nlohmann_json 3.11 REQUIRED CONFIG)
		33	+find_package(CURL REQUIRED) # system libcurl (macOS SDK / Linux)
		34	+find_package(Threads REQUIRED)
		35	+if(RAID_WITH_ARROW)
		36	+ find_package(Arrow CONFIG QUIET)
		37	+ find_package(Parquet CONFIG QUIET)
		38	+ if(NOT Arrow_FOUND OR NOT Parquet_FOUND)
		39	+ message(WARNING "RAID_WITH_ARROW=ON but Arrow/Parquet not found; building without Arrow")
		40	+ set(RAID_WITH_ARROW OFF)
		41	+ endif()
		42	+endif()
		43	+
		44	+# ---- compile options -------------------------------------------------------
		45	+add_library(raid_options INTERFACE)
		46	+target_compile_options(raid_options INTERFACE
		47	+ $<$<CXX_COMPILER_ID:Clang,AppleClang,GNU>:-Wall -Wextra -Wpedantic -Wshadow -Wconversion -Wno-sign-conversion>
		48	+ $<$<AND:$<CXX_COMPILER_ID:Clang,AppleClang,GNU>,$<BOOL:${RAID_WERROR}>>:-Werror>
		49	+ $<$<AND:$<CXX_COMPILER_ID:Clang,AppleClang,GNU>,$<CONFIG:Release>>:-O3>
		50	+)
		51	+if(RAID_NATIVE_ARCH AND CMAKE_SYSTEM_PROCESSOR MATCHES "arm64|aarch64")
		52	+ target_compile_options(raid_options INTERFACE -mcpu=native)
		53	+endif()
		54	+# Floating-point semantics: strict IEEE (no -ffast-math) so that CPU FP64 is a true reference.
		55	+target_compile_options(raid_options INTERFACE $<$<CXX_COMPILER_ID:Clang,AppleClang>:-ffp-model=precise>)
		56	+
		57	+# ---- core library ----------------------------------------------------------
		58	+file(GLOB_RECURSE RAID_CORE_SOURCES CONFIGURE_DEPENDS
		59	+ src/core/*.cpp src/data/*.cpp src/statistics/*.cpp src/estimators/*.cpp src/models/*.cpp backends/cpu/*.cpp)
		60	+if(NOT RAID_WITH_ARROW)
		61	+ list(FILTER RAID_CORE_SOURCES EXCLUDE REGEX ".*parquet_arrow\\.cpp$")
		62	+endif()
		63	+add_library(raid ${RAID_CORE_SOURCES})
		64	+add_library(raid::raid ALIAS raid)
		65	+target_include_directories(raid PUBLIC
		66	+ $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
		67	+ $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>)
		68	+target_link_libraries(raid PUBLIC Eigen3::Eigen nlohmann_json::nlohmann_json Threads::Threads CURL::libcurl raid_options)
		69	+target_compile_definitions(raid PUBLIC RAID_VERSION="${PROJECT_VERSION}")
		70	+if(RAID_WITH_ARROW)
		71	+ target_link_libraries(raid PUBLIC Arrow::arrow_shared Parquet::parquet_shared)
		72	+ target_compile_definitions(raid PUBLIC RAID_WITH_ARROW=1)
		73	+endif()
		74	+if(APPLE AND RAID_WITH_ACCELERATE)
		75	+ target_compile_definitions(raid PUBLIC RAID_WITH_ACCELERATE=1 EIGEN_USE_BLAS=1 ACCELERATE_NEW_LAPACK=1 ACCELERATE_LAPACK_ILP64=0)
		76	+ target_link_libraries(raid PUBLIC "-framework Accelerate")
		77	+endif()
		78	+
		79	+# ---- Metal backend ---------------------------------------------------------
		80	+if(RAID_ENABLE_METAL)
		81	+ if(NOT APPLE)
		82	+ message(FATAL_ERROR "RAID_ENABLE_METAL requires macOS")
		83	+ endif()
		84	+ file(GLOB _raid_metal_probe CONFIGURE_DEPENDS backends/metal/host/*.mm)
		85	+ if(NOT _raid_metal_probe)
		86	+ message(STATUS "Metal backend sources not present yet (Phase 3): building without Metal")
		87	+ set(RAID_ENABLE_METAL OFF)
		88	+ endif()
		89	+endif()
		90	+if(RAID_ENABLE_METAL)
		91	+ enable_language(OBJCXX)
		92	+ set(CMAKE_OBJCXX_STANDARD 20)
		93	+ file(GLOB RAID_METAL_HOST CONFIGURE_DEPENDS backends/metal/host/*.mm backends/metal/host/*.cpp)
		94	+ file(GLOB RAID_METAL_SHADERS CONFIGURE_DEPENDS backends/metal/shaders/*.metal)
		95	+ add_library(raid_metal ${RAID_METAL_HOST})
		96	+ target_link_libraries(raid_metal PUBLIC raid "-framework Metal" "-framework Foundation")
		97	+ target_compile_options(raid_metal PRIVATE -fobjc-arc)
		98	+ target_compile_definitions(raid PUBLIC RAID_ENABLE_METAL=1)
		99	+ # Embed the MSL sources as a string (runtime compilation fallback / cold-compile benchmark)
		100	+ set(RAID_MSL_BUNDLE "${CMAKE_CURRENT_BINARY_DIR}/raid_msl_bundle.cpp")
		101	+ add_custom_command(OUTPUT ${RAID_MSL_BUNDLE}
		102	+ COMMAND ${CMAKE_COMMAND} -DOUT=${RAID_MSL_BUNDLE} "-DSHADERS=${RAID_METAL_SHADERS}" -P ${CMAKE_CURRENT_SOURCE_DIR}/cmake/EmbedMSL.cmake
		103	+ DEPENDS ${RAID_METAL_SHADERS} ${CMAKE_CURRENT_SOURCE_DIR}/cmake/EmbedMSL.cmake COMMENT "Embedding MSL sources")
		104	+ target_sources(raid_metal PRIVATE ${RAID_MSL_BUNDLE})
		105	+ # Optional offline metallib (requires the Metal Toolchain). Missing toolchain => runtime compile only.
		106	+ find_program(RAID_XCRUN xcrun)
		107	+ if(RAID_XCRUN AND RAID_METAL_SHADERS)
		108	+ execute_process(COMMAND ${RAID_XCRUN} -sdk macosx metal --version RESULT_VARIABLE _mt_rc OUTPUT_QUIET ERROR_QUIET)
		109	+ if(_mt_rc EQUAL 0)
		110	+ set(RAID_METALLIB "${CMAKE_CURRENT_BINARY_DIR}/raid.metallib")
		111	+ set(_airs)
		112	+ foreach(s ${RAID_METAL_SHADERS})
		113	+ get_filename_component(n ${s} NAME_WE)
		114	+ add_custom_command(OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/${n}.air
		115	+ COMMAND ${RAID_XCRUN} -sdk macosx metal -std=metal3.2 -O2 -c ${s} -o ${CMAKE_CURRENT_BINARY_DIR}/${n}.air
		116	+ DEPENDS ${s} COMMENT "metal ${n}")
		117	+ list(APPEND _airs ${CMAKE_CURRENT_BINARY_DIR}/${n}.air)
		118	+ endforeach()
		119	+ add_custom_command(OUTPUT ${RAID_METALLIB} COMMAND ${RAID_XCRUN} -sdk macosx metallib ${_airs} -o ${RAID_METALLIB} DEPENDS ${_airs} COMMENT "metallib")
		120	+ add_custom_target(raid_metallib ALL DEPENDS ${RAID_METALLIB})
		121	+ add_dependencies(raid_metal raid_metallib)
		122	+ target_compile_definitions(raid_metal PRIVATE RAID_METALLIB_PATH="${RAID_METALLIB}")
		123	+ message(STATUS "Metal toolchain found: building raid.metallib")
		124	+ else()
		125	+ message(STATUS "Metal toolchain not found: runtime MSL compilation only")
		126	+ endif()
		127	+ endif()
		128	+endif()
		129	+
		130	+# ---- CLI --------------------------------------------------------------------
		131	+if(RAID_BUILD_CLI)
		132	+ add_executable(raid_cli cli/raid.cpp)
		133	+ set_target_properties(raid_cli PROPERTIES OUTPUT_NAME raid)
		134	+ target_link_libraries(raid_cli PRIVATE raid)
		135	+ if(RAID_ENABLE_METAL)
		136	+ target_link_libraries(raid_cli PRIVATE raid_metal)
		137	+ endif()
		138	+endif()
		139	+
		140	+# ---- examples -----------------------------------------------------------------
		141	+add_executable(example_minimal examples/cpp/minimal.cpp)
		142	+target_link_libraries(example_minimal PRIVATE raid)
		143	+if(RAID_ENABLE_METAL)
		144	+ target_link_libraries(example_minimal PRIVATE raid_metal)
		145	+endif()
		146	+
		147	+# ---- tests ------------------------------------------------------------------
		148	+if(RAID_BUILD_TESTS)
		149	+ find_package(Catch2 3 CONFIG REQUIRED)
		150	+ include(CTest)
		151	+ include(Catch)
		152	+ file(GLOB_RECURSE RAID_TEST_SOURCES CONFIGURE_DEPENDS tests/unit/*.cpp tests/property/*.cpp tests/reference/*.cpp tests/simulation/*.cpp tests/stress/*.cpp tests/integration/*.cpp)
		153	+ add_executable(raid_tests ${RAID_TEST_SOURCES})
		154	+ target_link_libraries(raid_tests PRIVATE raid Catch2::Catch2WithMain)
		155	+ target_compile_definitions(raid_tests PRIVATE RAID_SOURCE_DIR="${CMAKE_CURRENT_SOURCE_DIR}")
		156	+ if(RAID_ENABLE_METAL)
		157	+ target_link_libraries(raid_tests PRIVATE raid_metal)
		158	+ endif()
		159	+ catch_discover_tests(raid_tests)
		160	+endif()
		161	+
		162	+# ---- benchmarks ---------------------------------------------------------------
		163	+if(RAID_BUILD_BENCH)
		164	+ file(GLOB RAID_BENCH_SOURCES CONFIGURE_DEPENDS benchmarks/micro/*.cpp benchmarks/end_to_end/*.cpp benchmarks/accuracy/*.cpp benchmarks/scalability/*.cpp)
		165	+ foreach(b ${RAID_BENCH_SOURCES})
		166	+ get_filename_component(n ${b} NAME_WE)
		167	+ add_executable(bench_${n} ${b})
		168	+ target_link_libraries(bench_${n} PRIVATE raid)
		169	+ if(RAID_ENABLE_METAL)
		170	+ target_link_libraries(bench_${n} PRIVATE raid_metal)
		171	+ endif()
		172	+ endforeach()
		173	+endif()
		174	+
		175	+# ---- Python -------------------------------------------------------------------
		176	+if(RAID_BUILD_PYTHON)
		177	+ add_subdirectory(bindings/python)
		178	+endif()
		179	+
		180	+# ---- install -----------------------------------------------------------------
		181	+install(TARGETS raid raid_options EXPORT raidTargets LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR} ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR})
		182	+if(RAID_ENABLE_METAL)
		183	+ install(TARGETS raid_metal EXPORT raidTargets LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR} ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR})
		184	+endif()
		185	+if(RAID_BUILD_CLI)
		186	+ install(TARGETS raid_cli RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR})
		187	+endif()
		188	+install(DIRECTORY include/ DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
		189	+install(EXPORT raidTargets NAMESPACE raid:: DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/raid)

added CMakePresets.json +25 −0

				@@ -0,0 +1,25 @@
		1	+{
		2	+ "version": 6,
		3	+ "configurePresets": [
		4	+ { "name": "base", "hidden": true, "generator": "Ninja", "binaryDir": "${sourceDir}/build/${presetName}",
		5	+ "cacheVariables": { "CMAKE_EXPORT_COMPILE_COMMANDS": "ON" } },
		6	+ { "name": "macos-release", "inherits": "base", "displayName": "macOS arm64 Release (CPU + Metal + Arrow)",
		7	+ "cacheVariables": { "CMAKE_BUILD_TYPE": "Release", "RAID_ENABLE_METAL": "ON", "RAID_WITH_ARROW": "ON", "CMAKE_PREFIX_PATH": "/opt/homebrew" } },
		8	+ { "name": "macos-debug", "inherits": "macos-release", "displayName": "macOS Debug + sanitizers",
		9	+ "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug", "CMAKE_CXX_FLAGS": "-fsanitize=address,undefined -fno-omit-frame-pointer", "CMAKE_EXE_LINKER_FLAGS": "-fsanitize=address,undefined", "RAID_NATIVE_ARCH": "OFF" } },
		10	+ { "name": "cpu-only", "inherits": "base", "displayName": "Portable CPU-only build (Linux/macOS)",
		11	+ "cacheVariables": { "CMAKE_BUILD_TYPE": "Release", "RAID_ENABLE_METAL": "OFF", "RAID_WITH_ARROW": "OFF", "RAID_WITH_ACCELERATE": "OFF", "RAID_NATIVE_ARCH": "OFF" } },
		12	+ { "name": "bundle", "inherits": "base", "displayName": "Self-contained bundle for cluster nodes (E7)",
		13	+ "cacheVariables": { "CMAKE_BUILD_TYPE": "Release", "RAID_ENABLE_METAL": "ON", "RAID_WITH_ARROW": "OFF", "RAID_NATIVE_ARCH": "OFF", "RAID_BUILD_TESTS": "OFF", "CMAKE_OSX_DEPLOYMENT_TARGET": "26.0", "CMAKE_PREFIX_PATH": "/opt/homebrew", "CMAKE_CXX_FLAGS": "-mcpu=apple-m1" } }
		14	+ ],
		15	+ "buildPresets": [
		16	+ { "name": "macos-release", "configurePreset": "macos-release" },
		17	+ { "name": "macos-debug", "configurePreset": "macos-debug" },
		18	+ { "name": "cpu-only", "configurePreset": "cpu-only" },
		19	+ { "name": "bundle", "configurePreset": "bundle" }
		20	+ ],
		21	+ "testPresets": [
		22	+ { "name": "macos-release", "configurePreset": "macos-release", "output": { "outputOnFailure": true } },
		23	+ { "name": "cpu-only", "configurePreset": "cpu-only", "output": { "outputOnFailure": true } }
		24	+ ]
		25	+}

added README.md +114 −0

				@@ -0,0 +1,114 @@
		1	+<!-- Author: Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca -->
		2	+# ⚡ RAID — Research Architecture for Intraday Data
		3	+
		4	+**UQO Working Paper No. 40** — *A Metal-Accelerated C++ Framework for Reproducible High-Frequency Financial Econometrics*
		5	+
		6	+[](paper/)
		7	+[](CMakeLists.txt)
		8	+[-000000?logo=apple&logoColor=white)](backends/metal/)
		9	+[](pyproject.toml)
		10	+[-4051b5)](docs/audit.md)
		11	+[](https://uqo.ca)
		12	+[](mailto:simon-pierre.boucher@uqo.ca)
		13	+
		14	+> **TL;DR** — RAID asks whether heterogeneous high-frequency econometric workloads (realized measures, rolling/batched
		15	+> regressions, HAR, GARCH-family QML, block bootstrap) can be turned into batched, fused, streaming computation on Apple
		16	+> Silicon **without** losing estimator accuracy, inference validity or reproducibility. It is a C++20 library with a FP64
		17	+> CPU reference backend, an optional FP32 Metal backend (Apple GPUs have no FP64), nanobind Python bindings, a CLI, a
		18	+> FinData OS client, and a benchmark protocol that reports break-even points and the cases where the CPU wins.
		19	+> Status: Phase 1 (foundation) — see `RESEARCH_STATUS.md`. No performance numbers are claimed yet.
		20	+
		21	+---
		22	+
		23	+## 📌 Status
		24	+
		25	+| Item | Value |
		26	+|---|---|
		27	+| Version | 0.1.0 (November 2026) — Phases 0–1 |
		28	+| SSRN | not yet submitted |
		29	+| Journal | targets: J. Financial Econometrics, J. Financial Data Science, J. Computational Finance |
		30	+| Code & data | CPU core + tests build and pass on macOS 27 / Xcode 27.2; synthetic fixtures only in git; real bars via FinData OS (private license) |
		31	+| Ledger | `RESEARCH_STATUS.md` · `docs/PROGRESS.md` · `docs/DECISIONS.md` · `docs/risks.md` |
		32	+
		33	+## 🎯 What this paper does
		34	+
		35	+Three falsifiable questions. **RQ1** At what data and batch sizes does Metal beat an optimized multicore CPU, once I/O,
		36	+shader compilation, synchronization and transfers are counted? **RQ2** Does batched estimation raise throughput while
		37	+preserving statistical coverage and convergence? **RQ3** Can an Arrow/Parquet-to-estimator pipeline materially reduce
		38	+copies, memory pressure and end-to-end latency? Negative results are published. Tier A methods (returns, calendar-aware
		39	+aggregation, RV/BPV/RSV/RQ/jumps/rCov, OLS/WLS/ridge with HC/HAC/cluster inference, HAR family, GARCH/GJR/EGARCH with
		40	+Gaussian/Student-t QML, block bootstrap) are validated against R `highfrequency`/`rugarch` and Python
		41	+`statsmodels`/`arch`; GPU results are validated against the FP64 CPU reference with per-method tolerances.
		42	+
		43	+## 🏆 Headline results
		44	+
		45	+None yet — benchmarks (E1–E7) run in Phase 6; every number in the paper will point to a record under
		46	+`experiments/results/` listed in `experiments/manifests/`.
		47	+
		48	+## 🗂 Repository layout
		49	+
		50	+```
		51	+wp40_raid/
		52	+├── include/raid/{core,data,statistics,estimators,models,backend}/ # public C++20 headers
		53	+├── src/… backends/cpu/ backends/metal/{host,shaders}/ # implementations (Metal: Phase 3)
		54	+├── bindings/python/ python/raid/ cli/ # nanobind module, Python API, `raid` CLI
		55	+├── tests/{unit,property,reference,simulation,stress,integration}/ # Catch2 (reference = vs statsmodels/arch/highfrequency)
		56	+├── benchmarks/ experiments/{configs,scripts,results,manifests}/ # harness + tracked artifacts (Phase 6)
		57	+├── datasets/{synthetic,fixtures}/ # synthetic only; real data cached outside git
		58	+├── docs/ paper/ scripts/ .github/workflows/
		59	+```
		60	+
		61	+## 🚀 Quick start
		62	+
		63	+```bash
		64	+brew install cmake ninja eigen apache-arrow catch2 nlohmann-json # macOS
		65	+cmake --preset macos-release && cmake --build --preset macos-release # CPU + Metal + Arrow
		66	+ctest --preset macos-release # unit + reference tests
		67	+./build/macos-release/raid devices
		68	+./build/macos-release/raid validate datasets/fixtures/synthetic_bars_1m.csv
		69	+./build/macos-release/raid estimate realized datasets/fixtures/synthetic_bars_1m.csv --backend auto
		70	+uv venv --python 3.12 .venv && uv pip install --python .venv/bin/python -e ".[dev]" # Python package (nanobind)
		71	+```
		72	+
		73	+Linux / CPU-only: `cmake --preset cpu-only && cmake --build --preset cpu-only && ctest --preset cpu-only`.
		74	+
		75	+## 🗃 Data
		76	+
		77	+Market data come from the owner's **FinData OS** lakehouse (`https://findata.spboucher.ai/api/v1`, FirstRate Data under
		78	+a private-use license): 1-minute OHLCV bars (bar-open timestamps, extended 04:00–20:00 ET, regular 09:30–16:00 with the
		79	+16:00 closing print, zero-volume minutes absent at the source), dataset versions pinned via `X-Dataset-Version`. Raw
		80	+bars are never committed; `datasets/fixtures/` contains synthetic bars generated by `scripts/make_fixtures.py`.
		81	+
		82	+## 🔬 Methodology
		83	+
		84	+See `docs/methods/registry.md` (one entry per method: equations, conventions, reference software and tolerance) and
		85	+`docs/architecture/overview.md`.
		86	+
		87	+## 🖼 Figures & tables inventory
		88	+
		89	+To be generated in Phase 6–7 from `experiments/results/`.
		90	+
		91	+## ⚠️ Limitations
		92	+
		93	+Apple Metal has no FP64: GPU paths are FP32 with compensated reductions and FP64 combination on the CPU, and are only
		94	+valid where the documented tolerance is met. No tick/quote data: microstructure-noise and order-flow methods are out of
		95	+scope. Energy is reported only when measured.
		96	+
		97	+## 📖 Citation
		98	+
		99	+```bibtex
		100	+@techreport{boucher2026raid,
		101	+ title = {RAID: A Metal-Accelerated C++ Framework for Reproducible High-Frequency Financial Econometrics},
		102	+ author = {Boucher, Simon-Pierre},
		103	+ institution = {Université du Québec en Outaouais, Département des sciences administratives},
		104	+ type = {UQO Working Paper}, number = {40}, year = {2026}
		105	+}
		106	+```
		107	+
		108	+## 👤 Author & contact
		109	+
		110	+Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca — Département des sciences administratives, UQO.
		111	+
		112	+## 📝 Notes
		113	+
		114	+Directory name `wp40_raid` (charter requirement) instead of the usual slug; git remote `gitsrv:srv/git/wp40_uqo.git`.

added RESEARCH_STATUS.md +40 −0

				@@ -0,0 +1,40 @@
		1	+# RESEARCH_STATUS.md — RAID (UQO WP 40)
		2	+
		3	+Last update: 2026-11-02 (Phase 1 passed). Legend: ✅ verified by tests/artifacts · 🧪 implemented, validation pending ·
		4	+🧭 experimental · ⛔ blocked · ⏳ planned · ✖ not implemented (deliberately).
		5	+
		6	+## Phases
		7	+| Phase | Gate | Status | Evidence |
		8	+|---|---|---|---|
		9	+| 0 Audit | scope & feasibility memo | ✅ passed 2026-11-02 | `docs/audit.md`, `docs/capabilities.md`, `docs/novelty_matrix.md`, `docs/risks.md`, `docs/PROGRESS.md` |
		10	+| 1 Foundation | clean build + tests + docs | ✅ passed 2026-11-02 | `ctest` 26/26 (314 assertions), `pytest` 7/7, `docs/PROGRESS.md` Phase 1 |
		11	+| 2 Data | offline synthetic tests + opt-in live tests | ⏳ | — |
		12	+| 3 Numerical primitives | accuracy + measured break-even | ⏳ | — |
		13	+| 4 Econometrics (Tier A) | references + Monte Carlo | ⏳ | — |
		14	+| 5 Extensions (Tier B) | per-feature specs + tests | ⏳ | — |
		15	+| 6 Benchmarking E1–E7 | every claim traceable | ⏳ | — |
		16	+| 7 Manuscript | reproducible build, no placeholders | ⏳ | — |
		17	+| 8 Independent review | clean-checkout reproduction | ⏳ | — |
		18	+
		19	+## Methods registry (summary; full entries in `docs/methods/registry.md` from Phase 4)
		20	+| Method | Tier | CPU FP64 | Metal FP32 | Reference software | Status |
		21	+|---|---|---|---|---|---|
		22	+| returns (log/arith), lag/lead, rolling windows, calendar-aware aggregation | A | ✅ | ⏳ (Phase 3) | numpy; exchange_calendars (calendar exact 2000–2026) | ✅ CPU |
		23	+| RV, BPV, RSV±, RQ, jump proxy, TriPV, rCov/rCorr | A | ✅ (numpy truth 1e−12) | ⏳ (Phase 3) | highfrequency 1.0.3 (R comparison Phase 4) | ✅ CPU |
		24	+| OLS/WLS/ridge, HC0–3, HAC, cluster, rolling OLS | A | ✅ (statsmodels 1e−9) | ⏳ (sufficient stats, Phase 3) | statsmodels 0.15, scipy 1.18 | ✅ CPU |
		25	+| HAR-RV, log-HAR, HAR-X, HARQ | A | ⏳ | ⏳ | highfrequency `HARmodel`, statsmodels | ⏳ |
		26	+| GARCH(1,1), GJR, EGARCH (N, t), QMLE/Hessian/OPG SE, forecasts | A | ✅ (arch 8.0: loglik 5e−4, params 5 %) | ⏳ (batched likelihood, Phase 3) | arch 8.0 ✅, rugarch 1.5.6 (Phase 4) | ✅ CPU |
		27	+| Bootstrap (stationary / circular block), Monte Carlo | A | ⏳ | ⏳ | arch.bootstrap | ⏳ |
		28	+| VAR, GFEVD, Diebold–Yilmaz | B | ⏳ | ⏳ | statsmodels VAR | ⏳ |
		29	+| Realized GARCH, HEAVY, DCC, PCA/shrinkage, Kalman | B | ✖ until Tier A passes | | | ⏳ |
		30	+| Pre-averaging, realized kernels, MSRV, BNS test | C | ✖ (1-min bars) | | highfrequency | 🧭 only if validated |
		31	+| Hawkes, order-flow, LOB methods | C | ✖ | ✖ | — | ✖ no admissible data (bars only) |
		32	+
		33	+## Blockers
		34	+- None at Phase 0. Seven cluster nodes unreachable (m2m16, M2M32, M2M32b, M4M36, m4ma, m4mb, m4mc): E7 reports them as
		35	+ not tested unless they come back.
		36	+
		37	+## Next safe action
		38	+Phase 2: FinData client (C++ libcurl: catalog, bars Arrow pages, exports with polling/download, provenance, cache,
		39	+token bucket, retries), `raid data` CLI, offline tests with a recorded OpenAPI/fixture server, opt-in live tests
		40	+(`RAID_LIVE=1`), aggregate-vs-FinData-5m check.

added backends/cpu/cpu_backend.cpp +115 −0

				@@ -0,0 +1,115 @@
		1	+// CPU reference backend: FP64, Neumaier-compensated per-series reductions, parallel over series.
		2	+#include "raid/backend/backend.hpp"
		3	+#include "raid/core/thread_pool.hpp"
		4	+#include "raid/core/error.hpp"
		5	+#include "raid/statistics/reductions.hpp"
		6	+#include "raid/statistics/rolling.hpp"
		7	+#include "raid/models/garch.hpp"
		8	+#include <chrono>
		9	+#include <thread>
		10	+#include <cmath>
		11	+#ifdef __APPLE__
		12	+#include <sys/sysctl.h>
		13	+#endif
		14	+
		15	+namespace raid::backend {
		16	+
		17	+namespace {
		18	+struct Timer { std::chrono::steady_clock::time_point t0 = std::chrono::steady_clock::now(); f64 s() const { return std::chrono::duration<f64>(std::chrono::steady_clock::now() - t0).count(); } };
		19	+void fill(KernelStats* st, const char* k, const Timer& t, usize bin, usize bout) { if (!st) return; st->kernel = k; st->backend = raid::Backend::cpu; st->precision = Precision::fp64; st->wall_seconds = t.s(); st->bytes_in = bin; st->bytes_out = bout; }
		20	+}
		21	+
		22	+class CpuBackend final : public Backend {
		23	+ public:
		24	+ explicit CpuBackend(unsigned threads) : pool_(threads == 0 ? nullptr : std::make_unique<ThreadPool>(threads)) {}
		25	+ std::string name() const override { return "cpu"; }
		26	+ raid::Backend kind() const override { return raid::Backend::cpu; }
		27	+ bool available() const override { return true; }
		28	+ Device device() const override {
		29	+ Device d; d.kind = raid::Backend::cpu; d.supports_fp64 = true; d.unified_memory = true; d.compute_units = static_cast<int>(std::thread::hardware_concurrency());
		30	+#ifdef __APPLE__
		31	+ char buf[256]; size_t n = sizeof buf; if (sysctlbyname("machdep.cpu.brand_string", buf, &n, nullptr, 0) == 0) d.name = std::string(buf, n - 1); else d.name = "cpu";
		32	+ i64 mem = 0; n = sizeof mem; if (sysctlbyname("hw.memsize", &mem, &n, nullptr, 0) == 0) d.memory_bytes = static_cast<u64>(mem);
		33	+#else
		34	+ d.name = "cpu";
		35	+#endif
		36	+ d.details = "threads=" + std::to_string(pool().size());
		37	+ return d;
		38	+ }
		39	+ ThreadPool& pool() const { return pool_ ? *pool_ : ThreadPool::global(); }
		40	+
		41	+ template <class F> std::vector<f64> per_series(const Batch& b, F f) {
		42	+ std::vector<f64> out(b.n_series());
		43	+ pool().parallel_for(b.n_series(), [&](usize i0, usize i1) { for (usize i = i0; i < i1; ++i) out[i] = f(b.series(i)); });
		44	+ return out;
		45	+ }
		46	+
		47	+ std::vector<f64> sum_squares(const Batch& b, KernelStats* st) override {
		48	+ Timer t; auto r = per_series(b, [](std::span<const f64> x) { return stats::sum_squares(x); }); fill(st, "sum_squares", t, b.values.size_bytes(), r.size() * 8); return r;
		49	+ }
		50	+ std::vector<f64> sum_abs_adjacent(const Batch& b, KernelStats* st) override {
		51	+ Timer t; auto r = per_series(b, [](std::span<const f64> x) { f64 s = 0, c = 0; for (usize i = 1; i < x.size(); ++i) { const f64 v = std::abs(x[i]) * std::abs(x[i - 1]), tt = s + v; if (std::abs(s) >= std::abs(v)) c += (s - tt) + v; else c += (v - tt) + s; s = tt; } return s + c; });
		52	+ fill(st, "sum_abs_adjacent", t, b.values.size_bytes(), r.size() * 8); return r;
		53	+ }
		54	+ void semivariances(const Batch& b, std::vector<f64>& neg, std::vector<f64>& pos, KernelStats* st) override {
		55	+ Timer t; neg.assign(b.n_series(), 0); pos.assign(b.n_series(), 0);
		56	+ pool().parallel_for(b.n_series(), [&](usize i0, usize i1) { for (usize i = i0; i < i1; ++i) { f64 sn = 0, cn = 0, sp = 0, cp = 0; for (f64 v : b.series(i)) { const f64 q = v * v; if (v < 0) { const f64 tt = sn + q; if (std::abs(sn) >= q) cn += (sn - tt) + q; else cn += (q - tt) + sn; sn = tt; } else if (v > 0) { const f64 tt = sp + q; if (std::abs(sp) >= q) cp += (sp - tt) + q; else cp += (q - tt) + sp; sp = tt; } } neg[i] = sn + cn; pos[i] = sp + cp; } });
		57	+ fill(st, "semivariances", t, b.values.size_bytes(), neg.size() * 16);
		58	+ }
		59	+ std::vector<f64> sum_fourth(const Batch& b, KernelStats* st) override {
		60	+ Timer t; auto r = per_series(b, [](std::span<const f64> x) { f64 s = 0, c = 0; for (f64 v : x) { const f64 q = v * v * v * v, tt = s + q; if (std::abs(s) >= q) c += (s - tt) + q; else c += (q - tt) + s; s = tt; } return s + c; });
		61	+ fill(st, "sum_fourth", t, b.values.size_bytes(), r.size() * 8); return r;
		62	+ }
		63	+ std::vector<f64> rolling_sum_squares(const Batch& b, usize window, KernelStats* st) override {
		64	+ Timer t; std::vector<f64> out(b.values.size());
		65	+ pool().parallel_for(b.n_series(), [&](usize i0, usize i1) { for (usize i = i0; i < i1; ++i) { auto x = b.series(i); std::vector<f64> sq(x.size()); for (usize j = 0; j < x.size(); ++j) sq[j] = x[j] * x[j]; auto r = stats::rolling_sum(sq, stats::RollingOptions{window, 0, 1024}); std::copy(r.begin(), r.end(), out.begin() + b.offsets[i]); } });
		66	+ fill(st, "rolling_sum_squares", t, b.values.size_bytes(), out.size() * 8); return out;
		67	+ }
		68	+ void regression_stats(std::span<const f64> x, std::span<const f64> y, std::span<const i64> offsets, usize k, std::vector<f64>& xtx, std::vector<f64>& xty, std::vector<f64>& yty, KernelStats* st) override {
		69	+ Timer t; const usize ns = offsets.size() - 1; xtx.assign(ns * k * k, 0); xty.assign(ns * k, 0); yty.assign(ns, 0);
		70	+ pool().parallel_for(ns, [&](usize i0, usize i1) { for (usize s = i0; s < i1; ++s) { const usize b0 = static_cast<usize>(offsets[s]), n = static_cast<usize>(offsets[s + 1] - offsets[s]);
		71	+ std::vector<f64> acc(k * k, 0), accc(k * k, 0), ay(k, 0), ayc(k, 0); f64 syy = 0, cyy = 0;
		72	+ for (usize r = 0; r < n; ++r) { const f64* xr = x.data() + (b0 + r) * k; const f64 yr = y[b0 + r];
		73	+ for (usize a = 0; a < k; ++a) { for (usize c = a; c < k; ++c) { const f64 v = xr[a] * xr[c], tt = acc[a * k + c] + v; if (std::abs(acc[a * k + c]) >= std::abs(v)) accc[a * k + c] += (acc[a * k + c] - tt) + v; else accc[a * k + c] += (v - tt) + acc[a * k + c]; acc[a * k + c] = tt; }
		74	+ const f64 v = xr[a] * yr, tt = ay[a] + v; if (std::abs(ay[a]) >= std::abs(v)) ayc[a] += (ay[a] - tt) + v; else ayc[a] += (v - tt) + ay[a]; ay[a] = tt; }
		75	+ const f64 q = yr * yr, tt = syy + q; if (std::abs(syy) >= q) cyy += (syy - tt) + q; else cyy += (q - tt) + syy; syy = tt; }
		76	+ for (usize a = 0; a < k; ++a) for (usize c = a; c < k; ++c) { const f64 v = acc[a * k + c] + accc[a * k + c]; xtx[s * k * k + a * k + c] = v; xtx[s * k * k + c * k + a] = v; }
		77	+ for (usize a = 0; a < k; ++a) xty[s * k + a] = ay[a] + ayc[a];
		78	+ yty[s] = syy + cyy; } });
		79	+ fill(st, "regression_stats", t, x.size_bytes() + y.size_bytes(), (xtx.size() + xty.size() + yty.size()) * 8);
		80	+ }
		81	+ std::vector<f64> garch_nll_batch(const Batch& returns, std::span<const f64> params, usize n_params, usize p, int model_code, f64 nu_fixed, KernelStats* st) override {
		82	+ Timer t; const usize ns = returns.n_series(); std::vector<f64> out(ns * n_params);
		83	+ pool().parallel_for(ns * n_params, [&](usize i0, usize i1) { for (usize idx = i0; idx < i1; ++idx) { const usize s = idx / n_params, q = idx % n_params; const f64* th = params.data() + (s * n_params + q) * p; out[idx] = models::garch_nll(returns.series(s), std::span<const f64>(th, p), static_cast<models::GarchModel>(model_code), nu_fixed); } });
		84	+ fill(st, "garch_nll_batch", t, returns.values.size_bytes() + params.size_bytes(), out.size() * 8); return out;
		85	+ }
		86	+ private:
		87	+ std::unique_ptr<ThreadPool> pool_;
		88	+};
		89	+
		90	+std::shared_ptr<Backend> make_cpu_backend(unsigned threads) { return std::make_shared<CpuBackend>(threads); }
		91	+
		92	+std::shared_ptr<Backend> resolve(const ExecutionPlan& plan, std::string* reason) {
		93	+ auto why = [&](const std::string& s) { if (reason) *reason = s; };
		94	+ if (plan.backend == raid::Backend::cpu) { why("cpu requested"); return make_cpu_backend(plan.threads); }
		95	+#ifdef RAID_ENABLE_METAL
		96	+ auto m = make_metal_backend(plan.precision);
		97	+ if (m && m->available()) { why(plan.backend == raid::Backend::metal ? "metal requested" : "auto: metal available"); return m; }
		98	+ if (plan.backend == raid::Backend::metal && !plan.allow_fallback) throw RaidError(ErrorCode::unsupported, "Metal backend requested but unavailable and fallback disabled");
		99	+ why("metal unavailable: falling back to cpu");
		100	+#else
		101	+ if (plan.backend == raid::Backend::metal && !plan.allow_fallback) throw RaidError(ErrorCode::unsupported, "RAID built without Metal support");
		102	+ why(plan.backend == raid::Backend::metal ? "built without Metal: falling back to cpu" : "auto: cpu (no Metal in this build)");
		103	+#endif
		104	+ return make_cpu_backend(plan.threads);
		105	+}
		106	+
		107	+std::vector<Device> list_devices() {
		108	+ std::vector<Device> d; d.push_back(make_cpu_backend()->device());
		109	+#ifdef RAID_ENABLE_METAL
		110	+ if (auto m = make_metal_backend()) if (m->available()) d.push_back(m->device());
		111	+#endif
		112	+ return d;
		113	+}
		114	+
		115	+} // namespace raid::backend

added bindings/python/CMakeLists.txt +9 −0

				@@ -0,0 +1,9 @@
		1	+find_package(Python 3.11 COMPONENTS Interpreter Development.Module REQUIRED)
		2	+execute_process(COMMAND "${Python_EXECUTABLE}" -m nanobind --cmake_dir OUTPUT_STRIP_TRAILING_WHITESPACE OUTPUT_VARIABLE nanobind_ROOT)
		3	+find_package(nanobind CONFIG REQUIRED)
		4	+nanobind_add_module(_core NB_STATIC raid_py.cpp)
		5	+target_link_libraries(_core PRIVATE raid)
		6	+if(RAID_ENABLE_METAL)
		7	+ target_link_libraries(_core PRIVATE raid_metal)
		8	+endif()
		9	+install(TARGETS _core LIBRARY DESTINATION raid)

added bindings/python/raid_py.cpp +102 −0

				@@ -0,0 +1,102 @@
		1	+// nanobind bindings for RAID. Arrays are passed as NumPy views (no copy for contiguous float64/int64), the GIL is
		2	+// released around every heavy call, and results come back as dicts (JSON-like) plus NumPy arrays for vectors.
		3	+#include <nanobind/nanobind.h>
		4	+#include <nanobind/ndarray.h>
		5	+#include <nanobind/stl/string.h>
		6	+#include <nanobind/stl/vector.h>
		7	+#include <nanobind/stl/optional.h>
		8	+#include <nlohmann/json.hpp>
		9	+#include "raid/core/types.hpp"
		10	+#include "raid/core/error.hpp"
		11	+#include "raid/backend/backend.hpp"
		12	+#include "raid/estimators/ols.hpp"
		13	+#include "raid/estimators/realized.hpp"
		14	+#include "raid/models/garch.hpp"
		15	+#include "raid/data/bar_series.hpp"
		16	+#include "raid/data/calendar.hpp"
		17	+#include "raid/data/returns.hpp"
		18	+
		19	+namespace nb = nanobind;
		20	+using namespace raid;
		21	+using json = nlohmann::json;
		22	+using F64 = nb::ndarray<const double, nb::c_contig, nb::device::cpu>;
		23	+using I64 = nb::ndarray<const int64_t, nb::c_contig, nb::device::cpu>;
		24	+
		25	+namespace {
		26	+nb::object json_to_py(const json& j) {
		27	+ switch (j.type()) {
		28	+ case json::value_t::null: return nb::none();
		29	+ case json::value_t::boolean: return nb::bool_(j.get<bool>());
		30	+ case json::value_t::number_integer: return nb::int_(j.get<long long>());
		31	+ case json::value_t::number_unsigned: return nb::int_(j.get<unsigned long long>());
		32	+ case json::value_t::number_float: return nb::float_(j.get<double>());
		33	+ case json::value_t::string: return nb::str(j.get<std::string>().c_str());
		34	+ case json::value_t::array: { nb::list l; for (auto& v : j) l.append(json_to_py(v)); return l; }
		35	+ case json::value_t::object: { nb::dict d; for (auto& [k, v] : j.items()) d[k.c_str()] = json_to_py(v); return d; }
		36	+ default: return nb::none();
		37	+ }
		38	+}
		39	+nb::ndarray<nb::numpy, double> to_np(std::vector<double> v) {
		40	+ auto* data = new std::vector<double>(std::move(v));
		41	+ nb::capsule owner(data, [](void* p) noexcept { delete static_cast<std::vector<double>*>(p); });
		42	+ return nb::ndarray<nb::numpy, double>(data->data(), {data->size()}, owner);
		43	+}
		44	+nb::ndarray<nb::numpy, int64_t> to_np_i64(std::vector<int64_t> v) {
		45	+ auto* data = new std::vector<int64_t>(std::move(v));
		46	+ nb::capsule owner(data, [](void* p) noexcept { delete static_cast<std::vector<int64_t>*>(p); });
		47	+ return nb::ndarray<nb::numpy, int64_t>(data->data(), {data->size()}, owner);
		48	+}
		49	+} // namespace
		50	+
		51	+NB_MODULE(_core, m) {
		52	+ m.doc() = "RAID compiled core (C++20, CPU + optional Metal)";
		53	+ m.attr("__version__") = RAID_VERSION;
		54	+ nb::exception<RaidError>(m, "RaidError");
		55	+
		56	+ m.def("devices", []() { json out = json::array(); for (auto& d : backend::list_devices()) out.push_back(d.to_json()); return json_to_py(out); });
		57	+
		58	+ m.def("ols", [](F64 X, F64 y, const std::string& cov, int hac_lags, std::optional<I64> clusters, double ridge, std::vector<usize> unpen, std::optional<F64> weights, std::vector<std::string> names) {
		59	+ RAID_REQUIRE(X.ndim() == 2 && y.ndim() == 1 && X.shape(0) == y.shape(0), invalid_argument, "X (n,k), y (n,)");
		60	+ const Eigen::Index n = static_cast<Eigen::Index>(X.shape(0)), k = static_cast<Eigen::Index>(X.shape(1));
		61	+ Eigen::Map<const Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> Xm(X.data(), n, k);
		62	+ Eigen::Map<const Eigen::VectorXd> ym(y.data(), n);
		63	+ est::OlsOptions o; o.cov.type = stats::covtype_from_string(cov); o.cov.hac_lags = hac_lags; o.ridge_lambda = ridge; o.unpenalized = unpen; o.names = names;
		64	+ std::vector<i64> cl; if (clusters) { cl.assign(clusters->data(), clusters->data() + clusters->shape(0)); o.cov.clusters = cl; }
		65	+ std::vector<f64> w; if (weights) { w.assign(weights->data(), weights->data() + weights->shape(0)); o.weights = w; }
		66	+ Eigen::MatrixXd Xd = Xm; json out; { nb::gil_scoped_release rel; auto r = est::ols(Xd, ym, o); out = r.estimate.to_json(); }
		67	+ return json_to_py(out);
		68	+ }, nb::arg("X"), nb::arg("y"), nb::arg("cov") = "classical", nb::arg("hac_lags") = -1, nb::arg("clusters") = nb::none(), nb::arg("ridge_lambda") = 0.0, nb::arg("unpenalized") = std::vector<usize>{}, nb::arg("weights") = nb::none(), nb::arg("names") = std::vector<std::string>{});
		69	+
		70	+ m.def("realized_measures", [](F64 r, I64 off, const std::string& backend_s, bool bpv_fs, bool deterministic) {
		71	+ est::RealizedOptions o; o.plan.backend = backend_from_string(backend_s); o.bpv_finite_sample = bpv_fs; o.plan.deterministic = deterministic;
		72	+ backend::Batch b{std::span<const f64>(r.data(), r.shape(0)), std::span<const i64>(off.data(), off.shape(0))};
		73	+ est::RealizedMeasures mres; { nb::gil_scoped_release rel; mres = est::realized_measures_batch(b, o); }
		74	+ nb::dict d; d["rv"] = to_np(mres.rv); d["bpv"] = to_np(mres.bpv); d["rsv_neg"] = to_np(mres.rsv_neg); d["rsv_pos"] = to_np(mres.rsv_pos); d["rq"] = to_np(mres.rq); d["jump"] = to_np(mres.jump); d["tripv"] = to_np(mres.tripv); d["ret"] = to_np(mres.daily_return);
		75	+ d["backend"] = std::string(to_string(mres.stats.backend)); d["precision"] = std::string(to_string(mres.stats.precision)); d["wall_seconds"] = mres.stats.wall_seconds; d["device_seconds"] = mres.stats.device_seconds; d["note"] = mres.stats.note; d["warnings"] = mres.warnings;
		76	+ return d;
		77	+ }, nb::arg("returns"), nb::arg("offsets"), nb::arg("backend") = "auto", nb::arg("bpv_finite_sample") = true, nb::arg("deterministic") = true);
		78	+
		79	+ m.def("garch_fit", [](F64 r, const std::string& model, const std::string& dist, int horizon, const std::string& cov, int max_iter, double tol) {
		80	+ models::GarchFitOptions o; o.model = model == "gjr" ? models::GarchModel::gjr : (model == "egarch" ? models::GarchModel::egarch : models::GarchModel::garch);
		81	+ o.dist = dist == "student_t" || dist == "t" ? models::GarchDist::student_t : models::GarchDist::gaussian; o.forecast_horizon = horizon; o.cov = cov; o.max_iter = max_iter; o.tol = tol;
		82	+ std::span<const f64> rs(r.data(), r.shape(0)); json out; { nb::gil_scoped_release rel; out = models::garch_fit(rs, o).to_json(); }
		83	+ return json_to_py(out);
		84	+ }, nb::arg("returns"), nb::arg("model") = "garch", nb::arg("dist") = "gaussian", nb::arg("horizon") = 0, nb::arg("cov") = "qmle", nb::arg("max_iter") = 500, nb::arg("tol") = 1e-8);
		85	+
		86	+ m.def("validate_bars", [](I64 t, F64 o, F64 h, F64 l, F64 c, F64 v, const std::string& symbol, const std::string& interval, const std::string& session) {
		87	+ BarSeries b; b.symbol = symbol; b.interval = interval_from_string(interval); const usize n = t.shape(0); b.reserve(n);
		88	+ for (usize i = 0; i < n; ++i) b.push_back(Timestamp(t.data()[i]), o.data()[i], h.data()[i], l.data()[i], c.data()[i], v.data()[i]);
		89	+ json out; { nb::gil_scoped_release rel; auto st = b.normalize(); MarketCalendar cal; auto q = b.validate(cal, session == "extended" ? SessionPolicy::extended : SessionPolicy::regular); q.duplicates = st.duplicates_removed; q.unsorted = st.reordered; out = q.to_json(); }
		90	+ return json_to_py(out);
		91	+ });
		92	+
		93	+ m.def("calendar_sessions", [](const std::string& a, const std::string& b) { MarketCalendar cal; std::vector<std::string> out; for (auto d : cal.sessions(date_from_iso(a), date_from_iso(b))) out.push_back(d.iso()); return out; });
		94	+
		95	+ m.def("intraday_returns", [](I64 t, F64 c, const std::string& kind) {
		96	+ BarSeries b; b.interval = Interval::m1; const usize n = t.shape(0); b.reserve(n);
		97	+ for (usize i = 0; i < n; ++i) b.push_back(Timestamp(t.data()[i]), c.data()[i], c.data()[i], c.data()[i], c.data()[i], 0.0);
		98	+ data::IntradayReturns ir; { nb::gil_scoped_release rel; b.normalize(); MarketCalendar cal; auto reg = b.filter_session(cal, SessionPolicy::regular); ir = data::intraday_returns(reg, kind == "arithmetic" ? data::ReturnKind::arithmetic : data::ReturnKind::log); }
		99	+ nb::dict d; d["r"] = to_np(ir.r); d["offsets"] = to_np_i64(ir.session_offsets); std::vector<std::string> s; for (auto dd : ir.session_dates) s.push_back(dd.iso()); d["sessions"] = s; d["overnight"] = to_np(ir.overnight); d["close_to_close"] = to_np(ir.close_to_close); d["open_to_close"] = to_np(ir.open_to_close);
		100	+ return d;
		101	+ });
		102	+}

added cli/raid.cpp +120 −0

				@@ -0,0 +1,120 @@
		1	+// raid — command-line tool: devices | validate | estimate | data | benchmark (Phase 1: devices, validate, estimate on CSV/Parquet).
		2	+#include <cstdio>
		3	+#include <cstring>
		4	+#include <iostream>
		5	+#include <fstream>
		6	+#include <string>
		7	+#include <vector>
		8	+#include <map>
		9	+#include <nlohmann/json.hpp>
		10	+#include "raid/core/types.hpp"
		11	+#include "raid/core/error.hpp"
		12	+#include "raid/core/manifest.hpp"
		13	+#include "raid/core/hash.hpp"
		14	+#include "raid/backend/backend.hpp"
		15	+#include "raid/data/io.hpp"
		16	+#include "raid/data/calendar.hpp"
		17	+#include "raid/data/returns.hpp"
		18	+#include "raid/estimators/realized.hpp"
		19	+#include "raid/estimators/ols.hpp"
		20	+#include "raid/models/garch.hpp"
		21	+#ifdef RAID_WITH_FINDATA
		22	+#include "raid/data/findata.hpp"
		23	+#endif
		24	+
		25	+using namespace raid;
		26	+using json = nlohmann::json;
		27	+
		28	+namespace {
		29	+void usage() {
		30	+ std::fprintf(stderr,
		31	+ "raid %s — Research Architecture for Intraday Data (UQO WP 40)\n"
		32	+ " raid devices list backends/devices (JSON)\n"
		33	+ " raid validate <bars.csv|.parquet> [--session regular|extended] quality report per symbol (JSON)\n"
		34	+ " raid estimate realized <bars> [--backend auto|cpu|metal] [--bpv-no-fs] per-session realized measures\n"
		35	+ " raid estimate garch <bars> [--model garch|gjr|egarch] [--dist gaussian|student_t] [--horizon H] [--returns daily|intraday]\n"
		36	+ " raid estimate ols <csv y,x1,x2...> [--cov classical|hc0..hc3|hac|cluster] [--lags L]\n"
		37	+ " raid data ... FinData access (Phase 2)\n"
		38	+ " raid benchmark ... benchmark harness (Phase 3+)\n", RAID_VERSION);
		39	+}
		40	+std::string arg(const std::vector<std::string>& a, const std::string& key, const std::string& def = "") { for (usize i = 0; i + 1 < a.size(); ++i) if (a[i] == key) return a[i + 1]; return def; }
		41	+bool flag(const std::vector<std::string>& a, const std::string& key) { for (auto& s : a) if (s == key) return true; return false; }
		42	+
		43	+std::map<std::string, BarSeries> load_bars(const std::string& path) {
		44	+ if (path.size() > 4 && path.substr(path.size() - 4) == ".csv") return data::read_bars_csv(path);
		45	+#ifdef RAID_WITH_ARROW
		46	+ if (path.size() > 8 && path.substr(path.size() - 8) == ".parquet") return data::read_bars_parquet(path).series;
		47	+ if (path.size() > 6 && path.substr(path.size() - 6) == ".arrow") return data::read_bars_arrow_ipc(path).series;
		48	+#endif
		49	+ throw RaidError(ErrorCode::io, "unsupported input format (csv"
		50	+#ifdef RAID_WITH_ARROW
		51	+ ", parquet, arrow"
		52	+#endif
		53	+ "): " + path);
		54	+}
		55	+
		56	+int cmd_devices() {
		57	+ json out = json::array(); for (auto& d : backend::list_devices()) out.push_back(d.to_json());
		58	+ json j = {{"devices", out}, {"host", host_info()}}; std::cout << j.dump(1) << "\n"; return 0;
		59	+}
		60	+
		61	+int cmd_validate(const std::vector<std::string>& a) {
		62	+ if (a.empty()) { usage(); return 2; }
		63	+ auto bars = load_bars(a[0]); MarketCalendar cal;
		64	+ const auto policy = arg(a, "--session", "regular") == "extended" ? SessionPolicy::extended : SessionPolicy::regular;
		65	+ json out = json::array();
		66	+ for (auto& [sym, b] : bars) { auto bb = b; auto st = bb.normalize(); auto q = bb.validate(cal, policy); q.duplicates = st.duplicates_removed; q.unsorted = st.reordered; out.push_back(q.to_json()); }
		67	+ std::cout << json({{"file", a[0]}, {"sha256", Sha256::of_file(a[0])}, {"reports", out}}).dump(1) << "\n"; return 0;
		68	+}
		69	+
		70	+int cmd_estimate(const std::vector<std::string>& a) {
		71	+ if (a.size() < 2) { usage(); return 2; }
		72	+ const std::string what = a[0], path = a[1];
		73	+ ExecutionPlan plan; plan.backend = backend_from_string(arg(a, "--backend", "auto"));
		74	+ if (what == "realized") {
		75	+ auto bars = load_bars(path); MarketCalendar cal; json out = json::object();
		76	+ est::RealizedOptions ro; ro.plan = plan; ro.bpv_finite_sample = !flag(a, "--bpv-no-fs");
		77	+ for (auto& [sym, b0] : bars) { auto b = b0; b.normalize(); auto reg = b.filter_session(cal, SessionPolicy::regular); auto ir = data::intraday_returns(reg); auto m = est::realized_measures(ir, ro);
		78	+ json rows = json::array(); for (usize k = 0; k < m.sessions.size(); ++k) rows.push_back({{"session", m.sessions[k].iso()}, {"n", m.n_returns[k]}, {"rv", m.rv[k]}, {"bpv", m.bpv[k]}, {"rsv_neg", m.rsv_neg[k]}, {"rsv_pos", m.rsv_pos[k]}, {"rq", m.rq[k]}, {"jump", m.jump[k]}, {"tripv", m.tripv[k]}, {"ret", m.daily_return[k]}});
		79	+ out[sym] = {{"measures", rows}, {"backend", std::string(to_string(m.stats.backend))}, {"precision", std::string(to_string(m.stats.precision))}, {"wall_seconds", m.stats.wall_seconds}, {"note", m.stats.note}, {"warnings", m.warnings}}; }
		80	+ std::cout << out.dump(1) << "\n"; return 0;
		81	+ }
		82	+ if (what == "garch") {
		83	+ auto bars = load_bars(path); MarketCalendar cal; json out = json::object();
		84	+ models::GarchFitOptions go; const std::string mdl = arg(a, "--model", "garch"); go.model = mdl == "gjr" ? models::GarchModel::gjr : (mdl == "egarch" ? models::GarchModel::egarch : models::GarchModel::garch);
		85	+ go.dist = arg(a, "--dist", "gaussian") == "student_t" ? models::GarchDist::student_t : models::GarchDist::gaussian; go.forecast_horizon = std::stoi(arg(a, "--horizon", "0")); go.plan = plan;
		86	+ const bool intraday = arg(a, "--returns", "daily") == "intraday";
		87	+ for (auto& [sym, b0] : bars) { auto b = b0; b.normalize(); auto reg = b.filter_session(cal, SessionPolicy::regular); auto ir = data::intraday_returns(reg);
		88	+ std::vector<f64> r; if (intraday) r = ir.r; else for (f64 v : ir.close_to_close) if (std::isfinite(v)) r.push_back(v);
		89	+ try { auto e = models::garch_fit(r, go); out[sym] = e.to_json(); } catch (const RaidError& ex) { out[sym] = {{"error", ex.what()}}; } }
		90	+ std::cout << out.dump(1) << "\n"; return 0;
		91	+ }
		92	+ if (what == "ols") {
		93	+ std::ifstream f(path); RAID_REQUIRE(f.good(), io, "cannot open " + path); std::string line; std::getline(f, line);
		94	+ std::vector<std::string> names; { usize s = 0; for (usize i = 0; i <= line.size(); ++i) if (i == line.size() || line[i] == ',') { names.push_back(line.substr(s, i - s)); s = i + 1; } }
		95	+ std::vector<std::vector<f64>> rows; while (std::getline(f, line)) { if (line.empty()) continue; std::vector<f64> r; usize s = 0; for (usize i = 0; i <= line.size(); ++i) if (i == line.size() || line[i] == ',') { r.push_back(std::stod(line.substr(s, i - s))); s = i + 1; } rows.push_back(r); }
		96	+ const usize n = rows.size(), k = names.size() - 1; Eigen::MatrixXd X(n, k + 1); Eigen::VectorXd y(n);
		97	+ for (usize i = 0; i < n; ++i) { y[static_cast<Eigen::Index>(i)] = rows[i][0]; X(static_cast<Eigen::Index>(i), 0) = 1.0; for (usize j = 0; j < k; ++j) X(static_cast<Eigen::Index>(i), static_cast<Eigen::Index>(j + 1)) = rows[i][j + 1]; }
		98	+ est::OlsOptions oo; oo.cov.type = stats::covtype_from_string(arg(a, "--cov", "classical")); oo.cov.hac_lags = std::stoi(arg(a, "--lags", "-1")); oo.names = {"const"}; for (usize j = 1; j < names.size(); ++j) oo.names.push_back(names[j]);
		99	+ std::cout << est::ols(X, y, oo).estimate.to_json().dump(1) << "\n"; return 0;
		100	+ }
		101	+ usage(); return 2;
		102	+}
		103	+} // namespace
		104	+
		105	+int main(int argc, char** argv) {
		106	+ std::vector<std::string> a(argv + 1, argv + argc);
		107	+ if (a.empty()) { usage(); return 2; }
		108	+ try {
		109	+ const std::string cmd = a[0]; a.erase(a.begin());
		110	+ if (cmd == "devices") return cmd_devices();
		111	+ if (cmd == "validate") return cmd_validate(a);
		112	+ if (cmd == "estimate") return cmd_estimate(a);
		113	+#ifdef RAID_WITH_FINDATA
		114	+ if (cmd == "data") return raid::findata::cli_main(a);
		115	+#endif
		116	+ if (cmd == "benchmark") { std::fprintf(stderr, "benchmark: use the bench_* executables (Phase 3+)\n"); return 2; }
		117	+ usage(); return 2;
		118	+ } catch (const RaidError& e) { std::fprintf(stderr, "raid error: %s\n", e.what()); return 1; }
		119	+ catch (const std::exception& e) { std::fprintf(stderr, "error: %s\n", e.what()); return 1; }
		120	+}

added cmake/EmbedMSL.cmake +9 −0

				@@ -0,0 +1,9 @@
		1	+# Concatenates all .metal sources into one C++ translation unit exposing raid::metal::msl_bundle().
		2	+set(_out "namespace raid { namespace metal { const char* msl_bundle() { return R\"RAIDMSL(\n")
		3	+set(_sep "")
		4	+foreach(s ${SHADERS})
		5	+ file(READ ${s} _c)
		6	+ string(APPEND _out "// ---- ${s}\n${_c}\n")
		7	+endforeach()
		8	+string(APPEND _out ")RAIDMSL\"; } } }\n")
		9	+file(WRITE ${OUT} "${_out}")

added datasets/fixtures/garch_returns.csv +3001 −0

				@@ -0,0 +1,3001 @@
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		2	+0.011072422834064762
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added datasets/fixtures/reference_values.json +205 −0

				@@ -0,0 +1,205 @@
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	\ No newline at end of file

added datasets/fixtures/synthetic_bars_1m.csv +6818 −0

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		6818	+SYN2,2024-12-03T00:59:00Z,23.1834,23.1843,23.172,23.1728,220.0

added datasets/fixtures/synthetic_bars_1m.expected.json +96 −0

				@@ -0,0 +1,96 @@
		1	+{
		2	+ "SYN1": {
		3	+ "2024-11-25": {
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		10	+ "expected_bars": 391
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		12	+ "2024-11-26": {
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		39	+ "2024-12-02": {
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		45	+ "rq": 5.8733435507599605e-08,
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		60	+ "n_regular_bars": 373,
		61	+ "rv": 0.0008353831935930781,
		62	+ "bpv": 0.0007977362074909713,
		63	+ "rsv_neg": 0.0004595924981014806,
		64	+ "rsv_pos": 0.00037579069549159734,
		65	+ "rq": 7.975104614921211e-07,
		66	+ "expected_bars": 391
		67	+ },
		68	+ "2024-11-27": {
		69	+ "n_regular_bars": 380,
		70	+ "rv": 0.0009433116234142414,
		71	+ "bpv": 0.0009175564582475878,
		72	+ "rsv_neg": 0.00048049767491522606,
		73	+ "rsv_pos": 0.00046281394849901546,
		74	+ "rq": 8.624495358732458e-07,
		75	+ "expected_bars": 391
		76	+ },
		77	+ "2024-11-29": {
		78	+ "n_regular_bars": 205,
		79	+ "rv": 0.00041835614920297196,
		80	+ "bpv": 0.000418010122181772,
		81	+ "rsv_neg": 0.00019707105657041914,
		82	+ "rsv_pos": 0.00022128509263255285,
		83	+ "rq": 1.713607175525038e-07,
		84	+ "expected_bars": 211
		85	+ },
		86	+ "2024-12-02": {
		87	+ "n_regular_bars": 377,
		88	+ "rv": 0.000813432649113324,
		89	+ "bpv": 0.0007490121342075762,
		90	+ "rsv_neg": 0.0004083859759489544,
		91	+ "rsv_pos": 0.00040504667316436957,
		92	+ "rq": 6.172699093052535e-07,
		93	+ "expected_bars": 391
		94	+ }
		95	+ }
		96	+}
	\ No newline at end of file

added docs/DECISIONS.md +14 −0

				@@ -0,0 +1,14 @@
		1	+# docs/DECISIONS.md — decision log
		2	+
		3	+| ID | Date | Decision | Rationale |
		4	+|---|---|---|---|
		5	+| D-001 | 2026-11-02 | Project lives at `Recherche-UQO/finance/wp40_raid/`, WP number **40**, remote `wp40_uqo.git` | Charter asks for `wp40_raid/`; highest existing WP is 39; finance is the closest domain (WP 12 / 6 / 39 precedents) |
		6	+| D-002 | 2026-11-02 | Primary development and benchmark machine = the owner's laptop (M5 Max, 40 GPU cores); cluster used only for E7 with a self-contained bundle | Most nodes lack Homebrew/cmake in SSH PATH; production nodes (M3U96a, M4M64a/b, M1M32) must not be loaded; laptop has the newest toolchain |
		7	+| D-003 | 2026-11-02 | GPU numerics are FP32 with compensated/pairwise reductions; FP64 only on CPU; every Estimate reports `precision` | Metal rejects `double` (verified) |
		8	+| D-004 | 2026-11-02 | Shaders are shipped as MSL source **and** a prebuilt `.metallib`; runtime `newLibraryWithSource:` is the fallback and the "first compilation" benchmark case | Nodes without the Metal toolchain can still run; the charter asks for cold vs cached compilation measurements |
		9	+| D-005 | 2026-11-02 | Build: CMake ≥ 3.28 + Ninja, Apple clang, C++20, Catch2 v3 tests, nanobind bindings, scikit-build-core wheels; Arrow C++ optional (`RAID_WITH_ARROW`, default ON on macOS) | Portability of the CPU core to Linux CI; Arrow is heavy |
		10	+| D-006 | 2026-11-02 | Data access only through the RAID FinData client (bars Arrow IPC pages ≤ 31 d, exports Parquet for panels, query only for universe selection); cache under `datasets/cache/` (git-ignored) keyed by dataset version | Charter §5; license; reproducibility via pinned `X-Dataset-Version` |
		11	+| D-007 | 2026-11-02 | Reference software pinned: highfrequency 1.0.3, rugarch 1.5.6, arch 8.0.0, statsmodels 0.15.0, numpy 2.5.4, scipy 1.18.1 | Installed and recorded during the audit |
		12	+| D-008 | 2026-11-02 | Session policy default = **regular hours 09:30–16:00 ET inclusive of the 16:00 closing print** (391 bars), extended optional; early closes from the exchange calendar (211 bars); no bar is ever manufactured | FinData conventions (`expected_bars`) and charter §5 |
		13	+| D-009 | 2026-11-02 | Tier A experiments use market data only (no fundamentals) to avoid PIT/survivorship issues in a methods paper | docs/risks.md R15 |
		14	+| D-010 | 2026-11-02 | Energy is reported only when measured with `powermetrics`; otherwise "not measured" | charter §8, risks R17 |

added docs/PROGRESS.md +56 −0

				@@ -0,0 +1,56 @@
		1	+# docs/PROGRESS.md — phase journal (actual commands and results)
		2	+
		3	+## Phase 0 — Audit (2026-11-02) — GATE PASSED
		4	+Plan: audit the UQO repository, FinData, hardware, competitors, literature and licensing; write the four audit documents.
		5	+
		6	+Executed (laptop, 14:05–14:40 ET):
		7	+- Read `Recherche-UQO/README.md`, `HARMONISATION.md`, `_modele-working-paper/*`, WP 12/39/33 READMEs and `PROGRESS.md`.
		8	+ Result: WP 40 is free; conventions recorded in `docs/audit.md` §1.
		9	+- FinData: `fdq health`, `fdq datasets`, `GET /openapi.json` (saved), `GET /market/bars?format=arrow` (SPY, DST weekend),
		10	+ `GET /market/sessions` (SPY, Thanksgiving week 2024 → 211 expected bars on 11-29), `POST /exports` (SPY+QQQ Jan 2024:
		11	+ 34 607 rows, 1.5 s, version 5162, SHA-256 recorded), `GET /query/views`, `GET /coverage/symbols`. Results in §2.
		12	+- Hardware: `sw_vers`, `sysctl`, `system_profiler SPDisplaysDataType`, `xcodebuild -version`, Metal device query
		13	+ program (`docs/evidence/` test), 29-node parallel SSH probe (22 reachable; files in `docs/evidence/nodes/`).
		14	+- Metal: trivial kernel compiled at runtime (34.6 ms) and executed (1 M squares, 0.029 ms GPU); `double` kernel rejected
		15	+ by both runtime and offline compilers; Metal Toolchain 27B47 downloaded (`xcodebuild -downloadComponent MetalToolchain`).
		16	+- Toolchain installs: `brew install eigen apache-arrow catch2 nlohmann-json ninja` (background), R
		17	+ `install.packages(c("highfrequency","rugarch","xts","lmtest"))` → highfrequency 1.0.3, rugarch 1.5.6, xts 0.14.3
		18	+ (lmtest failed: Java runtime missing for a dependency; not needed), `uv venv --python 3.12` + pip install of the
		19	+ Python reference stack (versions in `docs/capabilities.md`).
		20	+- Literature: 39 key references resolved on Crossref (`docs/literature/crossref_resolved.json`); preprint hits were
		21	+ re-resolved to journal versions; three DOIs verified directly (Econometrica 2008 realized kernels, SPA 2009
		22	+ pre-averaging, OUP 2010 semivariance). Still to resolve: Hansen–Lunde–Nason 2011 Econometrica MCS DOI; software
		23	+ citations (Arrow, Eigen, nanobind, Metal docs) to be added with URLs/versions.
		24	+- Legal: FirstRate private-use license (no redistribution), MIT for RAID, Metal proprietary → optional backend.
		25	+
		26	+Gate memo (scope and feasibility): feasible on the laptop with the measured toolchain; FP64-on-GPU impossible → FP32
		27	+strategy; FinData provides all Tier A inputs (1-minute OHLCV bars, versions, sessions, exports); no trade/quote data →
		28	+Tier C order-flow methods excluded; cluster usable for E7 with a static bundle; 7 nodes unreachable. Decisions D-001…D-010.
		29	+
		30	+## Phase 1 — Foundation (2026-11-02, 14:40–15:40 ET) — GATE PASSED
		31	+Plan: CMake/Ninja build, C++20 core types, CPU backend, data abstractions (calendar, bars, returns, aggregation),
		32	+Arrow/Parquet reader, OLS/robust covariance, realized measures, GARCH-family QML (needed by the backend contract),
		33	+Catch2 tests, nanobind Python package, CLI, docs, CI.
		34	+
		35	+Executed:
		36	+- `cmake --preset macos-release && cmake --build --preset macos-release` (Apple clang 21, Eigen 5.0.1 + Accelerate,
		37	+ Arrow 26.0.0, Catch2 3.16.1, nlohmann-json 3.12). Metal sources absent → the build reports "Metal backend sources not
		38	+ present yet (Phase 3)" and continues CPU-only (`raid devices` lists the CPU only).
		39	+- `ctest --preset macos-release` → **26/26 test cases, 314 assertions** (unit: core, calendar, stats, OLS,
		40	+ realized/GARCH; reference: statsmodels 0.15 OLS/HC0/HC3/HAC/cluster at 1e−9, scipy t p-values at 1e−12,
		41	+ exchange_calendars 4.13 XNYS 2000–2026 exact session/holiday/early-close sets, numpy realized truth at 1e−12, arch 8.0
		42	+ GARCH/GJR/EGARCH/Student-t log-likelihood within 5e−4 and parameters within 5 % + 0.01, forecasts within 5 %).
		43	+- Python: `cmake -DRAID_BUILD_PYTHON=ON` builds `raid._core` (nanobind 3.1.0, Python 3.12); `pytest python/tests` →
		44	+ 7 passed (OLS HC3 vs statsmodels, realized vs numpy, GARCH convergence, calendar, bar validation).
		45	+- Real-data integration (file not committed): `raid validate /tmp/raid-export-test.parquet` (SPY+QQQ 1m January 2024,
		46	+ FinData export v5162) → 21 sessions each, coverage 1.0 (391 regular bars incl. the 16:00 print), 0 duplicates,
		47	+ 0 errors; `raid estimate realized … --backend cpu` → 21 sessions × 390 returns, mean RV(SPY) 2.71e−5, 0.25 ms.
		48	+- Bugs found and fixed during the phase (recorded for the paper's engineering section): thread-pool completion race
		49	+ (local mutex destroyed before the last job released it → SIGABRT under load); NLL rescaling sign; EGARCH ω
		50	+ wrongly log-transformed; `tgamma` overflow for ν > 340 (→ `lgamma`); fixture truth computed on unrounded prices.
		51	+- Environment manifest: `experiments/manifests/environment_<host>.json` via `scripts/record_environment.sh`.
		52	+- Repository: bare `gitsrv:srv/git/wp40_uqo.git` created with the SPB Git post-receive hook; `origin` set; pushed.
		53	+
		54	+Gate evidence: clean build on the macOS preset, all tests green, docs (`docs/architecture/overview.md`,
		55	+`docs/methods/registry.md`, `docs/api/cpp.md`, README) written. Open items carried to later phases: `bars.aggregate`
		56	+vs FinData derived 5m (Phase 2), GARCH backcast option (Phase 4), HAR/bootstrap (Phase 4), Metal (Phase 3).

added docs/api/cpp.md +28 −0

				@@ -0,0 +1,28 @@
		1	+# C++ API (Phase 1)
		2	+
		3	+```cpp
		4	+#include "raid/data/io.hpp" // read_bars_csv / read_bars_parquet / read_bars_arrow_ipc
		5	+#include "raid/data/calendar.hpp" // MarketCalendar (XNYS)
		6	+#include "raid/data/returns.hpp" // intraday_returns
		7	+#include "raid/estimators/realized.hpp"
		8	+#include "raid/estimators/ols.hpp"
		9	+#include "raid/models/garch.hpp"
		10	+#include "raid/backend/backend.hpp" // resolve(plan), list_devices()
		11	+
		12	+auto bars = raid::data::read_bars_csv("datasets/fixtures/synthetic_bars_1m.csv");
		13	+raid::MarketCalendar cal;
		14	+auto& b = bars["SYN1"]; b.normalize(); // sort, dedupe, session index
		15	+auto q = b.validate(cal); // QualityReport (counts, coverage)
		16	+auto reg = b.filter_session(cal, raid::SessionPolicy::regular);
		17	+auto ir = raid::data::intraday_returns(reg); // CSR: r + session_offsets
		18	+raid::est::RealizedOptions ro; ro.plan.backend = raid::Backend::automatic;
		19	+auto m = raid::est::realized_measures(ir, ro); // rv, bpv, rsv±, rq, jump, tripv; m.stats.backend/precision
		20	+raid::est::OlsOptions oo; oo.cov.type = raid::stats::CovType::hac_bartlett; oo.cov.hac_lags = 4;
		21	+auto fit = raid::est::ols(X, y, oo); // fit.estimate.to_json()
		22	+raid::models::GarchFitOptions go; go.model = raid::models::GarchModel::gjr; go.forecast_horizon = 5;
		23	+auto g = raid::models::garch_fit(daily_returns, go); // g.ok() ⇔ converged
		24	+```
		25	+
		26	+Error handling: all failures throw `raid::RaidError` with an `ErrorCode` (invalid_argument, data_quality, numerical,
		27	+not_converged, unsupported, io, api, insufficient_samples, memory, cancelled). Optimisers do not throw on
		28	+non-convergence; they return `Estimate` with `convergence.converged = false`.

added docs/architecture/overview.md +44 −0

				@@ -0,0 +1,44 @@
		1	+# Architecture overview (Phase 1, 2026-11-02)
		2	+
		3	+```
		4	+ ┌──────────────────────────── Python (nanobind, GIL released) ────────────────────────────┐
		5	+ │ raid.api: ols · realized_measures · garch_fit · validate_bars · calendar_sessions · … │
		6	+ └───────────────────────────────────────┬────────────────────────────────────────────────┘
		7	+ CLI `raid` ──────────────────────────────────────────┤
		8	+ ▼
		9	+ ┌─────────────── core ───────────────┐ ┌───────────── data ─────────────┐ ┌──────── statistics / estimators / models ────────┐
		10	+ │ types (Timestamp µs UTC, Date, │ │ MarketCalendar (XNYS rules, │ │ reductions (Neumaier, pairwise, Welford) │
		11	+ │ Interval, Backend, Precision) │ │ DST, early closes, 391/211 bars)│ │ rolling (sum/mean/var/cov/corr, re-summation) │
		12	+ │ error (RaidError + codes) │ │ BarSeries (columnar OHLCV + │ │ covariance (classical, HC0–3, HAC, cluster) │
		13	+ │ log (structured, redaction) │ │ session index, QualityReport) │ │ ols (QR/SVD/ridge/WLS, sufficient stats, rolling) │
		14	+ │ rng (xoshiro256++ streams) │ │ returns (session-aware), resample│ │ realized (RV, BPV, RSV±, RQ, J, TriPV, RCov) │
		15	+ │ thread_pool, hash (SHA-256) │ │ io: CSV fixtures, Parquet/Arrow │ │ models/garch (GARCH/GJR/EGARCH, N/t, QML, forecasts)│
		16	+ │ estimate / plan / manifest │ │ findata client (Phase 2) │ │ har, bootstrap (Phase 4) │
		17	+ └────────────────────────────────────┘ └────────────────────────────────┘ └───────────────────────┬──────────────────────────┘
		18	+ ▼
		19	+ ┌───────────── backend::Backend (batched primitives over CSR panels) ─────────────┐
		20	+ │ sum_squares · sum_abs_adjacent · semivariances · sum_fourth · rolling_sum_squares │
		21	+ │ regression_stats (X'X, X'y, y'y) · garch_nll_batch │
		22	+ ├───────────────────────────────┬───────────────────────────────────────────────────┤
		23	+ │ CpuBackend (FP64, threads) │ MetalBackend (FP32 + compensation, Phase 3) │
		24	+ └───────────────────────────────┴───────────────────────────────────────────────────┘
		25	+```
		26	+
		27	+Design rules (from the charter, enforced in code):
		28	+- **FP64 reference first.** Every primitive has a CPU FP64 implementation; the Metal backend must match it within the
		29	+ method's documented tolerance or report `precision=fp32` with a warning — never silently.
		30	+- **Estimators are backend-independent.** They consume `backend::Backend` through `resolve(plan)`; the plan decides
		31	+ `auto|cpu|metal`, determinism, threads, chunking and fallback. `Estimate` always carries backend, precision,
		32	+ convergence, warnings, runtime and provenance.
		33	+- **No silent data manufacturing.** `BarSeries` never forward-fills; `QualityReport` counts what is missing/invalid;
		34	+ returns are computed within sessions only; the 16:00 closing print is part of the regular session (391 bars).
		35	+- **CSR panels.** Batched kernels take `values + offsets`; ragged panels (different session lengths, delisted series)
		36	+ are first-class; rectangular panels get a dense view for linear algebra.
		37	+- **Provenance.** Inputs are hashed (SHA-256), FinData dataset versions and request IDs are kept, manifests record host,
		38	+ compiler and plan; `Estimate::to_json()` is the interchange format for Python, CLI and experiment records.
		39	+- **Failure is explicit.** `Estimate::ok()` requires convergence; optimisers report iterations, evaluations, gradient
		40	+ norm and a message; rank deficiency and ill-conditioning raise warnings; invalid parameters give +inf likelihood.
		41	+
		42	+Threading: a global `ThreadPool` (hardware concurrency or `RAID_THREADS`) with `parallel_for` over series; the calling
		43	+thread helps; exceptions propagate. Determinism: per-series Neumaier sums have a fixed order, so CPU results are bitwise
		44	+reproducible across thread counts (tested).

added docs/audit.md +136 −0

				@@ -0,0 +1,136 @@
		1	+# docs/audit.md — Phase 0 audit (2026-11-02)
		2	+
		3	+All facts below were measured on 2026-11-02 (America/Toronto) from the owner's laptop; raw probe outputs are in
		4	+`docs/evidence/` (per-node probe files, FinData OpenAPI snapshot). Nothing is assumed from older inventories.
		5	+
		6	+## 1. UQO Research repository
		7	+
		8	+- Root: `~/Desktop/UQO/Recherche/Recherche-UQO/` — one directory per working paper, grouped by domain
		9	+ (`immobilier/`, `finance/`, `economie/`, `ia/`, `methodes/`, `mathematiques/`), each paper is its own git repository
		10	+ with `origin = gitsrv:srv/git/wpN_uqo.git` (spbgit on M1M32). Highest existing number: **WP 39** (`wp39_uqo.git`,
		11	+ `finance/invisible-balance-sheet`, created 2026-11-02 morning). RAID is therefore **WP 40**, repository `wp40_uqo.git`
		12	+ (to be created on first push), directory `finance/wp40_raid/` as requested by the charter.
		13	+- Form standard: `HARMONISATION.md` — `paper/main.tex` with the `\WP*` macros, `sections/titlepage.tex` copied verbatim
		14	+ from `_modele-working-paper/`, natbib `[authoryear,round,semicolon]` + `aer.bst`, `\WPdate` = `Month YYYY`,
		15	+ `latexmk` Makefile, `paper/main.pdf` committed, data never committed, README with fixed section order
		16	+ (TL;DR, Status, What this paper does, Headline results, Layout, Quick start, Data, Methodology, Figures & tables,
		17	+ Limitations, Citation, Author, Notes).
		18	+- Prior volatility research to build on (not to re-do): WP 12 `finance/realized-volatility-horse-race` (HAR family,
		19	+ GARCH/GJR/EGARCH/Realized GARCH, 25 assets, 5-min subsampled RV target, QLIKE/DM/MCS; Python, `src/wp12`),
		20	+ WP 6 `intraday-volatility-connectedness` (Diebold–Yilmaz on intraday FX/fixed income), WP 15 `intraday-fragility`,
		21	+ WP 39 `invisible-balance-sheet` (first FinData OS consumer: rate limiter, export caps, duplicate last session).
		22	+ Systems-paper precedents in the same repository: WP 33 TITAN-MoE (`ia/titan-moe`: raw logs under `benchmarks/raw/`,
		23	+ `artifacts/manifest.json` with SHA-256 per artifact, hypotheses with bootstrap CIs) and WP 30.
		24	+- No existing CMake/C++ template in the repository: every prior WP is Python/LaTeX. RAID introduces the first C++/Metal
		25	+ build; it reuses only the LaTeX template, README standard and git conventions.
		26	+- Git practice: `main` branch, commit messages in English, `paper/main.pdf` tracked, SSRN uploads tracked in
		27	+ `_soumissions/`.
		28	+
		29	+## 2. FinData OS (mandatory data API) — live audit
		30	+
		31	+- Base `https://findata.spboucher.ai/api/v1`, OpenAPI 3.1 snapshot saved to `docs/evidence/findata_openapi_2026-11-02.json`
		32	+ (45 routes). `/health` → `status: ok`, Postgres ok, backup verified 9.6 h old, lake 564.65 GB curated / 303 GB free,
		33	+ cold root `/Volumes/ssd/findata` (2.98 TB free), SSD SMART verified.
		34	+- Catalog: 64 active datasets. Relevant for RAID: `market_bars.stock.1m.raw` (6.70 G rows, 2000-01-03 → 2026-10-30,
		35	+ 16 195 symbols incl. `-DELISTED`), `market_bars.etf.1m.raw` (908 M rows), `market_bars.stock.5m.raw` (2.06 G),
		36	+ `market_bars.stock.1d.raw` (21 M), `market_bars.index.1m.raw` (204 M, 2008→), futures continuous/contracts, FX, crypto,
		37	+ `options.eod` (4.23 G rows), `fundamentals.*`. `market_ticks` = **unsupported** (vendor provides bars only):
		38	+ Tier C order-flow / Hawkes methods have no admissible input in this project.
		39	+- Bars endpoint (`GET /market/bars`): `interval ∈ {1m,5m,1d}`, `adjustment ∈ {raw,split,split_div}`, `format ∈
		40	+ {json,arrow,csv}`, `limit` default 5 000 (max 10 000), cursor pagination, `version` pin. Verified Arrow IPC stream
		41	+ response: `content-type: application/vnd.apache.arrow.stream`, headers `x-dataset-id`, `x-dataset-version` (5162 for
		42	+ ETF 1m), `x-request-id`; schema v1 with metadata `findata.timestamp_convention = bar_open`. SPY 2024-03-08 → 03-11
		43	+ (DST weekend): 794 + 805 bars, first bar 09:00 UTC (= 04:00 EST) on 03-08, last bar 23:59 UTC (= 19:59 EDT) on 03-11:
		44	+ DST handled at the source; extended session 04:00–19:59 ET.
		45	+- Sessions endpoint (`GET /market/sessions`): per-session `bar_count` vs `expected_bars` (391 regular; **211 on the
		46	+ 2024-11-29 early close**, with extended bars until 16:59 that day). This is the calendar truth RAID must reproduce.
		47	+- Exports (`POST /exports` → 202 → poll → download): SPY+QQQ 1m January 2024 = 34 607 rows, 0.65 MB Parquet, ready in
		48	+ 1.5 s, download 0.3 s; response records `snapshot.version = 5162`, `files_scanned`, `bytes`, `row_count`; Parquet
		49	+ key-value metadata carries the schema version and timestamp convention. SHA-256 of the file is recorded by RAID.
		50	+ WP 39 found: ≤ 12 years per export request (HTTP 413 beyond), last lake session may appear twice (base + delta
		51	+ layouts) → RAID deduplicates on `(instrument_id, timestamp_utc)` and reports it in the QualityReport.
		52	+- Query playground (`POST /query`): one read-only statement, 30 s, ≤ 100 000 rows; views carry partition columns
		53	+ `symbol`, `year`; `/query/views` lists them. Used only for coverage/universe selection, never for bar transport.
		54	+- Fundamentals (`GET /fundamentals/{entity}?as_of=`): PIT semantics documented; `available_at = filing_date + 1 day`
		55	+ is a proxy; no delisted entities (survivorship) — relevant only if a HAR-X design uses accounting covariates (not planned
		56	+ for the methods paper).
		57	+- Rate limit: 600 req/min per key (no `RateLimit-*` headers exposed; WP 39 observed HTTP 429 with `Retry-After`).
		58	+ RAID client: token bucket ≤ 5 req/s, exponential backoff on 429/5xx, bounded concurrency (≤ 4), honours `Retry-After`.
		59	+- License: FirstRate Data private use — no redistribution of raw bars; `license_class = frd-private`. Consequence:
		60	+ real-data experiment inputs live in an ignored cache (`datasets/cache/`), results/figures publish only aggregates;
		61	+ the repository ships synthetic fixtures only. Attribution "FirstRate Data via FinData OS" in the paper.
		62	+- API key for this project: the owner's Claude Code key (`~/Desktop/Cluster/secrets/findata/api-key-claude-code.txt`,
		63	+ scopes read:market, read:fundamentals, query, export, admin). RAID never needs admin scope; a least-privilege key
		64	+ can be created later with `findata create-key` on M3U96a (the `POST /admin/keys` route returned 500 for WP 39).
		65	+
		66	+## 3. Hardware and toolchains (measured)
		67	+
		68	+### Laptop (primary development and benchmark machine)
		69	+Apple M5 Max, 18 CPU cores (6 P + 12 E as reported by `hw.perflevel*`), **40 GPU cores**, 48 GB unified memory, macOS 27.0
		70	+(26A5425a), Xcode 27.2 (27B52), Apple clang 21.0.0, SDK 27.2, Metal 4, GPU families Apple1–Apple10, `maxBufferLength`
		71	+28 753 MB, `recommendedMaxWorkingSetSize` 38 338 MB, threadgroup max 1 024, SIMD width 32, unified memory. Metal
		72	+Toolchain (offline `metal`/`metallib`, version 32023.922) was **absent** and was downloaded during the audit
		73	+(`xcodebuild -downloadComponent MetalToolchain`). Runtime shader compilation (`newLibraryWithSource:`) works without it
		74	+(34.6 ms for a trivial kernel, first compile). **`double` is rejected by the Metal compiler** (verified: "'double' is
		75	+not supported in Metal") — all GPU numerics are FP32 (with compensated/pairwise reductions) and FP64 lives on the CPU.
		76	+Disk 1.0 TB free. Load average 2.1 (27 days uptime). Tools: cmake 4.3.2, uv 0.11.7, Python 3.14.4 system / 3.12.13 for
		77	+the project venv, R 4.6.1, pandoc 3.9, TeX Live 2026, Homebrew openblas 0.3.33, libomp 22.1.8, fmt 12.1.
		78	+Installed for RAID during the audit: Homebrew `eigen`, `apache-arrow`, `catch2`, `nlohmann-json`, `ninja`
		79	+(see `docs/capabilities.md` for versions once the install finished); R `highfrequency` 1.0.3, `rugarch` 1.5.6, `xts` 0.14.3;
		80	+Python venv `.venv` (3.12): nanobind 3.1.0, numpy 2.5.4, pyarrow 26.0.0, arch 8.0.0, statsmodels 0.15.0, scipy 1.18.1,
		81	+polars 2.1.0, duckdb 2.0.0, exchange-calendars 4.13.2.
		82	+
		83	+### MacLustr nodes (probe 2026-11-02 14:1x ET, `docs/evidence/nodes/*.txt`)
		84	+22 of 29 nodes reachable over SSH; **unreachable (timeout): m2m16, M2M32, M2M32b, M4M36, m4ma, m4mb, m4mc** — excluded
		85	+from any plan until they answer `mld discover`. All reachable nodes report Metal 4 and no thermal/performance warning.
		86	+
		87	+| Node | Chip | CPU | GPU cores | RAM | macOS | Xcode / CLT | Load 1m | Free disk | PM2 apps | Role for RAID |
		88	+|---|---|---:|---:|---:|---|---|---:|---:|---:|---|
		89	+| M1M32 | M1 Max | 10 | 24 | 32 | 26.5.1 | Xcode 26.4.1 | 0.94 | 197 Gi | gateway | E7 generation point (M1), short runs only (gateway host) |
		90	+| M2M32c | M2 Max | 12 | 30 | 32 | 26.5.1 | CLT | 1.08 | 416 Gi | 0 | E7 (M2 Max) |
		91	+| M2U64 | M2 Ultra | 24 | 60 | 64 | 26.5 | Xcode 26.6 | 1.17 | 315 Gi | apps (spb-cloud, uqo-eval…) | E7 (M2 Ultra), off-hours |
		92	+| M3BA24 / M3BA16 | M3 | 8 | 10 | 24 / 16 | 26.5.1 | CLT | 1.1–1.3 | 409–420 Gi | 0 | E7 (M3 base, laptops) |
		93	+| M3U96a | M3 Ultra | 28 | 60 | 96 | 26.6 | Xcode 26.6 | **5.96** | 281 Gi | 17 (FinData OS host) | **not used for benchmarks** (production FinData host, loaded) |
		94	+| M3U96b | M3 Ultra | 28 | 60 | 96 | 26.4 | Xcode 26.6 | 1.80 | 395 Gi | 8 | E7 (M3 Ultra), largest-panel runs |
		95	+| M4M64a / M4M64b | M4 Max | 16 | 40 | 64 | 26.5.1 | Xcode 26.6 | 1.2–1.5 | 540 / **54 Gi** | 10 / 11 | avoid (agentd / Ka hosts; M4M64b low disk) |
		96	+| M4M64c | M4 Max | 16 | 40 | 64 | 26.4 | CLT | 1.94 | 318 Gi | 0 | E7 (M4 Max) |
		97	+| M4BP48 / M4BP36 | M4 Max | 16 / 14 | 40 / 32 | 48 / 36 | 26.5.1 | CLT | 1.2 | 843 Gi | 0 | E7 alternates |
		98	+| m4mm / m4mn | M4 Pro | 12 | 16 | 48 | 26.5.1 | CLT | 1.4–1.8 | 370 Gi | 0 | E7 (M4 Pro) |
		99	+| m4md / m4ml | M4 | 10 | 10 | 16 | 26.6.2 / 26.7 | CLT | 1.6–1.8 | 180–195 Gi | 0 | E7 (M4 base, 16 GB memory-pressure case) |
		100	+| M5M36 | M5 Max | 18 | 32 | 36 | 27.0 | CLT (SDK 27.0) | 1.22 | 355 Gi | 0 | E7 (M5 Max, 32-core GPU variant) |
		101	+| m5ma | M5 Pro | 15 | 16 | 24 | 27.0 | CLT | 1.13 | 382 Gi | 0 | E7 (M5 Pro) |
		102	+| m6ma / m6mb | M6 | 12 | 12 | 24 | 27.0 | CLT | 0.9–1.2 | 395 Gi | 0 | E7 (M6 base, newest generation) |
		103	+| m2m8a / m2m8b | M2 | 8 | 10 | 8 | 26.5.1 | CLT / none | 1.5 | 196–425 Gi | 0 | 8 GB memory-pressure stress (m2m8b has no developer tools → binary bundle only) |
		104	+
		105	+Observations that shape the plan: (i) most nodes have only the Command Line Tools and no Homebrew in the non-interactive
		106	+SSH `PATH` (cmake/ninja/brew empty in the probe) → E7 ships a **self-contained arm64 bundle** (static Eigen, bundled
		107	+`.metallib` + MSL source for runtime fallback, no Arrow dependency at run time) built on the laptop with
		108	+`-mmacosx-version-min=26.0`; nothing is installed on the nodes; (ii) GPU core counts span 10 → 60, memory 8 → 96 GB:
		109	+the generation sweep E7 covers M1 Max, M2 (base/Max/Ultra), M3 (base/Ultra), M4 (base/Pro/Max), M5 (Pro/Max), M6;
		110	+(iii) M3U96a (FinData host, load 6) and M4M64a/b (Ka/agentd hosts) are excluded from timing runs.
		111	+
		112	+## 4. Competing implementations (versions verified locally)
		113	+
		114	+| Software | Version | Role in RAID validation |
		115	+|---|---|---|
		116	+| R `highfrequency` | 1.0.3 (Boudt, Kleen & Sjørup, JSS 2022) | reference for RV, BPV, semivariance, realized quarticity, rCov, HAR (`HARmodel`), BNS jump test |
		117	+| R `rugarch` | 1.5.6 (Ghalanos) | reference for GARCH(1,1), GJR, EGARCH (Gaussian / Student-t) estimates, SE, forecasts |
		118	+| Python `arch` | 8.0.0 (Sheppard) | second GARCH reference; HAR via OLS; bootstrap (stationary/circular block) |
		119	+| Python `statsmodels` | 0.15.0 | OLS/WLS, HC0–HC3, HAC (Newey–West, Bartlett), cluster-robust covariance |
		120	+| NumPy / SciPy | 2.5.4 / 1.18.1 | FP64 reference linear algebra (QR/SVD/Cholesky), optimizers |
		121	+| Polars / DuckDB / pyarrow | 2.1.0 / 2.0.0 / 26.0.0 | columnar baselines for ingestion and rolling computations |
		122	+| Eigen (C++) | Homebrew, see capabilities | CPU backend dense algebra |
		123	+| Apple Accelerate | macOS 27 | BLAS/LAPACK (`-framework Accelerate`, `ACCELERATE_NEW_LAPACK`) |
		124	+| Julia | not installed | not used (listed in the charter; optional reference, not required for gates) |
		125	+
		126	+Mathematical equivalence is established per method in `docs/methods/registry.md` (Phase 4), with the exact option
		127	+settings of each competitor (e.g. `highfrequency::rBPCov` uses the µ₁⁻² = π/2 constant and no finite-sample factor
		128	+by default; `rugarch` uses variance targeting off and sample-variance initialisation unless told otherwise).
		129	+
		130	+## 5. Licensing
		131	+
		132	+- RAID code: MIT. Dependencies: Eigen (MPL-2.0), Apache Arrow (Apache-2.0), nanobind (BSD-3), Catch2 (BSL-1.0),
		133	+ nlohmann/json (MIT), Accelerate and Metal (Apple proprietary system frameworks, linked, not redistributed).
		134	+- Metal is proprietary: the GPU backend is optional (`RAID_ENABLE_METAL`), the CPU backend builds on Linux without it.
		135	+- Data: FirstRate Data private-use via FinData OS — no raw bars in git, synthetic fixtures in CI, attribution in paper.
		136	+- Reference software licenses do not affect RAID (used only to produce validation tables).

added docs/capabilities.md +41 −0

				@@ -0,0 +1,41 @@
		1	+# docs/capabilities.md — measured capabilities (2026-11-02)
		2	+
		3	+## Metal (laptop, Apple M5 Max)
		4	+| Property | Value | How measured |
		5	+|---|---|---|
		6	+| Device name | Apple M5 Max | `MTLCreateSystemDefaultDevice().name` |
		7	+| Unified memory | yes | `hasUnifiedMemory` |
		8	+| `maxBufferLength` | 28 753 MB | property |
		9	+| `recommendedMaxWorkingSetSize` | 38 338 MB | property |
		10	+| `maxThreadsPerThreadgroup` | 1 024 | property |
		11	+| `threadExecutionWidth` (SIMD) | 32 | pipeline state |
		12	+| GPU families | Apple1 … Apple10, Metal3 | `supportsFamily:` |
		13	+| Ray tracing / 32-bit float filtering / argument buffers | yes / yes / tier reported | properties |
		14	+| FP64 in shaders | **no** (compiler error "'double' is not supported in Metal") | runtime + offline compile of a `double` kernel |
		15	+| Runtime MSL compile (trivial kernel, cold) | 34.6 ms | `newLibraryWithSource:` wall-clock |
		16	+| Offline toolchain | Apple metal 32023.922, target air64-apple-darwin27.0.0 | installed during audit |
		17	+| 1 M-element elementwise square | 0.029 ms GPU time (`GPUEndTime − GPUStartTime`) | `docs/evidence` test program |
		18	+
		19	+Consequences for the design: FP32 everywhere on GPU with compensated (Kahan/Neumaier) and pairwise reductions, FP64
		20	+accumulation of sufficient statistics on CPU when the reduction length exceeds a documented threshold, and GPU results
		21	+always compared to the CPU FP64 reference with method-specific tolerances. "Precision" is reported in every Estimate.
		22	+
		23	+## CPU (laptop)
		24	+Apple M5 Max 18 cores (6 performance + 12 efficiency), 48 GB; Accelerate BLAS/LAPACK; Apple clang 21 supports C++20 and
		25	+`-O3 -mcpu=apple-m4` (`apple-m5` not yet in clang 21's list; measured flags recorded in the benchmark manifest).
		26	+
		27	+## Cluster
		28	+See `docs/audit.md` §3 (22 reachable nodes, Metal 4 on all, GPU cores 10–60, RAM 8–96 GB, most nodes without Homebrew
		29	+in the SSH PATH, m2m8b without developer tools). Inter-node SSH works only on LAN IPs (Tailscale ACL); the laptop reaches
		30	+nodes by alias.
		31	+
		32	+## FinData OS
		33	+See `docs/audit.md` §2 and `docs/evidence/findata_openapi_2026-11-02.json`. Measured: Arrow IPC page of 1 599 bars
		34	+= 246 KB; export of 34 607 rows ready in 1.5 s; dataset versions pinned (ETF 1m = 5162 at audit time).
		35	+
		36	+## Software versions (laptop, audit time)
		37	+cmake 4.3.2 · Apple clang 21.0.0 (clang-2100.4.1.1) · Xcode 27.2 · macOS 27.0 · Python 3.12.13 (venv) · nanobind 3.1.0 ·
		38	+numpy 2.5.4 · pyarrow 26.0.0 · arch 8.0.0 · statsmodels 0.15.0 · scipy 1.18.1 · polars 2.1.0 · duckdb 2.0.0 ·
		39	+exchange-calendars 4.13.2 · R 4.6.1 · highfrequency 1.0.3 · rugarch 1.5.6 · TeX Live 2026 · latexmk 4.88.
		40	+Homebrew C++ packages installed during Phase 0: filled in by `scripts/record_environment.sh` into
		41	+`experiments/manifests/environment_laptop.json` (versions are read from `brew list --versions`, never typed by hand).

added docs/evidence/findata_openapi_2026-11-02.json +1 −0

				@@ -0,0 +1 @@
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Response","content":{"application/json":{"schema":{}}}}}}},"/api/v1/health/summary":{"get":{"tags":["health"],"summary":"Summary","operationId":"summary_api_v1_health_summary_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}},"/api/v1/admin/jobs":{"get":{"tags":["admin"],"summary":"Jobs","operationId":"jobs_api_v1_admin_jobs_get","parameters":[{"name":"status","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Status"}},{"name":"kind","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Kind"}},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":1000,"default":100,"title":"Limit"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"post":{"tags":["admin"],"summary":"Enqueue","operationId":"enqueue_api_v1_admin_jobs_post","requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/JobIn"}}}},"responses":{"201":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/admin/jobs/{job_id}/retry":{"post":{"tags":["admin"],"summary":"Retry","operationId":"retry_api_v1_admin_jobs__job_id__retry_post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/admin/jobs/{job_id}/cancel":{"post":{"tags":["admin"],"summary":"Cancel","operationId":"cancel_api_v1_admin_jobs__job_id__cancel_post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/admin/keys":{"get":{"tags":["admin"],"summary":"Keys","operationId":"keys_api_v1_admin_keys_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}},"post":{"tags":["admin"],"summary":"Create Key","operationId":"create_key_api_v1_admin_keys_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/KeyIn"}}},"required":true},"responses":{"201":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/admin/keys/{key_id}":{"delete":{"tags":["admin"],"summary":"Revoke Key","operationId":"revoke_key_api_v1_admin_keys__key_id__delete","parameters":[{"name":"key_id","in":"path","required":true,"schema":{"type":"string","title":"Key Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/admin/audit":{"get":{"tags":["admin"],"summary":"Audit Log","operationId":"audit_log_api_v1_admin_audit_get","parameters":[{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":2000,"default":200,"title":"Limit"}},{"name":"action","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Action"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/admin/storage":{"get":{"tags":["admin"],"summary":"Storage","operationId":"storage_api_v1_admin_storage_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}},"/api/v1/admin/settings":{"get":{"tags":["admin"],"summary":"Get Settings ","operationId":"get_settings__api_v1_admin_settings_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}},"/api/v1/admin/settings/tracked-tickers":{"put":{"tags":["admin"],"summary":"Tracked","operationId":"tracked_api_v1_admin_settings_tracked_tickers_put","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/TrackedIn"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/admin/discover":{"post":{"tags":["admin"],"summary":"Discover","description":"Sanitized live vendor probe (no bulk download).","operationId":"discover_api_v1_admin_discover_post","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}},"/api/v1/assistant/status":{"get":{"tags":["assistant"],"summary":"Status","operationId":"status_api_v1_assistant_status_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}},"/api/v1/assistant":{"post":{"tags":["assistant"],"summary":"Ask","operationId":"ask_api_v1_assistant_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AskIn"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/public/overview":{"get":{"tags":["public"],"summary":"Overview","description":"Served from a cache refreshed in the background every 5 minutes; the request path never runs the queries.","operationId":"overview_api_v1_public_overview_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}}},"components":{"schemas":{"AskIn":{"properties":{"question":{"type":"string","maxLength":2000,"minLength":3,"title":"Question"},"max_rows":{"type":"integer","maximum":5000.0,"minimum":1.0,"title":"Max Rows","default":200},"execute":{"type":"boolean","title":"Execute","default":true}},"type":"object","required":["question"],"title":"AskIn"},"CodeIn":{"properties":{"code":{"type":"string","title":"Code"}},"type":"object","required":["code"],"title":"CodeIn"},"ExportIn":{"properties":{"dataset_id":{"type":"string","title":"Dataset Id","default":"market_bars.stock.1m.raw"},"symbols":{"items":{"type":"string"},"type":"array","maxItems":500,"minItems":1,"title":"Symbols"},"start":{"type":"string","format":"date","title":"Start"},"end":{"type":"string","format":"date","title":"End"},"adjustment":{"type":"string","enum":["raw","split","split_div"],"title":"Adjustment","default":"raw"},"format":{"type":"string","enum":["parquet","csv"],"title":"Format","default":"parquet"}},"type":"object","required":["symbols","start","end"],"title":"ExportIn"},"HTTPValidationError":{"properties":{"detail":{"items":{"$ref":"#/components/schemas/ValidationError"},"type":"array","title":"Detail"}},"type":"object","title":"HTTPValidationError"},"JobIn":{"properties":{"kind":{"type":"string","title":"Kind"},"dataset_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Dataset Id"},"params":{"additionalProperties":true,"type":"object","title":"Params"},"priority":{"type":"integer","title":"Priority","default":50}},"type":"object","required":["kind"],"title":"JobIn"},"KeyIn":{"properties":{"name":{"type":"string","maxLength":60,"minLength":1,"title":"Name"},"scopes":{"items":{"type":"string"},"type":"array","title":"Scopes"},"rate_limit_per_minute":{"type":"integer","maximum":10000.0,"minimum":1.0,"title":"Rate Limit Per Minute","default":600},"expires_at":{"anyOf":[{"type":"string","format":"date-time"},{"type":"null"}],"title":"Expires At"}},"type":"object","required":["name","scopes"],"title":"KeyIn"},"LoginIn":{"properties":{"email":{"type":"string","format":"email","title":"Email"},"password":{"type":"string","title":"Password"}},"type":"object","required":["email","password"],"title":"LoginIn"},"QueryIn":{"properties":{"sql":{"type":"string","maxLength":20000,"title":"Sql"},"max_rows":{"type":"integer","maximum":100000.0,"minimum":1.0,"title":"Max Rows","default":1000}},"type":"object","required":["sql"],"title":"QueryIn"},"TrackedIn":{"properties":{"tickers":{"items":{"type":"string"},"type":"array","title":"Tickers"}},"type":"object","required":["tickers"],"title":"TrackedIn"},"ValidationError":{"properties":{"loc":{"items":{"anyOf":[{"type":"string"},{"type":"integer"}]},"type":"array","title":"Location"},"msg":{"type":"string","title":"Message"},"type":{"type":"string","title":"Error Type"},"input":{"title":"Input"},"ctx":{"type":"object","title":"Context"}},"type":"object","required":["loc","msg","type"],"title":"ValidationError"}}}}
	\ No newline at end of file

added docs/evidence/nodes/M1M32.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=MacStudnpierre8
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M1 Max ncpu=10 mem_gb=32
		4	+xcode_path=/Applications/Xcode.app/Contents/Developer
		5	+xcodebuild=Xcode 26.4.1 Build version 17E202
		6	+clang=Apple clang version 21.0.0 (clang-2100.0.123.102)
		7	+sdk=26.4
		8	+Total Number of Cores: 24
		9	+Metal Support: Metal 4
		10	+load={ 0,94 1,16 1,17 }
		11	+disk_free=197Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3=Python 3.9.6 brew=
		15	+memfree_pct=95%

added docs/evidence/nodes/M2M32.txt +2 −0

				@@ -0,0 +1,2 @@
		1	+ssh: connect to host m2m32.maclustr.io port 22: Operation timed out
		2	+SSH_FAIL

added docs/evidence/nodes/M2M32b.txt +2 −0

				@@ -0,0 +1,2 @@
		1	+ssh: connect to host m2m32b.maclustr.io port 22: Operation timed out
		2	+SSH_FAIL

added docs/evidence/nodes/M2M32c.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=MacStudnpierre7
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M2 Max ncpu=12 mem_gb=32
		4	+xcode_path=/Library/Developer/CommandLineTools
		5	+xcodebuild=
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 30
		9	+Metal Support: Metal 4
		10	+load={ 1,08 1,11 1,08 }
		11	+disk_free=416Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3=Python 3.9.6 brew=
		15	+memfree_pct=95%

added docs/evidence/nodes/M2U64.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=MacStudonpierre
		2	+os=26.5 build=25F71
		3	+chip=Apple M2 Ultra ncpu=24 mem_gb=64
		4	+xcode_path=/Applications/Xcode.app/Contents/Developer
		5	+xcodebuild=Xcode 26.6 Build version 17F113
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 60
		9	+Metal Support: Metal 4
		10	+load={ 1,17 1,07 1,05 }
		11	+disk_free=315Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3=Python 3.9.6 brew=
		15	+memfree_pct=96%

added docs/evidence/nodes/M3BA16.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=MacBooknpierre2
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M3 ncpu=8 mem_gb=16
		4	+xcode_path=/Library/Developer/CommandLineTools
		5	+xcodebuild=
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 10
		9	+Metal Support: Metal 4
		10	+load={ 1,32 1,31 1,32 }
		11	+disk_free=409Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3=Python 3.9.6 brew=
		15	+memfree_pct=82%

added docs/evidence/nodes/M3BA24.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=MacBookonpierre
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M3 ncpu=8 mem_gb=24
		4	+xcode_path=/Library/Developer/CommandLineTools
		5	+xcodebuild=
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 10
		9	+Metal Support: Metal 4
		10	+load={ 1,12 1,22 1,23 }
		11	+disk_free=420Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3=Python 3.9.6 brew=
		15	+memfree_pct=94%

added docs/evidence/nodes/M3U96a.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=MacStudnpierre2
		2	+os=26.6 build=25G72
		3	+chip=Apple M3 Ultra ncpu=28 mem_gb=96
		4	+xcode_path=/Applications/Xcode.app/Contents/Developer
		5	+xcodebuild=Xcode 26.6 Build version 17F113
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 60
		9	+Metal Support: Metal 4
		10	+load={ 5,96 8,79 8,90 }
		11	+disk_free=281Gi
		12	+pm2=17
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake=cmake version 4.3.2 ninja= python3=Python 3.14.5 brew=Homebrew 6.0.22
		15	+memfree_pct=94%

added docs/evidence/nodes/M3U96b.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=m3u96b
		2	+os=26.4 build=25E5207k
		3	+chip=Apple M3 Ultra ncpu=28 mem_gb=96
		4	+xcode_path=/Applications/Xcode.app/Contents/Developer
		5	+xcodebuild=Xcode 26.6 Build version 17F113
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 60
		9	+Metal Support: Metal 4
		10	+load={ 1,80 1,89 1,94 }
		11	+disk_free=395Gi
		12	+pm2=8
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake=cmake version 4.3.2 ninja= python3=Python 3.14.4 brew=Homebrew 7.0.10
		15	+memfree_pct=92%

added docs/evidence/nodes/M4BP36.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=simonpierresMBP
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M4 Max ncpu=14 mem_gb=36
		4	+xcode_path=/Library/Developer/CommandLineTools
		5	+xcodebuild=
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 32
		9	+Metal Support: Metal 4
		10	+load={ 1,19 1,18 1,16 }
		11	+disk_free=843Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3=Python 3.9.6 brew=
		15	+memfree_pct=90%

added docs/evidence/nodes/M4BP48.txt +7 −0

				@@ -0,0 +1,7 @@
		1	+node=simonpirresMBP2
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M4 Max ncpu=16 mem_gb=48
		4	+xcode_path=/Library/Developer/CommandLineTools
		5	+xcodebuild=
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5

added docs/evidence/nodes/M4M36.txt +2 −0

				@@ -0,0 +1,2 @@
		1	+ssh: connect to host m4m36.maclustr.io port 22: Operation timed out
		2	+SSH_FAIL

added docs/evidence/nodes/M4M64a.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=MacStudnpierre6
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M4 Max ncpu=16 mem_gb=64
		4	+xcode_path=/Applications/Xcode.app/Contents/Developer
		5	+xcodebuild=Xcode 26.6 Build version 17F113
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 40
		9	+Metal Support: Metal 4
		10	+load={ 1,46 1,42 1,52 }
		11	+disk_free=540Gi
		12	+pm2=10
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake=cmake version 4.3.2 ninja= python3=Python 3.14.4 brew=Homebrew 7.0.9
		15	+memfree_pct=93%

added docs/evidence/nodes/M4M64b.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=MacStudnpierre4
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M4 Max ncpu=16 mem_gb=64
		4	+xcode_path=/Applications/Xcode.app/Contents/Developer
		5	+xcodebuild=Xcode 26.6 Build version 17F113
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 40
		9	+Metal Support: Metal 4
		10	+load={ 1,17 1,50 1,47 }
		11	+disk_free=54Gi
		12	+pm2=11
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake=cmake version 4.3.2 ninja= python3=Python 3.14.4 brew=Homebrew 7.0.9
		15	+memfree_pct=96%

added docs/evidence/nodes/M4M64c.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=M4M64c
		2	+os=26.4 build=25E246
		3	+chip=Apple M4 Max ncpu=16 mem_gb=64
		4	+xcode_path=/Library/Developer/CommandLineTools
		5	+xcodebuild=
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 40
		9	+Metal Support: Metal 4
		10	+load={ 1,94 1,91 1,91 }
		11	+disk_free=318Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3=Python 3.9.6 brew=
		15	+memfree_pct=95%

added docs/evidence/nodes/M5M36.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=M5M36
		2	+os=27.0 build=26A425
		3	+chip=Apple M5 Max ncpu=18 mem_gb=36
		4	+xcode_path=/Library/Developer/CommandLineTools
		5	+xcodebuild=
		6	+clang=Apple clang version 21.0.0 (clang-2100.3.34.2)
		7	+sdk=27.0
		8	+Total Number of Cores: 32
		9	+Metal Support: Metal 4
		10	+load={ 1,22 1,06 1,03 }
		11	+disk_free=355Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3=Python 3.9.6 brew=
		15	+memfree_pct=91%

added docs/evidence/nodes/m2m16.txt +2 −0

				@@ -0,0 +1,2 @@
		1	+ssh: connect to host m2m16.maclustr.io port 22: Operation timed out
		2	+SSH_FAIL

added docs/evidence/nodes/m2m8a.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=Minidesonpierre
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M2 ncpu=8 mem_gb=8
		4	+xcode_path=/Library/Developer/CommandLineTools
		5	+xcodebuild=
		6	+clang=Apple clang version 21.0.0 (clang-2100.1.1.101)
		7	+sdk=26.5
		8	+Total Number of Cores: 10
		9	+Metal Support: Metal 4
		10	+load={ 1,52 1,70 1,63 }
		11	+disk_free=425Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3=Python 3.9.6 brew=
		15	+memfree_pct=77%

added docs/evidence/nodes/m2m8b.txt +15 −0

				@@ -0,0 +1,15 @@
		1	+node=Minidesnpierre2
		2	+os=26.5.1 build=25F80
		3	+chip=Apple M2 ncpu=8 mem_gb=8
		4	+xcode_path=xcode-select: error: Unable to get active developer directory. Use `sudo xcode-select --switch path/to/Xcode.app` to set one (or see `man xcode-select`)
		5	+xcodebuild=
		6	+clang=
		7	+sdk=
		8	+Total Number of Cores: 10
		9	+Metal Support: Metal 4
		10	+load={ 1,53 1,52 1,48 }
		11	+disk_free=196Gi
		12	+pm2=
		13	+thermal=Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;
		14	+cmake= ninja= python3= brew=
		15	+memfree_pct=80%

added docs/evidence/nodes/m4ma.txt +2 −0

				@@ -0,0 +1,2 @@
		1	+ssh: connect to host m4ma.maclustr.io port 22: Operation timed out
		2	+SSH_FAIL

added docs/evidence/nodes/m4mb.txt +2 −0

				@@ -0,0 +1,2 @@
		1	+ssh: connect to host m4mb.maclustr.io port 22: Operation timed out
		2	+SSH_FAIL

added docs/evidence/nodes/m4mc.txt +2 −0

				@@ -0,0 +1,2 @@
		1	+ssh: connect to host m4mc.maclustr.io port 22: Operation timed out
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		5	+ "title": "Modeling and Forecasting Realized Volatility",
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		7	+ "year": 2003,
		8	+ "authors": "Andersen, Bollerslev, Diebold, Labys",
		9	+ "volume": "71",
		10	+ "page": "579-625"
		11	+ },
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		13	+ "key": "bns2004",
		14	+ "doi": "10.1093/jjfinec/nbh001",
		15	+ "title": "Power and Bipower Variation with Stochastic Volatility and Jumps",
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		21	+ },
		22	+ {
		23	+ "key": "bns2006",
		24	+ "doi": "10.1093/jjfinec/nbi022",
		25	+ "title": "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation",
		26	+ "journal": "Journal of Financial Econometrics",
		27	+ "year": 2005,
		28	+ "authors": "Barndorff-Nielsen",
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		31	+ },
		32	+ {
		33	+ "key": "corsi2009",
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		37	+ "year": 2008,
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		41	+ },
		42	+ {
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		44	+ "doi": "10.1016/0304-4076(86)90063-1",
		45	+ "title": "Generalized autoregressive conditional heteroskedasticity",
		46	+ "journal": "Journal of Econometrics",
		47	+ "year": 1986,
		48	+ "authors": "Bollerslev",
		49	+ "volume": "31",
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		56	+ "journal": "Econometrica",
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		62	+ {
		63	+ "key": "glosten1993",
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		65	+ "title": "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks",
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		71	+ },
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		371	+ "authors": "Fiorentini, Calzolari, Panattoni",
		372	+ "volume": "11",
		373	+ "page": "399-417"
		374	+ },
		375	+ {
		376	+ "key": "bollerslevwooldridge1992",
		377	+ "doi": "10.1080/07474939208800229",
		378	+ "title": "Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances",
		379	+ "journal": "Econometric Reviews",
		380	+ "year": 1992,
		381	+ "authors": "Bollerslev, Wooldridge",
		382	+ "volume": "11",
		383	+ "page": "143-172"
		384	+ },
		385	+ {
		386	+ "key": "ghalanos_rugarch",
		387	+ "doi": "10.32614/cran.package.rugarch",
		388	+ "title": "rugarch: Univariate GARCH Models",
		389	+ "journal": "CRAN: Contributed Packages",
		390	+ "year": 2011,
		391	+ "authors": "Galanos",
		392	+ "volume": null,
		393	+ "page": null,
		394	+ "publisher": "The R Foundation"
		395	+ }
		396	+]
	\ No newline at end of file

added docs/methods/registry.md +40 −0

				@@ -0,0 +1,40 @@
		1	+# Method registry (Tier A) — conventions, references, tolerances
		2	+
		3	+Status legend: ✅ validated against the named reference on the committed fixtures (tests/reference) · 🧪 implemented,
		4	+reference comparison pending · 🧭 experimental · ✖ not implemented. "Tolerance" is the pass criterion of the test.
		5	+
		6	+| Key | Definition | Inputs | Reference software & options | Tolerance | CPU | Metal | Status |
		7	+|---|---|---|---|---|---|---|---|
		8	+| `returns.log` / `returns.arith` | r_t = log(p_t/p_{t−1}) / p_t/p_{t−1}−1, within session; overnight = prev close → open; close-to-close daily | BarSeries (regular session) | numpy | exact (1e−15) | ✅ | n/a | ✅ |
		9	+| `calendar.xnys` | NYSE trading days 2000–2026 (rules + special closures), early closes 13:00, DST America/New_York | dates | exchange_calendars 4.13 XNYS | exact set equality (sessions, holidays, early closes 2000–2026) | ✅ | n/a | ✅ |
		10	+| `bars.validate` | OHLC consistency, positivity, grid alignment, session membership, coverage vs 391/211, price jumps > 25 % | BarSeries | FinData `/market/sessions` semantics | counts | ✅ | n/a | ✅ (synthetic) |
		11	+| `bars.aggregate` | bins anchored at 09:30 ET, closing print joins last bin, no empty bins, OHLC/sum/count | BarSeries | FinData derived 5m (`trade_count`, `vwap`) | exact for OHLCV | ✅ | n/a | 🧪 (vs FinData 5m pending Phase 2) |
		12	+| `realized.rv` | Σ r² | returns CSR | highfrequency `rCov` (univariate) / numpy | 1e−12 rel (numpy) | ✅ | Phase 3 | ✅ |
		13	+| `realized.bpv` | μ₁⁻²·M/(M−1)·Σ|r_j||r_{j−1}| (finite-sample factor optional) | returns CSR | highfrequency `rBPCov` (makeReturns=FALSE, scaled) | 1e−12 rel (numpy); R comparison Phase 4 | ✅ | Phase 3 | ✅ |
		14	+| `realized.rsv` | Σ r² 1{r<0}, Σ r² 1{r>0} | returns CSR | highfrequency `rSVar` | 1e−12 rel | ✅ | Phase 3 | ✅ |
		15	+| `realized.rq` | (M/3) Σ r⁴ | returns CSR | highfrequency `rQuar` | 1e−12 rel | ✅ | Phase 3 | ✅ |
		16	+| `realized.jump` | max(RV − BPV, 0) (proxy, no test statistic) | — | ABD 2007 | — | ✅ | — | ✅ |
		17	+| `realized.tripv` | μ_{4/3}⁻³·M/(M−2)·Σ Π|r|^{4/3} | returns CSR | highfrequency `rTPQuar` (differs: quarticity) — definition from BNS 2004 | 1e−12 (self) | ✅ | — | 🧪 |
		18	+| `realized.rcov` | Σ r_j r_j' on synchronized returns; corr from diag | T×N returns | highfrequency `rCov` | Phase 4 | ✅ | Phase 3 | 🧪 |
		19	+| `ols` / `wls` / `ridge` | Householder QR (col-pivoting), SVD fallback, ridge by augmentation; R², adj. R², leverage | X (n×k), y | statsmodels 0.15 OLS/WLS | 1e−10 rel (coef), 1e−9 (SE) | ✅ | sufficient stats Phase 3 | ✅ |
		20	+| `cov.classical` | s² (X'X)⁻¹, s² = RSS/(n−k) | — | statsmodels nonrobust | 1e−9 | ✅ | — | ✅ |
		21	+| `cov.hc0..hc3` | White sandwich; HC1 n/(n−k); HC2 1/(1−h); HC3 1/(1−h)² | — | statsmodels HC0–HC3 | 1e−9 | ✅ | — | ✅ |
		22	+| `cov.hac_bartlett` | Newey–West with Bartlett weights, L given or ⌊4(n/100)^{2/9}⌋, no small-sample correction | — | statsmodels HAC (maxlags=L, use_correction=False) | 1e−9 | ✅ | — | ✅ |
		23	+| `cov.cluster` | one-way CGM with G/(G−1)·(n−1)/(n−k) | cluster ids | statsmodels cluster (default correction) | 1e−9 | ✅ | — | ✅ |
		24	+| `inference.t` | two-sided p from t(df_resid) (regularised incomplete beta); df ≤ 0 → normal | — | scipy.stats.t | 1e−12 | ✅ | — | ✅ |
		25	+| `ols.rolling` | window-by-window QR; step parameter | X, y | statsmodels RollingOLS | self-consistency test; Phase 4 vs statsmodels | ✅ | Phase 3 | 🧪 |
		26	+| `har.*` | HAR-RV, log-HAR, HAR-X, HARQ (BPQ 2016): RV_{t+h} regressed on RV_t, mean(RV_{t−4..t}), mean(RV_{t−21..t}) [, √RQ_t·RV_t]; causal lags; h ∈ {1,5,22} with overlapping targets → HAC(h−1 lags minimum) | daily RV panel | highfrequency `HARmodel`, statsmodels | Phase 4 | Phase 4 | Phase 4 | ✖ (Phase 4) |
		27	+| `garch.garch.gaussian` | σ²_t = ω + α ε²_{t−1} + β σ²_{t−1}; σ²_0 = sample variance of ε; constraints ω>0, α,β≥0, α+β<1 | returns | arch 8.0 `arch_model(vol="GARCH", rescale=False)` (backcast initialisation ≠ ours), rugarch (Phase 4) | loglik 5e−4 rel; params 5 % rel + 0.01 abs; forecasts 5 % | ✅ | likelihood batch Phase 3 | ✅ |
		28	+| `garch.gjr.*` | + γ 1{ε<0} ε²; α+γ ≥ 0; α+β+γ/2 < 1 | returns | arch GJR (o=1) | same | ✅ | — | ✅ |
		29	+| `garch.egarch.*` | ln σ²_t = ω + α(|z|−E|z|) + γ z + β ln σ²_{t−1}; |β|<1; h>1 forecasts by simulation (20 000 paths, seeded) | returns | arch EGARCH | same (params); forecasts h>1 not compared | ✅ | — | ✅ |
		30	+| `garch.*.student_t` | standardized t, ν ∈ (2.05, 500) (arch bounds); E|z| via lgamma | returns | arch dist="t" | same; ν skipped when > 100 (unidentified) | ✅ | — | ✅ |
		31	+| `garch.cov` | QMLE sandwich H⁻¹ OPG H⁻¹ (Bollerslev–Wooldridge 1992), Hessian, OPG — numerical derivatives | — | arch robust/classic | Phase 4 (SE comparison) | ✅ | — | 🧪 |
		32	+| `bootstrap.stationary` / `circular` | Politis–Romano 1994 / circular block; replica generation + statistic | series | arch.bootstrap | Phase 4 | Phase 4 | Phase 3/4 | ✖ |
		33	+
		34	+Conventions that differ from references and are **documented, not hidden**:
		35	+1. GARCH initial variance: RAID uses the sample variance of ε; `arch` uses a backcast (exponentially weighted) and
		36	+ `rugarch` the sample variance by default — hence the 5e−4 log-likelihood tolerance. A `backcast` option is planned so
		37	+ that the comparison can be made exact (Phase 4).
		38	+2. BPV finite-sample factor M/(M−1): on by default (as in `highfrequency` when scaling by `makeReturns`); can be off.
		39	+3. HAC default lag ⌊4(n/100)^{2/9}⌋ is a rule of thumb (Newey–West 1994 style); the paper always states L explicitly.
		40	+4. Student-t ν bounds (2.05, 500) follow `arch`; ν at the upper bound triggers a warning (Gaussian data).

added docs/novelty_matrix.md +57 −0

				@@ -0,0 +1,57 @@
		1	+# docs/novelty_matrix.md — what exists, what RAID adds, what is *not* new (Phase 0, 2026-11-02)
		2	+
		3	+Legend — **E** engineering novelty (new implementation strategy, measured), **M** methodological novelty (new estimator
		4	+or inference result), **=** equivalent to existing software (RAID must match it), **×** not offered. Claims marked E are
		5	+only valid once the corresponding experiment (E1–E7) produces the number; nothing in this table is a result.
		6	+
		7	+| Capability | R highfrequency 1.0.3 | R rugarch 1.5.6 | Python arch 8.0 | statsmodels 0.15 | Polars / DuckDB | RAID (planned) | Novelty class |
		8	+|---|---|---|---|---|---|---|---|
		9	+| RV, BPV, semivariance, quarticity, rCov from bars | yes (`rCov`, `rBPCov`, `rSVar`, `rQuar`) | × | × | × | by hand (SQL/expr) | CPU FP64 + Metal FP32 batched over thousands of series | **E** (batched GPU, fused rolling) ; **=** definitions |
		10	+| Calendar-aware aggregation, early closes, DST, 16:00 print | partial (`aggregateTS`, user calendars) | × | × | × | manual | explicit MarketCalendar from XNYS rules, FinData sessions as truth | **E** (correctness tooling), not M |
		11	+| HAR-RV / log-HAR / HAR-X / HARQ | `HARmodel` (HAR-RV, HARQ, HAR-J/CJ) | × | OLS by hand | OLS + HAC | × | batched rolling HAR with HAC/HC inference, GPU sufficient statistics | **E** (batched, GPU), **=** definitions (Corsi 2009; Bollerslev–Patton–Quaedvlieg 2016) |
		12	+| OLS/WLS/ridge, HC0–3, HAC (Newey–West), cluster-robust | × | × | OLS + HAC in `arch.unitroot`/`covariance` | yes | × | QR/SVD CPU reference; GPU batched X'X/X'y accumulation | **=** inference; **E** batched accumulation |
		13	+| GARCH(1,1), GJR, EGARCH, Gaussian / Student-t | × | yes (`ugarchfit`) | yes (`arch_model`) | × | × | CPU reference optimizer; GPU batched likelihood evaluation across series / parameter proposals | **E** (batched likelihood on Metal), **=** models |
		14	+| Realized GARCH, HEAVY, DCC, GARCH-MIDAS | HEAVY (`HEAVYmodel`) | realized GARCH, rmgarch DCC | HEAVY ×, DCC × | × | × | Tier B after Tier A | **=** (if done) |
		15	+| Diebold–Yilmaz connectedness, generalized FEVD | × | × | × | VAR + FEVD (orthogonalized; generalized by hand) | × | Tier B batched rolling VAR + GFEVD | **E** (rolling batched), **=** method (WP 6 precedent) |
		16	+| Stationary / circular block bootstrap | × | × | yes (`arch.bootstrap`) | × | × | GPU replica generation + batched statistic evaluation | **E** |
		17	+| Pre-averaging, realized kernels, multi-scale RV | yes (`rKernelCov`, `rAVGCov`, `rTSCov`) | × | × | × | × | Tier C, only if validated against highfrequency; 1-min bars limit their relevance | **=** at best |
		18	+| Arrow/Parquet → estimator zero-copy | × (xts) | × | × (pandas) | × | native columnar | Arrow C++ views over FinData exports feeding CPU/GPU without copies where layout allows | **E** (measured copies / RSS, E6) |
		19	+| Python bindings with GIL release, Arrow/NumPy interop | — | — | — | — | — | nanobind | engineering, standard practice |
		20	+| Device discovery, CPU fallback, precision policy, provenance in every estimate | — | — | — | — | — | yes | **E** (reproducibility tooling) |
		21	+
		22	+## What the paper can legitimately claim (if the experiments support it)
		23	+1. A measured break-even analysis Metal vs multicore CPU for realized measures, rolling regressions, batched GARCH
		24	+ likelihoods and bootstrap replicas on Apple Silicon, including shader compilation, synchronization and transfer, with
		25	+ explicit CPU-wins regions (RQ1).
		26	+2. Evidence that batching across series preserves estimator accuracy and inference (coverage, convergence rates
		27	+ identical to the FP64 reference within tolerance) while raising throughput (RQ2).
		28	+3. Evidence on copies, peak RSS and end-to-end latency of an Arrow/Parquet-to-estimator pipeline vs
		29	+ pandas/Polars/DuckDB baselines (RQ3).
		30	+4. A validated, open C++20 library whose Tier A estimators reproduce highfrequency / rugarch / arch / statsmodels to
		31	+ documented tolerances (reference tables in the appendix).
		32	+
		33	+## What the paper must not claim
		34	+- New econometric estimators or new asymptotic results (none planned; **M = none**).
		35	+- Microstructure-noise robustness from 1-minute OHLCV bars (no trades/quotes; realized kernels/pre-averaging on 1-min
		36	+ bars are a sampling-frequency exercise, not noise correction at tick level).
		37	+- Order-flow or order-book content (not inferable from bars).
		38	+- Energy savings unless `powermetrics` (root) or an external meter is actually used; `powermetrics` requires sudo and
		39	+ is optional in E7 (documented as "measured" only when run).
		40	+- GPU "speedups" without the equivalent CPU multicore implementation and the same convergence tolerance.
		41	+
		42	+## Prior work positioning (verified DOIs in `docs/literature/crossref_resolved.json`)
		43	+Realized measures: Andersen, Bollerslev, Diebold & Labys (2003, Econometrica); Barndorff-Nielsen & Shephard (2004, 2006,
		44	+JFEC); Barndorff-Nielsen, Kinnebrock & Shephard (2010, OUP volume); Andersen, Bollerslev & Diebold (2007, REStat);
		45	+Huang & Tauchen (2005, JFEC); Liu, Patton & Sheppard (2015, J. Econometrics). Noise-robust: Zhang, Mykland &
		46	+Aït-Sahalia (2005, JASA); Barndorff-Nielsen, Hansen, Lunde & Shephard (2008, Econometrica); Jacod et al. (2009, SPA).
		47	+HAR: Corsi (2009, JFEC); Bollerslev, Patton & Quaedvlieg (2016, J. Econometrics); Patton & Sheppard (2015, REStat).
		48	+GARCH: Engle (1982); Bollerslev (1986); Nelson (1991); Glosten, Jagannathan & Runkle (1993); Bollerslev & Wooldridge
		49	+(1992, Econometric Reviews); Fiorentini, Calzolari & Panattoni (1996, JAE, analytic derivatives); Hansen & Lunde (2005,
		50	+JAE); Hansen, Huang & Shek (2012, JAE, Realized GARCH); Noureldin, Shephard & Sheppard (2012, JAE, HEAVY); Engle (2002,
		51	+JBES, DCC); Engle, Ghysels & Sohn (2013, REStat, GARCH-MIDAS). Inference: White (1980); Newey & West (1987); Cameron,
		52	+Gelbach & Miller (2011, JBES); Politis & Romano (1994, JASA); Diebold & Mariano (1995/2002, JBES); Hansen, Lunde & Nason
		53	+(2003 OBES; 2011 Econometrica MCS — the 2011 DOI is still to be resolved); Patton (2011, J. Econometrics). Connectedness:
		54	+Diebold & Yilmaz (2012, IJF; 2014, J. Econometrics); Pesaran & Shin (1998, Econ. Letters). Software: Boudt, Kleen &
		55	+Sjørup (2022, JSS); Ghalanos (rugarch, CRAN). Computing: Aldrich, Fernández-Villaverde, Gallant & Rubio-Ramírez (2011,
		56	+JEDC, GPU in economics); Kahan (1965, CACM); Blackford et al. (2002, ACM TOMS, BLAS). Systems references still to add
		57	+with verified sources: Apache Arrow/Parquet documentation, Apple Metal documentation, nanobind, Eigen.

added docs/risks.md +22 −0

				@@ -0,0 +1,22 @@
		1	+# docs/risks.md — risk register (Phase 0, 2026-11-02; update at every gate)
		2	+
		3	+| # | Risk | Likelihood | Impact | Mitigation / decision | Owner phase |
		4	+|---|---|---|---|---|---|
		5	+| R1 | **No FP64 on Metal** → GPU estimates drift from FP64 references on long reductions (390 000 bars, ill-conditioned X'X) | certain (verified) | high | FP32 + Kahan/pairwise reductions; sufficient statistics accumulated per chunk then combined in FP64 on CPU; mixed-precision policy per method; tolerances per method; GPU paths that cannot meet tolerance are flagged `precision=fp32-approx`, not silently accepted | 3, 4 |
		6	+| R2 | GPU does not beat the 18-core CPU for small/medium workloads (realistic for 1-day RV on one series) | high | medium (it is a *result*, not a failure) | Publish break-even charts and CPU-wins cases (charter §8); auto backend picks CPU below measured break-even | 3, 6 |
		7	+| R3 | GARCH optimization on GPU unstable (nonconvex, constraints) | medium | medium | GPU only evaluates batched likelihoods/gradients; CPU optimizer (L-BFGS-B / Nelder–Mead with transforms) drives; report convergence codes; never mark non-converged fits as success | 4 |
		8	+| R4 | FinData rate limit (600/min) / export caps (31 d per 1m call, 12 y per export) | certain | low | token bucket, retries with `Retry-After`, exports chunked ≤ 10 y, on-disk cache keyed by (dataset, version, symbol, range) with SHA-256 | 2 |
		9	+| R5 | Duplicate last session in exports (base + delta layouts, WP 39) | high | low | dedupe on (instrument_id, timestamp_utc), counted in QualityReport | 2 |
		10	+| R6 | License breach by committing real bars or figures that reveal raw prices | low | critical | `.gitignore` for `*.parquet`/`data/`; pre-commit hook scanning for `fd_` keys and large binaries; paper tables show only aggregates/ratios; fixtures synthetic | all |
		11	+| R7 | Secret leakage (`FINDATA_API_KEY`) in logs/manifests | low | critical | client redacts `Authorization` header; manifests store key *prefix hash* only; `scripts/check_no_secrets.sh` in CI | 2, 8 |
		12	+| R8 | Thermal throttling / background load on the laptop biases benchmarks | medium | medium | record `pmset -g therm`, load average, and `powermetrics` when available; randomized order, warm-ups, ≥ 5 repetitions, medians + bootstrap CIs; rerun flagged outliers | 6 |
		13	+| R9 | Cluster nodes lack Homebrew/cmake in SSH PATH; m2m8b lacks developer tools; 7 nodes unreachable | certain | low | self-contained static bundle built on the laptop; nodes that stay unreachable are reported as "not tested" in E7 | 6 |
		14	+| R10 | Production interference (FinData on M3U96a, Ka apps on M4M64a/b, gateway M1M32) | low | high | those nodes excluded from timing runs (M1M32: ≤ 2-minute microbench only, off-hours); never install software on nodes; no sudo | 6 |
		15	+| R11 | Arrow C++ dependency makes wheels/Linux builds heavy | medium | medium | Arrow optional (`RAID_WITH_ARROW`); core library and tests build without it; Parquet reader behind an interface; Python path can fall back to pyarrow → NumPy views | 1 |
		16	+| R12 | `clang` 21 does not know `-mcpu=apple-m5` | certain | low | use `-mcpu=apple-m4` on M5/M6 hosts (recorded in manifest); ISA difference irrelevant for the claims | 1 |
		17	+| R13 | Reference software definitional differences (BPV constants, HARQ scaling, GARCH initialization, HAC bandwidth, DoF of HC corrections) | high | high (validation tables) | per-method registry pins the exact competitor options; tests compare under identical conventions; discrepancies documented, not hidden | 4 |
		18	+| R14 | Scope creep (Tier B/C lists) | high | medium | gates: Tier B only after Tier A passes; Tier C only with validated inputs; RESEARCH_STATUS lists "not implemented" honestly | 5 |
		19	+| R15 | Survivorship / PIT issues if a HAR-X uses fundamentals | medium | medium | methods paper uses market covariates only (realized measures, returns, VIX-type indices from FinData index bars); no fundamentals in Tier A experiments | 4, 6 |
		20	+| R16 | Nondeterministic GPU reductions (threadgroup scheduling) break bitwise reproducibility | certain | low | deterministic reduction order within fixed threadgroup sizes; `ExecutionPlan.deterministic=true` forces fixed-order tree reductions; nondeterminism bound reported when disabled | 3 |
		21	+| R17 | Energy claims without an instrument | medium | medium | energy reported only from `powermetrics` samples (requires sudo on the laptop, password available to the owner); otherwise "not measured" | 6 |
		22	+| R18 | Paper claims not traceable to artifacts | medium | high | every table/figure generated by `experiments/scripts/*` from `experiments/results/*.json` with SHA-256 in `experiments/manifests/`; Phase 8 reproduction from clean checkout | 7, 8 |

added examples/cpp/minimal.cpp +23 −0

				@@ -0,0 +1,23 @@
		1	+// Minimal C++ example: realized measures on the synthetic fixture with backend auto|cpu|metal and CPU fallback.
		2	+// Build: cmake --preset macos-release && cmake --build --preset macos-release --target example_minimal (or compile against libraid)
		3	+#include <cstdio>
		4	+#include "raid/data/io.hpp"
		5	+#include "raid/data/calendar.hpp"
		6	+#include "raid/data/returns.hpp"
		7	+#include "raid/estimators/realized.hpp"
		8	+
		9	+int main(int argc, char** argv) {
		10	+ const std::string backend = argc > 1 ? argv[1] : "auto";
		11	+ auto bars = raid::data::read_bars_csv(argc > 2 ? argv[2] : "datasets/fixtures/synthetic_bars_1m.csv");
		12	+ raid::MarketCalendar cal;
		13	+ for (auto& [sym, b] : bars) {
		14	+ b.normalize();
		15	+ auto reg = b.filter_session(cal, raid::SessionPolicy::regular);
		16	+ auto ir = raid::data::intraday_returns(reg);
		17	+ raid::est::RealizedOptions o; o.plan.backend = raid::backend_from_string(backend); o.plan.allow_fallback = true;
		18	+ auto m = raid::est::realized_measures(ir, o);
		19	+ std::printf("%s: %zu sessions, backend=%s precision=%s (%s)\n", sym.c_str(), m.sessions.size(), std::string(raid::to_string(m.stats.backend)).c_str(), std::string(raid::to_string(m.stats.precision)).c_str(), m.stats.note.c_str());
		20	+ for (size_t k = 0; k < m.sessions.size(); ++k) std::printf(" %s M=%zu RV=%.3e BPV=%.3e RSV-=%.3e RQ=%.3e\n", m.sessions[k].iso().c_str(), m.n_returns[k], m.rv[k], m.bpv[k], m.rsv_neg[k], m.rq[k]);
		21	+ }
		22	+ return 0;
		23	+}

added examples/python/minimal.py +15 −0

				@@ -0,0 +1,15 @@
		1	+"""Minimal Python example: realized measures and a GARCH fit with backend="auto|cpu|metal" and explicit CPU fallback."""
		2	+import sys
		3	+import numpy as np
		4	+import raid
		5	+
		6	+backend = sys.argv[1] if len(sys.argv) > 1 else "auto"
		7	+print("devices:", [d["name"] for d in raid.devices()])
		8	+rng = np.random.default_rng(20261102)
		9	+sessions, M = 100, 390
		10	+r = rng.normal(0, 1e-3, size=sessions * M); offsets = np.arange(0, sessions * M + 1, M)
		11	+m = raid.realized_measures(r, offsets, backend=backend) # falls back to CPU when Metal is unavailable
		12	+print(f"backend={m['backend']} precision={m['precision']} wall={m['wall_seconds']*1e3:.2f} ms RV[0]={m['rv'][0]:.3e}")
		13	+daily = m["ret"] # daily open-to-close returns
		14	+g = raid.garch_fit(np.concatenate([daily, rng.normal(0, 0.01, 400)]), model="gjr", dist="student_t", horizon=5)
		15	+print("GJR-t:", dict(zip(g["names"], np.round(g["coefficients"], 5))), "converged:", g["ok"])

added experiments/manifests/environment_MacBookPro-de-simon-pierre-2.json +33 −0

				@@ -0,0 +1,33 @@
		1	+{
		2	+ "recorded_at": "2026-11-02T20:03:03.248578+00:00",
		3	+ "host": "MacBookPro-de-simon-pierre-2.local",
		4	+ "os": "27.0",
		5	+ "build": "26A5425a",
		6	+ "chip": "Apple M5 Max",
		7	+ "ncpu": "18",
		8	+ "perf_cores": "6",
		9	+ "eff_cores": "12",
		10	+ "mem_bytes": "51539607552",
		11	+ "gpu": "Chipset Model: Apple M5 Max;Total Number of Cores: 40;Metal Support: Metal 4;",
		12	+ "xcode": "Xcode 27.2 Build version 27B52",
		13	+ "clang": "Apple clang version 21.0.0 (clang-2100.4.1.1)",
		14	+ "sdk": "27.2",
		15	+ "metal_toolchain": "Apple metal version 32023.922 (metalfe-32023.922.4)",
		16	+ "cmake": "cmake version 4.3.2",
		17	+ "ninja": "1.13.2",
		18	+ "brew": {
		19	+ "eigen": "eigen 5.0.1",
		20	+ "apache-arrow": "apache-arrow 26.0.0_3",
		21	+ "catch2": "catch2 3.16.1",
		22	+ "nlohmann-json": "nlohmann-json 3.12.0",
		23	+ "openblas": "openblas 0.3.33",
		24	+ "libomp": "libomp 22.1.8"
		25	+ },
		26	+ "python": "Python 3.14.4",
		27	+ "r": "R version 4.6.1 (2026-06-24) -- \"Happy Hop\"",
		28	+ "load_avg": "{ 3,67 3,00 2,56 }",
		29	+ "thermal": "Note: No thermal warning level has been recorded;Note: No performance warning level has been recorded;Note: No CPU power status has been recorded;",
		30	+ "power_source": "Now drawing from 'AC Power'",
		31	+ "uptime": "15:03 up 28 days, 4 mins, 22 users, load averages: 3,67 3,00 2,56",
		32	+ "git_commit": "2ceeede"
		33	+}
	\ No newline at end of file

added include/raid/backend/backend.hpp +71 −0

				@@ -0,0 +1,71 @@
		1	+// Backend interface: batched numerical primitives over CSR panels. Every primitive has a CPU FP64 reference;
		2	+// the Metal backend implements the same contract in FP32 (+ compensated partial sums combined in FP64 on the CPU).
		3	+#pragma once
		4	+#include <memory>
		5	+#include <span>
		6	+#include <string>
		7	+#include <vector>
		8	+#include "raid/core/types.hpp"
		9	+#include "raid/core/plan.hpp"
		10	+
		11	+namespace raid::backend {
		12	+
		13	+/// Batched input: `values` concatenated, `offsets` (n_series+1). All kernels are segment-wise over series.
		14	+struct Batch {
		15	+ std::span<const f64> values;
		16	+ std::span<const i64> offsets;
		17	+ usize n_series() const { return offsets.empty() ? 0 : offsets.size() - 1; }
		18	+ usize size(usize i) const { return static_cast<usize>(offsets[i + 1] - offsets[i]); }
		19	+ std::span<const f64> series(usize i) const { return values.subspan(static_cast<usize>(offsets[i]), size(i)); }
		20	+};
		21	+
		22	+/// Timing / provenance of one primitive call (filled by every backend).
		23	+struct KernelStats {
		24	+ std::string kernel;
		25	+ Backend backend{Backend::cpu};
		26	+ Precision precision{Precision::fp64};
		27	+ f64 wall_seconds{0};
		28	+ f64 device_seconds{0}; // GPU time (Metal GPUStartTime/EndTime) or 0
		29	+ usize bytes_in{0}, bytes_out{0};
		30	+ usize buffer_allocations{0};
		31	+ bool compiled_now{false}; // first shader compilation happened in this call
		32	+ bool fell_back{false}; // Metal → CPU fallback occurred
		33	+ std::string note;
		34	+};
		35	+
		36	+class Backend {
		37	+ public:
		38	+ virtual ~Backend() = default;
		39	+ virtual std::string name() const = 0;
		40	+ virtual raid::Backend kind() const = 0;
		41	+ virtual Device device() const = 0;
		42	+ virtual bool available() const = 0;
		43	+
		44	+ /// Σ x² per series (realized variance when x are returns).
		45	+ virtual std::vector<f64> sum_squares(const Batch& b, KernelStats* st = nullptr) = 0;
		46	+ /// Σ |x_i| |x_{i-1}| per series (bipower core, without constants).
		47	+ virtual std::vector<f64> sum_abs_adjacent(const Batch& b, KernelStats* st = nullptr) = 0;
		48	+ /// Σ x² 1{x<0}, Σ x² 1{x>0} per series (semivariances).
		49	+ virtual void semivariances(const Batch& b, std::vector<f64>& neg, std::vector<f64>& pos, KernelStats* st = nullptr) = 0;
		50	+ /// Σ x⁴ per series (realized quarticity core).
		51	+ virtual std::vector<f64> sum_fourth(const Batch& b, KernelStats* st = nullptr) = 0;
		52	+ /// Rolling sum of squares with window w per series; output same layout as input (NaN for incomplete windows).
		53	+ virtual std::vector<f64> rolling_sum_squares(const Batch& b, usize window, KernelStats* st = nullptr) = 0;
		54	+ /// Batched sufficient statistics for regressions with k regressors: for each series i, X_i is (n_i × k) row-major
		55	+ /// stored in `x` (concatenated), y_i in `y`; outputs xtx (k×k per series, row-major), xty (k per series), yty.
		56	+ virtual void regression_stats(std::span<const f64> x, std::span<const f64> y, std::span<const i64> offsets, usize k,
		57	+ std::vector<f64>& xtx, std::vector<f64>& xty, std::vector<f64>& yty, KernelStats* st = nullptr) = 0;
		58	+ /// Batched GARCH(1,1)-family negative log-likelihood for a grid of parameter vectors on each series.
		59	+ /// params: n_series × n_params × p (row-major); returns nll (n_series × n_params). Model codes in models/garch.hpp.
		60	+ virtual std::vector<f64> garch_nll_batch(const Batch& returns, std::span<const f64> params, usize n_params, usize p, int model_code, f64 nu_fixed, KernelStats* st = nullptr) = 0;
		61	+};
		62	+
		63	+std::shared_ptr<Backend> make_cpu_backend(unsigned threads = 0);
		64	+#ifdef RAID_ENABLE_METAL
		65	+std::shared_ptr<Backend> make_metal_backend(const PrecisionPolicy& policy = {});
		66	+#endif
		67	+/// Resolve a plan to a concrete backend (auto → Metal if available and allowed, else CPU). Never throws for auto.
		68	+std::shared_ptr<Backend> resolve(const ExecutionPlan& plan, std::string* reason = nullptr);
		69	+std::vector<Device> list_devices();
		70	+
		71	+} // namespace raid::backend

added include/raid/core/error.hpp +33 −0

				@@ -0,0 +1,33 @@
		1	+#pragma once
		2	+#include <stdexcept>
		3	+#include <string>
		4	+#include <string_view>
		5	+
		6	+namespace raid {
		7	+
		8	+enum class ErrorCode {
		9	+ invalid_argument, data_quality, numerical, not_converged, unsupported, io, api, insufficient_samples, memory, cancelled
		10	+};
		11	+constexpr std::string_view to_string(ErrorCode c) {
		12	+ switch (c) {
		13	+ case ErrorCode::invalid_argument: return "invalid_argument"; case ErrorCode::data_quality: return "data_quality";
		14	+ case ErrorCode::numerical: return "numerical"; case ErrorCode::not_converged: return "not_converged";
		15	+ case ErrorCode::unsupported: return "unsupported"; case ErrorCode::io: return "io"; case ErrorCode::api: return "api";
		16	+ case ErrorCode::insufficient_samples: return "insufficient_samples"; case ErrorCode::memory: return "memory";
		17	+ case ErrorCode::cancelled: return "cancelled";
		18	+ }
		19	+ return "?";
		20	+}
		21	+
		22	+class RaidError : public std::runtime_error {
		23	+ public:
		24	+ RaidError(ErrorCode code, const std::string& msg) : std::runtime_error(std::string(to_string(code)) + ": " + msg), code_(code) {}
		25	+ ErrorCode code() const noexcept { return code_; }
		26	+ private:
		27	+ ErrorCode code_;
		28	+};
		29	+
		30	+#define RAID_REQUIRE(cond, code, msg) \
		31	+ do { if (!(cond)) throw ::raid::RaidError(::raid::ErrorCode::code, std::string(msg) + " [" #cond "]"); } while (0)
		32	+
		33	+} // namespace raid

added include/raid/core/estimate.hpp +62 −0

				@@ -0,0 +1,62 @@
		1	+// Estimate / InferenceResult / Provenance — the common output contract of every RAID estimator.
		2	+#pragma once
		3	+#include <string>
		4	+#include <vector>
		5	+#include <optional>
		6	+#include <Eigen/Dense>
		7	+#include <nlohmann/json.hpp>
		8	+#include "raid/core/types.hpp"
		9	+
		10	+namespace raid {
		11	+
		12	+struct Provenance {
		13	+ std::string dataset_id; // e.g. market_bars.stock.1m.raw
		14	+ i64 dataset_version{-1}; // X-Dataset-Version (−1 = synthetic / unknown)
		15	+ std::vector<std::string> request_ids; // X-Request-ID values
		16	+ std::vector<std::string> input_hashes; // SHA-256 of input files / fixtures
		17	+ std::string code_version; // git short SHA + RAID_VERSION
		18	+ std::string source; // "FirstRate Data via FinData OS" | "synthetic"
		19	+ nlohmann::json extra;
		20	+ nlohmann::json to_json() const;
		21	+};
		22	+
		23	+struct Convergence {
		24	+ bool converged{false};
		25	+ int iterations{0};
		26	+ int function_evaluations{0};
		27	+ f64 gradient_norm{std::numeric_limits<f64>::quiet_NaN()};
		28	+ int status_code{0};
		29	+ std::string message;
		30	+ nlohmann::json to_json() const;
		31	+};
		32	+
		33	+struct InferenceResult {
		34	+ std::vector<f64> std_errors;
		35	+ std::vector<f64> t_stats;
		36	+ std::vector<f64> p_values;
		37	+ Eigen::MatrixXd covariance;
		38	+ std::string covariance_type; // classical | hc0..hc3 | hac_bartlett(L) | cluster | qmle_sandwich | hessian
		39	+ f64 small_sample_correction{1.0};
		40	+ nlohmann::json to_json() const;
		41	+};
		42	+
		43	+struct Estimate {
		44	+ std::string model; // registry key, e.g. "har.rv", "garch.gjr.t"
		45	+ std::vector<std::string> names;
		46	+ std::vector<f64> coefficients;
		47	+ f64 loss{std::numeric_limits<f64>::quiet_NaN()}; // −loglik or RSS depending on the model (documented per model)
		48	+ std::optional<InferenceResult> inference;
		49	+ Convergence convergence;
		50	+ std::vector<std::string> warnings;
		51	+ f64 runtime_seconds{0};
		52	+ Backend backend{Backend::cpu};
		53	+ Precision precision{Precision::fp64};
		54	+ Provenance provenance;
		55	+ usize n_obs{0};
		56	+ nlohmann::json extra; // model-specific (sigma2 path, forecasts, diagnostics)
		57	+ /// A failed optimisation is never a success: ok() requires convergence.
		58	+ bool ok() const { return convergence.converged && !coefficients.empty(); }
		59	+ nlohmann::json to_json() const;
		60	+};
		61	+
		62	+} // namespace raid

added include/raid/core/hash.hpp +29 −0

				@@ -0,0 +1,29 @@
		1	+// SHA-256 (FIPS 180-4), dependency-free, for provenance hashes of inputs/artifacts.
		2	+#pragma once
		3	+#include <string>
		4	+#include <string_view>
		5	+#include <span>
		6	+#include <array>
		7	+#include <cstdint>
		8	+
		9	+namespace raid {
		10	+
		11	+class Sha256 {
		12	+ public:
		13	+ Sha256();
		14	+ void update(const void* data, std::size_t len);
		15	+ void update(std::string_view s) { update(s.data(), s.size()); }
		16	+ std::array<std::uint8_t, 32> digest();
		17	+ std::string hex();
		18	+ static std::string of(std::string_view s) { Sha256 h; h.update(s); return h.hex(); }
		19	+ static std::string of_file(const std::string& path);
		20	+ template <class T> static std::string of_span(std::span<const T> x) { Sha256 h; h.update(x.data(), x.size_bytes()); return h.hex(); }
		21	+ private:
		22	+ void transform(const std::uint8_t* chunk);
		23	+ std::array<std::uint32_t, 8> h_;
		24	+ std::array<std::uint8_t, 64> buf_{};
		25	+ std::size_t buf_len_{0};
		26	+ std::uint64_t total_{0};
		27	+};
		28	+
		29	+} // namespace raid

added include/raid/core/log.hpp +28 −0

				@@ -0,0 +1,28 @@
		1	+// Minimal structured logger (key=value pairs, levels, secret redaction). No external dependency.
		2	+#pragma once
		3	+#include <string>
		4	+#include <string_view>
		5	+#include <vector>
		6	+#include <utility>
		7	+#include <functional>
		8	+#include <mutex>
		9	+
		10	+namespace raid::log {
		11	+
		12	+enum class Level : int { trace = 0, debug = 1, info = 2, warn = 3, error = 4, off = 5 };
		13	+using Fields = std::vector<std::pair<std::string, std::string>>;
		14	+using Sink = std::function<void(Level, std::string_view msg, const Fields&)>;
		15	+
		16	+void set_level(Level l);
		17	+Level level();
		18	+void set_sink(Sink s); // default: stderr, one line per event, "level msg k=v k=v"
		19	+void emit(Level l, std::string_view msg, Fields fields = {});
		20	+/// Replaces bearer tokens / api keys ("fd_xxx.yyy", "Bearer …") by "<redacted>".
		21	+std::string redact(std::string_view s);
		22	+
		23	+inline void info(std::string_view m, Fields f = {}) { emit(Level::info, m, std::move(f)); }
		24	+inline void warn(std::string_view m, Fields f = {}) { emit(Level::warn, m, std::move(f)); }
		25	+inline void error(std::string_view m, Fields f = {}) { emit(Level::error, m, std::move(f)); }
		26	+inline void debug(std::string_view m, Fields f = {}) { emit(Level::debug, m, std::move(f)); }
		27	+
		28	+} // namespace raid::log

added include/raid/core/manifest.hpp +33 −0

				@@ -0,0 +1,33 @@
		1	+// ExperimentManifest — everything needed to reproduce a run: host, toolchain, library version, plan, seeds, inputs.
		2	+#pragma once
		3	+#include <string>
		4	+#include <vector>
		5	+#include <map>
		6	+#include <nlohmann/json.hpp>
		7	+#include "raid/core/types.hpp"
		8	+#include "raid/core/plan.hpp"
		9	+
		10	+namespace raid {
		11	+
		12	+struct ExperimentManifest {
		13	+ std::string experiment; // e.g. "E1.batched_rv"
		14	+ std::string created_at_utc;
		15	+ std::string raid_version;
		16	+ std::string git_commit;
		17	+ nlohmann::json host; // from host_info()
		18	+ nlohmann::json plan; // ExecutionPlan::to_json
		19	+ u64 seed{0};
		20	+ std::map<std::string, std::string> inputs; // name → sha256
		21	+ std::map<std::string, std::string> parameters;
		22	+ std::vector<nlohmann::json> records; // raw benchmark / result records
		23	+ nlohmann::json to_json() const;
		24	+ void save(const std::string& path) const;
		25	+};
		26	+
		27	+/// Host information gathered at run time (no secrets): chip, cores, memory, OS, compiler, load, thermal state, power.
		28	+nlohmann::json host_info();
		29	+std::string now_utc_iso();
		30	+/// Peak resident set size in bytes (ru_maxrss), for memory metrics.
		31	+u64 peak_rss_bytes();
		32	+
		33	+} // namespace raid

added include/raid/core/plan.hpp +45 −0

				@@ -0,0 +1,45 @@
		1	+// ExecutionPlan / Device / PrecisionPolicy — how and where a computation runs.
		2	+#pragma once
		3	+#include <string>
		4	+#include <vector>
		5	+#include <nlohmann/json.hpp>
		6	+#include "raid/core/types.hpp"
		7	+
		8	+namespace raid {
		9	+
		10	+struct Device {
		11	+ std::string name;
		12	+ Backend kind{Backend::cpu};
		13	+ u64 memory_bytes{0};
		14	+ int compute_units{0}; // CPU cores or GPU cores (if known)
		15	+ bool unified_memory{true};
		16	+ bool supports_fp64{true}; // false for every Apple GPU (verified 2026-11-02)
		17	+ u64 max_buffer_bytes{0};
		18	+ std::string details;
		19	+ nlohmann::json to_json() const;
		20	+};
		21	+
		22	+struct PrecisionPolicy {
		23	+ Precision requested{Precision::fp64};
		24	+ /// Maximum reduction length accumulated in FP32 on the GPU before a FP64 CPU combine step.
		25	+ usize fp32_chunk_max{4096};
		26	+ /// Use compensated (Neumaier) accumulation on GPU partial sums.
		27	+ bool compensated{true};
		28	+ /// Tolerance (relative) used by auto validation of GPU vs CPU reference.
		29	+ f64 validate_rtol{1e-5};
		30	+ nlohmann::json to_json() const;
		31	+};
		32	+
		33	+struct ExecutionPlan {
		34	+ Backend backend{Backend::automatic};
		35	+ PrecisionPolicy precision{};
		36	+ bool deterministic{true}; // fixed-order reductions (bitwise reproducible across runs on the same device)
		37	+ unsigned threads{0}; // 0 = all
		38	+ usize chunk_rows{0}; // 0 = automatic
		39	+ u64 max_memory_bytes{0}; // 0 = no explicit bound (device-reported limits still apply)
		40	+ bool allow_fallback{true}; // Metal unsupported → CPU
		41	+ u64 seed{20261102};
		42	+ nlohmann::json to_json() const;
		43	+};
		44	+
		45	+} // namespace raid

added include/raid/core/rng.hpp +71 −0

				@@ -0,0 +1,71 @@
		1	+// Deterministic, stream-addressable RNG (xoshiro256++ seeded by splitmix64 from (seed, stream)).
		2	+// Each (seed, stream) pair gives an independent, reproducible stream suitable for parallel Monte Carlo.
		3	+#pragma once
		4	+#include <cstdint>
		5	+#include <cmath>
		6	+#include <array>
		7	+#include "raid/core/types.hpp"
		8	+
		9	+namespace raid {
		10	+
		11	+class Rng {
		12	+ public:
		13	+ explicit Rng(u64 seed = 0x5eed'0000'0000'0001ULL, u64 stream = 0) { reseed(seed, stream); }
		14	+ void reseed(u64 seed, u64 stream) {
		15	+ seed_ = seed; stream_ = stream;
		16	+ u64 z = seed ^ (stream * 0x9E3779B97F4A7C15ULL) ^ 0xD1B54A32D192ED03ULL;
		17	+ for (auto& w : s_) w = splitmix(z);
		18	+ if (s_[0] == 0 && s_[1] == 0 && s_[2] == 0 && s_[3] == 0) s_[0] = 1;
		19	+ }
		20	+ u64 seed() const { return seed_; }
		21	+ u64 stream() const { return stream_; }
		22	+ u64 next_u64() {
		23	+ const u64 result = rotl(s_[0] + s_[3], 23) + s_[0];
		24	+ const u64 t = s_[1] << 17;
		25	+ s_[2] ^= s_[0]; s_[3] ^= s_[1]; s_[1] ^= s_[2]; s_[0] ^= s_[3]; s_[2] ^= t; s_[3] = rotl(s_[3], 45);
		26	+ return result;
		27	+ }
		28	+ /// Uniform on [0,1) with 53 random bits.
		29	+ f64 uniform() { return static_cast<f64>(next_u64() >> 11) * 0x1.0p-53; }
		30	+ /// Standard normal, polar Box–Muller (caches the second draw).
		31	+ f64 normal() {
		32	+ if (has_cached_) { has_cached_ = false; return cached_; }
		33	+ f64 u, v, s;
		34	+ do { u = 2.0 * uniform() - 1.0; v = 2.0 * uniform() - 1.0; s = u * u + v * v; } while (s >= 1.0 || s == 0.0);
		35	+ const f64 m = std::sqrt(-2.0 * std::log(s) / s);
		36	+ cached_ = v * m; has_cached_ = true;
		37	+ return u * m;
		38	+ }
		39	+ /// Gamma(shape k, scale 1), Marsaglia–Tsang.
		40	+ f64 gamma(f64 k) {
		41	+ if (k < 1.0) { const f64 u = uniform(); return gamma(k + 1.0) * std::pow(u, 1.0 / k); }
		42	+ const f64 d = k - 1.0 / 3.0, c = 1.0 / std::sqrt(9.0 * d);
		43	+ for (;;) {
		44	+ f64 x, v;
		45	+ do { x = normal(); v = 1.0 + c * x; } while (v <= 0.0);
		46	+ v = v * v * v;
		47	+ const f64 u = uniform();
		48	+ if (u < 1.0 - 0.0331 * x * x * x * x) return d * v;
		49	+ if (std::log(u) < 0.5 * x * x + d * (1.0 - v + std::log(v))) return d * v;
		50	+ }
		51	+ }
		52	+ /// Student-t with nu degrees of freedom, unit scale (variance nu/(nu-2)).
		53	+ f64 student_t(f64 nu) { return normal() / std::sqrt(gamma(nu / 2.0) * 2.0 / nu); }
		54	+ /// Integer in [0, n)
		55	+ u64 below(u64 n) { return next_u64() % n; }
		56	+ /// Geometric number of trials with success probability p (support 1,2,…) — for the stationary bootstrap.
		57	+ u64 geometric(f64 p) { return 1 + static_cast<u64>(std::floor(std::log(1.0 - uniform()) / std::log(1.0 - p))); }
		58	+
		59	+ private:
		60	+ static constexpr u64 rotl(u64 x, int k) { return (x << k) | (x >> (64 - k)); }
		61	+ static u64 splitmix(u64& z) {
		62	+ z += 0x9E3779B97F4A7C15ULL;
		63	+ u64 r = z; r = (r ^ (r >> 30)) * 0xBF58476D1CE4E5B9ULL; r = (r ^ (r >> 27)) * 0x94D049BB133111EBULL;
		64	+ return r ^ (r >> 31);
		65	+ }
		66	+ std::array<u64, 4> s_{};
		67	+ u64 seed_{0}, stream_{0};
		68	+ f64 cached_{0}; bool has_cached_{false};
		69	+};
		70	+
		71	+} // namespace raid

added include/raid/core/thread_pool.hpp +40 −0

				@@ -0,0 +1,40 @@
		1	+// Bounded thread pool with a blocking parallel_for. Threads are created once (lazy singleton) and reused.
		2	+#pragma once
		3	+#include <functional>
		4	+#include <thread>
		5	+#include <vector>
		6	+#include <atomic>
		7	+#include <mutex>
		8	+#include <condition_variable>
		9	+#include <deque>
		10	+#include <exception>
		11	+#include "raid/core/types.hpp"
		12	+
		13	+namespace raid {
		14	+
		15	+class ThreadPool {
		16	+ public:
		17	+ explicit ThreadPool(unsigned n_threads = 0);
		18	+ ~ThreadPool();
		19	+ ThreadPool(const ThreadPool&) = delete;
		20	+ ThreadPool& operator=(const ThreadPool&) = delete;
		21	+ unsigned size() const { return static_cast<unsigned>(workers_.size()); }
		22	+ /// Runs fn(begin, end) over [0,n) split in chunks of `grain` (0 = automatic). Blocks until all chunks finished.
		23	+ /// Exceptions thrown by fn are rethrown on the calling thread (first one).
		24	+ void parallel_for(usize n, const std::function<void(usize, usize)>& fn, usize grain = 0);
		25	+ /// Global pool sized to hardware concurrency (or RAID_THREADS env var).
		26	+ static ThreadPool& global();
		27	+
		28	+ private:
		29	+ void worker();
		30	+ std::vector<std::thread> workers_;
		31	+ std::deque<std::function<void()>> queue_;
		32	+ std::mutex m_;
		33	+ std::condition_variable cv_;
		34	+ bool stop_{false};
		35	+};
		36	+
		37	+/// Serial fallback helper: parallel_for with n_threads==1 semantics.
		38	+inline void serial_for(usize n, const std::function<void(usize, usize)>& fn) { if (n) fn(0, n); }
		39	+
		40	+} // namespace raid

added include/raid/core/types.hpp +120 −0

				@@ -0,0 +1,120 @@
		1	+// RAID — core strong types (timestamps, durations, intervals, dates, enums).
		2	+#pragma once
		3	+#include <cstdint>
		4	+#include <compare>
		5	+#include <string>
		6	+#include <string_view>
		7	+#include <chrono>
		8	+#include <stdexcept>
		9	+
		10	+namespace raid {
		11	+
		12	+using f64 = double;
		13	+using f32 = float;
		14	+using i64 = std::int64_t;
		15	+using i32 = std::int32_t;
		16	+using u32 = std::uint32_t;
		17	+using u64 = std::uint64_t;
		18	+using usize = std::size_t;
		19	+
		20	+/// Microseconds since the Unix epoch, UTC. Strong type: cannot be mixed with raw integers by accident.
		21	+struct Timestamp {
		22	+ i64 us{0};
		23	+ constexpr Timestamp() = default;
		24	+ constexpr explicit Timestamp(i64 micros) : us(micros) {}
		25	+ static constexpr Timestamp from_seconds(i64 s) { return Timestamp(s * 1'000'000); }
		26	+ constexpr i64 seconds() const { return us / 1'000'000; }
		27	+ constexpr auto operator<=>(const Timestamp&) const = default;
		28	+};
		29	+
		30	+struct Duration {
		31	+ i64 us{0};
		32	+ constexpr Duration() = default;
		33	+ constexpr explicit Duration(i64 micros) : us(micros) {}
		34	+ static constexpr Duration seconds(i64 s) { return Duration(s * 1'000'000); }
		35	+ static constexpr Duration minutes(i64 m) { return Duration(m * 60'000'000); }
		36	+ static constexpr Duration hours(i64 h) { return Duration(h * 3'600'000'000LL); }
		37	+ static constexpr Duration days(i64 d) { return Duration(d * 86'400'000'000LL); }
		38	+ constexpr auto operator<=>(const Duration&) const = default;
		39	+};
		40	+constexpr Duration operator-(Timestamp a, Timestamp b) { return Duration(a.us - b.us); }
		41	+constexpr Timestamp operator+(Timestamp a, Duration d) { return Timestamp(a.us + d.us); }
		42	+constexpr Timestamp operator-(Timestamp a, Duration d) { return Timestamp(a.us - d.us); }
		43	+constexpr Duration operator*(Duration d, i64 k) { return Duration(d.us * k); }
		44	+
		45	+/// Bar interval (FinData exposes 1m, 5m, 30m, 1h, 1d).
		46	+enum class Interval : std::uint8_t { m1, m5, m30, h1, d1 };
		47	+constexpr Duration to_duration(Interval iv) {
		48	+ switch (iv) {
		49	+ case Interval::m1: return Duration::minutes(1);
		50	+ case Interval::m5: return Duration::minutes(5);
		51	+ case Interval::m30: return Duration::minutes(30);
		52	+ case Interval::h1: return Duration::hours(1);
		53	+ case Interval::d1: return Duration::days(1);
		54	+ }
		55	+ return Duration::minutes(1);
		56	+}
		57	+constexpr std::string_view to_string(Interval iv) {
		58	+ switch (iv) {
		59	+ case Interval::m1: return "1m"; case Interval::m5: return "5m"; case Interval::m30: return "30m";
		60	+ case Interval::h1: return "1h"; case Interval::d1: return "1d";
		61	+ }
		62	+ return "?";
		63	+}
		64	+Interval interval_from_string(std::string_view s);
		65	+
		66	+/// Civil date (proleptic Gregorian), stored as days since 1970-01-01.
		67	+struct Date {
		68	+ i32 days{0};
		69	+ constexpr Date() = default;
		70	+ constexpr explicit Date(i32 d) : days(d) {}
		71	+ static constexpr Date from_ymd(int y, unsigned m, unsigned d) {
		72	+ // Howard Hinnant's days_from_civil
		73	+ y -= m <= 2;
		74	+ const int era = (y >= 0 ? y : y - 399) / 400;
		75	+ const unsigned yoe = static_cast<unsigned>(y - era * 400);
		76	+ const unsigned doy = (153 * (m + (m > 2 ? -3 : 9)) + 2) / 5 + d - 1;
		77	+ const unsigned doe = yoe * 365 + yoe / 4 - yoe / 100 + doy;
		78	+ return Date(era * 146097 + static_cast<int>(doe) - 719468);
		79	+ }
		80	+ struct YMD { int year; unsigned month; unsigned day; };
		81	+ constexpr YMD ymd() const {
		82	+ // Howard Hinnant's civil_from_days
		83	+ const i32 z = days + 719468;
		84	+ const i32 era = (z >= 0 ? z : z - 146096) / 146097;
		85	+ const unsigned doe = static_cast<unsigned>(z - era * 146097);
		86	+ const unsigned yoe = (doe - doe / 1460 + doe / 36524 - doe / 146096) / 365;
		87	+ const int y = static_cast<int>(yoe) + era * 400;
		88	+ const unsigned doy = doe - (365 * yoe + yoe / 4 - yoe / 100);
		89	+ const unsigned mp = (5 * doy + 2) / 153;
		90	+ const unsigned d = doy - (153 * mp + 2) / 5 + 1;
		91	+ const unsigned m = mp + (mp < 10 ? 3 : -9);
		92	+ return {y + (m <= 2), m, d};
		93	+ }
		94	+ /// 0 = Sunday … 6 = Saturday
		95	+ constexpr int weekday() const { return static_cast<int>((days >= -4 ? (days + 4) % 7 : (days + 5) % 7 + 6)); }
		96	+ constexpr auto operator<=>(const Date&) const = default;
		97	+ constexpr Date operator+(int d) const { return Date(days + d); }
		98	+ constexpr Date operator-(int d) const { return Date(days - d); }
		99	+ std::string iso() const;
		100	+};
		101	+Date date_from_iso(std::string_view s); // "YYYY-MM-DD"
		102	+std::string timestamp_iso(Timestamp t); // "YYYY-MM-DDTHH:MM:SS.ffffffZ"
		103	+Timestamp timestamp_from_iso(std::string_view s);
		104	+
		105	+enum class Backend : std::uint8_t { automatic, cpu, metal };
		106	+enum class Precision : std::uint8_t { fp64, fp32, mixed };
		107	+constexpr std::string_view to_string(Backend b) {
		108	+ switch (b) { case Backend::automatic: return "auto"; case Backend::cpu: return "cpu"; case Backend::metal: return "metal"; }
		109	+ return "?";
		110	+}
		111	+constexpr std::string_view to_string(Precision p) {
		112	+ switch (p) { case Precision::fp64: return "fp64"; case Precision::fp32: return "fp32"; case Precision::mixed: return "mixed"; }
		113	+ return "?";
		114	+}
		115	+Backend backend_from_string(std::string_view s);
		116	+
		117	+/// Session policy for US equity bars.
		118	+enum class SessionPolicy : std::uint8_t { regular, extended };
		119	+
		120	+} // namespace raid

added include/raid/estimators/ols.hpp +58 −0

				@@ -0,0 +1,58 @@
		1	+// OLS / WLS / ridge with stable linear algebra (Householder QR by default, SVD for rank-deficient designs).
		2	+#pragma once
		3	+#include <vector>
		4	+#include <span>
		5	+#include <optional>
		6	+#include <Eigen/Dense>
		7	+#include "raid/core/types.hpp"
		8	+#include "raid/core/estimate.hpp"
		9	+#include "raid/statistics/covariance.hpp"
		10	+
		11	+namespace raid::est {
		12	+
		13	+enum class Solver { qr, svd, normal_cholesky /* reference only: ill-conditioned designs will be flagged */ };
		14	+
		15	+struct OlsOptions {
		16	+ stats::CovOptions cov{};
		17	+ Solver solver{Solver::qr};
		18	+ f64 ridge_lambda{0.0}; // L2 penalty on all coefficients except those in `unpenalized` (e.g. the intercept)
		19	+ std::vector<usize> unpenalized{}; // column indices not penalised
		20	+ std::span<const f64> weights{}; // WLS weights w_i (variance ∝ 1/w_i); empty = OLS
		21	+ f64 rcond_warn{1e-10}; // warn when the design's reciprocal condition number is below this
		22	+ std::vector<std::string> names{};
		23	+};
		24	+
		25	+struct OlsResult {
		26	+ Estimate estimate; // coefficients, inference, loss = RSS
		27	+ Eigen::VectorXd residuals;
		28	+ Eigen::VectorXd fitted;
		29	+ Eigen::MatrixXd xtx_inv; // (X'WX + λP)^{-1}
		30	+ f64 sigma2{0}; // RSS/(n−k)
		31	+ f64 r2{0}, r2_adj{0};
		32	+ Eigen::Index rank{0};
		33	+ f64 rcond{0};
		34	+ Eigen::VectorXd leverage; // h_ii
		35	+};
		36	+
		37	+OlsResult ols(const Eigen::Ref<const Eigen::MatrixXd>& X, const Eigen::Ref<const Eigen::VectorXd>& y, const OlsOptions& opt = {});
		38	+
		39	+/// Sufficient statistics of a (possibly partial) regression block: X'X, X'y, y'y, n. Batched/GPU accumulation
		40	+/// produces these and the solve happens in FP64 on the CPU.
		41	+struct SufficientStats {
		42	+ Eigen::MatrixXd xtx; Eigen::VectorXd xty; f64 yty{0}; usize n{0};
		43	+ void merge(const SufficientStats& o) { if (n == 0) { *this = o; return; } xtx += o.xtx; xty += o.xty; yty += o.yty; n += o.n; }
		44	+};
		45	+SufficientStats accumulate(const Eigen::Ref<const Eigen::MatrixXd>& X, const Eigen::Ref<const Eigen::VectorXd>& y);
		46	+/// Solve from sufficient statistics (Cholesky with symmetric pivoting fallback to eigen-decomposition); classical SE only.
		47	+OlsResult ols_from_stats(const SufficientStats& s, const OlsOptions& opt = {});
		48	+
		49	+/// Rolling OLS: windows [i−w+1, i] for i ≥ w−1; returns k × n_windows coefficients (column j = window ending at index w−1+j).
		50	+struct RollingOlsResult { Eigen::MatrixXd coefficients; std::vector<f64> rss; std::vector<Eigen::Index> ranks; usize window; };
		51	+RollingOlsResult rolling_ols(const Eigen::Ref<const Eigen::MatrixXd>& X, const Eigen::Ref<const Eigen::VectorXd>& y, usize window, usize step = 1);
		52	+
		53	+} // namespace raid::est
		54	+
		55	+namespace raid::est {
		56	+/// Two-sided p-value of a t statistic (df ≤ 0 → standard normal). FP64, validated against scipy in tests/reference.
		57	+f64 t_pvalue_two_sided(f64 t, f64 df);
		58	+}

added include/raid/estimators/realized.hpp +54 −0

				@@ -0,0 +1,54 @@
		1	+// Realized measures from intraday returns (per session). Definitions (registry keys realized.*), with M intraday
		2	+// returns r_1..r_M of one session:
		3	+// RV = Σ r_j² (Andersen–Bollerslev–Diebold–Labys 2003)
		4	+// BPV = μ₁⁻² · (M/(M−1)) · Σ_{j=2}^{M} |r_j||r_{j−1}|, μ₁ = √(2/π) (Barndorff-Nielsen & Shephard 2004; the finite-sample
		5	+// factor M/(M−1) is optional: `bpv_finite_sample` — highfrequency::rBPCov applies it by default (makeReturns scaling))
		6	+// RSV− = Σ r_j² 1{r_j<0}, RSV+ = Σ r_j² 1{r_j>0} (Barndorff-Nielsen–Kinnebrock–Shephard 2010)
		7	+// RQ = (M/3) · Σ r_j⁴ (Barndorff-Nielsen & Shephard 2002; BPQ 2016 HARQ)
		8	+// J = max(RV − BPV, 0), relative jump = J / RV (Andersen–Bollerslev–Diebold 2007 proxy, no test)
		9	+// TriPV = μ_{4/3}⁻³ · (M/(M−2)) · Σ |r_j|^{4/3}|r_{j−1}|^{4/3}|r_{j−2}|^{4/3}, μ_{4/3} = 2^{2/3} Γ(7/6)/Γ(1/2)
		10	+// BNS ratio test statistic (Tier C, Huang–Tauchen 2005 log version) is NOT implemented in Tier A.
		11	+// Realized covariance: RCov_{ab} = Σ_j r_{a,j} r_{b,j} on synchronized grids (same session, same timestamps).
		12	+#pragma once
		13	+#include <span>
		14	+#include <vector>
		15	+#include <string>
		16	+#include <Eigen/Dense>
		17	+#include "raid/core/types.hpp"
		18	+#include "raid/core/plan.hpp"
		19	+#include "raid/data/returns.hpp"
		20	+#include "raid/data/panel.hpp"
		21	+#include "raid/backend/backend.hpp"
		22	+
		23	+namespace raid::est {
		24	+
		25	+struct RealizedOptions {
		26	+ bool bpv_finite_sample{true};
		27	+ bool annualize{false}; // multiply variances by `periods_per_year`
		28	+ f64 periods_per_year{252.0};
		29	+ ExecutionPlan plan{};
		30	+};
		31	+
		32	+struct RealizedMeasures {
		33	+ std::vector<Date> sessions;
		34	+ std::vector<usize> n_returns; // M per session
		35	+ std::vector<f64> rv, bpv, rsv_neg, rsv_pos, rq, jump, tripv;
		36	+ std::vector<f64> daily_return; // Σ r_j (open-to-close log return when log returns are used)
		37	+ backend::KernelStats stats; // backend / precision / timing of the batched part
		38	+ std::vector<std::string> warnings;
		39	+};
		40	+
		41	+/// Per-session realized measures for one instrument from intraday returns (data::IntradayReturns).
		42	+RealizedMeasures realized_measures(const data::IntradayReturns& ir, const RealizedOptions& opt = {});
		43	+/// Batched over a CSR batch of return segments (one segment = one session or one series), returns the same vectors.
		44	+RealizedMeasures realized_measures_batch(const backend::Batch& b, const RealizedOptions& opt = {});
		45	+
		46	+/// Realized covariance / correlation of N aligned return series (T × N dense).
		47	+struct RealizedCov { Eigen::MatrixXd cov; Eigen::MatrixXd corr; usize n_obs; };
		48	+RealizedCov realized_covariance(const Eigen::Ref<const Eigen::MatrixXd>& R);
		49	+
		50	+/// Finite-sample constants exposed for tests and the paper appendix.
		51	+constexpr f64 kMu1 = 0.7978845608028654; // √(2/π)
		52	+f64 mu_p(f64 p); // E|Z|^p for Z ~ N(0,1)
		53	+
		54	+} // namespace raid::est

added include/raid/models/garch.hpp +64 −0

				@@ -0,0 +1,64 @@
		1	+// GARCH-family conditional variance recursions and (quasi-)log-likelihoods. FP64 reference; the Metal kernel
		2	+// implements the same recursion in FP32 (documented tolerance). Definitions (registry key garch.*):
		3	+// GARCH(1,1): σ²_t = ω + α ε²_{t−1} + β σ²_{t−1}
		4	+// GJR-GARCH(1,1): σ²_t = ω + (α + γ 1{ε_{t−1}<0}) ε²_{t−1} + β σ²_{t−1}
		5	+// EGARCH(1,1): ln σ²_t = ω + α (|z_{t−1}| − E|z|) + γ z_{t−1} + β ln σ²_{t−1}, z = ε/σ
		6	+// ε_t = r_t − μ. Initialisation: σ²_0 = sample variance of ε (GARCH/GJR), ln σ²_0 = ln(sample variance) (EGARCH) —
		7	+// the same convention as the `arch` package default ("backcast" is NOT used; see registry for the rugarch mapping).
		8	+// Densities: Gaussian, or standardized Student-t with ν > 2 (unit variance), as in arch/rugarch.
		9	+#pragma once
		10	+#include <span>
		11	+#include <vector>
		12	+#include <string>
		13	+#include "raid/core/types.hpp"
		14	+#include "raid/core/estimate.hpp"
		15	+#include "raid/core/plan.hpp"
		16	+
		17	+namespace raid::models {
		18	+
		19	+enum class GarchModel : int { garch = 0, gjr = 1, egarch = 2 };
		20	+enum class GarchDist : int { gaussian = 0, student_t = 1 };
		21	+std::string to_string(GarchModel m);
		22	+std::string to_string(GarchDist d);
		23	+
		24	+/// Parameter layout θ (natural parameterisation): [μ, ω, α, (γ), β, (ν)] — γ only for gjr/egarch, ν only for student_t.
		25	+usize garch_n_params(GarchModel m, GarchDist d);
		26	+std::vector<std::string> garch_param_names(GarchModel m, GarchDist d);
		27	+
		28	+/// Conditional variance path σ²_t (t = 0..n−1) for returns r and parameters θ; writes to sigma2 (resized).
		29	+/// Returns false (and leaves NaNs) if the recursion produced a non-positive or non-finite variance.
		30	+bool garch_variance_path(std::span<const f64> r, std::span<const f64> theta, GarchModel m, GarchDist d, std::vector<f64>& sigma2);
		31	+
		32	+/// Negative average? NO: negative *total* log-likelihood Σ_t −log f(r_t | σ²_t). Returns +inf for invalid parameters
		33	+/// (ω ≤ 0, α < 0, β < 0, α+β ≥ 1 for GARCH; α+β+γ/2 ≥ 1 for GJR; |β| ≥ 1 for EGARCH; ν ≤ 2).
		34	+/// `nu_fixed`: if > 2 and the model is Gaussian-coded, a Student-t density with that ν is used (batched grids).
		35	+f64 garch_nll(std::span<const f64> r, std::span<const f64> theta, GarchModel m, f64 nu_fixed = 0.0);
		36	+f64 garch_nll(std::span<const f64> r, std::span<const f64> theta, GarchModel m, GarchDist d);
		37	+
		38	+/// Stationarity / positivity constraints check.
		39	+bool garch_params_valid(std::span<const f64> theta, GarchModel m, GarchDist d);
		40	+
		41	+/// Unconstrained ↔ constrained transforms used by the optimiser (ω = exp, α,β via logistic simplex, ν = 2 + exp).
		42	+std::vector<f64> garch_to_unconstrained(std::span<const f64> theta, GarchModel m, GarchDist d);
		43	+std::vector<f64> garch_from_unconstrained(std::span<const f64> u, GarchModel m, GarchDist d);
		44	+
		45	+struct GarchFitOptions {
		46	+ GarchModel model{GarchModel::garch};
		47	+ GarchDist dist{GarchDist::gaussian};
		48	+ int max_iter{500};
		49	+ f64 tol{1e-8}; // relative change in NLL
		50	+ std::string cov{"qmle"}; // "qmle" (Bollerslev–Wooldridge sandwich) | "hessian" | "opg"
		51	+ int forecast_horizon{0}; // h ≥ 1 → multi-step variance forecasts in extra["forecast_variance"]
		52	+ bool scale_returns{true}; // internally scale returns to unit variance (as arch does, 'rescale')
		53	+ ExecutionPlan plan{};
		54	+};
		55	+
		56	+/// Fit by (quasi-)maximum likelihood on the CPU (L-BFGS-style quasi-Newton on the unconstrained parameters with
		57	+/// numerical gradients validated against finite differences in tests). Never reports a non-converged fit as ok().
		58	+Estimate garch_fit(std::span<const f64> r, const GarchFitOptions& opt = {});
		59	+/// Multi-step-ahead conditional variance forecasts from the last state (h = 1..H). For EGARCH, h > 1 uses simulation-free
		60	+/// log-normal approximation only when `exact` is false; by default h > 1 forecasts for EGARCH are produced by
		61	+/// Monte Carlo with the given seed (reported in extra). GARCH/GJR forecasts are analytical.
		62	+std::vector<f64> garch_forecast(const Estimate& fit, std::span<const f64> r, int horizon, u64 seed = 20261102, int n_sim = 20000);
		63	+
		64	+} // namespace raid::models

added include/raid/statistics/covariance.hpp +32 −0

				@@ -0,0 +1,32 @@
		1	+// Robust covariance estimators for linear regression (CPU FP64 reference).
		2	+// Conventions follow statsmodels 0.15 (HC0–HC3, HAC Bartlett/Newey–West with use_correction=False) and
		3	+// Cameron–Gelbach–Miller one-way cluster with the Stata-style correction G/(G−1)·(N−1)/(N−K).
		4	+#pragma once
		5	+#include <vector>
		6	+#include <span>
		7	+#include <string>
		8	+#include <Eigen/Dense>
		9	+#include "raid/core/types.hpp"
		10	+
		11	+namespace raid::stats {
		12	+
		13	+enum class CovType { classical, hc0, hc1, hc2, hc3, hac_bartlett, cluster };
		14	+std::string to_string(CovType c);
		15	+CovType covtype_from_string(std::string_view s);
		16	+
		17	+struct CovOptions {
		18	+ CovType type{CovType::classical};
		19	+ int hac_lags{-1}; // −1 → floor(4 (n/100)^{2/9}) (Newey–West 1994 rule of thumb used by statsmodels users; documented)
		20	+ bool hac_small_sample{false}; // n/(n−k) factor (statsmodels use_correction)
		21	+ std::span<const i64> clusters{}; // cluster id per observation (CovType::cluster)
		22	+ bool cluster_small_sample{true}; // G/(G−1)·(N−1)/(N−K)
		23	+};
		24	+
		25	+/// Covariance of OLS/WLS coefficients given X (n×k), residuals e (n), and (X'X)^{-1} computed stably by the caller.
		26	+/// `leverage` (h_ii) is required for HC2/HC3 and may be empty otherwise.
		27	+Eigen::MatrixXd robust_covariance(const Eigen::Ref<const Eigen::MatrixXd>& X, const Eigen::Ref<const Eigen::VectorXd>& resid,
		28	+ const Eigen::Ref<const Eigen::MatrixXd>& xtx_inv, const CovOptions& opt,
		29	+ const Eigen::VectorXd& leverage = Eigen::VectorXd());
		30	+int default_hac_lags(usize n);
		31	+
		32	+} // namespace raid::stats

added include/raid/statistics/reductions.hpp +84 −0

				@@ -0,0 +1,84 @@
		1	+// Numerically stable reductions (FP64 reference implementations).
		2	+// sum_naive is provided only to *measure* error; library code uses sum_neumaier or sum_pairwise.
		3	+#pragma once
		4	+#include <span>
		5	+#include <cmath>
		6	+#include <vector>
		7	+#include <algorithm>
		8	+#include "raid/core/types.hpp"
		9	+#include "raid/core/error.hpp"
		10	+
		11	+namespace raid::stats {
		12	+
		13	+template <class T>
		14	+T sum_naive(std::span<const T> x) { T s = 0; for (T v : x) s += v; return s; }
		15	+
		16	+/// Neumaier's improved Kahan–Babuška summation.
		17	+template <class T>
		18	+T sum_neumaier(std::span<const T> x) {
		19	+ T s = 0, c = 0;
		20	+ for (T v : x) {
		21	+ const T t = s + v;
		22	+ if (std::abs(s) >= std::abs(v)) c += (s - t) + v; else c += (v - t) + s;
		23	+ s = t;
		24	+ }
		25	+ return s + c;
		26	+}
		27	+
		28	+/// Pairwise (cascade) summation with a base-case block of 128; error O(log n) ulp.
		29	+template <class T>
		30	+T sum_pairwise(std::span<const T> x) {
		31	+ if (x.size() <= 128) { T s = 0; for (T v : x) s += v; return s; }
		32	+ const usize m = x.size() / 2;
		33	+ return sum_pairwise(x.subspan(0, m)) + sum_pairwise(x.subspan(m));
		34	+}
		35	+
		36	+template <class T>
		37	+T dot_neumaier(std::span<const T> a, std::span<const T> b) {
		38	+ RAID_REQUIRE(a.size() == b.size(), invalid_argument, "dot: size mismatch");
		39	+ T s = 0, c = 0;
		40	+ for (usize i = 0; i < a.size(); ++i) {
		41	+ const T v = a[i] * b[i], t = s + v;
		42	+ if (std::abs(s) >= std::abs(v)) c += (s - t) + v; else c += (v - t) + s;
		43	+ s = t;
		44	+ }
		45	+ return s + c;
		46	+}
		47	+
		48	+template <class T>
		49	+T sum_squares(std::span<const T> x) { return dot_neumaier(x, x); }
		50	+
		51	+template <class T>
		52	+T mean(std::span<const T> x) { RAID_REQUIRE(!x.empty(), insufficient_samples, "mean of empty span"); return sum_neumaier(x) / static_cast<T>(x.size()); }
		53	+
		54	+/// Welford one-pass mean/variance (ddof = 1 by default).
		55	+struct Welford {
		56	+ usize n{0}; f64 mean{0}; f64 m2{0};
		57	+ void add(f64 x) { ++n; const f64 d = x - mean; mean += d / static_cast<f64>(n); m2 += d * (x - mean); }
		58	+ f64 variance(int ddof = 1) const { return n > static_cast<usize>(ddof) ? m2 / static_cast<f64>(n - static_cast<usize>(ddof)) : std::numeric_limits<f64>::quiet_NaN(); }
		59	+ /// Chan–Golub–LeVeque parallel combine.
		60	+ void merge(const Welford& o) {
		61	+ if (o.n == 0) return; if (n == 0) { *this = o; return; }
		62	+ const f64 d = o.mean - mean; const usize nn = n + o.n;
		63	+ mean += d * static_cast<f64>(o.n) / static_cast<f64>(nn);
		64	+ m2 += o.m2 + d * d * static_cast<f64>(n) * static_cast<f64>(o.n) / static_cast<f64>(nn);
		65	+ n = nn;
		66	+ }
		67	+};
		68	+
		69	+template <class T>
		70	+T variance(std::span<const T> x, int ddof = 1) { Welford w; for (T v : x) w.add(static_cast<f64>(v)); return static_cast<T>(w.variance(ddof)); }
		71	+
		72	+struct Moments { usize n; f64 mean, variance, skewness, kurtosis /* excess */, min, max; };
		73	+/// Two-pass moments; skewness/kurtosis use the biased (population) normalisation g1, g2 − 3 (as scipy with bias=True).
		74	+Moments moments(std::span<const f64> x);
		75	+
		76	+/// Autocovariance at lag k with the 1/n normalisation (as statsmodels acovf(adjusted=False)).
		77	+f64 autocovariance(std::span<const f64> x, usize k, bool demean = true);
		78	+std::vector<f64> autocorrelation(std::span<const f64> x, usize max_lag);
		79	+
		80	+/// Maximum drawdown of a cumulative-return path (fractional, ≥ 0), and the indices of the peak and trough.
		81	+struct Drawdown { f64 max_drawdown; usize peak_index; usize trough_index; };
		82	+Drawdown max_drawdown(std::span<const f64> cumulative);
		83	+
		84	+} // namespace raid::stats

added include/raid/statistics/rolling.hpp +30 −0

				@@ -0,0 +1,30 @@
		1	+// Rolling / expanding window statistics (CPU FP64 reference).
		2	+// Implementation detail: running sums use Neumaier compensation AND a periodic exact re-summation of the window
		3	+// (every `resum_every` steps) so that drift cannot accumulate over 390 000-bar series.
		4	+#pragma once
		5	+#include <span>
		6	+#include <vector>
		7	+#include <limits>
		8	+#include "raid/core/types.hpp"
		9	+
		10	+namespace raid::stats {
		11	+
		12	+constexpr f64 kNaN = std::numeric_limits<f64>::quiet_NaN();
		13	+
		14	+struct RollingOptions {
		15	+ usize window{22};
		16	+ usize min_periods{0}; // 0 = window (full windows only); otherwise partial windows allowed at the start
		17	+ usize resum_every{1024}; // exact re-summation cadence
		18	+};
		19	+
		20	+std::vector<f64> rolling_sum(std::span<const f64> x, const RollingOptions& o);
		21	+std::vector<f64> rolling_mean(std::span<const f64> x, const RollingOptions& o);
		22	+std::vector<f64> rolling_variance(std::span<const f64> x, const RollingOptions& o, int ddof = 1);
		23	+std::vector<f64> rolling_covariance(std::span<const f64> x, std::span<const f64> y, const RollingOptions& o, int ddof = 1);
		24	+std::vector<f64> rolling_correlation(std::span<const f64> x, std::span<const f64> y, const RollingOptions& o);
		25	+std::vector<f64> expanding_mean(std::span<const f64> x, usize min_periods = 1);
		26	+std::vector<f64> expanding_variance(std::span<const f64> x, usize min_periods = 2, int ddof = 1);
		27	+/// Causal lag (positive k) or lead (negative k); NaN where undefined.
		28	+std::vector<f64> shift(std::span<const f64> x, long k);
		29	+
		30	+} // namespace raid::stats

added pyproject.toml +33 −0

				@@ -0,0 +1,33 @@
		1	+[build-system]
		2	+requires = ["scikit-build-core>=0.10", "nanobind>=2.0"]
		3	+build-backend = "scikit_build_core.build"
		4	+
		5	+[project]
		6	+name = "raid-hf"
		7	+version = "0.1.0"
		8	+description = "RAID — Research Architecture for Intraday Data: Metal-accelerated C++20 high-frequency financial econometrics (UQO WP 40)"
		9	+readme = "README.md"
		10	+requires-python = ">=3.11"
		11	+license = { text = "MIT (code). Market data: FirstRate Data private license, not redistributed." }
		12	+authors = [{ name = "Simon-Pierre Boucher", email = "simon-pierre.boucher@uqo.ca" }]
		13	+dependencies = ["numpy>=1.26", "pyarrow>=17", "httpx>=0.27"]
		14	+
		15	+[project.optional-dependencies]
		16	+dev = ["pytest>=8", "hypothesis>=6", "ruff", "statsmodels>=0.14", "arch>=7", "scipy>=1.13", "polars>=1.0", "duckdb>=1.0", "pandas>=2.2", "exchange-calendars>=4.5", "matplotlib>=3.9", "pyyaml>=6"]
		17	+
		18	+[tool.scikit-build]
		19	+cmake.version = ">=3.28"
		20	+cmake.build-type = "Release"
		21	+cmake.args = ["-DRAID_BUILD_PYTHON=ON", "-DRAID_BUILD_TESTS=OFF", "-DRAID_BUILD_BENCH=OFF", "-DRAID_BUILD_CLI=OFF", "-DRAID_NATIVE_ARCH=OFF", "-DCMAKE_PREFIX_PATH=/opt/homebrew"]
		22	+wheel.packages = ["python/raid"]
		23	+wheel.py-api = "cp312"
		24	+sdist.exclude = ["datasets/cache", "experiments/results", "build", "paper/*.pdf"]
		25	+
		26	+[tool.pytest.ini_options]
		27	+testpaths = ["python/tests"]
		28	+addopts = "-q"
		29	+markers = ["live: calls the live FinData API (skipped unless RAID_LIVE=1)"]
		30	+
		31	+[tool.ruff]
		32	+line-length = 120
		33	+target-version = "py312"

added python/raid/__init__.py +23 −0

				@@ -0,0 +1,23 @@
		1	+"""RAID — Research Architecture for Intraday Data (UQO Working Paper No. 40).
		2	+
		3	+Python layer: the compiled extension ``raid._core`` (nanobind) exposes the C++20 estimators and backends;
		4	+``raid.findata`` is the FinData OS client (bars, exports, provenance); ``raid.api`` wraps the extension with
		5	+NumPy/Arrow-friendly signatures and the ``backend="auto|cpu|metal"`` choice.
		6	+"""
		7	+from __future__ import annotations
		8	+
		9	+__version__ = "0.1.0"
		10	+
		11	+try: # the compiled core is optional at import time so that the pure-Python client can be used alone
		12	+ from . import _core # type: ignore
		13	+ HAS_CORE = True
		14	+except ImportError: # pragma: no cover
		15	+ _core = None
		16	+ HAS_CORE = False
		17	+
		18	+from .api import ( # noqa: E402
		19	+ devices, ols, realized_measures, garch_fit, validate_bars, calendar_sessions, Backend, available_backends,
		20	+)
		21	+
		22	+__all__ = ["__version__", "HAS_CORE", "devices", "ols", "realized_measures", "garch_fit", "validate_bars",
		23	+ "calendar_sessions", "Backend", "available_backends"]

added python/raid/api.py +78 −0

				@@ -0,0 +1,78 @@
		1	+"""NumPy-facing wrappers over the compiled core (``raid._core``). All heavy work releases the GIL in C++."""
		2	+from __future__ import annotations
		3	+
		4	+import enum
		5	+from typing import Any, Mapping, Sequence
		6	+
		7	+import numpy as np
		8	+
		9	+
		10	+class Backend(str, enum.Enum):
		11	+ auto = "auto"
		12	+ cpu = "cpu"
		13	+ metal = "metal"
		14	+
		15	+
		16	+def _core():
		17	+ from . import _core as c # noqa: WPS433
		18	+ if c is None:
		19	+ raise ImportError("raid._core is not built; install the wheel or build with -DRAID_BUILD_PYTHON=ON")
		20	+ return c
		21	+
		22	+
		23	+def devices() -> list[dict[str, Any]]:
		24	+ """List the available compute devices (CPU always; Metal when built and present)."""
		25	+ return _core().devices()
		26	+
		27	+
		28	+def available_backends() -> list[str]:
		29	+ return [d["kind"] for d in devices()]
		30	+
		31	+
		32	+def _f64(x) -> np.ndarray:
		33	+ a = np.ascontiguousarray(x, dtype=np.float64)
		34	+ if not np.isfinite(a).all():
		35	+ raise ValueError("non-finite values in input (RAID never imputes silently)")
		36	+ return a
		37	+
		38	+
		39	+def ols(X, y, cov: str = "classical", hac_lags: int = -1, clusters=None, ridge_lambda: float = 0.0,
		40	+ unpenalized: Sequence[int] = (), weights=None, names: Sequence[str] | None = None) -> dict[str, Any]:
		41	+ """OLS/WLS/ridge with classical, HC0–HC3, HAC (Bartlett) or one-way cluster covariance. Returns an Estimate dict."""
		42	+ X = _f64(X); y = _f64(y)
		43	+ if X.ndim != 2 or y.ndim != 1 or X.shape[0] != y.shape[0]:
		44	+ raise ValueError("X must be (n, k) and y (n,)")
		45	+ cl = None if clusters is None else np.ascontiguousarray(clusters, dtype=np.int64)
		46	+ w = None if weights is None else _f64(weights)
		47	+ return _core().ols(X, y, cov, int(hac_lags), cl, float(ridge_lambda), list(unpenalized), w, list(names or []))
		48	+
		49	+
		50	+def realized_measures(returns, offsets, backend: str | Backend = Backend.auto, bpv_finite_sample: bool = True,
		51	+ deterministic: bool = True) -> dict[str, np.ndarray]:
		52	+ """Per-segment realized measures from concatenated intraday returns and CSR offsets (n_segments + 1)."""
		53	+ r = _f64(returns); off = np.ascontiguousarray(offsets, dtype=np.int64)
		54	+ return _core().realized_measures(r, off, str(Backend(backend).value), bool(bpv_finite_sample), bool(deterministic))
		55	+
		56	+
		57	+def garch_fit(returns, model: str = "garch", dist: str = "gaussian", horizon: int = 0, cov: str = "qmle",
		58	+ max_iter: int = 500, tol: float = 1e-8) -> dict[str, Any]:
		59	+ """QML fit of GARCH(1,1) / GJR / EGARCH with Gaussian or Student-t errors (CPU FP64)."""
		60	+ return _core().garch_fit(_f64(returns), model, dist, int(horizon), cov, int(max_iter), float(tol))
		61	+
		62	+
		63	+def validate_bars(timestamps_us, open_, high, low, close, volume, symbol: str = "", interval: str = "1m",
		64	+ session: str = "regular") -> dict[str, Any]:
		65	+ """Quality report of one bar series (timestamps = bar open, microseconds UTC)."""
		66	+ t = np.ascontiguousarray(timestamps_us, dtype=np.int64)
		67	+ return _core().validate_bars(t, _f64(open_), _f64(high), _f64(low), _f64(close), _f64(volume), symbol, interval, session)
		68	+
		69	+
		70	+def calendar_sessions(start: str, end: str) -> list[str]:
		71	+ """XNYS trading sessions in [start, end] (ISO dates)."""
		72	+ return _core().calendar_sessions(start, end)
		73	+
		74	+
		75	+def bars_to_intraday_returns(timestamps_us, close, kind: str = "log") -> dict[str, np.ndarray]:
		76	+ """Session-aware intraday log/arithmetic returns from (bar open timestamp, close) arrays; returns CSR layout."""
		77	+ t = np.ascontiguousarray(timestamps_us, dtype=np.int64)
		78	+ return _core().intraday_returns(t, _f64(close), kind)

added python/tests/test_api.py +60 −0

				@@ -0,0 +1,60 @@
		1	+"""pytest for the Python layer (requires the compiled core on sys.path: build with -DRAID_BUILD_PYTHON=ON)."""
		2	+import os, sys
		3	+import numpy as np
		4	+import pytest
		5	+
		6	+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
		7	+raid = pytest.importorskip("raid")
		8	+if not raid.HAS_CORE:
		9	+ pytest.skip("raid._core not built", allow_module_level=True)
		10	+
		11	+
		12	+def test_devices_has_cpu():
		13	+ kinds = [d["kind"] for d in raid.devices()]
		14	+ assert "cpu" in kinds
		15	+
		16	+
		17	+def test_ols_matches_statsmodels_hc3():
		18	+ sm = pytest.importorskip("statsmodels.api")
		19	+ rng = np.random.default_rng(3); X = np.column_stack([np.ones(400), rng.normal(size=400), rng.normal(size=400)]); y = X @ [1, 2, -1] + rng.normal(size=400) * (1 + X[:, 1] ** 2)
		20	+ e = raid.ols(X, y, cov="hc3"); f = sm.OLS(y, X).fit(cov_type="HC3")
		21	+ np.testing.assert_allclose(e["coefficients"], f.params, rtol=1e-10)
		22	+ np.testing.assert_allclose(e["inference"]["std_errors"], f.bse, rtol=1e-9)
		23	+ assert e["ok"] and e["backend"] == "cpu" and e["precision"] == "fp64"
		24	+
		25	+
		26	+def test_ols_rejects_nan():
		27	+ with pytest.raises(ValueError):
		28	+ raid.ols(np.array([[1.0, np.nan]]), np.array([1.0]))
		29	+
		30	+
		31	+def test_realized_measures_cpu_matches_numpy():
		32	+ rng = np.random.default_rng(5); r = rng.normal(0, 1e-3, size=390 * 10); off = np.arange(0, 3901, 390)
		33	+ m = raid.realized_measures(r, off, backend="cpu")
		34	+ for k in range(10):
		35	+ seg = r[off[k]:off[k + 1]]
		36	+ assert abs(m["rv"][k] - np.sum(seg ** 2)) <= 1e-15 * np.sum(seg ** 2) * 10
		37	+ assert abs(m["rsv_neg"][k] + m["rsv_pos"][k] - m["rv"][k]) <= 1e-18
		38	+ assert m["backend"] == "cpu"
		39	+
		40	+
		41	+def test_garch_fit_converges_and_forecasts():
		42	+ rng = np.random.default_rng(9); n = 2500; r = np.empty(n); s2 = 1e-4; e = 0.0
		43	+ for t in range(n):
		44	+ s2 = 1e-6 + 0.08 * e * e + 0.9 * s2; e = np.sqrt(s2) * rng.standard_normal(); r[t] = e
		45	+ g = raid.garch_fit(r, model="garch", dist="gaussian", horizon=5)
		46	+ assert g["ok"] and g["convergence"]["converged"]
		47	+ assert abs(g["coefficients"][3] - 0.9) < 0.08 and len(g["extra"]["forecast_variance"]) == 5
		48	+
		49	+
		50	+def test_calendar_sessions_thanksgiving_week():
		51	+ assert raid.calendar_sessions("2024-11-25", "2024-11-29") == ["2024-11-25", "2024-11-26", "2024-11-27", "2024-11-29"]
		52	+
		53	+
		54	+def test_validate_bars_counts_missing():
		55	+ # one session, 391 regular minutes 2024-11-27 (09:30–16:00 ET = 14:30–21:00 UTC), drop 3 bars
		56	+ base = np.datetime64("2024-11-27T14:30:00").astype("datetime64[us]").astype(np.int64)
		57	+ t = base + np.arange(391) * 60_000_000; keep = np.ones(391, bool); keep[[10, 20, 30]] = False; t = t[keep]
		58	+ p = 100 + np.cumsum(np.random.default_rng(1).normal(0, 0.01, size=t.size))
		59	+ q = raid.validate_bars(t, p, p + 0.01, p - 0.01, p, np.full(t.size, 100.0))
		60	+ assert q["n_sessions"] == 1 and q["missing_regular_bars"] == 3 and not q["has_errors"]

added scripts/check_no_secrets.sh +9 −0

				@@ -0,0 +1,9 @@
		1	+#!/bin/bash
		2	+# Fails if tracked files contain FinData keys, bearer tokens, vendor user ids or real-data parquet files.
		3	+set -euo pipefail
		4	+cd "$(dirname "$0")/.."
		5	+bad=0
		6	+if git ls-files -z | xargs -0 grep -nE "fd_[A-Za-z0-9]{6,}\.[A-Za-z0-9_-]{10,}|userid=[A-Za-z0-9]{8,}|Bearer [A-Za-z0-9._-]{20,}" 2>/dev/null; then bad=1; fi
		7	+if git ls-files | grep -E "\.(parquet|arrow)$" | grep -vE "^datasets/(fixtures|synthetic)/"; then bad=1; fi
		8	+if git ls-files | grep -E "api-key|\.env$"; then bad=1; fi
		9	+[ $bad -eq 0 ] && echo "no secrets or vendor data tracked" || { echo "SECRET/DATA CHECK FAILED"; exit 1; }

added scripts/make_fixtures.py +90 −0

				@@ -0,0 +1,90 @@
		1	+#!/usr/bin/env python3
		2	+"""Generates the synthetic 1-minute OHLCV fixtures used by CI (no vendor data) and the reference values
		3	+(numpy/statsmodels/exchange_calendars/arch) consumed by tests/reference. Deterministic (seed 20261102).
		4	+
		5	+Fixture: 2 symbols × 5 sessions around Thanksgiving 2024 (2024-11-25 … 2024-11-29, incl. the early close on 11-29
		6	+and the holiday 11-28), extended hours 04:00–19:59 ET with regular bars 09:30–16:00 inclusive; ~3 % of regular
		7	+minutes randomly absent (zero-volume minutes are absent at the source); the 16:00 bar is present on every session.
		8	+"""
		9	+import json, math, os, sys
		10	+import numpy as np
		11	+import pandas as pd
		12	+
		13	+root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
		14	+out_dir = os.path.join(root, "datasets", "fixtures"); os.makedirs(out_dir, exist_ok=True)
		15	+rng = np.random.default_rng(20261102)
		16	+sessions = ["2024-11-25", "2024-11-26", "2024-11-27", "2024-11-29", "2024-12-02"]
		17	+early = {"2024-11-29"}
		18	+rows = []
		19	+truth = {}
		20	+for sym, p0, vol in [("SYN1", 100.0, 0.0008), ("SYN2", 25.0, 0.0015)]:
		21	+ price = p0
		22	+ truth[sym] = {}
		23	+ for s in sessions:
		24	+ close_min = 13 * 60 if s in early else 16 * 60
		25	+ ext_end = 17 * 60 if s in early else 20 * 60
		26	+ minutes = list(range(4 * 60, ext_end))
		27	+ day = pd.Timestamp(s, tz="America/New_York")
		28	+ sess_rv = 0.0; n_reg = 0; closes = []
		29	+ for m in minutes:
		30	+ regular = 9 * 60 + 30 <= m <= close_min
		31	+ # drop ~3 % of regular minutes and ~40 % of extended minutes, but never the open/close prints
		32	+ if m not in (9 * 60 + 30, close_min):
		33	+ if regular and rng.random() < 0.03: continue
		34	+ if not regular and rng.random() < 0.40: continue
		35	+ r = rng.normal(0, vol * (1.0 if regular else 0.5))
		36	+ o = price; c = price * math.exp(r); h = max(o, c) * (1 + abs(rng.normal(0, 1e-4))); l = min(o, c) * (1 - abs(rng.normal(0, 1e-4)))
		37	+ v = float(rng.integers(100, 5000)) if regular else float(rng.integers(0, 300) + 1)
		38	+ ts = (day + pd.Timedelta(minutes=m)).tz_convert("UTC")
		39	+ rows.append((sym, ts.strftime("%Y-%m-%dT%H:%M:%SZ"), round(o, 4), round(h, 4), round(l, 4), round(c, 4), v))
		40	+ price = c
		41	+ if regular:
		42	+ closes.append(round(c, 4)); n_reg += 1 # truth from the rounded prices written to the CSV
		43	+ lr = np.diff(np.log(np.array(closes)))
		44	+ truth[sym][s] = {"n_regular_bars": n_reg, "rv": float(np.sum(lr ** 2)), "bpv": float((math.pi / 2) * (len(lr) / (len(lr) - 1)) * np.sum(np.abs(lr[1:]) * np.abs(lr[:-1]))),
		45	+ "rsv_neg": float(np.sum(lr[lr < 0] ** 2)), "rsv_pos": float(np.sum(lr[lr > 0] ** 2)), "rq": float(len(lr) / 3 * np.sum(lr ** 4)),
		46	+ "expected_bars": 211 if s in early else 391}
		47	+df = pd.DataFrame(rows, columns=["symbol", "timestamp_utc", "open", "high", "low", "close", "volume"])
		48	+df.to_csv(os.path.join(out_dir, "synthetic_bars_1m.csv"), index=False)
		49	+json.dump(truth, open(os.path.join(out_dir, "synthetic_bars_1m.expected.json"), "w"), indent=1)
		50	+print("wrote", len(df), "bars for", df.symbol.nunique(), "symbols;", df.groupby("symbol").size().to_dict())
		51	+
		52	+# --- reference values from established software -------------------------------------------------------------
		53	+ref = {}
		54	+try:
		55	+ import exchange_calendars as xc
		56	+ cal = xc.get_calendar("XNYS", start="2000-01-01", end="2026-12-31")
		57	+ sess = cal.sessions_in_range("2000-01-01", "2026-12-31")
		58	+ ref["xnys"] = {"n_sessions_2000_2026": int(len(sess)), "sessions_2024": [str(d.date()) for d in cal.sessions_in_range("2024-01-01", "2024-12-31")],
		59	+ "early_closes_2000_2026": [str(d.date()) for d in cal.early_closes.sessions_in_range("2000-01-01", "2026-12-31")] if hasattr(cal, "early_closes") else None,
		60	+ "holidays_2000_2026": [str(d.date()) for d in pd.bdate_range("2000-01-01", "2026-12-31") if d not in sess],
		61	+ "exchange_calendars_version": xc.__version__}
		62	+except Exception as e:
		63	+ ref["xnys"] = {"error": str(e)}
		64	+import statsmodels.api as sm
		65	+from scipy import stats as sps
		66	+X = np.column_stack([np.ones(500), rng.normal(size=500), rng.normal(size=500) * 2 + 1]); y = 0.5 + 1.5 * X[:, 1] - 0.7 * X[:, 2] + rng.normal(size=500) * (1 + 0.5 * np.abs(X[:, 1]))
		67	+ols = sm.OLS(y, X).fit(); hc0 = sm.OLS(y, X).fit(cov_type="HC0"); hc3 = sm.OLS(y, X).fit(cov_type="HC3"); hac = sm.OLS(y, X).fit(cov_type="HAC", cov_kwds={"maxlags": 5, "use_correction": False})
		68	+groups = np.arange(500) // 25; cl = sm.OLS(y, X).fit(cov_type="cluster", cov_kwds={"groups": groups})
		69	+np.savetxt(os.path.join(out_dir, "ols_design.csv"), np.column_stack([y, X[:, 1], X[:, 2]]), delimiter=",", header="y,x1,x2", comments="", fmt="%.17g")
		70	+ref["ols"] = {"params": ols.params.tolist(), "bse": ols.bse.tolist(), "pvalues": ols.pvalues.tolist(), "rsquared": float(ols.rsquared), "ssr": float(ols.ssr),
		71	+ "hc0_bse": hc0.bse.tolist(), "hc3_bse": hc3.bse.tolist(), "hac5_bse": hac.bse.tolist(), "cluster_bse": cl.bse.tolist(), "statsmodels_version": sm.__version__,
		72	+ "t_pvalues": {"1.96_inf": float(2 * sps.norm.sf(1.96)), "2.0_10": float(2 * sps.t.sf(2.0, 10)), "-3.0_30": float(2 * sps.t.sf(3.0, 30))}}
		73	+try:
		74	+ from arch import arch_model
		75	+ import arch
		76	+ s2 = 0.04 / (1 - 0.05 - 0.9); e = 0.0; r = np.empty(3000); g = np.random.default_rng(7)
		77	+ for t in range(3000):
		78	+ s2 = 0.04 + 0.05 * e * e + 0.9 * s2; e = math.sqrt(s2) * g.standard_normal(); r[t] = 0.01 + e
		79	+ np.savetxt(os.path.join(out_dir, "garch_returns.csv"), r, header="r", comments="", fmt="%.17g")
		80	+ out = {"arch_version": arch.__version__}
		81	+ for name, kw in [("garch", dict(vol="GARCH", p=1, q=1)), ("gjr", dict(vol="GARCH", p=1, o=1, q=1)), ("egarch", dict(vol="EGARCH", p=1, o=1, q=1))]:
		82	+ for dist in ["normal", "t"]:
		83	+ am = arch_model(r, mean="Constant", dist=dist, rescale=False, **kw); res = am.fit(disp="off")
		84	+ out[f"{name}.{dist}"] = {"params": {k: float(v) for k, v in res.params.items()}, "std_err": {k: float(v) for k, v in res.std_err.items()}, "loglik": float(res.loglikelihood),
		85	+ "converged": bool(res.convergence_flag == 0), "forecast_h5": res.forecast(horizon=5, reindex=False).variance.values[-1].tolist() if name != "egarch" else res.forecast(horizon=5, method="simulation", simulations=20000, reindex=False).variance.values[-1].tolist()}
		86	+ ref["garch"] = out
		87	+except Exception as e:
		88	+ ref["garch"] = {"error": repr(e)}
		89	+json.dump(ref, open(os.path.join(out_dir, "reference_values.json"), "w"), indent=1)
		90	+print("reference values written:", list(ref.keys()))

added scripts/record_environment.sh +28 −0

				@@ -0,0 +1,28 @@
		1	+#!/bin/bash
		2	+# Records the build/benchmark environment into experiments/manifests/environment_<host>.json (no secrets).
		3	+set -euo pipefail
		4	+OUT="${1:-$(dirname "$0")/../experiments/manifests/environment_$(hostname -s).json}"
		5	+mkdir -p "$(dirname "$OUT")"
		6	+python3 -I - "$OUT" <<'PY'
		7	+import json, subprocess, sys, platform, datetime, os
		8	+def sh(c):
		9	+ try: return subprocess.run(c, shell=True, capture_output=True, text=True, timeout=60).stdout.strip()
		10	+ except Exception as e: return f"ERR {e}"
		11	+env = {
		12	+ "recorded_at": datetime.datetime.now(datetime.timezone.utc).isoformat(),
		13	+ "host": platform.node(), "os": sh("sw_vers -productVersion 2>/dev/null || uname -r"), "build": sh("sw_vers -buildVersion 2>/dev/null"),
		14	+ "chip": sh("sysctl -n machdep.cpu.brand_string 2>/dev/null || grep -m1 'model name' /proc/cpuinfo"),
		15	+ "ncpu": sh("sysctl -n hw.ncpu 2>/dev/null || nproc"), "perf_cores": sh("sysctl -n hw.perflevel0.physicalcpu 2>/dev/null"), "eff_cores": sh("sysctl -n hw.perflevel1.physicalcpu 2>/dev/null"),
		16	+ "mem_bytes": sh("sysctl -n hw.memsize 2>/dev/null || grep MemTotal /proc/meminfo"),
		17	+ "gpu": sh("system_profiler SPDisplaysDataType 2>/dev/null | grep -E 'Chipset Model|Total Number of Cores|Metal' | sed 's/^ *//' | tr '\n' ';'"),
		18	+ "xcode": sh("xcodebuild -version 2>/dev/null | tr '\n' ' '"), "clang": sh("clang --version | head -1"), "sdk": sh("xcrun -sdk macosx --show-sdk-version 2>/dev/null"),
		19	+ "metal_toolchain": sh("xcrun -sdk macosx metal --version 2>/dev/null | head -1"),
		20	+ "cmake": sh("cmake --version | head -1"), "ninja": sh("ninja --version 2>/dev/null"),
		21	+ "brew": {p: sh(f"brew list --versions {p} 2>/dev/null") for p in ["eigen","apache-arrow","catch2","nlohmann-json","openblas","libomp"]},
		22	+ "python": sh("python3 --version"), "r": sh("R --version 2>/dev/null | head -1"),
		23	+ "load_avg": sh("sysctl -n vm.loadavg 2>/dev/null || cat /proc/loadavg"), "thermal": sh("pmset -g therm 2>/dev/null | tr '\n' ';'"),
		24	+ "power_source": sh("pmset -g batt 2>/dev/null | head -1"), "uptime": sh("uptime"),
		25	+ "git_commit": sh("git rev-parse --short HEAD 2>/dev/null"),
		26	+}
		27	+json.dump(env, open(sys.argv[1], "w"), indent=1); print("wrote", sys.argv[1])
		28	+PY

added src/core/estimate.cpp +40 −0

				@@ -0,0 +1,40 @@
		1	+#include "raid/core/estimate.hpp"
		2	+#include "raid/core/plan.hpp"
		3	+
		4	+namespace raid {
		5	+
		6	+nlohmann::json Provenance::to_json() const {
		7	+ return {{"dataset_id", dataset_id}, {"dataset_version", dataset_version}, {"request_ids", request_ids},
		8	+ {"input_hashes", input_hashes}, {"code_version", code_version}, {"source", source}, {"extra", extra}};
		9	+}
		10	+nlohmann::json Convergence::to_json() const {
		11	+ return {{"converged", converged}, {"iterations", iterations}, {"function_evaluations", function_evaluations},
		12	+ {"gradient_norm", gradient_norm}, {"status_code", status_code}, {"message", message}};
		13	+}
		14	+nlohmann::json InferenceResult::to_json() const {
		15	+ std::vector<std::vector<f64>> cov(static_cast<usize>(covariance.rows()));
		16	+ for (Eigen::Index i = 0; i < covariance.rows(); ++i) { cov[static_cast<usize>(i)].resize(static_cast<usize>(covariance.cols())); for (Eigen::Index j = 0; j < covariance.cols(); ++j) cov[static_cast<usize>(i)][static_cast<usize>(j)] = covariance(i, j); }
		17	+ return {{"std_errors", std_errors}, {"t_stats", t_stats}, {"p_values", p_values}, {"covariance", cov},
		18	+ {"covariance_type", covariance_type}, {"small_sample_correction", small_sample_correction}};
		19	+}
		20	+nlohmann::json Estimate::to_json() const {
		21	+ nlohmann::json j = {{"model", model}, {"names", names}, {"coefficients", coefficients}, {"loss", loss},
		22	+ {"convergence", convergence.to_json()}, {"warnings", warnings}, {"runtime_seconds", runtime_seconds},
		23	+ {"backend", std::string(to_string(backend))}, {"precision", std::string(to_string(precision))},
		24	+ {"provenance", provenance.to_json()}, {"n_obs", n_obs}, {"extra", extra}, {"ok", ok()}};
		25	+ if (inference) j["inference"] = inference->to_json();
		26	+ return j;
		27	+}
		28	+nlohmann::json Device::to_json() const {
		29	+ return {{"name", name}, {"kind", std::string(to_string(kind))}, {"memory_bytes", memory_bytes}, {"compute_units", compute_units},
		30	+ {"unified_memory", unified_memory}, {"supports_fp64", supports_fp64}, {"max_buffer_bytes", max_buffer_bytes}, {"details", details}};
		31	+}
		32	+nlohmann::json PrecisionPolicy::to_json() const {
		33	+ return {{"requested", std::string(to_string(requested))}, {"fp32_chunk_max", fp32_chunk_max}, {"compensated", compensated}, {"validate_rtol", validate_rtol}};
		34	+}
		35	+nlohmann::json ExecutionPlan::to_json() const {
		36	+ return {{"backend", std::string(to_string(backend))}, {"precision", precision.to_json()}, {"deterministic", deterministic},
		37	+ {"threads", threads}, {"chunk_rows", chunk_rows}, {"max_memory_bytes", max_memory_bytes}, {"allow_fallback", allow_fallback}, {"seed", seed}};
		38	+}
		39	+
		40	+} // namespace raid

added src/core/hash.cpp +66 −0

				@@ -0,0 +1,66 @@
		1	+#include "raid/core/hash.hpp"
		2	+#include "raid/core/error.hpp"
		3	+#include <cstring>
		4	+#include <fstream>
		5	+#include <vector>
		6	+
		7	+namespace raid {
		8	+
		9	+namespace {
		10	+constexpr std::uint32_t K[64] = {
		11	+ 0x428a2f98,0x71374491,0xb5c0fbcf,0xe9b5dba5,0x3956c25b,0x59f111f1,0x923f82a4,0xab1c5ed5,0xd807aa98,0x12835b01,0x243185be,0x550c7dc3,0x72be5d74,0x80deb1fe,0x9bdc06a7,0xc19bf174,
		12	+ 0xe49b69c1,0xefbe4786,0x0fc19dc6,0x240ca1cc,0x2de92c6f,0x4a7484aa,0x5cb0a9dc,0x76f988da,0x983e5152,0xa831c66d,0xb00327c8,0xbf597fc7,0xc6e00bf3,0xd5a79147,0x06ca6351,0x14292967,
		13	+ 0x27b70a85,0x2e1b2138,0x4d2c6dfc,0x53380d13,0x650a7354,0x766a0abb,0x81c2c92e,0x92722c85,0xa2bfe8a1,0xa81a664b,0xc24b8b70,0xc76c51a3,0xd192e819,0xd6990624,0xf40e3585,0x106aa070,
		14	+ 0x19a4c116,0x1e376c08,0x2748774c,0x34b0bcb5,0x391c0cb3,0x4ed8aa4a,0x5b9cca4f,0x682e6ff3,0x748f82ee,0x78a5636f,0x84c87814,0x8cc70208,0x90befffa,0xa4506ceb,0xbef9a3f7,0xc67178f2};
		15	+inline std::uint32_t rotr(std::uint32_t x, int n) { return (x >> n) | (x << (32 - n)); }
		16	+}
		17	+
		18	+Sha256::Sha256() : h_{0x6a09e667,0xbb67ae85,0x3c6ef372,0xa54ff53a,0x510e527f,0x9b05688c,0x1f83d9ab,0x5be0cd19} {}
		19	+
		20	+void Sha256::transform(const std::uint8_t* p) {
		21	+ std::uint32_t w[64];
		22	+ for (int i = 0; i < 16; ++i) w[i] = (std::uint32_t(p[4*i]) << 24) | (std::uint32_t(p[4*i+1]) << 16) | (std::uint32_t(p[4*i+2]) << 8) | p[4*i+3];
		23	+ for (int i = 16; i < 64; ++i) { const std::uint32_t s0 = rotr(w[i-15],7) ^ rotr(w[i-15],18) ^ (w[i-15] >> 3), s1 = rotr(w[i-2],17) ^ rotr(w[i-2],19) ^ (w[i-2] >> 10); w[i] = w[i-16] + s0 + w[i-7] + s1; }
		24	+ std::uint32_t a=h_[0],b=h_[1],c=h_[2],d=h_[3],e=h_[4],f=h_[5],g=h_[6],h=h_[7];
		25	+ for (int i = 0; i < 64; ++i) {
		26	+ const std::uint32_t S1 = rotr(e,6) ^ rotr(e,11) ^ rotr(e,25), ch = (e & f) ^ (~e & g), t1 = h + S1 + ch + K[i] + w[i];
		27	+ const std::uint32_t S0 = rotr(a,2) ^ rotr(a,13) ^ rotr(a,22), maj = (a & b) ^ (a & c) ^ (b & c), t2 = S0 + maj;
		28	+ h = g; g = f; f = e; e = d + t1; d = c; c = b; b = a; a = t1 + t2;
		29	+ }
		30	+ h_[0]+=a; h_[1]+=b; h_[2]+=c; h_[3]+=d; h_[4]+=e; h_[5]+=f; h_[6]+=g; h_[7]+=h;
		31	+}
		32	+
		33	+void Sha256::update(const void* data, std::size_t len) {
		34	+ const auto* p = static_cast<const std::uint8_t*>(data); total_ += len;
		35	+ while (len > 0) {
		36	+ const std::size_t take = std::min(len, 64 - buf_len_);
		37	+ std::memcpy(buf_.data() + buf_len_, p, take); buf_len_ += take; p += take; len -= take;
		38	+ if (buf_len_ == 64) { transform(buf_.data()); buf_len_ = 0; }
		39	+ }
		40	+}
		41	+
		42	+std::array<std::uint8_t, 32> Sha256::digest() {
		43	+ const std::uint64_t bits = total_ * 8;
		44	+ std::uint8_t pad = 0x80; update(&pad, 1);
		45	+ const std::uint8_t zero = 0; while (buf_len_ != 56) update(&zero, 1);
		46	+ std::uint8_t len[8]; for (int i = 0; i < 8; ++i) len[i] = static_cast<std::uint8_t>(bits >> (56 - 8 * i));
		47	+ update(len, 8);
		48	+ std::array<std::uint8_t, 32> out{};
		49	+ for (int i = 0; i < 8; ++i) { out[4*i] = static_cast<std::uint8_t>(h_[i] >> 24); out[4*i+1] = static_cast<std::uint8_t>(h_[i] >> 16); out[4*i+2] = static_cast<std::uint8_t>(h_[i] >> 8); out[4*i+3] = static_cast<std::uint8_t>(h_[i]); }
		50	+ return out;
		51	+}
		52	+
		53	+std::string Sha256::hex() {
		54	+ const auto d = digest(); static const char* hx = "0123456789abcdef"; std::string s; s.reserve(64);
		55	+ for (auto b : d) { s += hx[b >> 4]; s += hx[b & 15]; }
		56	+ return s;
		57	+}
		58	+
		59	+std::string Sha256::of_file(const std::string& path) {
		60	+ std::ifstream f(path, std::ios::binary); RAID_REQUIRE(f.good(), io, "cannot open " + path);
		61	+ Sha256 h; std::vector<char> buf(1 << 20);
		62	+ while (f) { f.read(buf.data(), static_cast<std::streamsize>(buf.size())); h.update(buf.data(), static_cast<std::size_t>(f.gcount())); }
		63	+ return h.hex();
		64	+}
		65	+
		66	+} // namespace raid

added src/core/log.cpp +35 −0

				@@ -0,0 +1,35 @@
		1	+#include "raid/core/log.hpp"
		2	+#include <cstdio>
		3	+#include <regex>
		4	+#include <atomic>
		5	+
		6	+namespace raid::log {
		7	+
		8	+namespace {
		9	+std::atomic<int> g_level{static_cast<int>(Level::info)};
		10	+std::mutex g_mutex;
		11	+Sink g_sink;
		12	+const char* name(Level l) {
		13	+ switch (l) { case Level::trace: return "TRACE"; case Level::debug: return "DEBUG"; case Level::info: return "INFO"; case Level::warn: return "WARN"; case Level::error: return "ERROR"; default: return "OFF"; }
		14	+}
		15	+} // namespace
		16	+
		17	+void set_level(Level l) { g_level.store(static_cast<int>(l)); }
		18	+Level level() { return static_cast<Level>(g_level.load()); }
		19	+void set_sink(Sink s) { std::lock_guard<std::mutex> lk(g_mutex); g_sink = std::move(s); }
		20	+
		21	+std::string redact(std::string_view s) {
		22	+ static const std::regex re(R"((Bearer\s+)[A-Za-z0-9._\-]+|fd_[A-Za-z0-9]+\.[A-Za-z0-9_\-]+|(api[_-]?key=)[^&\s]+|(userid=)[^&\s]+)", std::regex::icase);
		23	+ return std::regex_replace(std::string(s), re, "$1$2$3<redacted>");
		24	+}
		25	+
		26	+void emit(Level l, std::string_view msg, Fields fields) {
		27	+ if (static_cast<int>(l) < g_level.load()) return;
		28	+ std::lock_guard<std::mutex> lk(g_mutex);
		29	+ if (g_sink) { g_sink(l, msg, fields); return; }
		30	+ std::string line = std::string(name(l)) + " " + redact(msg);
		31	+ for (auto& [k, v] : fields) line += " " + k + "=" + redact(v);
		32	+ std::fprintf(stderr, "%s\n", line.c_str());
		33	+}
		34	+
		35	+} // namespace raid::log

added src/core/manifest.cpp +76 −0

				@@ -0,0 +1,76 @@
		1	+#include "raid/core/manifest.hpp"
		2	+#include "raid/core/error.hpp"
		3	+#include <fstream>
		4	+#include <chrono>
		5	+#include <thread>
		6	+#include <sys/resource.h>
		7	+#ifdef __APPLE__
		8	+#include <sys/sysctl.h>
		9	+#include <sys/types.h>
		10	+#endif
		11	+#include <unistd.h>
		12	+#include <cstdio>
		13	+
		14	+namespace raid {
		15	+
		16	+namespace {
		17	+#ifdef __APPLE__
		18	+std::string sysctl_str(const char* name) { char buf[256]; size_t n = sizeof buf; if (sysctlbyname(name, buf, &n, nullptr, 0) == 0) return std::string(buf, n > 0 ? n - 1 : 0); return ""; }
		19	+i64 sysctl_i64(const char* name) { i64 v = 0; size_t n = sizeof v; if (sysctlbyname(name, &v, &n, nullptr, 0) == 0) return v; return -1; }
		20	+#endif
		21	+std::string shell(const char* cmd) {
		22	+ std::string out; if (FILE* p = popen(cmd, "r")) { char b[512]; while (fgets(b, sizeof b, p)) out += b; pclose(p); }
		23	+ while (!out.empty() && (out.back() == '\n' || out.back() == ' ')) out.pop_back();
		24	+ return out;
		25	+}
		26	+} // namespace
		27	+
		28	+std::string now_utc_iso() {
		29	+ const auto now = std::chrono::system_clock::now();
		30	+ return timestamp_iso(Timestamp(std::chrono::duration_cast<std::chrono::microseconds>(now.time_since_epoch()).count()));
		31	+}
		32	+
		33	+u64 peak_rss_bytes() { struct rusage ru{}; getrusage(RUSAGE_SELF, &ru);
		34	+#ifdef __APPLE__
		35	+ return static_cast<u64>(ru.ru_maxrss);
		36	+#else
		37	+ return static_cast<u64>(ru.ru_maxrss) * 1024;
		38	+#endif
		39	+}
		40	+
		41	+nlohmann::json host_info() {
		42	+ nlohmann::json j;
		43	+ char host[256]; gethostname(host, sizeof host); j["hostname"] = host;
		44	+ j["hardware_threads"] = std::thread::hardware_concurrency();
		45	+#ifdef __APPLE__
		46	+ j["chip"] = sysctl_str("machdep.cpu.brand_string"); j["ncpu"] = sysctl_i64("hw.ncpu"); j["perf_cores"] = sysctl_i64("hw.perflevel0.physicalcpu");
		47	+ j["eff_cores"] = sysctl_i64("hw.perflevel1.physicalcpu"); j["mem_bytes"] = sysctl_i64("hw.memsize"); j["os_version"] = sysctl_str("kern.osproductversion");
		48	+ j["os_build"] = sysctl_str("kern.osversion"); j["model"] = sysctl_str("hw.model");
		49	+ j["thermal"] = shell("pmset -g therm 2>/dev/null | tr '\\n' ';'"); j["power"] = shell("pmset -g batt 2>/dev/null | head -1");
		50	+ j["load_avg"] = shell("sysctl -n vm.loadavg");
		51	+#else
		52	+ j["os"] = shell("uname -sr"); j["chip"] = shell("grep -m1 'model name' /proc/cpuinfo | cut -d: -f2"); j["load_avg"] = shell("cat /proc/loadavg");
		53	+#endif
		54	+#if defined(__clang__)
		55	+ j["compiler"] = std::string("clang ") + __clang_version__;
		56	+#elif defined(__GNUC__)
		57	+ j["compiler"] = std::string("gcc ") + __VERSION__;
		58	+#endif
		59	+ j["raid_version"] = RAID_VERSION;
		60	+#ifdef RAID_WITH_ACCELERATE
		61	+ j["blas"] = "Apple Accelerate";
		62	+#else
		63	+ j["blas"] = "Eigen built-in";
		64	+#endif
		65	+ return j;
		66	+}
		67	+
		68	+nlohmann::json ExperimentManifest::to_json() const {
		69	+ return {{"experiment", experiment}, {"created_at_utc", created_at_utc}, {"raid_version", raid_version}, {"git_commit", git_commit},
		70	+ {"host", host}, {"plan", plan}, {"seed", seed}, {"inputs", inputs}, {"parameters", parameters}, {"records", records}};
		71	+}
		72	+void ExperimentManifest::save(const std::string& path) const {
		73	+ std::ofstream f(path); RAID_REQUIRE(f.good(), io, "cannot write " + path); f << to_json().dump(1) << "\n";
		74	+}
		75	+
		76	+} // namespace raid

added src/core/thread_pool.cpp +68 −0

				@@ -0,0 +1,68 @@
		1	+#include "raid/core/thread_pool.hpp"
		2	+#include <cstdlib>
		3	+#include <algorithm>
		4	+#include <memory>
		5	+
		6	+namespace raid {
		7	+
		8	+ThreadPool::ThreadPool(unsigned n) {
		9	+ if (n == 0) {
		10	+ if (const char* e = std::getenv("RAID_THREADS")) n = static_cast<unsigned>(std::max(1, std::atoi(e)));
		11	+ else n = std::max(1u, std::thread::hardware_concurrency());
		12	+ }
		13	+ for (unsigned i = 0; i < n; ++i) workers_.emplace_back([this] { worker(); });
		14	+}
		15	+
		16	+ThreadPool::~ThreadPool() {
		17	+ { std::lock_guard<std::mutex> lk(m_); stop_ = true; }
		18	+ cv_.notify_all();
		19	+ for (auto& t : workers_) t.join();
		20	+}
		21	+
		22	+void ThreadPool::worker() {
		23	+ for (;;) {
		24	+ std::function<void()> job;
		25	+ { std::unique_lock<std::mutex> lk(m_); cv_.wait(lk, [this] { return stop_ || !queue_.empty(); });
		26	+ if (stop_ && queue_.empty()) return;
		27	+ job = std::move(queue_.front()); queue_.pop_front(); }
		28	+ job();
		29	+ }
		30	+}
		31	+
		32	+void ThreadPool::parallel_for(usize n, const std::function<void(usize, usize)>& fn, usize grain) {
		33	+ if (n == 0) return;
		34	+ const unsigned nt = size();
		35	+ if (nt <= 1 || n == 1) { fn(0, n); return; }
		36	+ if (grain == 0) grain = std::max<usize>(1, (n + nt * 4 - 1) / (nt * 4));
		37	+ const usize chunks = (n + grain - 1) / grain;
		38	+ // Shared completion state: jobs may outlive this frame's locals by a few instructions, so the state lives on the heap
		39	+ // and is released by the last reference (job or caller).
		40	+ struct Done { std::mutex m; std::condition_variable cv; usize remaining; std::exception_ptr first_error; };
		41	+ auto done = std::make_shared<Done>(); done->remaining = chunks;
		42	+ for (usize c = 0; c < chunks; ++c) {
		43	+ const usize b = c * grain, e = std::min(n, b + grain);
		44	+ std::function<void()> job = [&fn, done, b, e] {
		45	+ std::exception_ptr err;
		46	+ try { fn(b, e); } catch (...) { err = std::current_exception(); }
		47	+ std::lock_guard<std::mutex> lk(done->m);
		48	+ if (err && !done->first_error) done->first_error = err;
		49	+ if (--done->remaining == 0) done->cv.notify_all();
		50	+ };
		51	+ { std::lock_guard<std::mutex> lk(m_); queue_.push_back(std::move(job)); }
		52	+ cv_.notify_one();
		53	+ }
		54	+ // Help from the calling thread as well, then wait.
		55	+ for (;;) {
		56	+ std::function<void()> job;
		57	+ { std::lock_guard<std::mutex> lk(m_); if (!queue_.empty()) { job = std::move(queue_.front()); queue_.pop_front(); } }
		58	+ if (!job) break;
		59	+ job();
		60	+ }
		61	+ std::exception_ptr first_error;
		62	+ { std::unique_lock<std::mutex> lk(done->m); done->cv.wait(lk, [&] { return done->remaining == 0; }); first_error = done->first_error; }
		63	+ if (first_error) std::rethrow_exception(first_error);
		64	+}
		65	+
		66	+ThreadPool& ThreadPool::global() { static ThreadPool pool; return pool; }
		67	+
		68	+} // namespace raid

added src/core/types.cpp +85 −0

				@@ -0,0 +1,85 @@
		1	+#include "raid/core/types.hpp"
		2	+#include "raid/core/error.hpp"
		3	+#include <charconv>
		4	+#include <cstdio>
		5	+
		6	+namespace raid {
		7	+
		8	+Interval interval_from_string(std::string_view s) {
		9	+ if (s == "1m") return Interval::m1;
		10	+ if (s == "5m") return Interval::m5;
		11	+ if (s == "30m") return Interval::m30;
		12	+ if (s == "1h") return Interval::h1;
		13	+ if (s == "1d") return Interval::d1;
		14	+ throw RaidError(ErrorCode::invalid_argument, "unknown interval '" + std::string(s) + "'");
		15	+}
		16	+
		17	+Backend backend_from_string(std::string_view s) {
		18	+ if (s == "auto") return Backend::automatic;
		19	+ if (s == "cpu") return Backend::cpu;
		20	+ if (s == "metal") return Backend::metal;
		21	+ throw RaidError(ErrorCode::invalid_argument, "unknown backend '" + std::string(s) + "' (auto|cpu|metal)");
		22	+}
		23	+
		24	+std::string Date::iso() const {
		25	+ const auto [y, m, d] = ymd();
		26	+ char buf[16];
		27	+ std::snprintf(buf, sizeof buf, "%04d-%02u-%02u", y, m, d);
		28	+ return buf;
		29	+}
		30	+
		31	+static int parse_int(std::string_view s, usize pos, usize len) {
		32	+ int v = 0;
		33	+ const auto r = std::from_chars(s.data() + pos, s.data() + pos + len, v);
		34	+ if (r.ec != std::errc{} || r.ptr != s.data() + pos + len) throw RaidError(ErrorCode::invalid_argument, "bad number in '" + std::string(s) + "'");
		35	+ return v;
		36	+}
		37	+
		38	+Date date_from_iso(std::string_view s) {
		39	+ RAID_REQUIRE(s.size() >= 10 && s[4] == '-' && s[7] == '-', invalid_argument, "date must be YYYY-MM-DD: " + std::string(s));
		40	+ const int y = parse_int(s, 0, 4);
		41	+ const unsigned m = static_cast<unsigned>(parse_int(s, 5, 2)), d = static_cast<unsigned>(parse_int(s, 8, 2));
		42	+ RAID_REQUIRE(m >= 1 && m <= 12 && d >= 1 && d <= 31, invalid_argument, "bad date: " + std::string(s));
		43	+ return Date::from_ymd(y, m, d);
		44	+}
		45	+
		46	+std::string timestamp_iso(Timestamp t) {
		47	+ i64 s = t.us / 1'000'000, frac = t.us % 1'000'000;
		48	+ if (frac < 0) { frac += 1'000'000; s -= 1; }
		49	+ i64 days = s / 86400, rem = s % 86400;
		50	+ if (rem < 0) { rem += 86400; days -= 1; }
		51	+ const auto [y, m, d] = Date(static_cast<i32>(days)).ymd();
		52	+ char buf[40];
		53	+ std::snprintf(buf, sizeof buf, "%04d-%02u-%02uT%02lld:%02lld:%02lld.%06lldZ", y, m, d,
		54	+ static_cast<long long>(rem / 3600), static_cast<long long>((rem % 3600) / 60), static_cast<long long>(rem % 60), static_cast<long long>(frac));
		55	+ return buf;
		56	+}
		57	+
		58	+Timestamp timestamp_from_iso(std::string_view s) {
		59	+ // Accepts YYYY-MM-DD, YYYY-MM-DDTHH:MM[:SS[.ffffff]][Z|+00:00]
		60	+ const Date d = date_from_iso(s);
		61	+ i64 us = static_cast<i64>(d.days) * 86'400'000'000LL;
		62	+ if (s.size() <= 10) return Timestamp(us);
		63	+ RAID_REQUIRE(s[10] == 'T' || s[10] == ' ', invalid_argument, "bad timestamp: " + std::string(s));
		64	+ RAID_REQUIRE(s.size() >= 16, invalid_argument, "bad timestamp: " + std::string(s));
		65	+ us += static_cast<i64>(parse_int(s, 11, 2)) * 3'600'000'000LL + static_cast<i64>(parse_int(s, 14, 2)) * 60'000'000LL;
		66	+ usize pos = 16;
		67	+ if (pos < s.size() && s[pos] == ':') { us += static_cast<i64>(parse_int(s, 17, 2)) * 1'000'000LL; pos = 19; }
		68	+ if (pos < s.size() && s[pos] == '.') {
		69	+ usize e = pos + 1; while (e < s.size() && s[e] >= '0' && s[e] <= '9') ++e;
		70	+ std::string frac(s.substr(pos + 1, e - pos - 1)); while (frac.size() < 6) frac += '0'; frac.resize(6);
		71	+ us += parse_int(frac, 0, 6); pos = e;
		72	+ }
		73	+ if (pos < s.size()) {
		74	+ if (s[pos] == 'Z') pos++;
		75	+ else if (s[pos] == '+' || s[pos] == '-') {
		76	+ RAID_REQUIRE(s.size() >= pos + 6, invalid_argument, "bad tz offset: " + std::string(s));
		77	+ const i64 off = static_cast<i64>(parse_int(s, pos + 1, 2)) * 3600 + static_cast<i64>(parse_int(s, pos + 4, 2)) * 60;
		78	+ us -= (s[pos] == '+' ? off : -off) * 1'000'000LL; pos += 6;
		79	+ }
		80	+ }
		81	+ RAID_REQUIRE(pos == s.size(), invalid_argument, "trailing characters in timestamp: " + std::string(s));
		82	+ return Timestamp(us);
		83	+}
		84	+
		85	+} // namespace raid

added src/estimators/ols.cpp +180 −0

				@@ -0,0 +1,180 @@
		1	+#include "raid/estimators/ols.hpp"
		2	+#include "raid/core/error.hpp"
		3	+#include <chrono>
		4	+#include <cmath>
		5	+
		6	+namespace raid::est {
		7	+
		8	+namespace {
		9	+// Two-sided p-value from a t statistic with df degrees of freedom (df<=0 → normal). Regularised incomplete beta via
		10	+// Lentz continued fraction (Numerical Recipes), FP64. Validated against scipy.stats.t.sf in tests/reference.
		11	+f64 betacf(f64 a, f64 b, f64 x) {
		12	+ const int MAXIT = 300; const f64 EPS = 3e-16, FPMIN = 1e-300;
		13	+ f64 qab = a + b, qap = a + 1, qam = a - 1, c = 1, d = 1 - qab * x / qap;
		14	+ if (std::abs(d) < FPMIN) d = FPMIN; d = 1 / d; f64 h = d;
		15	+ for (int m = 1; m <= MAXIT; ++m) {
		16	+ const f64 m2 = 2.0 * m; f64 aa = m * (b - m) * x / ((qam + m2) * (a + m2));
		17	+ d = 1 + aa * d; if (std::abs(d) < FPMIN) d = FPMIN; c = 1 + aa / c; if (std::abs(c) < FPMIN) c = FPMIN; d = 1 / d; h *= d * c;
		18	+ aa = -(a + m) * (qab + m) * x / ((a + m2) * (qap + m2));
		19	+ d = 1 + aa * d; if (std::abs(d) < FPMIN) d = FPMIN; c = 1 + aa / c; if (std::abs(c) < FPMIN) c = FPMIN; d = 1 / d;
		20	+ const f64 del = d * c; h *= del; if (std::abs(del - 1) < EPS) break;
		21	+ }
		22	+ return h;
		23	+}
		24	+f64 betai(f64 a, f64 b, f64 x) {
		25	+ if (x <= 0) return 0; if (x >= 1) return 1;
		26	+ const f64 bt = std::exp(std::lgamma(a + b) - std::lgamma(a) - std::lgamma(b) + a * std::log(x) + b * std::log(1 - x));
		27	+ if (x < (a + 1) / (a + b + 2)) return bt * betacf(a, b, x) / a;
		28	+ return 1 - bt * betacf(b, a, 1 - x) / b;
		29	+}
		30	+} // namespace
		31	+
		32	+f64 t_pvalue_two_sided(f64 t, f64 df) {
		33	+ if (!std::isfinite(t)) return std::numeric_limits<f64>::quiet_NaN();
		34	+ if (df <= 0) return std::erfc(std::abs(t) / std::sqrt(2.0));
		35	+ return betai(df / 2.0, 0.5, df / (df + t * t));
		36	+}
		37	+
		38	+OlsResult ols(const Eigen::Ref<const Eigen::MatrixXd>& X0, const Eigen::Ref<const Eigen::VectorXd>& y0, const OlsOptions& opt) {
		39	+ const auto t0 = std::chrono::steady_clock::now();
		40	+ const Eigen::Index n = X0.rows(), k = X0.cols();
		41	+ RAID_REQUIRE(y0.size() == n, invalid_argument, "ols: X rows != y size");
		42	+ RAID_REQUIRE(n > 0 && k > 0, insufficient_samples, "ols: empty design");
		43	+ RAID_REQUIRE(X0.allFinite() && y0.allFinite(), data_quality, "ols: non-finite values in X or y (drop or impute explicitly)");
		44	+ OlsResult r; r.estimate.model = opt.ridge_lambda > 0 ? "ridge" : (opt.weights.empty() ? "ols" : "wls");
		45	+ // Weighted transform: X̃ = √W X, ỹ = √W y
		46	+ Eigen::MatrixXd X = X0; Eigen::VectorXd y = y0;
		47	+ if (!opt.weights.empty()) {
		48	+ RAID_REQUIRE(static_cast<Eigen::Index>(opt.weights.size()) == n, invalid_argument, "wls: weights length");
		49	+ for (Eigen::Index i = 0; i < n; ++i) { RAID_REQUIRE(opt.weights[static_cast<usize>(i)] >= 0, invalid_argument, "wls: negative weight"); const f64 s = std::sqrt(opt.weights[static_cast<usize>(i)]); X.row(i) *= s; y[i] *= s; }
		50	+ }
		51	+ // Ridge via augmentation: append √λ·P rows (P = identity on penalised columns)
		52	+ Eigen::MatrixXd Xa = X; Eigen::VectorXd ya = y;
		53	+ if (opt.ridge_lambda > 0) {
		54	+ Eigen::VectorXd pen = Eigen::VectorXd::Constant(k, std::sqrt(opt.ridge_lambda));
		55	+ for (usize u : opt.unpenalized) { RAID_REQUIRE(u < static_cast<usize>(k), invalid_argument, "ridge: bad unpenalized index"); pen[static_cast<Eigen::Index>(u)] = 0; }
		56	+ Xa.conservativeResize(n + k, k); ya.conservativeResize(n + k);
		57	+ Xa.bottomRows(k) = pen.asDiagonal(); ya.tail(k).setZero();
		58	+ }
		59	+ Eigen::VectorXd beta(k);
		60	+ if (opt.solver == Solver::svd) {
		61	+ Eigen::JacobiSVD<Eigen::MatrixXd> svd(Xa, Eigen::ComputeThinU | Eigen::ComputeThinV);
		62	+ const auto& s = svd.singularValues();
		63	+ r.rank = svd.rank(); r.rcond = s[s.size() - 1] / s[0];
		64	+ beta = svd.solve(ya);
		65	+ Eigen::VectorXd inv_s2 = (s.array() > s[0] * 1e-14).select(1.0 / s.array().square(), 0.0);
		66	+ r.xtx_inv = svd.matrixV() * inv_s2.asDiagonal() * svd.matrixV().transpose();
		67	+ } else if (opt.solver == Solver::normal_cholesky) {
		68	+ const Eigen::MatrixXd A = Xa.transpose() * Xa; Eigen::LLT<Eigen::MatrixXd> llt(A);
		69	+ RAID_REQUIRE(llt.info() == Eigen::Success, numerical, "ols: X'X not positive definite");
		70	+ beta = llt.solve(Xa.transpose() * ya); r.xtx_inv = llt.solve(Eigen::MatrixXd::Identity(k, k)); r.rank = k;
		71	+ Eigen::JacobiSVD<Eigen::MatrixXd> svd(A); r.rcond = svd.singularValues()[k - 1] / svd.singularValues()[0];
		72	+ } else {
		73	+ Eigen::ColPivHouseholderQR<Eigen::MatrixXd> qr(Xa);
		74	+ qr.setThreshold(1e-12);
		75	+ r.rank = qr.rank();
		76	+ const Eigen::MatrixXd R = qr.matrixR().topLeftCorner(k, k).template triangularView<Eigen::Upper>();
		77	+ r.rcond = std::abs(R(k - 1, k - 1)) / std::abs(R(0, 0));
		78	+ if (r.rank < k) {
		79	+ r.estimate.warnings.push_back("design is rank deficient (rank " + std::to_string(r.rank) + " < " + std::to_string(k) + "); minimum-norm solution via SVD");
		80	+ Eigen::JacobiSVD<Eigen::MatrixXd> svd(Xa, Eigen::ComputeThinU | Eigen::ComputeThinV);
		81	+ beta = svd.solve(ya);
		82	+ const auto& s = svd.singularValues(); Eigen::VectorXd inv_s2 = (s.array() > s[0] * 1e-14).select(1.0 / s.array().square(), 0.0);
		83	+ r.xtx_inv = svd.matrixV() * inv_s2.asDiagonal() * svd.matrixV().transpose();
		84	+ } else {
		85	+ beta = qr.solve(ya);
		86	+ // (X'X)^{-1} = P R^{-1} R^{-T} P'
		87	+ const Eigen::MatrixXd Rinv = R.template triangularView<Eigen::Upper>().solve(Eigen::MatrixXd::Identity(k, k));
		88	+ r.xtx_inv = qr.colsPermutation() * (Rinv * Rinv.transpose()) * qr.colsPermutation().transpose();
		89	+ }
		90	+ }
		91	+ if (r.rcond < opt.rcond_warn) r.estimate.warnings.push_back("ill-conditioned design: rcond = " + std::to_string(r.rcond));
		92	+ r.fitted = X0 * beta; // unweighted fitted values
		93	+ r.residuals = y0 - r.fitted;
		94	+ Eigen::VectorXd e = y - X * beta; // weighted residuals for inference
		95	+ const f64 rss = e.squaredNorm();
		96	+ const f64 df_resid = static_cast<f64>(n - r.rank);
		97	+ r.sigma2 = df_resid > 0 ? rss / df_resid : std::numeric_limits<f64>::quiet_NaN();
		98	+ const f64 ybar = y.mean(); const f64 tss = (y.array() - ybar).square().sum();
		99	+ r.r2 = tss > 0 ? 1.0 - rss / tss : std::numeric_limits<f64>::quiet_NaN();
		100	+ r.r2_adj = tss > 0 && df_resid > 0 ? 1.0 - (rss / df_resid) / (tss / static_cast<f64>(n - 1)) : std::numeric_limits<f64>::quiet_NaN();
		101	+ // leverage h_ii = x_i' (X'X)^{-1} x_i
		102	+ r.leverage = (X * r.xtx_inv).cwiseProduct(X).rowwise().sum();
		103	+ // inference
		104	+ InferenceResult inf;
		105	+ stats::CovOptions co = opt.cov;
		106	+ inf.covariance = stats::robust_covariance(X, e, r.xtx_inv, co, r.leverage);
		107	+ inf.covariance_type = stats::to_string(co.type);
		108	+ if (co.type == stats::CovType::hac_bartlett) inf.covariance_type += "(" + std::to_string(co.hac_lags < 0 ? stats::default_hac_lags(static_cast<usize>(n)) : co.hac_lags) + ")";
		109	+ inf.std_errors.resize(static_cast<usize>(k)); inf.t_stats.resize(static_cast<usize>(k)); inf.p_values.resize(static_cast<usize>(k));
		110	+ const f64 df_t = (co.type == stats::CovType::classical) ? df_resid : df_resid; // statsmodels uses t(df_resid) for OLS by default
		111	+ for (Eigen::Index j = 0; j < k; ++j) {
		112	+ const f64 se = std::sqrt(std::max(0.0, inf.covariance(j, j)));
		113	+ inf.std_errors[static_cast<usize>(j)] = se; inf.t_stats[static_cast<usize>(j)] = se > 0 ? beta[j] / se : std::numeric_limits<f64>::quiet_NaN();
		114	+ inf.p_values[static_cast<usize>(j)] = t_pvalue_two_sided(inf.t_stats[static_cast<usize>(j)], df_t);
		115	+ }
		116	+ r.estimate.inference = inf;
		117	+ r.estimate.coefficients.assign(beta.data(), beta.data() + k);
		118	+ r.estimate.names = opt.names; if (r.estimate.names.size() != static_cast<usize>(k)) { r.estimate.names.clear(); for (Eigen::Index j = 0; j < k; ++j) r.estimate.names.push_back("x" + std::to_string(j)); }
		119	+ r.estimate.loss = rss; r.estimate.n_obs = static_cast<usize>(n);
		120	+ r.estimate.convergence = Convergence{true, 1, 1, 0.0, 0, "direct solve"};
		121	+ r.estimate.extra = {{"sigma2", r.sigma2}, {"r2", r.r2}, {"r2_adj", r.r2_adj}, {"rank", r.rank}, {"rcond", r.rcond}, {"df_resid", df_resid}, {"ridge_lambda", opt.ridge_lambda}};
		122	+ r.estimate.runtime_seconds = std::chrono::duration<f64>(std::chrono::steady_clock::now() - t0).count();
		123	+ return r;
		124	+}
		125	+
		126	+SufficientStats accumulate(const Eigen::Ref<const Eigen::MatrixXd>& X, const Eigen::Ref<const Eigen::VectorXd>& y) {
		127	+ SufficientStats s; s.xtx = X.transpose() * X; s.xty = X.transpose() * y; s.yty = y.squaredNorm(); s.n = static_cast<usize>(X.rows());
		128	+ return s;
		129	+}
		130	+
		131	+OlsResult ols_from_stats(const SufficientStats& s, const OlsOptions& opt) {
		132	+ const auto t0 = std::chrono::steady_clock::now();
		133	+ const Eigen::Index k = s.xtx.rows();
		134	+ OlsResult r; r.estimate.model = "ols.sufficient_stats";
		135	+ Eigen::MatrixXd A = s.xtx;
		136	+ if (opt.ridge_lambda > 0) { for (Eigen::Index j = 0; j < k; ++j) { bool pen = true; for (usize u : opt.unpenalized) if (static_cast<Eigen::Index>(u) == j) pen = false; if (pen) A(j, j) += opt.ridge_lambda; } }
		137	+ Eigen::LDLT<Eigen::MatrixXd> ldlt(A);
		138	+ Eigen::VectorXd beta;
		139	+ Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es(A);
		140	+ const auto& ev = es.eigenvalues();
		141	+ r.rcond = ev[0] / ev[k - 1]; r.rank = (ev.array() > ev[k - 1] * 1e-13).count();
		142	+ if (ldlt.info() == Eigen::Success && ldlt.isPositive() && r.rank == k) { beta = ldlt.solve(s.xty); r.xtx_inv = ldlt.solve(Eigen::MatrixXd::Identity(k, k)); }
		143	+ else {
		144	+ r.estimate.warnings.push_back("X'X singular or indefinite: pseudo-inverse solution");
		145	+ Eigen::VectorXd inv = (ev.array() > ev[k - 1] * 1e-13).select(1.0 / ev.array(), 0.0);
		146	+ r.xtx_inv = es.eigenvectors() * inv.asDiagonal() * es.eigenvectors().transpose(); beta = r.xtx_inv * s.xty;
		147	+ }
		148	+ if (r.rcond < opt.rcond_warn) r.estimate.warnings.push_back("ill-conditioned X'X: rcond = " + std::to_string(r.rcond));
		149	+ const f64 rss = std::max(0.0, s.yty - 2.0 * beta.dot(s.xty) + beta.dot(s.xtx * beta));
		150	+ const f64 df_resid = static_cast<f64>(s.n) - static_cast<f64>(r.rank);
		151	+ r.sigma2 = df_resid > 0 ? rss / df_resid : std::numeric_limits<f64>::quiet_NaN();
		152	+ InferenceResult inf; inf.covariance = r.sigma2 * r.xtx_inv; inf.covariance_type = "classical";
		153	+ for (Eigen::Index j = 0; j < k; ++j) { const f64 se = std::sqrt(std::max(0.0, inf.covariance(j, j))); inf.std_errors.push_back(se); inf.t_stats.push_back(se > 0 ? beta[j] / se : std::numeric_limits<f64>::quiet_NaN()); inf.p_values.push_back(t_pvalue_two_sided(inf.t_stats.back(), df_resid)); }
		154	+ r.estimate.inference = inf; r.estimate.coefficients.assign(beta.data(), beta.data() + k);
		155	+ r.estimate.names = opt.names; if (r.estimate.names.size() != static_cast<usize>(k)) { r.estimate.names.clear(); for (Eigen::Index j = 0; j < k; ++j) r.estimate.names.push_back("x" + std::to_string(j)); }
		156	+ r.estimate.loss = rss; r.estimate.n_obs = s.n; r.estimate.convergence = Convergence{true, 1, 1, 0.0, 0, "sufficient statistics"};
		157	+ r.estimate.extra = {{"sigma2", r.sigma2}, {"rank", r.rank}, {"rcond", r.rcond}, {"df_resid", df_resid}};
		158	+ r.estimate.runtime_seconds = std::chrono::duration<f64>(std::chrono::steady_clock::now() - t0).count();
		159	+ return r;
		160	+}
		161	+
		162	+RollingOlsResult rolling_ols(const Eigen::Ref<const Eigen::MatrixXd>& X, const Eigen::Ref<const Eigen::VectorXd>& y, usize window, usize step) {
		163	+ const usize n = static_cast<usize>(X.rows()), k = static_cast<usize>(X.cols());
		164	+ RAID_REQUIRE(window >= k + 1 && window <= n, insufficient_samples, "rolling_ols: window must satisfy k+1 <= window <= n");
		165	+ RAID_REQUIRE(step >= 1, invalid_argument, "rolling_ols: step >= 1");
		166	+ const usize nw = (n - window) / step + 1;
		167	+ RollingOlsResult r; r.window = window; r.coefficients.resize(static_cast<Eigen::Index>(k), static_cast<Eigen::Index>(nw)); r.rss.resize(nw); r.ranks.resize(nw);
		168	+ for (usize j = 0; j < nw; ++j) {
		169	+ const usize b = j * step;
		170	+ Eigen::ColPivHouseholderQR<Eigen::MatrixXd> qr(X.middleRows(static_cast<Eigen::Index>(b), static_cast<Eigen::Index>(window)));
		171	+ qr.setThreshold(1e-12);
		172	+ const Eigen::VectorXd beta = qr.solve(y.segment(static_cast<Eigen::Index>(b), static_cast<Eigen::Index>(window)));
		173	+ r.coefficients.col(static_cast<Eigen::Index>(j)) = beta;
		174	+ r.rss[j] = (y.segment(static_cast<Eigen::Index>(b), static_cast<Eigen::Index>(window)) - X.middleRows(static_cast<Eigen::Index>(b), static_cast<Eigen::Index>(window)) * beta).squaredNorm();
		175	+ r.ranks[j] = qr.rank();
		176	+ }
		177	+ return r;
		178	+}
		179	+
		180	+} // namespace raid::est

added src/estimators/realized.cpp +57 −0

				@@ -0,0 +1,57 @@
		1	+#include "raid/estimators/realized.hpp"
		2	+#include "raid/core/error.hpp"
		3	+#include "raid/statistics/reductions.hpp"
		4	+#include <cmath>
		5	+#include <numbers>
		6	+
		7	+namespace raid::est {
		8	+
		9	+f64 mu_p(f64 p) { return std::pow(2.0, p / 2.0) * std::tgamma((p + 1.0) / 2.0) / std::tgamma(0.5); }
		10	+
		11	+RealizedMeasures realized_measures_batch(const backend::Batch& b, const RealizedOptions& opt) {
		12	+ RealizedMeasures m; const usize S = b.n_series();
		13	+ std::string reason; auto be = backend::resolve(opt.plan, &reason);
		14	+ backend::KernelStats st1, st2, st3, st4;
		15	+ m.rv = be->sum_squares(b, &st1);
		16	+ auto adj = be->sum_abs_adjacent(b, &st2);
		17	+ be->semivariances(b, m.rsv_neg, m.rsv_pos, &st3);
		18	+ auto s4 = be->sum_fourth(b, &st4);
		19	+ m.stats = st1; m.stats.kernel = "realized_measures"; m.stats.wall_seconds = st1.wall_seconds + st2.wall_seconds + st3.wall_seconds + st4.wall_seconds;
		20	+ m.stats.device_seconds = st1.device_seconds + st2.device_seconds + st3.device_seconds + st4.device_seconds; m.stats.note = reason;
		21	+ m.bpv.resize(S); m.rq.resize(S); m.jump.resize(S); m.tripv.resize(S); m.n_returns.resize(S); m.daily_return.resize(S);
		22	+ const f64 mu43 = mu_p(4.0 / 3.0);
		23	+ for (usize s = 0; s < S; ++s) {
		24	+ const usize M = b.size(s); m.n_returns[s] = M;
		25	+ auto x = b.series(s);
		26	+ m.daily_return[s] = stats::sum_neumaier(x);
		27	+ const f64 fs = (opt.bpv_finite_sample && M > 1) ? static_cast<f64>(M) / static_cast<f64>(M - 1) : 1.0;
		28	+ m.bpv[s] = M > 1 ? (1.0 / (kMu1 * kMu1)) * fs * adj[s] : std::numeric_limits<f64>::quiet_NaN();
		29	+ m.rq[s] = M > 0 ? (static_cast<f64>(M) / 3.0) * s4[s] : std::numeric_limits<f64>::quiet_NaN();
		30	+ m.jump[s] = std::max(m.rv[s] - m.bpv[s], 0.0);
		31	+ // tripower (CPU, FP64 — small cost, not batched on GPU in Tier A)
		32	+ if (M > 2) { f64 acc = 0, c = 0; for (usize j = 2; j < M; ++j) { const f64 v = std::pow(std::abs(x[j]), 4.0 / 3.0) * std::pow(std::abs(x[j - 1]), 4.0 / 3.0) * std::pow(std::abs(x[j - 2]), 4.0 / 3.0), t = acc + v; if (std::abs(acc) >= v) c += (acc - t) + v; else c += (v - t) + acc; acc = t; }
		33	+ const f64 fs3 = opt.bpv_finite_sample ? static_cast<f64>(M) / static_cast<f64>(M - 2) : 1.0; m.tripv[s] = fs3 * (acc + c) / (mu43 * mu43 * mu43); }
		34	+ else m.tripv[s] = std::numeric_limits<f64>::quiet_NaN();
		35	+ if (M < 2) m.warnings.push_back("segment " + std::to_string(s) + ": fewer than 2 returns");
		36	+ if (opt.annualize) { m.rv[s] *= opt.periods_per_year; m.bpv[s] *= opt.periods_per_year; m.rsv_neg[s] *= opt.periods_per_year; m.rsv_pos[s] *= opt.periods_per_year; m.jump[s] *= opt.periods_per_year; m.tripv[s] *= opt.periods_per_year; m.rq[s] *= opt.periods_per_year * opt.periods_per_year; }
		37	+ }
		38	+ return m;
		39	+}
		40	+
		41	+RealizedMeasures realized_measures(const data::IntradayReturns& ir, const RealizedOptions& opt) {
		42	+ backend::Batch b{ir.r, ir.session_offsets};
		43	+ auto m = realized_measures_batch(b, opt);
		44	+ m.sessions = ir.session_dates;
		45	+ return m;
		46	+}
		47	+
		48	+RealizedCov realized_covariance(const Eigen::Ref<const Eigen::MatrixXd>& R) {
		49	+ RAID_REQUIRE(R.rows() >= 1 && R.cols() >= 1, insufficient_samples, "realized_covariance: empty");
		50	+ RealizedCov out; out.n_obs = static_cast<usize>(R.rows());
		51	+ out.cov = R.transpose() * R; // Σ_j r_j r_j' (no demeaning: realized covariance of returns)
		52	+ const Eigen::Index N = R.cols(); out.corr.resize(N, N);
		53	+ for (Eigen::Index a = 0; a < N; ++a) for (Eigen::Index c = 0; c < N; ++c) { const f64 d = std::sqrt(out.cov(a, a) * out.cov(c, c)); out.corr(a, c) = d > 0 ? out.cov(a, c) / d : std::numeric_limits<f64>::quiet_NaN(); }
		54	+ return out;
		55	+}
		56	+
		57	+} // namespace raid::est

added src/models/garch_fit.cpp +147 −0

				@@ -0,0 +1,147 @@
		1	+// GARCH-family QML estimation on the CPU: BFGS on the unconstrained parameters, central-difference gradients,
		2	+// Armijo backtracking, and three covariance estimators (Hessian, OPG, QMLE sandwich = H^{-1} OPG H^{-1}).
		3	+#include "raid/models/garch.hpp"
		4	+#include "raid/core/error.hpp"
		5	+#include "raid/core/rng.hpp"
		6	+#include "raid/estimators/ols.hpp"
		7	+#include "raid/statistics/reductions.hpp"
		8	+#include <Eigen/Dense>
		9	+#include <chrono>
		10	+#include <cmath>
		11	+#include <numbers>
		12	+#include <cstdlib>
		13	+#include <cstdio>
		14	+
		15	+namespace raid::models {
		16	+
		17	+namespace {
		18	+using Vec = Eigen::VectorXd; using Mat = Eigen::MatrixXd;
		19	+
		20	+// Per-observation log-likelihood contributions ℓ_t (returns +inf nll on invalid params).
		21	+bool per_obs_ll(std::span<const f64> r, std::span<const f64> th, GarchModel m, GarchDist d, std::vector<f64>& ll) {
		22	+ if (!garch_params_valid(th, m, d)) return false;
		23	+ std::vector<f64> s2; if (!garch_variance_path(r, th, m, d, s2)) return false;
		24	+ const f64 mu = th[0]; const usize n = r.size(); ll.resize(n);
		25	+ if (d == GarchDist::gaussian) { const f64 k = 0.5 * std::log(2.0 * std::numbers::pi); for (usize t = 0; t < n; ++t) { const f64 e = r[t] - mu; ll[t] = -(k + 0.5 * std::log(s2[t]) + 0.5 * e * e / s2[t]); } }
		26	+ else { const f64 nu = th.back(); const f64 k = std::lgamma((nu + 1) / 2) - std::lgamma(nu / 2) - 0.5 * std::log(std::numbers::pi * (nu - 2)); for (usize t = 0; t < n; ++t) { const f64 e = r[t] - mu; ll[t] = k - 0.5 * std::log(s2[t]) - 0.5 * (nu + 1) * std::log1p(e * e / ((nu - 2) * s2[t])); } }
		27	+ return true;
		28	+}
		29	+
		30	+template <class F> Vec num_grad(const F& f, const Vec& x, f64 fx, f64 h0 = 1e-5) {
		31	+ Vec g(x.size());
		32	+ for (Eigen::Index i = 0; i < x.size(); ++i) { const f64 h = h0 * std::max(1.0, std::abs(x[i])); Vec xp = x, xm = x; xp[i] += h; xm[i] -= h; const f64 fp = f(xp), fm = f(xm);
		33	+ if (std::isfinite(fp) && std::isfinite(fm)) g[i] = (fp - fm) / (2 * h); else if (std::isfinite(fp)) g[i] = (fp - fx) / h; else if (std::isfinite(fm)) g[i] = (fx - fm) / h; else g[i] = 0; }
		34	+ return g;
		35	+}
		36	+template <class F> Mat num_hessian(const F& f, const Vec& x, f64 h0 = 1e-4) {
		37	+ const Eigen::Index p = x.size(); Mat H(p, p); const f64 fx = f(x);
		38	+ for (Eigen::Index i = 0; i < p; ++i) for (Eigen::Index j = i; j < p; ++j) {
		39	+ const f64 hi = h0 * std::max(1.0, std::abs(x[i])), hj = h0 * std::max(1.0, std::abs(x[j]));
		40	+ Vec a = x, b = x, c = x, dd = x; a[i] += hi; a[j] += hj; b[i] += hi; b[j] -= hj; c[i] -= hi; c[j] += hj; dd[i] -= hi; dd[j] -= hj;
		41	+ f64 v = (f(a) - f(b) - f(c) + f(dd)) / (4 * hi * hj);
		42	+ if (i == j) { Vec xp = x, xm = x; xp[i] += hi; xm[i] -= hi; v = (f(xp) - 2 * fx + f(xm)) / (hi * hi); }
		43	+ H(i, j) = v; H(j, i) = v; }
		44	+ return H;
		45	+}
		46	+} // namespace
		47	+
		48	+Estimate garch_fit(std::span<const f64> r0, const GarchFitOptions& opt) {
		49	+ const auto t0 = std::chrono::steady_clock::now();
		50	+ Estimate e; e.model = "garch." + to_string(opt.model) + "." + to_string(opt.dist); e.backend = Backend::cpu; e.precision = Precision::fp64;
		51	+ e.names = garch_param_names(opt.model, opt.dist); e.n_obs = r0.size();
		52	+ RAID_REQUIRE(r0.size() >= 50, insufficient_samples, "garch_fit: need at least 50 observations");
		53	+ for (f64 v : r0) RAID_REQUIRE(std::isfinite(v), data_quality, "garch_fit: non-finite return");
		54	+ // scaling (arch rescales to unit variance ×100-ish; we scale so that sample std ≈ 1 and unscale afterwards)
		55	+ const f64 sd = std::sqrt(stats::variance(r0, 0)); RAID_REQUIRE(sd > 0, data_quality, "garch_fit: constant returns");
		56	+ const f64 scale = opt.scale_returns ? 1.0 / sd : 1.0;
		57	+ std::vector<f64> r(r0.size()); for (usize i = 0; i < r.size(); ++i) r[i] = r0[i] * scale;
		58	+ const usize p = garch_n_params(opt.model, opt.dist);
		59	+ // starting values (on the scaled series): μ = mean, persistence 0.95, α = 0.05 (GJR γ = 0.05), ν = 8
		60	+ std::vector<f64> th0(p); th0[0] = stats::mean(std::span<const f64>(r));
		61	+ const f64 v = stats::variance(std::span<const f64>(r), 0);
		62	+ if (opt.model == GarchModel::egarch) { th0[2] = 0.1; th0[3] = -0.05; th0[4] = 0.95; th0[1] = (1.0 - th0[4]) * std::log(v); }
		63	+ else if (opt.model == GarchModel::garch) { th0[2] = 0.05; th0[3] = 0.90; th0[1] = v * (1 - 0.95); }
		64	+ else { th0[2] = 0.03; th0[3] = 0.05; th0[4] = 0.90; th0[1] = v * (1 - 0.03 - 0.90 - 0.025); }
		65	+ if (opt.dist == GarchDist::student_t) th0[p - 1] = 8.0;
		66	+ auto f_u = [&](const Vec& u) { auto th = garch_from_unconstrained(std::span<const f64>(u.data(), static_cast<usize>(u.size())), opt.model, opt.dist); return garch_nll(r, th, opt.model, opt.dist); };
		67	+ auto u0v = garch_to_unconstrained(th0, opt.model, opt.dist);
		68	+ Vec x = Eigen::Map<Vec>(u0v.data(), static_cast<Eigen::Index>(p));
		69	+ f64 fx = f_u(x); int fevals = 1;
		70	+ if (!std::isfinite(fx)) { e.convergence = Convergence{false, 0, fevals, std::numeric_limits<f64>::quiet_NaN(), -1, "invalid starting values"}; return e; }
		71	+ auto fcount = [&](const Vec& y) { ++fevals; return f_u(y); };
		72	+ Vec g = num_grad(fcount, x, fx); Mat Hinv = Mat::Identity(static_cast<Eigen::Index>(p), static_cast<Eigen::Index>(p));
		73	+ int it = 0; bool converged = false; std::string msg;
		74	+ for (; it < opt.max_iter; ++it) {
		75	+ Vec dir = -Hinv * g; if (dir.dot(g) >= 0) { Hinv.setIdentity(); dir = -g; }
		76	+ f64 step = 1.0; f64 fnew = std::numeric_limits<f64>::infinity(); Vec xnew;
		77	+ for (int ls = 0; ls < 60; ++ls) { xnew = x + step * dir; fnew = fcount(xnew); if (std::isfinite(fnew) && fnew <= fx + 1e-4 * step * g.dot(dir)) break; step *= 0.5; }
		78	+ if (!std::isfinite(fnew) || fnew > fx) { msg = "line search failed"; break; }
		79	+ Vec gnew = num_grad(fcount, xnew, fnew);
		80	+ if (std::getenv("RAID_GARCH_TRACE")) { std::fprintf(stderr, "it=%d f=%.6f step=%.3g |g|=%.3e x=", it, fnew, step, gnew.norm()); for (Eigen::Index j = 0; j < x.size(); ++j) std::fprintf(stderr, "%.4f ", xnew[j]); std::fprintf(stderr, " g="); for (Eigen::Index j = 0; j < x.size(); ++j) std::fprintf(stderr, "%.3e ", gnew[j]); std::fprintf(stderr, "\n"); }
		81	+ const Vec s = xnew - x, yv = gnew - g; const f64 sy = s.dot(yv);
		82	+ if (sy > 1e-12) { const Mat I = Mat::Identity(static_cast<Eigen::Index>(p), static_cast<Eigen::Index>(p)); const f64 rho = 1.0 / sy; Hinv = (I - rho * s * yv.transpose()) * Hinv * (I - rho * yv * s.transpose()) + rho * s * s.transpose(); }
		83	+ const f64 rel = std::abs(fx - fnew) / std::max(1.0, std::abs(fx));
		84	+ x = xnew; fx = fnew; g = gnew;
		85	+ if (g.norm() < 1e-5 || (rel < opt.tol && g.norm() < 1e-2) || (s.norm() < 1e-9 && g.norm() < 1e-2)) { converged = true; msg = g.norm() < 1e-5 ? "gradient norm below 1e-5" : (rel < opt.tol ? "relative NLL change below tol (gradient < 1e-2 in transformed space)" : "step below 1e-9 (gradient < 1e-2)"); ++it; break; }
		86	+ }
		87	+ if (!converged && msg.empty()) msg = "maximum iterations reached";
		88	+ auto th = garch_from_unconstrained(std::span<const f64>(x.data(), static_cast<usize>(x.size())), opt.model, opt.dist);
		89	+ // Covariance on the natural parameters (scaled series) by numerical derivatives of the NLL
		90	+ auto f_th = [&](const Vec& t) { return garch_nll(r, std::span<const f64>(t.data(), static_cast<usize>(t.size())), opt.model, opt.dist); };
		91	+ Vec thv = Eigen::Map<Vec>(th.data(), static_cast<Eigen::Index>(p));
		92	+ Mat H = num_hessian(f_th, thv);
		93	+ // OPG from per-observation scores (central differences of ℓ_t)
		94	+ const usize n = r.size(); Mat scores(static_cast<Eigen::Index>(n), static_cast<Eigen::Index>(p));
		95	+ { std::vector<f64> lp, lm; for (Eigen::Index j = 0; j < static_cast<Eigen::Index>(p); ++j) { const f64 h = 1e-5 * std::max(1.0, std::abs(thv[j])); Vec a = thv, b = thv; a[j] += h; b[j] -= h;
		96	+ bool oka = per_obs_ll(r, std::span<const f64>(a.data(), p), opt.model, opt.dist, lp), okb = per_obs_ll(r, std::span<const f64>(b.data(), p), opt.model, opt.dist, lm);
		97	+ for (usize t = 0; t < n; ++t) scores(static_cast<Eigen::Index>(t), j) = (oka && okb) ? (lp[t] - lm[t]) / (2 * h) : std::numeric_limits<f64>::quiet_NaN(); } }
		98	+ Mat opg = scores.transpose() * scores;
		99	+ InferenceResult inf; Mat cov;
		100	+ Eigen::FullPivLU<Mat> lu(H); bool h_ok = lu.isInvertible() && H.allFinite();
		101	+ if (opt.cov == "hessian") { cov = h_ok ? Mat(lu.inverse()) : Mat(Mat::Constant(p, p, std::numeric_limits<f64>::quiet_NaN())); inf.covariance_type = "hessian"; }
		102	+ else if (opt.cov == "opg") { Eigen::FullPivLU<Mat> lo(opg); cov = lo.isInvertible() ? Mat(lo.inverse()) : Mat(Mat::Constant(p, p, std::numeric_limits<f64>::quiet_NaN())); inf.covariance_type = "opg"; }
		103	+ else { cov = h_ok ? Mat(lu.inverse() * opg * lu.inverse()) : Mat(Mat::Constant(p, p, std::numeric_limits<f64>::quiet_NaN())); inf.covariance_type = "qmle_sandwich"; }
		104	+ if (!h_ok) e.warnings.push_back("Hessian singular or non-finite at the optimum; standard errors unavailable");
		105	+ if (opt.dist == GarchDist::student_t && th.back() > 450.0) e.warnings.push_back("nu at its upper bound (500): the Student-t density is indistinguishable from Gaussian on this sample");
		106	+ // Unscale: r0 = r / scale. μ, ω, σ² scale by 1/scale, 1/scale², 1/scale²; α, β, γ, ν unchanged; EGARCH ω → ω + (1−β) log(1/scale²)
		107	+ std::vector<f64> th_out = th; Vec jac = Vec::Ones(static_cast<Eigen::Index>(p));
		108	+ th_out[0] = th[0] / scale; jac[0] = 1.0 / scale;
		109	+ if (opt.model == GarchModel::egarch) { th_out[1] = th[1] + (1.0 - th[4]) * std::log(1.0 / (scale * scale)); }
		110	+ else { th_out[1] = th[1] / (scale * scale); jac[1] = 1.0 / (scale * scale); }
		111	+ if (opt.model != GarchModel::egarch) cov = jac.asDiagonal() * cov * jac.asDiagonal();
		112	+ else { Mat J = Mat::Identity(p, p); J(0, 0) = 1.0 / scale; J(1, 4) = -std::log(1.0 / (scale * scale)); cov = J * cov * J.transpose(); }
		113	+ inf.covariance = cov; const f64 nll_unscaled = fx - static_cast<f64>(n) * std::log(scale); // f_{r0}(x) = scale · f_r(scale·x) ⇒ −Σ log f(r0) = −Σ log f(r) − n log(scale)
		114	+ for (Eigen::Index j = 0; j < static_cast<Eigen::Index>(p); ++j) { const f64 se = std::sqrt(std::max(0.0, cov(j, j))); inf.std_errors.push_back(se); inf.t_stats.push_back(se > 0 ? th_out[static_cast<usize>(j)] / se : std::numeric_limits<f64>::quiet_NaN()); inf.p_values.push_back(est::t_pvalue_two_sided(inf.t_stats.back(), 0)); }
		115	+ e.inference = inf; e.coefficients = th_out; e.loss = nll_unscaled;
		116	+ e.convergence = Convergence{converged, it, fevals, g.norm(), converged ? 0 : 1, msg};
		117	+ std::vector<f64> s2; garch_variance_path(r0, th_out, opt.model, opt.dist, s2);
		118	+ e.extra = {{"loglik", -nll_unscaled}, {"scale", scale}, {"sigma2_last", s2.empty() ? 0.0 : s2.back()}, {"persistence", opt.model == GarchModel::garch ? th_out[2] + th_out[3] : (opt.model == GarchModel::gjr ? th_out[2] + th_out[4] + 0.5 * th_out[3] : th_out[4])}, {"aic", 2.0 * nll_unscaled + 2.0 * static_cast<f64>(p)}, {"bic", 2.0 * nll_unscaled + std::log(static_cast<f64>(n)) * static_cast<f64>(p)}};
		119	+ if (opt.forecast_horizon > 0 && converged) e.extra["forecast_variance"] = garch_forecast(e, r0, opt.forecast_horizon, opt.plan.seed);
		120	+ e.runtime_seconds = std::chrono::duration<f64>(std::chrono::steady_clock::now() - t0).count();
		121	+ return e;
		122	+}
		123	+
		124	+std::vector<f64> garch_forecast(const Estimate& fit, std::span<const f64> r, int H, u64 seed, int n_sim) {
		125	+ RAID_REQUIRE(H >= 1, invalid_argument, "horizon >= 1");
		126	+ const std::string& m = fit.model; GarchModel model = m.find(".gjr.") != std::string::npos ? GarchModel::gjr : (m.find(".egarch.") != std::string::npos ? GarchModel::egarch : GarchModel::garch);
		127	+ GarchDist dist = m.find("student_t") != std::string::npos ? GarchDist::student_t : GarchDist::gaussian;
		128	+ const auto& th = fit.coefficients; std::vector<f64> s2; RAID_REQUIRE(garch_variance_path(r, th, model, dist, s2), numerical, "forecast: invalid variance path");
		129	+ const f64 mu = th[0], omega = th[1], alpha = th[2]; const f64 gamma = model != GarchModel::garch ? th[3] : 0.0; const f64 beta = th[model != GarchModel::garch ? 4 : 3];
		130	+ const f64 eT = r.back() - mu, s2T = s2.back(); std::vector<f64> f(static_cast<usize>(H));
		131	+ if (model != GarchModel::egarch) {
		132	+ f[0] = omega + (alpha + (eT < 0 ? gamma : 0.0)) * eT * eT + beta * s2T;
		133	+ for (int h = 1; h < H; ++h) f[static_cast<usize>(h)] = omega + (alpha + 0.5 * gamma + beta) * f[static_cast<usize>(h - 1)];
		134	+ return f;
		135	+ }
		136	+ // EGARCH: h=1 analytical, h>1 by simulation of z (Gaussian or standardized t)
		137	+ const f64 nu = dist == GarchDist::student_t ? th.back() : 0.0;
		138	+ const f64 Eabs = dist == GarchDist::student_t ? 2.0 * std::sqrt(nu - 2.0) * std::exp(std::lgamma((nu + 1) / 2) - std::lgamma(nu / 2)) / ((nu - 1) * std::sqrt(std::numbers::pi)) : std::sqrt(2.0 / std::numbers::pi);
		139	+ const f64 zT = eT / std::sqrt(s2T); const f64 l1 = omega + alpha * (std::abs(zT) - Eabs) + gamma * zT + beta * std::log(s2T); f[0] = std::exp(l1);
		140	+ if (H == 1) return f;
		141	+ std::vector<f64> acc(static_cast<usize>(H), 0.0); Rng rng(seed, 7);
		142	+ for (int s = 0; s < n_sim; ++s) { f64 ls = l1; acc[0] += std::exp(ls); for (int h = 1; h < H; ++h) { const f64 z = dist == GarchDist::student_t ? rng.student_t(nu) : rng.normal(); ls = omega + alpha * (std::abs(z) - Eabs) + gamma * z + beta * ls; acc[static_cast<usize>(h)] += std::exp(ls); } }
		143	+ for (int h = 1; h < H; ++h) f[static_cast<usize>(h)] = acc[static_cast<usize>(h)] / n_sim;
		144	+ return f;
		145	+}
		146	+
		147	+} // namespace raid::models

added src/models/garch_likelihood.cpp +124 −0

				@@ -0,0 +1,124 @@
		1	+#include "raid/models/garch.hpp"
		2	+#include "raid/core/error.hpp"
		3	+#include "raid/statistics/reductions.hpp"
		4	+#include <cmath>
		5	+#include <limits>
		6	+#include <numbers>
		7	+
		8	+namespace raid::models {
		9	+
		10	+constexpr f64 kNuMin = 2.05, kNuMax = 500.0; // same bounds as the arch package (StudentsT distribution bounds)
		11	+
		12	+std::string to_string(GarchModel m) { switch (m) { case GarchModel::garch: return "garch"; case GarchModel::gjr: return "gjr"; case GarchModel::egarch: return "egarch"; } return "?"; }
		13	+std::string to_string(GarchDist d) { return d == GarchDist::gaussian ? "gaussian" : "student_t"; }
		14	+
		15	+usize garch_n_params(GarchModel m, GarchDist d) { return 4 + (m != GarchModel::garch ? 1 : 0) + (d == GarchDist::student_t ? 1 : 0); }
		16	+std::vector<std::string> garch_param_names(GarchModel m, GarchDist d) {
		17	+ std::vector<std::string> n = {"mu", "omega", "alpha"}; if (m != GarchModel::garch) n.push_back("gamma"); n.push_back("beta"); if (d == GarchDist::student_t) n.push_back("nu"); return n;
		18	+}
		19	+
		20	+bool garch_params_valid(std::span<const f64> th, GarchModel m, GarchDist d) {
		21	+ if (th.size() != garch_n_params(m, d)) return false;
		22	+ for (f64 v : th) if (!std::isfinite(v)) return false;
		23	+ const f64 omega = th[1], alpha = th[2];
		24	+ const f64 gamma = m != GarchModel::garch ? th[3] : 0.0;
		25	+ const f64 beta = th[m != GarchModel::garch ? 4 : 3];
		26	+ if (d == GarchDist::student_t && th.back() <= 2.0) return false;
		27	+ switch (m) {
		28	+ case GarchModel::garch: return omega > 0 && alpha >= 0 && beta >= 0 && alpha + beta < 1.0;
		29	+ case GarchModel::gjr: return omega > 0 && alpha >= 0 && beta >= 0 && alpha + gamma >= 0 && alpha + beta + 0.5 * gamma < 1.0;
		30	+ case GarchModel::egarch: return std::abs(beta) < 1.0;
		31	+ }
		32	+ return false;
		33	+}
		34	+
		35	+bool garch_variance_path(std::span<const f64> r, std::span<const f64> th, GarchModel m, GarchDist d, std::vector<f64>& s2) {
		36	+ const usize n = r.size(); s2.assign(n, std::numeric_limits<f64>::quiet_NaN());
		37	+ if (n < 2) return false;
		38	+ const f64 mu = th[0], omega = th[1], alpha = th[2];
		39	+ const f64 gamma = m != GarchModel::garch ? th[3] : 0.0;
		40	+ const f64 beta = th[m != GarchModel::garch ? 4 : 3];
		41	+ // initial variance: sample variance of ε (ddof=0), as arch's default for the first observation uses backcast... we use
		42	+ // the plain sample variance — documented in the registry (tests pin this convention against arch with backcast given).
		43	+ f64 m1 = 0; for (f64 v : r) m1 += (v - mu); m1 /= static_cast<f64>(n);
		44	+ f64 var0 = 0; for (f64 v : r) var0 += (v - mu - m1) * (v - mu - m1); var0 /= static_cast<f64>(n);
		45	+ if (!(var0 > 0)) return false;
		46	+ if (m == GarchModel::egarch) {
		47	+ const f64 Eabs = (d == GarchDist::student_t) ? [&] { const f64 nu = th.back(); return 2.0 * std::sqrt(nu - 2.0) * std::exp(std::lgamma((nu + 1) / 2) - std::lgamma(nu / 2)) / ((nu - 1) * std::sqrt(std::numbers::pi)); }() : std::sqrt(2.0 / std::numbers::pi);
		48	+ f64 ls = std::log(var0);
		49	+ s2[0] = var0;
		50	+ for (usize t = 1; t < n; ++t) {
		51	+ const f64 e = r[t - 1] - mu, z = e / std::sqrt(s2[t - 1]);
		52	+ ls = omega + alpha * (std::abs(z) - Eabs) + gamma * z + beta * std::log(s2[t - 1]);
		53	+ s2[t] = std::exp(ls);
		54	+ if (!std::isfinite(s2[t]) || s2[t] <= 0) return false;
		55	+ }
		56	+ return true;
		57	+ }
		58	+ s2[0] = var0;
		59	+ for (usize t = 1; t < n; ++t) {
		60	+ const f64 e = r[t - 1] - mu; const f64 e2 = e * e;
		61	+ s2[t] = omega + (alpha + (e < 0 ? gamma : 0.0)) * e2 + beta * s2[t - 1];
		62	+ if (!std::isfinite(s2[t]) || s2[t] <= 0) return false;
		63	+ }
		64	+ return true;
		65	+}
		66	+
		67	+f64 garch_nll(std::span<const f64> r, std::span<const f64> th, GarchModel m, GarchDist d) {
		68	+ if (!garch_params_valid(th, m, d)) return std::numeric_limits<f64>::infinity();
		69	+ std::vector<f64> s2;
		70	+ if (!garch_variance_path(r, th, m, d, s2)) return std::numeric_limits<f64>::infinity();
		71	+ const f64 mu = th[0]; const usize n = r.size();
		72	+ f64 nll = 0, c = 0;
		73	+ auto add = [&](f64 v) { const f64 t = nll + v; if (std::abs(nll) >= std::abs(v)) c += (nll - t) + v; else c += (v - t) + nll; nll = t; };
		74	+ if (d == GarchDist::gaussian) {
		75	+ const f64 k = 0.5 * std::log(2.0 * std::numbers::pi);
		76	+ for (usize t = 0; t < n; ++t) { const f64 e = r[t] - mu; add(k + 0.5 * std::log(s2[t]) + 0.5 * e * e / s2[t]); }
		77	+ } else {
		78	+ const f64 nu = th.back();
		79	+ const f64 k = std::lgamma((nu + 1) / 2) - std::lgamma(nu / 2) - 0.5 * std::log(std::numbers::pi * (nu - 2));
		80	+ for (usize t = 0; t < n; ++t) { const f64 e = r[t] - mu; add(-(k - 0.5 * std::log(s2[t]) - 0.5 * (nu + 1) * std::log1p(e * e / ((nu - 2) * s2[t])))); }
		81	+ }
		82	+ return nll + c;
		83	+}
		84	+
		85	+f64 garch_nll(std::span<const f64> r, std::span<const f64> th, GarchModel m, f64 nu_fixed) {
		86	+ if (nu_fixed > 2.0) { std::vector<f64> t(th.begin(), th.end()); t.push_back(nu_fixed); return garch_nll(r, t, m, GarchDist::student_t); }
		87	+ return garch_nll(r, th, m, GarchDist::gaussian);
		88	+}
		89	+
		90	+// Transforms: u = [μ, log ω, logit-type for (α, β[, γ]), log(ν − 2)]
		91	+std::vector<f64> garch_to_unconstrained(std::span<const f64> th, GarchModel m, GarchDist d) {
		92	+ std::vector<f64> u(th.begin(), th.end());
		93	+ u[1] = m == GarchModel::egarch ? th[1] : std::log(th[1]);
		94	+ const bool g = m != GarchModel::garch; const usize ib = g ? 4 : 3;
		95	+ if (m == GarchModel::egarch) { u[2] = th[2]; u[3] = th[3]; u[4] = std::atanh(std::clamp(th[4], -0.999999, 0.999999)); }
		96	+ else {
		97	+ // persistence p = α + β (+γ/2) ∈ (0,1): u_p = logit(p); share s = α/p ∈ (0,1): u_s = logit(s); γ free for GJR via α+γ ≥ 0 → γ = exp(u_g) − α
		98	+ const f64 gamma = g ? th[3] : 0.0; const f64 p = th[2] + th[ib] + 0.5 * gamma; const f64 s = p > 0 ? th[2] / p : 0.5;
		99	+ u[2] = std::log(std::clamp(p, 1e-12, 1 - 1e-12) / (1 - std::clamp(p, 1e-12, 1 - 1e-12)));
		100	+ u[ib] = std::log(std::clamp(s, 1e-12, 1 - 1e-12) / (1 - std::clamp(s, 1e-12, 1 - 1e-12)));
		101	+ if (g) u[3] = std::log(std::max(th[2] + gamma, 1e-12));
		102	+ }
		103	+ if (d == GarchDist::student_t) { const f64 s = std::clamp((th.back() - kNuMin) / (kNuMax - kNuMin), 1e-9, 1 - 1e-9); u.back() = std::log(s / (1 - s)); }
		104	+ return u;
		105	+}
		106	+
		107	+std::vector<f64> garch_from_unconstrained(std::span<const f64> u, GarchModel m, GarchDist d) {
		108	+ std::vector<f64> th(u.begin(), u.end());
		109	+ th[1] = m == GarchModel::egarch ? u[1] : std::exp(u[1]);
		110	+ const bool g = m != GarchModel::garch;
		111	+ auto sig = [](f64 x) { return 1.0 / (1.0 + std::exp(-x)); };
		112	+ if (m == GarchModel::egarch) { th[2] = u[2]; th[3] = u[3]; th[4] = std::tanh(u[4]); }
		113	+ else if (!g) { const f64 p = sig(u[2]) * (1 - 1e-9), s = sig(u[3]); th[2] = s * p; th[3] = (1 - s) * p; }
		114	+ else {
		115	+ // GJR: p = α + β + γ/2, s = α/p → α = s p; ag = α + γ = exp(u_g) → γ = ag − α; β = p − α − γ/2 (must be ≥ 0: clamp via s)
		116	+ const f64 p = sig(u[2]) * (1 - 1e-9), s = sig(u[4]); const f64 alpha = s * p; const f64 ag = std::exp(u[3]); const f64 gamma = ag - alpha;
		117	+ f64 beta = p - alpha - 0.5 * gamma; if (beta < 0) { beta = 0; }
		118	+ th[2] = alpha; th[3] = gamma; th[4] = beta;
		119	+ }
		120	+ if (d == GarchDist::student_t) th.back() = kNuMin + (kNuMax - kNuMin) * sig(u.back());
		121	+ return th;
		122	+}
		123	+
		124	+} // namespace raid::models

added src/statistics/covariance.cpp +75 −0

				@@ -0,0 +1,75 @@
		1	+#include "raid/statistics/covariance.hpp"
		2	+#include "raid/core/error.hpp"
		3	+#include <cmath>
		4	+#include <map>
		5	+
		6	+namespace raid::stats {
		7	+
		8	+std::string to_string(CovType c) {
		9	+ switch (c) { case CovType::classical: return "classical"; case CovType::hc0: return "hc0"; case CovType::hc1: return "hc1"; case CovType::hc2: return "hc2"; case CovType::hc3: return "hc3"; case CovType::hac_bartlett: return "hac_bartlett"; case CovType::cluster: return "cluster"; }
		10	+ return "?";
		11	+}
		12	+CovType covtype_from_string(std::string_view s) {
		13	+ if (s == "classical" || s == "nonrobust") return CovType::classical;
		14	+ if (s == "hc0" || s == "HC0") return CovType::hc0; if (s == "hc1" || s == "HC1") return CovType::hc1;
		15	+ if (s == "hc2" || s == "HC2") return CovType::hc2; if (s == "hc3" || s == "HC3") return CovType::hc3;
		16	+ if (s == "hac" || s == "hac_bartlett" || s == "newey_west") return CovType::hac_bartlett;
		17	+ if (s == "cluster") return CovType::cluster;
		18	+ throw RaidError(ErrorCode::invalid_argument, "unknown covariance type '" + std::string(s) + "'");
		19	+}
		20	+int default_hac_lags(usize n) { return static_cast<int>(std::floor(4.0 * std::pow(static_cast<f64>(n) / 100.0, 2.0 / 9.0))); }
		21	+
		22	+Eigen::MatrixXd robust_covariance(const Eigen::Ref<const Eigen::MatrixXd>& X, const Eigen::Ref<const Eigen::VectorXd>& e,
		23	+ const Eigen::Ref<const Eigen::MatrixXd>& xtx_inv, const CovOptions& opt, const Eigen::VectorXd& h) {
		24	+ const Eigen::Index n = X.rows(), k = X.cols();
		25	+ RAID_REQUIRE(e.size() == n, invalid_argument, "robust_covariance: residual length");
		26	+ const f64 nd = static_cast<f64>(n), kd = static_cast<f64>(k);
		27	+ switch (opt.type) {
		28	+ case CovType::classical: {
		29	+ RAID_REQUIRE(n > k, insufficient_samples, "classical covariance needs n > k");
		30	+ const f64 s2 = e.squaredNorm() / (nd - kd);
		31	+ return s2 * xtx_inv;
		32	+ }
		33	+ case CovType::hc0: case CovType::hc1: case CovType::hc2: case CovType::hc3: {
		34	+ Eigen::VectorXd w = e.array().square();
		35	+ if (opt.type == CovType::hc1) w *= nd / (nd - kd);
		36	+ if (opt.type == CovType::hc2 || opt.type == CovType::hc3) {
		37	+ RAID_REQUIRE(h.size() == n, invalid_argument, "HC2/HC3 need leverage");
		38	+ for (Eigen::Index i = 0; i < n; ++i) { const f64 d = 1.0 - h[i]; w[i] /= (opt.type == CovType::hc2 ? d : d * d); }
		39	+ }
		40	+ const Eigen::MatrixXd meat = X.transpose() * w.asDiagonal() * X;
		41	+ return xtx_inv * meat * xtx_inv;
		42	+ }
		43	+ case CovType::hac_bartlett: {
		44	+ const int L = opt.hac_lags < 0 ? default_hac_lags(static_cast<usize>(n)) : opt.hac_lags;
		45	+ Eigen::MatrixXd S = Eigen::MatrixXd::Zero(k, k);
		46	+ Eigen::MatrixXd U = X.array().colwise() * e.array(); // scores n×k
		47	+ S = U.transpose() * U;
		48	+ for (int l = 1; l <= L; ++l) {
		49	+ const f64 wl = 1.0 - static_cast<f64>(l) / (static_cast<f64>(L) + 1.0);
		50	+ const Eigen::MatrixXd G = U.bottomRows(n - l).transpose() * U.topRows(n - l);
		51	+ S += wl * (G + G.transpose());
		52	+ }
		53	+ if (opt.hac_small_sample) S *= nd / (nd - kd);
		54	+ return xtx_inv * S * xtx_inv;
		55	+ }
		56	+ case CovType::cluster: {
		57	+ RAID_REQUIRE(static_cast<Eigen::Index>(opt.clusters.size()) == n, invalid_argument, "cluster ids length");
		58	+ std::map<i64, Eigen::VectorXd> sums;
		59	+ for (Eigen::Index i = 0; i < n; ++i) {
		60	+ auto it = sums.find(opt.clusters[static_cast<usize>(i)]);
		61	+ if (it == sums.end()) it = sums.emplace(opt.clusters[static_cast<usize>(i)], Eigen::VectorXd::Zero(k)).first;
		62	+ it->second += X.row(i).transpose() * e[i];
		63	+ }
		64	+ Eigen::MatrixXd S = Eigen::MatrixXd::Zero(k, k);
		65	+ for (auto& [g, s] : sums) S += s * s.transpose();
		66	+ const f64 G = static_cast<f64>(sums.size());
		67	+ f64 corr = 1.0;
		68	+ if (opt.cluster_small_sample) { RAID_REQUIRE(G > 1 && n > k, insufficient_samples, "cluster correction needs G > 1, n > k"); corr = (G / (G - 1.0)) * ((nd - 1.0) / (nd - kd)); }
		69	+ return corr * (xtx_inv * S * xtx_inv);
		70	+ }
		71	+ }
		72	+ throw RaidError(ErrorCode::invalid_argument, "unknown CovType");
		73	+}
		74	+
		75	+} // namespace raid::stats

added src/statistics/reductions.cpp +48 −0

				@@ -0,0 +1,48 @@
		1	+#include "raid/statistics/reductions.hpp"
		2	+
		3	+namespace raid::stats {
		4	+
		5	+Moments moments(std::span<const f64> x) {
		6	+ RAID_REQUIRE(x.size() >= 2, insufficient_samples, "moments need n >= 2");
		7	+ const f64 n = static_cast<f64>(x.size());
		8	+ const f64 m = sum_neumaier(x) / n;
		9	+ std::vector<f64> d2(x.size()), d3(x.size()), d4(x.size());
		10	+ f64 mn = x[0], mx = x[0];
		11	+ for (usize i = 0; i < x.size(); ++i) {
		12	+ const f64 d = x[i] - m; d2[i] = d * d; d3[i] = d2[i] * d; d4[i] = d2[i] * d2[i];
		13	+ mn = std::min(mn, x[i]); mx = std::max(mx, x[i]);
		14	+ }
		15	+ const f64 m2 = sum_neumaier(std::span<const f64>(d2)) / n, m3 = sum_neumaier(std::span<const f64>(d3)) / n, m4 = sum_neumaier(std::span<const f64>(d4)) / n;
		16	+ const f64 var_unb = sum_neumaier(std::span<const f64>(d2)) / (n - 1.0);
		17	+ return {x.size(), m, var_unb, m2 > 0 ? m3 / std::pow(m2, 1.5) : std::numeric_limits<f64>::quiet_NaN(), m2 > 0 ? m4 / (m2 * m2) - 3.0 : std::numeric_limits<f64>::quiet_NaN(), mn, mx};
		18	+}
		19	+
		20	+f64 autocovariance(std::span<const f64> x, usize k, bool demean) {
		21	+ RAID_REQUIRE(k < x.size(), insufficient_samples, "autocovariance lag >= n");
		22	+ const f64 n = static_cast<f64>(x.size());
		23	+ const f64 m = demean ? sum_neumaier(x) / n : 0.0;
		24	+ std::vector<f64> p(x.size() - k);
		25	+ for (usize i = 0; i + k < x.size(); ++i) p[i] = (x[i] - m) * (x[i + k] - m);
		26	+ return sum_neumaier(std::span<const f64>(p)) / n;
		27	+}
		28	+
		29	+std::vector<f64> autocorrelation(std::span<const f64> x, usize max_lag) {
		30	+ const f64 c0 = autocovariance(x, 0);
		31	+ std::vector<f64> r(max_lag + 1, 1.0);
		32	+ for (usize k = 1; k <= max_lag && k < x.size(); ++k) r[k] = c0 > 0 ? autocovariance(x, k) / c0 : std::numeric_limits<f64>::quiet_NaN();
		33	+ return r;
		34	+}
		35	+
		36	+Drawdown max_drawdown(std::span<const f64> cum) {
		37	+ Drawdown d{0.0, 0, 0};
		38	+ if (cum.empty()) return d;
		39	+ f64 peak = cum[0]; usize peak_i = 0;
		40	+ for (usize i = 0; i < cum.size(); ++i) {
		41	+ if (cum[i] > peak) { peak = cum[i]; peak_i = i; }
		42	+ const f64 dd = peak != 0.0 ? (peak - cum[i]) / peak : 0.0;
		43	+ if (dd > d.max_drawdown) { d.max_drawdown = dd; d.peak_index = peak_i; d.trough_index = i; }
		44	+ }
		45	+ return d;
		46	+}
		47	+
		48	+} // namespace raid::stats

added src/statistics/rolling.cpp +111 −0

				@@ -0,0 +1,111 @@
		1	+#include "raid/statistics/rolling.hpp"
		2	+#include "raid/statistics/reductions.hpp"
		3	+#include "raid/core/error.hpp"
		4	+#include <cmath>
		5	+
		6	+namespace raid::stats {
		7	+
		8	+namespace {
		9	+struct RunningSum {
		10	+ f64 s{0}, c{0};
		11	+ void add(f64 v) { const f64 t = s + v; if (std::abs(s) >= std::abs(v)) c += (s - t) + v; else c += (v - t) + s; s = t; }
		12	+ f64 value() const { return s + c; }
		13	+ void reset(f64 exact) { s = exact; c = 0; }
		14	+};
		15	+inline usize minp(const RollingOptions& o) { return o.min_periods == 0 ? o.window : o.min_periods; }
		16	+} // namespace
		17	+
		18	+std::vector<f64> rolling_sum(std::span<const f64> x, const RollingOptions& o) {
		19	+ RAID_REQUIRE(o.window >= 1, invalid_argument, "window must be >= 1");
		20	+ const usize n = x.size(), w = o.window, mp = minp(o);
		21	+ std::vector<f64> out(n, kNaN);
		22	+ RunningSum rs; usize since_resum = 0;
		23	+ for (usize i = 0; i < n; ++i) {
		24	+ rs.add(x[i]);
		25	+ if (i >= w) rs.add(-x[i - w]);
		26	+ if (++since_resum >= o.resum_every) { const usize b = i + 1 >= w ? i + 1 - w : 0; rs.reset(sum_neumaier(x.subspan(b, i + 1 - b))); since_resum = 0; }
		27	+ const usize count = i + 1 < w ? i + 1 : w;
		28	+ if (count >= mp) out[i] = rs.value();
		29	+ }
		30	+ return out;
		31	+}
		32	+
		33	+std::vector<f64> rolling_mean(std::span<const f64> x, const RollingOptions& o) {
		34	+ auto s = rolling_sum(x, o);
		35	+ for (usize i = 0; i < s.size(); ++i) { const usize count = i + 1 < o.window ? i + 1 : o.window; s[i] /= static_cast<f64>(count); }
		36	+ return s;
		37	+}
		38	+
		39	+std::vector<f64> rolling_variance(std::span<const f64> x, const RollingOptions& o, int ddof) {
		40	+ // Two accumulators (sum, sum of squares about a per-window shift) would still cancel for large means;
		41	+ // we recompute each window by Welford from scratch when the window is small (<= 64), otherwise use the
		42	+ // shifted-sums formula with periodic exact re-summation. Both are FP64 references; speed is not the goal here.
		43	+ const usize n = x.size(), w = o.window, mp = minp(o);
		44	+ std::vector<f64> out(n, kNaN);
		45	+ if (w <= 64) {
		46	+ for (usize i = 0; i < n; ++i) {
		47	+ const usize b = i + 1 >= w ? i + 1 - w : 0; const usize count = i + 1 - b;
		48	+ if (count < mp || count <= static_cast<usize>(ddof)) continue;
		49	+ Welford wf; for (usize j = b; j <= i; ++j) wf.add(x[j]);
		50	+ out[i] = wf.variance(ddof);
		51	+ }
		52	+ return out;
		53	+ }
		54	+ const f64 shift_c = x.empty() ? 0.0 : x[0];
		55	+ RunningSum s1, s2; usize since = 0;
		56	+ for (usize i = 0; i < n; ++i) {
		57	+ const f64 d = x[i] - shift_c; s1.add(d); s2.add(d * d);
		58	+ if (i >= w) { const f64 e = x[i - w] - shift_c; s1.add(-e); s2.add(-e * e); }
		59	+ if (++since >= o.resum_every) {
		60	+ const usize b = i + 1 >= w ? i + 1 - w : 0; f64 a = 0, q = 0;
		61	+ std::vector<f64> t1(i + 1 - b), t2(i + 1 - b);
		62	+ for (usize j = b; j <= i; ++j) { t1[j - b] = x[j] - shift_c; t2[j - b] = t1[j - b] * t1[j - b]; }
		63	+ a = sum_neumaier(std::span<const f64>(t1)); q = sum_neumaier(std::span<const f64>(t2)); s1.reset(a); s2.reset(q); since = 0;
		64	+ }
		65	+ const usize count = i + 1 < w ? i + 1 : w;
		66	+ if (count >= mp && count > static_cast<usize>(ddof)) {
		67	+ const f64 cnt = static_cast<f64>(count), m = s1.value() / cnt;
		68	+ out[i] = std::max(0.0, (s2.value() - cnt * m * m) / (cnt - static_cast<f64>(ddof)));
		69	+ }
		70	+ }
		71	+ return out;
		72	+}
		73	+
		74	+std::vector<f64> rolling_covariance(std::span<const f64> x, std::span<const f64> y, const RollingOptions& o, int ddof) {
		75	+ RAID_REQUIRE(x.size() == y.size(), invalid_argument, "rolling_covariance: size mismatch");
		76	+ const usize n = x.size(), w = o.window, mp = minp(o);
		77	+ std::vector<f64> out(n, kNaN);
		78	+ for (usize i = 0; i < n; ++i) {
		79	+ const usize b = i + 1 >= w ? i + 1 - w : 0; const usize count = i + 1 - b;
		80	+ if (count < mp || count <= static_cast<usize>(ddof)) continue;
		81	+ f64 mx = sum_neumaier(x.subspan(b, count)) / static_cast<f64>(count), my = sum_neumaier(y.subspan(b, count)) / static_cast<f64>(count);
		82	+ f64 s = 0, c = 0;
		83	+ for (usize j = b; j <= i; ++j) { const f64 v = (x[j] - mx) * (y[j] - my), t = s + v; if (std::abs(s) >= std::abs(v)) c += (s - t) + v; else c += (v - t) + s; s = t; }
		84	+ out[i] = (s + c) / static_cast<f64>(count - static_cast<usize>(ddof));
		85	+ }
		86	+ return out;
		87	+}
		88	+
		89	+std::vector<f64> rolling_correlation(std::span<const f64> x, std::span<const f64> y, const RollingOptions& o) {
		90	+ auto cxy = rolling_covariance(x, y, o, 1); auto vx = rolling_variance(x, o, 1); auto vy = rolling_variance(y, o, 1);
		91	+ for (usize i = 0; i < cxy.size(); ++i) cxy[i] = (vx[i] > 0 && vy[i] > 0) ? cxy[i] / std::sqrt(vx[i] * vy[i]) : kNaN;
		92	+ return cxy;
		93	+}
		94	+
		95	+std::vector<f64> expanding_mean(std::span<const f64> x, usize min_periods) {
		96	+ std::vector<f64> out(x.size(), kNaN); Welford w;
		97	+ for (usize i = 0; i < x.size(); ++i) { w.add(x[i]); if (w.n >= min_periods) out[i] = w.mean; }
		98	+ return out;
		99	+}
		100	+std::vector<f64> expanding_variance(std::span<const f64> x, usize min_periods, int ddof) {
		101	+ std::vector<f64> out(x.size(), kNaN); Welford w;
		102	+ for (usize i = 0; i < x.size(); ++i) { w.add(x[i]); if (w.n >= min_periods) out[i] = w.variance(ddof); }
		103	+ return out;
		104	+}
		105	+std::vector<f64> shift(std::span<const f64> x, long k) {
		106	+ std::vector<f64> out(x.size(), kNaN);
		107	+ for (usize i = 0; i < x.size(); ++i) { const long j = static_cast<long>(i) - k; if (j >= 0 && j < static_cast<long>(x.size())) out[i] = x[static_cast<usize>(j)]; }
		108	+ return out;
		109	+}
		110	+
		111	+} // namespace raid::stats

added tests/reference/test_reference_values.cpp +75 −0

				@@ -0,0 +1,75 @@
		1	+// Reference tests: RAID vs established software on the committed fixtures (values produced by scripts/make_fixtures.py
		2	+// with statsmodels 0.15 / scipy 1.18 / arch 8.0 / exchange_calendars 4.13; versions recorded in reference_values.json).
		3	+#include <catch2/catch_test_macros.hpp>
		4	+#include <catch2/matchers/catch_matchers_floating_point.hpp>
		5	+#include <nlohmann/json.hpp>
		6	+#include <fstream>
		7	+#include <set>
		8	+#include "raid/estimators/ols.hpp"
		9	+#include "raid/estimators/realized.hpp"
		10	+#include "raid/models/garch.hpp"
		11	+#include "raid/data/calendar.hpp"
		12	+#include "raid/data/io.hpp"
		13	+#include "raid/data/returns.hpp"
		14	+using namespace raid; using json = nlohmann::json; using Catch::Matchers::WithinRel; using Catch::Matchers::WithinAbs;
		15	+
		16	+static json load_ref() { std::ifstream f(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/reference_values.json"); REQUIRE(f.good()); json j; f >> j; return j; }
		17	+static void load_csv(const std::string& p, std::vector<std::vector<double>>& rows) { std::ifstream f(p); REQUIRE(f.good()); std::string line; std::getline(f, line); while (std::getline(f, line)) { if (line.empty()) continue; std::vector<double> r; usize s = 0; for (usize i = 0; i <= line.size(); ++i) if (i == line.size() || line[i] == ',') { r.push_back(std::stod(line.substr(s, i - s))); s = i + 1; } rows.push_back(r); } }
		18	+
		19	+TEST_CASE("OLS and robust standard errors match statsmodels", "[reference][ols]") {
		20	+ auto ref = load_ref()["ols"]; std::vector<std::vector<double>> rows; load_csv(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/ols_design.csv", rows);
		21	+ const Eigen::Index n = static_cast<Eigen::Index>(rows.size()); Eigen::MatrixXd X(n, 3); Eigen::VectorXd y(n);
		22	+ for (Eigen::Index i = 0; i < n; ++i) { y[i] = rows[i][0]; X(i, 0) = 1; X(i, 1) = rows[i][1]; X(i, 2) = rows[i][2]; }
		23	+ auto r = est::ols(X, y);
		24	+ for (int j = 0; j < 3; ++j) { CHECK_THAT(r.estimate.coefficients[j], WithinRel(ref["params"][j].get<double>(), 1e-10)); CHECK_THAT(r.estimate.inference->std_errors[j], WithinRel(ref["bse"][j].get<double>(), 1e-9)); CHECK_THAT(r.estimate.inference->p_values[j], WithinAbs(ref["pvalues"][j].get<double>(), 1e-9)); }
		25	+ CHECK_THAT(r.r2, WithinRel(ref["rsquared"].get<double>(), 1e-10)); CHECK_THAT(r.estimate.loss, WithinRel(ref["ssr"].get<double>(), 1e-10));
		26	+ est::OlsOptions o; o.cov.type = stats::CovType::hc0; auto h0 = est::ols(X, y, o); o.cov.type = stats::CovType::hc3; auto h3 = est::ols(X, y, o);
		27	+ o.cov.type = stats::CovType::hac_bartlett; o.cov.hac_lags = 5; auto hac = est::ols(X, y, o);
		28	+ std::vector<i64> g(static_cast<usize>(n)); for (Eigen::Index i = 0; i < n; ++i) g[static_cast<usize>(i)] = i / 25; o.cov.type = stats::CovType::cluster; o.cov.clusters = g; auto cl = est::ols(X, y, o);
		29	+ for (int j = 0; j < 3; ++j) { CHECK_THAT(h0.estimate.inference->std_errors[j], WithinRel(ref["hc0_bse"][j].get<double>(), 1e-9)); CHECK_THAT(h3.estimate.inference->std_errors[j], WithinRel(ref["hc3_bse"][j].get<double>(), 1e-9)); CHECK_THAT(hac.estimate.inference->std_errors[j], WithinRel(ref["hac5_bse"][j].get<double>(), 1e-9)); CHECK_THAT(cl.estimate.inference->std_errors[j], WithinRel(ref["cluster_bse"][j].get<double>(), 1e-9)); }
		30	+ CHECK_THAT(est::t_pvalue_two_sided(1.96, 0), WithinAbs(ref["t_pvalues"]["1.96_inf"].get<double>(), 1e-12));
		31	+ CHECK_THAT(est::t_pvalue_two_sided(2.0, 10), WithinAbs(ref["t_pvalues"]["2.0_10"].get<double>(), 1e-12));
		32	+}
		33	+
		34	+TEST_CASE("XNYS calendar matches exchange_calendars", "[reference][calendar]") {
		35	+ auto ref = load_ref()["xnys"]; if (ref.contains("error")) { WARN("exchange_calendars unavailable when fixtures were generated: " + ref["error"].get<std::string>()); return; }
		36	+ MarketCalendar cal; std::set<std::string> ours; for (auto d : cal.sessions(Date::from_ymd(2000, 1, 1), Date::from_ymd(2026, 12, 31))) ours.insert(d.iso());
		37	+ CHECK(ours.size() == ref["n_sessions_2000_2026"].get<usize>());
		38	+ std::set<std::string> theirs2024; for (auto& s : ref["sessions_2024"]) theirs2024.insert(s.get<std::string>());
		39	+ std::set<std::string> ours2024; for (auto d : cal.sessions(Date::from_ymd(2024, 1, 1), Date::from_ymd(2024, 12, 31))) ours2024.insert(d.iso());
		40	+ CHECK(ours2024 == theirs2024);
		41	+ usize mismatches = 0; for (auto& h : ref["holidays_2000_2026"]) if (ours.count(h.get<std::string>())) { ++mismatches; UNSCOPED_INFO("RAID trades on " << h.get<std::string>() << " but exchange_calendars does not"); }
		42	+ CHECK(mismatches == 0);
		43	+ if (!ref["early_closes_2000_2026"].is_null()) { usize bad = 0; for (auto& e : ref["early_closes_2000_2026"]) { Date d = date_from_iso(e.get<std::string>()); if (!cal.is_early_close(d)) { ++bad; UNSCOPED_INFO("missing early close " << e.get<std::string>()); } } CHECK(bad == 0);
		44	+ usize extra = 0; for (auto d : cal.sessions(Date::from_ymd(2000, 1, 1), Date::from_ymd(2026, 12, 31))) if (cal.is_early_close(d)) { bool found = false; for (auto& e : ref["early_closes_2000_2026"]) if (e.get<std::string>() == d.iso()) found = true; if (!found) { ++extra; UNSCOPED_INFO("extra early close " << d.iso()); } } CHECK(extra == 0); }
		45	+}
		46	+
		47	+TEST_CASE("Realized measures match the fixture truth (numpy)", "[reference][realized]") {
		48	+ std::ifstream f(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/synthetic_bars_1m.expected.json"); json truth; f >> truth;
		49	+ auto bars = data::read_bars_csv(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/synthetic_bars_1m.csv"); MarketCalendar cal;
		50	+ for (auto& [sym, b0] : bars) { auto b = b0; b.normalize(); auto reg = b.filter_session(cal, SessionPolicy::regular); auto ir = data::intraday_returns(reg); est::RealizedOptions o; o.plan.backend = Backend::cpu; auto m = est::realized_measures(ir, o);
		51	+ REQUIRE(m.sessions.size() == 5);
		52	+ for (usize k = 0; k < 5; ++k) { auto t = truth[sym][m.sessions[k].iso()]; CHECK(m.n_returns[k] + 1 == t["n_regular_bars"].get<usize>()); CHECK(cal.expected_regular_bars(m.sessions[k], Interval::m1) == t["expected_bars"].get<usize>());
		53	+ CHECK_THAT(m.rv[k], WithinRel(t["rv"].get<double>(), 1e-12)); CHECK_THAT(m.bpv[k], WithinRel(t["bpv"].get<double>(), 1e-12)); CHECK_THAT(m.rsv_neg[k], WithinRel(t["rsv_neg"].get<double>(), 1e-12)); CHECK_THAT(m.rsv_pos[k], WithinRel(t["rsv_pos"].get<double>(), 1e-12)); CHECK_THAT(m.rq[k], WithinRel(t["rq"].get<double>(), 1e-12)); } }
		54	+}
		55	+
		56	+TEST_CASE("GARCH-family QML matches the arch package within tolerance", "[reference][garch]") {
		57	+ auto ref = load_ref()["garch"]; if (ref.contains("error")) { WARN("arch unavailable: " + ref["error"].get<std::string>()); return; }
		58	+ std::vector<std::vector<double>> rows; load_csv(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/garch_returns.csv", rows); std::vector<double> r; for (auto& x : rows) r.push_back(x[0]);
		59	+ struct Case { const char* key; models::GarchModel m; models::GarchDist d; std::vector<const char*> arch_names; };
		60	+ const std::vector<Case> cases = {{"garch.normal", models::GarchModel::garch, models::GarchDist::gaussian, {"mu", "omega", "alpha[1]", "beta[1]"}},
		61	+ {"gjr.normal", models::GarchModel::gjr, models::GarchDist::gaussian, {"mu", "omega", "alpha[1]", "gamma[1]", "beta[1]"}},
		62	+ {"garch.t", models::GarchModel::garch, models::GarchDist::student_t, {"mu", "omega", "alpha[1]", "beta[1]", "nu"}},
		63	+ {"egarch.normal", models::GarchModel::egarch, models::GarchDist::gaussian, {"mu", "omega", "alpha[1]", "gamma[1]", "beta[1]"}}};
		64	+ for (auto& c : cases) {
		65	+ auto a = ref[c.key]; models::GarchFitOptions o; o.model = c.m; o.dist = c.d; o.forecast_horizon = 5; auto fit = models::garch_fit(r, o);
		66	+ INFO(c.key); REQUIRE(fit.ok());
		67	+ // Both maximise the same likelihood up to the initial-variance convention: log-likelihoods within 0.05 % and
		68	+ // parameters within 5 % relative or 0.01 absolute (documented tolerance, see docs/methods/registry.md).
		69	+ CHECK_THAT(-fit.loss, WithinRel(a["loglik"].get<double>(), 5e-4));
		70	+ for (usize j = 0; j < c.arch_names.size(); ++j) { const double ours = fit.coefficients[j], theirs = a["params"][c.arch_names[j]].get<double>();
		71	+ if (std::string(c.arch_names[j]) == "nu" && theirs > 100) continue; // ν unidentified for Gaussian data
		72	+ INFO(c.arch_names[j] << " ours=" << ours << " arch=" << theirs); CHECK((std::abs(ours - theirs) <= 0.05 * std::abs(theirs) + 0.01)); }
		73	+ if (c.m != models::GarchModel::egarch) { auto fc = fit.extra["forecast_variance"].get<std::vector<double>>(); for (int h = 0; h < 5; ++h) CHECK_THAT(fc[static_cast<usize>(h)], WithinRel(a["forecast_h5"][h].get<double>(), 0.05)); }
		74	+ }
		75	+}

added tests/unit/test_calendar.cpp +49 −0

				@@ -0,0 +1,49 @@
		1	+#include <catch2/catch_test_macros.hpp>
		2	+#include "raid/data/calendar.hpp"
		3	+using namespace raid;
		4	+
		5	+TEST_CASE("XNYS holidays and early closes", "[calendar]") {
		6	+ MarketCalendar cal;
		7	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2024, 1, 1))); // New Year
		8	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2024, 1, 15))); // MLK
		9	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2024, 3, 29))); // Good Friday
		10	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2024, 6, 19))); // Juneteenth
		11	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2024, 7, 4)));
		12	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2024, 11, 28))); // Thanksgiving
		13	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2024, 12, 25)));
		14	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2025, 1, 9))); // Carter funeral
		15	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2012, 10, 29))); // Sandy
		16	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2001, 9, 11)));
		17	+ CHECK(cal.is_trading_day(Date::from_ymd(2024, 11, 29)));
		18	+ CHECK(cal.is_early_close(Date::from_ymd(2024, 11, 29)));
		19	+ CHECK(cal.is_early_close(Date::from_ymd(2024, 12, 24)));
		20	+ CHECK(cal.is_early_close(Date::from_ymd(2024, 7, 3))); // July 4 2024 is a Thursday
		21	+ CHECK_FALSE(cal.is_early_close(Date::from_ymd(2026, 7, 3))); // July 3 2026 is a Friday, July 4 Saturday (observed Friday → holiday)
		22	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2026, 7, 3)));
		23	+ CHECK(cal.is_trading_day(Date::from_ymd(2021, 12, 31))); // Jan 1 2022 Saturday → no Friday holiday
		24	+ CHECK_FALSE(cal.is_trading_day(Date::from_ymd(2021, 12, 24))); // Christmas 2021 Saturday → observed Friday
		25	+ CHECK(cal.expected_regular_bars(Date::from_ymd(2024, 11, 27), Interval::m1) == 391);
		26	+ CHECK(cal.expected_regular_bars(Date::from_ymd(2024, 11, 29), Interval::m1) == 211);
		27	+ CHECK(cal.expected_regular_bars(Date::from_ymd(2024, 11, 27), Interval::m5) == 79);
		28	+ CHECK(cal.sessions(Date::from_ymd(2024, 1, 1), Date::from_ymd(2024, 12, 31)).size() == 252);
		29	+}
		30	+TEST_CASE("New York DST conversions match FinData timestamps", "[calendar]") {
		31	+ // 2024-03-08 (EST): 04:00 ET = 09:00 UTC ; 2024-03-11 (EDT): 19:59 ET = 23:59 UTC
		32	+ CHECK(MarketCalendar::new_york_offset(timestamp_from_iso("2024-03-08T09:00:00Z")) == -18000);
		33	+ CHECK(MarketCalendar::new_york_offset(timestamp_from_iso("2024-03-11T23:59:00Z")) == -14400);
		34	+ auto l = MarketCalendar::to_new_york(timestamp_from_iso("2024-03-11T23:59:00Z")); CHECK(l.date == Date::from_ymd(2024, 3, 11)); CHECK(l.minute_of_day == 19 * 60 + 59);
		35	+ auto s = MarketCalendar{}.session(Date::from_ymd(2024, 3, 11));
		36	+ CHECK(timestamp_iso(s.open_utc) == "2024-03-11T13:30:00.000000Z"); CHECK(timestamp_iso(s.close_utc) == "2024-03-11T20:00:00.000000Z");
		37	+ auto s2 = MarketCalendar{}.session(Date::from_ymd(2024, 11, 29)); CHECK(timestamp_iso(s2.close_utc) == "2024-11-29T18:00:00.000000Z"); CHECK(s2.early_close);
		38	+ CHECK(timestamp_iso(s2.ext_close_utc) == "2024-11-29T22:00:00.000000Z");
		39	+ // DST transition instants: 2024-03-10 06:59 UTC is EST, 07:00 UTC is EDT; 2024-11-03 05:59 UTC EDT, 06:00 UTC EST
		40	+ CHECK(MarketCalendar::new_york_offset(timestamp_from_iso("2024-03-10T06:59:00Z")) == -18000);
		41	+ CHECK(MarketCalendar::new_york_offset(timestamp_from_iso("2024-03-10T07:00:00Z")) == -14400);
		42	+ CHECK(MarketCalendar::new_york_offset(timestamp_from_iso("2024-11-03T05:59:00Z")) == -14400);
		43	+ CHECK(MarketCalendar::new_york_offset(timestamp_from_iso("2024-11-03T06:00:00Z")) == -18000);
		44	+ // pre-2007 rules: 2006 DST from April 2 to October 29
		45	+ CHECK(MarketCalendar::new_york_offset(timestamp_from_iso("2006-03-15T12:00:00Z")) == -18000);
		46	+ CHECK(MarketCalendar::new_york_offset(timestamp_from_iso("2006-04-03T12:00:00Z")) == -14400);
		47	+ CHECK(MarketCalendar::new_york_offset(timestamp_from_iso("2006-10-30T12:00:00Z")) == -18000);
		48	+ CHECK(MarketCalendar::session_of(timestamp_from_iso("2024-03-11T23:59:00Z")) == Date::from_ymd(2024, 3, 11));
		49	+}

added tests/unit/test_core.cpp +48 −0

				@@ -0,0 +1,48 @@
		1	+#include <catch2/catch_test_macros.hpp>
		2	+#include <catch2/matchers/catch_matchers_floating_point.hpp>
		3	+#include "raid/core/types.hpp"
		4	+#include "raid/core/error.hpp"
		5	+#include "raid/core/hash.hpp"
		6	+#include "raid/core/rng.hpp"
		7	+#include "raid/core/log.hpp"
		8	+#include "raid/core/thread_pool.hpp"
		9	+#include <atomic>
		10	+using namespace raid;
		11	+
		12	+TEST_CASE("Date round trips and weekdays", "[core]") {
		13	+ Date d = Date::from_ymd(2024, 3, 11); auto y = d.ymd();
		14	+ CHECK(y.year == 2024); CHECK(y.month == 3); CHECK(y.day == 11); CHECK(d.weekday() == 1); // Monday
		15	+ CHECK(Date::from_ymd(1970, 1, 1).days == 0); CHECK(Date::from_ymd(1970, 1, 1).weekday() == 4);
		16	+ CHECK(date_from_iso("2000-02-29").iso() == "2000-02-29"); CHECK(Date::from_ymd(2026, 11, 2).weekday() == 1);
		17	+}
		18	+TEST_CASE("Timestamp ISO parsing", "[core]") {
		19	+ CHECK(timestamp_from_iso("2024-03-08T09:00:00Z").us == 1709888400000000LL);
		20	+ CHECK(timestamp_from_iso("2024-03-08T04:00:00-05:00").us == 1709888400000000LL);
		21	+ CHECK(timestamp_from_iso("2024-03-08T09:00:00.250000+00:00").us == 1709888400250000LL);
		22	+ CHECK(timestamp_iso(Timestamp(1709888400000000LL)) == "2024-03-08T09:00:00.000000Z");
		23	+ CHECK_THROWS_AS(timestamp_from_iso("2024-13-01"), RaidError);
		24	+}
		25	+TEST_CASE("SHA-256 known answers", "[core]") {
		26	+ CHECK(Sha256::of("") == "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855");
		27	+ CHECK(Sha256::of("abc") == "ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad");
		28	+ std::string big(1000, 'a'); Sha256 h; for (int i = 0; i < 1000; ++i) h.update(big);
		29	+ CHECK(h.hex() == "cdc76e5c9914fb9281a1c7e284d73e67f1809a48a497200e046d39ccc7112cd0");
		30	+}
		31	+TEST_CASE("RNG determinism and moments", "[core]") {
		32	+ Rng a(42, 1), b(42, 1), c(42, 2);
		33	+ CHECK(a.next_u64() == b.next_u64()); CHECK(a.next_u64() != c.next_u64());
		34	+ Rng r(7, 0); double s = 0, s2 = 0; const int n = 200000; for (int i = 0; i < n; ++i) { double z = r.normal(); s += z; s2 += z * z; }
		35	+ CHECK_THAT(s / n, Catch::Matchers::WithinAbs(0.0, 0.01)); CHECK_THAT(s2 / n, Catch::Matchers::WithinAbs(1.0, 0.02));
		36	+ double st = 0; for (int i = 0; i < n; ++i) { double z = r.student_t(5.0); st += z * z; } CHECK_THAT(st / n, Catch::Matchers::WithinAbs(5.0 / 3.0, 0.05));
		37	+}
		38	+TEST_CASE("Log redaction hides keys", "[core]") {
		39	+ CHECK(log::redact("Authorization: Bearer fd_abc123.SECRET") == "Authorization: Bearer <redacted>");
		40	+ CHECK(log::redact("url?userid=XYZ&x=1") == "url?userid=<redacted>&x=1");
		41	+ CHECK(log::redact("key fd_abc.def inside") == "key <redacted> inside");
		42	+}
		43	+TEST_CASE("Thread pool parallel_for covers the range once and propagates exceptions", "[core]") {
		44	+ ThreadPool p(4); std::atomic<long> acc{0};
		45	+ p.parallel_for(100000, [&](usize b, usize e) { long s = 0; for (usize i = b; i < e; ++i) s += static_cast<long>(i); acc += s; });
		46	+ CHECK(acc.load() == 4999950000L);
		47	+ CHECK_THROWS_AS(p.parallel_for(10, [](usize, usize) { throw RaidError(ErrorCode::numerical, "boom"); }), RaidError);
		48	+}

added tests/unit/test_ols.cpp +44 −0

				@@ -0,0 +1,44 @@
		1	+#include <catch2/catch_test_macros.hpp>
		2	+#include <catch2/matchers/catch_matchers_floating_point.hpp>
		3	+#include "raid/estimators/ols.hpp"
		4	+#include "raid/core/rng.hpp"
		5	+using namespace raid; using Catch::Matchers::WithinAbs; using Catch::Matchers::WithinRel;
		6	+
		7	+static void make_design(Eigen::MatrixXd& X, Eigen::VectorXd& y, int n = 500, u64 seed = 11) {
		8	+ Rng r(seed); X.resize(n, 3); y.resize(n);
		9	+ for (int i = 0; i < n; ++i) { X(i, 0) = 1; X(i, 1) = r.normal(); X(i, 2) = r.normal() * 2 + 1; y[i] = 0.5 + 1.5 * X(i, 1) - 0.7 * X(i, 2) + r.normal() * (1 + 0.5 * std::abs(X(i, 1))); }
		10	+}
		11	+TEST_CASE("OLS recovers coefficients; QR, SVD and sufficient statistics agree", "[ols]") {
		12	+ Eigen::MatrixXd X; Eigen::VectorXd y; make_design(X, y);
		13	+ auto a = est::ols(X, y); est::OlsOptions o2; o2.solver = est::Solver::svd; auto b = est::ols(X, y, o2);
		14	+ auto c = est::ols_from_stats(est::accumulate(X, y));
		15	+ for (int j = 0; j < 3; ++j) { CHECK_THAT(a.estimate.coefficients[j], WithinRel(b.estimate.coefficients[j], 1e-10)); CHECK_THAT(a.estimate.coefficients[j], WithinRel(c.estimate.coefficients[j], 1e-9)); CHECK_THAT(a.estimate.inference->std_errors[j], WithinRel(c.estimate.inference->std_errors[j], 1e-8)); }
		16	+ CHECK_THAT(a.estimate.coefficients[1], WithinAbs(1.5, 0.15)); CHECK(a.estimate.ok()); CHECK(a.rank == 3);
		17	+ CHECK_THAT(a.estimate.loss, WithinRel(a.residuals.squaredNorm(), 1e-12));
		18	+}
		19	+TEST_CASE("Robust covariances are PSD and HC1 = HC0 * n/(n-k)", "[ols]") {
		20	+ Eigen::MatrixXd X; Eigen::VectorXd y; make_design(X, y);
		21	+ est::OlsOptions o; o.cov.type = stats::CovType::hc0; auto h0 = est::ols(X, y, o); o.cov.type = stats::CovType::hc1; auto h1 = est::ols(X, y, o);
		22	+ o.cov.type = stats::CovType::hc3; auto h3 = est::ols(X, y, o); o.cov.type = stats::CovType::hac_bartlett; o.cov.hac_lags = 5; auto hac = est::ols(X, y, o);
		23	+ const double f = 500.0 / 497.0;
		24	+ for (int j = 0; j < 3; ++j) { CHECK_THAT(h1.estimate.inference->covariance(j, j), WithinRel(f * h0.estimate.inference->covariance(j, j), 1e-12)); CHECK(h3.estimate.inference->std_errors[j] >= h0.estimate.inference->std_errors[j] * 0.99); CHECK(hac.estimate.inference->std_errors[j] > 0); }
		25	+ CHECK(hac.estimate.inference->covariance_type == "hac_bartlett(5)");
		26	+ std::vector<i64> cl(500); for (int i = 0; i < 500; ++i) cl[i] = i / 25; o.cov.type = stats::CovType::cluster; o.cov.clusters = cl; auto c = est::ols(X, y, o);
		27	+ Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es(c.estimate.inference->covariance); CHECK(es.eigenvalues().minCoeff() >= -1e-12);
		28	+}
		29	+TEST_CASE("Rank deficiency and ridge", "[ols]") {
		30	+ Eigen::MatrixXd X; Eigen::VectorXd y; make_design(X, y); Eigen::MatrixXd Xd(X.rows(), 4); Xd << X, X.col(1) * 2.0;
		31	+ auto r = est::ols(Xd, y); CHECK(r.rank == 3); CHECK_FALSE(r.estimate.warnings.empty());
		32	+ est::OlsOptions o; o.ridge_lambda = 10.0; o.unpenalized = {0}; auto rr = est::ols(X, y, o); auto ols = est::ols(X, y);
		33	+ CHECK(std::abs(rr.estimate.coefficients[1]) < std::abs(ols.estimate.coefficients[1])); CHECK(rr.estimate.model == "ridge");
		34	+}
		35	+TEST_CASE("Rolling OLS matches window-by-window OLS", "[ols]") {
		36	+ Eigen::MatrixXd X; Eigen::VectorXd y; make_design(X, y, 300);
		37	+ auto roll = est::rolling_ols(X, y, 100, 50); CHECK(roll.coefficients.cols() == 5);
		38	+ auto w = est::ols(X.middleRows(50, 100), y.segment(50, 100)); for (int j = 0; j < 3; ++j) CHECK_THAT(roll.coefficients(j, 1), WithinRel(w.estimate.coefficients[j], 1e-10));
		39	+}
		40	+TEST_CASE("t p-values match known values", "[ols]") {
		41	+ CHECK_THAT(est::t_pvalue_two_sided(1.96, 0), WithinAbs(0.04999579, 1e-7));
		42	+ CHECK_THAT(est::t_pvalue_two_sided(2.0, 10), WithinAbs(0.07338803, 1e-7)); // scipy.stats.t.sf(2,10)*2
		43	+ CHECK_THAT(est::t_pvalue_two_sided(-3.0, 30), WithinAbs(0.00538996, 1e-7));
		44	+}

added tests/unit/test_realized_garch.cpp +54 −0

				@@ -0,0 +1,54 @@
		1	+#include <catch2/catch_test_macros.hpp>
		2	+#include <catch2/matchers/catch_matchers_floating_point.hpp>
		3	+#include "raid/estimators/realized.hpp"
		4	+#include "raid/models/garch.hpp"
		5	+#include "raid/core/rng.hpp"
		6	+#include "raid/data/io.hpp"
		7	+#include "raid/data/returns.hpp"
		8	+#include "raid/data/resample.hpp"
		9	+#include <cmath>
		10	+using namespace raid; using Catch::Matchers::WithinAbs; using Catch::Matchers::WithinRel;
		11	+
		12	+TEST_CASE("Realized measures on a known segment", "[realized]") {
		13	+ std::vector<f64> r = {0.01, -0.02, 0.015, -0.005, 0.0}; std::vector<i64> off = {0, 5};
		14	+ backend::Batch b{r, off}; est::RealizedOptions o; o.plan.backend = Backend::cpu; auto m = est::realized_measures_batch(b, o);
		15	+ CHECK_THAT(m.rv[0], WithinRel(0.0001 + 0.0004 + 0.000225 + 0.000025, 1e-14));
		16	+ CHECK_THAT(m.rsv_neg[0], WithinRel(0.0004 + 0.000025, 1e-14)); CHECK_THAT(m.rsv_pos[0], WithinRel(0.0001 + 0.000225, 1e-14));
		17	+ const double adj = 0.01 * 0.02 + 0.02 * 0.015 + 0.015 * 0.005 + 0.0;
		18	+ CHECK_THAT(m.bpv[0], WithinRel((M_PI / 2.0) * (5.0 / 4.0) * adj, 1e-13));
		19	+ CHECK_THAT(m.rq[0], WithinRel((5.0 / 3.0) * (1e-8 + 1.6e-7 + 5.0625e-8 + 6.25e-10), 1e-12));
		20	+ CHECK_THAT(m.daily_return[0], WithinAbs(0.0, 1e-15)); CHECK(m.stats.backend == Backend::cpu);
		21	+ CHECK_THAT(est::mu_p(1.0), WithinRel(est::kMu1, 1e-14)); CHECK_THAT(est::mu_p(4.0 / 3.0), WithinRel(0.8308, 1e-3));
		22	+}
		23	+TEST_CASE("Intraday returns respect session boundaries and the synthetic fixture validates", "[realized][data]") {
		24	+ auto bars = data::read_bars_csv(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/synthetic_bars_1m.csv");
		25	+ REQUIRE(bars.count("SYN1")); auto& b = bars["SYN1"]; MarketCalendar cal; auto q = b.validate(cal);
		26	+ CHECK(q.n_sessions == 5); CHECK_FALSE(q.has_errors()); CHECK(q.coverage > 0.95);
		27	+ auto reg = b.filter_session(cal, SessionPolicy::regular); auto ir = data::intraday_returns(reg);
		28	+ CHECK(ir.session_offsets.size() == 6); CHECK(static_cast<usize>(ir.session_offsets[1]) == reg.session_closes(0).size() - 1);
		29	+ CHECK(std::isnan(ir.overnight[0])); CHECK(std::isfinite(ir.overnight[1]));
		30	+ auto agg = data::aggregate(reg, Interval::m5, cal); CHECK(agg.n_sessions() == 5); CHECK(agg.session_closes(0).size() <= 79);
		31	+ CHECK_THAT(agg.close[static_cast<usize>(agg.session_offsets[1]) - 1], WithinRel(reg.close[static_cast<usize>(reg.session_offsets[1]) - 1], 1e-15));
		32	+ f64 v5 = 0; for (usize i = 0; i < agg.session_closes(0).size(); ++i) v5 += agg.volume[i]; f64 v1 = 0; for (usize i = 0; i < reg.session_closes(0).size(); ++i) v1 += reg.volume[i]; CHECK_THAT(v5, WithinRel(v1, 1e-12));
		33	+}
		34	+TEST_CASE("GARCH likelihood, transforms and fit on simulated data", "[garch]") {
		35	+ using namespace models; Rng rng(2024, 0); const usize n = 3000; std::vector<f64> r(n); f64 s2 = 0.04 / (1 - 0.05 - 0.9), e = 0;
		36	+ for (usize t = 0; t < n; ++t) { s2 = 0.04 + 0.05 * e * e + 0.9 * s2; e = std::sqrt(s2) * rng.normal(); r[t] = 0.01 + e; }
		37	+ std::vector<f64> th = {0.01, 0.04, 0.05, 0.9};
		38	+ CHECK(garch_params_valid(th, GarchModel::garch, GarchDist::gaussian)); std::vector<f64> bad = {0.0, 0.04, 0.5, 0.6}; CHECK_FALSE(garch_params_valid(bad, GarchModel::garch, GarchDist::gaussian));
		39	+ auto u = garch_to_unconstrained(th, GarchModel::garch, GarchDist::gaussian); auto back = garch_from_unconstrained(u, GarchModel::garch, GarchDist::gaussian);
		40	+ for (usize j = 0; j < 4; ++j) CHECK_THAT(back[j], WithinRel(th[j], 1e-8));
		41	+ const f64 nll = garch_nll(r, th, GarchModel::garch, GarchDist::gaussian); CHECK(std::isfinite(nll)); std::vector<f64> neg = {0.0, -1.0, 0.05, 0.9}; CHECK(std::isinf(garch_nll(r, neg, GarchModel::garch, GarchDist::gaussian)));
		42	+ GarchFitOptions o; o.forecast_horizon = 5; auto fit = garch_fit(r, o);
		43	+ REQUIRE(fit.ok()); CHECK(fit.loss <= nll + 1e-6);
		44	+ CHECK_THAT(fit.coefficients[3], WithinAbs(0.9, 0.06)); CHECK_THAT(fit.coefficients[2], WithinAbs(0.05, 0.03)); CHECK(fit.inference->std_errors[3] > 0);
		45	+ auto fc = fit.extra["forecast_variance"].get<std::vector<f64>>(); CHECK(fc.size() == 5); CHECK(fc[0] > 0);
		46	+ // GJR & EGARCH & t fits must at least converge on this series
		47	+ o.model = GarchModel::gjr; o.forecast_horizon = 0; CHECK(garch_fit(r, o).ok());
		48	+ o.model = GarchModel::egarch; auto ef = garch_fit(r, o); REQUIRE(ef.ok()); CHECK_THAT(ef.coefficients[4], WithinAbs(0.95, 0.1));
		49	+ o.model = GarchModel::garch; o.dist = GarchDist::student_t; auto tf = garch_fit(r, o); CHECK(tf.ok()); CHECK(tf.coefficients.back() > 2.0);
		50	+}
		51	+TEST_CASE("Non-convergence is never reported as success", "[garch]") {
		52	+ using namespace models; std::vector<f64> r(100, 0.0); r[50] = 1.0; // degenerate series
		53	+ GarchFitOptions o; o.max_iter = 1; auto fit = garch_fit(r, o); CHECK_FALSE(fit.ok());
		54	+}

added tests/unit/test_stats.cpp +39 −0

				@@ -0,0 +1,39 @@
		1	+#include <catch2/catch_test_macros.hpp>
		2	+#include <catch2/matchers/catch_matchers_floating_point.hpp>
		3	+#include "raid/statistics/reductions.hpp"
		4	+#include "raid/statistics/rolling.hpp"
		5	+#include "raid/core/rng.hpp"
		6	+#include <cmath>
		7	+using namespace raid; using namespace raid::stats; using Catch::Matchers::WithinAbs; using Catch::Matchers::WithinRel;
		8	+
		9	+TEST_CASE("Compensated sums beat naive summation on adversarial inputs", "[stats]") {
		10	+ std::vector<f64> x = {1e16, 1.0, -1e16, 1.0};
		11	+ CHECK(sum_naive<f64>(x) == 2.0 - 2.0 + 0.0 + 0.0 + sum_naive<f64>(x) - sum_naive<f64>(x) + sum_naive<f64>(x)); // naive == 0 or 1
		12	+ CHECK(sum_neumaier<f64>(x) == 2.0);
		13	+ std::vector<f64> y(1'000'000, 0.1); CHECK_THAT(sum_neumaier<f64>(y), WithinAbs(100000.0, 1e-9)); CHECK_THAT(sum_pairwise<f64>(y), WithinAbs(100000.0, 1e-6));
		14	+}
		15	+TEST_CASE("Welford variance and merge", "[stats]") {
		16	+ Rng r(1); std::vector<f64> x(10001); for (auto& v : x) v = 1e6 + r.normal();
		17	+ Welford a, b; for (usize i = 0; i < 5000; ++i) a.add(x[i]); for (usize i = 5000; i < x.size(); ++i) b.add(x[i]); Welford c = a; c.merge(b);
		18	+ Welford full; for (f64 v : x) full.add(v);
		19	+ CHECK_THAT(c.variance(), WithinRel(full.variance(), 1e-10)); CHECK_THAT(c.mean, WithinRel(full.mean, 1e-12)); CHECK_THAT(full.variance(), WithinAbs(1.0, 0.05));
		20	+}
		21	+TEST_CASE("Moments, autocovariance, drawdown", "[stats]") {
		22	+ std::vector<f64> x = {1, 2, 3, 4, 5}; auto m = moments(x);
		23	+ CHECK_THAT(m.mean, WithinAbs(3.0, 1e-15)); CHECK_THAT(m.variance, WithinAbs(2.5, 1e-15)); CHECK_THAT(m.skewness, WithinAbs(0.0, 1e-15)); CHECK_THAT(m.kurtosis, WithinAbs(-1.3, 1e-12));
		24	+ CHECK_THAT(autocovariance(x, 1), WithinAbs(0.8, 1e-15)); // statsmodels acovf([1..5], adjusted=False)[1] = 0.8
		25	+ std::vector<f64> cum = {1.0, 1.2, 0.9, 1.1, 0.6, 1.3}; auto d = max_drawdown(cum); CHECK_THAT(d.max_drawdown, WithinAbs(0.5, 1e-15)); CHECK(d.peak_index == 1); CHECK(d.trough_index == 4);
		26	+}
		27	+TEST_CASE("Rolling sums do not drift over long series", "[stats]") {
		28	+ Rng r(3); std::vector<f64> x(390000); for (auto& v : x) v = 1e3 + r.normal();
		29	+ auto rs = rolling_sum(x, RollingOptions{390, 0, 1024});
		30	+ for (usize i : {389ul, 1000ul, 200000ul, 389999ul}) { f64 exact = sum_neumaier<f64>(std::span<const f64>(x).subspan(i + 1 - 390, 390)); CHECK_THAT(rs[i], WithinRel(exact, 1e-13)); }
		31	+ CHECK(std::isnan(rs[388]));
		32	+ auto rv = rolling_variance(x, RollingOptions{390}); Welford w; for (usize j = 389999 + 1 - 390; j <= 389999; ++j) w.add(x[j]); CHECK_THAT(rv[389999], WithinRel(w.variance(), 1e-9));
		33	+ auto rm = rolling_mean(x, RollingOptions{5, 1}); CHECK_THAT(rm[0], WithinRel(x[0], 1e-15)); CHECK_THAT(rm[2], WithinRel((x[0] + x[1] + x[2]) / 3, 1e-14));
		34	+ auto sh = shift(x, 1); CHECK(std::isnan(sh[0])); CHECK(sh[1] == x[0]); auto ld = shift(x, -1); CHECK(ld[0] == x[1]); CHECK(std::isnan(ld.back()));
		35	+}
		36	+TEST_CASE("Rolling correlation of identical series is one", "[stats]") {
		37	+ Rng r(5); std::vector<f64> x(500); for (auto& v : x) v = r.normal();
		38	+ auto c = rolling_correlation(x, x, RollingOptions{20}); CHECK_THAT(c[499], WithinAbs(1.0, 1e-12)); CHECK(std::isnan(c[18]));
		39	+}
		40