Import python venv for stability

2026-02-15 21:24:16 -08:00
parent 1343e93a59
commit 7d784705c9
4997 changed files with 1628270 additions and 0 deletions
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+import pandas as pd
+from typing import Dict, Optional
+
+from yfinance import utils
+from yfinance.config import YfConfig
+from yfinance.const import _BASE_URL_
+from yfinance.data import YfData
+from yfinance.exceptions import YFDataException
+
+_QUOTE_SUMMARY_URL_ = f"{_BASE_URL_}/v10/finance/quoteSummary/"
+
+class FundsData:
+    """
+    ETF and Mutual Funds Data
+    Queried Modules: quoteType, summaryProfile, fundProfile, topHoldings
+
+    Notes: 
+    - fundPerformance module is not implemented as better data is queryable using history
+    """
+    def __init__(self, data: YfData, symbol: str):
+        """
+        Args:
+            data (YfData): The YfData object for fetching data.
+            symbol (str): The symbol of the fund.
+        """
+        self._data = data
+        self._symbol = symbol
+        
+        # quoteType
+        self._quote_type = None
+
+        # summaryProfile
+        self._description = None
+
+        # fundProfile
+        self._fund_overview = None
+        self._fund_operations = None
+
+        # topHoldings
+        self._asset_classes = None
+        self._top_holdings = None
+        self._equity_holdings = None
+        self._bond_holdings = None
+        self._bond_ratings = None
+        self._sector_weightings = None
+
+    def quote_type(self) -> str:
+        """
+        Returns the quote type of the fund.
+
+        Returns:
+            str: The quote type.
+        """
+        if self._quote_type is None:
+            self._fetch_and_parse()
+        return self._quote_type
+    
+    @property
+    def description(self) -> str:
+        """
+        Returns the description of the fund.
+
+        Returns:
+            str: The description.
+        """
+        if self._description is None:
+            self._fetch_and_parse()
+        return self._description
+    
+    @property
+    def fund_overview(self) -> Dict[str, Optional[str]]:
+        """
+        Returns the fund overview.
+
+        Returns:
+            Dict[str, Optional[str]]: The fund overview.
+        """
+        if self._fund_overview is None:
+            self._fetch_and_parse()
+        return self._fund_overview
+
+    @property
+    def fund_operations(self) -> pd.DataFrame:
+        """
+        Returns the fund operations.
+
+        Returns:
+            pd.DataFrame: The fund operations.
+        """
+        if self._fund_operations is None:
+            self._fetch_and_parse()
+        return self._fund_operations
+
+    @property
+    def asset_classes(self) -> Dict[str, float]:
+        """
+        Returns the asset classes of the fund.
+
+        Returns:
+            Dict[str, float]: The asset classes.
+        """
+        if self._asset_classes is None:
+            self._fetch_and_parse()
+        return self._asset_classes
+
+    @property
+    def top_holdings(self) -> pd.DataFrame:
+        """
+        Returns the top holdings of the fund.
+
+        Returns:
+            pd.DataFrame: The top holdings.
+        """
+        if self._top_holdings is None:
+            self._fetch_and_parse()
+        return self._top_holdings
+
+    @property
+    def equity_holdings(self) -> pd.DataFrame:
+        """
+        Returns the equity holdings of the fund.
+
+        Returns:
+            pd.DataFrame: The equity holdings.
+        """
+        if self._equity_holdings is None:
+            self._fetch_and_parse()
+        return self._equity_holdings
+
+    @property
+    def bond_holdings(self) -> pd.DataFrame:
+        """
+        Returns the bond holdings of the fund.
+
+        Returns:
+            pd.DataFrame: The bond holdings.
+        """
+        if self._bond_holdings is None:
+            self._fetch_and_parse()
+        return self._bond_holdings
+
+    @property
+    def bond_ratings(self) -> Dict[str, float]:
+        """
+        Returns the bond ratings of the fund.
+
+        Returns:
+            Dict[str, float]: The bond ratings.
+        """
+        if self._bond_ratings is None:
+            self._fetch_and_parse()
+        return self._bond_ratings
+
+    @property
+    def sector_weightings(self) -> Dict[str,float]:
+        """
+        Returns the sector weightings of the fund.
+
+        Returns:
+            Dict[str, float]: The sector weightings.
+        """
+        if self._sector_weightings is None:
+            self._fetch_and_parse()
+        return self._sector_weightings
+
+    def _fetch(self):
+        """
+        Fetches the raw JSON data from the API.
+
+        Returns:
+            dict: The raw JSON data.
+        """
+        modules = ','.join(["quoteType", "summaryProfile", "topHoldings", "fundProfile"])
+        params_dict = {"modules": modules, "corsDomain": "finance.yahoo.com", "symbol": self._symbol, "formatted": "false"}
+        result = self._data.get_raw_json(_QUOTE_SUMMARY_URL_+self._symbol, params=params_dict)
+        return result
+
+    def _fetch_and_parse(self) -> None:
+        """
+        Fetches and parses the data from the API.
+        """
+        result = self._fetch()
+        try:
+            data = result["quoteSummary"]["result"][0]
+            # check quote type
+            self._quote_type = data["quoteType"]["quoteType"]
+            
+            # parse "summaryProfile", "topHoldings", "fundProfile"
+            self._parse_description(data["summaryProfile"])
+            self._parse_top_holdings(data["topHoldings"])
+            self._parse_fund_profile(data["fundProfile"])
+        except KeyError:
+            if not YfConfig.debug.hide_exceptions:
+                raise
+            raise YFDataException(f"{self._symbol}: No Fund data found.")
+        except Exception as e:
+            if not YfConfig.debug.hide_exceptions:
+                raise
+            logger = utils.get_yf_logger()
+            logger.error(f"Failed to get fund data for '{self._symbol}' reason: {e}")
+            logger.debug("Got response: ")
+            logger.debug("-------------")
+            logger.debug(f" {data}")
+            logger.debug("-------------")
+
+    @staticmethod
+    def _parse_raw_values(data, default=None):
+        """
+        Parses raw values from the data.
+
+        Args:
+            data: The data to parse.
+            default: The default value if data is not a dictionary.
+
+        Returns:
+            The parsed value or the default value.
+        """
+        if not isinstance(data, dict):
+            return data
+        
+        return data.get("raw", default)
+
+    def _parse_description(self, data) -> None:
+        """
+        Parses the description from the data.
+
+        Args:
+            data: The data to parse.
+        """
+        self._description = data.get("longBusinessSummary", "")
+
+    def _parse_top_holdings(self, data) -> None:
+        """
+        Parses the top holdings from the data.
+
+        Args:
+            data: The data to parse.
+        """
+        # asset classes
+        self._asset_classes = {
+            "cashPosition": self._parse_raw_values(data.get("cashPosition", None)),
+            "stockPosition": self._parse_raw_values(data.get("stockPosition", None)),
+            "bondPosition": self._parse_raw_values(data.get("bondPosition", None)),
+            "preferredPosition": self._parse_raw_values(data.get("preferredPosition", None)),
+            "convertiblePosition": self._parse_raw_values(data.get("convertiblePosition", None)),
+            "otherPosition": self._parse_raw_values(data.get("otherPosition", None))
+        }
+
+        # top holdings
+        _holdings = data.get("holdings", [])
+        _symbol, _name, _holding_percent = [], [], []
+
+        for item in _holdings:
+            _symbol.append(item["symbol"])
+            _name.append(item["holdingName"])
+            _holding_percent.append(item["holdingPercent"])
+        
+        self._top_holdings = pd.DataFrame({
+            "Symbol": _symbol,
+            "Name": _name,
+            "Holding Percent": _holding_percent
+        }).set_index("Symbol")
+
+        # equity holdings
+        _equity_holdings = data.get("equityHoldings", {})
+        self._equity_holdings = pd.DataFrame({
+            "Average": ["Price/Earnings", "Price/Book", "Price/Sales", "Price/Cashflow", "Median Market Cap", "3 Year Earnings Growth"],
+            self._symbol: [
+                self._parse_raw_values(_equity_holdings.get("priceToEarnings", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToBook", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToSales", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToCashflow", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("medianMarketCap", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("threeYearEarningsGrowth", pd.NA)),
+            ],
+            "Category Average": [
+                self._parse_raw_values(_equity_holdings.get("priceToEarningsCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToBookCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToSalesCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToCashflowCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("medianMarketCapCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("threeYearEarningsGrowthCat", pd.NA)),
+            ]
+        }).set_index("Average")
+        
+        # bond holdings
+        _bond_holdings = data.get("bondHoldings", {})
+        self._bond_holdings = pd.DataFrame({
+            "Average": ["Duration", "Maturity", "Credit Quality"],
+            self._symbol: [
+                self._parse_raw_values(_bond_holdings.get("duration", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("maturity", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("creditQuality", pd.NA)),
+            ],
+            "Category Average": [
+                self._parse_raw_values(_bond_holdings.get("durationCat", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("maturityCat", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("creditQualityCat", pd.NA)),
+            ]
+        }).set_index("Average")
+
+        # bond ratings
+        self._bond_ratings = dict((key, d[key]) for d in data.get("bondRatings", []) for key in d)
+
+        # sector weightings
+        self._sector_weightings = dict((key, d[key]) for d in data.get("sectorWeightings", []) for key in d)
+        
+    def _parse_fund_profile(self, data):
+        """
+        Parses the fund profile from the data.
+
+        Args:
+            data: The data to parse.
+        """
+        self._fund_overview = {
+            "categoryName": data.get("categoryName", None), 
+            "family":       data.get("family", None), 
+            "legalType":    data.get("legalType", None)
+        }
+        
+        _fund_operations = data.get("feesExpensesInvestment", {})
+        _fund_operations_cat = data.get("feesExpensesInvestmentCat", {})
+
+        self._fund_operations = pd.DataFrame({
+            "Attributes": ["Annual Report Expense Ratio", "Annual Holdings Turnover", "Total Net Assets"],
+            self._symbol: [
+                self._parse_raw_values(_fund_operations.get("annualReportExpenseRatio", pd.NA)),
+                self._parse_raw_values(_fund_operations.get("annualHoldingsTurnover", pd.NA)),
+                self._parse_raw_values(_fund_operations.get("totalNetAssets", pd.NA))
+            ],
+            "Category Average": [
+                self._parse_raw_values(_fund_operations_cat.get("annualReportExpenseRatio", pd.NA)),
+                self._parse_raw_values(_fund_operations_cat.get("annualHoldingsTurnover", pd.NA)),
+                self._parse_raw_values(_fund_operations_cat.get("totalNetAssets", pd.NA))
+            ]
+        }).set_index("Attributes")