sync dev -> main

.gitignore

@@ -10,6 +10,7 @@ build/
 *.html
 *.css
 *.png
+test.ipynb
 
 # Environments
 .env

tests/test_prices.py

@@ -864,7 +864,7 @@ def test_repair_bad_stock_splits(self):
 
             fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
             correct_df = _pd.read_csv(fp, index_col="Date")
-            correct_df.index = _pd.to_datetime(correct_df.index)
+            correct_df.index = _pd.to_datetime(correct_df.index, utc=True)
 
             repaired_df = repaired_df.sort_index()
             correct_df = correct_df.sort_index()

tests/test_ticker.py

@@ -35,8 +35,6 @@
     ("recommendations", Union[pd.DataFrame, dict]),
     ("recommendations_summary", Union[pd.DataFrame, dict]),
     ("upgrades_downgrades", Union[pd.DataFrame, dict]),
-    ("earnings", pd.DataFrame),
-    ("quarterly_earnings", pd.DataFrame),
     ("quarterly_cashflow", pd.DataFrame),
     ("cashflow", pd.DataFrame),
     ("quarterly_balance_sheet", pd.DataFrame),
@@ -114,9 +112,6 @@ def test_badTicker(self):
         for attribute_name, attribute_type in ticker_attributes:
             assert_attribute_type(self, dat, attribute_name, attribute_type)
 
-        with self.assertRaises(YFNotImplementedError):
-            assert isinstance(dat.earnings, pd.Series)
-            assert dat.earnings.empty
         assert isinstance(dat.dividends, pd.Series)
         assert dat.dividends.empty
         assert isinstance(dat.splits, pd.Series)
@@ -309,22 +304,6 @@ def test_earnings_dates_with_limit(self):
 
     # Below will fail because not ported to Yahoo API
 
-    # def test_earnings(self):
-    #     data = self.ticker.earnings
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-
-    #     data_cached = self.ticker.earnings
-    #     self.assertIs(data, data_cached, "data not cached")
-
-    # def test_quarterly_earnings(self):
-    #     data = self.ticker.quarterly_earnings
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-
-    #     data_cached = self.ticker.quarterly_earnings
-    #     self.assertIs(data, data_cached, "data not cached")
-
     # def test_earnings_forecasts(self):
     #     data = self.ticker.earnings_forecasts
     #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
@@ -707,15 +686,14 @@ def test_calendar(self):
         data_cached = self.ticker.calendar
         self.assertIs(data, data_cached, "data not cached")
 
-    # Below will fail because not ported to Yahoo API
 
-    # def test_sustainability(self):
-    #     data = self.ticker.sustainability
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
+    def test_sustainability(self):
+        data = self.ticker.sustainability
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
 
-    #     data_cached = self.ticker.sustainability
-    #     self.assertIs(data, data_cached, "data not cached")
+        data_cached = self.ticker.sustainability
+        self.assertIs(data, data_cached, "data not cached")
 
     # def test_shares(self):
     #     data = self.ticker.shares

yfinance/__init__.py

@@ -29,21 +29,7 @@
 __version__ = version.version
 __author__ = "Ran Aroussi"
 
+import warnings
+warnings.filterwarnings('default', category=DeprecationWarning, module='^yfinance')
 
-def pdr_override():
-    """
-    make pandas datareader optional
-    otherwise can be called via fix_yahoo_finance.download(...)
-    """
-    from .utils import print_once
-    print_once("yfinance: pandas_datareader support is deprecated & semi-broken so will be removed in a future verison. Just use yfinance.")
-    try:
-        import pandas_datareader
-        pandas_datareader.data.get_data_yahoo = download
-        pandas_datareader.data.get_data_yahoo_actions = download
-        pandas_datareader.data.DataReader = download
-    except Exception:
-        pass
-
-
-__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'enable_debug_mode', 'set_tz_cache_location']
+__all__ = ['download', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location']

yfinance/base.py

@@ -230,7 +230,7 @@ def get_fast_info(self, proxy=None):
 
     @property
     def basic_info(self):
-        warnings.warn("'Ticker.basic_info' is renamed to 'Ticker.fast_info', hopefully purpose is clearer", DeprecationWarning)
+        warnings.warn("'Ticker.basic_info' is deprecated and will be removed in future, Switch to 'Ticker.fast_info'", DeprecationWarning)
         return self.fast_info
 
     def get_sustainability(self, proxy=None, as_dict=False):
@@ -289,6 +289,8 @@ def get_earnings(self, proxy=None, as_dict=False, freq="yearly"):
                 Default is None
         """
         self._fundamentals.proxy = proxy or self.proxy
+        if self._fundamentals.earnings is None:
+            return None
         data = self._fundamentals.earnings[freq]
         if as_dict:
             dict_data = data.to_dict()

yfinance/const.py

@@ -30,7 +30,9 @@
                    "AmortizationOfIntangiblesIncomeStatement", "DepreciationIncomeStatement", "ResearchAndDevelopment",
                    "SellingGeneralAndAdministration", "SellingAndMarketingExpense", "GeneralAndAdministrativeExpense",
                    "OtherGandA", "InsuranceAndClaims", "RentAndLandingFees", "SalariesAndWages", "GrossProfit",
-                   "CostOfRevenue", "TotalRevenue", "ExciseTaxes", "OperatingRevenue"],
+                   "CostOfRevenue", "TotalRevenue", "ExciseTaxes", "OperatingRevenue", "LossAdjustmentExpense",
+                   "NetPolicyholderBenefitsAndClaims", "PolicyholderBenefitsGross", "PolicyholderBenefitsCeded",
+                   "OccupancyAndEquipment", "ProfessionalExpenseAndContractServicesExpense", "OtherNonInterestExpense"],
     'balance-sheet': ["TreasurySharesNumber", "PreferredSharesNumber", "OrdinarySharesNumber", "ShareIssued", "NetDebt",
                       "TotalDebt", "TangibleBookValue", "InvestedCapital", "WorkingCapital", "NetTangibleAssets",
                       "CapitalLeaseObligations", "CommonStockEquity", "PreferredStockEquity", "TotalCapitalization",
@@ -74,7 +76,8 @@
                       "DuefromRelatedPartiesCurrent", "TaxesReceivable", "AccruedInterestReceivable", "NotesReceivable",
                       "LoansReceivable", "AccountsReceivable", "AllowanceForDoubtfulAccountsReceivable",
                       "GrossAccountsReceivable", "CashCashEquivalentsAndShortTermInvestments",
-                      "OtherShortTermInvestments", "CashAndCashEquivalents", "CashEquivalents", "CashFinancial"],
+                      "OtherShortTermInvestments", "CashAndCashEquivalents", "CashEquivalents", "CashFinancial",
+                      "CashCashEquivalentsAndFederalFundsSold"],
     'cash-flow': ["ForeignSales", "DomesticSales", "AdjustedGeographySegmentData", "FreeCashFlow",
                   "RepurchaseOfCapitalStock", "RepaymentOfDebt", "IssuanceOfDebt", "IssuanceOfCapitalStock",
                   "CapitalExpenditure", "InterestPaidSupplementalData", "IncomeTaxPaidSupplementalData",

yfinance/scrapers/fundamentals.py

@@ -1,13 +1,13 @@
 import datetime
 import json
+import warnings
 
 import pandas as pd
 
 from yfinance import utils, const
 from yfinance.data import YfData
 from yfinance.exceptions import YFException, YFNotImplementedError
 
-
 class Fundamentals:
 
     def __init__(self, data: YfData, symbol: str, proxy=None):
@@ -30,9 +30,8 @@ def financials(self) -> "Financials":
 
     @property
     def earnings(self) -> dict:
-        if self._earnings is None:
-            raise YFNotImplementedError('earnings')
-        return self._earnings
+        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:

yfinance/scrapers/history.py

@@ -97,10 +97,13 @@ def history(self, period="1mo", interval="1d",
                 end = utils._parse_user_dt(end, tz)
             if start is None:
                 if interval == "1m":
-                    start = end - 604800  # Subtract 7 days
+                    start = end - 604800   # 7 days
+                elif interval in ("5m", "15m", "30m", "90m"):
+                    start = end - 5184000  # 60 days
+                elif interval in ("1h", '60m'):
+                    start = end - 63072000  # 730 days
                 else:
-                    max_start_datetime = pd.Timestamp.utcnow().floor("D") - _datetime.timedelta(days=99 * 365)
-                    start = int(max_start_datetime.timestamp())
+                    start = end - 3122064000  # 99 years
             else:
                 start = utils._parse_user_dt(start, tz)
             params = {"period1": start, "period2": end}
@@ -236,7 +239,7 @@ def history(self, period="1mo", interval="1d",
         # 2) fix weired bug with Yahoo! - returning 60m for 30m bars
         if interval.lower() == "30m":
             logger.debug(f'{self.ticker}: resampling 30m OHLC from 15m')
-            quotes2 = quotes.resample('30T')
+            quotes2 = quotes.resample('30min')
             quotes = pd.DataFrame(index=quotes2.last().index, data={
                 'Open': quotes2['Open'].first(),
                 'High': quotes2['High'].max(),
@@ -265,6 +268,7 @@ def history(self, period="1mo", interval="1d",
             tps = self._history_metadata["tradingPeriods"]
             if not isinstance(tps, pd.DataFrame):
                 self._history_metadata = utils.format_history_metadata(self._history_metadata, tradingPeriodsOnly=True)
+                self._history_metadata_formatted = True
                 tps = self._history_metadata["tradingPeriods"]
             quotes = utils.fix_Yahoo_returning_prepost_unrequested(quotes, params["interval"], tps)
         logger.debug(f'{self.ticker}: OHLC after cleaning: {quotes.index[0]} -> {quotes.index[-1]}')
@@ -391,7 +395,7 @@ def history(self, period="1mo", interval="1d",
     def get_history_metadata(self, proxy=None) -> dict:
         if self._history_metadata is None:
             # Request intraday data, because then Yahoo returns exchange schedule.
-            self.history(period="1wk", interval="1h", prepost=True, proxy=proxy)
+            self.history(period="5d", interval="1h", prepost=True, proxy=proxy)
 
         if self._history_metadata_formatted is False:
             self._history_metadata = utils.format_history_metadata(self._history_metadata)
@@ -863,6 +867,7 @@ def _fix_unit_random_mixups(self, df, interval, tz_exchange, prepost):
             df_orig = df[~f_zeroes]  # all row slicing must be applied to both df and df2
         else:
             df2_zeroes = None
+            df_orig = df
         if df2.shape[0] <= 1:
             logger.info("price-repair-100x: Insufficient good data for detecting 100x price errors")
             if "Repaired?" not in df.columns:
@@ -1025,6 +1030,10 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
             f_zero_or_nan_ignore = np.isin(f_prices_bad.index.date, dts)
             df2_reserve = df2[f_zero_or_nan_ignore]
             df2 = df2[~f_zero_or_nan_ignore]
+            if df2.empty:
+                # No good data
+                return df
+            df2 = df2.copy()
             f_prices_bad = (df2[price_cols] == 0.0) | df2[price_cols].isna()
 
         f_change = df2["High"].to_numpy() != df2["Low"].to_numpy()
@@ -1033,7 +1042,19 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
             f_vol_bad = None
         else:
             f_high_low_good = (~df2["High"].isna().to_numpy()) & (~df2["Low"].isna().to_numpy())
-            f_vol_bad = (df2["Volume"] == 0).to_numpy() & f_high_low_good & f_change
+            f_vol_zero = (df2["Volume"] == 0).to_numpy()
+            f_vol_bad = f_vol_zero & f_high_low_good & f_change
+            # ^ intra-interval price changed without volume, bad
+
+            if not intraday:
+                # Interday data: if close changes between intervals with volume=0 then volume is wrong.
+                # Possible can repair with intraday, but usually Yahoo does not have the volume.
+                close_diff = df2['Close'].diff()
+                close_diff.iloc[0] = 0
+                close_chg_pct_abs = np.abs(close_diff / df2['Close'])
+                f_bad_price_chg = (close_chg_pct_abs > 0.05).to_numpy() & f_vol_zero
+                f_bad_price_chg = f_bad_price_chg & (~f_vol_bad)  # exclude where already know volume is bad
+                f_vol_bad = f_vol_bad | f_bad_price_chg
 
         # If stock split occurred, then trading must have happened.
         # I should probably rename the function, because prices aren't zero ...
@@ -1068,7 +1089,8 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
         for i in range(len(price_cols)):
             c = price_cols[i]
             df2.loc[f_prices_bad[:, i], c] = tag
-        df2.loc[f_vol_bad, "Volume"] = tag
+        if f_vol_bad is not None:
+            df2.loc[f_vol_bad, "Volume"] = tag
         # If volume=0 or NaN for bad prices, then tag volume for repair
         f_vol_zero_or_nan = (df2["Volume"].to_numpy() == 0) | (df2["Volume"].isna().to_numpy())
         df2.loc[f_prices_bad.any(axis=1) & f_vol_zero_or_nan, "Volume"] = tag
@@ -1229,7 +1251,11 @@ def _fix_bad_stock_splits(self, df, interval, tz_exchange):
             if cutoff_idx == df.shape[0]-1:
                 df = df_pre_split_repaired
             else:
-                df = pd.concat([df_pre_split_repaired.sort_index(), df.iloc[cutoff_idx+1:]])
+                df_post_cutoff = df.iloc[cutoff_idx+1:]
+                if df_post_cutoff.empty:
+                    df = df_pre_split_repaired.sort_index()
+                else:
+                    df = pd.concat([df_pre_split_repaired.sort_index(), df_post_cutoff])
         return df
 
     @utils.log_indent_decorator
@@ -1594,9 +1620,9 @@ def map_signals_to_ranges(f, f_up, f_down):
                 f_open_and_closed_fixed = f_open_fixed & f_close_fixed
                 f_open_xor_closed_fixed = np.logical_xor(f_open_fixed, f_close_fixed)
                 if f_open_and_closed_fixed.any():
-                    df2.loc[f_open_and_closed_fixed, "Volume"] *= m_rcp
+                    df2.loc[f_open_and_closed_fixed, "Volume"] = (df2.loc[f_open_and_closed_fixed, "Volume"] * m_rcp).round().astype('int')
                 if f_open_xor_closed_fixed.any():
-                    df2.loc[f_open_xor_closed_fixed, "Volume"] *= 0.5 * m_rcp
+                    df2.loc[f_open_xor_closed_fixed, "Volume"] = (df2.loc[f_open_xor_closed_fixed, "Volume"] * 0.5 * m_rcp).round().astype('int')
 
             df2.loc[f_corrected, 'Repaired?'] = True
 
@@ -1649,7 +1675,8 @@ def map_signals_to_ranges(f, f_up, f_down):
                 for c in ['Open', 'High', 'Low', 'Close', 'Adj Close']:
                     df2.iloc[r[0]:r[1], df2.columns.get_loc(c)] *= m
                 if correct_volume:
-                    df2.iloc[r[0]:r[1], df2.columns.get_loc("Volume")] *= m_rcp
+                    col_loc = df2.columns.get_loc("Volume")
+                    df2.iloc[r[0]:r[1], col_loc] = (df2.iloc[r[0]:r[1], col_loc] * m_rcp).round().astype('int')
                 df2.iloc[r[0]:r[1], df2.columns.get_loc('Repaired?')] = True
                 if r[0] == r[1] - 1:
                     if interday:

yfinance/scrapers/quote.py

@@ -1,7 +1,5 @@
 import datetime
 import json
-import warnings
-from collections.abc import MutableMapping
 
 import numpy as _np
 import pandas as pd
@@ -10,7 +8,7 @@
 from yfinance import utils
 from yfinance.data import YfData
 from yfinance.const import quote_summary_valid_modules, _BASE_URL_
-from yfinance.exceptions import YFNotImplementedError, YFDataException, YFException
+from yfinance.exceptions import YFDataException, YFException
 
 info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume", "volume24Hr"}
 info_retired_keys_price.update({"regularMarket"+s for s in ["DayHigh", "DayLow", "Open", "PreviousClose", "Price", "Volume"]})
@@ -25,57 +23,6 @@
 _QUOTE_SUMMARY_URL_ = f"{_BASE_URL_}/v10/finance/quoteSummary"
 
 
-class InfoDictWrapper(MutableMapping):
-    """ Simple wrapper around info dict, intercepting 'gets' to
-    print how-to-migrate messages for specific keys. Requires
-    override dict API"""
-
-    def __init__(self, info):
-        self.info = info
-
-    def keys(self):
-        return self.info.keys()
-
-    def __str__(self):
-        return self.info.__str__()
-
-    def __repr__(self):
-        return self.info.__repr__()
-
-    def __contains__(self, k):
-        return k in self.info.keys()
-
-    def __getitem__(self, k):
-        if k in info_retired_keys_price:
-            warnings.warn(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead", DeprecationWarning)
-            return None
-        elif k in info_retired_keys_exchange:
-            warnings.warn(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead", DeprecationWarning)
-            return None
-        elif k in info_retired_keys_marketCap:
-            warnings.warn(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead", DeprecationWarning)
-            return None
-        elif k in info_retired_keys_symbol:
-            warnings.warn(f"Symbol removed from info (key='{k}'). You know this already", DeprecationWarning)
-            return None
-        return self.info[self._keytransform(k)]
-
-    def __setitem__(self, k, value):
-        self.info[self._keytransform(k)] = value
-
-    def __delitem__(self, k):
-        del self.info[self._keytransform(k)]
-
-    def __iter__(self):
-        return iter(self.info)
-
-    def __len__(self):
-        return len(self.info)
-
-    def _keytransform(self, k):
-        return k
-
-
 class FastInfo:
     # Contain small subset of info[] items that can be fetched faster elsewhere.
     # Imitates a dict.
@@ -565,7 +512,15 @@ def info(self) -> dict:
     @property
     def sustainability(self) -> pd.DataFrame:
         if self._sustainability is None:
-            raise YFNotImplementedError('sustainability')
+            result = self._fetch(self.proxy, modules=['esgScores'])
+            if result is None:
+                self._sustainability = pd.DataFrame()
+            else:
+                try:
+                    data = result["quoteSummary"]["result"][0]
+                except (KeyError, IndexError):
+                    raise YFDataException(f"Failed to parse json response from Yahoo Finance: {result}")
+                self._sustainability = pd.DataFrame(data)
         return self._sustainability
 
     @property