V0.0.18.0 mergers and acquisitions (#27)

* update pre-commit

* update version

* added mergers and aquisitions and tests

Author: TexasCoding

poetry.lock

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "fmp-py"
-version = "0.0.17.0"
+version = "0.0.18.0"
 description = "Python package for Financial Modeling Prep API"
 authors = ["TexasCoding <[email protected]>"]
 readme = "README.md"

pyproject.toml

@@ -0,0 +1,123 @@
+import pandas as pd
+from fmp_py.fmp_base import FmpBase
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+"""
+The FmpMergersAndAquisitions class provides methods for retrieving mergers and acquisitions data from the Financial Modeling Prep API.
+Refer to the official documentation (https://site.financialmodelingprep.com/developer/docs#mergers-&-acquisitions) for more information.
+
+def ma_rss_feed(self, page: int = 0) -> pd.DataFrame:
+    Reference: https://site.financialmodelingprep.com/developer/docs#m&a-rss-feed-mergers-&-acquisitions
+
+def search_ma(self, query: str) -> pd.DataFrame:
+    Reference: https://site.financialmodelingprep.com/developer/docs#search-m&a-mergers-&-acquisitions
+"""
+
+
+class FmpMergersAndAquisitions(FmpBase):
+    def __init__(self, api_key: str = os.getenv("FMP_API_KEY")) -> None:
+        super().__init__(api_key)
+
+    #####################################
+    # Mergers and Acquisitions Search
+    #####################################
+    def search_ma(self, name: str) -> pd.DataFrame:
+        """
+        Retrieves mergers and acquisitions data based on a search name.
+
+        Args:
+            name (str): The search name of company.
+
+        Returns:
+            pd.DataFrame: A DataFrame containing the mergers and acquisitions data.
+        """
+        url = "v4/mergers-acquisitions/search"
+        params = {"name": name}
+        response = self.get_request(url, params)
+
+        if not response:
+            raise ValueError("No data found for the specified parameters.")
+
+        data_df = (
+            pd.DataFrame(response)
+            .fillna("")
+            .rename(
+                columns={
+                    "companyName": "company_name",
+                    "symbol": "symbol",
+                    "targetedCompanyName": "targeted_company_name",
+                    "targetedCik": "targeted_cik",
+                    "targetedSymbol": "targeted_symbol",
+                    "transactionDate": "transaction_date",
+                    "acceptanceTime": "acceptance_time",
+                    "url": "url",
+                }
+            )
+            .astype(
+                {
+                    "company_name": "str",
+                    "symbol": "str",
+                    "targeted_company_name": "str",
+                    "targeted_cik": "str",
+                    "targeted_symbol": "str",
+                    "transaction_date": "datetime64[ns]",
+                    "acceptance_time": "datetime64[ns]",
+                    "url": "str",
+                }
+            )
+        )
+        return data_df
+
+    #####################################
+    # Mergers and Acquisitions RSS Feed
+    #####################################
+    def ma_rss_feed(self, page: int = 0) -> pd.DataFrame:
+        """
+        Retrieves mergers and acquisitions data from the RSS feed.
+
+        Args:
+            page (int): The page number for the RSS feed.
+
+        Returns:
+            pd.DataFrame: A DataFrame containing the mergers and acquisitions data.
+        """
+        url = "v4/mergers-acquisitions-rss-feed"
+        params = {"page": page}
+        response = self.get_request(url, params)
+
+        if not response:
+            raise ValueError("No data found for the specified parameters.")
+
+        data_df = (
+            pd.DataFrame(response)
+            .fillna("")
+            .rename(
+                columns={
+                    "companyName": "company_name",
+                    "symbol": "symbol",
+                    "targetedCompanyName": "targeted_company_name",
+                    "targetedCik": "targeted_cik",
+                    "targetedSymbol": "targeted_symbol",
+                    "transactionDate": "transaction_date",
+                    "acceptanceTime": "acceptance_time",
+                    "url": "url",
+                }
+            )
+            .astype(
+                {
+                    "company_name": "str",
+                    "symbol": "str",
+                    "targeted_company_name": "str",
+                    "targeted_cik": "str",
+                    "targeted_symbol": "str",
+                    "transaction_date": "datetime64[ns]",
+                    "acceptance_time": "datetime64[ns]",
+                    "url": "str",
+                }
+            )
+        )
+        return data_df

src/fmp_py/fmp_mergers_and_aquisitions.py

@@ -0,0 +1,90 @@
+import pytest
+import pandas as pd
+
+from fmp_py.fmp_mergers_and_aquisitions import FmpMergersAndAquisitions
+
+
+@pytest.fixture
+def fmp_mergers_and_aquisitions():
+    return FmpMergersAndAquisitions()
+
+
+def test_fmp_mergers_and_aquisitions_init(fmp_mergers_and_aquisitions):
+    assert isinstance(fmp_mergers_and_aquisitions, FmpMergersAndAquisitions)
+
+
+def test_fmp_mergers_and_aquisitions_ma_rss_feed(fmp_mergers_and_aquisitions):
+    result = fmp_mergers_and_aquisitions.ma_rss_feed()
+    assert isinstance(result, pd.DataFrame)
+    assert not result.empty
+    assert result.columns.tolist() == [
+        "company_name",
+        "cik",
+        "symbol",
+        "targeted_company_name",
+        "targeted_cik",
+        "targeted_symbol",
+        "transaction_date",
+        "acceptance_time",
+        "url",
+    ]
+    assert isinstance(result["company_name"].iloc[0], str)
+    assert isinstance(result["cik"].iloc[0], str)
+    assert isinstance(result["symbol"].iloc[0], str)
+    assert isinstance(result["targeted_company_name"].iloc[0], str)
+    assert isinstance(result["targeted_cik"].iloc[0], str)
+    assert isinstance(result["targeted_symbol"].iloc[0], str)
+    assert isinstance(result["transaction_date"].iloc[0], pd.Timestamp)
+    assert isinstance(result["acceptance_time"].iloc[0], pd.Timestamp)
+    assert isinstance(result["url"].iloc[0], str)
+
+
+def test_fmp_mergers_and_aquisitions_ma_rss_feed_by_page(fmp_mergers_and_aquisitions):
+    result = fmp_mergers_and_aquisitions.ma_rss_feed(page=1)
+    assert isinstance(result, pd.DataFrame)
+    assert not result.empty
+    assert result.columns.tolist() == [
+        "company_name",
+        "cik",
+        "symbol",
+        "targeted_company_name",
+        "targeted_cik",
+        "targeted_symbol",
+        "transaction_date",
+        "acceptance_time",
+        "url",
+    ]
+    assert isinstance(result["company_name"].iloc[0], str)
+    assert isinstance(result["cik"].iloc[0], str)
+    assert isinstance(result["symbol"].iloc[0], str)
+    assert isinstance(result["targeted_company_name"].iloc[0], str)
+    assert isinstance(result["targeted_cik"].iloc[0], str)
+    assert isinstance(result["targeted_symbol"].iloc[0], str)
+    assert isinstance(result["transaction_date"].iloc[0], pd.Timestamp)
+    assert isinstance(result["acceptance_time"].iloc[0], pd.Timestamp)
+    assert isinstance(result["url"].iloc[0], str)
+
+
+def test_fmp_mergers_and_aquisitions_search_ma(fmp_mergers_and_aquisitions):
+    result = fmp_mergers_and_aquisitions.search_ma(name="test")
+    assert isinstance(result, pd.DataFrame)
+    assert not result.empty
+    assert result.columns.tolist() == [
+        "company_name",
+        "cik",
+        "symbol",
+        "targeted_company_name",
+        "targeted_cik",
+        "targeted_symbol",
+        "transaction_date",
+        "acceptance_time",
+        "url",
+    ]
+    assert isinstance(result["company_name"].iloc[0], str)
+    assert isinstance(result["symbol"].iloc[0], str)
+    assert isinstance(result["targeted_company_name"].iloc[0], str)
+    assert isinstance(result["targeted_cik"].iloc[0], str)
+    assert isinstance(result["targeted_symbol"].iloc[0], str)
+    assert isinstance(result["transaction_date"].iloc[0], pd.Timestamp)
+    assert isinstance(result["acceptance_time"].iloc[0], pd.Timestamp)
+    assert isinstance(result["url"].iloc[0], str)