[Feature] Add Chart To econometrics.correlation_matrix (#6750)

* add chart to correlation matrix

* lint

* handling for data.results without chart

---------

Co-authored-by: Igor Radovanovic <[email protected]>

Author: deeleeramone

openbb_platform/extensions/econometrics/integration/test_econometrics_api.py

@@ -71,8 +71,8 @@ def get_data(menu: Literal["equity", "crypto"]):
 @pytest.mark.parametrize(
     "params, data_type",
     [
-        ({"data": ""}, "equity"),
-        ({"data": ""}, "crypto"),
+        ({"data": "", "method": "pearson"}, "equity"),
+        ({"data": "", "method": "pearson"}, "crypto"),
     ],
 )
 @pytest.mark.integration

openbb_platform/extensions/econometrics/integration/test_econometrics_python.py

@@ -66,8 +66,8 @@ def get_data(menu: Literal["equity", "crypto"]):
 @parametrize(
     "params, data_type",
     [
-        ({"data": ""}, "equity"),
-        ({"data": ""}, "crypto"),
+        ({"data": "", "method": "pearson"}, "equity"),
+        ({"data": "", "method": "pearson"}, "crypto"),
     ],
 )
 @pytest.mark.integration

openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_router.py

@@ -25,7 +25,9 @@
         APIEx(parameters={"data": APIEx.mock_data("timeseries")}),
     ],
 )
-def correlation_matrix(data: List[Data]) -> OBBject[List[Data]]:
+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.
@@ -37,6 +39,11 @@ def correlation_matrix(data: List[Data]) -> OBBject[List[Data]]:
     ----------
     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
     -------
@@ -49,9 +56,14 @@ def correlation_matrix(data: List[Data]) -> OBBject[List[Data]]:
 
     df = basemodel_to_df(data)
     # remove non float columns from the dataframe to perform the correlation
-    df = df.select_dtypes(include=["float64"])
 
-    corr = df.corr()
+    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)

openbb_platform/extensions/econometrics/openbb_econometrics/econometrics_views.py

@@ -0,0 +1,39 @@
+"""Views for the Econometrics Extension."""
+
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from openbb_charting.core.openbb_figure import (
+        OpenBBFigure,
+    )
+
+
+class EconometricsViews:
+    """Econometrics Views."""
+
+    @staticmethod
+    def econometrics_correlation_matrix(  # noqa: PLR0912
+        **kwargs,
+    ) -> tuple["OpenBBFigure", dict[str, Any]]:
+        """Correlation Matrix Chart.
+
+        Parameters
+        ----------
+        data : Union[list[Data], DataFrame]
+            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
+        colorscale : str
+            Plotly colorscale to use for the heatmap. Default is "RdBu".
+        title : str
+            Title of the chart. Default is "Asset Correlation Matrix".
+        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)

openbb_platform/extensions/econometrics/pyproject.toml

@@ -21,3 +21,6 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry.plugins."openbb_core_extension"]
 econometrics = "openbb_econometrics.econometrics_router:router"
+
+[tool.poetry.plugins."openbb_charting_extension"]
+econometrics = "openbb_econometrics.econometrics_views:EconometricsViews"

openbb_platform/obbject_extensions/charting/integration/test_charting_api.py

@@ -897,3 +897,44 @@ def test_charting_economy_survey_bls_series(params, headers):
     assert chart
     assert not fig
     assert list(chart.keys()) == ["content", "format"]
+
+
+@parametrize(
+    "params",
+    [
+        (
+            {
+                "data": "",
+                "method": "pearson",
+                "chart": True,
+            }
+        )
+    ],
+)
+@pytest.mark.integration
+def test_charting_econometrics_correlation_matrix(params, headers):
+    """Test chart econometrics correlation matrix."""
+    # pylint:disable=import-outside-toplevel
+    from pandas import DataFrame
+
+    url = "http://0.0.0.0:8000/api/v1/equity/price/historical?symbol=AAPL,MSFT,GOOG&provider=yfinance"
+    result = requests.get(url, headers=headers, timeout=10)
+    df = DataFrame(result.json()["results"])
+    df = df.pivot(index="date", columns="symbol", values="close").reset_index()
+    body = df.to_dict(orient="records")
+
+    params = {p: v for p, v in params.items() if v}
+
+    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=headers, timeout=10, data=json.dumps(body))
+
+    assert isinstance(result, requests.Response)
+    assert result.status_code == 200
+
+    chart = result.json()["chart"]
+    fig = chart.pop("fig", {})
+
+    assert chart
+    assert not fig
+    assert list(chart.keys()) == ["content", "format"]

openbb_platform/obbject_extensions/charting/integration/test_charting_python.py

@@ -728,3 +728,34 @@ def test_charting_economy_survey_bls_series(params, obb):
     assert len(result.results) > 0
     assert result.chart.content
     assert isinstance(result.chart.fig, OpenBBFigure)
+
+
+@parametrize(
+    "params",
+    [
+        (
+            {
+                "data": "",
+                "method": "pearson",
+                "chart": True,
+            }
+        )
+    ],
+)
+@pytest.mark.integration
+def test_charting_econometrics_correlation_matrix(params, obb):
+    """Test chart econometrics correlation matrix."""
+
+    symbols = "XRT,XLB,XLI,XLH,XLC,XLY,XLU,XLK".split(",")
+    params["data"] = (
+        obb.equity.price.historical(symbol=symbols, provider="yfinance")
+        .to_df()
+        .pivot(columns="symbol", values="close")
+        .filter(items=symbols, axis=1)
+    )
+    result = obb.econometrics.correlation_matrix(**params)
+    assert result
+    assert isinstance(result, OBBject)
+    assert len(result.results) > 0
+    assert result.chart.content
+    assert isinstance(result.chart.fig, OpenBBFigure)

openbb_platform/obbject_extensions/charting/openbb_charting/charting.py

@@ -1,6 +1,6 @@
 """Charting Class implementation."""
 
-# pylint: disable=too-many-arguments,unused-argument
+# pylint: disable=too-many-arguments,unused-argument,too-many-positional-arguments
 
 from typing import (
     TYPE_CHECKING,
@@ -57,6 +57,8 @@ class Charting:
         Create a line chart from external data.
     create_bar_chart
         Create a bar chart, on a single x-axis with one or more values for the y-axis, from external data.
+    create_correlation_matrix
+        Create a correlation matrix from external data.
     toggle_chart_style
         Toggle the chart style, of an existing chart, between light and dark mode.
     """
@@ -367,6 +369,54 @@ def create_bar_chart(
 
         return fig
 
+    def create_correlation_matrix(
+        self,
+        data: Union[
+            list[Data],
+            "DataFrame",
+        ],
+        method: Literal["pearson", "kendall", "spearman"] = "pearson",
+        colorscale: str = "RdBu",
+        title: str = "Asset Correlation Matrix",
+        layout_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        """Create a correlation matrix from external data.
+
+        Parameters
+        ----------
+        data : Union[list[Data], DataFrame]
+            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
+        colorscale : str
+            Plotly colorscale to use for the heatmap. Default is "RdBu".
+        title : str
+            Title of the chart. Default is "Asset Correlation Matrix".
+        layout_kwargs : Dict[str, Any]
+            Additional keyword arguments to apply with figure.update_layout(), by default None.
+
+        Returns
+        -------
+        OpenBBFigure
+            The OpenBBFigure object.
+        """
+        # pylint: disable=import-outside-toplevel
+        from openbb_charting.charts.correlation_matrix import correlation_matrix
+
+        kwargs = {
+            "data": data,
+            "method": method,
+            "colorscale": colorscale,
+            "title": title,
+            "layout_kwargs": layout_kwargs,
+        }
+        fig, _ = correlation_matrix(**kwargs)
+        fig = self._set_chart_style(fig)
+        return fig
+
     # pylint: disable=inconsistent-return-statements
     def show(self, render: bool = True, **kwargs):
         """Display chart and save it to the OBBject."""

openbb_platform/obbject_extensions/charting/openbb_charting/charts/correlation_matrix.py

@@ -0,0 +1,117 @@
+"""Correlation Matrix Chart."""
+
+from typing import TYPE_CHECKING, Any, Union
+
+if TYPE_CHECKING:
+    from plotly.graph_objs import Figure  # noqa
+    from openbb_charting.core.openbb_figure import OpenBBFigure  # noqa
+
+
+def correlation_matrix(  # noqa: PLR0912
+    **kwargs,
+) -> tuple[Union["OpenBBFigure", "Figure"], dict[str, Any]]:
+    """Correlation Matrix Chart."""
+    # pylint: disable=import-outside-toplevel
+    from numpy import ones_like, triu  # noqa
+    from openbb_core.app.utils import basemodel_to_df  # noqa
+    from openbb_charting.core.openbb_figure import OpenBBFigure
+    from openbb_charting.core.chart_style import ChartStyle
+    from plotly.graph_objs import Figure, Heatmap, Layout
+    from pandas import DataFrame
+
+    if "data" in kwargs and isinstance(kwargs["data"], DataFrame):
+        corr = kwargs["data"]
+    elif "data" in kwargs and isinstance(kwargs["data"], list):
+        corr = basemodel_to_df(kwargs["data"], index=kwargs.get("index", "date"))  # type: ignore
+    else:
+        corr = basemodel_to_df(
+            kwargs["obbject_item"], index=kwargs.get("index", "date")  # type: ignore
+        )
+    if (
+        "symbol" in corr.columns
+        and len(corr.symbol.unique()) > 1
+        and "close" in corr.columns
+    ):
+        corr = corr.pivot(
+            columns="symbol",
+            values="close",
+        )
+
+    method = kwargs.get("method") or "pearson"
+    corr = corr.corr(method=method, numeric_only=True)
+
+    X = corr.columns.to_list()
+    x_replace = X[-1]
+    Y = X.copy()
+    y_replace = Y[0]
+    X = [x if x != x_replace else "" for x in X]
+    Y = [y if y != y_replace else "" for y in Y]
+    mask = triu(ones_like(corr, dtype=bool))
+    df = corr.mask(mask)
+    title = kwargs.get("title") or "Asset Correlation Matrix"
+    text_color = "white" if ChartStyle().plt_style == "dark" else "black"
+    colorscale = kwargs.get("colorscale") or "RdBu"
+    heatmap = Heatmap(
+        z=df,
+        x=X,
+        y=Y,
+        xgap=1,
+        ygap=1,
+        colorscale=colorscale,
+        colorbar=dict(
+            orientation="v",
+            x=0.9,
+            y=0.45,
+            xanchor="left",
+            yanchor="middle",
+            len=0.75,
+            bgcolor="rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)",
+        ),
+        text=df.fillna(""),
+        texttemplate="%{text:.4f}",
+        hoverinfo="skip",
+    )
+    layout = Layout(
+        title=title,
+        title_x=0.5,
+        title_y=0.95,
+        xaxis=dict(
+            showgrid=False,
+            showline=False,
+            ticklen=0,
+            tickfont=dict(size=16),
+            ticklabelstandoff=10,
+            domain=[0.05, 1],
+        ),
+        yaxis=dict(
+            showgrid=False,
+            side="left",
+            autorange="reversed",
+            showline=False,
+            ticklen=0,
+            tickfont=dict(size=16),
+            ticklabelstandoff=15,
+            domain=[0.05, 1],
+        ),
+        margin=dict(r=20, t=0, b=50),
+        dragmode=False,
+    )
+    fig = Figure(data=[heatmap], layout=layout)
+    figure = OpenBBFigure(fig=fig)
+    figure.update_layout(
+        font=dict(color=text_color),
+        paper_bgcolor=(
+            "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)"
+        ),
+        plot_bgcolor=(
+            "rgba(0,0,0,0)" if text_color == "white" else "rgba(255,255,255,0)"
+        ),
+    )
+    layout_kwargs = kwargs.get("layout_kwargs", {})
+
+    if layout_kwargs:
+        figure.update_layout(**layout_kwargs)
+
+    content = figure.show(external=True).to_plotly_json()  # type: ignore
+
+    return figure, content