init commit, draft PR

yfinance/scrapers/fund_profile.py

@@ -0,0 +1,132 @@
+import pandas as pd
+
+from yfinance.data import YfData
+from yfinance.const import _BASE_URL_
+from yfinance.exceptions import YFinanceDataException
+
+_QUOTE_SUMMARY_URL_ = f"{_BASE_URL_}/v10/finance/quoteSummary/"
+
+
+class FundProfile:
+    def __init__(self, data: YfData, symbol: str, proxy=None):
+        self._data = data
+        self._symbol = symbol
+        self.proxy = proxy
+
+        self._description = None
+        self._top_holdings = None
+        self._equity_holdings = None
+        self._bond_holdings = None
+        self._bond_ratings = None
+        self._sector_weightings = None
+        self._profile = None
+        self._perfomance = None
+
+    @property
+    def description(self) -> str:
+        if self._description is None:
+            self._fetch_and_parse()
+        return self._description
+
+    @property
+    def top_holdings(self) -> pd.DataFrame:
+        if self._top_holdings is None:
+            self._fetch_and_parse()
+        return self._top_holdings
+
+    @property
+    def equity_holdings(self) -> pd.DataFrame:
+        if self._equity_holdings is None:
+            self._fetch_and_parse()
+        return self._equity_holdings
+
+    @property
+    def bond_holdings(self) -> pd.DataFrame:
+        if self._bond_holdings is None:
+            self._fetch_and_parse()
+        return self._bond_holdings
+
+    @property
+    def bond_ratings(self) -> pd.DataFrame:
+        if self._bond_ratings is None:
+            self._fetch_and_parse()
+        return self._bond_ratings
+
+    @property
+    def sector_weightings(self) -> pd.DataFrame:
+        if self._sector_weightings is None:
+            self._fetch_and_parse()
+        return self._sector_weightings
+
+    @property
+    def profile(self) -> pd.DataFrame:
+        if self._profile is None:
+            self._fetch_and_parse()
+        return self._profile
+
+    @property
+    def perfomance(self) -> pd.DataFrame:
+        if self._perfomance is None:
+            self._fetch_and_parse()
+        return self._perfomance
+
+    def _fetch(self, proxy):
+        modules = ','.join(["quoteType", "summaryProfile", "topHoldings", "fundPerformance", "fundProfile"])
+        params_dict = {"modules": modules, "corsDomain": "finance.yahoo.com", "symbol": self._symbol, "formatted": "false"}
+        result = self._data.get_raw_json(_QUOTE_SUMMARY_URL_, user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=proxy)
+        return result
+
+    def _fetch_and_parse(self):
+        result = self._fetch(self.proxy)
+        try:
+            data = result["quoteSummary"]["result"][0]
+            # check quote type
+            try:
+                quote_type = data["quoteType"]["quoteType"]
+                if quote_type != "ETF":
+                    raise YFinanceDataException("Only ETFs are supported.")
+            except KeyError:
+                raise YFinanceDataException("Failed to parse quote type. No ETF data found.")
+            # parse "summaryProfile", "topHoldings", "fundPerformance", "fundProfile",
+            self._parse_description(data["summaryProfile"])
+            self._parse_top_holdings(data["topHoldings"])
+            self._parse_fund_performance(data["fundPerformance"])
+            self._parse_fund_profile(data["fundProfile"])
+        except (KeyError, IndexError):
+            raise YFinanceDataException("Failed to parse fund json data.")
+
+    @staticmethod
+    def _parse_raw_values(data):
+        if isinstance(data, dict) and "raw" in data:
+            return data["raw"]
+        return data
+
+    def _parse_description(self, data):
+        self._description = data.get("longBusinessSummary", "")
+
+    def _parse_top_holdings(self, data):
+        if "maxAge" in data:
+            del data["maxAge"]
+        df = pd.DataFrame.from_dict(data, orient="index")
+        if not df.empty:
+            df.columns.name = "Symbol"
+            df.rename(columns={df.columns[0]: 'Name'}, inplace=True)
+        self._top_holdings = df
+
+    def _parse_fund_performance(self, data):
+        if "maxAge" in data:
+            del data["maxAge"]
+        df = pd.DataFrame.from_dict(data, orient="index")
+        if not df.empty:
+            df.columns.name = "Performance"
+            df.rename(columns={df.columns[0]: 'Value'}, inplace=True)
+        self._perfomance = df
+
+    def _parse_fund_profile(self, data):
+        if "maxAge" in data:
+            del data["maxAge"]
+        df = pd.DataFrame.from_dict(data, orient="index")
+        if not df.empty:
+            df.columns.name = "Profile"
+            df.rename(columns={df.columns[0]: 'Value'}, inplace=True)
+        self._profile = df