Hotfix/ext map test (#5459)

* add tests and rebuild

* remove files before rebuild to avoid leftovers

* replace NoneType with None

* docstring

20 files changed, 172 insertions, 3918 deletions

diff --git a/noxfile.py b/noxfile.py
index 62e602e956b..9c305b66e32 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -1,6 +1,10 @@
 import nox
 
-test_locations = ["openbb_sdk/sdk/core/tests", "openbb_sdk/providers"]
+test_locations = [
+    "openbb_sdk/tests",
+    "openbb_sdk/sdk/core/tests",
+    "openbb_sdk/providers",
+]
 
 
 @nox.session(python=["3.8", "3.9", "3.10", "3.11"])
diff --git a/openbb_sdk/openbb/package/__extensions__.py b/openbb_sdk/openbb/package/__extensions__.py
index 445cb6fa972..f7de27006cf 100644
--- a/openbb_sdk/openbb/package/__extensions__.py
+++ b/openbb_sdk/openbb/package/__extensions__.py
@@ -7,15 +7,11 @@ from openbb_core.app.static.container import Container
 class Extensions(Container):
     """
     /crypto
-    /econometrics
     /economy
     /fixedincome
     /forex
-    /futures
     /news
-    /qa
     /stocks
-    /ta
     """
 
     def __repr__(self) -> str:
@@ -28,12 +24,6 @@ class Extensions(Container):
         return crypto.CLASS_crypto(command_runner=self._command_runner)
 
     @property
-    def econometrics(self):  # route = "/econometrics"
-        from . import econometrics
-
-        return econometrics.CLASS_econometrics(command_runner=self._command_runner)
-
-    @property
     def economy(self):  # route = "/economy"
         from . import economy
 
@@ -52,31 +42,13 @@ class Extensions(Container):
         return forex.CLASS_forex(command_runner=self._command_runner)
 
     @property
-    def futures(self):  # route = "/futures"
-        from . import futures
-
-        return futures.CLASS_futures(command_runner=self._command_runner)
-
-    @property
     def news(self):  # route = "/news"
         from . import news
 
         return news.CLASS_news(command_runner=self._command_runner)
 
     @property
-    def qa(self):  # route = "/qa"
-        from . import qa
-
-        return qa.CLASS_qa(command_runner=self._command_runner)
-
-    @property
     def stocks(self):  # route = "/stocks"
         from . import stocks
 
         return stocks.CLASS_stocks(command_runner=self._command_runner)
-
-    @property
-    def ta(self):  # route = "/ta"
-        from . import ta
-
-        return ta.CLASS_ta(command_runner=self._command_runner)
diff --git a/openbb_sdk/openbb/package/crypto.py b/openbb_sdk/openbb/package/crypto.py
index f0d9e513cc5..7eaa0d7bbf2 100644
--- a/openbb_sdk/openbb/package/crypto.py
+++ b/openbb_sdk/openbb/package/crypto.py
@@ -38,7 +38,7 @@ class CLASS_crypto(Container):
                 description="End date of the data, in YYYY-MM-DD format."
             ),
         ] = None,
-        provider: Union[Literal["fmp", "polygon", "yfinance"], None] = None,
+        provider: Union[Literal["fmp", "polygon"], None] = None,
         **kwargs
     ) -> OBBject[List]:
         """Crypto Historical Price.
@@ -51,7 +51,7 @@ class CLASS_crypto(Container):
             Start date of the data, in YYYY-MM-DD format.
         end_date : Union[datetime.date, None, str]
             End date of the data, in YYYY-MM-DD format.
-        provider : Union[Literal['fmp', 'polygon', 'yfinance'], None]
+        provider : Union[Literal['fmp', 'polygon'], None]
             The provider to use for the query, by default None.
             If None, the provider specified in defaults is selected or 'fmp' if there is
             no default.
@@ -67,17 +67,13 @@ class CLASS_crypto(Container):
             Whether the data is adjusted. (provider: polygon)
         multiplier : PositiveInt
             Multiplier of the timespan. (provider: polygon)
-        interval : Literal['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo']
-            Data granularity. (provider: yfinance)
-        period : Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']
-            Period of the data to return. (provider: yfinance)
 
         Returns
         -------
         OBBject
             results : List[CryptoHistorical]
                 Serializable results.
-            provider : Union[Literal['fmp', 'polygon', 'yfinance'], NoneType]
+            provider : Union[Literal['fmp', 'polygon'], None]
                 Provider name.
             warnings : Optional[List[Warning_]]
                 List of warnings.
diff --git a/openbb_sdk/openbb/package/econometrics.py b/openbb_sdk/openbb/package/econometrics.py
deleted file mode 100644
index 62f7f350f5b..00000000000
--- a/openbb_sdk/openbb/package/econometrics.py
+++ /dev/null
@@ -1,519 +0,0 @@
-### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
-
-from typing import Dict, List, Literal, Union
-
-import openbb_provider
-import openbb_provider.abstract.data
-import pandas
-import pydantic
-import pydantic.types
-from openbb_core.app.model.obbject import OBBject
-from openbb_core.app.static.container import Container
-from openbb_core.app.static.filters import filter_inputs
-from pydantic import validate_arguments
-
-
-class CLASS_econometrics(Container):
-    """/econometrics
-    bgot
-    coint
-    corr
-    dwat
-    granger
-    ols
-    ols_summary
-    panelbols
-    panelfd
-    panelfmac
-    panelols
-    panelpols
-    panelre
-    unitroot
-    """
-
-    def __repr__(self) -> str:
-        return self.__doc__ or ""
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def bgot(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-        lags: pydantic.types.PositiveInt = 1,
-    ) -> OBBject[openbb_provider.abstract.data.Data]:
-        """Perform Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation.
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-        lags: PositiveInt
-            Number of lags to use in the test.
-        Returns
-        -------
-        OBBject[Data]
-            OBBject with the results being the score from the test.
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-            lags=lags,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/bgot",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def coint(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        columns: List[str],
-    ) -> OBBject[openbb_provider.abstract.data.Data]:
-        """Show co-integration between two timeseries using the two step Engle-Granger test.
-
-        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.
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            columns=columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/coint",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def corr(
-        self, data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame]
-    ) -> OBBject[List]:
-        """Get the corrlelation matrix of an input dataset.
-
-        Parameters
-        ----------
-        data : List[Data]
-            Input dataset.
-
-        Returns
-        -------
-        OBBject[List[Data]]
-            Correlation matrix.
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/corr",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def dwat(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-    ) -> OBBject[Dict]:
-        """Perform Durbin-Watson test for autocorrelation
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-
-        Returns
-        -------
-        OBBject[Data]
-            OBBject with the results being the score from the test.
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/dwat",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def granger(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_column: str,
-        lag: pydantic.types.PositiveInt = 3,
-    ) -> OBBject[openbb_provider.abstract.data.Data]:
-        """Perform Granger causality test to determine if X "causes" y.
-
-        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.
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_column=x_column,
-            lag=lag,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/granger",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def ols(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-    ) -> OBBject[Dict]:
-        """Perform OLS regression.  This returns the model and results objects from statsmodels.
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-
-        Returns
-        -------
-        OBBject[Dict]
-            OBBject with the results being model and results objects.
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/ols",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def ols_summary(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-    ) -> OBBject[openbb_provider.abstract.data.Data]:
-        """Perform OLS regression.  This returns the summary object from statsmodels.
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-
-        Returns
-        -------
-        OBBject[Dict]
-            OBBject with the results being summary object.
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/ols_summary",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def panelbols(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-    ) -> OBBject[Dict]:
-        """Perform a Between estimator regression on panel data
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-
-        Returns
-        -------
-        OBBject[Dict]
-            OBBject with the fit model returned
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/panelbols",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def panelfd(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-    ) -> OBBject[Dict]:
-        """Perform a first-difference estimate for panel data
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-
-        Returns
-        -------
-        OBBject[Dict]
-            OBBject with the fit model returned
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/panelfd",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def panelfmac(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-    ) -> OBBject[Dict]:
-        """Fama-MacBeth estimator for panel data
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-
-        Returns
-        -------
-        OBBject[Dict]
-            OBBject with the fit model returned
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/panelfmac",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def panelols(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-    ) -> OBBject[Dict]:
-        """One- and two-way fixed effects estimator for panel data
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-
-        Returns
-        -------
-        OBBject[Dict]
-            OBBject with the fit model returned
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/panelols",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def panelpols(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-    ) -> OBBject[Dict]:
-        """Perform a Pooled coefficvient estimator regression on panel data
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-
-        Returns
-        -------
-        OBBject[Dict]
-            OBBject with the fit model returned
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/panelpols",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def panelre(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        y_column: str,
-        x_columns: List[str],
-    ) -> OBBject[Dict]:
-        """Perform One-way Random Effects model for panel data
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        y_column: str
-            Target column.
-        x_columns: str
-            List of columns to use as exogenous variables.
-
-        Returns
-        -------
-        OBBject[Dict]
-            OBBject with the fit model returned
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            y_column=y_column,
-            x_columns=x_columns,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/panelre",
-            **inputs,
-        )
-
-    @validate_arguments(config=dict(arbitrary_types_allowed=True))
-    def unitroot(
-        self,
-        data: Union[List[openbb_provider.abstract.data.Data], pandas.DataFrame],
-        column: str,
-        regression: Literal["c", "ct", "ctt"] = "c",
-    ) -> OBBject[openbb_provider.abstract.data.Data]:
-        """Perform Augmented Dickey-Fuller unit root test.
-
-        Parameters
-        ----------
-        data: List[Data]
-            Input dataset.
-        column: str
-            Data columns to check unit root
-        regression: str
-            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.
-        """  # noqa: E501
-
-        inputs = filter_inputs(
-            data=data,
-            column=column,
-            regression=regression,
-        )
-
-        return self._command_runner.run(
-            "/econometrics/unitroot",
-            **inputs,
-        )
diff --git a/openbb_sdk/openbb/package/economy.py b/openbb_sdk/openbb/package/economy.py
index 58e0e9ea5cf..48753544223 100644
--- a/openbb_sdk/openbb/package/economy.py
+++ b/openbb_sdk/openbb/package/economy.py
@@ -8,23 +8,16 @@ from openbb_core.app.model.custom_parameter import OpenBBCustomParameter
 from openbb_core.app.model.obbject import OBBject
 from openbb_core.app.static.container import Container
 from openbb_core.app.static.filters import filter_inputs
-from pydantic import BaseModel, validate_arguments
+from pydantic import validate_arguments
 
 
 class CLASS_economy(Container):
     """/economy
     available_indices
     const
-    cot
-    cot_search
     cpi
-    european_index
-    european_index_constituents
     index
-    index_search
-    index_snapshots
     risk
-    sp500_multiples
     """
 
     def __repr__(self) -> str:
@@ -32,25 +25,23 @@ class CLASS_economy(Container):
 
     @validate_arguments
     def available_indices(
-        self, provider: Union[Literal["cboe", "fmp", "yfinance"], None] = None, **kwargs
+        self, provider: Union[Literal["fmp"], None] = None, **kwargs
     ) -> OBBject[List]:
         """Lists of available indices from a provider.
 
         Parameters
         ----------
-        provider : Union[Literal['cboe', 'fmp', 'yfinance'], None]
+        provider : Union[Literal['fmp'], None]
             The provider to use for the query, by default None.
-            If None, the provider specified in defaults is selected or 'cboe' if there is
+            If None, the provider specified in defaults is selected or 'fmp' if there is
             no default.
-        europe : bool
-            Filter for European indices. False for US indices. (provider: cboe)
 
         Returns
         -------
         OBBject
             results : List[AvailableIndices]
                 Serializable results.
-            provider : Union[Literal['cboe', 'fmp', 'yfinance'], NoneType]
+            provider : Union[Literal['fmp'], None]
                 Provider name.
             warnings : Optional[List[Warning_]]
                 List of warnings.
@@ -65,34 +56,11 @@ class CLASS_economy(Container):
             Name of the index.
         currency : Optional[str]
             Currency the index is traded in.
-        isin : Optional[str]
-            ISIN code for the index. Valid only for European indices. (provider: cboe)
-        region : Optional[str]
-            Region for the index. Valid only for European indices (provider: cboe)
-        symbol : Optional[str]
-            Symbol for the index. (provider: cboe, yfinance)
-        description : Optional[str]
-            Description for the index. Valid only for US indices. (provider: cboe)
-        data_delay : Optional[int]
-            Data delay for the index. Valid only for US indices. (provider: cboe)
-        open_time : Optional[time]
-            Opening time for the index. Valid only for US indices. (provider: cboe)
-        close_time : Optional[time]
-            Closing time for the index. Valid only for US indices. (provider: cboe)
-        time_zone : Optional[str]
-            Time zone for the index. Valid only for US indices. (provider: cboe)
-        tick_days : Optional[str]
-            The trading days for t