From 35aacde5413511ba737d2b051e419cbb97070d13 Mon Sep 17 00:00:00 2001 From: Danglewood <85772166+deeleeramone@users.noreply.github.com> Date: Mon, 1 Jun 2026 09:12:06 -0700 Subject: [PATCH] refactor econometrics for v5 (#7505) Co-authored-by: deeleeramone <> --- .../workflows/publish-openbb-econometrics.yml | 163 + .../test-unit-openbb-econometrics.yml | 122 + .../extensions/econometrics/README.md | 87 +- .../econometrics/integration/conftest.py | 82 + .../integration/test_econometrics_api.py | 463 +- .../integration/test_econometrics_python.py | 478 +-- .../openbb_econometrics/diagnostics.py | 353 ++ .../econometrics_router.py | 1049 ----- .../openbb_econometrics/econometrics_views.py | 3 +- .../econometrics/openbb_econometrics/panel.py | 211 + .../openbb_econometrics/regression.py | 227 + .../openbb_econometrics/router.py | 18 + .../openbb_econometrics/time_series.py | 537 +++ .../econometrics/openbb_econometrics/tools.py | 157 + .../econometrics/openbb_econometrics/utils.py | 62 +- .../extensions/econometrics/poetry.lock | 2662 ------------ .../extensions/econometrics/pyproject.toml | 105 +- .../extensions/econometrics/tests/conftest.py | 94 + .../econometrics/tests/test_diagnostics.py | 116 + .../tests/test_econometrics_utils.py | 1 + .../tests/test_econometrics_views.py | 55 + .../econometrics/tests/test_panel.py | 103 + .../econometrics/tests/test_regression.py | 86 + .../econometrics/tests/test_router.py | 11 + .../econometrics/tests/test_time_series.py | 248 ++ .../econometrics/tests/test_tools.py | 68 + .../extensions/econometrics/uv.lock | 3821 +++++++++++++++++ 27 files changed, 6954 insertions(+), 4428 deletions(-) create mode 100644 .github/workflows/publish-openbb-econometrics.yml create mode 100644 .github/workflows/test-unit-openbb-econometrics.yml create mode 100644 openbb_platform/extensions/econometrics/integration/conftest.py create mode 100644 openbb_platform/extensions/econometrics/openbb_econometrics/diagnostics.py delete mode 100644 openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_router.py create mode 100644 openbb_platform/extensions/econometrics/openbb_econometrics/panel.py create mode 100644 openbb_platform/extensions/econometrics/openbb_econometrics/regression.py create mode 100644 openbb_platform/extensions/econometrics/openbb_econometrics/router.py create mode 100644 openbb_platform/extensions/econometrics/openbb_econometrics/time_series.py create mode 100644 openbb_platform/extensions/econometrics/openbb_econometrics/tools.py delete mode 100644 openbb_platform/extensions/econometrics/poetry.lock create mode 100644 openbb_platform/extensions/econometrics/tests/conftest.py create mode 100644 openbb_platform/extensions/econometrics/tests/test_diagnostics.py create mode 100644 openbb_platform/extensions/econometrics/tests/test_econometrics_views.py create mode 100644 openbb_platform/extensions/econometrics/tests/test_panel.py create mode 100644 openbb_platform/extensions/econometrics/tests/test_regression.py create mode 100644 openbb_platform/extensions/econometrics/tests/test_router.py create mode 100644 openbb_platform/extensions/econometrics/tests/test_time_series.py create mode 100644 openbb_platform/extensions/econometrics/tests/test_tools.py create mode 100644 openbb_platform/extensions/econometrics/uv.lock diff --git a/.github/workflows/publish-openbb-econometrics.yml b/.github/workflows/publish-openbb-econometrics.yml new file mode 100644 index 00000000000..3a6582eec6c --- /dev/null +++ b/.github/workflows/publish-openbb-econometrics.yml @@ -0,0 +1,163 @@ +name: ๐Ÿ“ฆ publish openbb-econometrics + +# Triggers exclusively on the merge of a ``release/openbb-econometrics-v*`` +# branch into ``develop`` (or ``v5``). Four sequential jobs โ€” verify the +# version bump, build once, test the built source across the full +# cross-platform matrix, and only publish to PyPI if the matrix is green. + +on: + pull_request: + types: [closed] + branches: [develop, v5] + workflow_dispatch: + +permissions: + contents: read + +jobs: + version-check: + name: Verify version bump + if: ${{ (github.event_name == 'pull_request' && github.event.pull_request.merged == true && startsWith(github.event.pull_request.head.ref, 'release/openbb-econometrics-v')) || github.event_name == 'workflow_dispatch' }} + runs-on: ubuntu-latest + outputs: + version: ${{ steps.parse.outputs.version }} + defaults: + run: + shell: bash + working-directory: openbb_platform/extensions/econometrics + steps: + - uses: actions/checkout@v6 + with: + ref: develop + + - name: Parse [project].version from pyproject.toml + id: parse + run: | + VERSION=$(grep -m1 -E '^version[[:space:]]*=' pyproject.toml | sed -E 's/^version[[:space:]]*=[[:space:]]*"([^"]+)".*/\1/') + if [ -z "$VERSION" ]; then + echo "::error::Could not parse [project].version from pyproject.toml." + exit 1 + fi + echo "Local version: $VERSION" + echo "version=$VERSION" >> "$GITHUB_OUTPUT" + + - name: Fail if version already exists on PyPI + env: + VERSION: ${{ steps.parse.outputs.version }} + run: | + STATUS=$(curl -sS -o /dev/null -w "%{http_code}" "https://pypi.org/pypi/openbb-econometrics/${VERSION}/json") + case "$STATUS" in + 200) + echo "::error::openbb-econometrics ${VERSION} is already published on PyPI." + echo "Bump openbb_platform/extensions/econometrics/pyproject.toml [project].version and re-merge a fresh release branch." + exit 1 + ;; + 404) + echo "openbb-econometrics ${VERSION} is not yet on PyPI โ€” proceeding." + ;; + *) + echo "::error::Unexpected status $STATUS from PyPI JSON API. Aborting to avoid a silent skip." + exit 1 + ;; + esac + + - name: Verify release branch name matches pyproject version + if: github.event_name == 'pull_request' + env: + VERSION: ${{ steps.parse.outputs.version }} + BRANCH: ${{ github.event.pull_request.head.ref }} + run: | + EXPECTED="release/openbb-econometrics-v${VERSION}" + if [ "$BRANCH" != "$EXPECTED" ]; then + echo "::error::Release branch '${BRANCH}' does not match pyproject version '${VERSION}'." + echo "Expected branch name: '${EXPECTED}'." + echo "Either rename the release branch or update [project].version in pyproject.toml so they agree." + exit 1 + fi + echo "Branch '${BRANCH}' matches pyproject version '${VERSION}'." + + build: + name: Build sdist + wheel + needs: version-check + runs-on: ubuntu-latest + defaults: + run: + shell: bash + working-directory: openbb_platform/extensions/econometrics + steps: + - uses: actions/checkout@v6 + with: + ref: develop + + - uses: actions/setup-python@v6 + with: + python-version: "3.11" + + - run: python -m pip install --upgrade pip uv + + - name: Build sdist + wheel + run: uv build --no-sources + + - uses: actions/upload-artifact@v4 + with: + name: openbb-econometrics-dist + path: openbb_platform/extensions/econometrics/dist/ + if-no-files-found: error + retention-days: 7 + + test: + name: Test (${{ matrix.os }} / Python ${{ matrix.python_version }}) + needs: build + strategy: + fail-fast: false + matrix: + include: + - { os: ubuntu-latest, python_version: "3.10" } + - { os: ubuntu-latest, python_version: "3.11" } + - { os: ubuntu-latest, python_version: "3.12" } + - { os: ubuntu-latest, python_version: "3.13" } + - { os: ubuntu-latest, python_version: "3.14" } + - { os: macos-latest, python_version: "3.10" } + - { os: macos-latest, python_version: "3.14" } + - { os: windows-latest, python_version: "3.10" } + - { os: windows-latest, python_version: "3.14" } + runs-on: ${{ matrix.os }} + defaults: + run: + shell: bash + working-directory: openbb_platform/extensions/econometrics + steps: + - uses: actions/checkout@v6 + with: + ref: develop + + - uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python_version }} + allow-prereleases: true + + - run: python -m pip install --upgrade pip uv + + - name: Install openbb-econometrics from source (+ dev group) + run: | + uv pip install --system -e . + uv pip install --system --group dev + + - name: Run unit tests + run: pytest tests --cov=openbb_econometrics --cov-report=term-missing + + publish: + name: Publish to PyPI + needs: test + runs-on: ubuntu-latest + permissions: + id-token: write # required for PyPI OIDC trusted publishing + steps: + - uses: actions/download-artifact@v4 + with: + name: openbb-econometrics-dist + path: dist + + - uses: pypa/gh-action-pypi-publish@release/v1 + with: + packages-dir: dist diff --git a/.github/workflows/test-unit-openbb-econometrics.yml b/.github/workflows/test-unit-openbb-econometrics.yml new file mode 100644 index 00000000000..002be9ace8a --- /dev/null +++ b/.github/workflows/test-unit-openbb-econometrics.yml @@ -0,0 +1,122 @@ +name: ๐Ÿงช test openbb-econometrics + +# PR-test for the Econometrics extension. Triggers when a PR touches any file +# under ``openbb_platform/extensions/econometrics/`` or this workflow itself. +# +# Build-once, test-many: a single ``build`` job produces the wheel + sdist; +# the ``lint`` and ``test`` jobs install the extension editable from source +# (``[tool.uv.sources]`` pins openbb-core to the sibling tree, since +# openbb-core>=2.0.0 is not yet on PyPI) and run across every supported +# (OS, Python) combination. The command suite is pure compute โ€” no network. + +on: + pull_request: + branches: [develop, main, v5, feature/v5-platform-api] + paths: + - "openbb_platform/extensions/econometrics/**" + - ".github/workflows/test-unit-openbb-econometrics.yml" + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: test-openbb-econometrics-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + build: + name: Build sdist + wheel + runs-on: ubuntu-latest + defaults: + run: + shell: bash + working-directory: openbb_platform/extensions/econometrics + steps: + - uses: actions/checkout@v6 + + - uses: actions/setup-python@v6 + with: + python-version: "3.11" + + - run: python -m pip install --upgrade pip uv + + - name: Build sdist + wheel + run: uv build --no-sources + + - uses: actions/upload-artifact@v4 + with: + name: openbb-econometrics-dist + path: openbb_platform/extensions/econometrics/dist/ + if-no-files-found: error + retention-days: 7 + + lint: + name: Lint (ruff + ty) + needs: build + runs-on: ubuntu-latest + defaults: + run: + shell: bash + working-directory: openbb_platform/extensions/econometrics + steps: + - uses: actions/checkout@v6 + + - uses: actions/setup-python@v6 + with: + python-version: "3.11" + + - run: python -m pip install --upgrade pip uv + + - name: Install openbb-econometrics in editable mode (+ dev group) + run: | + uv pip install --system -e . + uv pip install --system --group dev + + - name: ruff check + run: ruff check openbb_econometrics tests integration + + - name: ruff format --check + run: ruff format --check openbb_econometrics tests integration + + - name: ty check + run: ty check openbb_econometrics + + test: + name: Test (${{ matrix.os }} / Python ${{ matrix.python_version }}) + needs: build + strategy: + fail-fast: false + matrix: + include: + - { os: ubuntu-latest, python_version: "3.10" } + - { os: ubuntu-latest, python_version: "3.11" } + - { os: ubuntu-latest, python_version: "3.12" } + - { os: ubuntu-latest, python_version: "3.13" } + - { os: ubuntu-latest, python_version: "3.14" } + - { os: macos-latest, python_version: "3.10" } + - { os: macos-latest, python_version: "3.14" } + - { os: windows-latest, python_version: "3.10" } + - { os: windows-latest, python_version: "3.14" } + runs-on: ${{ matrix.os }} + defaults: + run: + shell: bash + working-directory: openbb_platform/extensions/econometrics + steps: + - uses: actions/checkout@v6 + + - uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python_version }} + allow-prereleases: true + + - run: python -m pip install --upgrade pip uv + + - name: Install openbb-econometrics from source (+ dev group) + run: | + uv pip install --system -e . + uv pip install --system --group dev + + - name: Run unit tests + run: pytest tests --cov=openbb_econometrics --cov-report=term-missing --cov-fail-under=100 diff --git a/openbb_platform/extensions/econometrics/README.md b/openbb_platform/extensions/econometrics/README.md index 85e451a12e9..37181e7a6cc 100644 --- a/openbb_platform/extensions/econometrics/README.md +++ b/openbb_platform/extensions/econometrics/README.md @@ -1,13 +1,90 @@ -# Econometrics extension for OpenBB Platform +# OpenBB Econometrics Extension -This extension provides a set of econometrics tools. +This package adds the `openbb-econometrics` extension to the Open Data Platform by OpenBB. + +It provides a toolkit of econometrics commands โ€” regression, regression diagnostics, +stationarity and cointegration tests, Granger causality, GARCH volatility modelling, and +panel-data estimators โ€” that operate on any tabular dataset passed in as `data`. ## Installation -To install the extension, run the following command in this folder: +Install from PyPI with: -```bash +```sh pip install openbb-econometrics ``` -Documentation available [here](https://docs.openbb.co/platform/developer_guide/contributing). +Then build the Python static assets by running: + +```sh +openbb-build +``` + +## Quick Start + +Every command is a `POST` endpoint that takes a `data` payload (a list of records, e.g. +the `.results` of another OpenBB command) plus typed parameters, and returns an `OBBject`. + +```python +from openbb import obb + +prices = obb.equity.price.historical( + symbol="AAPL", start_date="2023-01-01", provider="yfinance" +).results + +# Coefficient table of an OLS regression. +obb.econometrics.ols_regression( + data=prices, y_column="close", x_columns=["open", "high", "low"] +) + +# Augmented Dickey-Fuller stationarity test. +obb.econometrics.unit_root(data=prices, column="close") + +# GARCH(1, 1) conditional volatility. +obb.econometrics.garch(data=prices, column="close") +``` + +To use the extension over HTTP, start the API server with `openbb-api` and POST to +`/api/v1/econometrics/`. + +## Coverage + +All commands are available under `obb.econometrics.*`. + +### Tools + +- `correlation_matrix` โ€” correlation matrix of a dataset +- `summary_statistics` โ€” per-column descriptive statistics + +### Regression + +- `ols_regression` โ€” Ordinary Least Squares coefficient table +- `ols_regression_summary` โ€” OLS goodness-of-fit summary + +### Diagnostics + +- `autocorrelation` โ€” Durbin-Watson autocorrelation test +- `residual_autocorrelation` โ€” Breusch-Godfrey residual autocorrelation test +- `heteroskedasticity` โ€” Breusch-Pagan and White heteroskedasticity tests +- `normality` โ€” Jarque-Bera normality test on residuals +- `variance_inflation_factor` โ€” multicollinearity (VIF) test + +### Time Series + +- `unit_root` โ€” Augmented Dickey-Fuller unit root test +- `kpss` โ€” KPSS stationarity test +- `cointegration` โ€” Engle-Granger pairwise cointegration test +- `cointegration_johansen` โ€” Johansen multivariate cointegration test +- `causality` โ€” Granger causality test +- `garch` โ€” GARCH(p, q) conditional volatility model + +### Panel + +- `panel_random_effects` โ€” one-way Random Effects model +- `panel_between` โ€” Between estimator +- `panel_pooled` โ€” Pooled OLS estimator +- `panel_fixed` โ€” Fixed Effects estimator +- `panel_first_difference` โ€” First-Difference estimator +- `panel_fmac` โ€” Fama-MacBeth estimator + +See the full docs [here](https://docs.openbb.co/odp/python/extensions/data-processing/econometrics) diff --git a/openbb_platform/extensions/econometrics/integration/conftest.py b/openbb_platform/extensions/econometrics/integration/conftest.py new file mode 100644 index 00000000000..981f6dcdaf2 --- /dev/null +++ b/openbb_platform/extensions/econometrics/integration/conftest.py @@ -0,0 +1,82 @@ +"""Integration-test fixtures for openbb-econometrics.""" + +import threading +import time + +import numpy as np +import pandas as pd +import pytest +from openbb_core.app.utils import df_to_basemodel + +from openbb_econometrics.utils import mock_multi_index_data + +_SEED = 20240517 +_N_ROWS = 120 + + +@pytest.fixture(scope="session", autouse=True) +def api_server(pytestconfig): + """Run the OpenBB REST API on 0.0.0.0:8000 for the integration session.""" + if pytestconfig.getoption("markexpr") == "not integration": + yield None + return + + import uvicorn + from openbb_core.api.rest_api import app + + config = uvicorn.Config(app, host="0.0.0.0", port=8000, log_level="warning") # noqa: S104 + server = uvicorn.Server(config) + thread = threading.Thread(target=server.run, daemon=True) + thread.start() + + deadline = time.time() + 60 + while time.time() < deadline: + if server.started: + break + time.sleep(0.25) + else: # pragma: no cover + raise RuntimeError("The OpenBB REST API did not start within 60s.") + + yield server + + server.should_exit = True + thread.join(timeout=15) + + +def _timeseries_records() -> list[dict]: + """Build a deterministic OHLCV time series as JSON-ready records.""" + rng = np.random.default_rng(_SEED) + t = np.arange(_N_ROWS) + base = 100 + 0.5 * t + rng.normal(0, 2, _N_ROWS).cumsum() * 0.3 + close = base + rng.normal(0, 1, _N_ROWS) + open_ = close + rng.normal(0, 0.5, _N_ROWS) + high = np.maximum(open_, close) + np.abs(rng.normal(1, 0.5, _N_ROWS)) + low = np.minimum(open_, close) - np.abs(rng.normal(1, 0.5, _N_ROWS)) + volume = 1_000_000 + rng.normal(0, 100_000, _N_ROWS) + 5_000 * t + dates = pd.date_range("2021-01-04", periods=_N_ROWS, freq="B") + return [ + { + "date": day.strftime("%Y-%m-%d"), + "open": round(float(o), 4), + "high": round(float(h), 4), + "low": round(float(lo), 4), + "close": round(float(c), 4), + "volume": round(float(v), 2), + } + for day, o, h, lo, c, v in zip(dates, open_, high, low, close, volume) + ] + + +@pytest.fixture(scope="session") +def timeseries_data() -> list[dict]: + """Deterministic OHLCV records for the time-series command tests.""" + return _timeseries_records() + + +@pytest.fixture(scope="session") +def panel_data() -> list[dict]: + """Deterministic entity/time panel records as JSON-safe dicts.""" + np.random.seed(_SEED) + return [ + row.model_dump(mode="json") for row in df_to_basemodel(mock_multi_index_data()) + ] diff --git a/openbb_platform/extensions/econometrics/integration/test_econometrics_api.py b/openbb_platform/extensions/econometrics/integration/test_econometrics_api.py index 1faef43cbf1..78fe0131b9f 100644 --- a/openbb_platform/extensions/econometrics/integration/test_econometrics_api.py +++ b/openbb_platform/extensions/econometrics/integration/test_econometrics_api.py @@ -1,335 +1,238 @@ -"""Test econometrics extension.""" +"""Integration tests for the openbb-econometrics HTTP API interface.""" import base64 -import json -import random -from typing import Literal import pytest import requests from openbb_core.env import Env -from openbb_core.provider.utils.helpers import get_querystring -data: dict = {} +_BASE_URL = "http://127.0.0.1:8000/api/v1/econometrics" +_headers: dict = {} def get_headers(): - """Get the headers for the API request.""" - if "headers" in data: - return data["headers"] + """Build the Basic-auth headers for the local API server.""" + if _headers: + return _headers userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" - userpass_bytes = userpass.encode("ascii") - base64_bytes = base64.b64encode(userpass_bytes) - - data["headers"] = {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} - return data["headers"] + base64_bytes = base64.b64encode(userpass.encode("ascii")) + _headers["Authorization"] = f"Basic {base64_bytes.decode('ascii')}" + return _headers -def request_data(menu: str, symbol: str, provider: str): - """Randomly pick a symbol and a provider and get data from the selected menu.""" - url = f"http://0.0.0.0:8000/api/v1/{menu}/price/historical?symbol={symbol}&provider={provider}" - result = requests.get(url, headers=get_headers(), timeout=10) - return result.json()["results"] - - -def get_equity_data(): - """Get equity data.""" - if "equity_data" in data: - return data["equity_data"] - - symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311 - provider = random.choice(["fmp", "yfinance"]) # noqa: S311 - - data["equity_data"] = request_data("equity", symbol=symbol, provider=provider) - return data["equity_data"] - - -def get_crypto_data(): - """Get crypto data.""" - if "crypto_data" in data: - return data["crypto_data"] - - # TODO : add more crypto providers and symbols - symbol = random.choice(["BTCUSD"]) # noqa: S311 - provider = random.choice(["fmp"]) # noqa: S311 - - data["crypto_data"] = request_data( - menu="crypto", - symbol=symbol, - provider=provider, +def _post(endpoint: str, payload: dict) -> requests.Response: + """POST a QueryParams payload to an econometrics endpoint.""" + return requests.post( + f"{_BASE_URL}/{endpoint}", headers=get_headers(), timeout=30, json=payload ) - return data["crypto_data"] -def get_data(menu: Literal["equity", "crypto"]): - """Get data based on the selected menu.""" - funcs = {"equity": get_equity_data, "crypto": get_crypto_data} - return funcs[menu]() - - -@pytest.mark.parametrize( - "params, data_type", - [ - ({"data": "", "method": "pearson"}, "equity"), - ({"data": "", "method": "pearson"}, "crypto"), - ], -) @pytest.mark.integration -def test_econometrics_correlation_matrix(params, data_type): - """Test the correlation matrix endpoint.""" - params = {p: v for p, v in params.items() if v} - - body = json.dumps(get_data(data_type)) - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/econometrics/correlation_matrix?{query_str}" - result = requests.post(url, headers=get_headers(), timeout=10, data=body) - assert isinstance(result, requests.Response) +def test_econometrics_correlation_matrix(timeseries_data): + result = _post("correlation_matrix", {"data": timeseries_data, "method": "pearson"}) assert result.status_code == 200 + assert result.json()["results"] -@pytest.mark.parametrize( - "params, data_type", - [ - ( - {"data": "", "y_column": "close", "x_columns": ["high"]}, - "equity", - ), - ( - {"data": "", "y_column": "close", "x_columns": ["high"]}, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_ols_regression_summary(params, data_type): - """Test the OLS regression summary endpoint.""" - params = {p: v for p, v in params.items() if v} +def test_econometrics_summary_statistics(timeseries_data): + result = _post("summary_statistics", {"data": timeseries_data}) + assert result.status_code == 200 + assert result.json()["results"] - body = json.dumps( + +@pytest.mark.integration +def test_econometrics_ols_regression(timeseries_data): + result = _post( + "ols_regression", + {"data": timeseries_data, "y_column": "close", "x_columns": ["high", "low"]}, + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_ols_regression_summary(timeseries_data): + result = _post( + "ols_regression_summary", + {"data": timeseries_data, "y_column": "close", "x_columns": ["high", "low"]}, + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_autocorrelation(timeseries_data): + result = _post( + "autocorrelation", + {"data": timeseries_data, "y_column": "close", "x_columns": ["open"]}, + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_residual_autocorrelation(timeseries_data): + result = _post( + "residual_autocorrelation", { - "data": get_data(data_type), - "x_columns": params.pop("x_columns"), - } + "data": timeseries_data, + "y_column": "close", + "x_columns": ["open"], + "lags": 4, + }, ) - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/econometrics/ols_regression_summary?{query_str}" - result = requests.post(url, headers=get_headers(), timeout=20, data=body) - assert isinstance(result, requests.Response) assert result.status_code == 200 + assert result.json()["results"] -@pytest.mark.parametrize( - "params, data_type", - [ - ( - {"data": "", "y_column": "volume", "x_columns": ["close"]}, - "equity", - ), - ( - {"data": "", "y_column": "volume", "x_columns": ["close"]}, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_autocorrelation(params, data_type): - """Test the autocorrelation endpoint.""" - params = {p: v for p, v in params.items() if v} +def test_econometrics_heteroskedasticity(timeseries_data): + result = _post( + "heteroskedasticity", + {"data": timeseries_data, "y_column": "close", "x_columns": ["open"]}, + ) + assert result.status_code == 200 + assert result.json()["results"] - body = json.dumps( + +@pytest.mark.integration +def test_econometrics_normality(timeseries_data): + result = _post( + "normality", + {"data": timeseries_data, "y_column": "close", "x_columns": ["open"]}, + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_variance_inflation_factor(timeseries_data): + result = _post( + "variance_inflation_factor", + {"data": timeseries_data, "columns": ["open", "high", "low"]}, + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_unit_root(timeseries_data): + result = _post( + "unit_root", {"data": timeseries_data, "column": "close", "regression": "c"} + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_kpss(timeseries_data): + result = _post( + "kpss", {"data": timeseries_data, "column": "close", "regression": "c"} + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_cointegration(timeseries_data): + result = _post( + "cointegration", {"data": timeseries_data, "columns": ["open", "close"]} + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_cointegration_johansen(timeseries_data): + result = _post( + "cointegration_johansen", + {"data": timeseries_data, "columns": ["open", "high", "close"]}, + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_causality(timeseries_data): + result = _post( + "causality", { - "data": get_data(data_type), - "x_columns": params.pop("x_columns"), - } + "data": timeseries_data, + "y_column": "close", + "x_column": "volume", + "lag": 3, + }, ) - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/econometrics/autocorrelation?{query_str}" - result = requests.post(url, headers=get_headers(), timeout=10, data=body) - assert isinstance(result, requests.Response) assert result.status_code == 200 + assert result.json()["results"] -@pytest.mark.parametrize( - "params, data_type", - [ - ( - { - "data": "", - "y_column": "volume", - "x_columns": ["close"], - "lags": "", - }, - "equity", - ), - ( - { - "data": "", - "y_column": "volume", - "x_columns": ["close"], - "lags": "2", - }, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_residual_autocorrelation(params, data_type): - """Test the residual autocorrelation endpoint.""" - params = {p: v for p, v in params.items() if v} - - body = json.dumps( - { - "data": get_data(data_type), - "x_columns": params.pop("x_columns"), - } +def test_econometrics_garch(timeseries_data): + result = _post( + "garch", {"data": timeseries_data, "column": "close", "p": 1, "q": 1} ) + assert result.status_code == 200 + assert result.json()["results"] - query_str = get_querystring(params, []) - url = ( - f"http://0.0.0.0:8000/api/v1/econometrics/residual_autocorrelation?{query_str}" + +@pytest.mark.integration +def test_econometrics_panel_random_effects(panel_data): + result = _post( + "panel_random_effects", + {"data": panel_data, "y_column": "income", "x_columns": ["age", "education"]}, ) - result = requests.post(url, headers=get_headers(), timeout=10, data=body) - assert isinstance(result, requests.Response) assert result.status_code == 200 + assert result.json()["results"] -@pytest.mark.parametrize( - "params, data_type", - [ - ( - { - "data": "", - "columns": ["close", "volume"], - }, - "equity", - ), - ( - { - "data": "", - "columns": ["close", "volume"], - }, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_cointegration(params, data_type): - """Test the cointegration endpoint.""" - params = {p: v for p, v in params.items() if v} - - body = json.dumps( - { - "data": get_data(data_type), - "columns": params.pop("columns"), - } +def test_econometrics_panel_between(panel_data): + result = _post( + "panel_between", + {"data": panel_data, "y_column": "income", "x_columns": ["age", "education"]}, ) - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/econometrics/cointegration?{query_str}" - result = requests.post(url, headers=get_headers(), timeout=10, data=body) - assert isinstance(result, requests.Response) assert result.status_code == 200 + assert result.json()["results"] -@pytest.mark.parametrize( - "params, data_type", - [ - ( - {"data": "", "y_column": "volume", "x_column": "close", "lag": ""}, - "equity", - ), - ( - {"data": "", "y_column": "volume", "x_column": "close", "lag": "2"}, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_causality(params, data_type): - """Test the causality endpoint.""" - params = {p: v for p, v in params.items() if v} - body = json.dumps(get_data(data_type)) - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/econometrics/causality?{query_str}" - result = requests.post(url, headers=get_headers(), timeout=10, data=body) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize( - "params, data_type", - [({"data": "", "column": "high", "regression": "c"}, "equity")], -) -@pytest.mark.integration -def test_econometrics_unit_root(params, data_type): - """Test the unit root endpoint.""" - params = {p: v for p, v in params.items() if v} - body = json.dumps(get_data(data_type)) - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/econometrics/unit_root?{query_str}" - result = requests.post(url, headers=get_headers(), timeout=10, data=body) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize( - "params, data_type", - [ - ( - {"data": "", "y_column": "close", "x_columns": ["high"]}, - "equity", - ), - ( - {"data": "", "y_column": "close", "x_columns": ["high"]}, - "crypto", - ), - ], -) -@pytest.mark.integration -def test_econometrics_ols_regression(params, data_type): - """Test the OLS regression function in econometrics extension.""" - params = {p: v for p, v in params.items() if v} - - body = json.dumps( - { - "data": get_data(data_type), - "x_columns": params.pop("x_columns"), - } +def test_econometrics_panel_pooled(panel_data): + result = _post( + "panel_pooled", + {"data": panel_data, "y_column": "income", "x_columns": ["age", "education"]}, ) - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/econometrics/ols_regression?{query_str}" - result = requests.post(url, headers=get_headers(), timeout=10, data=body) - assert isinstance(result, requests.Response) assert result.status_code == 200 + assert result.json()["results"] -@pytest.mark.parametrize( - "params, data_type", - [ - ({"data": "", "columns": ["high", "low"]}, "equity"), - ({"data": "", "columns": ["high", "low"]}, "crypto"), - ], -) @pytest.mark.integration -def test_econometrics_variance_inflation_factor(params, data_type): - """Test the variance inflation factor endpoint.""" - params = {p: v for p, v in params.items() if v} - - body = json.dumps( - { - "data": get_data(data_type), - "columns": params.pop("columns"), - } +def test_econometrics_panel_fixed(panel_data): + result = _post( + "panel_fixed", + {"data": panel_data, "y_column": "income", "x_columns": ["age", "education"]}, ) - - url = "http://0.0.0.0:8000/api/v1/econometrics/variance_inflation_factor" - result = requests.post(url, headers=get_headers(), timeout=10, data=body) - assert isinstance(result, requests.Response) assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_panel_first_difference(panel_data): + result = _post( + "panel_first_difference", + {"data": panel_data, "y_column": "income", "x_columns": ["age"]}, + ) + assert result.status_code == 200 + assert result.json()["results"] + + +@pytest.mark.integration +def test_econometrics_panel_fmac(panel_data): + result = _post( + "panel_fmac", + {"data": panel_data, "y_column": "income", "x_columns": ["age", "education"]}, + ) + assert result.status_code == 200 + assert result.json()["results"] diff --git a/openbb_platform/extensions/econometrics/integration/test_econometrics_python.py b/openbb_platform/extensions/econometrics/integration/test_econometrics_python.py index 56ed9cee342..f48cdf4af4e 100644 --- a/openbb_platform/extensions/econometrics/integration/test_econometrics_python.py +++ b/openbb_platform/extensions/econometrics/integration/test_econometrics_python.py @@ -1,423 +1,195 @@ -"""Test econometrics extension.""" - -import random -from typing import Literal +"""Integration tests for the openbb-econometrics Python interface.""" import pytest from openbb_core.app.model.obbject import OBBject -from openbb_econometrics.utils import mock_multi_index_data -# pylint: disable=inconsistent-return-statements @pytest.fixture(scope="session") def obb(pytestconfig): - """Fixture to setup obb.""" + """Return the obb application object for the integration session.""" if pytestconfig.getoption("markexpr") != "not integration": - import openbb # pylint: disable=import-outside-toplevel + import openbb return openbb.obb + return None -# pylint: disable=redefined-outer-name - -data: dict = {} - - -def get_stocks_data(): - """Get stocks data.""" - import openbb # pylint:disable=import-outside-toplevel - - if "stocks_data" in data: - return data["stocks_data"] - - symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311 - provider = random.choice(["fmp", "yfinance"]) # noqa: S311 - - data["stocks_data"] = openbb.obb.equity.price.historical( - symbol=symbol, provider=provider - ).results - return data["stocks_data"] - - -def get_crypto_data(): - """Get crypto data.""" - import openbb # pylint:disable=import-outside-toplevel - - if "crypto_data" in data: - return data["crypto_data"] - - # TODO : add more crypto providers and symbols - symbol = random.choice(["BTCUSD"]) # noqa: S311 - provider = random.choice(["fmp"]) # noqa: S311 - - data["crypto_data"] = openbb.obb.crypto.price.historical( - symbol=symbol, provider=provider - ).results - return data["crypto_data"] - - -def get_data(menu: Literal["equity", "crypto"]): - """Get data.""" - funcs = {"equity": get_stocks_data, "crypto": get_crypto_data} - return funcs[menu]() - - -@pytest.mark.parametrize( - "params, data_type", - [ - ({"data": "", "method": "pearson"}, "equity"), - ({"data": "", "method": "pearson"}, "crypto"), - ], -) @pytest.mark.integration -def test_econometrics_correlation_matrix(params, data_type, obb): - """Test the econometrics correlation matrix.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = get_data(data_type) - - result = obb.econometrics.correlation_matrix(**params) - assert result +def test_econometrics_correlation_matrix(obb, timeseries_data): + result = obb.econometrics.correlation_matrix(data=timeseries_data, method="pearson") assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params, data_type", - [ - ( - {"data": "", "y_column": "close", "x_columns": ["high"]}, - "equity", - ), - ( - {"data": "", "y_column": "close", "x_columns": ["high"]}, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_ols_regression(params, data_type, obb): - """Test the econometrics OLS regression.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = get_data(data_type) - - result = obb.econometrics.ols_regression(**params) - assert result +def test_econometrics_summary_statistics(obb, timeseries_data): + result = obb.econometrics.summary_statistics(data=timeseries_data) assert isinstance(result, OBBject) - assert result.results is not None + assert result.results -@pytest.mark.parametrize( - "params, data_type", - [ - ( - {"data": "", "y_column": "close", "x_columns": ["high"]}, - "equity", - ), - ( - {"data": "", "y_column": "close", "x_columns": ["high"]}, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_ols_regression_summary(params, data_type, obb): - """Test the econometrics OLS regression summary.""" - params = {p: v for p, v in params.items() if v} - params["data"] = get_data(data_type) - - result = obb.econometrics.ols_regression_summary(**params) - assert result +def test_econometrics_ols_regression(obb, timeseries_data): + result = obb.econometrics.ols_regression( + data=timeseries_data, y_column="close", x_columns=["high", "low"] + ) assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params, data_type", - [ - ( - {"data": "", "y_column": "volume", "x_columns": ["close"]}, - "equity", - ), - ( - {"data": "", "y_column": "volume", "x_columns": ["close"]}, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_autocorrelation(params, data_type, obb): - """Test the econometrics autocorrelation.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = get_data(data_type) - - result = obb.econometrics.autocorrelation(**params) - assert result +def test_econometrics_ols_regression_summary(obb, timeseries_data): + result = obb.econometrics.ols_regression_summary( + data=timeseries_data, y_column="close", x_columns=["high", "low"] + ) assert isinstance(result, OBBject) + assert result.results -@pytest.mark.parametrize( - "params, data_type", - [ - ( - { - "data": "", - "y_column": "volume", - "x_columns": ["close"], - "lags": "", - }, - "equity", - ), - ( - { - "data": "", - "y_column": "volume", - "x_columns": ["close"], - "lags": "2", - }, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_residual_autocorrelation(params, data_type, obb): - """Test the econometrics residual autocorrelation.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = get_data(data_type) - - result = obb.econometrics.residual_autocorrelation(**params) - assert result +def test_econometrics_autocorrelation(obb, timeseries_data): + result = obb.econometrics.autocorrelation( + data=timeseries_data, y_column="close", x_columns=["open"] + ) assert isinstance(result, OBBject) + assert result.results -@pytest.mark.parametrize( - "params, data_type", - [ - ( - { - "data": "", - "columns": ["close", "volume"], - }, - "equity", - ), - ( - { - "data": "", - "columns": ["close", "volume"], - }, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_cointegration(params, data_type, obb): - """Test the econometrics cointegration.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = get_data(data_type) - - result = obb.econometrics.cointegration(**params) - assert result +def test_econometrics_residual_autocorrelation(obb, timeseries_data): + result = obb.econometrics.residual_autocorrelation( + data=timeseries_data, y_column="close", x_columns=["open"], lags=4 + ) assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params, data_type", - [ - ( - { - "data": "", - "y_column": "volume", - "x_column": "close", - "lag": "", - }, - "equity", - ), - ( - { - "data": "", - "y_column": "volume", - "x_column": "close", - "lag": "2", - }, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_causality(params, data_type, obb): - """Test the econometrics causality.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = get_data(data_type) - - result = obb.econometrics.causality(**params) - assert result +def test_econometrics_heteroskedasticity(obb, timeseries_data): + result = obb.econometrics.heteroskedasticity( + data=timeseries_data, y_column="close", x_columns=["open"] + ) assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params, data_type", - [ - ({"data": "", "column": "close", "regression": "c"}, "equity"), - ( - {"data": "", "column": "volume", "regression": "ctt"}, - "crypto", - ), - ], -) @pytest.mark.integration -def test_econometrics_unit_root(params, data_type, obb): - """Test the econometrics unit root.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = get_data(data_type) - - result = obb.econometrics.unit_root(**params) - assert result +def test_econometrics_normality(obb, timeseries_data): + result = obb.econometrics.normality( + data=timeseries_data, y_column="close", x_columns=["open"] + ) assert isinstance(result, OBBject) + assert result.results -@pytest.mark.parametrize( - "params", - [ - {"data": "", "y_column": "income", "x_columns": ["age"]}, - {"data": "", "y_column": "income", "x_columns": ["age"]}, - ], -) @pytest.mark.integration -def test_econometrics_panel_random_effects(params, obb): - """Test the econometrics panel random effects.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = mock_multi_index_data() - - result = obb.econometrics.panel_random_effects(**params) - assert result +def test_econometrics_variance_inflation_factor(obb, timeseries_data): + result = obb.econometrics.variance_inflation_factor( + data=timeseries_data, columns=["open", "high", "low"] + ) assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params", - [ - {"data": "", "y_column": "income", "x_columns": ["age"]}, - {"data": "", "y_column": "income", "x_columns": ["age"]}, - ], -) @pytest.mark.integration -def test_econometrics_panel_between(params, obb): - """Test the econometrics panel between.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = mock_multi_index_data() - - result = obb.econometrics.panel_between(**params) - assert result +def test_econometrics_unit_root(obb, timeseries_data): + result = obb.econometrics.unit_root( + data=timeseries_data, column="close", regression="c" + ) assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params", - [ - {"data": "", "y_column": "income", "x_columns": ["age"]}, - {"data": "", "y_column": "income", "x_columns": ["age"]}, - ], -) @pytest.mark.integration -def test_econometrics_panel_pooled(params, obb): - """Test the econometrics panel pooled.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = mock_multi_index_data() - - result = obb.econometrics.panel_pooled(**params) - assert result +def test_econometrics_kpss(obb, timeseries_data): + result = obb.econometrics.kpss(data=timeseries_data, column="close", regression="c") assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params", - [ - {"data": "", "y_column": "income", "x_columns": ["age"]}, - {"data": "", "y_column": "income", "x_columns": ["age"]}, - ], -) @pytest.mark.integration -def test_econometrics_panel_fixed(params, obb): - """Test the econometrics panel fixed.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = mock_multi_index_data() - - result = obb.econometrics.panel_fixed(**params) - assert result +def test_econometrics_cointegration(obb, timeseries_data): + result = obb.econometrics.cointegration( + data=timeseries_data, columns=["open", "close"] + ) assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params", - [ - {"data": "", "y_column": "income", "x_columns": ["age"]}, - {"data": "", "y_column": "income", "x_columns": ["age"]}, - ], -) @pytest.mark.integration -def test_econometrics_panel_first_difference(params, obb): - """Test the econometrics panel first difference.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = mock_multi_index_data() - - result = obb.econometrics.panel_first_difference(**params) - assert result +def test_econometrics_cointegration_johansen(obb, timeseries_data): + result = obb.econometrics.cointegration_johansen( + data=timeseries_data, columns=["open", "high", "close"] + ) assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params", - [ - {"data": "", "y_column": "income", "x_columns": ["age"]}, - {"data": "", "y_column": "income", "x_columns": ["age"]}, - ], -) @pytest.mark.integration -def test_econometrics_panel_fmac(params, obb): - """Test the econometrics panel fmac.""" - params = {p: v for p, v in params.items() if v} - - params["data"] = mock_multi_index_data() - - result = obb.econometrics.panel_fmac(**params) - """Test the econometrics panel fmac.""" - assert result +def test_econometrics_causality(obb, timeseries_data): + result = obb.econometrics.causality( + data=timeseries_data, y_column="close", x_column="volume", lag=3 + ) assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results -@pytest.mark.parametrize( - "params", - [ - ({"data": "", "columns": ["income", "age"]}), - ({"data": "", "columns": ["education"]}), - ], -) @pytest.mark.integration -def test_econometrics_variance_inflation_factor(params, obb): - params = {p: v for p, v in params.items() if v} - - params["data"] = mock_multi_index_data() - - result = obb.econometrics.variance_inflation_factor(**params) - assert result +def test_econometrics_garch(obb, timeseries_data): + result = obb.econometrics.garch(data=timeseries_data, column="close", p=1, q=1) assert isinstance(result, OBBject) - assert len(result.results) > 0 + assert result.results + + +@pytest.mark.integration +def test_econometrics_panel_random_effects(obb, panel_data): + result = obb.econometrics.panel_random_effects( + data=panel_data, y_column="income", x_columns=["age", "education"] + ) + assert isinstance(result, OBBject) + assert result.results + + +@pytest.mark.integration +def test_econometrics_panel_between(obb, panel_data): + result = obb.econometrics.panel_between( + data=panel_data, y_column="income", x_columns=["age", "education"] + ) + assert isinstance(result, OBBject) + assert result.results + + +@pytest.mark.integration +def test_econometrics_panel_pooled(obb, panel_data): + result = obb.econometrics.panel_pooled( + data=panel_data, y_column="income", x_columns=["age", "education"] + ) + assert isinstance(result, OBBject) + assert result.results + + +@pytest.mark.integration +def test_econometrics_panel_fixed(obb, panel_data): + result = obb.econometrics.panel_fixed( + data=panel_data, y_column="income", x_columns=["age", "education"] + ) + assert isinstance(result, OBBject) + assert result.results + + +@pytest.mark.integration +def test_econometrics_panel_first_difference(obb, panel_data): + result = obb.econometrics.panel_first_difference( + data=panel_data, y_column="income", x_columns=["age"] + ) + assert isinstance(result, OBBject) + assert result.results + + +@pytest.mark.integration +def test_econometrics_panel_fmac(obb, panel_data): + result = obb.econometrics.panel_fmac( + data=panel_data, y_column="income", x_columns=["age", "education"] + ) + assert isinstance(result, OBBject) + assert result.results diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/diagnostics.py b/openbb_platform/extensions/econometrics/openbb_econometrics/diagnostics.py new file mode 100644 index 00000000000..e7f3b581514 --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/diagnostics.py @@ -0,0 +1,353 @@ +"""Econometrics regression-diagnostic commands.""" + +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import Field, PositiveInt + +router = Router(prefix="", description="Econometrics regression-diagnostic commands.") + + +def _ols_fit(data, y_column, x_columns): + """Fit an OLS model and return the statsmodels results object.""" + import statsmodels.api as sm + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + df = basemodel_to_df(data) + exog = sm.add_constant(get_target_columns(df, x_columns)) + endog = get_target_column(df, y_column) + return sm.OLS(endog, exog).fit() + + +class AutocorrelationQueryParams(QueryParams): + """Query parameters for the Durbin-Watson autocorrelation endpoint.""" + + __category__ = "diagnostic" + __output_columns__ = ("durbin_watson",) + + data: list[Data] = Field(description="Input dataset.") + y_column: str = Field(description="Name of the dependent (target) column.") + x_columns: list[str] = Field( + description="Names of the independent (exogenous) columns." + ) + + +class AutocorrelationData(Data): + """Durbin-Watson autocorrelation test result.""" + + durbin_watson: float = Field( + description="Durbin-Watson statistic (near 2 = no autocorrelation)." + ) + + +class ResidualAutocorrelationQueryParams(QueryParams): + """Query parameters for the Breusch-Godfrey residual autocorrelation endpoint.""" + + __category__ = "diagnostic" + __output_columns__ = ( + "lm_statistic", + "lm_p_value", + "f_statistic", + "f_p_value", + ) + + data: list[Data] = Field(description="Input dataset.") + y_column: str = Field(description="Name of the dependent (target) column.") + x_columns: list[str] = Field( + description="Names of the independent (exogenous) columns." + ) + lags: PositiveInt = Field( + default=1, description="Number of lags to include in the test." + ) + + +class ResidualAutocorrelationData(Data): + """Breusch-Godfrey residual autocorrelation test result.""" + + lm_statistic: float = Field(description="Lagrange Multiplier test statistic.") + lm_p_value: float = Field(description="p-value of the LM statistic.") + f_statistic: float = Field(description="F-statistic of the auxiliary regression.") + f_p_value: float = Field(description="p-value of the F-statistic.") + + +class HeteroskedasticityQueryParams(QueryParams): + """Query parameters for the heteroskedasticity endpoint.""" + + __category__ = "diagnostic" + __output_columns__ = ( + "breusch_pagan_lm_statistic", + "breusch_pagan_lm_p_value", + "breusch_pagan_f_statistic", + "breusch_pagan_f_p_value", + "white_lm_statistic", + "white_lm_p_value", + "white_f_statistic", + "white_f_p_value", + ) + + data: list[Data] = Field(description="Input dataset.") + y_column: str = Field(description="Name of the dependent (target) column.") + x_columns: list[str] = Field( + description="Names of the independent (exogenous) columns." + ) + robust: bool = Field( + default=True, + description="Use the robust (studentized Koenker) Breusch-Pagan variant," + + " which does not assume normally distributed residuals.", + ) + + +class HeteroskedasticityData(Data): + """Breusch-Pagan and White heteroskedasticity test results.""" + + breusch_pagan_lm_statistic: float = Field(description="Breusch-Pagan LM statistic.") + breusch_pagan_lm_p_value: float = Field( + description="p-value of the Breusch-Pagan LM statistic." + ) + breusch_pagan_f_statistic: float = Field(description="Breusch-Pagan F statistic.") + breusch_pagan_f_p_value: float = Field( + description="p-value of the Breusch-Pagan F statistic." + ) + white_lm_statistic: float = Field(description="White LM statistic.") + white_lm_p_value: float = Field(description="p-value of the White LM statistic.") + white_f_statistic: float = Field(description="White F statistic.") + white_f_p_value: float = Field(description="p-value of the White F statistic.") + + +class NormalityQueryParams(QueryParams): + """Query parameters for the Jarque-Bera normality endpoint.""" + + __category__ = "diagnostic" + __output_columns__ = ("jarque_bera", "p_value", "skew", "kurtosis") + + data: list[Data] = Field(description="Input dataset.") + y_column: str = Field(description="Name of the dependent (target) column.") + x_columns: list[str] = Field( + description="Names of the independent (exogenous) columns." + ) + + +class NormalityData(Data): + """Jarque-Bera normality test result for regression residuals.""" + + jarque_bera: float = Field(description="Jarque-Bera test statistic.") + p_value: float = Field(description="p-value of the test statistic.") + skew: float = Field(description="Skewness of the residuals.") + kurtosis: float = Field(description="Kurtosis of the residuals.") + + +class VarianceInflationFactorQueryParams(QueryParams): + """Query parameters for the variance inflation factor endpoint.""" + + __category__ = "diagnostic" + __output_columns__ = ("variable", "vif") + + data: list[Data] = Field(description="Input dataset.") + columns: list[str] | None = Field( + default=None, + description="Columns to test for collinearity. If omitted, all numeric columns.", + ) + + +class VarianceInflationFactorData(Data): + """Variance inflation factor for a single variable.""" + + variable: str = Field(description="Name of the variable.") + vif: float = Field( + description="Variance inflation factor (above 5 indicates high collinearity)." + ) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def autocorrelation( + params: AutocorrelationQueryParams, +) -> OBBject[AutocorrelationData]: + """Perform the Durbin-Watson test for autocorrelation in regression residuals. + + The Durbin-Watson statistic ranges from 0 to 4. A value near 2 suggests no + autocorrelation; values toward 0 indicate positive autocorrelation and values + toward 4 indicate negative autocorrelation. + """ + from statsmodels.stats.stattools import durbin_watson + + results = _ols_fit(params.data, params.y_column, params.x_columns) + return OBBject( + results=AutocorrelationData(durbin_watson=float(durbin_watson(results.resid))) + ) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def residual_autocorrelation( + params: ResidualAutocorrelationQueryParams, +) -> OBBject[ResidualAutocorrelationData]: + """Perform the Breusch-Godfrey LM test for residual autocorrelation. + + The Breusch-Godfrey test detects autocorrelation in regression residuals up to a + chosen number of lags. Significant statistics indicate the model has not captured + some of the data's serial dependence. + """ + from statsmodels.stats.diagnostic import acorr_breusch_godfrey + + results = _ols_fit(params.data, params.y_column, params.x_columns) + lm_stat, lm_p_value, f_stat, f_p_value = acorr_breusch_godfrey( + results, nlags=params.lags + ) + return OBBject( + results=ResidualAutocorrelationData( + lm_statistic=float(lm_stat), + lm_p_value=float(lm_p_value), + f_statistic=float(f_stat), + f_p_value=float(f_p_value), + ) + ) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def heteroskedasticity( + params: HeteroskedasticityQueryParams, +) -> OBBject[HeteroskedasticityData]: + """Perform the Breusch-Pagan and White tests for heteroskedasticity. + + Heteroskedasticity - non-constant variance of the regression residuals - violates a + key OLS assumption and makes standard errors unreliable. The Breusch-Pagan test + detects linear forms; the White test additionally captures non-linear forms. + """ + from statsmodels.stats.diagnostic import het_breuschpagan, het_white + + results = _ols_fit(params.data, params.y_column, params.x_columns) + breusch_pagan = het_breuschpagan( + results.resid, results.model.exog, robust=params.robust + ) + white = het_white(results.resid, results.model.exog) + return OBBject( + results=HeteroskedasticityData( + breusch_pagan_lm_statistic=float(breusch_pagan[0]), + breusch_pagan_lm_p_value=float(breusch_pagan[1]), + breusch_pagan_f_statistic=float(breusch_pagan[2]), + breusch_pagan_f_p_value=float(breusch_pagan[3]), + white_lm_statistic=float(white[0]), + white_lm_p_value=float(white[1]), + white_f_statistic=float(white[2]), + white_f_p_value=float(white[3]), + ) + ) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def normality(params: NormalityQueryParams) -> OBBject[NormalityData]: + """Perform the Jarque-Bera normality test on regression residuals. + + The Jarque-Bera test checks whether OLS residuals are normally distributed by + comparing their skewness and kurtosis to those of a normal distribution. + """ + from statsmodels.stats.stattools import jarque_bera + + results = _ols_fit(params.data, params.y_column, params.x_columns) + jb_stat, jb_p_value, skew, kurtosis = jarque_bera(results.resid) + return OBBject( + results=NormalityData( + jarque_bera=float(jb_stat), + p_value=float(jb_p_value), + skew=float(skew), + kurtosis=float(kurtosis), + ) + ) + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Calculate the variance inflation factor.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + 'obb.econometrics.variance_inflation_factor(data=stock_data, columns=["open", "high", "low", "close"])', # noqa: E501 + ], + ), + APIEx( + parameters={ + "columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def variance_inflation_factor( + params: VarianceInflationFactorQueryParams, +) -> OBBject[list[VarianceInflationFactorData]]: + """Calculate the variance inflation factor (VIF) to detect multicollinearity. + + VIF quantifies how much the variance of an estimated coefficient is inflated by + collinearity with the other regressors. A VIF between 1 and 5 is generally + acceptable; values above 5 indicate high collinearity. + """ + from openbb_core.app.utils import basemodel_to_df + from statsmodels.stats.outliers_influence import ( + variance_inflation_factor as _vif, + ) + from statsmodels.tools.tools import add_constant + + dataset = basemodel_to_df(params.data) + dataset = dataset if params.columns is None else dataset[params.columns] + df = add_constant(dataset).select_dtypes( + exclude=["object", "datetime", "timedelta"] + ) + + out = [ + VarianceInflationFactorData( + variable=str(df.columns[i]), vif=float(_vif(df.values, i)) + ) + for i in range(1, len(df.columns)) + ] + + return OBBject(results=out) diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_router.py b/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_router.py deleted file mode 100644 index 4852c8a9f42..00000000000 --- a/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_router.py +++ /dev/null @@ -1,1049 +0,0 @@ -"""Econometrics Router.""" - -# pylint: disable=too-many-lines - -from itertools import combinations -from typing import Any, Literal - -from openbb_core.app.model.example import APIEx, PythonEx -from openbb_core.app.model.obbject import OBBject -from openbb_core.app.router import Router -from openbb_core.provider.abstract.data import Data -from pydantic import BaseModel, PositiveInt, model_serializer - -router = Router(prefix="", description="Econometrics analysis tools.") - - -class OLSRegressionResults(BaseModel): - """OLS Regression Results that serializes statsmodels objects.""" - - model: Any - results: Any - - class Config: - """Pydantic config.""" - - arbitrary_types_allowed = True - - @model_serializer - def serialize_model(self) -> dict: - """Serialize statsmodels objects to a dictionary.""" - results = self.results - conf_int = results.conf_int() - conf_int_dict = ( - conf_int.to_dict() - if hasattr(conf_int, "to_dict") - else conf_int.to_dict("index") - ) - return { - "params": ( - results.params.to_dict() - if hasattr(results.params, "to_dict") - else dict(results.params) - ), - "rsquared": float(results.rsquared), - "rsquared_adj": float(results.rsquared_adj), - "fvalue": float(results.fvalue) if results.fvalue is not None else None, - "f_pvalue": ( - float(results.f_pvalue) if results.f_pvalue is not None else None - ), - "aic": float(results.aic), - "bic": float(results.bic), - "llf": float(results.llf), - "nobs": int(results.nobs), - "df_model": float(results.df_model), - "df_resid": float(results.df_resid), - "pvalues": ( - results.pvalues.to_dict() - if hasattr(results.pvalues, "to_dict") - else dict(results.pvalues) - ), - "tvalues": ( - results.tvalues.to_dict() - if hasattr(results.tvalues, "to_dict") - else dict(results.tvalues) - ), - "bse": ( - results.bse.to_dict() - if hasattr(results.bse, "to_dict") - else dict(results.bse) - ), - "conf_int": conf_int_dict, - } - - -@router.command( - methods=["POST"], - examples=[ - PythonEx( - description="Get the correlation matrix of a dataset.", - code=[ - "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 - "obb.econometrics.correlation_matrix(data=stock_data)", - ], - ), - APIEx(parameters={"data": APIEx.mock_data("timeseries")}), - ], -) -def correlation_matrix( - data: list[Data], method: Literal["pearson", "kendall", "spearman"] = "pearson" -) -> OBBject[list[Data]]: - """Get the correlation matrix of an input dataset. - - The correlation matrix provides a view of how different variables in your dataset relate to one another. - By quantifying the degree to which variables move in relation to each other, this matrix can help identify patterns, - trends, and potential areas for deeper analysis. The correlation score ranges from -1 to 1, with -1 indicating a - perfect negative correlation, 0 indicating no correlation, and 1 indicating a perfect positive correlation. - - Parameters - ---------- - data : list[Data] - Input dataset. - method : Literal["pearson", "kendall", "spearman"] - Method to use for correlation calculation. Default is "pearson". - pearson : standard correlation coefficient - kendall : Kendall Tau correlation coefficient - spearman : Spearman rank correlation - - Returns - ------- - OBBject[list[Data]] - Correlation matrix. - """ - # pylint: disable=import-outside-toplevel - import numpy as np - from openbb_core.app.utils import basemodel_to_df - - df = basemodel_to_df(data) - # remove non float columns from the dataframe to perform the correlation - - if "symbol" in df.columns and len(df.symbol.unique()) > 1 and "close" in df.columns: - df = df.pivot( - columns="symbol", - values="close", - ) - - corr = df.corr(method=method, numeric_only=True) - - # replace nan values with None to allow for json serialization - corr = corr.replace(np.nan, None) - - ret = [] - for k, v in corr.items(): - v["comp_to"] = k - ret.append(Data(**v)) - return OBBject(results=ret) - - -@router.command( - methods=["POST"], - examples=[ - PythonEx( - description="Perform Ordinary Least Squares (OLS) regression.", - code=[ - "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 - 'obb.econometrics.ols_regression(data=stock_data, y_column="close", x_columns=["open", "high", "low"])', - ], - ), - APIEx( - parameters={ - "y_column": "close", - "x_columns": ["open", "high", "low"], - "data": APIEx.mock_data("timeseries"), - } - ), - ], -) -def ols_regression( - data: list[Data], - y_column: str, - x_columns: list[str], -) -> OBBject[OLSRegressionResults]: - """Perform Ordinary Least Squares (OLS) regression. - - OLS regression is a fundamental statistical method to explore and model the relationship between a - dependent variable and one or more independent variables. By fitting the best possible linear equation to the data, - it helps uncover how changes in the independent variables are associated with changes in the dependent variable. - This returns the model and results objects from statsmodels library. - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - - Returns - ------- - OBBject[OLSRegressionResults] - OBBject with the results being model and results objects. - """ - # pylint: disable=import-outside-toplevel - import statsmodels.api as sm - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - - X = sm.add_constant(get_target_columns(basemodel_to_df(data), x_columns)) - y = get_target_column(basemodel_to_df(data), y_column) - model = sm.OLS(y, X) - results = model.fit() - return OBBject(results=OLSRegressionResults(model=model, results=results)) - - -@router.command( - methods=["POST"], - examples=[ - PythonEx( - description="Perform Ordinary Least Squares (OLS) regression and return the summary.", - code=[ - "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 pylint: disable=line-too-long - 'obb.econometrics.ols_regression_summary(data=stock_data, y_column="close", x_columns=["open", "high", "low"])', # noqa: E501 pylint: disable=line-too-long - ], - ), - APIEx( - parameters={ - "y_column": "close", - "x_columns": ["open", "high", "low"], - "data": APIEx.mock_data("timeseries"), - } - ), - ], -) -def ols_regression_summary( - data: list[Data], - y_column: str, - x_columns: list[str], -) -> OBBject[Data]: - """Perform Ordinary Least Squares (OLS) regression. - - This returns the summary object from statsmodels. - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - - Returns - ------- - OBBject[Data] - OBBject with the results being summary object. - """ - # pylint: disable=import-outside-toplevel - import re # noqa - import statsmodels.api as sm # noqa - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - - X = sm.add_constant(get_target_columns(basemodel_to_df(data), x_columns)) - y = get_target_column(basemodel_to_df(data), y_column) - - try: - X = X.astype(float) - y = y.astype(float) - except ValueError as exc: - raise ValueError("All columns must be numeric") from exc - - results = sm.OLS(y, X).fit() - results_summary = results.summary() - results = {} - - for item in results_summary.tables[0].data: - results[item[0].strip()] = item[1].strip() - results[item[2].strip()] = str(item[3]).strip() - - table_1 = results_summary.tables[1] - headers = table_1.data[0] # Assuming the headers are in the first row - for i, row in enumerate(table_1.data): - if i == 0: # Skipping the header row - continue - for j, cell in enumerate(row): - if j == 0: # Skipping the row index - continue - key = f"{row[0].strip()}_{headers[j].strip()}" # Combining row index and column header - results[key] = cell.strip() - - for item in results_summary.tables[2].data: - results[item[0].strip()] = item[1].strip() - results[item[2].strip()] = str(item[3]).strip() - - results = {k: v for k, v in results.items() if v} - clean_results = {} - for k, v in results.items(): - new_key = re.sub(r"[.,\]\[:-]", "", k).lower().strip().replace(" ", "_") - clean_results[new_key] = v - - clean_results["raw"] = str(results_summary) - - return OBBject(results=clean_results) # type: ignore[arg-type] - - -@router.command( - methods=["POST"], - examples=[ - PythonEx( - description="Perform Durbin-Watson test for autocorrelation.", - code=[ - "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 - 'obb.econometrics.autocorrelation(data=stock_data, y_column="close", x_columns=["open", "high", "low"])', - ], - ), - APIEx( - parameters={ - "y_column": "close", - "x_columns": ["open", "high", "low"], - "data": APIEx.mock_data("timeseries"), - } - ), - ], -) -def autocorrelation( - data: list[Data], - y_column: str, - x_columns: list[str], -) -> OBBject[Data]: - """Perform Durbin-Watson test for autocorrelation. - - The Durbin-Watson test is a widely used method for detecting the presence of autocorrelation in the residuals - from a statistical or econometric model. Autocorrelation occurs when past values in the data series influence - future values, which can be a critical issue in time-series analysis, affecting the reliability of - model predictions. The test provides a statistic that ranges from 0 to 4, where a value around 2 suggests - no autocorrelation, values towards 0 indicate positive autocorrelation, and values towards 4 suggest - negative autocorrelation. Understanding the degree of autocorrelation helps in refining models to better capture - the underlying dynamics of the data, ensuring more accurate and trustworthy results. - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - - Returns - ------- - OBBject[dict] - OBBject with the results being the score from the test. - """ - # pylint: disable=import-outside-toplevel - import statsmodels.api as sm - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - from statsmodels.stats.stattools import durbin_watson - - X = sm.add_constant(get_target_columns(basemodel_to_df(data), x_columns)) - y = get_target_column(basemodel_to_df(data), y_column) - results = sm.OLS(y, X).fit() - return OBBject(results=Data(score=durbin_watson(results.resid))) # type: ignore[call-arg] - - -@router.command( - methods=["POST"], - examples=[ - PythonEx( - description="Perform Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation.", - code=[ - "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 - 'obb.econometrics.residual_autocorrelation(data=stock_data, y_column="close", x_columns=["open", "high", "low"])', # noqa: E501 pylint: disable=line-too-long - ], - ), - APIEx( - parameters={ - "y_column": "close", - "x_columns": ["open", "high", "low"], - "data": APIEx.mock_data("timeseries"), - } - ), - ], -) -def residual_autocorrelation( - data: list[Data], - y_column: str, - x_columns: list[str], - lags: PositiveInt = 1, -) -> OBBject[Data]: - """Perform Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation. - - The Breusch-Godfrey Lagrange Multiplier test is a sophisticated tool for uncovering autocorrelation within the - residuals of a regression model. Autocorrelation in residuals can indicate that a model fails to capture some - aspect of the underlying data structure, possibly leading to biased or inefficient estimates. - By specifying the number of lags, you can control the depth of the test to check for autocorrelation, - allowing for a tailored analysis that matches the specific characteristics of your data. - This test is particularly valuable in econometrics and time-series analysis, where understanding the independence - of errors is crucial for model validity. - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - lags: PositiveInt - Number of lags to use in the test. - - Returns - ------- - OBBject[Data] - from statsmodels.stats.diagnostic import ( - acorr_breusch_godfrey, # type: ignore # pylint: disable=import-outside-toplevel - ) - """ - # pylint: disable=import-outside-toplevel - import statsmodels.api as sm - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - from statsmodels.stats.diagnostic import ( - acorr_breusch_godfrey, - ) - - X = sm.add_constant(get_target_columns(basemodel_to_df(data), x_columns)) - y = get_target_column(basemodel_to_df(data), y_column) - model = sm.OLS(y, X) - results = model.fit() - lm_stat, p_value, f_stat, fp_value = acorr_breusch_godfrey(results, nlags=lags) - - results = { - "lm_stat": lm_stat, - "p_value": p_value, - "f_stat": f_stat, - "fp_value": fp_value, - } - - return OBBject(results=results) # type: ignore[arg-type] - - -@router.command( - methods=["POST"], - examples=[ - PythonEx( - description="Perform co-integration test between two timeseries.", - code=[ - "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 - 'obb.econometrics.cointegration(data=stock_data, columns=["open", "close"])', - ], - ), - ], -) -def cointegration( - data: list[Data], - columns: list[str], -) -> OBBject[Data]: - """Show co-integration between two timeseries using the two step Engle-Granger test. - - The two-step Engle-Granger test is a method designed to detect co-integration between two time series. - Co-integration is a statistical property indicating that two or more time series move together over the long term, - even if they are individually non-stationary. This concept is crucial in economics and finance, where identifying - pairs or groups of assets that share a common stochastic trend can inform long-term investment strategies - and risk management practices. The Engle-Granger test first checks for a stable, long-term relationship by - regressing one time series on the other and then tests the residuals for stationarity. - If the residuals are found to be stationary, it suggests that despite any short-term deviations, - the series are bound by an equilibrium relationship over time. - - Parameters - ---------- - data: list[Data] - Input dataset. - columns: list[str] - Data columns to check cointegration - maxlag: PositiveInt - Number of lags to use in the test. - - Returns - ------- - OBBject[Data] - OBBject with the results being the score from the test. - """ - # pylint: disable=import-outside-toplevel - from openbb_core.app.utils import basemodel_to_df, get_target_columns # noqa - from openbb_econometrics.utils import ( # noqa - get_engle_granger_two_step_cointegration_test, - ) - - pairs = list(combinations(columns, 2)) - dataset = get_target_columns(basemodel_to_df(data), columns) - result = {} - for x, y in pairs: - ( - c, - gamma, - alpha, - _, # z - adfstat, - pvalue, - ) = get_engle_granger_two_step_cointegration_test(dataset[x], dataset[y]) - result[f"{x}/{y}"] = { - "c": c, - "gamma": gamma, - "alpha": alpha, - "adfstat": adfstat, - "pvalue": pvalue, - } - - return OBBject(results=result) # type: ignore[arg-type] - - -@router.command( - methods=["POST"], - examples=[ - PythonEx( - description="Perform Granger causality test to determine if X 'causes' y.", - code=[ - "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 - 'obb.econometrics.causality(data=stock_data, y_column="close", x_column="open")', - ], - ), - APIEx( - description="Example with mock data.", - parameters={ - "y_column": "close", - "x_column": "open", - "lag": 1, - "data": APIEx.mock_data("timeseries"), - }, - ), - ], -) -def causality( - data: list[Data], - y_column: str, - x_column: str, - lag: PositiveInt = 3, -) -> OBBject[Data]: - """Perform Granger causality test to determine if X 'causes' y. - - The Granger causality test is a statistical hypothesis test to determine if one time series is useful in - forecasting another. While 'causality' in this context does not imply a cause-and-effect relationship in - the philosophical sense, it does test whether changes in one variable are systematically followed by changes - in another variable, suggesting a predictive relationship. By specifying a lag, you set the number of periods to - look back in the time series to assess this relationship. This test is particularly useful in economic and - financial data analysis, where understanding the lead-lag relationship between indicators can inform investment - decisions and policy making. - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_column: str - Columns to use as exogenous variables. - lag: PositiveInt - Number of lags to use in the test. - - Returns - ------- - OBBject[Data] - OBBject with the results being the score from the test. - """ - # pylint: disable=import-outside-toplevel - from openbb_core.app.utils import basemodel_to_df, get_target_column - from pandas import DataFrame, concat - from statsmodels.tsa.stattools import grangercausalitytests - - X = get_target_column(basemodel_to_df(data), x_column) - y = get_target_column(basemodel_to_df(data), y_column) - - granger = grangercausalitytests(concat([y, X], axis=1), [lag], verbose=False) - - for test in granger[lag][0]: - # As ssr_chi2test and lrtest have one less value in the tuple, we fill - # this value with a '-' to allow the conversion to a DataFrame - if len(granger[lag][0][test]) != 4: - pars = granger[lag][0][test] - granger[lag][0][test] = (pars[0], pars[1], "-", pars[2]) - - df = DataFrame(granger[lag][0], index=["F-test", "P-value", "Count", "Lags"]).T - results = df.to_dict() - - return OBBject(results=results) - - -@router.command( - methods=["POST"], - examples=[ - PythonEx( - description="Perform Augmented Dickey-Fuller (ADF) unit root test.", - code=[ - "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 - 'obb.econometrics.unit_root(data=stock_data, column="close")', - 'obb.econometrics.unit_root(data=stock_data, column="close", regression="ct")', - ], - ), - APIEx( - parameters={ - "column": "close", - "data": APIEx.mock_data("timeseries"), - } - ), - ], -) -def unit_root( - data: list[Data], - column: str, - regression: Literal["c", "ct", "ctt"] = "c", -) -> OBBject[Data]: - """Perform Augmented Dickey-Fuller (ADF) unit root test. - - The ADF test is a popular method for testing the presence of a unit root in a time series. - A unit root indicates that the series may be non-stationary, meaning its statistical properties such as mean, - variance, and autocorrelation can change over time. The presence of a unit root suggests that the time series might - be influenced by a random walk process, making it unpredictable and challenging for modeling and forecasting. - The 'regression' parameter allows you to specify the model used in the test: 'c' for a constant term, - 'ct' for a constant and trend term, and 'ctt' for a constant, linear, and quadratic trend. - This flexibility helps tailor the test to the specific characteristics of your data, providing a more accurate - assessment of its stationarity. - - Parameters - ---------- - data: list[Data] - Input dataset. - column: str - Data columns to check unit root - regression: Literal["c", "ct", "ctt"] - Regression type to use in the test. Either "c" for constant only, "ct" for constant and trend, or "ctt" for - constant, trend, and trend-squared. - - Returns - ------- - OBBject[Data] - OBBject with the results being the score from the test. - """ - # pylint: disable=import-outside-toplevel - from openbb_core.app.utils import basemodel_to_df, get_target_column - from statsmodels.tsa.stattools import adfuller - - dataset = get_target_column(basemodel_to_df(data), column) - adfstat, pvalue, usedlag, nobs, _, icbest = adfuller(dataset, regression=regression) - results = { - "adfstat": adfstat, - "pvalue": pvalue, - "usedlag": usedlag, - "nobs": nobs, - "icbest": icbest, - } - return OBBject(results=results) # type: ignore[arg-type] - - -@router.command( - methods=["POST"], - examples=[ - APIEx( - parameters={ - "y_column": "portfolio_value", - "x_columns": ["risk_free_rate"], - "data": APIEx.mock_data("panel"), - } - ), - ], -) -def panel_random_effects( - data: list[Data], - y_column: str, - x_columns: list[str], -) -> OBBject[dict]: - """Perform One-way Random Effects model for panel data. - - One-way Random Effects model to panel data is offering a nuanced approach to analyzing data that spans across both - time and entities (such as individuals, companies, countries, etc.). By acknowledging and modeling the random - variation that exists within these entities, this method provides insights into the general patterns that - emerge across the dataset. - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - - Returns - ------- - OBBject[dict] - OBBject with the fit model returned - """ - # pylint: disable=import-outside-toplevel - import statsmodels.api as sm - from linearmodels.panel import RandomEffects - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - - X = get_target_columns(basemodel_to_df(data), x_columns) - if len(X) < 3: - raise ValueError("This analysis requires at least 3 items in the dataset.") - y = get_target_column(basemodel_to_df(data), y_column) - exogenous = sm.add_constant(X) - results = RandomEffects(y, exogenous).fit() - return OBBject(results={"results": results}) - - -@router.command( - methods=["POST"], - examples=[ - APIEx( - parameters={ - "y_column": "portfolio_value", - "x_columns": ["risk_free_rate"], - "data": APIEx.mock_data("panel"), - } - ), - ], -) -def panel_between( - data: list[Data], - y_column: str, - x_columns: list[str], -) -> OBBject[dict]: - """Perform a Between estimator regression on panel data. - - The Between estimator for regression analysis on panel data is focusing on the differences between entities - (such as individuals, companies, or countries) over time. By aggregating the data for each entity and analyzing the - average outcomes, this method provides insights into the overall impact of explanatory variables (x_columns) on - the dependent variable (y_column) across all entities. - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - - Returns - ------- - OBBject[dict] - OBBject with the fit model returned - """ - # pylint: disable=import-outside-toplevel - import statsmodels.api as sm - from linearmodels.panel import BetweenOLS - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - - X = get_target_columns(basemodel_to_df(data), x_columns) - y = get_target_column(basemodel_to_df(data), y_column) - exogenous = sm.add_constant(X) - results = BetweenOLS(y, exogenous).fit() - return OBBject(results={"results": results}) - - -@router.command( - methods=["POST"], - examples=[ - APIEx( - parameters={ - "y_column": "portfolio_value", - "x_columns": ["risk_free_rate"], - "data": APIEx.mock_data("panel"), - } - ), - ], -) -def panel_pooled( - data: list[Data], - y_column: str, - x_columns: list[str], -) -> OBBject[dict]: - """Perform a Pooled coefficient estimator regression on panel data. - - The Pooled coefficient estimator for regression analysis on panel data is treating the data as a large - cross-section without distinguishing between variations across time or entities - (such as individuals, companies, or countries). By assuming that the explanatory variables (x_columns) have a - uniform effect on the dependent variable (y_column) across all entities and time periods, this method simplifies - the analysis and provides a generalized view of the relationships within the data. - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - - Returns - ------- - OBBject[dict] - OBBject with the fit model returned - """ - # pylint: disable=import-outside-toplevel - import statsmodels.api as sm - from linearmodels.panel import PooledOLS - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - - X = get_target_columns(basemodel_to_df(data), x_columns) - y = get_target_column(basemodel_to_df(data), y_column) - exogenous = sm.add_constant(X) - results = PooledOLS(y, exogenous).fit() - return OBBject(results={"results": results}) - - -@router.command( - methods=["POST"], - examples=[ - APIEx( - parameters={ - "y_column": "portfolio_value", - "x_columns": ["risk_free_rate"], - "data": APIEx.mock_data("panel"), - } - ), - ], -) -def panel_fixed( - data: list[Data], - y_column: str, - x_columns: list[str], -) -> OBBject[dict]: - """One- and two-way fixed effects estimator for panel data. - - The Fixed Effects estimator to panel data is enabling a focused analysis on the unique characteristics of entities - (such as individuals, companies, or countries) and/or time periods. By controlling for entity-specific and/or - time-specific influences, this method isolates the effect of explanatory variables (x_columns) on the dependent - variable (y_column), under the assumption that these entity or time effects capture unobserved heterogeneity. - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - - Returns - ------- - OBBject[dict] - OBBject with the fit model returned - """ - # pylint: disable=import-outside-toplevel - import statsmodels.api as sm - from linearmodels.panel import PanelOLS - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - - X = get_target_columns(basemodel_to_df(data), x_columns) - y = get_target_column(basemodel_to_df(data), y_column) - exogenous = sm.add_constant(X) - results = PanelOLS(y, exogenous).fit() - return OBBject(results={"results": results}) - - -@router.command( - methods=["POST"], - examples=[ - APIEx( - parameters={ - "y_column": "portfolio_value", - "x_columns": ["risk_free_rate"], - "data": APIEx.mock_data("panel"), - } - ), - ], -) -def panel_first_difference( - data: list[Data], - y_column: str, - x_columns: list[str], -) -> OBBject[dict]: - """Perform a first-difference estimate for panel data. - - The First-Difference estimator for panel data analysis is focusing on the changes between consecutive observations - for each entity (such as individuals, companies, or countries). By differencing the data, this method effectively - removes entity-specific effects that are constant over time, allowing for the examination of the impact of changes - in explanatory variables (x_columns) on the change in the dependent variable (y_column). - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - - Returns - ------- - OBBject[dict] - OBBject with the fit model returned - """ - # pylint: disable=import-outside-toplevel - from linearmodels.panel import FirstDifferenceOLS - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - - X = get_target_columns(basemodel_to_df(data), x_columns) - y = get_target_column(basemodel_to_df(data), y_column) - exogenous = X - results = FirstDifferenceOLS(y, exogenous).fit() - return OBBject(results={"results": results}) - - -@router.command( - methods=["POST"], - examples=[ - APIEx( - parameters={ - "y_column": "portfolio_value", - "x_columns": ["risk_free_rate"], - "data": APIEx.mock_data("panel"), - } - ), - ], -) -def panel_fmac( - data: list[Data], - y_column: str, - x_columns: list[str], -) -> OBBject[dict]: - """Fama-MacBeth estimator for panel data. - - The Fama-MacBeth estimator, a two-step procedure renowned for its application in finance to estimate the risk - premiums and evaluate the capital asset pricing model. By first estimating cross-sectional regressions for each - time period and then averaging the regression coefficients over time, this method provides insights into the - relationship between the dependent variable (y_column) and explanatory variables (x_columns) across different - entities (such as individuals, companies, or countries). - - Parameters - ---------- - data: list[Data] - Input dataset. - y_column: str - Target column. - x_columns: list[str] - list of columns to use as exogenous variables. - - Returns - ------- - OBBject[dict] - OBBject with the fit model returned - """ - # pylint: disable=import-outside-toplevel - import statsmodels.api as sm - from linearmodels.panel import FamaMacBeth - from openbb_core.app.utils import ( - basemodel_to_df, - get_target_column, - get_target_columns, - ) - - X = get_target_columns(basemodel_to_df(data), x_columns) - y = get_target_column(basemodel_to_df(data), y_column) - exogenous = sm.add_constant(X) - results = FamaMacBeth(y, exogenous).fit() - return OBBject(results={"results": results}) - - -@router.command( - methods=["POST"], - examples=[ - PythonEx( - description="Calculate the variance inflation factor.", - code=[ - "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='yfinance').to_df()", # noqa: E501 pylint: disable= C0301 - 'obb.econometrics.variance_inflation_factor(data=stock_data, columns=["open", "high", "low", "close"])', # noqa: E501 pylint: disable= C0301 - ], - ), - APIEx( - parameters={ - "columns": ["open", "high", "low"], - "data": APIEx.mock_data("timeseries"), - } - ), - ], -) -def variance_inflation_factor( - data: list[Data], columns: list[str] | None = None -) -> OBBject[list[Data]]: - """Calculate VIF (variance inflation factor), which tests for collinearity. - - It quantifies the severity of multicollinearity in an ordinary least squares regression analysis. The square - root of the variance inflation factor indicates how much larger the standard error increases compared to if - that variable had 0 correlation to other predictor variables in the model. - - It is defined as: - - $ VIF_i = 1 / (1 - R_i^2) $ - where $ R_i $ is the coefficient of determination of the regression equation with the column i being the result - from the i:th series being the exogenous variable. - - A VIF over 5 indicates a high collinearity and correlation. Values over 10 indicates causes problems, while a - value of 1 indicates no correlation. Thus VIF values between 1 and 5 are most commonly considered acceptable. - In order to improve the results one can often remove a column with high VIF. - - For further information see: https://en.wikipedia.org/wiki/Variance_inflation_factor - - Parameters - ---------- - dataset: list[Data] - Dataset to calculate VIF on - columns: Optional[list] - The columns to calculate to test for collinearity - - Returns - ------- - OBBject[list[Data]] - The resulting VIF values for the selected columns - """ - # pylint: disable=import-outside-toplevel - from openbb_core.app.utils import ( - basemodel_to_df, - df_to_basemodel, - ) - from pandas import DataFrame - from statsmodels.stats.outliers_influence import variance_inflation_factor as vif - from statsmodels.tools.tools import add_constant - - # Convert to pandas dataframe - dataset = basemodel_to_df(data) - - # Add a constant - df = add_constant(dataset if columns is None else dataset[columns]) - - # Remove date and string type because VIF doesn't work for these types - df = df.select_dtypes(exclude=["object", "datetime", "timedelta"]) # type: ignore - - # Calculate the VIF values - vif_values: dict = {} - for i in range(len(df.columns))[1:]: - vif_values[f"{df.columns[i]}"] = vif(df.values, i) - - results = df_to_basemodel(DataFrame(vif_values, index=[0])) - return OBBject(results=results) diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_views.py b/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_views.py index 3406b88885e..6e1618d10b0 100644 --- a/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_views.py +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_views.py @@ -33,7 +33,6 @@ class EconometricsViews: layout_kwargs : Dict[str, Any] Additional keyword arguments to apply with figure.update_layout(), by default None. """ - # pylint: disable=import-outside-toplevel from openbb_charting.charts.correlation_matrix import correlation_matrix - return correlation_matrix(**kwargs) # type: ignore + return correlation_matrix(**kwargs) diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/panel.py b/openbb_platform/extensions/econometrics/openbb_econometrics/panel.py new file mode 100644 index 00000000000..c5a6f24bb4e --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/panel.py @@ -0,0 +1,211 @@ +"""Econometrics panel-data regression commands.""" + +from typing import Literal + +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import Field + +router = Router(prefix="", description="Econometrics panel-data regression commands.") + + +class PanelRegressionQueryParams(QueryParams): + """Query parameters shared by every panel-data regression endpoint.""" + + __category__ = "panel" + __output_columns__ = ( + "variable", + "coefficient", + "standard_error", + "t_statistic", + "p_value", + "conf_int_lower", + "conf_int_upper", + ) + + data: list[Data] = Field( + description="Input panel dataset, indexed by entity and time." + ) + y_column: str = Field(description="Name of the dependent (target) column.") + x_columns: list[str] = Field( + description="Names of the independent (exogenous) columns." + ) + cov_type: Literal["unadjusted", "robust"] = Field( + default="unadjusted", + description="Covariance estimator: 'unadjusted' (homoskedastic) or 'robust'" + + " (heteroskedasticity-robust standard errors).", + ) + + +class PanelFixedQueryParams(PanelRegressionQueryParams): + """Query parameters for the fixed-effects panel regression endpoint.""" + + entity_effects: bool = Field( + default=True, description="Include entity-specific fixed effects." + ) + time_effects: bool = Field( + default=False, description="Include time-specific fixed effects." + ) + + +class PanelRegressionData(Data): + """One estimated coefficient of a panel-data regression.""" + + variable: str = Field(description="Name of the regressor.") + coefficient: float = Field(description="Estimated coefficient.") + standard_error: float = Field(description="Standard error of the coefficient.") + t_statistic: float = Field( + description="t-statistic for the null hypothesis that the coefficient is zero." + ) + p_value: float = Field(description="p-value of the t-statistic.") + conf_int_lower: float = Field( + description="Lower bound of the 95% confidence interval." + ) + conf_int_upper: float = Field( + description="Upper bound of the 95% confidence interval." + ) + + +def _fit_panel(estimator, params, *, add_constant=True, min_observations=0): + """Fit a linearmodels panel estimator and return its results object.""" + import statsmodels.api as sm + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + df = basemodel_to_df(params.data) + exog = get_target_columns(df, params.x_columns) + if min_observations and len(exog) < min_observations: + raise ValueError( + f"This analysis requires at least {min_observations} items in the dataset." + ) + endog = get_target_column(df, params.y_column) + if add_constant: + exog = sm.add_constant(exog) + model_kwargs: dict = {} + if hasattr(params, "entity_effects"): + model_kwargs["entity_effects"] = params.entity_effects + model_kwargs["time_effects"] = params.time_effects + return estimator(endog, exog, **model_kwargs).fit(cov_type=params.cov_type) + + +def _to_rows(results) -> list[PanelRegressionData]: + """Convert a linearmodels results object into PanelRegressionData rows.""" + conf_int = results.conf_int() + return [ + PanelRegressionData( + variable=str(variable), + coefficient=float(results.params[variable]), + standard_error=float(results.std_errors[variable]), + t_statistic=float(results.tstats[variable]), + p_value=float(results.pvalues[variable]), + conf_int_lower=float(conf_int.loc[variable, "lower"]), + conf_int_upper=float(conf_int.loc[variable, "upper"]), + ) + for variable in results.params.index + ] + + +_PANEL_EXAMPLE = APIEx( + parameters={ + "y_column": "portfolio_value", + "x_columns": ["risk_free_rate"], + "data": APIEx.mock_data("panel"), + } +) + + +@router.command(methods=["POST"], examples=[_PANEL_EXAMPLE]) +def panel_random_effects( + params: PanelRegressionQueryParams, +) -> OBBject[list[PanelRegressionData]]: + """Estimate a one-way Random Effects model for panel data. + + The Random Effects model treats entity-specific deviations as random draws, + capturing variation across entities while pooling information for efficient + estimation. Requires at least three observations. + """ + from linearmodels.panel import RandomEffects + + results = _fit_panel(RandomEffects, params, min_observations=3) + return OBBject(results=_to_rows(results)) + + +@router.command(methods=["POST"], examples=[_PANEL_EXAMPLE]) +def panel_between( + params: PanelRegressionQueryParams, +) -> OBBject[list[PanelRegressionData]]: + """Estimate a Between regression on panel data. + + The Between estimator regresses the entity-level means, summarising the long-run + cross-sectional relationship between the regressors and the target. + """ + from linearmodels.panel import BetweenOLS + + results = _fit_panel(BetweenOLS, params) + return OBBject(results=_to_rows(results)) + + +@router.command(methods=["POST"], examples=[_PANEL_EXAMPLE]) +def panel_pooled( + params: PanelRegressionQueryParams, +) -> OBBject[list[PanelRegressionData]]: + """Estimate a Pooled OLS regression on panel data. + + The Pooled estimator treats the panel as one large cross-section, assuming the + regressors have a uniform effect across all entities and time periods. + """ + from linearmodels.panel import PooledOLS + + results = _fit_panel(PooledOLS, params) + return OBBject(results=_to_rows(results)) + + +@router.command(methods=["POST"], examples=[_PANEL_EXAMPLE]) +def panel_fixed( + params: PanelFixedQueryParams, +) -> OBBject[list[PanelRegressionData]]: + """Estimate a Fixed Effects regression on panel data. + + The Fixed Effects estimator controls for entity- and/or time-specific effects, + isolating the effect of the regressors from unobserved heterogeneity. + """ + from linearmodels.panel import PanelOLS + + results = _fit_panel(PanelOLS, params, add_constant=False) + return OBBject(results=_to_rows(results)) + + +@router.command(methods=["POST"], examples=[_PANEL_EXAMPLE]) +def panel_first_difference( + params: PanelRegressionQueryParams, +) -> OBBject[list[PanelRegressionData]]: + """Estimate a First-Difference regression on panel data. + + Differencing consecutive observations removes time-invariant entity effects, so the + estimator measures how changes in the regressors relate to changes in the target. + """ + from linearmodels.panel import FirstDifferenceOLS + + results = _fit_panel(FirstDifferenceOLS, params, add_constant=False) + return OBBject(results=_to_rows(results)) + + +@router.command(methods=["POST"], examples=[_PANEL_EXAMPLE]) +def panel_fmac( + params: PanelRegressionQueryParams, +) -> OBBject[list[PanelRegressionData]]: + """Estimate a Fama-MacBeth regression on panel data. + + The Fama-MacBeth procedure runs a cross-sectional regression each period and + averages the coefficients over time - widely used to estimate risk premia. + """ + from linearmodels.panel import FamaMacBeth + + results = _fit_panel(FamaMacBeth, params) + return OBBject(results=_to_rows(results)) diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/regression.py b/openbb_platform/extensions/econometrics/openbb_econometrics/regression.py new file mode 100644 index 00000000000..c8802d7c9df --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/regression.py @@ -0,0 +1,227 @@ +"""Econometrics regression commands - Ordinary Least Squares.""" + +from typing import Literal + +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import Field + +router = Router(prefix="", description="Econometrics regression commands.") + + +class OlsRegressionQueryParams(QueryParams): + """Query parameters for the OLS regression endpoint.""" + + __category__ = "regression" + __output_columns__ = ( + "variable", + "coefficient", + "standard_error", + "t_statistic", + "p_value", + "conf_int_lower", + "conf_int_upper", + ) + + data: list[Data] = Field(description="Input dataset.") + y_column: str = Field(description="Name of the dependent (target) column.") + x_columns: list[str] = Field( + description="Names of the independent (exogenous) columns." + ) + cov_type: Literal["nonrobust", "HC0", "HC1", "HC2", "HC3"] = Field( + default="nonrobust", + description="Covariance estimator for the standard errors: 'nonrobust', or" + + " heteroskedasticity-robust 'HC0', 'HC1', 'HC2', or 'HC3'.", + ) + + +class OlsRegressionData(Data): + """One estimated coefficient of an OLS regression.""" + + variable: str = Field( + description="Name of the regressor ('const' is the intercept)." + ) + coefficient: float = Field(description="Estimated coefficient.") + standard_error: float = Field(description="Standard error of the coefficient.") + t_statistic: float = Field( + description="t-statistic for the null hypothesis that the coefficient is zero." + ) + p_value: float = Field(description="Two-sided p-value of the t-statistic.") + conf_int_lower: float = Field( + description="Lower bound of the 95% confidence interval." + ) + conf_int_upper: float = Field( + description="Upper bound of the 95% confidence interval." + ) + + +class OlsRegressionSummaryQueryParams(QueryParams): + """Query parameters for the OLS regression summary endpoint.""" + + __category__ = "regression" + __output_columns__ = ( + "r_squared", + "adjusted_r_squared", + "f_statistic", + "f_p_value", + "aic", + "bic", + "log_likelihood", + "nobs", + "df_model", + "df_residuals", + ) + + data: list[Data] = Field(description="Input dataset.") + y_column: str = Field(description="Name of the dependent (target) column.") + x_columns: list[str] = Field( + description="Names of the independent (exogenous) columns." + ) + cov_type: Literal["nonrobust", "HC0", "HC1", "HC2", "HC3"] = Field( + default="nonrobust", + description="Covariance estimator for the standard errors: 'nonrobust', or" + + " heteroskedasticity-robust 'HC0', 'HC1', 'HC2', or 'HC3'.", + ) + + +class OlsRegressionSummaryData(Data): + """Goodness-of-fit summary of an OLS regression.""" + + r_squared: float = Field(description="Coefficient of determination.") + adjusted_r_squared: float = Field( + description="R-squared adjusted for the number of regressors." + ) + f_statistic: float | None = Field( + default=None, description="F-statistic of the overall regression." + ) + f_p_value: float | None = Field( + default=None, description="p-value of the F-statistic." + ) + aic: float = Field(description="Akaike Information Criterion.") + bic: float = Field(description="Bayesian Information Criterion.") + log_likelihood: float = Field(description="Log-likelihood of the fitted model.") + nobs: int = Field(description="Number of observations.") + df_model: float = Field(description="Model degrees of freedom.") + df_residuals: float = Field(description="Residual degrees of freedom.") + summary: str = Field(description="Full statsmodels text summary.") + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Perform Ordinary Least Squares (OLS) regression.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + 'obb.econometrics.ols_regression(data=stock_data, y_column="close", x_columns=["open", "high", "low"])', # noqa: E501 + ], + ), + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def ols_regression( + params: OlsRegressionQueryParams, +) -> OBBject[list[OlsRegressionData]]: + """Perform Ordinary Least Squares (OLS) regression. + + OLS fits the best linear relationship between a dependent variable and one or more + independent variables. Returns the estimated coefficient table - one row per + regressor, with standard errors, t-statistics, p-values, and confidence intervals. + """ + import statsmodels.api as sm + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + df = basemodel_to_df(params.data) + exog = sm.add_constant(get_target_columns(df, params.x_columns)) + endog = get_target_column(df, params.y_column) + results = sm.OLS(endog, exog).fit(cov_type=params.cov_type) + conf_int = results.conf_int() + + out = [] + for variable in results.params.index: + bounds = conf_int.loc[variable] + out.append( + OlsRegressionData( + variable=str(variable), + coefficient=float(results.params[variable]), + standard_error=float(results.bse[variable]), + t_statistic=float(results.tvalues[variable]), + p_value=float(results.pvalues[variable]), + conf_int_lower=float(bounds.iloc[0]), + conf_int_upper=float(bounds.iloc[1]), + ) + ) + + return OBBject(results=out) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "y_column": "close", + "x_columns": ["open", "high", "low"], + "data": APIEx.mock_data("timeseries"), + } + ), + ], +) +def ols_regression_summary( + params: OlsRegressionSummaryQueryParams, +) -> OBBject[OlsRegressionSummaryData]: + """Perform Ordinary Least Squares (OLS) regression and return the fit summary. + + Returns the model-level goodness-of-fit statistics - R-squared, F-statistic, + information criteria, log-likelihood, and degrees of freedom - along with the full + statsmodels text summary. + """ + import statsmodels.api as sm + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + + df = basemodel_to_df(params.data) + exog = sm.add_constant(get_target_columns(df, params.x_columns)) + endog = get_target_column(df, params.y_column) + + try: + exog = exog.astype(float) + endog = endog.astype(float) + except ValueError as exc: + raise ValueError("All columns must be numeric.") from exc + + results = sm.OLS(endog, exog).fit(cov_type=params.cov_type) + + return OBBject( + results=OlsRegressionSummaryData( + r_squared=float(results.rsquared), + adjusted_r_squared=float(results.rsquared_adj), + f_statistic=(float(results.fvalue) if results.fvalue is not None else None), + f_p_value=( + float(results.f_pvalue) if results.f_pvalue is not None else None + ), + aic=float(results.aic), + bic=float(results.bic), + log_likelihood=float(results.llf), + nobs=int(results.nobs), + df_model=float(results.df_model), + df_residuals=float(results.df_resid), + summary=str(results.summary()), + ) + ) diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/router.py b/openbb_platform/extensions/econometrics/openbb_econometrics/router.py new file mode 100644 index 00000000000..6f1ff7758b8 --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/router.py @@ -0,0 +1,18 @@ +"""Top-level router for the openbb-econometrics extension.""" + +from openbb_core.app.router import Router + +from openbb_econometrics.diagnostics import router as _diagnostics_router +from openbb_econometrics.panel import router as _panel_router +from openbb_econometrics.regression import router as _regression_router +from openbb_econometrics.time_series import router as _time_series_router +from openbb_econometrics.tools import router as _tools_router + +router = Router(prefix="", description="Econometrics analysis tools.") +router.include_router(_tools_router) +router.include_router(_regression_router) +router.include_router(_diagnostics_router) +router.include_router(_time_series_router) +router.include_router(_panel_router) + +__all__ = ["router"] diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/time_series.py b/openbb_platform/extensions/econometrics/openbb_econometrics/time_series.py new file mode 100644 index 00000000000..8c49b38a9e7 --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/time_series.py @@ -0,0 +1,537 @@ +"""Econometrics time-series commands - stationarity, cointegration, causality, GARCH.""" + +from datetime import ( + date as dateType, + datetime, +) +from itertools import combinations +from typing import Literal + +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import Field, NonNegativeInt, PositiveFloat, PositiveInt + +router = Router(prefix="", description="Econometrics time-series commands.") + + +class UnitRootQueryParams(QueryParams): + """Query parameters for the Augmented Dickey-Fuller unit root endpoint.""" + + __category__ = "time_series" + __output_columns__ = ("adf_stat", "p_value", "used_lag", "nobs", "ic_best") + + data: list[Data] = Field(description="Input dataset.") + column: str = Field(description="Column to test for a unit root.") + regression: Literal["c", "ct", "ctt"] = Field( + default="c", + description="Deterministic terms: 'c' constant, 'ct' constant and trend," + + " 'ctt' constant, trend, and quadratic trend.", + ) + maxlag: NonNegativeInt | None = Field( + default=None, + description="Maximum lag to include. If None, a data-dependent default is used.", + ) + autolag: Literal["AIC", "BIC", "t-stat"] | None = Field( + default="AIC", + description="Lag-selection method: 'AIC', 'BIC', or 't-stat'; None uses" + + " maxlag directly without selection.", + ) + + +class UnitRootData(Data): + """Augmented Dickey-Fuller unit root test result.""" + + adf_stat: float = Field( + description="Test statistic (more negative favours stationarity)." + ) + p_value: float = Field(description="MacKinnon p-value.") + used_lag: int = Field(description="Number of lags used.") + nobs: int = Field(description="Number of observations used.") + ic_best: float | None = Field( + default=None, + description="Maximised information criterion (None when autolag is disabled).", + ) + critical_values: dict = Field( + description="Critical values at the 1%, 5%, and 10% levels." + ) + + +class KpssQueryParams(QueryParams): + """Query parameters for the KPSS stationarity endpoint.""" + + __category__ = "time_series" + __output_columns__ = ("kpss_stat", "p_value", "p_value_interpolated", "lags") + + data: list[Data] = Field(description="Input dataset.") + column: str = Field(description="Column to test.") + regression: Literal["c", "ct"] = Field( + default="c", + description="Null hypothesis: 'c' level-stationary, 'ct' trend-stationary.", + ) + nlags: Literal["auto", "legacy"] | NonNegativeInt = Field( + default="auto", + description="Lags for the variance estimator: 'auto', 'legacy', or an" + + " explicit non-negative integer.", + ) + + +class KpssData(Data): + """KPSS stationarity test result.""" + + kpss_stat: float = Field(description="Test statistic.") + p_value: float = Field(description="p-value of the test statistic.") + p_value_interpolated: bool = Field( + description="False when p_value is a clamped table boundary" + + " (the true p-value is more extreme than reported)." + ) + lags: int = Field(description="Number of lags used in the test.") + critical_values: dict = Field( + description="Critical values at the 10%, 5%, 2.5%, and 1% levels." + ) + + +class CointegrationQueryParams(QueryParams): + """Query parameters for the Engle-Granger cointegration endpoint.""" + + __category__ = "time_series" + __output_columns__ = ("pair", "c", "gamma", "alpha", "adf_stat", "p_value") + + data: list[Data] = Field(description="Input dataset.") + columns: list[str] = Field( + description="Columns to test pairwise for cointegration." + ) + + +class CointegrationData(Data): + """Engle-Granger two-step cointegration result for one column pair.""" + + pair: str = Field(description="The tested column pair, formatted as 'x/y'.") + c: float = Field(description="Constant of the long-run relationship.") + gamma: float = Field(description="Slope of the long-run relationship.") + alpha: float = Field(description="Error-correction speed of adjustment.") + adf_stat: float = Field(description="Dickey-Fuller statistic on the residuals.") + p_value: float = Field(description="p-value of the Dickey-Fuller statistic.") + + +class CointegrationJohansenQueryParams(QueryParams): + """Query parameters for the Johansen cointegration endpoint.""" + + __category__ = "time_series" + __output_columns__ = ( + "rank", + "eigenvalue", + "trace_statistic", + "max_eig_statistic", + ) + + data: list[Data] = Field(description="Input dataset.") + columns: list[str] = Field( + description="Columns (time series) to test jointly for cointegration." + ) + deterministic_order: Literal[-1, 0, 1] = Field( + default=0, + description="Deterministic trend: -1 none, 0 constant, 1 constant and trend.", + ) + k_ar_diff: PositiveInt = Field( + default=1, description="Number of lagged differences in the model." + ) + + +class CointegrationJohansenData(Data): + """Johansen cointegration result for one cointegration rank hypothesis.""" + + rank: int = Field(description="Cointegration rank hypothesis (r <= rank).") + eigenvalue: float = Field(description="Eigenvalue associated with the rank.") + trace_statistic: float = Field(description="Trace test statistic.") + trace_crit_90: float = Field(description="Trace critical value at the 90% level.") + trace_crit_95: float = Field(description="Trace critical value at the 95% level.") + trace_crit_99: float = Field(description="Trace critical value at the 99% level.") + max_eig_statistic: float = Field(description="Maximum-eigenvalue test statistic.") + max_eig_crit_90: float = Field( + description="Maximum-eigenvalue critical value at the 90% level." + ) + max_eig_crit_95: float = Field( + description="Maximum-eigenvalue critical value at the 95% level." + ) + max_eig_crit_99: float = Field( + description="Maximum-eigenvalue critical value at the 99% level." + ) + + +class CausalityQueryParams(QueryParams): + """Query parameters for the Granger causality endpoint.""" + + __category__ = "time_series" + __output_columns__ = ("test", "statistic", "p_value", "lag") + + data: list[Data] = Field(description="Input dataset.") + y_column: str = Field(description="Target column.") + x_column: str = Field(description="Candidate causal column.") + lag: PositiveInt = Field(default=3, description="Number of lags to test.") + + +class CausalityData(Data): + """One sub-test of the Granger causality result.""" + + test: str = Field(description="Name of the Granger sub-test.") + statistic: float = Field(description="Test statistic.") + p_value: float = Field(description="p-value of the test statistic.") + lag: int = Field(description="Number of lags tested.") + + +class GarchQueryParams(QueryParams): + """Query parameters for the GARCH volatility endpoint.""" + + __category__ = "time_series" + __output_columns__ = ("date", "conditional_volatility") + + data: list[Data] = Field(description="Input dataset.") + column: str = Field(description="Column to model. Best applied to a return series.") + x_columns: list[str] | None = Field( + default=None, + description="Exogenous regressor columns for the mean model" + + " (used by the 'LS', 'ARX', and 'HARX' mean models).", + ) + mean: Literal["Constant", "Zero", "LS", "AR", "ARX", "HAR", "HARX"] = Field( + default="Constant", + description="Mean model: 'Constant' (constant mean), 'Zero' (zero mean)," + + " 'LS' (least squares), 'AR'/'ARX' (autoregressive, 'ARX' adds exogenous" + + " regressors), 'HAR'/'HARX' (heterogeneous autoregressive).", + ) + lags: NonNegativeInt = Field( + default=0, + description="Lag order of the 'AR', 'ARX', 'HAR', and 'HARX' mean models.", + ) + vol: Literal["GARCH", "ARCH", "EGARCH", "FIGARCH", "APARCH", "HARCH"] = Field( + default="GARCH", + description="Volatility process: 'GARCH', 'ARCH', 'EGARCH' (exponential GARCH," + + " captures leverage), 'FIGARCH' (fractionally integrated, long memory)," + + " 'APARCH' (asymmetric power ARCH), 'HARCH' (heterogeneous ARCH).", + ) + p: PositiveInt = Field( + default=1, description="Order of the symmetric ARCH (innovation) term." + ) + o: NonNegativeInt = Field( + default=0, description="Order of the asymmetric (leverage) term." + ) + q: NonNegativeInt = Field( + default=1, description="Order of the lagged volatility (GARCH) term." + ) + power: PositiveFloat = Field( + default=2.0, + description="Power applied to the conditional volatility (2.0 is standard" + + " GARCH; other values give power-GARCH and APARCH variants).", + ) + distribution: Literal["normal", "t", "skewt", "ged"] = Field( + default="normal", + description="Distribution of the model innovations: 'normal' (Gaussian)," + + " 't' (Student's t, fat tails), 'skewt' (skewed Student's t)," + + " or 'ged' (generalized error distribution).", + ) + + +class GarchData(Data): + """One observation of the GARCH conditional volatility series.""" + + date: datetime | dateType | str | int = Field( + description="Observation date (integer position when the input has no date)." + ) + conditional_volatility: float = Field( + description="Estimated conditional volatility at the observation." + ) + + +@router.command( + methods=["POST"], + examples=[ + APIEx(parameters={"column": "close", "data": APIEx.mock_data("timeseries")}) + ], +) +def unit_root(params: UnitRootQueryParams) -> OBBject[UnitRootData]: + """Perform the Augmented Dickey-Fuller (ADF) unit root test. + + The ADF test checks whether a series has a unit root - a sign of non-stationarity. + Rejecting the null hypothesis indicates the series is stationary. + """ + from openbb_core.app.utils import basemodel_to_df, get_target_column + from statsmodels.tsa.stattools import adfuller + + series = get_target_column(basemodel_to_df(params.data), params.column) + result = adfuller( + series, + maxlag=params.maxlag, + regression=params.regression, + autolag=params.autolag, + ) + adf_stat, p_value, used_lag, nobs, critical_values = result[:5] + ic_best = result[5] if len(result) > 5 else None + return OBBject( + results=UnitRootData( + adf_stat=float(adf_stat), + p_value=float(p_value), + used_lag=int(used_lag), + nobs=int(nobs), + ic_best=float(ic_best) if ic_best is not None else None, + critical_values=dict(critical_values), + ) + ) + + +@router.command( + methods=["POST"], + examples=[ + APIEx(parameters={"column": "close", "data": APIEx.mock_data("timeseries")}) + ], +) +def kpss(params: KpssQueryParams) -> OBBject[KpssData]: + """Perform the KPSS test for stationarity. + + The KPSS null hypothesis is that the series IS stationary - the opposite of the ADF + test - so running both tests gives a more robust read on stationarity. + """ + import warnings + + from openbb_core.app.utils import basemodel_to_df, get_target_column + from statsmodels.tools.sm_exceptions import InterpolationWarning + from statsmodels.tsa.stattools import kpss as _kpss + + series = get_target_column(basemodel_to_df(params.data), params.column) + # Capture statsmodels' off-table InterpolationWarning as a typed result field. + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always", InterpolationWarning) + statistic, p_value, lags, critical_values = _kpss( + series, regression=params.regression, nlags=params.nlags + ) + p_value_interpolated = not any( + issubclass(w.category, InterpolationWarning) for w in caught + ) + return OBBject( + results=KpssData( + kpss_stat=float(statistic), + p_value=float(p_value), + p_value_interpolated=p_value_interpolated, + lags=int(lags), + critical_values=dict(critical_values), + ) + ) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "columns": ["open", "close"], + "data": APIEx.mock_data("timeseries"), + } + ) + ], +) +def cointegration(params: CointegrationQueryParams) -> OBBject[list[CointegrationData]]: + """Test pairs of time series for cointegration via the Engle-Granger two-step test. + + Cointegration means two non-stationary series share a long-run equilibrium. The + Engle-Granger test regresses one series on the other and checks the residuals for + stationarity. + """ + from openbb_core.app.utils import basemodel_to_df, get_target_columns + + from openbb_econometrics.utils import ( + get_engle_granger_two_step_cointegration_test, + ) + + dataset = get_target_columns(basemodel_to_df(params.data), params.columns) + out = [] + for x_col, y_col in combinations(params.columns, 2): + c, gamma, alpha, _, adf_stat, p_value = ( + get_engle_granger_two_step_cointegration_test( + dataset[x_col], dataset[y_col] + ) + ) + out.append( + CointegrationData( + pair=f"{x_col}/{y_col}", + c=float(c), + gamma=float(gamma), + alpha=float(alpha), + adf_stat=float(adf_stat), + p_value=float(p_value), + ) + ) + + return OBBject(results=out) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + parameters={ + "columns": ["open", "close"], + "data": APIEx.mock_data("timeseries"), + } + ) + ], +) +def cointegration_johansen( + params: CointegrationJohansenQueryParams, +) -> OBBject[list[CointegrationJohansenData]]: + """Perform the Johansen cointegration test on multiple time series. + + The Johansen test detects cointegrating relationships among two or more series + simultaneously, reporting the number of cointegrating vectors via the trace and + maximum-eigenvalue statistics. + """ + from openbb_core.app.utils import basemodel_to_df, get_target_columns + from statsmodels.tsa.vector_ar.vecm import coint_johansen + + dataset = get_target_columns(basemodel_to_df(params.data), params.columns) + result = coint_johansen( + dataset, det_order=params.deterministic_order, k_ar_diff=params.k_ar_diff + ) + out = [ + CointegrationJohansenData( + rank=rank, + eigenvalue=float(result.eig[rank]), + trace_statistic=float(result.lr1[rank]), + trace_crit_90=float(result.cvt[rank][0]), + trace_crit_95=float(result.cvt[rank][1]), + trace_crit_99=float(result.cvt[rank][2]), + max_eig_statistic=float(result.lr2[rank]), + max_eig_crit_90=float(result.cvm[rank][0]), + max_eig_crit_95=float(result.cvm[rank][1]), + max_eig_crit_99=float(result.cvm[rank][2]), + ) + for rank in range(len(result.lr1)) + ] + + return OBBject(results=out) + + +@router.command( + methods=["POST"], + examples=[ + APIEx( + description="Granger causality test with mock data.", + parameters={ + "y_column": "close", + "x_column": "open", + "lag": 1, + "data": APIEx.mock_data("timeseries"), + }, + ) + ], +) +def causality(params: CausalityQueryParams) -> OBBject[list[CausalityData]]: + """Perform the Granger causality test to assess whether X helps predict y. + + Granger causality tests whether past values of one series improve the forecast of + another. It is a predictive, not a structural, notion of causality. + """ + from contextlib import redirect_stdout + from io import StringIO + + from openbb_core.app.utils import basemodel_to_df, get_target_column + from pandas import concat + from statsmodels.tsa.stattools import grangercausalitytests + + df = basemodel_to_df(params.data) + x_series = get_target_column(df, params.x_column) + y_series = get_target_column(df, params.y_column) + + with redirect_stdout(StringIO()): + granger = grangercausalitytests( + concat([y_series, x_series], axis=1), [params.lag] + ) + out = [ + CausalityData( + test=str(test), + statistic=float(values[0]), + p_value=float(values[1]), + lag=params.lag, + ) + for test, values in granger[params.lag][0].items() + ] + + return OBBject(results=out) + + +@router.command( + methods=["POST"], + examples=[ + APIEx(parameters={"column": "close", "data": APIEx.mock_data("timeseries")}) + ], +) +def garch(params: GarchQueryParams) -> OBBject[list[GarchData]]: + """Fit a configurable volatility model from the GARCH family to a series. + + The mean model, volatility process, lag orders, power, and innovation distribution + are all configurable. The estimated conditional volatility series is returned; the + fitted parameters and information criteria are attached to ``OBBject.extra``. Best + applied to a return series. + """ + from arch import arch_model + from openbb_core.app.utils import ( + basemodel_to_df, + get_target_column, + get_target_columns, + ) + from pandas import DataFrame, Series + + df = basemodel_to_df(params.data, index="date") + series = get_target_column(df, params.column).astype(float) + exog = None + if params.x_columns: + combined = ( + get_target_columns(df, params.x_columns) + .astype(float) + .join(series.rename("_endog_")) + .dropna() + ) + series = combined["_endog_"] + exog = combined.drop(columns="_endog_") + else: + series = series.dropna() + + results = arch_model( + series, + x=exog, + mean=params.mean, + lags=params.lags, + vol=params.vol, + p=params.p, + o=params.o, + q=params.q, + power=params.power, + dist=params.distribution, + ).fit(disp="off") + + volatility = Series(results.conditional_volatility).dropna() + frame = DataFrame( + { + "date": volatility.index, + "conditional_volatility": volatility.to_numpy(), + } + ) + out = [ + GarchData( + date=record["date"], + conditional_volatility=float(record["conditional_volatility"]), + ) + for record in frame.to_dict(orient="records") + ] + + return OBBject( + results=out, + extra={ + "results_metadata": { + "params": results.params.to_dict(), + "aic": float(results.aic), + "bic": float(results.bic), + "log_likelihood": float(results.loglikelihood), + } + }, + ) diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/tools.py b/openbb_platform/extensions/econometrics/openbb_econometrics/tools.py new file mode 100644 index 00000000000..05f63515f32 --- /dev/null +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/tools.py @@ -0,0 +1,157 @@ +"""Econometrics data tools - correlation matrix and descriptive statistics.""" + +from typing import Literal + +from openbb_core.app.model.example import APIEx, PythonEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.router import Router +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import Field, PositiveInt + +router = Router( + prefix="", description="Econometrics correlation and descriptive tools." +) + + +class CorrelationMatrixQueryParams(QueryParams): + """Query parameters for the correlation matrix endpoint.""" + + __category__ = "tools" + __output_columns__ = ("comp_to",) + + data: list[Data] = Field(description="Input dataset.") + method: Literal["pearson", "kendall", "spearman"] = Field( + default="pearson", + description="Correlation method: 'pearson' (standard), 'kendall' (Kendall Tau)," + + " or 'spearman' (rank correlation).", + ) + min_periods: PositiveInt = Field( + default=1, + description="Minimum number of observations required per pair of columns" + + " to compute a correlation.", + ) + + +class CorrelationMatrixData(Data): + """One row of the correlation matrix.""" + + comp_to: str = Field( + description="The variable each correlation in the row is measured against." + ) + + +class SummaryStatisticsQueryParams(QueryParams): + """Query parameters for the summary statistics endpoint.""" + + __category__ = "tools" + __output_columns__ = ( + "column", + "count", + "mean", + "std", + "min", + "p25", + "median", + "p75", + "max", + "skew", + "kurtosis", + ) + + data: list[Data] = Field(description="Input dataset.") + + +class SummaryStatisticsData(Data): + """Descriptive statistics for a single numeric column.""" + + column: str = Field(description="Name of the column.") + count: int = Field(description="Number of non-null observations.") + mean: float = Field(description="Arithmetic mean.") + std: float = Field(description="Sample standard deviation.") + min: float = Field(description="Minimum value.") + p25: float = Field(description="25th percentile.") + median: float = Field(description="Median (50th percentile).") + p75: float = Field(description="75th percentile.") + max: float = Field(description="Maximum value.") + skew: float = Field(description="Skewness of the distribution.") + kurtosis: float = Field(description="Excess kurtosis of the distribution.") + + +@router.command( + methods=["POST"], + examples=[ + PythonEx( + description="Get the correlation matrix of a dataset.", + code=[ + "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()", # noqa: E501 + "obb.econometrics.correlation_matrix(data=stock_data)", + ], + ), + APIEx(parameters={"data": APIEx.mock_data("timeseries")}), + ], +) +def correlation_matrix( + params: CorrelationMatrixQueryParams, +) -> OBBject[list[CorrelationMatrixData]]: + """Get the correlation matrix of an input dataset. + + The correlation matrix shows how strongly the variables in the dataset move in + relation to one another. Scores range from -1 (perfect negative correlation) through + 0 (no correlation) to 1 (perfect positive correlation). + """ + import numpy as np + from openbb_core.app.utils import basemodel_to_df + + df = basemodel_to_df(params.data) + + if "symbol" in df.columns and len(df.symbol.unique()) > 1 and "close" in df.columns: + df = df.pivot(columns="symbol", values="close") + + corr = df.corr( + method=params.method, min_periods=params.min_periods, numeric_only=True + ).replace(np.nan, None) + + results = [] + for key, series in corr.items(): + row = series.to_dict() + row["comp_to"] = str(key) + results.append(CorrelationMatrixData.model_validate(row)) + + return OBBject(results=results) + + +@router.command( + methods=["POST"], + examples=[APIEx(parameters={"data": APIEx.mock_data("timeseries")})], +) +def summary_statistics( + params: SummaryStatisticsQueryParams, +) -> OBBject[list[SummaryStatisticsData]]: + """Compute descriptive summary statistics for each numeric column. + + Returns per-column count, mean, standard deviation, minimum, quartiles, maximum, + skewness, and excess kurtosis - a quick overview of each variable's distribution. + """ + from openbb_core.app.utils import basemodel_to_df + + df = basemodel_to_df(params.data).select_dtypes(include="number") + + results = [ + SummaryStatisticsData( + column=str(column), + count=int(df[column].count()), + mean=float(df[column].mean()), + std=float(df[column].std()), + min=float(df[column].min()), + p25=float(df[column].quantile(0.25)), + median=float(df[column].median()), + p75=float(df[column].quantile(0.75)), + max=float(df[column].max()), + skew=float(df[column].skew()), + kurtosis=float(df[column].kurtosis()), + ) + for column in df.columns + ] + + return OBBject(results=results) diff --git a/openbb_platform/extensions/econometrics/openbb_econometrics/utils.py b/openbb_platform/extensions/econometrics/openbb_econometrics/utils.py index 343a842a3b5..b6dfe5317f7 100644 --- a/openbb_platform/extensions/econometrics/openbb_econometrics/utils.py +++ b/openbb_platform/extensions/econometrics/openbb_econometrics/utils.py @@ -10,60 +10,7 @@ if TYPE_CHECKING: def get_engle_granger_two_step_cointegration_test( dependent_series: "Series", independent_series: "Series" ) -> tuple[float, float, float, "Series", float, float]: - """Estimate long-run and short-run cointegration relationship for series y and x. - - Then apply the two-step Engle & Granger test for cointegration. - - Uses a 2-step process to first estimate coefficients for the long-run relationship - y_t = c + gamma * x_t + z_t - - and then the short-term relationship, - y_t - y_(t-1) = alpha * z_(t-1) + epsilon_t, - - with z the found residuals of the first equation. - - Then tests cointegration by Dickey-Fuller phi=1 vs phi < 1 in - z_t = phi * z_(t-1) + eta_t - - If this implies phi < 1, the z series is stationary is concluded to be - stationary, and thus the series y and x are concluded to be cointegrated. - - Parameters - ---------- - dependent_series : pd.Series - The first time series of the pair to analyse. - independent_series : pd.Series - The second time series of the pair to analyse. - - Returns - ------- - Tuple[float, float, float, pd.Series, float, float] - c : float - The constant term in the long-run relationship y_t = c + gamma * x_t + z_t. This - describes the static shift of y with respect to gamma * x. - - gamma : float - The gamma term in the long-run relationship y_t = c + gamma * x_t + z_t. This - describes the ratio between the const-shifted y and x. - - alpha : float - The alpha term in the short-run relationship y_t - y_(t-1) = alpha * z_(t-1) + epsilon. This - gives an indication of the strength of the error correction toward the long-run mean. - - z : pd.Series - Series of residuals z_t from the long-run relationship y_t = c + gamma * x_t + z_t, representing - the value of the error correction term. - - dfstat : float - The Dickey Fuller test-statistic for phi = 1 vs phi < 1 in the second equation. A more - negative value implies the existence of stronger cointegration. - - pvalue : float - The p-value corresponding to the Dickey Fuller test-statistic. A lower value implies - stronger rejection of no-cointegration, thus stronger evidence of cointegration. - - """ - # pylint: disable=import-outside-toplevel + """Run the Engle-Granger two-step cointegration test on a series pair.""" import statsmodels.api as sm from statsmodels.tsa.stattools import adfuller @@ -81,11 +28,7 @@ def get_engle_granger_two_step_cointegration_test( alpha = short_run_ols_fit.params.iloc[0] - # NOTE: The p-value returned by the adfuller function assumes we do not estimate z - # first, but test stationarity of an unestimated series directly. This assumption - # should have limited effect for high N, however. Critical values taking this into - # account more accurately are provided in e.g. McKinnon (1990) and Engle & Yoo (1987). - + # adfuller's p-value assumes z is not pre-estimated; limited effect for high N. adfstat, pvalue, _, _, _ = adfuller(z, maxlag=1, autolag=None) return c, gamma, alpha, z, adfstat, pvalue @@ -93,7 +36,6 @@ def get_engle_granger_two_step_cointegration_test( def mock_multi_index_data(): """Create a mock multi-index dataframe for testing purposes.""" - # pylint: disable=import-outside-toplevel from numpy import random from pandas import DataFrame, MultiIndex diff --git a/openbb_platform/extensions/econometrics/poetry.lock b/openbb_platform/extensions/econometrics/poetry.lock deleted file mode 100644 index e6840e1eff2..00000000000 --- a/openbb_platform/extensions/econometrics/poetry.lock +++ /dev/null @@ -1,2662 +0,0 @@ -# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand. - -[[package]] -name = "aiohappyeyeballs" -version = "2.6.2" -description = "Happy Eyeballs for asyncio" -optional = false -python-versions = ">=3.10" -groups = ["main"] -files = [ - 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a/openbb_platform/extensions/econometrics/pyproject.toml b/openbb_platform/extensions/econometrics/pyproject.toml index ea47d2c81f4..2710c3a6698 100644 --- a/openbb_platform/extensions/econometrics/pyproject.toml +++ b/openbb_platform/extensions/econometrics/pyproject.toml @@ -1,26 +1,97 @@ -[tool.poetry] +[project] name = "openbb-econometrics" -version = "1.7.2" +version = "2.0.0" description = "Econometrics Toolkit for OpenBB" -authors = ["OpenBB Team "] -license = "AGPL-3.0-only" +authors = [{ name = "OpenBB Team", email = "hello@openbb.co" }] +license = { text = "AGPL-3.0-only" } readme = "README.md" -packages = [{ include = "openbb_econometrics" }] +requires-python = ">=3.10,<4" +dependencies = [ + "openbb-core[pandas]>=2.0.0", + "statsmodels>=0.14", + "linearmodels>=6", + "scipy>=1.11", + "arch>=7.2", +] -[tool.poetry.dependencies] -python = ">=3.10,<4" # scipy forces python <4.0 explicitly -openbb-core = "^1.6.10" -pandas-ta-openbb = "^0.4.23" -arch = "^7.2" -linearmodels = "^6" 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"PLR0911", "PLR0913", "PLR0914", "PLR0915", "PLR0917"] +"**/__init__.py" = ["F401"] + +[tool.ruff.lint.isort] +combine-as-imports = true +force-wrap-aliases = true + +[tool.ruff.lint.pydocstyle] +convention = "numpy" + +[tool.ty] +[tool.ty.environment] +python-version = "3.10" + +[tool.ty.src] +include = ["openbb_econometrics"] +exclude = ["tests", "integration"] + +[tool.ty.rules] +unresolved-import = "warn" + +[tool.coverage.report] +exclude_lines = ["pragma: no cover", "if TYPE_CHECKING:", "raise NotImplementedError", "@overload"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +addopts = "-q --strict-markers" +testpaths = ["tests"] +markers = [ + "integration: end-to-end tests that hit live services (excluded from the coverage gate)", +] +filterwarnings = [ + "ignore:Inheritance class ClientSession from ClientSession is discouraged:DeprecationWarning", +] diff --git a/openbb_platform/extensions/econometrics/tests/conftest.py b/openbb_platform/extensions/econometrics/tests/conftest.py new file mode 100644 index 00000000000..ac412d2d4bd --- /dev/null +++ b/openbb_platform/extensions/econometrics/tests/conftest.py @@ -0,0 +1,94 @@ +"""Shared fixtures for the econometrics test suite.""" + +import numpy as np +import pandas as pd +import pytest +from openbb_core.app.utils import df_to_basemodel + +from openbb_econometrics.utils import mock_multi_index_data + +SEED = 42 +N_ROWS = 120 + + +@pytest.fixture(scope="session") +def timeseries_df() -> pd.DataFrame: + """Build a smooth, slightly-noisy OHLCV timeseries DataFrame.""" + rng = np.random.default_rng(SEED) + t = np.arange(N_ROWS) + base = 100 + 0.5 * t + rng.normal(0, 2, N_ROWS).cumsum() * 0.3 + close = base + rng.normal(0, 1, N_ROWS) + + return pd.DataFrame( + { + "open": close + rng.normal(0, 0.5, N_ROWS), + "high": close + np.abs(rng.normal(1, 0.5, N_ROWS)), + "low": close - np.abs(rng.normal(1, 0.5, N_ROWS)), + "close": close, + "volume": (1e6 + rng.normal(0, 1e5, N_ROWS) + 5000 * t).astype(float), + } + ) + + +@pytest.fixture(scope="session") +def timeseries_data(timeseries_df) -> list: + """Expose the timeseries DataFrame as a ``list[Data]``.""" + return df_to_basemodel(timeseries_df) + + +@pytest.fixture(scope="session") +def symbol_df() -> pd.DataFrame: + """Build a long-format DataFrame with a ``symbol`` column and >1 symbol.""" + rng = np.random.default_rng(SEED) + rows = 30 + frames = [] + for sym in ("AAA", "BBB", "CCC"): + close = 100 + rng.normal(0, 1, rows).cumsum() + frames.append(pd.DataFrame({"symbol": sym, "close": close})) + return pd.concat(frames, ignore_index=True) + + +@pytest.fixture(scope="session") +def symbol_data(symbol_df) -> list: + """Expose the multi-symbol DataFrame as a ``list[Data]``.""" + return df_to_basemodel(symbol_df) + + +@pytest.fixture(scope="session") +def panel_df() -> pd.DataFrame: + """Build a panel (entity/time MultiIndex) DataFrame.""" + np.random.seed(SEED) + return mock_multi_index_data() + + +@pytest.fixture(scope="session") +def panel_data(panel_df) -> list: + """Expose the panel DataFrame as a ``list[Data]`` (MultiIndex preserved).""" + return df_to_basemodel(panel_df, index=True) + + +@pytest.fixture(scope="session") +def non_numeric_data() -> list: + """A dataset whose ``open`` column is non-numeric.""" + rng = np.random.default_rng(SEED) + rows = 20 + df = pd.DataFrame( + { + "open": [f"label_{i}" for i in range(rows)], + "high": rng.normal(10, 1, rows), + "close": rng.normal(10, 1, rows), + } + ) + return df_to_basemodel(df) + + +@pytest.fixture(scope="session") +def small_panel_data() -> list: + """A panel dataset with only 2 rows.""" + df = pd.DataFrame( + {"portfolio_value": [10.0, 12.0], "risk_free_rate": [0.01, 0.02]}, + index=pd.MultiIndex.from_tuples( + [("entity_1", 1), ("entity_1", 2)], names=("entity", "time") + ), + ) + return df_to_basemodel(df, index=True) diff --git a/openbb_platform/extensions/econometrics/tests/test_diagnostics.py b/openbb_platform/extensions/econometrics/tests/test_diagnostics.py new file mode 100644 index 00000000000..954aaac2a29 --- /dev/null +++ b/openbb_platform/extensions/econometrics/tests/test_diagnostics.py @@ -0,0 +1,116 @@ +"""Tests for ``openbb_econometrics.diagnostics`` - regression diagnostics.""" + +from openbb_econometrics import diagnostics + +X_COLUMNS = ["open", "high", "low"] + + +def test_autocorrelation(timeseries_data): + """The Durbin-Watson statistic lies in the [0, 4] range.""" + out = diagnostics.autocorrelation( + diagnostics.AutocorrelationQueryParams( + data=timeseries_data, y_column="close", x_columns=X_COLUMNS + ) + ) + result = out.results + dumped = result.model_dump() + assert 0.0 <= dumped["durbin_watson"] <= 4.0 + + +def test_residual_autocorrelation(timeseries_data): + """The Breusch-Godfrey test returns LM and F statistics with p-values.""" + out = diagnostics.residual_autocorrelation( + diagnostics.ResidualAutocorrelationQueryParams( + data=timeseries_data, + y_column="close", + x_columns=X_COLUMNS, + lags=2, + ) + ) + dumped = out.results.model_dump() + assert 0.0 <= dumped["lm_p_value"] <= 1.0 + assert 0.0 <= dumped["f_p_value"] <= 1.0 + assert isinstance(dumped["lm_statistic"], float) + assert isinstance(dumped["f_statistic"], float) + + +def test_residual_autocorrelation_default_lags(timeseries_data): + """The ``lags`` parameter defaults to 1.""" + params = diagnostics.ResidualAutocorrelationQueryParams( + data=timeseries_data, y_column="close", x_columns=X_COLUMNS + ) + assert params.lags == 1 + out = diagnostics.residual_autocorrelation(params) + assert out.results.lm_statistic is not None + + +def test_heteroskedasticity(timeseries_data): + """The heteroskedasticity command returns Breusch-Pagan and White results.""" + params = diagnostics.HeteroskedasticityQueryParams( + data=timeseries_data, y_column="close", x_columns=X_COLUMNS + ) + assert params.robust is True + out = diagnostics.heteroskedasticity(params) + dumped = out.results.model_dump() + for prefix in ("breusch_pagan", "white"): + assert 0.0 <= dumped[f"{prefix}_lm_p_value"] <= 1.0 + assert 0.0 <= dumped[f"{prefix}_f_p_value"] <= 1.0 + assert isinstance(dumped[f"{prefix}_lm_statistic"], float) + assert isinstance(dumped[f"{prefix}_f_statistic"], float) + + +def test_heteroskedasticity_non_robust(timeseries_data): + """The heteroskedasticity command accepts the non-robust Breusch-Pagan variant.""" + out = diagnostics.heteroskedasticity( + diagnostics.HeteroskedasticityQueryParams( + data=timeseries_data, + y_column="close", + x_columns=X_COLUMNS, + robust=False, + ) + ) + dumped = out.results.model_dump() + assert 0.0 <= dumped["breusch_pagan_lm_p_value"] <= 1.0 + + +def test_normality(timeseries_data): + """The Jarque-Bera normality test returns a statistic and p-value.""" + out = diagnostics.normality( + diagnostics.NormalityQueryParams( + data=timeseries_data, y_column="close", x_columns=X_COLUMNS + ) + ) + dumped = out.results.model_dump() + assert dumped["jarque_bera"] >= 0.0 + assert 0.0 <= dumped["p_value"] <= 1.0 + assert isinstance(dumped["skew"], float) + assert isinstance(dumped["kurtosis"], float) + + +def test_variance_inflation_factor_with_columns(timeseries_data): + """VIF with an explicit ``columns`` list returns one row per requested column.""" + out = diagnostics.variance_inflation_factor( + diagnostics.VarianceInflationFactorQueryParams( + data=timeseries_data, columns=X_COLUMNS + ) + ) + results = out.results + assert {row.variable for row in results} == set(X_COLUMNS) + for row in results: + assert row.model_dump()["vif"] > 0.0 + + +def test_variance_inflation_factor_without_columns(timeseries_data): + """Without ``columns`` VIF uses every numeric column in the dataset.""" + params = diagnostics.VarianceInflationFactorQueryParams(data=timeseries_data) + assert params.columns is None + out = diagnostics.variance_inflation_factor(params) + # open/high/low/close/volume. + assert len(out.results) == 5 + assert {row.variable for row in out.results} == { + "open", + "high", + "low", + "close", + "volume", + } diff --git a/openbb_platform/extensions/econometrics/tests/test_econometrics_utils.py b/openbb_platform/extensions/econometrics/tests/test_econometrics_utils.py index 04aacc34833..e38a5fdfc4a 100644 --- a/openbb_platform/extensions/econometrics/tests/test_econometrics_utils.py +++ b/openbb_platform/extensions/econometrics/tests/test_econometrics_utils.py @@ -2,6 +2,7 @@ import numpy as np import pandas as pd + from openbb_econometrics.utils import ( get_engle_granger_two_step_cointegration_test, mock_multi_index_data, diff --git a/openbb_platform/extensions/econometrics/tests/test_econometrics_views.py b/openbb_platform/extensions/econometrics/tests/test_econometrics_views.py new file mode 100644 index 00000000000..162c46894b2 --- /dev/null +++ b/openbb_platform/extensions/econometrics/tests/test_econometrics_views.py @@ -0,0 +1,55 @@ +"""Tests for ``openbb_econometrics.econometrics_views`` - the charting views. + +``openbb_charting`` is an optional dependency that is not installed in the test +environment. ``EconometricsViews`` imports ``openbb_charting.charts`` lazily, so +a fake package is injected into ``sys.modules`` to exercise the wrapper. +""" + +import sys +import types + +import pytest + +from openbb_econometrics.econometrics_views import EconometricsViews + + +@pytest.fixture +def fake_charting(monkeypatch): + """Inject a fake ``openbb_charting.charts.correlation_matrix`` package tree. + + The fake ``correlation_matrix`` records the keyword arguments it received and + returns a ``(figure, content)`` tuple, mirroring the real charting API. + """ + calls: dict = {} + + def _correlation_matrix(**kwargs): + calls["kwargs"] = kwargs + return ("FAKE_FIGURE", {"chart": "correlation_matrix"}) + + charting = types.ModuleType("openbb_charting") + charts = types.ModuleType("openbb_charting.charts") + corr_mod = types.ModuleType("openbb_charting.charts.correlation_matrix") + corr_mod.correlation_matrix = _correlation_matrix + charts.correlation_matrix = corr_mod + charting.charts = charts + + monkeypatch.setitem(sys.modules, "openbb_charting", charting) + monkeypatch.setitem(sys.modules, "openbb_charting.charts", charts) + monkeypatch.setitem( + sys.modules, "openbb_charting.charts.correlation_matrix", corr_mod + ) + return calls + + +def test_econometrics_correlation_matrix(fake_charting): + """The view delegates to ``openbb_charting`` and returns its tuple.""" + figure, content = EconometricsViews.econometrics_correlation_matrix( + data=[{"a": 1.0, "b": 2.0}], method="pearson" + ) + assert figure == "FAKE_FIGURE" + assert content == {"chart": "correlation_matrix"} + # The kwargs are forwarded unchanged. + assert fake_charting["kwargs"] == { + "data": [{"a": 1.0, "b": 2.0}], + "method": "pearson", + } diff --git a/openbb_platform/extensions/econometrics/tests/test_panel.py b/openbb_platform/extensions/econometrics/tests/test_panel.py new file mode 100644 index 00000000000..8491492b940 --- /dev/null +++ b/openbb_platform/extensions/econometrics/tests/test_panel.py @@ -0,0 +1,103 @@ +"""Tests for ``openbb_econometrics.panel`` - panel-data regression commands.""" + +import pytest + +from openbb_econometrics import panel + +PANEL_COMMANDS = [ + "panel_random_effects", + "panel_between", + "panel_pooled", + "panel_first_difference", + "panel_fmac", +] + + +@pytest.mark.parametrize("command_name", PANEL_COMMANDS) +def test_panel_regressions(panel_data, command_name): + """Each panel estimator returns finite coefficient rows.""" + command = getattr(panel, command_name) + out = command( + panel.PanelRegressionQueryParams( + data=panel_data, + y_column="income", + x_columns=["age", "education"], + ) + ) + results = out.results + assert isinstance(results, list) + assert len(results) > 0 + for row in results: + dumped = row.model_dump() + assert isinstance(dumped["variable"], str) + assert isinstance(dumped["coefficient"], float) + assert isinstance(dumped["standard_error"], float) + assert isinstance(dumped["t_statistic"], float) + assert isinstance(dumped["p_value"], float) + assert dumped["conf_int_lower"] <= dumped["conf_int_upper"] + + +def test_panel_first_difference_no_constant(panel_data): + """First-difference regression drops the constant term.""" + out = panel.panel_first_difference( + panel.PanelRegressionQueryParams( + data=panel_data, + y_column="income", + x_columns=["age", "education"], + ) + ) + variables = {row.variable for row in out.results} + assert "const" not in variables + + +def test_panel_random_effects_too_few_items_raises(small_panel_data): + """A panel with fewer than three observations triggers a ValueError.""" + params = panel.PanelRegressionQueryParams( + data=small_panel_data, + y_column="portfolio_value", + x_columns=["risk_free_rate"], + ) + with pytest.raises(ValueError, match="at least 3 items"): + panel.panel_random_effects(params) + + +def test_panel_fixed(panel_data): + """Fixed-effects regression includes entity effects and omits the constant.""" + out = panel.panel_fixed( + panel.PanelFixedQueryParams( + data=panel_data, y_column="income", x_columns=["age", "education"] + ) + ) + results = out.results + assert len(results) > 0 + assert "const" not in {row.variable for row in results} + for row in results: + dumped = row.model_dump() + assert isinstance(dumped["coefficient"], float) + assert dumped["conf_int_lower"] <= dumped["conf_int_upper"] + + +def test_panel_fixed_time_effects(panel_data): + """Fixed-effects regression accepts entity and time effects together.""" + out = panel.panel_fixed( + panel.PanelFixedQueryParams( + data=panel_data, + y_column="income", + x_columns=["age", "education"], + time_effects=True, + ) + ) + assert out.results + + +def test_panel_robust_covariance(panel_data): + """Panel estimators accept the robust covariance estimator.""" + out = panel.panel_random_effects( + panel.PanelRegressionQueryParams( + data=panel_data, + y_column="income", + x_columns=["age", "education"], + cov_type="robust", + ) + ) + assert out.results diff --git a/openbb_platform/extensions/econometrics/tests/test_regression.py b/openbb_platform/extensions/econometrics/tests/test_regression.py new file mode 100644 index 00000000000..64116e6bb28 --- /dev/null +++ b/openbb_platform/extensions/econometrics/tests/test_regression.py @@ -0,0 +1,86 @@ +"""Tests for ``openbb_econometrics.regression`` - OLS regression commands.""" + +import pytest + +from openbb_econometrics import regression + + +def test_ols_regression(timeseries_data): + """OLS regression returns one coefficient row per regressor plus the constant.""" + params = regression.OlsRegressionQueryParams( + data=timeseries_data, + y_column="close", + x_columns=["open", "high", "low"], + ) + assert params.cov_type == "nonrobust" + out = regression.ols_regression(params) + results = out.results + assert isinstance(results, list) + # const + open + high + low. + assert len(results) == 4 + variables = {row.variable for row in results} + assert variables == {"const", "open", "high", "low"} + for row in results: + dumped = row.model_dump() + assert dumped["conf_int_lower"] <= dumped["conf_int_upper"] + assert isinstance(dumped["coefficient"], float) + assert isinstance(dumped["t_statistic"], float) + assert isinstance(dumped["p_value"], float) + + +def test_ols_regression_summary(timeseries_data): + """The OLS summary reports finite goodness-of-fit statistics.""" + out = regression.ols_regression_summary( + regression.OlsRegressionSummaryQueryParams( + data=timeseries_data, + y_column="close", + x_columns=["open", "high", "low"], + ) + ) + result = out.results + dumped = result.model_dump() + assert 0.0 <= dumped["r_squared"] <= 1.0 + assert dumped["nobs"] == 120 + assert dumped["f_statistic"] is not None + assert dumped["f_p_value"] is not None + assert isinstance(dumped["aic"], float) + assert isinstance(dumped["bic"], float) + assert "OLS Regression Results" in dumped["summary"] + + +def test_ols_regression_summary_non_numeric_raises(non_numeric_data): + """A non-numeric regressor column triggers the numeric-coercion ValueError.""" + params = regression.OlsRegressionSummaryQueryParams( + data=non_numeric_data, + y_column="close", + x_columns=["open", "high"], + ) + with pytest.raises(ValueError, match="All columns must be numeric."): + regression.ols_regression_summary(params) + + +@pytest.mark.parametrize("cov_type", ["nonrobust", "HC0", "HC1", "HC2", "HC3"]) +def test_ols_regression_cov_type(timeseries_data, cov_type): + """OLS regression accepts the nonrobust and heteroskedasticity-robust estimators.""" + out = regression.ols_regression( + regression.OlsRegressionQueryParams( + data=timeseries_data, + y_column="close", + x_columns=["open", "high", "low"], + cov_type=cov_type, + ) + ) + assert len(out.results) == 4 + + +def test_ols_regression_summary_cov_type(timeseries_data): + """The OLS summary accepts a heteroskedasticity-robust covariance estimator.""" + out = regression.ols_regression_summary( + regression.OlsRegressionSummaryQueryParams( + data=timeseries_data, + y_column="close", + x_columns=["open", "high", "low"], + cov_type="HC3", + ) + ) + assert 0.0 <= out.results.r_squared <= 1.0 diff --git a/openbb_platform/extensions/econometrics/tests/test_router.py b/openbb_platform/extensions/econometrics/tests/test_router.py new file mode 100644 index 00000000000..ff2d7983b0c --- /dev/null +++ b/openbb_platform/extensions/econometrics/tests/test_router.py @@ -0,0 +1,11 @@ +"""Tests for ``openbb_econometrics.router`` - the aggregating router.""" + + +def test_router_imports_and_aggregates(): + """Importing the router wires together every sub-router.""" + from openbb_core.app.router import Router + + from openbb_econometrics.router import router + + assert isinstance(router, Router) + assert "router" in dir() diff --git a/openbb_platform/extensions/econometrics/tests/test_time_series.py b/openbb_platform/extensions/econometrics/tests/test_time_series.py new file mode 100644 index 00000000000..5efac56d948 --- /dev/null +++ b/openbb_platform/extensions/econometrics/tests/test_time_series.py @@ -0,0 +1,248 @@ +"""Tests for ``openbb_econometrics.time_series`` - stationarity and volatility.""" + +import pytest + +from openbb_econometrics import time_series + + +@pytest.mark.parametrize("regression", ["c", "ct", "ctt"]) +def test_unit_root(timeseries_data, regression): + """The ADF unit-root test returns a statistic and critical values.""" + out = time_series.unit_root( + time_series.UnitRootQueryParams( + data=timeseries_data, column="close", regression=regression + ) + ) + dumped = out.results.model_dump() + assert isinstance(dumped["adf_stat"], float) + assert 0.0 <= dumped["p_value"] <= 1.0 + assert dumped["used_lag"] >= 0 + assert dumped["nobs"] > 0 + assert isinstance(dumped["ic_best"], float) + assert set(dumped["critical_values"]) == {"1%", "5%", "10%"} + + +def test_unit_root_defaults(timeseries_data): + """The ADF parameters default to a constant regression with AIC lag selection.""" + params = time_series.UnitRootQueryParams(data=timeseries_data, column="close") + assert params.regression == "c" + assert params.maxlag is None + assert params.autolag == "AIC" + + +def test_unit_root_no_autolag(timeseries_data): + """With ``autolag`` disabled the ADF test uses ``maxlag`` directly.""" + out = time_series.unit_root( + time_series.UnitRootQueryParams( + data=timeseries_data, column="close", maxlag=4, autolag=None + ) + ) + dumped = out.results.model_dump() + assert dumped["used_lag"] == 4 + assert dumped["ic_best"] is None + + +@pytest.mark.parametrize("regression", ["c", "ct"]) +def test_kpss(timeseries_data, regression): + """The KPSS stationarity test returns a statistic and critical values.""" + out = time_series.kpss( + time_series.KpssQueryParams( + data=timeseries_data, column="close", regression=regression + ) + ) + dumped = out.results.model_dump() + assert isinstance(dumped["kpss_stat"], float) + assert 0.0 <= dumped["p_value"] <= 1.0 + assert dumped["lags"] >= 0 + assert dumped["critical_values"] + assert isinstance(dumped["p_value_interpolated"], bool) + assert dumped["p_value_interpolated"] == (0.01 < dumped["p_value"] < 0.1) + + +def test_kpss_defaults(timeseries_data): + """The KPSS parameters default to a constant regression and automatic lags.""" + params = time_series.KpssQueryParams(data=timeseries_data, column="close") + assert params.regression == "c" + assert params.nlags == "auto" + + +@pytest.mark.parametrize("nlags", ["auto", "legacy", 5]) +def test_kpss_nlags(timeseries_data, nlags): + """The KPSS test accepts automatic, legacy, and explicit integer lag settings.""" + out = time_series.kpss( + time_series.KpssQueryParams(data=timeseries_data, column="close", nlags=nlags) + ) + dumped = out.results.model_dump() + assert dumped["kpss_stat"] >= 0.0 + assert dumped["lags"] >= 0 + + +def test_cointegration(timeseries_data): + """Engle-Granger cointegration returns one row per column pair.""" + out = time_series.cointegration( + time_series.CointegrationQueryParams( + data=timeseries_data, columns=["open", "high", "close"] + ) + ) + results = out.results + # 3 columns -> 3 pairwise combinations. + assert len(results) == 3 + pairs = {row.pair for row in results} + assert pairs == {"open/high", "open/close", "high/close"} + for row in results: + dumped = row.model_dump() + assert isinstance(dumped["c"], float) + assert isinstance(dumped["gamma"], float) + assert isinstance(dumped["alpha"], float) + assert isinstance(dumped["adf_stat"], float) + + +@pytest.mark.parametrize("deterministic_order", [-1, 0, 1]) +def test_cointegration_johansen(timeseries_data, deterministic_order): + """The Johansen test returns one row per cointegration rank hypothesis.""" + out = time_series.cointegration_johansen( + time_series.CointegrationJohansenQueryParams( + data=timeseries_data, + columns=["open", "close"], + deterministic_order=deterministic_order, + ) + ) + results = out.results + # Two series -> two rank hypotheses. + assert len(results) == 2 + for rank, row in enumerate(results): + dumped = row.model_dump() + assert dumped["rank"] == rank + assert isinstance(dumped["eigenvalue"], float) + assert isinstance(dumped["trace_statistic"], float) + assert isinstance(dumped["max_eig_statistic"], float) + assert dumped["trace_crit_90"] <= dumped["trace_crit_99"] + + +def test_cointegration_johansen_defaults(timeseries_data): + """The Johansen ``deterministic_order``/``k_ar_diff`` defaults are 0 and 1.""" + params = time_series.CointegrationJohansenQueryParams( + data=timeseries_data, columns=["open", "close"] + ) + assert params.deterministic_order == 0 + assert params.k_ar_diff == 1 + + +def test_causality(timeseries_data): + """The Granger causality test returns one row per Granger sub-test.""" + out = time_series.causality( + time_series.CausalityQueryParams( + data=timeseries_data, y_column="close", x_column="open", lag=2 + ) + ) + results = out.results + assert len(results) > 0 + for row in results: + dumped = row.model_dump() + assert dumped["lag"] == 2 + assert isinstance(dumped["test"], str) + assert isinstance(dumped["statistic"], float) + assert 0.0 <= dumped["p_value"] <= 1.0 + + +def test_causality_default_lag(timeseries_data): + """The Granger causality ``lag`` parameter defaults to 3.""" + params = time_series.CausalityQueryParams( + data=timeseries_data, y_column="close", x_column="open" + ) + assert params.lag == 3 + + +@pytest.mark.parametrize( + ("p", "q", "distribution"), + [ + (1, 1, "normal"), + (2, 1, "t"), + (1, 2, "skewt"), + (2, 2, "ged"), + ], +) +def test_garch(timeseries_data, p, q, distribution): + """GARCH returns a conditional-volatility series and fit metadata in ``extra``.""" + out = time_series.garch( + time_series.GarchQueryParams( + data=timeseries_data, + column="close", + p=p, + q=q, + distribution=distribution, + ) + ) + results = out.results + assert len(results) == 120 + for index, row in enumerate(results): + dumped = row.model_dump() + assert dumped["date"] == index + assert dumped["conditional_volatility"] > 0.0 + + metadata = out.extra["results_metadata"] + assert set(metadata) == {"params", "aic", "bic", "log_likelihood"} + assert isinstance(metadata["params"], dict) + assert isinstance(metadata["aic"], float) + assert isinstance(metadata["bic"], float) + assert isinstance(metadata["log_likelihood"], float) + + +def test_garch_defaults(timeseries_data): + """The GARCH model-configuration parameters default to a plain GARCH(1, 1).""" + params = time_series.GarchQueryParams(data=timeseries_data, column="close") + assert params.mean == "Constant" + assert params.lags == 0 + assert params.vol == "GARCH" + assert params.p == 1 + assert params.o == 0 + assert params.q == 1 + assert params.power == 2.0 + assert params.distribution == "normal" + assert params.x_columns is None + + +def test_garch_configurable_volatility_model(timeseries_data): + """garch fits non-default mean and volatility processes.""" + out = time_series.garch( + time_series.GarchQueryParams( + data=timeseries_data, + column="close", + mean="AR", + lags=2, + vol="EGARCH", + o=1, + ) + ) + assert out.results + assert all(row.conditional_volatility > 0.0 for row in out.results) + + +def test_garch_with_exogenous_regressor(timeseries_data): + """garch accepts exogenous mean regressors through x_columns.""" + out = time_series.garch( + time_series.GarchQueryParams( + data=timeseries_data, + column="close", + x_columns=["open"], + mean="LS", + ) + ) + assert out.results + assert all(row.conditional_volatility > 0.0 for row in out.results) + + +def test_garch_preserves_input_dates(): + """garch reports each observation's input date, not a 0-based position.""" + rows = [ + { + "date": f"2022-{(i // 28) + 1:02d}-{(i % 28) + 1:02d}", + "close": 100.0 + (i % 7) - (i % 3) * 1.5 + i * 0.2, + } + for i in range(60) + ] + out = time_series.garch(time_series.GarchQueryParams(data=rows, column="close")) + dates = [str(row.model_dump()["date"]) for row in out.results] + assert len(dates) == 60 + assert dates[0] == "2022-01-01" + assert dates[-1] == "2022-03-04" diff --git a/openbb_platform/extensions/econometrics/tests/test_tools.py b/openbb_platform/extensions/econometrics/tests/test_tools.py new file mode 100644 index 00000000000..b9888d8e328 --- /dev/null +++ b/openbb_platform/extensions/econometrics/tests/test_tools.py @@ -0,0 +1,68 @@ +"""Tests for ``openbb_econometrics.tools`` - correlation matrix and statistics.""" + +import pytest + +from openbb_econometrics import tools + + +@pytest.mark.parametrize("method", ["pearson", "kendall", "spearman"]) +def test_correlation_matrix_methods(timeseries_data, method): + """The correlation matrix returns one row per numeric column for each method.""" + out = tools.correlation_matrix( + tools.CorrelationMatrixQueryParams(data=timeseries_data, method=method) + ) + results = out.results + assert isinstance(results, list) + # open/high/low/close/volume -> 5 numeric columns. + assert len(results) == 5 + comp_to = {row.comp_to for row in results} + assert comp_to == {"open", "high", "low", "close", "volume"} + for row in results: + dumped = row.model_dump() + assert dumped["comp_to"] in comp_to + # The self-correlation is 1.0. + assert dumped[row.comp_to] == pytest.approx(1.0) + + +def test_correlation_matrix_defaults(timeseries_data): + """The correlation matrix defaults to the pearson method with min_periods of 1.""" + params = tools.CorrelationMatrixQueryParams(data=timeseries_data) + assert params.method == "pearson" + assert params.min_periods == 1 + out = tools.correlation_matrix(params) + assert len(out.results) == 5 + + +def test_correlation_matrix_min_periods(timeseries_data): + """The correlation matrix honours an explicit min_periods threshold.""" + out = tools.correlation_matrix( + tools.CorrelationMatrixQueryParams(data=timeseries_data, min_periods=10) + ) + assert len(out.results) == 5 + + +def test_correlation_matrix_symbol_pivot(symbol_data): + """A dataset with a ``symbol`` column and >1 symbol pivots on ``close``.""" + out = tools.correlation_matrix(tools.CorrelationMatrixQueryParams(data=symbol_data)) + results = out.results + # Pivoted on the three symbols. + assert len(results) == 3 + assert {row.comp_to for row in results} == {"AAA", "BBB", "CCC"} + + +def test_summary_statistics(timeseries_data): + """Summary statistics returns one fully-populated row per numeric column.""" + out = tools.summary_statistics( + tools.SummaryStatisticsQueryParams(data=timeseries_data) + ) + results = out.results + assert len(results) == 5 + columns = {row.column for row in results} + assert columns == {"open", "high", "low", "close", "volume"} + for row in results: + dumped = row.model_dump() + assert 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