Machine learning platform for market analysis and forecasting, with a focus on stock volatility prediction, market trend forecasting, and portfolio optimization.
MeridianAlgo/AraAI має індекс здоров’я 57 зі 100, що відповідає смузі «Помірний». Найвищий показник — Vitality (84/100), найнижчий — Security (33/100). Останнє оновлення було 12 днів тому. Більшість нещодавньої роботи виконує один учасник.
57
загалом / 100
Помірний
Індекс здоров'я програмного забезпечення
Метрики згруповано у зважені категорії на шкалі 1–100. Загальна оцінка починається як їхнє зважене середнє, відкаліброване за розподілом публічного реєстру, тож діапазони мають перцентильний зміст; коли публічні дані активують Політику юрисдикцій високого ризику, рейтинг коригується й отримує верхню межу 34 («У зоні ризику»).
57
Винятковий93-100Верхній щабель реєстру (≈ топ-5%); відповідає практично всім перевіреним критеріям
Відмінний80-92Сильний за всіма напрямами; незначні прогалини
Добрий65-79Здоровий; прогалини обмежені та керовані
Помірний50-64Прийнятний, але з помітними прогалинами; рекомендовано перевірку
Слабкий35-49Суттєві недоліки в кількох сферах
У зоні ризику20-34Суттєві слабкі місця; впровадження потребує обережності
Критичний1-19Серйозні проблеми (покинутий, єдиний мейнтейнер, без базової гігієни)
Профіль оцінок
Кожна вісь — окрема категорія. Форма важить більше, ніж середнє: здоровий об'єкт заповнює всю фігуру, тоді як профіль із піками та провалами означає, що сила в одному вимірі маскує ризик в іншому.
Зважений загальний бал 55 калібровано до 57 за шкалою опублікованого індексу (калібрування реєстру 2026-08-02).
Наскільки репозиторій оснащений для розробки та супроводу за участі ШІ-агентів? Має свідомо малу вагу (4%): агентний інструментарій — реальний сигнал супроводу, але репозиторій без нього все одно може отримати 100/100.
Типізований код — Python без конфігурації перевірки типів
52.8/55
Керовані розміри файлів — 1/25 файлів вихідного коду понад 60 КБ
Використані вхідні дані
primary_language
Python
largest_source_bytes
70 050
source_files_sampled
25
oversized_source_files
1
Ключові факти
15зірок GitHub
4контриб'юторів
178комітів за останні 12 місяців
12днів від останнього пушу
3релізів
1бас-фактор
0відкритих issue
PyPIпакетних екосистем
Попередження щодо збору даних
Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
Could not fetch pypi package 'ara-ai' from its registry
GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
Докладніше
Історія зірок і форків 0 ★ / 4 ⇿
0Зірки
4Форки
2Релізи
Коли додано кожну зірку й форк — зібрано з GitHub і згруповано за днями. Кумулятивне зростання розміщено просто над денними додаваннями, з яких воно складається, тож їх видно одне проти одного: рівномірне органічне накопичення виглядає зовсім інакше, ніж різкий короткочасний сплеск. Там, де цю різницю можна виміряти, її подано як автентичність росту.
Мажорні 1Мінорні 0Патчі 1
OpenSSF Scorecard 3.3 / 10
3.3сукупно
Незалежна, не прив'язана до інструментів оцінка безпеки від відкритого проєкту OpenSSF Scorecard. Кожна перевірка винагороджує практику безпеки, а не інструмент конкретного постачальника. Перевірки, які Scorecard не зміг визначити, позначено н/д і виключено з оцінки безпеки (вони ніколи не зараховуються як нуль).Scorecard v5.5.0 · 2026-08-02 21:14 UTC
Не вдалося зібрати розв'язаний набір залежностей для цього звіту: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
Звіт у форматі JSON машиночитний
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"body": "Pulled production Meridian.AI stock/forex checkpoints from HF and ran them on\nthe existing holdout slice. Even in-sample (they train through the holdout)\nthey fail to beat the always-up baseline; reinforces the no-daily-edge finding.",
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"body": "…hmarking\n\n- Retract the v1.0.0 forex edge claim (63.5%, z=8.4): the daily *=X bars are\n internally inconsistent (day-t high/low leak the t+1 close; OLS on day-t\n OHL ratios alone scores 81% sign accuracy). Forex now trains and evaluates\n with a 1-day embargo (embargo_days=1, persisted in checkpo\n[…]\nof-sample numbers.\n- CI: retrying warm-start download (scripts/hf_download.py, 429-aware),\n hourly crons kept staggered (stocks :00, forex :30); ASCII console output\n for Windows cp1252 portability.",
"is_bot": false,
"headline": "v1.2.0: v7 checkpoint format, forex data-leak retraction, honest benc…",
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"is_bot": false,
"headline": "Fix HF 429 push failures by committing model and card atomically with…",
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"is_bot": false,
"headline": "docs: correct all stale v5-era specs and filenames for v1.0.0 [skip ci]",
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"headline": "Fix HF push rate-limit failure and bump Node-20 actions [skip ci]",
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"body": "While live-testing the published models I hit two user-facing breaks in\nthe documented entry points:\n\n- predict_ultimate / predict_forex did float(pred_return[0]) on the\n model's (1, 1) output. Under numpy 2.x that raises \"only 0-dimensional\n arrays can be converted to Python scalars\" and every pr\n[…]\nt (ml.predict -> ml.predict_forex)\nin MODEL_CARD.md and QUICK_START.md.\n\nVerified live: predict_ultimate(\"AAPL\") and predict_forex on all three\npair formats now return full multi-day prediction dicts.",
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"headline": "Fix prediction convenience APIs and forex pair parsing [skip ci]",
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"body": "Silences the \"Node.js 20 is deprecated\" annotations on every run.\n\n- actions/checkout v4 -> v6\n- actions/setup-python v5 -> v6\n- actions/upload-artifact v4 -> v7\n- actions/download-artifact v4 -> v8\n\nupload-artifact v7 / download-artifact v8 are the same artifact-backend\ngeneration, so the upload/download pair in daily-model-tests stays\ncompatible.",
"is_bot": false,
"headline": "Bump GitHub Actions to Node-24 majors [skip ci]",
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"body": "test_direction_accuracy_above_chance used a hard >= 50.0 gate on the\nstored validation direction accuracy. Daily price direction is near\nefficient and the chronological holdout is small (n ranged from ~128 to\n4096 across runs), so the metric fluctuates around 50% by pure sampling\nnoise. The gate fai\n[…]\n%. The test now fails only on a\n statistically real collapse below chance (the old inverted-model bug),\n not on run-to-run noise. Conservative 45% fallback for older\n checkpoints lacking the count.",
"is_bot": false,
"headline": "Fix flaky model-test: noise-aware direction-accuracy floor [skip ci]",
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"body": "Add scripts/sanity_check_model.py and wire it into both training workflows so a collapsed/biased model is deleted instead of pushed. Rewrite the HF model card and README to the real v6 architecture and honest next-day performance; bump version to 6.0.1. [skip ci]",
"is_bot": false,
"headline": "v6.0.1: sanity gate to block degenerate models + honest docs",
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"headline": "v6.0.0: shrink model to 430K params and disable dropout to extract re…",
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"body": "… data",
"is_bot": false,
"headline": "v5.2.3: raise step budget to 2000 so model trains on clean per-symbol…",
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"committed_at": "2026-05-28T16:27:27Z",
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"oid": "c3e30633dc7ec434ca55bf5e9c409b631834820e",
"body": "… [skip ci]\n\nThe model predicted 'down' ~92% of the time and scored below a coin flip\nlive, despite claiming 74% validation accuracy. Root cause was data\ncontamination in the training pipeline:\n\n- unified_ml: windows + indicators + next-day returns were computed over\n one flat array of ~100 symbols\n[…]\nmixing or\n split-induced fake returns).\n- train_stocks/forex: load filters interval='1d'.\n- large_torch_model: scaler fit on train split only (was leaking val stats);\n stock target clip 1.0 -> 0.25.",
"is_bot": false,
"headline": "fix: train per-symbol on clean daily data to kill the prediction bias…",
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"committed_at": "2026-05-27T20:19:29Z",
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"oid": "848a93b890d8cbba94b1570990aa9994d45e27d9",
"body": "Per-symbol assertions on 30-sample windows had ~16% false-failure rate.\nSwitch to a single pooled test per asset class (150+ stock samples,\n90+ forex). 40% floor at n=150 is p<0.1% -- catches broken models,\nnot hard market periods. Per-symbol scores still printed in logs.",
"is_bot": false,
"headline": "refactor: aggregate directional accuracy across all symbols [skip ci]",
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"oid": "782b49237f8159c88aa82f13c0e468fa8919b663",
"body": "0.45 still flags normal market noise. At n=90 samples, 0.40 is ~2 sigma\nbelow chance — it catches genuinely degenerate outputs without tripping\non models that are simply struggling in hard-to-predict markets.",
"is_bot": false,
"headline": "fix: lower live directional accuracy floor to 0.40",
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"oid": "30c448b7a88d4516aea95b9aa4dfa5f3d2610a61",
"body": "n=40 samples has too much variance — a model at true 50% accuracy\nfails the old threshold ~30% of the time by chance. 90 samples halves\nthat false-failure rate. Floor of 0.45 still catches degenerate outputs.",
"is_bot": false,
"headline": "fix: raise n_steps to 90 and lower live accuracy floor to 0.45",
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"headline": "fix: add accelerate to denorm + directional signal jobs",
"author_name": "MeridianAlgo",
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{
"oid": "d5a0c20f89a322b7fa3b6cbf58915832e8ef1ab0",
"body": "- Add psutil to denorm + directional signal jobs (large_torch_model imports it)\n- Soften training_history test — require one finite val_loss, not all\n- Auto-create missing GH labels before opening failure issues",
"is_bot": false,
"headline": "fix: patch daily test workflow after first run",
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"body": "Runs nightly at 23:00 UTC — checkpoint health, inference, denorm, live signal, and auto issue reporting.",
"is_bot": false,
"headline": "ci: add daily end-of-day model test workflow",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-27T17:14:39Z",
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"oid": "056b7fb37ebd7bd3e0ddbd6936b56459ed08ca33",
"body": null,
"is_bot": false,
"headline": "Update README.md",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-26T17:27:04Z",
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"body": "…s actually train (v5.2.2)",
"is_bot": false,
"headline": "feat: step-based LR schedule + raise step budget to 300 so capped run…",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-26T17:22:10Z",
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"oid": "1bf0cfbdea86eb4b63f62bb281351316997dbd95",
"body": null,
"is_bot": false,
"headline": "chore: remove stale datasets and deprecated revolutionary_model shim",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-26T16:33:38Z",
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"oid": "c4d519c6b3d76aff2759fafd618f40062eb880d9",
"body": "…HF push (v5.2.1)",
"is_bot": false,
"headline": "fix: batch validation forward pass to stop CI runner OOM-kill before …",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-26T16:25:40Z",
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{
"oid": "4af7b95f73284fa10cb6274bfbf4c63bfc819b3d",
"body": "…ation script",
"is_bot": false,
"headline": "release v5.2.0 — bump version, fix readme badges, drop duplicate migr…",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-24T02:59:29Z",
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"headline": "style: auto-format and lint [skip ci]",
"author_name": "GitHub Action",
"author_login": "actions-user",
"committed_at": "2026-05-24T02:54:59Z",
"body_truncated": false,
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"oid": "4681bca4a40b0e657135074ee63deab6d54fd28f",
"body": "…migration script + rename scripts",
"is_bot": false,
"headline": "consolidate to single-job workflow + per-step comet logging + legacy …",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
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"oid": "36d744270b979684f94c5621b2600ac70b1e5092",
"body": "Confirmed via Hugging Face that no pushes have landed since 2026-05-15\neven though training loops have been completing 70 steps cleanly. The\ntraining script gets SIGTERMed within ~25 seconds of 'Step limit\nreached' — before the 4096-sample CPU validation, Comet log_model\n(132 MB upload), final 2048-\n[…]\nantees the artifact step\nfinds a valid checkpoint when we don't.\n\nAlso added flush=True to the step/time limit print lines so CI logs\nshow the message immediately, not on Python's stdout buffer flush.",
"is_bot": false,
"headline": "fix(train): safety-save model BEFORE post-train validation/Comet",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-24T02:44:34Z",
"body_truncated": true,
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{
"oid": "87455e6539c1e7303dd09b28d9c106143d203bf0",
"body": "Every stage now shows what it's doing:\n\nSetup job\n- Run Information: run#, trigger, commit, branch, timestamp, CPU/RAM/disk\n- Install Dependencies: show package versions after install\n- Free Disk Space: before + after disk stats\n- Fetch Data: elapsed time\n- Check if Data was Fetched: full per-symbol\n[…]\n timestamp\n- Model Download Status: confirm artifact arrived + size\n- Upload to HF Hub: show file size, repo, start time, elapsed time\n\nCleanup job\n- Pipeline Summary: all four job results at a glance",
"is_bot": false,
"headline": "feat(ci): add comprehensive logging to stock + forex workflows",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-24T01:27:41Z",
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"body": "…ompletion\n\nTraining completes 70 steps in ~31min then needs unbounded time for\npost-training validation, atomic model save, and Comet log_model upload\n(~130 MB .pt to Comet). The 75-min step timeout was killing the process\nafter training finished but before the model was saved, causing:\n - artifac\n[…]\nted\n\nFix:\n- Remove timeout-minutes from Train Model step (both forex + stocks)\n- Raise job timeout 95 -> 360 min (GitHub public-repo maximum)\n- Only --max-steps 70 governs when the training loop exits",
"is_bot": false,
"headline": "fix(ci): remove step timeout -- let 70 steps + post-training run to c…",
"author_name": "MeridianAlgo",
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"author_name": "GitHub Action",
"author_login": "actions-user",
"committed_at": "2026-05-22T17:28:17Z",
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"oid": "423bd8b072e49cc648f9795be740cf30351bff00",
"body": "…imit)\n\n- Remove --max-time from both training scripts (was firing at step 47,\n before 70 steps could complete, causing the post-training validation\n to run past the step timeout and get canceled)\n- Add --max-steps 70 to both workflows — training stops cleanly at\n exactly step 70 then saves + pus\n[…]\n max_time_minutes=None (unlimited) in both training functions\n- Expand Train Model step timeout 55→75min, job timeout 75→95min to\n accommodate 70 steps (~47min) + validation/save/Comet flush (~15min)",
"is_bot": false,
"headline": "switch CI from time-based to step-based training (70 steps, no time l…",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-22T17:27:59Z",
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"oid": "2ba096a6167312a3615d0304f41edce7caeaf5ca",
"body": "…+ push HF on partial runs\n\nThree coupled changes so a training run that hits the time budget still\nsaves, gets logged, and ends up on Hugging Face:\n\n1. Step timeout 38min -> 55min, script --max-time 35min -> 32min.\n The script stopped training at 33min and then ran final validation\n (forward pa\n[…]\ndel.outcome ==\n 'success' + hashFiles). With the atomic _save_model + SIGTERM handler\n already in place, a partial-but-improved checkpoint reaches HF on\n timeout runs instead of being discarded.",
"is_bot": false,
"headline": "fix(ci): stop exit-143 on training cleanup + register model in Comet …",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-22T16:04:33Z",
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"headline": "style: auto-format and lint [skip ci]",
"author_name": "GitHub Action",
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{
"oid": "06d11d1c9562170d3a07115fec755bc714c0bbad",
"body": "One-shot maintenance script: cancels all in-progress runs and deletes\nevery run for non-lint workflows in MeridianAlgo/AraAI, so the Actions\ntab shows a clean slate for the v5 release (lint runs are preserved).\n\n- GitHub REST API directly (no gh CLI dependency)\n- --dry-run flag to preview cancel/delete plan\n- Cancels active runs, waits, then deletes; gentle pacing between calls\n- Token from $GITHUB_TOKEN / $GH_TOKEN or --token; needs repo+workflow",
"is_bot": false,
"headline": "chore: add scripts/clean_workflow_runs.py for v5 history wipe",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-22T13:39:04Z",
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"headline": "style: auto-format and lint [skip ci]",
"author_name": "GitHub Action",
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"committed_at": "2026-05-22T13:08:11Z",
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{
"oid": "197873c7d1bbefadf3e57e0c4cf97f95dcdf7cbb",
"body": "Runs were dying at the 50-min job timeout (exit 143 / \"operation was\ncanceled\") because the math under-counted setup time: ~17min for\ninstall + artifact download + HF model fetch, then 33min of training,\nthen validation+save pushed total past 50min.\n\n- meridian-stocks.yml / meridian-forex.yml: timeo\n[…]\nlocal variable 'ema_decay' where it is not associated\nwith a value\": the Comet log_parameters block referenced ema_decay\nbefore it was defined further down. Moved the assignment above the\nComet block.",
"is_bot": false,
"headline": "fix(ci): raise training job timeout to 75min and fix ema_decay NameError",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-22T13:07:49Z",
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{
"oid": "89f8e421e505d1f28bef647c3d09ddacd2d9ce48",
"body": "…y migration\n\nFix recurring \"operation was canceled\" CI failure at ~44min by making the\ntraining pipeline signal-safe and tightening the time budget:\n\n- SIGTERM/SIGINT handler in AdvancedMLSystem.train sets a shutdown flag\n the step loop polls; final save always runs before exit\n- _save_model is no\n[…]\n,\n comet_ai_token) via load_dotenv at script start\n- MODEL_CARD refreshed: new layout, hardening notes, Comet links, v5.1.0 metadata\n\nVersion bumps: pyproject.toml + meridianalgo/__init__.py -> 5.1.0",
"is_bot": false,
"headline": "v5.1.0: harden CI training pipeline + full Comet telemetry + HF legac…",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-21T16:58:34Z",
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"oid": "5546666c0f061e66b8dc48c1a731b94ed8a3fc15",
"body": null,
"is_bot": false,
"headline": "fix: extend forex and stock training job timeouts to 120 minutes",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-05-17T23:38:41Z",
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"headline": "style: auto-format and lint [skip ci]",
"author_name": "GitHub Action",
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"committed_at": "2026-05-15T19:08:27Z",
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{
"oid": "f47595c607dd8547941d795814e0f134f4cf3d4a",
"body": "The alias was a leftover compat shim in the try-block; MeridianModel is\nthe only name used in this file. Also fixes ruff I001 import sort in\nrevolutionary_model.py.",
"is_bot": false,
"headline": "fix: remove unused RevolutionaryFinancialModel import alias (ruff F401)",
"author_name": "Ishaan",
"author_login": "ishaanman7898",
"committed_at": "2026-05-15T19:08:09Z",
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{
"oid": "d3e258aececdf98b55fa5da04d5302713ac6681d",
"body": "- CHANGELOG.md: full version history from v1.0.0-Beta to v5.0.0, built\n from all git tags and GitHub releases\n- QUICK_START.md: rewritten with accurate v5.0 specs, correct training\n commands, troubleshooting for known pre-v5 bugs\n- MODEL_CARD.md: updated to v5.0 — MeridianModel, checkpoint format\n\n[…]\nth, class weighting,\n logit scaling rationale, direction metrics table\n- INDEX.md (new): single-page documentation index with source layout\n- LICENSE: fix year (2025→2026) and placeholder GitHub URLs",
"is_bot": false,
"headline": "docs: comprehensive v5.0 documentation overhaul",
"author_name": "Ishaan",
"author_login": "ishaanman7898",
"committed_at": "2026-05-15T19:05:14Z",
"body_truncated": true,
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},
{
"oid": "10c85542c70ce70efc2b27a2a455658590ccb489",
"body": "Documents all 7 training bugs fixed in v5.0, the architecture rename\nfrom RevolutionaryFinancialModel to MeridianModel, hourly CI schedule,\nCPU vs GPU model specs, checkpoint format reference, and updated project\nstructure.",
"is_bot": false,
"headline": "docs: update README for MeridianModel v5.0",
"author_name": "Ishaan",
"author_login": "ishaanman7898",
"committed_at": "2026-05-15T18:55:50Z",
"body_truncated": false,
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},
{
"oid": "404e0ed76343122c86fa1545ea5e31f0dff52683",
"body": "Architecture rename:\n- meridian_model.py: new canonical file — classes MeridianModel, MeridianBlock\n- revolutionary_model.py: backward-compat shim (3-line re-export)\n- Checkpoint string: \"MeridianModel-2026\" (v5.0); _load_model accepts both old\n \"RevolutionaryFinancialModel-2026\" and new string so \n[…]\nremove hardcoded dim=384/num_heads=6/version=\"4.1\"\n so new models (dim=256/4/5.0) and old HF models both pass\n- Accepts architecture strings \"MeridianModel-2026\" or \"RevolutionaryFinancialModel-2026\"",
"is_bot": false,
"headline": "feat: rename to MeridianModel v5.0, hourly CI, LR warmup",
"author_name": "Ishaan",
"author_login": "ishaanman7898",
"committed_at": "2026-05-15T18:26:33Z",
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"oid": "ff044c6313eb36169bce3ff2568f25109be20bdd",
"body": "Without this, new models saved with mamba_state_dim=4 would build the wrong\ngraph shape when the directional-accuracy tests reconstruct the model for\ninference, causing a state-dict load mismatch. Defaults to 16 so existing\nHuggingFace checkpoints (which predate this field) continue to load correctly.",
"is_bot": false,
"headline": "fix: pass mamba_state_dim to RevolutionaryFinancialModel in signal tests",
"author_name": "Ishaan",
"author_login": "ishaanman7898",
"committed_at": "2026-05-15T16:36:40Z",
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"headline": "style: auto-format and lint [skip ci]",
"author_name": "GitHub Action",
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"committed_at": "2026-05-15T16:35:48Z",
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{
"oid": "4902160f30b330654ee6467eab45a20b044168cf",
"body": "Architecture changes (speed):\n- RevolutionaryFinancialModel: thread mamba_state_dim through to MambaBlock\n so state_dim is configurable without rebuilding the graph\n- New CPU training config: dim=256, num_heads=4, use_mamba=False, mamba_state_dim=4\n (~11M params, ~27 min/epoch on 60K samples vs 23\n[…]\n 180s time-limit buffer (from 120s) to guarantee validation always fits\n- Checkpoint: save actual use_mamba from model (was hardcoded True)\n and persist mamba_state_dim for faithful round-trip reload",
"is_bot": false,
"headline": "perf+fix: CPU-optimised architecture + 7 training correctness fixes",
"author_name": "Ishaan",
"author_login": "ishaanman7898",
"committed_at": "2026-05-15T16:32:19Z",
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{
"oid": "a5eeb6b082cc8b60b05d7fac485b684f786725e5",
"body": "* fix: clip target return outliers, remove min-max remap, add model test suite\n\nThe trained checkpoints on HuggingFace shipped with metadata showing\nbest_val_loss=Infinity across all 50 training runs and direction_accuracy=0\nbecause the target distribution was poisoned by single bad price ticks\n(sto\n[…]\nt CI training run uses the fixed code path.\n\n* style: auto-format and lint [skip ci]\n\n---------\n\nCo-authored-by: MeridianAlgo <meridianalgo@gmail.com>\nCo-authored-by: GitHub Action <action@github.com>",
"is_bot": false,
"headline": "fix: clip target return outliers; add rigorous model test suite (#154)",
"author_name": "Ishaan M.",
"author_login": "ishaanman7898",
"committed_at": "2026-05-01T13:05:48Z",
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"headline": "style: auto-format and lint [skip ci]",
"author_name": "GitHub Action",
"author_login": "actions-user",
"committed_at": "2026-04-07T23:57:18Z",
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"oid": "ff417e840bde00ec372eeb959e66f9de549c1730",
"body": null,
"is_bot": false,
"headline": "fix: remove unused mem_model_mb variable (ruff F841)",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-04-07T23:56:46Z",
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"oid": "22e2a896a86590de24d13632091edce221e5c9ba",
"body": "X was deleted (line 557) to free numpy memory but still referenced in\n_update_metadata. Save n_samples before del and pass that instead.\n\nAlso update MODEL_CARD.md with correct v4.1 specs (45M params, not 388M).",
"is_bot": false,
"headline": "fix: resolve UnboundLocalError on X after del in training loop",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-04-07T23:54:08Z",
"body_truncated": false,
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{
"oid": "2caf5092a82a9433bfcddb8814bcdf0a63fef74b",
"body": "- --max-time 45 (45min training from script start)\n- timeout-minutes: 60 (15min buffer for CI overhead)\n- cancel-in-progress: false (queued runs wait, never kill running ones)\n- Stocks every 6h at :00, Forex every 6h at :30",
"is_bot": false,
"headline": "fix: simple 45min training / 60min timeout, no cancel-in-progress",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-04-07T01:28:42Z",
"body_truncated": false,
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},
{
"oid": "716fcdd880ea5a7d9e560f1a7a19e977eaf768fd",
"body": "- float32 everywhere (was float64) — halves dataset memory\n- Delete numpy arrays after torch conversion (del X, y)\n- In-place normalization (X_tensor.sub_().div_()) — no copy\n- Cap dataset at 300K samples (~1.5GB) with warning\n- Free feature_matrix after building X windows\n- Log memory usage for diagnostics\n\nPeak memory estimate with 50 stocks:\n Before: ~10.5GB (float64 + copies) → OOM\n After: ~3.5GB (float32 + in-place) → fits in 7GB",
"is_bot": false,
"headline": "fix: fit training within 7GB GitHub Actions RAM limit",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-04-07T01:25:07Z",
"body_truncated": false,
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"body": null,
"is_bot": false,
"headline": "style: auto-format and lint [skip ci]",
"author_name": "GitHub Action",
"author_login": "actions-user",
"committed_at": "2026-04-07T01:19:35Z",
"body_truncated": false,
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},
{
"oid": "35fef6b1c3b01d9fd73d9cd95f2564f9b1a2a2f1",
"body": "Workflows:\n- timeout-minutes: 350, --max-time 320 (5h20m training budget)\n- Concurrency groups: new scheduled runs cancel stale in-progress ones\n- Staggered crons: stocks at 0/6/12/18h, forex at 3/9/15/21h\n- Workflow dispatch now accepts epochs and max_time inputs\n- Smart issue handling: no duplicat\n[…]\ning time\n- _update_metadata() extracted for checkpoint + final save reuse\n- Training history capped at 50 entries to prevent unbounded growth\n- Patience increased to 20 epochs for longer training runs",
"is_bot": false,
"headline": "feat: robust training pipeline with smart scheduling and issue handling",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-04-07T01:19:23Z",
"body_truncated": true,
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},
{
"oid": "eb9cf3c7dbcb3b44a53c8c6e2d1935a03b52d314",
"body": "- timeout-minutes: 60 was too tight — CI overhead (checkout, install,\n download model) takes ~15min before script starts, leaving no room\n- Now: timeout-minutes: 340, --max-time 320 (5h20m training budget)\n- Cron reduced to every 6h to match longer training runs\n- Final validation capped at 2048 samples and skipped if time is tight\n- global_start_time ensures Python never overshoots its own budget",
"is_bot": false,
"headline": "fix: increase job timeout to 340min, train for 5h+ per run",
"author_name": "MeridianAlgo",
"author_login": "MeridianAlgo-Developer",
"committed_at": "2026-04-07T01:12:07Z",
"body_truncated": false,
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},
{
"oid": "8784faabd00deeb92f8860dedb910677b557407b",
"body": "The MeridianAlgo account token didn't have write access.\nSwitched all model download/upload references to meridianal/ARA.AI\nwhere the write token works.",
"is_bot": false,
"headline": "fix: switch HuggingFace repo to meridianal/ARA.AI",
"author_name": "MeridianAlgo",
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{
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{
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{
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{
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{
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{
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{
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"reason": "no SAST tool detected",
"documentation_url": "https://github.com/ossf/scorecard/blob/c395761df6afe1a69e476bc60a013a94bcbc153f/docs/checks.md#sast"
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{
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{
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{
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{
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{
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{
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"name": "Security",
"value": 33,
"weight": 0.16,
"metrics": [
{
"key": "security_posture",
"band": "at_risk",
"name": "Security posture",
"note": "Excluded from scoring (no data or not applicable): CI-Tests, Packaging, Signed-Releases. Remaining weights renormalized.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
"ci_tests",
"packaging",
"signed_releases"
]
}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 33,
"inputs": {
"source": "openssf_scorecard",
"checks_evaluated": 15,
"scorecard_version": "v5.5.0",
"checks_inconclusive": 3,
"scorecard_aggregate": 3.3
},
"components": [
{
"key": "binary_artifacts",
"name": "Binary-Artifacts",
"detail": "no binaries found in the repo",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "branch_protection",
"name": "Branch-Protection",
"detail": "branch protection is not maximal on development and all release branches",
"points": 0.8,
"status": "partial",
"details": [],
"max_points": 7.5
},
{
"key": "ci_tests",
"name": "CI-Tests",
"detail": "no pull request found",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 2.5
},
{
"key": "cii_best_practices",
"name": "CII-Best-Practices",
"detail": "no effort to earn an OpenSSF best practices badge detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 2.5
},
{
"key": "code_review",
"name": "Code-Review",
"detail": "Found 0/30 approved changesets -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "contributors",
"name": "Contributors",
"detail": "project has 2 contributing companies or organizations -- score normalized to 6",
"points": 1.5,
"status": "partial",
"details": [],
"max_points": 2.5
},
{
"key": "dangerous_workflow",
"name": "Dangerous-Workflow",
"detail": "no dangerous workflow patterns detected",
"points": 10,
"status": "met",
"details": [],
"max_points": 10
},
{
"key": "dependency_update_tool",
"name": "Dependency-Update-Tool",
"detail": "no update tool detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "fuzzing",
"name": "Fuzzing",
"detail": "project is not fuzzed",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "license",
"name": "License",
"detail": "license file detected",
"points": 2.2,
"status": "partial",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "packaging",
"name": "Packaging",
"detail": "packaging workflow not detected",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 5
},
{
"key": "pinned_dependencies",
"name": "Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "sast",
"name": "SAST",
"detail": "no SAST tool detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "security_policy",
"name": "Security-Policy",
"detail": "security policy file not detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "signed_releases",
"name": "Signed-Releases",
"detail": "no releases found",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 7.5
},
{
"key": "token_permissions",
"name": "Token-Permissions",
"detail": "detected GitHub workflow tokens with excessive permissions",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "vulnerabilities",
"name": "Vulnerabilities",
"detail": "36 existing vulnerabilities detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
"band": "exceptional",
"name": "High-Risk Jurisdiction Exposure",
"note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
"notes": [
{
"code": "jurisdiction_evidence_limits",
"params": {}
}
],
"value": 100,
"inputs": {
"meaning": "self-published location evidence; not nationality or citizenship",
"red_flag": false,
"exposures": [],
"policy_countries": [
"Russia",
"Iran",
"North Korea"
],
"commit_weight_rule": {
"min_commits": 50,
"min_commit_share": 0.1
},
"review_only_matches": 0,
"below_threshold_exposures": [],
"assessed_self_published_locations": 2
},
"components": [
{
"key": "policy_exposure_multiplier",
"name": "Policy exposure multiplier",
"detail": "no confirmed policy-scope location match",
"points": 100,
"status": "met",
"details": [
{
"code": "jurisdiction_no_match",
"params": {}
}
],
"max_points": 100
}
]
}
],
"description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
},
{
"key": "ai_readiness",
"band": "at_risk",
"name": "AI Readiness",
"value": 34,
"weight": 0.04,
"metrics": [
{
"key": "ai_agent_context",
"band": "weak",
"name": "Agent context & guidance",
"note": null,
"notes": [],
"value": 40,
"inputs": {
"has_llms_txt": false,
"legible_history_share": 0.845,
"agent_instruction_files": [],
"agent_instruction_max_bytes": null
},
"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": "no CLAUDE.md / AGENTS.md / editor rules",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_instructions",
"params": {}
}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
"name": "Legible commit history",
"detail": "82 of 97 human commits state their intent (structured subject or explanatory body)",
"points": 40,
"status": "met",
"details": [
{
"code": "legible_history",
"params": {
"legible": 82,
"sampled": 97
}
}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "at_risk",
"name": "Verify loop (build / test / typecheck)",
"note": null,
"notes": [],
"value": 22,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [],
"has_dockerfile": false,
"typed_language": false,
"bootstrap_files": [],
"has_devcontainer": false,
"has_linter_config": false,
"typecheck_configs": [],
"agent_commit_share": 0,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 18
},
{
"key": "automated_tests",
"name": "Automated tests",
"detail": null,
"points": 22,
"status": "met",
"details": [],
"max_points": 22
},
{
"key": "lint_format_config",
"name": "Lint / format config",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 11
},
{
"key": "static_type_checking",
"name": "Static type checking",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 11
},
{
"key": "reproducible_environment",
"name": "Reproducible environment",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
"name": "Demonstrated agent practice",
"detail": "no agent-authored commits among the last 100",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_authored_commits",
"params": {
"sampled": 100
}
}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "no automated dependency updates observed",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_dependency_automation",
"params": {}
}
],
"max_points": 8
},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
}
]
},
{
"key": "ai_code_legibility",
"band": "moderate",
"name": "Code legibility for models",
"note": null,
"notes": [],
"value": 53,
"inputs": {
"primary_language": "Python",
"largest_source_bytes": 70050,
"source_files_sampled": 25,
"oversized_source_files": 1
},
"components": [
{
"key": "type_checkable_code",
"name": "Type-checkable code",
"detail": "Python without a type-check config",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_typecheck_config_language",
"params": {
"language": "Python"
}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
"name": "Manageable file sizes",
"detail": "1/25 source files over 60KB",
"points": 52.8,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 25,
"oversized": 1
}
}
],
"max_points": 55
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight: agent tooling is a real maintenance signal, but its absence must never gate the top of the scale (calibration saturates at raw 91, so 100/100 remains reachable with AI Readiness at zero)."
}
],
"classification": {
"labels": [],
"scores": {},
"primary": null,
"evidence": [],
"artifacts": [],
"confidence": "none",
"host_extension": false,
"runs_as_process": false,
"consumed_by_code": false
},
"metrics_version": "2.3.1"
},
"warnings": [
"Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
"Could not fetch pypi package 'ara-ai' from its registry",
"GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository"
],
"report_type": "repository",
"generated_at": "2026-08-02T21:14:42.479219Z",
"schema_version": "0.29.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/m/MeridianAlgo/AraAI.svg",
"full_name": "MeridianAlgo/AraAI",
"license_state": "custom",
"license_spdx": null
}
Пов’язані записи
Перевірені репозиторії, що мають спільні каталожні теги або екосистеми з MeridianAlgo/AraAI. Читати поруч →
Оцінки — це сигнали, а не гарантії. Вони відображають публічно видимі практики на GitHub — це не аудит коду й не гарантія безпеки.
Відсутні дані виключаються, а ваги перенормовуються — нуль за відсутність ніколи не ставиться. Методологія версіонована й відкрита: метрики v2.3.1, схема v0.29.0 — повна методологія · вікі метрик.
Як окремий результат виглядає на тлі всього реєстру: сукупна статистика.
