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
@@ -0,0 +1,163 @@
+import datetime
+import json
+import warnings
+
+import pandas as pd
+
+from yfinance import utils, const
+from yfinance.config import YfConfig
+from yfinance.data import YfData
+from yfinance.exceptions import YFException, YFNotImplementedError
+
+class Fundamentals:
+
+    def __init__(self, data: YfData, symbol: str):
+        self._data = data
+        self._symbol = symbol
+
+        self._earnings = None
+        self._financials = None
+        self._shares = None
+
+        self._financials_data = None
+        self._fin_data_quote = None
+        self._basics_already_scraped = False
+        self._financials = Financials(data, symbol)
+
+    @property
+    def financials(self) -> "Financials":
+        return self._financials
+
+    @property
+    def earnings(self) -> dict:
+        warnings.warn("'Ticker.earnings' is deprecated as not available via API. Look for \"Net Income\" in Ticker.income_stmt.", DeprecationWarning)
+        return None
+
+    @property
+    def shares(self) -> pd.DataFrame:
+        if self._shares is None:
+            raise YFNotImplementedError('shares')
+        return self._shares
+
+
+class Financials:
+    def __init__(self, data: YfData, symbol: str):
+        self._data = data
+        self._symbol = symbol
+        self._income_time_series = {}
+        self._balance_sheet_time_series = {}
+        self._cash_flow_time_series = {}
+
+    def get_income_time_series(self, freq="yearly") -> pd.DataFrame:
+        res = self._income_time_series
+        if freq not in res:
+            res[freq] = self._fetch_time_series("income", freq)
+        return res[freq]
+
+    def get_balance_sheet_time_series(self, freq="yearly") -> pd.DataFrame:
+        res = self._balance_sheet_time_series
+        if freq not in res:
+            res[freq] = self._fetch_time_series("balance-sheet", freq)
+        return res[freq]
+
+    def get_cash_flow_time_series(self, freq="yearly") -> pd.DataFrame:
+        res = self._cash_flow_time_series
+        if freq not in res:
+            res[freq] = self._fetch_time_series("cash-flow", freq)
+        return res[freq]
+
+    @utils.log_indent_decorator
+    def _fetch_time_series(self, name, timescale):
+        # Fetching time series preferred over scraping 'QuoteSummaryStore',
+        # because it matches what Yahoo shows. But for some tickers returns nothing,
+        # despite 'QuoteSummaryStore' containing valid data.
+
+        allowed_names = ["income", "balance-sheet", "cash-flow"]
+        allowed_timescales = ["yearly", "quarterly", "trailing"]
+
+        if name not in allowed_names:
+            raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
+        if timescale not in allowed_timescales:
+            raise ValueError(f"Illegal argument: timescale must be one of: {allowed_timescales}")
+        if timescale == "trailing" and name not in ('income', 'cash-flow'):
+            raise ValueError("Illegal argument: frequency 'trailing'" +
+                             " only available for cash-flow or income data.")
+
+        try:
+            statement = self._create_financials_table(name, timescale)
+
+            if statement is not None:
+                return statement
+        except YFException as e:
+            if not YfConfig.debug.hide_exceptions:
+                raise
+            utils.get_yf_logger().error(f"{self._symbol}: Failed to create {name} financials table for reason: {e}")
+        return pd.DataFrame()
+
+    def _create_financials_table(self, name, timescale):
+        if name == "income":
+            # Yahoo stores the 'income' table internally under 'financials' key
+            name = "financials"
+
+        keys = const.fundamentals_keys[name]
+
+        try:
+            return self._get_financials_time_series(timescale, keys)
+        except Exception:
+            if not YfConfig.debug.hide_exceptions:
+                raise
+            pass
+
+    def _get_financials_time_series(self, timescale, keys: list) -> pd.DataFrame:
+        timescale_translation = {"yearly": "annual", "quarterly": "quarterly", "trailing": "trailing"}
+        timescale = timescale_translation[timescale]
+
+        # Step 2: construct url:
+        ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._symbol}?symbol={self._symbol}"
+        url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
+        # Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
+        start_dt = datetime.datetime(2016, 12, 31)
+        end = pd.Timestamp.now('UTC').ceil("D")
+        url += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"
+
+        # Step 3: fetch and reshape data
+        json_str = self._data.cache_get(url=url).text
+        json_data = json.loads(json_str)
+        data_raw = json_data["timeseries"]["result"]
+        # data_raw = [v for v in data_raw if len(v) > 1] # Discard keys with no data
+        for d in data_raw:
+            del d["meta"]
+
+        # Now reshape data into a table:
+        # Step 1: get columns and index:
+        timestamps = set()
+        data_unpacked = {}
+        for x in data_raw:
+            for k in x.keys():
+                if k == "timestamp":
+                    timestamps.update(x[k])
+                else:
+                    data_unpacked[k] = x[k]
+        timestamps = sorted(list(timestamps))
+        dates = pd.to_datetime(timestamps, unit="s")
+        df = pd.DataFrame(columns=dates, index=list(data_unpacked.keys()))
+        for k, v in data_unpacked.items():
+            if df is None:
+                df = pd.DataFrame(columns=dates, index=[k])
+            df.loc[k] = {pd.Timestamp(x["asOfDate"]): x["reportedValue"]["raw"] for x in v}
+
+        df.index = df.index.str.replace("^" + timescale, "", regex=True)
+
+        # Ensure float type, not object
+        for d in df.columns:
+            df[d] = df[d].astype('float')
+
+        # Reorder table to match order on Yahoo website
+        df = df.reindex([k for k in keys if k in df.index])
+        df = df[sorted(df.columns, reverse=True)]
+
+        # Trailing 12 months return only the first column.
+        if (timescale == "trailing"):
+            df = df.iloc[:, [0]]
+
+        return df