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WP40 RAID: Phase 3 gate — primitives benchmark records, summary, figure; status/progress updated

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 2, 2026) parent 5aa6672 Browse files

8 changed files +5,287 −8

8 changed files
Unified Split
modified RESEARCH_STATUS.md +7 −8
@@ -1,6 +1,6 @@
1 1 # RESEARCH_STATUS.md — RAID (UQO WP 40)
2 2
3 −Last update: 2026-11-02 (Phase 2 passed). Legend: ✅ verified by tests/artifacts · 🧪 implemented, validation pending ·
3 +Last update: 2026-11-02 (Phase 3 passed). Legend: ✅ verified by tests/artifacts · 🧪 implemented, validation pending ·
4 4 🧭 experimental · ⛔ blocked · ⏳ planned · ✖ not implemented (deliberately).
5 5
6 6 ## Phases
@@ -9,7 +9,7 @@ Last update: 2026-11-02 (Phase 2 passed). Legend: ✅ verified by tests/artifact
9 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 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 11 | 2 Data | offline synthetic tests + opt-in live tests | ✅ passed 2026-11-02 | offline `[findata]` 4/4, live `[live]` 2/2 (60 assertions), `docs/operations/findata.md` |
12 −| 3 Numerical primitives | accuracy + measured break-even | ⏳ | — |
12 +| 3 Numerical primitives | accuracy + measured break-even | ✅ passed 2026-11-02 | `[metal]` 6 cases / 6 411 assertions; `experiments/results/bench_primitives_summary.json` (Metal wins: fused realized ≥ 39 k elements up to 3.4×, GARCH NLL P=64 up to 7.9×; CPU wins: rolling sums, GARCH P=1, small panels) |
13 13 | 4 Econometrics (Tier A) | references + Monte Carlo | ⏳ | — |
14 14 | 5 Extensions (Tier B) | per-feature specs + tests | ⏳ | — |
15 15 | 6 Benchmarking E1–E7 | every claim traceable | ⏳ | — |
@@ -20,10 +20,10 @@ Last update: 2026-11-02 (Phase 2 passed). Legend: ✅ verified by tests/artifact
20 20 | Method | Tier | CPU FP64 | Metal FP32 | Reference software | Status |
21 21 |---|---|---|---|---|---|
22 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 |
23 +| RV, BPV, RSV±, RQ, jump proxy, TriPV, rCov/rCorr | A | ✅ (numpy truth 1e−12) | ✅ fused kernel 1e−6 vs FP64 | highfrequency 1.0.3 (R comparison Phase 4) | ✅ CPU |
24 +| OLS/WLS/ridge, HC0–3, HAC, cluster, rolling OLS | A | ✅ (statsmodels 1e−9) | ✅ sufficient statistics 1e−6 (k ≤ 8) | statsmodels 0.15, scipy 1.18 | ✅ CPU |
25 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 |
26 +| GARCH(1,1), GJR, EGARCH (N, t), QMLE/Hessian/OPG SE, forecasts | A | ✅ (arch 8.0: loglik 5e−4, params 5 %) | ✅ batched NLL 2e−5 (grids) | arch 8.0 ✅, rugarch 1.5.6 (Phase 4) | ✅ CPU |
27 27 | Bootstrap (stationary / circular block), Monte Carlo | A | ⏳ | ⏳ | arch.bootstrap | ⏳ |
28 28 | VAR, GFEVD, Diebold–Yilmaz | B | ⏳ | ⏳ | statsmodels VAR | ⏳ |
29 29 | Realized GARCH, HEAVY, DCC, PCA/shrinkage, Kalman | B | ✖ until Tier A passes | | | ⏳ |
@@ -35,6 +35,5 @@ Last update: 2026-11-02 (Phase 2 passed). Legend: ✅ verified by tests/artifact
35 35 not tested unless they come back.
36 36
37 37 ## Next safe action
38 −Phase 3: Metal backend (device discovery, runtime + prebuilt shader library, buffer pool, batched reductions,
39 −rolling sums, regression sufficient statistics, GARCH likelihood batch), FP32-vs-FP64 accuracy tests, first break-even
40 −microbenchmarks (bench_reductions) with manifests.
38 +Phase 4: HAR family (fit, rolling, batched via GPU sufficient statistics), block bootstrap, R references
39 +(highfrequency / rugarch), Monte Carlo (bias/RMSE/coverage), gradient checks, registry completion.
modified docs/PROGRESS.md +39 −0
@@ -81,3 +81,42 @@ Executed:
81 81
82 82 Gate evidence: offline tests in `ctest`; live run transcript above; no vendor data in git (`scripts/check_no_secrets.sh`).
83 83 Open: least-privilege key for benchmarks (needs `findata create-key` on M3U96a — owner action, optional).
84 +
85 +## Phase 3 — Numerical primitives (2026-11-02, 15:40–16:30 ET) — GATE PASSED
86 +Plan: Metal backend with FP32 "double-float" (error-free-transform) accumulation, fused realized kernel, rolling sums,
87 +regression sufficient statistics, batched GARCH likelihood; CPU/GPU agreement tests; measured break-even analysis.
88 +
89 +Executed:
90 +- `backends/metal/shaders/raid_kernels.metal` (TwoSum/Fast2Sum double-float, deterministic threadgroup tree reductions,
91 + chunked partial sums combined in FP64 on the CPU) and `backends/metal/host/metal_backend.mm` (device discovery,
92 + prebuilt `raid.metallib` with runtime-MSL fallback, pipeline cache, buffer pool by power-of-two size class, GPU
93 + timing from `GPUStartTime/GPUEndTime`, `-fno-fast-math` / `fastMathEnabled = NO` so the compensation is not optimised
94 + away). Metal has no `lgamma`/`log1p`: Student-t constants are computed in FP64 on the host and passed per parameter
95 + vector.
96 +- Tests `[metal]` (6 cases, 6 411 assertions): fused and single-statistic accumulators vs CPU FP64 within 1e−6
97 + (Σx⁴ 1e−5) for n = 390/3 900/39 000 across chunk boundaries (4 096); bitwise determinism across runs; rolling sums
98 + 1e−5; regression statistics 1e−6 (and the solve from GPU statistics agrees with CPU QR to 1e−5); batched GARCH NLL
99 + 2e−5 relative (GJR + Student-t fixed ν 5e−5), argmin over a 16-point grid identical; +inf on invalid parameters.
100 + Full suite: `ctest` 38/38 (2 live skipped), `pytest` 7/7.
101 +- `bench_bench_primitives` (laptop M5 Max 40-core GPU, 18 CPU threads, load ≈ 3–4 from other sessions, no thermal
102 + warning, 5 repetitions, shuffled order, 2 warm-ups; raw records `experiments/results/raw/bench_primitives.jsonl`
103 + + manifest; summary `experiments/results/bench_primitives_summary.{json,csv}`, figure
104 + `fig_bench_primitives_throughput.pdf`). Medians of wall-clock including FP64→FP32 conversion and buffer traffic;
105 + verdicts use non-overlapping bootstrap 95 % CIs of the median:
106 + - realized_fused: Metal wins from 39 000 elements (100 × 390: 2.4×), 2.0–3.4× vs 18-thread CPU at 3.9 M–39 M
107 + elements (max 3.4× at 1 000 × 39 000); ties at 390 000 for 10 × 39 000 and 100 × 3 900 shapes; CPU 1 thread is
108 + 27–40× slower than Metal at 39 M.
109 + - regression_stats (k = 4): break-even ≈ 390 000 elements for a single long series (3.9×); ties for most shapes
110 + between 390 k and 3.9 M; 1.1–1.4× at 39 M. CPU wins below 39 000 elements.
111 + - rolling_sumsq (w = 22, direct O(w) kernel): **CPU wins at every size** (Metal 0.4–0.9× of CPU-MT); the GPU kernel
112 + is memory/arithmetic-bound by its O(w) design and the CPU running sum is O(1) per element — a negative result kept
113 + for the paper (a scan-based kernel is future work).
114 + - garch_nll: with one parameter vector per series (P = 1) the **CPU wins everywhere** (0.01–0.33×; the recursion is
115 + sequential and the GPU is launch-bound); with P = 64 parameter vectors per series Metal wins from 100 series
116 + (1.9×) up to 7.9× at 1 000 × 3 900 — batched likelihood evaluation is where the GPU pays off.
117 + - Device time is 5–20× smaller than wall time for the reductions: FP64→FP32 conversion and buffer fills dominate
118 + ("unified memory is not free computation"); E6 will add the FP32-resident ablation.
119 + - Shader library: prebuilt metallib loaded in 0.23 ms; first kernel call 14 ms (pipeline creation); runtime MSL
120 + compilation of the bundle is measured separately in E7 (toolchain-less nodes).
121 +
122 +Gate evidence: accuracy tests and the measured break-even table above; no claim beyond the tested shapes.
added experiments/results/bench_primitives_summary.csv +208 −0
@@ -0,0 +1,208 @@
1 +kernel,S,n,elements,backend,threads,median_s,ci_lo,ci_hi,device_s,elements_per_s,reps,precision,peak_rss_mb
2 +garch_nll_P1,1,390,390,cpu,1,1.7909333109855652e-06,1.7499551177024841e-06,1.8328428268432617e-06,0.0,217763552.44929796,5,fp64,3255.451648
3 +garch_nll_P1,1,390,390,cpu,18,1.7499551177024841e-06,1.6670674085617065e-06,1.8333084881305695e-06,0.0,222862858.62692922,5,fp64,3368.943616
4 +garch_nll_P1,1,390,390,metal,0,0.0009180000051856041,0.00044724997133016586,0.0009358339011669159,0.00032679166179150343,424836.59890736995,5,fp32,2962.440192
5 +garch_nll_P1,1,3900,3900,cpu,1,1.6250181943178177e-05,1.6207806766033173e-05,1.637497916817665e-05,0.0,239997312.86930108,5,fp64,2962.440192
6 +garch_nll_P1,1,3900,3900,cpu,18,1.695798709988594e-05,1.691700890660286e-05,1.9416213035583496e-05,0.0,229980125.41395503,5,fp64,3431.858176
7 +garch_nll_P1,1,3900,3900,metal,0,0.001348957885056734,0.0010095001198351383,0.0015331669710576534,0.0011987084290012717,2891120.6518771155,5,fp32,2962.440192
8 +garch_nll_P1,10,390,3900,cpu,1,2.1916814148426056e-05,2.1541956812143326e-05,7.208297029137611e-05,0.0,177945570.62847915,5,fp64,2962.440192
9 +garch_nll_P1,10,390,3900,cpu,18,2.929195761680603e-05,1.7000362277030945e-05,4.200031980872154e-05,0.0,133142347.50095384,5,fp64,2962.440192
10 +garch_nll_P1,10,390,3900,metal,0,0.0003432091325521469,0.0003193751908838749,0.0009956671856343746,0.00019762502051889896,11363333.98531415,5,fp32,2920.169472
11 +garch_nll_P1,10,3900,39000,cpu,1,0.0002155420370399952,0.00016279099509119987,0.00037720799446105957,0.0,180939182.60948467,5,fp64,576.782336
12 +garch_nll_P1,10,3900,39000,cpu,18,6.916606798768044e-05,6.245775148272514e-05,7.533328607678413e-05,0.0,563860302.2358668,5,fp64,781.43488
13 +garch_nll_P1,10,3900,39000,metal,0,0.0014300826005637646,0.0012115412391722202,0.0016847080551087856,0.0010766249615699053,27271152.019208886,5,fp32,3360.11264
14 +garch_nll_P1,100,390,39000,cpu,1,0.0001916671171784401,0.0001916252076625824,0.0002199173904955387,0.0,203477782.59580857,5,fp64,3431.87456
15 +garch_nll_P1,100,390,39000,cpu,18,0.00016116676852107048,0.00015066703781485558,0.00017320923507213593,0.0,241985369.30335766,5,fp64,3359.932416
16 +garch_nll_P1,100,390,39000,metal,0,0.0006238752976059914,0.0006115422584116459,0.0006384579464793205,0.0004310833755880594,62512492.720348835,5,fp32,3355.901952
17 +garch_nll_P1,100,3900,390000,cpu,1,0.0016154590994119644,0.0016071251593530178,0.0016200002282857895,0.0,241417439.87326083,5,fp64,3222.339584
18 +garch_nll_P1,100,3900,390000,cpu,18,0.00020695803686976433,0.00020262505859136581,0.0002222089096903801,0.0,1884439985.5098226,5,fp64,2962.440192
19 +garch_nll_P1,100,3900,390000,metal,0,0.0016493331640958786,0.0015722080133855343,0.004429208114743233,0.0009533748961985111,236459199.68740082,5,fp32,2962.440192
20 +garch_nll_P1,1000,390,390000,cpu,1,0.0016390001401305199,0.001627500168979168,0.0016581248492002487,0.0,237949949.14945084,5,fp64,3359.932416
21 +garch_nll_P1,1000,390,390000,cpu,18,0.0002765827812254429,0.00027295900508761406,0.00033191684633493423,0.0,1410066086.8042636,5,fp64,2962.440192
22 +garch_nll_P1,1000,390,390000,metal,0,0.0011531668715178967,0.0010962923988699913,0.0011774590238928795,0.00017799995839595795,338199101.65009224,5,fp32,3222.339584
23 +garch_nll_P1,1000,3900,3900000,cpu,1,0.018310166895389557,0.01771716633811593,0.019527375232428312,0.0,212996420.09172553,5,fp64,572.112896
24 +garch_nll_P1,1000,3900,3900000,cpu,18,0.0014978330582380295,0.001383374910801649,0.0015932917594909668,0.0,2603761466.306366,5,fp64,3432.742912
25 +garch_nll_P1,1000,3900,3900000,metal,0,0.0072068748995661736,0.007158916909247637,0.009398084133863449,0.0010924999369308352,541149951.171591,5,fp32,3360.11264
26 +garch_nll_P1,10000,390,3900000,cpu,1,0.017741417046636343,0.01734916679561138,0.017816499806940556,0.0,219824605.3146817,5,fp64,3355.901952
27 +garch_nll_P1,10000,390,3900000,cpu,18,0.0022433330304920673,0.002103041857481003,0.002292540855705738,0.0,1738484632.905596,5,fp64,2962.440192
28 +garch_nll_P1,10000,390,3900000,metal,0,0.00675466563552618,0.006540417205542326,0.007071124855428934,0.00046787504106760025,577378690.5880197,5,fp32,2962.440192
29 +garch_nll_P1,10000,3900,39000000,cpu,1,0.16226391727104783,0.16133012529462576,0.16484179068356752,0.0,240349183.3298581,5,fp64,3255.451648
30 +garch_nll_P1,10000,3900,39000000,cpu,18,0.01383341709151864,0.012941500172019005,0.014138792175799608,0.0,2819260038.3538756,5,fp64,3254.272
31 +garch_nll_P1,10000,3900,39000000,metal,0,0.06106641702353954,0.05950908316299319,0.0616582497023046,0.004054000019095838,638648899.0334327,5,fp32,3355.901952
32 +garch_nll_P64,1,390,390,cpu,1,8.287513628602028e-05,8.27503390610218e-05,8.458364754915237e-05,0.0,4705874.614239238,5,fp64,577.159168
33 +garch_nll_P64,1,390,390,cpu,18,0.00017224997282028198,0.00012754183262586594,0.0002139592543244362,0.0,2264151.3006617934,5,fp64,3431.514112
34 +garch_nll_P64,1,390,390,metal,0,0.0005012080073356628,0.00047737499698996544,0.0006123748607933521,0.0003255000337958336,778120.0505418383,5,fp32,3431.46496
35 +garch_nll_P64,1,3900,3900,cpu,1,0.0008078329265117645,0.0008031250908970833,0.0008203750476241112,0.0,4827730.923076213,5,fp64,3431.87456
36 +garch_nll_P64,1,3900,3900,cpu,18,0.00014187488704919815,0.000135000329464674,0.00021779071539640427,0.0,27489008.668937948,5,fp64,3222.339584
37 +garch_nll_P64,1,3900,3900,metal,0,0.0014235423877835274,0.00134983379393816,0.0033927499316632748,0.00122337497305125,2739644.4485733556,5,fp32,3431.87456
38 +garch_nll_P64,10,390,3900,cpu,1,0.0008257082663476467,0.000809917226433754,0.0008320827037096024,0.0,4723217.8227436915,5,fp64,3432.742912
39 +garch_nll_P64,10,390,3900,cpu,18,0.0002438751980662346,0.00013074977323412895,0.0002598753198981285,0.0,15991786.089460358,5,fp64,3368.943616
40 +garch_nll_P64,10,390,3900,metal,0,0.0010179998353123665,0.0004632081836462021,0.0010751672089099884,0.00032300001475960016,3831041.877136759,5,fp32,3431.530496
41 +garch_nll_P64,10,3900,39000,cpu,1,0.008083874825388193,0.008056249935179949,0.00812424998730421,0.0,4824419.086440666,5,fp64,3355.901952
42 +garch_nll_P64,10,3900,39000,cpu,18,0.000775500200688839,0.0006874999962747097,0.0008766250684857368,0.0,50290122.38211957,5,fp64,3432.742912
43 +garch_nll_P64,10,3900,39000,metal,0,0.0013822498731315136,0.0008970410563051701,0.0033571659587323666,0.0006641249638050795,28214869.654243305,5,fp32,3431.530496
44 +garch_nll_P64,100,390,39000,cpu,1,0.008346542250365019,0.008285791147500277,0.008543540723621845,0.0,4672593.611839012,5,fp64,3431.514112
45 +garch_nll_P64,100,390,39000,cpu,18,0.001005750149488449,0.0009415419772267342,0.0010966667905449867,0.0,38777026.30204572,5,fp64,781.484032
46 +garch_nll_P64,100,390,39000,metal,0,0.0005332920700311661,0.0005230000242590904,0.0005429168231785297,0.00033650000113993883,73130658.02332444,5,fp32,2962.440192
47 +garch_nll_P64,100,3900,390000,cpu,1,0.0801338329911232,0.07999316696077585,0.08033083425834775,0.0,4866858.172667244,5,fp64,3359.932416
48 +garch_nll_P64,100,3900,390000,cpu,18,0.006173874717205763,0.005890249740332365,0.006268457975238562,0.0,63169406.22606451,5,fp64,3432.742912
49 +garch_nll_P64,100,3900,390000,metal,0,0.0021704169921576977,0.0018634586594998837,0.0022297906689345837,0.0010580417001619935,179688972.86059555,5,fp32,3431.858176
50 +garch_nll_P64,1000,390,390000,cpu,1,0.09679854195564985,0.08949454175308347,0.10223195794969797,0.0,4028986.306205791,5,fp64,2962.440192
51 +garch_nll_P64,1000,390,390000,cpu,18,0.00848708301782608,0.008417499717324972,0.008939292281866074,0.0,45952183.94598623,5,fp64,2962.440192
52 +garch_nll_P64,1000,390,390000,metal,0,0.002336665987968445,0.002312499564141035,0.0027117920108139515,0.001452916651032865,166904470.73228282,5,fp32,3359.932416
53 +garch_nll_P64,1000,3900,3900000,cpu,1,0.8561791670508683,0.8505779169499874,0.9446879159659147,0.0,4555121.34619399,5,fp64,2962.440192
54 +garch_nll_P64,1000,3900,3900000,cpu,18,0.08150566695258021,0.0744811249896884,0.10778554202988744,0.0,47849433.61384933,5,fp64,3255.451648
55 +garch_nll_P64,1000,3900,3900000,metal,0,0.010272707790136337,0.009604125283658504,0.010956583078950644,0.0029698750004172325,379646737.7125929,5,fp32,2962.440192
56 +realized_fused,1,390,390,cpu,1,1.4998950064182281e-06,1.416075974702835e-06,4.041939973831177e-06,0.0,260018200.16144055,5,fp64,2962.440192
57 +realized_fused,1,390,390,cpu,18,1.4998950064182281e-06,1.4579854905605316e-06,1.6670674085617065e-06,0.0,260018200.16144055,5,fp64,2962.440192
58 +realized_fused,1,390,390,metal,0,0.0002344171516597271,0.00019499985501170158,0.00025337468832731247,3.199989441782236e-05,1663700.7882687368,5,fp32,3432.742912
59 +realized_fused,1,3900,3900,cpu,1,1.1624768376350403e-05,1.1375173926353455e-05,1.2792181223630905e-05,0.0,335490555.4879026,5,fp64,2962.440192
60 +realized_fused,1,3900,3900,cpu,18,1.116609200835228e-05,1.1124648153781891e-05,1.2124888598918915e-05,0.0,349271705.5423496,5,fp64,3432.742912
61 +realized_fused,1,3900,3900,metal,0,0.0006333747878670692,0.0001420830376446247,0.000777125358581543,2.037500962615013e-05,6157491.7011356,5,fp32,2913.878016
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4849 + },
4850 + "realized_fused": {
4851 + "smallest_metal_win_elements": 39000,
4852 + "largest_cpu_win_elements": 39000,
4853 + "max_speedup_vs_mt": 3.4364769430726474,
4854 + "n_cases": 17
4855 + },
4856 + "regression_stats": {
4857 + "smallest_metal_win_elements": 390000,
4858 + "largest_cpu_win_elements": 390000,
4859 + "max_speedup_vs_mt": 3.851129874745179,
4860 + "n_cases": 17
4861 + },
4862 + "rolling_sumsq": {
4863 + "smallest_metal_win_elements": null,
4864 + "largest_cpu_win_elements": 39000000,
4865 + "max_speedup_vs_mt": 0.909589128259996,
4866 + "n_cases": 17
4867 + }
4868 + },
4869 + "shader_library": [
4870 + {
4871 + "backend": "metal",
4872 + "compiled_now": false,
4873 + "kernel": "shader_library_load",
4874 + "note": "metallib:/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/finance/wp40_raid/build/macos-release/raid.metallib chunks=1 convert_s=0.000001",
4875 + "seconds": 0.000230708,
4876 + "t": 0.03212570771574974,
4877 + "wall_first_call": 0.013961625
4878 + }
4879 + ]
4880 +}
\ No newline at end of file
added experiments/results/fig_bench_primitives_throughput.pdf +0 −0

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added experiments/results/fig_bench_primitives_throughput.png +0 −0

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added include/raid/estimators/har.hpp +59 −0
@@ -0,0 +1,59 @@
1 +// HAR family (registry keys har.*). Daily series RV_t (t = 1..T), trading-day lags, causal construction:
2 +// HAR-RV (Corsi 2009): y_{t+h} = β0 + βd RV_t + βw RV^{(5)}_t + βm RV^{(22)}_t + ε, RV^{(k)}_t = (1/k) Σ_{j=0}^{k−1} RV_{t−j}
3 +// log-HAR: same with log RV on both sides (forecast = exp(ŷ) × smearing optional, not applied by default)
4 +// HAR-X: + user covariates X_t (already aligned at t)
5 +// HARQ (Bollerslev–Patton–Quaedvlieg 2016): βd → (βd + βq √RQ_t): term βq · √RQ_t · RV_t added
6 +// Target for horizon h: y_{t+h} = (1/h) Σ_{j=1}^{h} RV_{t+j} (average over the next h days, "overlapping" when h > 1)
7 +// or the single-day RV_{t+h} (`target = single`). Overlapping targets induce MA(h−1) errors → HAC with ≥ h−1 lags.
8 +#pragma once
9 +#include <vector>
10 +#include <span>
11 +#include <string>
12 +#include <Eigen/Dense>
13 +#include "raid/core/types.hpp"
14 +#include "raid/core/estimate.hpp"
15 +#include "raid/core/plan.hpp"
16 +#include "raid/estimators/ols.hpp"
17 +#include "raid/backend/backend.hpp"
18 +
19 +namespace raid::est {
20 +
21 +enum class HarTarget { average, single };
22 +
23 +struct HarSpec {
24 + std::vector<usize> lags{1, 5, 22}; // daily, weekly, monthly windows
25 + int horizon{1};
26 + HarTarget target{HarTarget::average};
27 + bool log{false}; // log-HAR
28 + bool harq{false}; // add √RQ_t · RV_t
29 + stats::CovOptions cov{stats::CovType::hac_bartlett, -1, false, {}, true}; // hac_lags −1 → max(h−1, ⌊4(n/100)^{2/9}⌋)
30 + std::string model_key() const { return std::string(log ? "har.logrv" : "har.rv") + (harq ? ".harq" : "") + ".h" + std::to_string(horizon); }
31 +};
32 +
33 +/// Builds the aligned design (y, X) from a daily RV series (and optional RQ and exogenous columns X_t, aligned at t).
34 +struct HarDesign { Eigen::MatrixXd X; Eigen::VectorXd y; std::vector<std::string> names; usize first_t{0}; usize n{0}; };
35 +HarDesign har_design(std::span<const f64> rv, const HarSpec& spec, std::span<const f64> rq = {}, const Eigen::MatrixXd& exog = Eigen::MatrixXd(), const std::vector<std::string>& exog_names = {});
36 +
37 +/// Fit one HAR on one series (CPU QR + robust covariance). Estimate.extra contains fitted/residual summaries.
38 +Estimate har_fit(std::span<const f64> rv, const HarSpec& spec, std::span<const f64> rq = {}, const Eigen::MatrixXd& exog = Eigen::MatrixXd(), const std::vector<std::string>& exog_names = {});
39 +
40 +/// Forecast ŷ for the last available t (origin = last observation) from a fitted HAR; for log models returns exp(ŷ)
41 +/// (naive back-transform; the smearing factor is reported in extra["smearing"] and not applied).
42 +f64 har_forecast(const Estimate& fit, std::span<const f64> rv, const HarSpec& spec, std::span<const f64> rq = {}, const Eigen::VectorXd& exog_last = Eigen::VectorXd());
43 +
44 +/// Rolling (or expanding when window = 0) re-estimation with one-origin-ahead forecasts, strictly causal:
45 +/// the model fitted on observations ≤ t predicts y_{t+h}. Returns forecasts aligned with the target index.
46 +struct HarRolling { std::vector<usize> origin; std::vector<f64> forecast, actual; Eigen::MatrixXd coefficients; usize n_fits{0}; f64 seconds{0}; };
47 +HarRolling har_rolling(std::span<const f64> rv, const HarSpec& spec, usize window, usize step = 1, std::span<const f64> rq = {});
48 +
49 +/// Batched HAR over a panel of daily RV series (CSR). Sufficient statistics are accumulated by the backend (Metal when
50 +/// available: regression_stats) and solved in FP64 on the CPU; inference is classical (HAC requires residuals: use
51 +/// har_fit per series when robust SE are needed). Returns one Estimate per series.
52 +struct HarBatchResult { std::vector<Estimate> fits; backend::KernelStats stats; };
53 +HarBatchResult har_batch(std::span<const f64> rv_concat, std::span<const i64> offsets, const HarSpec& spec, const ExecutionPlan& plan = {});
54 +
55 +/// Forecast losses for volatility: MSE and QLIKE (Patton 2011): QLIKE = y/ŷ − log(y/ŷ) − 1.
56 +struct ForecastLoss { f64 mse, qlike; usize n; };
57 +ForecastLoss forecast_loss(std::span<const f64> actual, std::span<const f64> forecast);
58 +
59 +} // namespace raid::est
added src/estimators/har.cpp +94 −0
@@ -0,0 +1,94 @@
1 +#include "raid/estimators/har.hpp"
2 +#include "raid/core/error.hpp"
3 +#include "raid/statistics/covariance.hpp"
4 +#include <chrono>
5 +#include <cmath>
6 +#include <algorithm>
7 +
8 +namespace raid::est {
9 +
10 +HarDesign har_design(std::span<const f64> rv, const HarSpec& spec, std::span<const f64> rq, const Eigen::MatrixXd& exog, const std::vector<std::string>& exog_names) {
11 + const usize T = rv.size(); RAID_REQUIRE(!spec.lags.empty() && spec.horizon >= 1, invalid_argument, "har: lags and horizon");
12 + const usize maxlag = *std::max_element(spec.lags.begin(), spec.lags.end()); const usize h = static_cast<usize>(spec.horizon);
13 + RAID_REQUIRE(T > maxlag + h + 2, insufficient_samples, "har: need T > maxlag + h + 2");
14 + for (f64 v : rv) RAID_REQUIRE(std::isfinite(v) && (!spec.log || v > 0), data_quality, "har: RV must be finite (and > 0 for log-HAR)");
15 + if (spec.harq) RAID_REQUIRE(rq.size() == T, invalid_argument, "harq: RQ series required with the same length");
16 + const usize nex = exog.size() ? static_cast<usize>(exog.cols()) : 0; if (nex) RAID_REQUIRE(static_cast<usize>(exog.rows()) == T, invalid_argument, "har-x: exog rows must equal T");
17 + std::vector<f64> z(T); for (usize t = 0; t < T; ++t) z[t] = spec.log ? std::log(rv[t]) : rv[t];
18 + // prefix sums for the window means (FP64, Neumaier)
19 + std::vector<f64> cs(T + 1, 0.0); { f64 s = 0, c = 0; for (usize t = 0; t < T; ++t) { const f64 v = z[t], tt = s + v; if (std::abs(s) >= std::abs(v)) c += (s - tt) + v; else c += (v - tt) + s; s = tt; cs[t + 1] = s + c; } }
20 + auto wmean = [&](usize t, usize k) { f64 s = 0; for (usize j = 0; j < k; ++j) s += z[t - j]; return s / static_cast<f64>(k); }; // direct sum (exact enough, k ≤ 22)
21 + const usize first = maxlag - 1, last = T - 1 - h; // origins t with full lags and a full target
22 + HarDesign d; d.first_t = first; d.n = last - first + 1; const usize k = 1 + spec.lags.size() + (spec.harq ? 1 : 0) + nex;
23 + d.X.resize(static_cast<Eigen::Index>(d.n), static_cast<Eigen::Index>(k)); d.y.resize(static_cast<Eigen::Index>(d.n));
24 + d.names.push_back("const"); for (usize l : spec.lags) d.names.push_back((spec.log ? "logrv_" : "rv_") + std::to_string(l)); if (spec.harq) d.names.push_back("sqrt_rq_x_rv"); for (usize j = 0; j < nex; ++j) d.names.push_back(j < exog_names.size() ? exog_names[j] : "x" + std::to_string(j));
25 + for (usize t = first; t <= last; ++t) {
26 + const Eigen::Index i = static_cast<Eigen::Index>(t - first); Eigen::Index c = 0; d.X(i, c++) = 1.0;
27 + for (usize l : spec.lags) d.X(i, c++) = wmean(t, l);
28 + if (spec.harq) d.X(i, c++) = std::sqrt(std::max(rq[t], 0.0)) * rv[t];
29 + for (usize j = 0; j < nex; ++j) d.X(i, c++) = exog(static_cast<Eigen::Index>(t), static_cast<Eigen::Index>(j));
30 + if (spec.target == HarTarget::single) d.y[i] = z[t + h]; else { f64 s = 0; for (usize j = 1; j <= h; ++j) s += spec.log ? rv[t + j] : z[t + j]; s /= static_cast<f64>(h); d.y[i] = spec.log ? std::log(s) : s; }
31 + }
32 + return d;
33 +}
34 +
35 +Estimate har_fit(std::span<const f64> rv, const HarSpec& spec, std::span<const f64> rq, const Eigen::MatrixXd& exog, const std::vector<std::string>& exog_names) {
36 + auto d = har_design(rv, spec, rq, exog, exog_names);
37 + OlsOptions o; o.cov = spec.cov; o.names = d.names;
38 + if (o.cov.type == stats::CovType::hac_bartlett && o.cov.hac_lags < 0) o.cov.hac_lags = std::max(spec.horizon - 1, stats::default_hac_lags(d.n));
39 + auto r = ols(d.X, d.y, o); Estimate e = r.estimate; e.model = spec.model_key();
40 + e.extra["horizon"] = spec.horizon; e.extra["target"] = spec.target == HarTarget::average ? "average" : "single"; e.extra["lags"] = spec.lags; e.extra["first_origin"] = d.first_t; e.extra["log"] = spec.log; e.extra["harq"] = spec.harq;
41 + if (spec.log) { f64 s = 0; for (Eigen::Index i = 0; i < r.residuals.size(); ++i) s += std::exp(r.residuals[i]); e.extra["smearing"] = s / static_cast<f64>(r.residuals.size()); }
42 + return e;
43 +}
44 +
45 +f64 har_forecast(const Estimate& fit, std::span<const f64> rv, const HarSpec& spec, std::span<const f64> rq, const Eigen::VectorXd& exog_last) {
46 + const usize T = rv.size(); const usize maxlag = *std::max_element(spec.lags.begin(), spec.lags.end()); RAID_REQUIRE(T >= maxlag, insufficient_samples, "har_forecast: not enough history");
47 + const usize t = T - 1; f64 yhat = fit.coefficients[0]; usize c = 1;
48 + for (usize l : spec.lags) { f64 s = 0; for (usize j = 0; j < l; ++j) s += spec.log ? std::log(rv[t - j]) : rv[t - j]; yhat += fit.coefficients[c++] * s / static_cast<f64>(l); }
49 + if (spec.harq) { RAID_REQUIRE(rq.size() == T, invalid_argument, "harq forecast needs RQ"); yhat += fit.coefficients[c++] * std::sqrt(std::max(rq[t], 0.0)) * rv[t]; }
50 + for (Eigen::Index j = 0; j < exog_last.size(); ++j) yhat += fit.coefficients[c++] * exog_last[j];
51 + return spec.log ? std::exp(yhat) : yhat;
52 +}
53 +
54 +HarRolling har_rolling(std::span<const f64> rv, const HarSpec& spec, usize window, usize step, std::span<const f64> rq) {
55 + const auto t0 = std::chrono::steady_clock::now(); HarRolling out; const usize T = rv.size(); const usize h = static_cast<usize>(spec.horizon);
56 + const usize maxlag = *std::max_element(spec.lags.begin(), spec.lags.end()); const usize minT = maxlag + h + 30;
57 + RAID_REQUIRE(window == 0 || window >= minT, insufficient_samples, "har_rolling: window too small"); RAID_REQUIRE(T > minT + h, insufficient_samples, "har_rolling: series too short");
58 + const usize k = 1 + spec.lags.size() + (spec.harq ? 1 : 0); std::vector<Eigen::VectorXd> coefs;
59 + for (usize end = (window ? window : minT); end + h <= T; end += step) { // model uses rv[0..end) (or the last `window`), forecasts target at origin end−1
60 + const usize b = window ? end - window : 0; std::span<const f64> hist = rv.subspan(b, end - b); std::span<const f64> rqh = spec.harq ? rq.subspan(b, end - b) : std::span<const f64>();
61 + Estimate e; try { HarSpec s2 = spec; s2.cov.type = stats::CovType::classical; e = har_fit(hist, s2, rqh); } catch (const RaidError&) { continue; }
62 + const f64 fc = har_forecast(e, hist, spec, rqh); const usize t = end - 1;
63 + f64 actual; if (spec.target == HarTarget::single) actual = rv[t + h]; else { f64 s = 0; for (usize j = 1; j <= h; ++j) s += rv[t + j]; actual = s / static_cast<f64>(h); }
64 + out.origin.push_back(t); out.forecast.push_back(fc); out.actual.push_back(actual); coefs.push_back(Eigen::Map<const Eigen::VectorXd>(e.coefficients.data(), static_cast<Eigen::Index>(k))); ++out.n_fits;
65 + }
66 + out.coefficients.resize(static_cast<Eigen::Index>(k), static_cast<Eigen::Index>(coefs.size())); for (usize j = 0; j < coefs.size(); ++j) out.coefficients.col(static_cast<Eigen::Index>(j)) = coefs[j];
67 + out.seconds = std::chrono::duration<f64>(std::chrono::steady_clock::now() - t0).count(); return out;
68 +}
69 +
70 +HarBatchResult har_batch(std::span<const f64> rv_concat, std::span<const i64> offsets, const HarSpec& spec, const ExecutionPlan& plan) {
71 + HarBatchResult out; const usize S = offsets.size() - 1; std::string why; auto be = backend::resolve(plan, &why);
72 + // Build all designs (CPU, FP64), concatenate row-major for the backend, accumulate X'X / X'y / y'y per series, solve in FP64.
73 + std::vector<HarDesign> designs(S); usize k = 0, rows = 0; std::vector<i64> roff(S + 1, 0); std::vector<usize> ok(S, 1);
74 + for (usize s = 0; s < S; ++s) { try { designs[s] = har_design(rv_concat.subspan(static_cast<usize>(offsets[s]), static_cast<usize>(offsets[s + 1] - offsets[s])), spec); k = static_cast<usize>(designs[s].X.cols()); rows += designs[s].n; } catch (const RaidError&) { ok[s] = 0; } roff[s + 1] = static_cast<i64>(rows); }
75 + RAID_REQUIRE(k > 0, insufficient_samples, "har_batch: no series with enough data");
76 + std::vector<f64> X(rows * k), y(rows); for (usize s = 0; s < S; ++s) { if (!ok[s]) continue; const usize b = static_cast<usize>(roff[s]); for (usize i = 0; i < designs[s].n; ++i) { y[b + i] = designs[s].y[static_cast<Eigen::Index>(i)]; for (usize j = 0; j < k; ++j) X[(b + i) * k + j] = designs[s].X(static_cast<Eigen::Index>(i), static_cast<Eigen::Index>(j)); } }
77 + std::vector<f64> xtx, xty, yty; be->regression_stats(X, y, roff, k, xtx, xty, yty, &out.stats);
78 + out.fits.resize(S);
79 + for (usize s = 0; s < S; ++s) {
80 + if (!ok[s]) { out.fits[s].model = spec.model_key(); out.fits[s].convergence = Convergence{false, 0, 0, 0, 1, "insufficient data"}; continue; }
81 + SufficientStats ss; ss.xtx = Eigen::Map<const Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>(xtx.data() + s * k * k, static_cast<Eigen::Index>(k), static_cast<Eigen::Index>(k)); ss.xty = Eigen::Map<const Eigen::VectorXd>(xty.data() + s * k, static_cast<Eigen::Index>(k)); ss.yty = yty[s]; ss.n = designs[s].n;
82 + OlsOptions o; o.names = designs[s].names; auto r = ols_from_stats(ss, o); out.fits[s] = r.estimate; out.fits[s].model = spec.model_key(); out.fits[s].backend = be->kind(); out.fits[s].precision = out.stats.precision;
83 + out.fits[s].extra["horizon"] = spec.horizon; out.fits[s].extra["note"] = "classical SE from sufficient statistics; use har_fit for HAC";
84 + }
85 + return out;
86 +}
87 +
88 +ForecastLoss forecast_loss(std::span<const f64> a, std::span<const f64> f) {
89 + RAID_REQUIRE(a.size() == f.size(), invalid_argument, "forecast_loss: size mismatch"); ForecastLoss l{0, 0, 0};
90 + for (usize i = 0; i < a.size(); ++i) { if (!(a[i] > 0) || !(f[i] > 0) || !std::isfinite(a[i]) || !std::isfinite(f[i])) continue; const f64 d = a[i] - f[i]; l.mse += d * d; const f64 q = a[i] / f[i]; l.qlike += q - std::log(q) - 1.0; ++l.n; }
91 + if (l.n) { l.mse /= static_cast<f64>(l.n); l.qlike /= static_cast<f64>(l.n); } return l;
92 +}
93 +
94 +} // namespace raid::est
95
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