Remove .formats and adjust imports
Ricardo Prins
parent
0503240973
commit
5729ce3cb6
140
yfinance/base.py
140
yfinance/base.py
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@ -30,8 +30,7 @@ from typing import Optional
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from urllib.parse import quote as urlencode
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import dateutil as _dateutil
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import numpy as _np
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import pandas as _pd
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import numpy as np
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import pandas as pd
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import requests
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@ -155,9 +154,9 @@ class TickerBase:
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shared._DFS[self.ticker] = utils.empty_df()
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shared._ERRORS[self.ticker] = err_msg
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if raise_errors:
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raise Exception('%s: %s' % (self.ticker, err_msg))
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raise Exception(f'{self.ticker}: {err_msg}')
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else:
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logger.error('%s: %s' % (self.ticker, err_msg))
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logger.error(f'{self.ticker}: {err_msg}')
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return utils.empty_df()
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if end is None:
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@ -198,15 +197,15 @@ class TickerBase:
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for k in ["period1", "period2"]:
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if k in params_pretty:
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params_pretty[k] = str(_pd.Timestamp(params[k], unit='s').tz_localize("UTC").tz_convert(tz))
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logger.debug('%s: %s' % (self.ticker, "Yahoo GET parameters: " + str(params_pretty)))
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logger.debug(f'{self.ticker}: Yahoo GET parameters: {str(params_pretty)}')
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# Getting data from json
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url = "{}/v8/finance/chart/{}".format(self._base_url, self.ticker)
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url = f"{self._base_url}/v8/finance/chart/{self.ticker}"
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data = None
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get_fn = self._data.get
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if end is not None:
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end_dt = _pd.Timestamp(end, unit='s').tz_localize("UTC")
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dt_now = _pd.Timestamp.utcnow()
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end_dt = pd.Timestamp(end, unit='s').tz_localize("UTC")
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dt_now = pd.Timestamp.utcnow()
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data_delay = _datetime.timedelta(minutes=30)
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if end_dt + data_delay <= dt_now:
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# Date range in past so safe to fetch through cache:
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@ -239,16 +238,16 @@ class TickerBase:
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if start_user is not None:
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err_msg += f'{start_user}'
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elif not intraday:
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err_msg += f'{_pd.Timestamp(start, unit="s").tz_localize("UTC").tz_convert(tz).date()}'
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err_msg += f'{pd.Timestamp(start, unit="s").tz_localize("UTC").tz_convert(tz).date()}'
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else:
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err_msg += f'{_pd.Timestamp(start, unit="s").tz_localize("UTC").tz_convert(tz)}'
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err_msg += f'{pd.Timestamp(start, unit="s").tz_localize("UTC").tz_convert(tz)}'
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err_msg += ' -> '
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if end_user is not None:
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err_msg += f'{end_user})'
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elif not intraday:
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err_msg += f'{_pd.Timestamp(end, unit="s").tz_localize("UTC").tz_convert(tz).date()})'
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err_msg += f'{pd.Timestamp(end, unit="s").tz_localize("UTC").tz_convert(tz).date()})'
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else:
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err_msg += f'{_pd.Timestamp(end, unit="s").tz_localize("UTC").tz_convert(tz)})'
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err_msg += f'{pd.Timestamp(end, unit="s").tz_localize("UTC").tz_convert(tz)})'
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else:
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err_msg += f' (period={period})'
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@ -256,7 +255,7 @@ class TickerBase:
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if data is None or type(data) is not dict:
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fail = True
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elif type(data) is dict and 'status_code' in data:
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err_msg += "(Yahoo status_code = {})".format(data["status_code"])
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err_msg += f"(Yahoo status_code = {data['status_code']})"
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fail = True
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elif "chart" in data and data["chart"]["error"]:
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err_msg = data["chart"]["error"]["description"]
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@ -266,16 +265,15 @@ class TickerBase:
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elif period is not None and "timestamp" not in data["chart"]["result"][0] and period not in \
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self._history_metadata["validRanges"]:
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# User provided a bad period. The minimum should be '1d', but sometimes Yahoo accepts '1h'.
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err_msg = "Period '{}' is invalid, must be one of {}".format(period, self._history_metadata[
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"validRanges"])
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err_msg = f"Period '{period}' is invalid, must be one of {self._history_metadata['validRanges']}"
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fail = True
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if fail:
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shared._DFS[self.ticker] = utils.empty_df()
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shared._ERRORS[self.ticker] = err_msg
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if raise_errors:
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raise Exception('%s: %s' % (self.ticker, err_msg))
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raise Exception(f'{self.ticker}: {err_msg}')
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else:
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logger.error('%s: %s' % (self.ticker, err_msg))
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logger.error(f'{self.ticker}: {err_msg}')
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return utils.empty_df()
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# parse quotes
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@ -283,16 +281,16 @@ class TickerBase:
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quotes = utils.parse_quotes(data["chart"]["result"][0])
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# Yahoo bug fix - it often appends latest price even if after end date
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if end and not quotes.empty:
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endDt = _pd.to_datetime(_datetime.datetime.utcfromtimestamp(end))
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endDt = pd.to_datetime(_datetime.datetime.utcfromtimestamp(end))
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if quotes.index[quotes.shape[0] - 1] >= endDt:
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quotes = quotes.iloc[0:quotes.shape[0] - 1]
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except Exception:
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shared._DFS[self.ticker] = utils.empty_df()
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shared._ERRORS[self.ticker] = err_msg
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if raise_errors:
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raise Exception('%s: %s' % (self.ticker, err_msg))
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raise Exception(f'{self.ticker}: {err_msg}')
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else:
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logger.error('%s: %s' % (self.ticker, err_msg))
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logger.error(f'{self.ticker}: {err_msg}')
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return shared._DFS[self.ticker]
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logger.debug(f'{self.ticker}: yfinance received OHLC data: {quotes.index[0]} -> {quotes.index[-1]}')
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@ -300,7 +298,7 @@ class TickerBase:
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if interval.lower() == "30m":
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logger.debug(f'{self.ticker}: resampling 30m OHLC from 15m')
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quotes2 = quotes.resample('30T')
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quotes = _pd.DataFrame(index=quotes2.last().index, data={
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quotes = pd.DataFrame(index=quotes2.last().index, data={
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'Open': quotes2['Open'].first(),
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'High': quotes2['High'].max(),
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'Low': quotes2['Low'].min(),
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@ -356,7 +354,7 @@ class TickerBase:
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if splits is not None:
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splits = splits.loc[startDt:]
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if end is not None:
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endDt = _pd.Timestamp(end, unit='s').tz_localize(tz)
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endDt = pd.Timestamp(end, unit='s').tz_localize(tz)
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if dividends is not None:
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dividends = dividends[dividends.index < endDt]
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if capital_gains is not None:
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@ -369,14 +367,11 @@ class TickerBase:
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if not intraday:
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# If localizing a midnight during DST transition hour when clocks roll back,
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# meaning clock hits midnight twice, then use the 2nd (ambiguous=True)
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quotes.index = _pd.to_datetime(quotes.index.date).tz_localize(tz_exchange, ambiguous=True,
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nonexistent='shift_forward')
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quotes.index = pd.to_datetime(quotes.index.date).tz_localize(tz_exchange, ambiguous=True, nonexistent='shift_forward')
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if dividends.shape[0] > 0:
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dividends.index = _pd.to_datetime(dividends.index.date).tz_localize(tz_exchange, ambiguous=True,
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nonexistent='shift_forward')
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dividends.index = pd.to_datetime(dividends.index.date).tz_localize(tz_exchange, ambiguous=True, nonexistent='shift_forward')
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if splits.shape[0] > 0:
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splits.index = _pd.to_datetime(splits.index.date).tz_localize(tz_exchange, ambiguous=True,
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nonexistent='shift_forward')
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splits.index = pd.to_datetime(splits.index.date).tz_localize(tz_exchange, ambiguous=True, nonexistent='shift_forward')
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# Combine
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df = quotes.sort_index()
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@ -431,9 +426,8 @@ class TickerBase:
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logger.error('%s: %s' % (self.ticker, err_msg))
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if rounding:
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df = _np.round(df, data[
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"chart"]["result"][0]["meta"]["priceHint"])
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df['Volume'] = df['Volume'].fillna(0).astype(_np.int64)
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df = np.round(df, data["chart"]["result"][0]["meta"]["priceHint"])
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df['Volume'] = df['Volume'].fillna(0).astype(np.int64)
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if intraday:
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df.index.name = "Datetime"
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@ -460,7 +454,7 @@ class TickerBase:
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# Reconstruct values in df using finer-grained price data. Delimiter marks what to reconstruct
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logger = utils.get_yf_logger()
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if not isinstance(df, _pd.DataFrame):
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if not isinstance(df, pd.DataFrame):
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raise Exception("'df' must be a Pandas DataFrame not", type(df))
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if interval == "1m":
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# Can't go smaller than 1m so can't reconstruct
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@ -489,7 +483,7 @@ class TickerBase:
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sub_interval = nexts[interval]
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td_range = itds[interval]
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else:
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logger.warning("Have not implemented price repair for '%s' interval. Contact developers", interval)
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logger.warning(f"Have not implemented price repair for '{interval}' interval. Contact developers")
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if "Repaired?" not in df.columns:
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df["Repaired?"] = False
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return df
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@ -498,7 +492,7 @@ class TickerBase:
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if self._reconstruct_start_interval is None:
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self._reconstruct_start_interval = interval
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if interval not in [self._reconstruct_start_interval, nexts[self._reconstruct_start_interval]]:
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logger.debug(f"{self.ticker}: Price repair has hit max depth of 2 ('%s'->'%s'->'%s')", self._reconstruct_start_interval, interval, sub_interval)
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logger.debug(f"{self.ticker}: Price repair has hit max depth of 2 ('{self._reconstruct_start_interval}'->'{interval}'->'{sub_interval}')")
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return df
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df = df.sort_index()
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@ -512,7 +506,7 @@ class TickerBase:
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min_dt = None
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else:
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m -= _datetime.timedelta(days=1) # allow space for 1-day padding
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min_dt = _pd.Timestamp.utcnow() - m
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min_dt = pd.Timestamp.utcnow() - m
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min_dt = min_dt.tz_convert(df.index.tz).ceil("D")
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logger.debug(f"min_dt={min_dt} interval={interval} sub_interval={sub_interval}")
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if min_dt is not None:
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@ -525,7 +519,7 @@ class TickerBase:
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return df
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dts_to_repair = df.index[f_repair_rows]
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indices_to_repair = _np.where(f_repair_rows)[0]
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indices_to_repair = np.where(f_repair_rows)[0]
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if len(dts_to_repair) == 0:
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logger.info("Nothing needs repairing (dts_to_repair[] empty)")
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@ -690,11 +684,11 @@ class TickerBase:
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df_new.index = df_new.index.tz_localize(df_fine.index.tz)
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else:
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df_fine["diff"] = df_fine["intervalID"].diff()
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new_index = _np.append([df_fine.index[0]], df_fine.index[df_fine["intervalID"].diff() > 0])
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new_index = np.append([df_fine.index[0]], df_fine.index[df_fine["intervalID"].diff() > 0])
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df_new.index = new_index
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# Calibrate!
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common_index = _np.intersect1d(df_block.index, df_new.index)
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common_index = np.intersect1d(df_block.index, df_new.index)
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if len(common_index) == 0:
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# Can't calibrate so don't attempt repair
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logger.warning(f"Can't calibrate {interval} block starting {start_d} so aborting repair")
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@ -711,9 +705,9 @@ class TickerBase:
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# good data. Which is case most of time.
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# But in case are repairing a chunk of bad 1d data, back/forward-fill the
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# good div-adjustments - not perfect, but a good backup.
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div_adjusts[f_tag] = _np.nan
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div_adjusts[f_tag] = np.nan
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div_adjusts = div_adjusts.fillna(method='bfill').fillna(method='ffill')
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for idx in _np.where(f_tag)[0]:
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for idx in np.where(f_tag)[0]:
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dt = df_new_calib.index[idx]
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n = len(div_adjusts)
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if df_new.loc[dt, "Dividends"] != 0:
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@ -760,9 +754,9 @@ class TickerBase:
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weights.index = df_new.index
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weights = weights[weights.index.isin(common_index)].to_numpy().astype(float)
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weights = weights[:, None] # transpose
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weights = _np.tile(weights, len(price_cols)) # 1D -> 2D
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weights = np.tile(weights, len(price_cols)) # 1D -> 2D
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weights = weights[calib_filter] # flatten
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ratio = _np.average(ratios, weights=weights)
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ratio = np.average(ratios, weights=weights)
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logger.debug(f"Price calibration ratio (raw) = {ratio:6f}")
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ratio_rcp = round(1.0 / ratio, 1)
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ratio = round(ratio, 1)
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@ -964,8 +958,8 @@ class TickerBase:
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if df2_zeroes is not None:
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if "Repaired?" not in df2_zeroes.columns:
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df2_zeroes["Repaired?"] = False
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df2 = _pd.concat([df2, df2_zeroes]).sort_index()
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df2.index = _pd.to_datetime()
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df2 = pd.concat([df2, df2_zeroes]).sort_index()
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df2.index = pd.to_datetime()
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return df2
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@ -1020,7 +1014,7 @@ class TickerBase:
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grp = pd.Series(f_prices_bad.any(axis=1), name="nan").groupby(f_prices_bad.index.date)
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nan_pct = grp.sum() / grp.count()
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dts = nan_pct.index[nan_pct > 0.5]
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f_zero_or_nan_ignore = _np.isin(f_prices_bad.index.date, dts)
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f_zero_or_nan_ignore = np.isin(f_prices_bad.index.date, dts)
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df2_reserve = df2[f_zero_or_nan_ignore]
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df2 = df2[~f_zero_or_nan_ignore]
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f_prices_bad = (df2[price_cols] == 0.0) | df2[price_cols].isna()
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@ -1085,7 +1079,7 @@ class TickerBase:
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if df2_reserve is not None:
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if "Repaired?" not in df2_reserve.columns:
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df2_reserve["Repaired?"] = False
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df2 = _pd.concat([df2, df2_reserve]).sort_index()
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df2 = pd.concat([df2, df2_reserve]).sort_index()
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# Restore original values where repair failed (i.e. remove tag values)
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f = df2[data_cols].to_numpy() == tag
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@ -1123,7 +1117,7 @@ class TickerBase:
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elif df2.index.tz != tz_exchange:
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df2.index = df2.index.tz_convert(tz_exchange)
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div_indices = _np.where(f_div)[0]
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div_indices = np.where(f_div)[0]
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last_div_idx = div_indices[-1]
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if last_div_idx == 0:
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# Not enough data to recalculate the div-adjustment,
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@ -1140,7 +1134,7 @@ class TickerBase:
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start_dt = df2.index[start_idx]
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f_no_adj = (df2['Close'] == df2['Adj Close']).to_numpy()[start_idx:last_div_idx]
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threshold_pct = 0.5
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Yahoo_failed = (_np.sum(f_no_adj) / len(f_no_adj)) > threshold_pct
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Yahoo_failed = (np.sum(f_no_adj) / len(f_no_adj)) > threshold_pct
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# Fix Yahoo
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if Yahoo_failed:
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@ -1218,13 +1212,13 @@ class TickerBase:
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# Calculate daily price % change. To reduce effect of price volatility,
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# calculate change for each OHLC column and select value nearest 1.0.
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_1d_change_x = _np.full((n, 4), 1.0)
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_1d_change_x = np.full((n, 4), 1.0)
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price_data = df2[OHLC].replace(0.0, 1.0).to_numpy()
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_1d_change_x[1:] = price_data[1:, ] / price_data[:-1, ]
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diff = _np.abs(_1d_change_x - 1.0)
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j_indices = _np.argmin(diff, axis=1)
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_1d_change_minx = _1d_change_x[_np.arange(n), j_indices]
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f_na = _np.isnan(_1d_change_minx)
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diff = np.abs(_1d_change_x - 1.0)
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j_indices = np.argmin(diff, axis=1)
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_1d_change_minx = _1d_change_x[np.arange(n), j_indices]
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f_na = np.isnan(_1d_change_minx)
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if f_na.any():
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# Possible if data was too old for reconstruction.
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_1d_change_minx[f_na] = 1.0
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@ -1233,19 +1227,19 @@ class TickerBase:
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# If all 1D changes are closer to 1.0 than split, exit
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split_max = max(split, split_rcp)
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if _np.max(_1d_change_minx) < (split_max - 1) * 0.5 + 1 and _np.min(_1d_change_minx) > 1.0 / ((split_max - 1) * 0.5 + 1):
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if np.max(_1d_change_minx) < (split_max - 1) * 0.5 + 1 and np.min(_1d_change_minx) > 1.0 / ((split_max - 1) * 0.5 + 1):
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logger.info(f"price-repair-split: No bad splits detected")
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return df
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# Calculate the true price variance, i.e. remove effect of bad split-adjustments.
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# Key = ignore 1D changes outside of interquartile range
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q1, q3 = _np.percentile(_1d_change_minx, [25, 75])
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q1, q3 = np.percentile(_1d_change_minx, [25, 75])
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iqr = q3 - q1
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lower_bound = q1 - 1.5 * iqr
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upper_bound = q3 + 1.5 * iqr
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f = (_1d_change_minx >= lower_bound) & (_1d_change_minx <= upper_bound)
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avg = _np.mean(_1d_change_minx[f])
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sd = _np.std(_1d_change_minx[f])
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avg = np.mean(_1d_change_minx[f])
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sd = np.std(_1d_change_minx[f])
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# Now can calculate SD as % of mean
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sd_pct = sd / avg
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logger.debug(f"price-repair-split: Estimation of true 1D change stats: mean = {avg:.2f}, StdDev = {sd:.4f} ({sd_pct*100.0:.1f}% of mean)")
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@ -1281,7 +1275,7 @@ class TickerBase:
|
|||
correct_columns_individually = False
|
||||
|
||||
if correct_columns_individually:
|
||||
_1d_change_x = _np.full((n, 4), 1.0)
|
||||
_1d_change_x = np.full((n, 4), 1.0)
|
||||
price_data = df2[OHLC].replace(0.0, 1.0).to_numpy()
|
||||
_1d_change_x[1:] = price_data[1:, ] / price_data[:-1, ]
|
||||
else:
|
||||
|
|
@ -1308,7 +1302,7 @@ class TickerBase:
|
|||
return df
|
||||
|
||||
def map_signals_to_ranges(f, f1):
|
||||
true_indices = _np.where(f)[0]
|
||||
true_indices = np.where(f)[0]
|
||||
ranges = []
|
||||
for i in range(len(true_indices) - 1):
|
||||
if i % 2 == 0:
|
||||
|
|
@ -1326,7 +1320,7 @@ class TickerBase:
|
|||
return ranges
|
||||
|
||||
if correct_columns_individually:
|
||||
f_corrected = _np.full(n, False)
|
||||
f_corrected = np.full(n, False)
|
||||
if correct_volume:
|
||||
# If Open or Close is repaired but not both,
|
||||
# then this means the interval has a mix of correct
|
||||
|
|
@ -1334,8 +1328,8 @@ class TickerBase:
|
|||
# so use a heuristic:
|
||||
# - if both Open & Close were Nx bad => Volume is Nx bad
|
||||
# - if only one of Open & Close are Nx bad => Volume is 0.5*Nx bad
|
||||
f_open_fixed = _np.full(n, False)
|
||||
f_close_fixed = _np.full(n, False)
|
||||
f_open_fixed = np.full(n, False)
|
||||
f_close_fixed = np.full(n, False)
|
||||
for j in range(len(OHLC)):
|
||||
c = OHLC[j]
|
||||
ranges = map_signals_to_ranges(f[:, j], f1[:, j])
|
||||
|
|
@ -1361,7 +1355,7 @@ class TickerBase:
|
|||
|
||||
if correct_volume:
|
||||
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)
|
||||
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
|
||||
if f_open_xor_closed_fixed.any():
|
||||
|
|
@ -1727,19 +1721,19 @@ class TickerBase:
|
|||
|
||||
# Process dates
|
||||
tz = self._get_ticker_tz(proxy=None, timeout=10)
|
||||
dt_now = _pd.Timestamp.utcnow().tz_convert(tz)
|
||||
dt_now = pd.Timestamp.utcnow().tz_convert(tz)
|
||||
if start is not None:
|
||||
start_ts = utils._parse_user_dt(start, tz)
|
||||
start = _pd.Timestamp.fromtimestamp(start_ts).tz_localize("UTC").tz_convert(tz)
|
||||
start = pd.Timestamp.fromtimestamp(start_ts).tz_localize("UTC").tz_convert(tz)
|
||||
start_d = start.date()
|
||||
if end is not None:
|
||||
end_ts = utils._parse_user_dt(end, tz)
|
||||
end = _pd.Timestamp.fromtimestamp(end_ts).tz_localize("UTC").tz_convert(tz)
|
||||
end = pd.Timestamp.fromtimestamp(end_ts).tz_localize("UTC").tz_convert(tz)
|
||||
end_d = end.date()
|
||||
if end is None:
|
||||
end = dt_now
|
||||
if start is None:
|
||||
start = end - _pd.Timedelta(days=548) # 18 months
|
||||
start = end - pd.Timedelta(days=548) # 18 months
|
||||
if start >= end:
|
||||
logger.error("Start date must be before end")
|
||||
return None
|
||||
|
|
@ -1767,7 +1761,7 @@ class TickerBase:
|
|||
if "shares_out" not in shares_data[0]:
|
||||
return None
|
||||
try:
|
||||
df = _pd.Series(shares_data[0]["shares_out"], index=_pd.to_datetime(shares_data[0]["timestamp"], unit="s"))
|
||||
df = pd.Series(shares_data[0]["shares_out"], index=pd.to_datetime(shares_data[0]["timestamp"], unit="s"))
|
||||
except Exception as e:
|
||||
logger.error(f"{self.ticker}: Failed to parse shares count data: {e}")
|
||||
return None
|
||||
|
|
@ -1796,9 +1790,7 @@ class TickerBase:
|
|||
if "shortName" in self._quote.info:
|
||||
q = self._quote.info['shortName']
|
||||
|
||||
url = 'https://markets.businessinsider.com/ajax/' \
|
||||
'SearchController_Suggest?max_results=25&query=%s' \
|
||||
% urlencode(q)
|
||||
url = f'https://markets.businessinsider.com/ajax/SearchController_Suggest?max_results=25&query={urlencode(q)}'
|
||||
data = self._data.cache_get(url=url, proxy=proxy).text
|
||||
|
||||
search_str = f'"{ticker}|'
|
||||
|
|
@ -1861,7 +1853,7 @@ class TickerBase:
|
|||
"the issue. Thank you for your patience.")
|
||||
|
||||
try:
|
||||
data = _pd.read_html(data)[0]
|
||||
data = pd.read_html(data)[0]
|
||||
except ValueError:
|
||||
if page_offset == 0:
|
||||
# Should not fail on first page
|
||||
|
|
@ -1872,7 +1864,7 @@ class TickerBase:
|
|||
if dates is None:
|
||||
dates = data
|
||||
else:
|
||||
dates = _pd.concat([dates, data], axis=0)
|
||||
dates = pd.concat([dates, data], axis=0)
|
||||
|
||||
page_offset += page_size
|
||||
# got less data then we asked for or already fetched all we requested, no need to fetch more pages
|
||||
|
|
@ -1910,7 +1902,7 @@ class TickerBase:
|
|||
tzinfo.columns = ["AM/PM", "TZ"]
|
||||
# - combine and parse
|
||||
dates[cn] = dates[cn] + ' ' + tzinfo["AM/PM"]
|
||||
dates[cn] = _pd.to_datetime(dates[cn], format="%b %d, %Y, %I %p")
|
||||
dates[cn] = pd.to_datetime(dates[cn], format="%b %d, %Y, %I %p")
|
||||
# - instead of attempting decoding of ambiguous timezone abbreviation, just use 'info':
|
||||
self._quote.proxy = proxy
|
||||
tz = self._get_ticker_tz(proxy=proxy, timeout=30)
|
||||
|
|
|
|||
Loading…
Reference in New Issue