2106 lines
91 KiB
Python
2106 lines
91 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# yfinance - market data downloader
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# https://github.com/ranaroussi/yfinance
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#
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# Copyright 2017-2019 Ran Aroussi
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from __future__ import print_function
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import datetime as _datetime
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import json as _json
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import logging
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import time as _time
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import warnings
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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 requests
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from . import shared
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from . import utils
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from .data import TickerData
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from .scrapers.analysis import Analysis
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from .scrapers.fundamentals import Fundamentals
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from .scrapers.holders import Holders
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from .scrapers.quote import Quote, FastInfo
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from .const import _BASE_URL_, _ROOT_URL_
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class TickerBase:
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def __init__(self, ticker, session=None):
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self.ticker = ticker.upper()
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self.session = session
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self._history = None
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self._history_metadata = None
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self._history_metadata_formatted = False
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self._base_url = _BASE_URL_
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self._tz = None
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self._isin = None
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self._news = []
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self._shares = None
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self._earnings_dates = {}
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self._earnings = None
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self._financials = None
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# accept isin as ticker
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if utils.is_isin(self.ticker):
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self.ticker = utils.get_ticker_by_isin(self.ticker, None, session)
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self._data: TickerData = TickerData(self.ticker, session=session)
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self._analysis = Analysis(self._data)
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self._holders = Holders(self._data)
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self._quote = Quote(self._data)
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self._fundamentals = Fundamentals(self._data)
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self._fast_info = None
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# Limit recursion depth when repairing prices
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self._reconstruct_start_interval = None
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@utils.log_indent_decorator
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def history(self, period="1mo", interval="1d",
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start=None, end=None, prepost=False, actions=True,
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auto_adjust=True, back_adjust=False, repair=False, keepna=False,
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proxy=None, rounding=False, timeout=10,
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debug=None, # deprecated
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raise_errors=False) -> pd.DataFrame:
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"""
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:Parameters:
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period : str
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Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
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Either Use period parameter or use start and end
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interval : str
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Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
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Intraday data cannot extend last 60 days
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start: str
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Download start date string (YYYY-MM-DD) or _datetime, inclusive.
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Default is 99 years ago
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E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
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end: str
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Download end date string (YYYY-MM-DD) or _datetime, exclusive.
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Default is now
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E.g. for end="2023-01-01", the last data point will be on "2022-12-31"
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prepost : bool
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Include Pre and Post market data in results?
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Default is False
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auto_adjust: bool
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Adjust all OHLC automatically? Default is True
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back_adjust: bool
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Back-adjusted data to mimic true historical prices
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repair: bool
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Detect currency unit 100x mixups and attempt repair.
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Default is False
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keepna: bool
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Keep NaN rows returned by Yahoo?
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Default is False
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proxy: str
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Optional. Proxy server URL scheme. Default is None
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rounding: bool
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Round values to 2 decimal places?
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Optional. Default is False = precision suggested by Yahoo!
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timeout: None or float
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If not None stops waiting for a response after given number of
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seconds. (Can also be a fraction of a second e.g. 0.01)
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Default is 10 seconds.
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debug: bool
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If passed as False, will suppress message printing to console.
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DEPRECATED, will be removed in future version
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raise_errors: bool
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If True, then raise errors as Exceptions instead of logging.
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"""
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logger = utils.get_yf_logger()
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if debug is not None:
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if debug:
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utils.print_once(f"yfinance: Ticker.history(debug={debug}) argument is deprecated and will be removed in future version. Do this instead: logging.getLogger('yfinance').setLevel(logging.ERROR)")
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logger.setLevel(logging.ERROR)
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else:
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utils.print_once(f"yfinance: Ticker.history(debug={debug}) argument is deprecated and will be removed in future version. Do this instead to suppress error messages: logging.getLogger('yfinance').setLevel(logging.CRITICAL)")
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logger.setLevel(logging.CRITICAL)
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start_user = start
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end_user = end
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if start or period is None or period.lower() == "max":
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# Check can get TZ. Fail => probably delisted
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tz = self._get_ticker_tz(proxy, timeout)
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if tz is None:
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# Every valid ticker has a timezone. Missing = problem
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err_msg = "No timezone found, symbol may be delisted"
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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(f'{self.ticker}: {err_msg}')
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else:
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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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end = int(_time.time())
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else:
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end = utils._parse_user_dt(end, tz)
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if start is None:
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if interval == "1m":
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start = end - 604800 # Subtract 7 days
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else:
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max_start_datetime = pd.Timestamp.utcnow().floor("D") - _datetime.timedelta(days=99 * 365)
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start = int(max_start_datetime.timestamp())
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else:
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start = utils._parse_user_dt(start, tz)
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params = {"period1": start, "period2": end}
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else:
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period = period.lower()
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params = {"range": period}
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params["interval"] = interval.lower()
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params["includePrePost"] = prepost
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# 1) fix weired bug with Yahoo! - returning 60m for 30m bars
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if params["interval"] == "30m":
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params["interval"] = "15m"
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# if the ticker is MUTUALFUND or ETF, then get capitalGains events
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params["events"] = "div,splits,capitalGains"
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params_pretty = dict(params)
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tz = self._get_ticker_tz(proxy, timeout)
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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(f'{self.ticker}: Yahoo GET parameters: {str(params_pretty)}')
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# Getting data from json
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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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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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get_fn = self._data.cache_get
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try:
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data = get_fn(
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url=url,
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params=params,
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proxy=proxy,
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timeout=timeout
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)
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if "Will be right back" in data.text or data is None:
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raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
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"Our engineers are working quickly to resolve "
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"the issue. Thank you for your patience.")
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data = data.json()
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except Exception:
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pass
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# Store the meta data that gets retrieved simultaneously
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try:
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self._history_metadata = data["chart"]["result"][0]["meta"]
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except Exception:
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self._history_metadata = {}
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intraday = params["interval"][-1] in ("m", 'h')
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err_msg = "No price data found, symbol may be delisted"
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if start or period is None or period.lower() == "max":
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err_msg += f' ({params["interval"]} '
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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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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 += ' -> '
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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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else:
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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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fail = False
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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 += 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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fail = True
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elif "chart" not in data or data["chart"]["result"] is None or not data["chart"]["result"]:
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fail = True
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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 = 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(f'{self.ticker}: {err_msg}')
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else:
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logger.error(f'{self.ticker}: {err_msg}')
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if self._reconstruct_start_interval is not None and self._reconstruct_start_interval == interval:
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self._reconstruct_start_interval = None
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return utils.empty_df()
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# parse quotes
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try:
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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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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(f'{self.ticker}: {err_msg}')
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else:
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logger.error(f'{self.ticker}: {err_msg}')
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if self._reconstruct_start_interval is not None and self._reconstruct_start_interval == interval:
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self._reconstruct_start_interval = None
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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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# 2) fix weired bug with Yahoo! - returning 60m for 30m bars
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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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'Open': quotes2['Open'].first(),
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'High': quotes2['High'].max(),
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'Low': quotes2['Low'].min(),
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'Close': quotes2['Close'].last(),
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'Adj Close': quotes2['Adj Close'].last(),
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'Volume': quotes2['Volume'].sum()
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})
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try:
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quotes['Dividends'] = quotes2['Dividends'].max()
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quotes['Stock Splits'] = quotes2['Stock Splits'].max()
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except Exception:
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pass
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# Select useful info from metadata
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quote_type = self._history_metadata["instrumentType"]
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expect_capital_gains = quote_type in ('MUTUALFUND', 'ETF')
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tz_exchange = self._history_metadata["exchangeTimezoneName"]
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# Note: ordering is important. If you change order, run the tests!
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quotes = utils.set_df_tz(quotes, params["interval"], tz_exchange)
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quotes = utils.fix_Yahoo_dst_issue(quotes, params["interval"])
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quotes = utils.fix_Yahoo_returning_live_separate(quotes, params["interval"], tz_exchange)
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intraday = params["interval"][-1] in ("m", 'h')
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if not prepost and intraday and "tradingPeriods" in self._history_metadata:
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tps = self._history_metadata["tradingPeriods"]
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if not isinstance(tps, pd.DataFrame):
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self._history_metadata = utils.format_history_metadata(self._history_metadata, tradingPeriodsOnly=True)
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tps = self._history_metadata["tradingPeriods"]
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quotes = utils.fix_Yahoo_returning_prepost_unrequested(quotes, params["interval"], tps)
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logger.debug(f'{self.ticker}: OHLC after cleaning: {quotes.index[0]} -> {quotes.index[-1]}')
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# actions
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dividends, splits, capital_gains = utils.parse_actions(data["chart"]["result"][0])
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if not expect_capital_gains:
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capital_gains = None
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if splits is not None:
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splits = utils.set_df_tz(splits, interval, tz_exchange)
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if dividends is not None:
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dividends = utils.set_df_tz(dividends, interval, tz_exchange)
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if capital_gains is not None:
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capital_gains = utils.set_df_tz(capital_gains, interval, tz_exchange)
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if start is not None:
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if not quotes.empty:
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startDt = quotes.index[0].floor('D')
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if dividends is not None:
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dividends = dividends.loc[startDt:]
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if capital_gains is not None:
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capital_gains = capital_gains.loc[startDt:]
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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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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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capital_gains = capital_gains[capital_gains.index < endDt]
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if splits is not None:
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splits = splits[splits.index < endDt]
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# Prepare for combine
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intraday = params["interval"][-1] in ("m", 'h')
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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, 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, 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, nonexistent='shift_forward')
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# Combine
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df = quotes.sort_index()
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if dividends.shape[0] > 0:
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df = utils.safe_merge_dfs(df, dividends, interval)
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if "Dividends" in df.columns:
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df.loc[df["Dividends"].isna(), "Dividends"] = 0
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else:
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df["Dividends"] = 0.0
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if splits.shape[0] > 0:
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df = utils.safe_merge_dfs(df, splits, interval)
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if "Stock Splits" in df.columns:
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df.loc[df["Stock Splits"].isna(), "Stock Splits"] = 0
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else:
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df["Stock Splits"] = 0.0
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if expect_capital_gains:
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if capital_gains.shape[0] > 0:
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df = utils.safe_merge_dfs(df, capital_gains, interval)
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if "Capital Gains" in df.columns:
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df.loc[df["Capital Gains"].isna(), "Capital Gains"] = 0
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else:
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df["Capital Gains"] = 0.0
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logger.debug(f'{self.ticker}: OHLC after combining events: {quotes.index[0]} -> {quotes.index[-1]}')
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df = df[~df.index.duplicated(keep='first')] # must do before repair
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if isinstance(repair, str) and repair=='silent':
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utils.log_once(logging.WARNING, f"yfinance: Ticker.history(repair='silent') value is deprecated and will be removed in future version. Repair now silent by default, use logging module to increase verbosity.")
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repair = True
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if repair:
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# Do this before auto/back adjust
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logger.debug(f'{self.ticker}: checking OHLC for repairs ...')
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df = self._fix_unit_mixups(df, interval, tz_exchange, prepost)
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df = self._fix_bad_stock_split(df, interval, tz_exchange)
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# Must repair 100x and split errors before price reconstruction
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df = self._fix_zeroes(df, interval, tz_exchange, prepost)
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df = self._fix_missing_div_adjust(df, interval, tz_exchange)
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df = df.sort_index()
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# Auto/back adjust
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try:
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if auto_adjust:
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df = utils.auto_adjust(df)
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elif back_adjust:
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df = utils.back_adjust(df)
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except Exception as e:
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if auto_adjust:
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err_msg = "auto_adjust failed with %s" % e
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else:
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err_msg = "back_adjust failed with %s" % e
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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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else:
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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["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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else:
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df.index.name = "Date"
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self._history = df.copy()
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# missing rows cleanup
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if not actions:
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df = df.drop(columns=["Dividends", "Stock Splits", "Capital Gains"], errors='ignore')
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if not keepna:
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mask_nan_or_zero = (df.isna() | (df == 0)).all(axis=1)
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|
df = df.drop(mask_nan_or_zero.index[mask_nan_or_zero])
|
|
|
|
logger.debug(f'{self.ticker}: yfinance returning OHLC: {df.index[0]} -> {df.index[-1]}')
|
|
|
|
if self._reconstruct_start_interval is not None and self._reconstruct_start_interval == interval:
|
|
self._reconstruct_start_interval = None
|
|
return df
|
|
|
|
# ------------------------
|
|
|
|
@utils.log_indent_decorator
|
|
def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
|
|
# Reconstruct values in df using finer-grained price data. Delimiter marks what to reconstruct
|
|
logger = utils.get_yf_logger()
|
|
|
|
if not isinstance(df, pd.DataFrame):
|
|
raise Exception("'df' must be a Pandas DataFrame not", type(df))
|
|
if interval == "1m":
|
|
# Can't go smaller than 1m so can't reconstruct
|
|
return df
|
|
|
|
if interval[1:] in ['d', 'wk', 'mo']:
|
|
# Interday data always includes pre & post
|
|
prepost = True
|
|
intraday = False
|
|
else:
|
|
intraday = True
|
|
|
|
price_cols = [c for c in ["Open", "High", "Low", "Close", "Adj Close"] if c in df]
|
|
data_cols = price_cols + ["Volume"]
|
|
|
|
# If interval is weekly then can construct with daily. But if smaller intervals then
|
|
# restricted to recent times:
|
|
intervals = ["1wk", "1d", "1h", "30m", "15m", "5m", "2m", "1m"]
|
|
itds = {i: utils._interval_to_timedelta(interval) for i in intervals}
|
|
nexts = {intervals[i]: intervals[i + 1] for i in range(len(intervals) - 1)}
|
|
min_lookbacks = {"1wk": None, "1d": None, "1h": _datetime.timedelta(days=730)}
|
|
for i in ["30m", "15m", "5m", "2m"]:
|
|
min_lookbacks[i] = _datetime.timedelta(days=60)
|
|
min_lookbacks["1m"] = _datetime.timedelta(days=30)
|
|
if interval in nexts:
|
|
sub_interval = nexts[interval]
|
|
td_range = itds[interval]
|
|
else:
|
|
logger.warning(f"Have not implemented price repair for '{interval}' interval. Contact developers")
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
|
|
# Limit max reconstruction depth to 2:
|
|
if self._reconstruct_start_interval is None:
|
|
self._reconstruct_start_interval = interval
|
|
if interval != self._reconstruct_start_interval and interval != nexts[self._reconstruct_start_interval]:
|
|
logger.debug(f"{self.ticker}: Price repair has hit max depth of 2 ('%s'->'%s'->'%s')", self._reconstruct_start_interval, nexts[self._reconstruct_start_interval], interval)
|
|
return df
|
|
|
|
df = df.sort_index()
|
|
|
|
f_repair = df[data_cols].to_numpy() == tag
|
|
f_repair_rows = f_repair.any(axis=1)
|
|
|
|
# Ignore old intervals for which Yahoo won't return finer data:
|
|
m = min_lookbacks[sub_interval]
|
|
if m is None:
|
|
min_dt = None
|
|
else:
|
|
m -= _datetime.timedelta(days=1) # allow space for 1-day padding
|
|
min_dt = pd.Timestamp.utcnow() - m
|
|
min_dt = min_dt.tz_convert(df.index.tz).ceil("D")
|
|
logger.debug(f"min_dt={min_dt} interval={interval} sub_interval={sub_interval}")
|
|
if min_dt is not None:
|
|
f_recent = df.index >= min_dt
|
|
f_repair_rows = f_repair_rows & f_recent
|
|
if not f_repair_rows.any():
|
|
logger.info("Data too old to repair")
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
|
|
dts_to_repair = df.index[f_repair_rows]
|
|
indices_to_repair = np.where(f_repair_rows)[0]
|
|
|
|
if len(dts_to_repair) == 0:
|
|
logger.info("Nothing needs repairing (dts_to_repair[] empty)")
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
|
|
df_v2 = df.copy()
|
|
if "Repaired?" not in df_v2.columns:
|
|
df_v2["Repaired?"] = False
|
|
f_good = ~(df[price_cols].isna().any(axis=1))
|
|
f_good = f_good & (df[price_cols].to_numpy() != tag).all(axis=1)
|
|
df_good = df[f_good]
|
|
|
|
# Group nearby NaN-intervals together to reduce number of Yahoo fetches
|
|
dts_groups = [[dts_to_repair[0]]]
|
|
last_dt = dts_to_repair[0]
|
|
last_ind = indices_to_repair[0]
|
|
td = utils._interval_to_timedelta(interval)
|
|
# Note on setting max size: have to allow space for adding good data
|
|
if sub_interval == "1mo":
|
|
grp_max_size = _dateutil.relativedelta.relativedelta(years=2)
|
|
elif sub_interval == "1wk":
|
|
grp_max_size = _dateutil.relativedelta.relativedelta(years=2)
|
|
elif sub_interval == "1d":
|
|
grp_max_size = _dateutil.relativedelta.relativedelta(years=2)
|
|
elif sub_interval == "1h":
|
|
grp_max_size = _dateutil.relativedelta.relativedelta(years=1)
|
|
elif sub_interval == "1m":
|
|
grp_max_size = _datetime.timedelta(days=5) # allow 2 days for buffer below
|
|
else:
|
|
grp_max_size = _datetime.timedelta(days=30)
|
|
logger.debug(f"grp_max_size = {grp_max_size}")
|
|
for i in range(1, len(dts_to_repair)):
|
|
ind = indices_to_repair[i]
|
|
dt = dts_to_repair[i]
|
|
if dt.date() < dts_groups[-1][0].date() + grp_max_size:
|
|
dts_groups[-1].append(dt)
|
|
else:
|
|
dts_groups.append([dt])
|
|
last_dt = dt
|
|
last_ind = ind
|
|
|
|
logger.debug("Repair groups:")
|
|
for g in dts_groups:
|
|
logger.debug(f"- {g[0]} -> {g[-1]}")
|
|
|
|
# Add some good data to each group, so can calibrate prices later:
|
|
for i in range(len(dts_groups)):
|
|
g = dts_groups[i]
|
|
g0 = g[0]
|
|
i0 = df_good.index.get_indexer([g0], method="nearest")[0]
|
|
if i0 > 0:
|
|
if (min_dt is None or df_good.index[i0 - 1] >= min_dt) and \
|
|
((not intraday) or df_good.index[i0 - 1].date() == g0.date()):
|
|
i0 -= 1
|
|
gl = g[-1]
|
|
il = df_good.index.get_indexer([gl], method="nearest")[0]
|
|
if il < len(df_good) - 1:
|
|
if (not intraday) or df_good.index[il + 1].date() == gl.date():
|
|
il += 1
|
|
good_dts = df_good.index[i0:il + 1]
|
|
dts_groups[i] += good_dts.to_list()
|
|
dts_groups[i].sort()
|
|
|
|
n_fixed = 0
|
|
for g in dts_groups:
|
|
df_block = df[df.index.isin(g)]
|
|
logger.debug("df_block:\n" + str(df_block))
|
|
|
|
start_dt = g[0]
|
|
start_d = start_dt.date()
|
|
reject = False
|
|
if sub_interval == "1h" and (_datetime.date.today() - start_d) > _datetime.timedelta(days=729):
|
|
reject = True
|
|
elif sub_interval in ["30m", "15m"] and (_datetime.date.today() - start_d) > _datetime.timedelta(days=59):
|
|
reject = True
|
|
if reject:
|
|
# Don't bother requesting more price data, Yahoo will reject
|
|
msg = f"Cannot reconstruct {interval} block starting"
|
|
if intraday:
|
|
msg += f" {start_dt}"
|
|
else:
|
|
msg += f" {start_d}"
|
|
msg += ", too old, Yahoo will reject request for finer-grain data"
|
|
logger.info(msg)
|
|
continue
|
|
|
|
td_1d = _datetime.timedelta(days=1)
|
|
end_dt = g[-1]
|
|
end_d = end_dt.date() + td_1d
|
|
if interval in "1wk":
|
|
fetch_start = start_d - td_range # need previous week too
|
|
fetch_end = g[-1].date() + td_range
|
|
elif interval == "1d":
|
|
fetch_start = start_d
|
|
fetch_end = g[-1].date() + td_range
|
|
else:
|
|
fetch_start = g[0]
|
|
fetch_end = g[-1] + td_range
|
|
|
|
# The first and last day returned by Yahoo can be slightly wrong, so add buffer:
|
|
fetch_start -= td_1d
|
|
fetch_end += td_1d
|
|
if intraday:
|
|
fetch_start = fetch_start.date()
|
|
fetch_end = fetch_end.date() + td_1d
|
|
if min_dt is not None:
|
|
fetch_start = max(min_dt.date(), fetch_start)
|
|
logger.debug(f"Fetching {sub_interval} prepost={prepost} {fetch_start}->{fetch_end}")
|
|
df_fine = self.history(start=fetch_start, end=fetch_end, interval=sub_interval, auto_adjust=False, actions=True, prepost=prepost, repair=True, keepna=True)
|
|
if df_fine is None or df_fine.empty:
|
|
msg = f"Cannot reconstruct {interval} block starting"
|
|
if intraday:
|
|
msg += f" {start_dt}"
|
|
else:
|
|
msg += f" {start_d}"
|
|
msg += ", too old, Yahoo is rejecting request for finer-grain data"
|
|
logger.debug(msg)
|
|
continue
|
|
# Discard the buffer
|
|
df_fine = df_fine.loc[g[0]: g[-1] + itds[sub_interval] - _datetime.timedelta(milliseconds=1)].copy()
|
|
if df_fine.empty:
|
|
msg = f"Cannot reconstruct {interval} block range"
|
|
if intraday:
|
|
msg += f" {start_dt}->{end_dt}"
|
|
else:
|
|
msg += f" {start_d}->{end_d}"
|
|
msg += ", Yahoo not returning finer-grain data within range"
|
|
logger.debug(msg)
|
|
continue
|
|
|
|
df_fine["ctr"] = 0
|
|
if interval == "1wk":
|
|
weekdays = ["MON", "TUE", "WED", "THU", "FRI", "SAT", "SUN"]
|
|
week_end_day = weekdays[(df_block.index[0].weekday() + 7 - 1) % 7]
|
|
df_fine["Week Start"] = df_fine.index.tz_localize(None).to_period("W-" + week_end_day).start_time
|
|
grp_col = "Week Start"
|
|
elif interval == "1d":
|
|
df_fine["Day Start"] = pd.to_datetime(df_fine.index.date)
|
|
grp_col = "Day Start"
|
|
else:
|
|
df_fine.loc[df_fine.index.isin(df_block.index), "ctr"] = 1
|
|
df_fine["intervalID"] = df_fine["ctr"].cumsum()
|
|
df_fine = df_fine.drop("ctr", axis=1)
|
|
grp_col = "intervalID"
|
|
df_fine = df_fine[~df_fine[price_cols + ['Dividends']].isna().all(axis=1)]
|
|
|
|
df_fine_grp = df_fine.groupby(grp_col)
|
|
df_new = df_fine_grp.agg(
|
|
Open=("Open", "first"),
|
|
Close=("Close", "last"),
|
|
AdjClose=("Adj Close", "last"),
|
|
Low=("Low", "min"),
|
|
High=("High", "max"),
|
|
Dividends=("Dividends", "sum"),
|
|
Volume=("Volume", "sum")).rename(columns={"AdjClose": "Adj Close"})
|
|
if grp_col in ["Week Start", "Day Start"]:
|
|
df_new.index = df_new.index.tz_localize(df_fine.index.tz)
|
|
else:
|
|
df_fine["diff"] = df_fine["intervalID"].diff()
|
|
new_index = np.append([df_fine.index[0]], df_fine.index[df_fine["intervalID"].diff() > 0])
|
|
df_new.index = new_index
|
|
logger.debug('df_new:' + '\n' + str(df_new))
|
|
|
|
# Calibrate!
|
|
common_index = np.intersect1d(df_block.index, df_new.index)
|
|
if len(common_index) == 0:
|
|
# Can't calibrate so don't attempt repair
|
|
logger.info(f"Can't calibrate {interval} block starting {start_d} so aborting repair")
|
|
continue
|
|
# First, attempt to calibrate the 'Adj Close' column. OK if cannot.
|
|
# Only necessary for 1d interval, because the 1h data is not div-adjusted.
|
|
if interval == '1d':
|
|
df_new_calib = df_new[df_new.index.isin(common_index)]
|
|
df_block_calib = df_block[df_block.index.isin(common_index)]
|
|
f_tag = df_block_calib['Adj Close'] == tag
|
|
if f_tag.any():
|
|
div_adjusts = df_block_calib['Adj Close'] / df_block_calib['Close']
|
|
# The loop below assumes each 1d repair is isoloated, i.e. surrounded by
|
|
# good data. Which is case most of time.
|
|
# But in case are repairing a chunk of bad 1d data, back/forward-fill the
|
|
# good div-adjustments - not perfect, but a good backup.
|
|
div_adjusts[f_tag] = np.nan
|
|
div_adjusts = div_adjusts.fillna(method='bfill').fillna(method='ffill')
|
|
for idx in np.where(f_tag)[0]:
|
|
dt = df_new_calib.index[idx]
|
|
n = len(div_adjusts)
|
|
if df_new.loc[dt, "Dividends"] != 0:
|
|
if idx < n - 1:
|
|
# Easy, take div-adjustment from next-day
|
|
div_adjusts[idx] = div_adjusts[idx + 1]
|
|
else:
|
|
# Take previous-day div-adjustment and reverse todays adjustment
|
|
div_adj = 1.0 - df_new_calib["Dividends"].iloc[idx] / df_new_calib['Close'].iloc[
|
|
idx - 1]
|
|
div_adjusts[idx] = div_adjusts[idx - 1] / div_adj
|
|
else:
|
|
if idx > 0:
|
|
# Easy, take div-adjustment from previous-day
|
|
div_adjusts[idx] = div_adjusts[idx - 1]
|
|
else:
|
|
# Must take next-day div-adjustment
|
|
div_adjusts[idx] = div_adjusts[idx + 1]
|
|
if df_new_calib["Dividends"].iloc[idx + 1] != 0:
|
|
div_adjusts[idx] *= 1.0 - df_new_calib["Dividends"].iloc[idx + 1] / \
|
|
df_new_calib['Close'].iloc[idx]
|
|
f_close_bad = df_block_calib['Close'] == tag
|
|
df_new['Adj Close'] = df_block['Close'] * div_adjusts
|
|
if f_close_bad.any():
|
|
df_new.loc[f_close_bad, 'Adj Close'] = df_new['Close'][f_close_bad] * div_adjusts[f_close_bad]
|
|
|
|
# Check whether 'df_fine' has different split-adjustment.
|
|
# If different, then adjust to match 'df'
|
|
calib_cols = ['Open', 'Close']
|
|
df_new_calib = df_new[df_new.index.isin(common_index)][calib_cols].to_numpy()
|
|
df_block_calib = df_block[df_block.index.isin(common_index)][calib_cols].to_numpy()
|
|
calib_filter = (df_block_calib != tag)
|
|
if not calib_filter.any():
|
|
# Can't calibrate so don't attempt repair
|
|
logger.info(f"Can't calibrate {interval} block starting {start_d} so aborting repair")
|
|
continue
|
|
# Avoid divide-by-zero warnings:
|
|
for j in range(len(calib_cols)):
|
|
f = ~calib_filter[:, j]
|
|
if f.any():
|
|
df_block_calib[f, j] = 1
|
|
df_new_calib[f, j] = 1
|
|
ratios = df_block_calib[calib_filter] / df_new_calib[calib_filter]
|
|
weights = df_fine_grp.size()
|
|
weights.index = df_new.index
|
|
weights = weights[weights.index.isin(common_index)].to_numpy().astype(float)
|
|
weights = weights[:, None] # transpose
|
|
weights = np.tile(weights, len(calib_cols)) # 1D -> 2D
|
|
weights = weights[calib_filter] # flatten
|
|
not1 = ~np.isclose(ratios, 1.0, rtol=0.00001)
|
|
if np.sum(not1) == len(calib_cols):
|
|
# Only 1 calibration row in df_new is different to df_block so ignore
|
|
ratio = 1.0
|
|
else:
|
|
ratio = np.average(ratios, weights=weights)
|
|
logger.debug(f"Price calibration ratio (raw) = {ratio:6f}")
|
|
ratio_rcp = round(1.0 / ratio, 1)
|
|
ratio = round(ratio, 1)
|
|
if ratio == 1 and ratio_rcp == 1:
|
|
# Good!
|
|
pass
|
|
else:
|
|
if ratio > 1:
|
|
# data has different split-adjustment than fine-grained data
|
|
# Adjust fine-grained to match
|
|
df_new[price_cols] *= ratio
|
|
df_new["Volume"] /= ratio
|
|
elif ratio_rcp > 1:
|
|
# data has different split-adjustment than fine-grained data
|
|
# Adjust fine-grained to match
|
|
df_new[price_cols] *= 1.0 / ratio_rcp
|
|
df_new["Volume"] *= ratio_rcp
|
|
|
|
# Repair!
|
|
bad_dts = df_block.index[(df_block[price_cols + ["Volume"]] == tag).to_numpy().any(axis=1)]
|
|
|
|
no_fine_data_dts = []
|
|
for idx in bad_dts:
|
|
if idx not in df_new.index:
|
|
# Yahoo didn't return finer-grain data for this interval,
|
|
# so probably no trading happened.
|
|
no_fine_data_dts.append(idx)
|
|
if len(no_fine_data_dts) > 0:
|
|
logger.debug(f"Yahoo didn't return finer-grain data for these intervals: " + str(no_fine_data_dts))
|
|
for idx in bad_dts:
|
|
if idx not in df_new.index:
|
|
# Yahoo didn't return finer-grain data for this interval,
|
|
# so probably no trading happened.
|
|
continue
|
|
df_new_row = df_new.loc[idx]
|
|
|
|
if interval == "1wk":
|
|
df_last_week = df_new.iloc[df_new.index.get_loc(idx) - 1]
|
|
df_fine = df_fine.loc[idx:]
|
|
|
|
df_bad_row = df.loc[idx]
|
|
bad_fields = df_bad_row.index[df_bad_row == tag].to_numpy()
|
|
if "High" in bad_fields:
|
|
df_v2.loc[idx, "High"] = df_new_row["High"]
|
|
if "Low" in bad_fields:
|
|
df_v2.loc[idx, "Low"] = df_new_row["Low"]
|
|
if "Open" in bad_fields:
|
|
if interval == "1wk" and idx != df_fine.index[0]:
|
|
# Exchange closed Monday. In this case, Yahoo sets Open to last week close
|
|
df_v2.loc[idx, "Open"] = df_last_week["Close"]
|
|
df_v2.loc[idx, "Low"] = min(df_v2.loc[idx, "Open"], df_v2.loc[idx, "Low"])
|
|
else:
|
|
df_v2.loc[idx, "Open"] = df_new_row["Open"]
|
|
if "Close" in bad_fields:
|
|
df_v2.loc[idx, "Close"] = df_new_row["Close"]
|
|
# Assume 'Adj Close' also corrupted, easier than detecting whether true
|
|
df_v2.loc[idx, "Adj Close"] = df_new_row["Adj Close"]
|
|
elif "Adj Close" in bad_fields:
|
|
df_v2.loc[idx, "Adj Close"] = df_new_row["Adj Close"]
|
|
if "Volume" in bad_fields:
|
|
df_v2.loc[idx, "Volume"] = df_new_row["Volume"]
|
|
df_v2.loc[idx, "Repaired?"] = True
|
|
n_fixed += 1
|
|
|
|
return df_v2
|
|
|
|
@utils.log_indent_decorator
|
|
def _fix_unit_mixups(self, df, interval, tz_exchange, prepost):
|
|
df2 = self._fix_unit_switch(df, interval, tz_exchange)
|
|
df3 = self._fix_unit_random_mixups(df2, interval, tz_exchange, prepost)
|
|
return df3
|
|
|
|
@utils.log_indent_decorator
|
|
def _fix_unit_random_mixups(self, df, interval, tz_exchange, prepost):
|
|
# Sometimes Yahoo returns few prices in cents/pence instead of $/£
|
|
# I.e. 100x bigger
|
|
# 2 ways this manifests:
|
|
# - random 100x errors spread throughout table
|
|
# - a sudden switch between $<->cents at some date
|
|
# This function fixes the first.
|
|
|
|
# Easy to detect and fix, just look for outliers = ~100x local median
|
|
logger = utils.get_yf_logger()
|
|
|
|
if df.shape[0] == 0:
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
if df.shape[0] == 1:
|
|
# Need multiple rows to confidently identify outliers
|
|
logger.info("price-repair-100x: Cannot check single-row table for 100x price errors")
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
|
|
df2 = df.copy()
|
|
|
|
if df2.index.tz is None:
|
|
df2.index = df2.index.tz_localize(tz_exchange)
|
|
elif df2.index.tz != tz_exchange:
|
|
df2.index = df2.index.tz_convert(tz_exchange)
|
|
|
|
# Only import scipy if users actually want function. To avoid
|
|
# adding it to dependencies.
|
|
from scipy import ndimage as _ndimage
|
|
|
|
data_cols = ["High", "Open", "Low", "Close", "Adj Close"] # Order important, separate High from Low
|
|
data_cols = [c for c in data_cols if c in df2.columns]
|
|
f_zeroes = (df2[data_cols] == 0).any(axis=1).to_numpy()
|
|
if f_zeroes.any():
|
|
df2_zeroes = df2[f_zeroes]
|
|
df2 = df2[~f_zeroes]
|
|
else:
|
|
df2_zeroes = None
|
|
if df2.shape[0] <= 1:
|
|
logger.info("price-repair-100x: Insufficient good data for detecting 100x price errors")
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
df2_data = df2[data_cols].to_numpy()
|
|
median = _ndimage.median_filter(df2_data, size=(3, 3), mode="wrap")
|
|
ratio = df2_data / median
|
|
ratio_rounded = (ratio / 20).round() * 20 # round ratio to nearest 20
|
|
f = ratio_rounded == 100
|
|
if not f.any():
|
|
logger.info("price-repair-100x: No sporadic 100x errors")
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
|
|
# Mark values to send for repair
|
|
tag = -1.0
|
|
for i in range(len(data_cols)):
|
|
fi = f[:, i]
|
|
c = data_cols[i]
|
|
df2.loc[fi, c] = tag
|
|
|
|
n_before = (df2_data == tag).sum()
|
|
df2 = self._reconstruct_intervals_batch(df2, interval, prepost, tag)
|
|
df2_tagged = df2[data_cols].to_numpy() == tag
|
|
n_after = (df2[data_cols].to_numpy() == tag).sum()
|
|
|
|
if n_after > 0:
|
|
# This second pass will *crudely* "fix" any remaining errors in High/Low
|
|
# simply by ensuring they don't contradict e.g. Low = 100x High.
|
|
f = df2_tagged
|
|
for i in range(f.shape[0]):
|
|
fi = f[i, :]
|
|
if not fi.any():
|
|
continue
|
|
idx = df2.index[i]
|
|
|
|
c = "Open"
|
|
j = data_cols.index(c)
|
|
if fi[j]:
|
|
df2.loc[idx, c] = df.loc[idx, c] * 0.01
|
|
#
|
|
c = "Close"
|
|
j = data_cols.index(c)
|
|
if fi[j]:
|
|
df2.loc[idx, c] = df.loc[idx, c] * 0.01
|
|
#
|
|
c = "Adj Close"
|
|
j = data_cols.index(c)
|
|
if fi[j]:
|
|
df2.loc[idx, c] = df.loc[idx, c] * 0.01
|
|
#
|
|
c = "High"
|
|
j = data_cols.index(c)
|
|
if fi[j]:
|
|
df2.loc[idx, c] = df2.loc[idx, ["Open", "Close"]].max()
|
|
#
|
|
c = "Low"
|
|
j = data_cols.index(c)
|
|
if fi[j]:
|
|
df2.loc[idx, c] = df2.loc[idx, ["Open", "Close"]].min()
|
|
|
|
df2_tagged = df2[data_cols].to_numpy() == tag
|
|
n_after_crude = df2_tagged.sum()
|
|
else:
|
|
n_after_crude = n_after
|
|
|
|
n_fixed = n_before - n_after_crude
|
|
n_fixed_crudely = n_after - n_after_crude
|
|
if n_fixed > 0:
|
|
report_msg = f"{self.ticker}: fixed {n_fixed}/{n_before} currency unit mixups "
|
|
if n_fixed_crudely > 0:
|
|
report_msg += f"({n_fixed_crudely} crudely) "
|
|
report_msg += f"in {interval} price data"
|
|
logger.info('price-repair-100x: ' + report_msg)
|
|
|
|
# Restore original values where repair failed
|
|
f = df2_tagged
|
|
for j in range(len(data_cols)):
|
|
fj = f[:, j]
|
|
if fj.any():
|
|
c = data_cols[j]
|
|
df2.loc[fj, c] = df.loc[fj, c]
|
|
if df2_zeroes is not None:
|
|
if "Repaired?" not in df2_zeroes.columns:
|
|
df2_zeroes["Repaired?"] = False
|
|
df2 = pd.concat([df2, df2_zeroes]).sort_index()
|
|
df2.index = pd.to_datetime()
|
|
|
|
return df2
|
|
|
|
@utils.log_indent_decorator
|
|
def _fix_unit_switch(self, df, interval, tz_exchange):
|
|
# Sometimes Yahoo returns few prices in cents/pence instead of $/£
|
|
# I.e. 100x bigger
|
|
# 2 ways this manifests:
|
|
# - random 100x errors spread throughout table
|
|
# - a sudden switch between $<->cents at some date
|
|
# This function fixes the second.
|
|
# Eventually Yahoo fixes but could take them 2 weeks.
|
|
|
|
# To detect, use 'bad split adjustment' algorithm. But only correct
|
|
# if no stock splits in data
|
|
|
|
f_splits = df['Stock Splits'].to_numpy() != 0.0
|
|
if f_splits.any():
|
|
utils.get_yf_logger().debug('price-repair-100x: Cannot check for chunked 100x errors because splits present')
|
|
return df
|
|
|
|
return self._fix_prices_sudden_change(df, interval, tz_exchange, 100.0)
|
|
|
|
@utils.log_indent_decorator
|
|
def _fix_zeroes(self, df, interval, tz_exchange, prepost):
|
|
# Sometimes Yahoo returns prices=0 or NaN when trades occurred.
|
|
# But most times when prices=0 or NaN returned is because no trades.
|
|
# Impossible to distinguish, so only attempt repair if few or rare.
|
|
|
|
logger = utils.get_yf_logger()
|
|
|
|
if df.shape[0] == 0:
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
|
|
intraday = interval[-1] in ("m", 'h')
|
|
|
|
df = df.sort_index() # important!
|
|
df2 = df.copy()
|
|
|
|
if df2.index.tz is None:
|
|
df2.index = df2.index.tz_localize(tz_exchange)
|
|
elif df2.index.tz != tz_exchange:
|
|
df2.index = df2.index.tz_convert(tz_exchange)
|
|
|
|
price_cols = [c for c in ["Open", "High", "Low", "Close", "Adj Close"] if c in df2.columns]
|
|
f_prices_bad = (df2[price_cols] == 0.0) | df2[price_cols].isna()
|
|
df2_reserve = None
|
|
if intraday:
|
|
# Ignore days with >50% intervals containing NaNs
|
|
grp = pd.Series(f_prices_bad.any(axis=1), name="nan").groupby(f_prices_bad.index.date)
|
|
nan_pct = grp.sum() / grp.count()
|
|
dts = nan_pct.index[nan_pct > 0.5]
|
|
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]
|
|
f_prices_bad = (df2[price_cols] == 0.0) | df2[price_cols].isna()
|
|
|
|
f_high_low_good = (~df2["High"].isna().to_numpy()) & (~df2["Low"].isna().to_numpy())
|
|
f_change = df2["High"].to_numpy() != df2["Low"].to_numpy()
|
|
f_vol_bad = (df2["Volume"] == 0).to_numpy() & f_high_low_good & f_change
|
|
|
|
# If stock split occurred, then trading must have happened.
|
|
# I should probably rename the function, because prices aren't zero ...
|
|
if 'Stock Splits' in df2.columns:
|
|
f_split = (df2['Stock Splits'] != 0.0).to_numpy()
|
|
if f_split.any():
|
|
f_change_expected_but_missing = f_split & ~f_change
|
|
if f_change_expected_but_missing.any():
|
|
f_prices_bad[f_change_expected_but_missing] = True
|
|
|
|
# Check whether worth attempting repair
|
|
f_prices_bad = f_prices_bad.to_numpy()
|
|
f_bad_rows = f_prices_bad.any(axis=1) | f_vol_bad
|
|
if not f_bad_rows.any():
|
|
logger.info("price-repair-missing: No price=0 errors to repair")
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
if f_prices_bad.sum() == len(price_cols) * len(df2):
|
|
# Need some good data to calibrate
|
|
logger.info("price-repair-missing: No good data for calibration so cannot fix price=0 bad data")
|
|
if "Repaired?" not in df.columns:
|
|
df["Repaired?"] = False
|
|
return df
|
|
|
|
data_cols = price_cols + ["Volume"]
|
|
|
|
# Mark values to send for repair
|
|
tag = -1.0
|
|
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 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
|
|
# If volume=0 or NaN but price moved in interval, then tag volume for repair
|
|
df2.loc[f_change & f_vol_zero_or_nan, "Volume"] = tag
|
|
|
|
df2_tagged = df2[data_cols].to_numpy() == tag
|
|
n_before = df2_tagged.sum()
|
|
dts_tagged = df2.index[df2_tagged.any(axis=1)]
|
|
df2 = self._reconstruct_intervals_batch(df2, interval, prepost, tag)
|
|
df2_tagged = df2[data_cols].to_numpy() == tag
|
|
n_after = df2_tagged.sum()
|
|
dts_not_repaired = df2.index[df2_tagged.any(axis=1)]
|
|
n_fixed = n_before - n_after
|
|
if n_fixed > 0:
|
|
msg = f"{self.ticker}: fixed {n_fixed}/{n_before} value=0 errors in {interval} price data"
|
|
if n_fixed < 4:
|
|
dts_repaired = sorted(list(set(dts_tagged).difference(dts_not_repaired)))
|
|
msg += f": {dts_repaired}"
|
|
logger.info('price-repair-missing: ' + msg)
|
|
|
|
if df2_reserve is not None:
|
|
if "Repaired?" not in df2_reserve.columns:
|
|
df2_reserve["Repaired?"] = False
|
|
df2 = pd.concat([df2, df2_reserve]).sort_index()
|
|
|
|
# Restore original values where repair failed (i.e. remove tag values)
|
|
f = df2[data_cols].to_numpy() == tag
|
|
for j in range(len(data_cols)):
|
|
fj = f[:, j]
|
|
if fj.any():
|
|
c = data_cols[j]
|
|
df2.loc[fj, c] = df.loc[fj, c]
|
|
|
|
return df2
|
|
|
|
@utils.log_indent_decorator
|
|
def _fix_missing_div_adjust(self, df, interval, tz_exchange):
|
|
# Sometimes, if a dividend occurred today, then Yahoo has not adjusted historic data.
|
|
# Easy to detect and correct BUT ONLY IF the data 'df' includes today's dividend.
|
|
# E.g. if fetching historic prices before todays dividend, then cannot fix.
|
|
|
|
logger = utils.get_yf_logger()
|
|
|
|
if df is None or df.empty:
|
|
return df
|
|
interday = interval in ['1d', '1wk', '1mo', '3mo']
|
|
if not interday:
|
|
return df
|
|
|
|
df = df.sort_index()
|
|
|
|
f_div = (df["Dividends"] != 0.0).to_numpy()
|
|
if not f_div.any():
|
|
logger.debug('div-adjust-repair: No dividends to check')
|
|
return df
|
|
|
|
df2 = df.copy()
|
|
if df2.index.tz is None:
|
|
df2.index = df2.index.tz_localize(tz_exchange)
|
|
elif df2.index.tz != tz_exchange:
|
|
df2.index = df2.index.tz_convert(tz_exchange)
|
|
|
|
div_indices = np.where(f_div)[0]
|
|
last_div_idx = div_indices[-1]
|
|
if last_div_idx == 0:
|
|
# Not enough data to recalculate the div-adjustment,
|
|
# because need close day before
|
|
logger.debug('div-adjust-repair: Insufficient data to recalculate div-adjustment')
|
|
return df
|
|
|
|
# To determine if Yahoo messed up, analyse price data between today's dividend and
|
|
# the previous dividend
|
|
if len(div_indices) == 1:
|
|
# No other divs in data
|
|
prev_idx = 0
|
|
prev_dt = None
|
|
else:
|
|
prev_idx = div_indices[-2]
|
|
prev_dt = df2.index[prev_idx]
|
|
f_no_adj = (df2['Close'] == df2['Adj Close']).to_numpy()[prev_idx:last_div_idx]
|
|
threshold_pct = 0.5
|
|
Yahoo_failed = (np.sum(f_no_adj) / len(f_no_adj)) > threshold_pct
|
|
|
|
# Fix Yahoo
|
|
if Yahoo_failed:
|
|
last_div_dt = df2.index[last_div_idx]
|
|
last_div_row = df2.loc[last_div_dt]
|
|
close_day_before = df2['Close'].iloc[last_div_idx - 1]
|
|
adj = 1.0 - df2['Dividends'].iloc[last_div_idx] / close_day_before
|
|
div = last_div_row['Dividends']
|
|
msg = f'Correcting missing div-adjustment preceding div = {div} @ {last_div_dt.date()} (prev_dt={prev_dt})'
|
|
logger.debug('div-adjust-repair: ' + msg)
|
|
|
|
if interval == '1d':
|
|
# exclusive
|
|
df2.loc[:last_div_dt - _datetime.timedelta(seconds=1), 'Adj Close'] *= adj
|
|
else:
|
|
# inclusive
|
|
df2.loc[:last_div_dt, 'Adj Close'] *= adj
|
|
|
|
return df2
|
|
|
|
@utils.log_indent_decorator
|
|
def _fix_bad_stock_split(self, df, interval, tz_exchange):
|
|
# Repair idea is to look for BIG daily price changes that closely match the
|
|
# most recent stock split ratio. This indicates Yahoo failed to apply a new
|
|
# stock split to old price data.
|
|
#
|
|
# There is a slight complication, because Yahoo does another stupid thing.
|
|
# Sometimes the old data is adjusted twice. So cannot simply assume
|
|
# which direction to reverse adjustment - have to analyse prices and detect.
|
|
# Not difficult.
|
|
|
|
logger = utils.get_yf_logger()
|
|
|
|
interday = interval in ['1d', '1wk', '1mo', '3mo']
|
|
if not interday:
|
|
return df
|
|
|
|
# Find the most recent stock split
|
|
df = df.sort_index(ascending=False)
|
|
split_f = df['Stock Splits'].to_numpy() != 0
|
|
if not split_f.any():
|
|
logger.debug('price-repair-split: No splits in data')
|
|
return df
|
|
most_recent_split_day = df.index[split_f].max()
|
|
split = df.loc[most_recent_split_day, 'Stock Splits']
|
|
if most_recent_split_day == df.index[0]:
|
|
logger.info(
|
|
"price-repair-split: Need 1+ day of price data after split to determine true price. Won't repair")
|
|
return df
|
|
|
|
logger.debug(f'price-repair-split: Most recent split = {split:.4f} @ {most_recent_split_day.date()}')
|
|
|
|
return self._fix_prices_sudden_change(df, interval, tz_exchange, split, correct_volume=True)
|
|
|
|
@utils.log_indent_decorator
|
|
def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_volume=False):
|
|
logger = utils.get_yf_logger()
|
|
|
|
df = df.sort_index(ascending=False)
|
|
split = change
|
|
split_rcp = 1.0 / split
|
|
interday = interval in ['1d', '1wk', '1mo', '3mo']
|
|
|
|
if change in [100.0, 0.01]:
|
|
fix_type = '100x error'
|
|
start_min = None
|
|
else:
|
|
fix_type = 'bad split'
|
|
# start_min = 1 year before oldest split
|
|
f = df['Stock Splits'].to_numpy() != 0.0
|
|
start_min = (df.index[f].min() - _dateutil.relativedelta.relativedelta(years=1)).date()
|
|
logger.debug(f'price-repair-split: start_min={start_min}')
|
|
|
|
OHLC = ['Open', 'High', 'Low', 'Close']
|
|
OHLCA = OHLC + ['Adj Close']
|
|
|
|
# Do not attempt repair of the split is small,
|
|
# could be mistaken for normal price variance
|
|
if 0.8 < split < 1.25:
|
|
logger.info("price-repair-split: Split ratio too close to 1. Won't repair")
|
|
return df
|
|
|
|
df2 = df.copy()
|
|
if df2.index.tz is None:
|
|
df2.index = df2.index.tz_localize(tz_exchange)
|
|
elif df2.index.tz != tz_exchange:
|
|
df2.index = df2.index.tz_convert(tz_exchange)
|
|
n = df2.shape[0]
|
|
|
|
# If stock is currently suspended and not in USA, then usually Yahoo introduces
|
|
# 100x errors into suspended intervals. Clue is no price change and 0 volume.
|
|
# Better to use last active trading interval as baseline.
|
|
f_no_activity = (df2['Low'] == df2['High']) & (df2['Volume']==0)
|
|
f_no_activity = f_no_activity | df2[OHLC].isna().all(axis=1)
|
|
appears_suspended = f_no_activity.any() and np.where(f_no_activity)[0][0]==0
|
|
f_active = ~f_no_activity
|
|
idx_latest_active = np.where(f_active & np.roll(f_active, 1))[0]
|
|
if len(idx_latest_active) == 0:
|
|
idx_latest_active = None
|
|
else:
|
|
idx_latest_active = int(idx_latest_active[0])
|
|
log_msg = f'price-repair-split: appears_suspended={appears_suspended}, idx_latest_active={idx_latest_active}'
|
|
if idx_latest_active is not None:
|
|
log_msg += f' ({df.index[idx_latest_active].date()})'
|
|
logger.debug(log_msg)
|
|
|
|
if logger.isEnabledFor(logging.DEBUG):
|
|
df_debug = df2.copy()
|
|
df_debug = df_debug.drop(['Adj Close', 'Volume', 'Dividends', 'Repaired?'], axis=1, errors='ignore')
|
|
debug_cols = ['Low', 'High']
|
|
df_debug = df_debug.drop([c for c in OHLC if c not in debug_cols], axis=1, errors='ignore')
|
|
else:
|
|
debug_cols = []
|
|
|
|
# Calculate daily price % change. To reduce effect of price volatility,
|
|
# calculate change for each OHLC column.
|
|
if interday and interval != '1d' and split not in [100.0, 100, 0.001]:
|
|
# Avoid using 'Low' and 'High'. For multiday intervals, these can be
|
|
# very volatile so reduce ability to detect genuine stock split errors
|
|
_1d_change_x = np.full((n, 2), 1.0)
|
|
price_data = df2[['Open','Close']].replace(0.0, 1.0).to_numpy()
|
|
else:
|
|
_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, ]
|
|
if interday and interval != '1d':
|
|
# average change
|
|
_1d_change_minx = np.average(_1d_change_x, axis=1)
|
|
else:
|
|
# change nearest to 1.0
|
|
diff = np.abs(_1d_change_x - 1.0)
|
|
j_indices = np.argmin(diff, axis=1)
|
|
_1d_change_minx = _1d_change_x[np.arange(n), j_indices]
|
|
f_na = np.isnan(_1d_change_minx)
|
|
if f_na.any():
|
|
# Possible if data was too old for reconstruction.
|
|
_1d_change_minx[f_na] = 1.0
|
|
if logger.isEnabledFor(logging.DEBUG):
|
|
df_debug['1D change X'] = _1d_change_minx
|
|
df_debug['1D change X'] = df_debug['1D change X'].round(2).astype('str')
|
|
|
|
# If all 1D changes are closer to 1.0 than split, exit
|
|
split_max = max(split, split_rcp)
|
|
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):
|
|
logger.info(f"price-repair-split: No {fix_type}s detected")
|
|
return df
|
|
|
|
# Calculate the true price variance, i.e. remove effect of bad split-adjustments.
|
|
# Key = ignore 1D changes outside of interquartile range
|
|
q1, q3 = np.percentile(_1d_change_minx, [25, 75])
|
|
iqr = q3 - q1
|
|
lower_bound = q1 - 1.5 * iqr
|
|
upper_bound = q3 + 1.5 * iqr
|
|
f = (_1d_change_minx >= lower_bound) & (_1d_change_minx <= upper_bound)
|
|
avg = np.mean(_1d_change_minx[f])
|
|
sd = np.std(_1d_change_minx[f])
|
|
# Now can calculate SD as % of mean
|
|
sd_pct = sd / avg
|
|
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)")
|
|
|
|
# Only proceed if split adjustment far exceeds normal 1D changes
|
|
largest_change_pct = 5 * sd_pct
|
|
if interday and interval != '1d':
|
|
largest_change_pct *= 3
|
|
if interval in ['1mo', '3mo']:
|
|
largest_change_pct *= 2
|
|
if max(split, split_rcp) < 1.0 + largest_change_pct:
|
|
logger.info("price-repair-split: Split ratio too close to normal price volatility. Won't repair")
|
|
logger.debug(f"sd_pct = {sd_pct:.4f} largest_change_pct = {largest_change_pct:.4f}")
|
|
if logger.isEnabledFor(logging.DEBUG):
|
|
logger.debug(f"sd_pct = {sd_pct:.4f} largest_change_pct = {largest_change_pct:.4f}")
|
|
return df
|
|
|
|
# Now can detect bad split adjustments
|
|
# Set threshold to halfway between split ratio and largest expected normal price change
|
|
r = _1d_change_minx / split_rcp
|
|
split_max = max(split, split_rcp)
|
|
logger.debug(f"price-repair-split: split_max={split_max:.3f} largest_change_pct={largest_change_pct:.4f}")
|
|
threshold = (split_max + 1.0 + largest_change_pct) * 0.5
|
|
logger.debug(f"price-repair-split: threshold={threshold:.3f}")
|
|
|
|
if 'Repaired?' not in df2.columns:
|
|
df2['Repaired?'] = False
|
|
|
|
if interday and interval != '1d':
|
|
# Yahoo creates multi-day intervals using potentiall corrupt data, e.g.
|
|
# the Close could be 100x Open. This means have to correct each OHLC column
|
|
# individually
|
|
correct_columns_individually = True
|
|
else:
|
|
correct_columns_individually = False
|
|
|
|
if correct_columns_individually:
|
|
_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:
|
|
_1d_change_x = _1d_change_minx
|
|
|
|
r = _1d_change_x / split_rcp
|
|
f_down = _1d_change_x < 1.0 / threshold
|
|
f_up = _1d_change_x > threshold
|
|
f = f_down | f_up
|
|
if logger.isEnabledFor(logging.DEBUG):
|
|
if not correct_columns_individually:
|
|
df_debug['r'] = r
|
|
df_debug['f_down'] = f_down
|
|
df_debug['f_up'] = f_up
|
|
df_debug['r'] = df_debug['r'].round(2).astype('str')
|
|
else:
|
|
for j in range(len(OHLC)):
|
|
c = OHLC[j]
|
|
if c in debug_cols:
|
|
df_debug[c + '_r'] = r[:, j]
|
|
df_debug[c + '_f_down'] = f_down[:, j]
|
|
df_debug[c + '_f_up'] = f_up[:, j]
|
|
df_debug[c + '_r'] = df_debug[c + '_r'].round(2).astype('str')
|
|
|
|
|
|
if not f.any():
|
|
logger.info(f'price-repair-split: No {fix_type}s detected')
|
|
return df
|
|
|
|
# if logger.isEnabledFor(logging.DEBUG):
|
|
# df_debug['i'] = list(range(0, df_debug.shape[0]))
|
|
# df_debug['i_rev'] = df_debug.shape[0]-1 - df_debug['i']
|
|
# with pd.option_context('display.max_rows', None, 'display.max_columns', 10, 'display.width', 1000): # more options can be specified also
|
|
# logger.debug(f"price-repair-split: my workings:" + '\n' + str(df_debug))
|
|
|
|
def map_signals_to_ranges(f, f_up, f_down):
|
|
# Ensure 0th element is False, because True is nonsense
|
|
if f[0]:
|
|
f = np.copy(f) ; f[0] = False
|
|
f_up = np.copy(f_up) ; f_up[0] = False
|
|
f_down = np.copy(f_down) ; f_down[0] = False
|
|
|
|
if not f.any():
|
|
return []
|
|
|
|
true_indices = np.where(f)[0]
|
|
ranges = []
|
|
|
|
for i in range(len(true_indices) - 1):
|
|
if i % 2 == 0:
|
|
if split > 1.0:
|
|
adj = 'split' if f_down[true_indices[i]] else '1.0/split'
|
|
else:
|
|
adj = '1.0/split' if f_down[true_indices[i]] else 'split'
|
|
ranges.append((true_indices[i], true_indices[i + 1], adj))
|
|
|
|
if len(true_indices) % 2 != 0:
|
|
if split > 1.0:
|
|
adj = 'split' if f_down[true_indices[-1]] else '1.0/split'
|
|
else:
|
|
adj = '1.0/split' if f_down[true_indices[-1]] else 'split'
|
|
ranges.append((true_indices[-1], len(f), adj))
|
|
|
|
return ranges
|
|
|
|
if idx_latest_active is not None:
|
|
idx_rev_latest_active = df.shape[0] - 1 - idx_latest_active
|
|
logger.debug(f'price-repair-split: idx_latest_active={idx_latest_active}, idx_rev_latest_active={idx_rev_latest_active}')
|
|
if correct_columns_individually:
|
|
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
|
|
# and errors. A problem for correcting Volume,
|
|
# 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)
|
|
|
|
OHLC_correct_ranges = [None, None, None, None]
|
|
for j in range(len(OHLC)):
|
|
c = OHLC[j]
|
|
idx_first_f = np.where(f)[0][0]
|
|
if appears_suspended and (idx_latest_active is not None and idx_latest_active >= idx_first_f):
|
|
# Suspended midway during data date range.
|
|
# 1: process data before suspension in index-ascending (date-descending) order.
|
|
# 2: process data after suspension in index-descending order. Requires signals to be reversed,
|
|
# then returned ranges to also be reversed, because this logic was originally written for
|
|
# index-ascending (date-descending) order.
|
|
fj = f[:, j]
|
|
f_upj = f_up[:, j]
|
|
f_downj = f_down[:, j]
|
|
ranges_before = map_signals_to_ranges(fj[idx_latest_active:], f_upj[idx_latest_active:], f_downj[idx_latest_active:])
|
|
if len(ranges_before) > 0:
|
|
# Shift each range back to global indexing
|
|
for i in range(len(ranges_before)):
|
|
r = ranges_before[i]
|
|
ranges_before[i] = (r[0] + idx_latest_active, r[1] + idx_latest_active, r[2])
|
|
f_rev_downj = np.flip(np.roll(f_upj, -1)) # correct
|
|
f_rev_upj = np.flip(np.roll(f_downj, -1)) # correct
|
|
f_revj = f_rev_upj | f_rev_downj
|
|
ranges_after = map_signals_to_ranges(f_revj[idx_rev_latest_active:], f_rev_upj[idx_rev_latest_active:], f_rev_downj[idx_rev_latest_active:])
|
|
if len(ranges_after) > 0:
|
|
# Shift each range back to global indexing:
|
|
for i in range(len(ranges_after)):
|
|
r = ranges_after[i]
|
|
ranges_after[i] = (r[0] + idx_rev_latest_active, r[1] + idx_rev_latest_active, r[2])
|
|
# Flip range to normal ordering
|
|
for i in range(len(ranges_after)):
|
|
r = ranges_after[i]
|
|
ranges_after[i] = (n-r[1], n-r[0], r[2])
|
|
ranges = ranges_before ; ranges.extend(ranges_after)
|
|
else:
|
|
ranges = map_signals_to_ranges(f[:, j], f_up[:, j], f_down[:, j])
|
|
logger.debug(f"column '{c}' ranges: {ranges}")
|
|
if start_min is not None:
|
|
# Prune ranges that are older than start_min
|
|
for i in range(len(ranges)-1, -1, -1):
|
|
r = ranges[i]
|
|
if df.index[r[0]].date() < start_min:
|
|
logger.debug(f'price-repair-split: Pruning {c} range {df.index[r[0]]}->{df.index[r[1]-1]} because too old.')
|
|
del ranges[i]
|
|
|
|
if len(ranges) > 0:
|
|
OHLC_correct_ranges[j] = ranges
|
|
|
|
count = sum([1 if x is not None else 0 for x in OHLC_correct_ranges])
|
|
if count == 0:
|
|
pass
|
|
elif count == 1:
|
|
# If only 1 column then assume false positive
|
|
idxs = [i if OHLC_correct_ranges[i] else -1 for i in range(len(OHLC))]
|
|
idx = np.where(np.array(idxs) != -1)[0][0]
|
|
col = OHLC[idx]
|
|
logger.debug(f'price-repair-split: Potential {fix_type} detected only in column {col}, so treating as false positive (ignore)')
|
|
else:
|
|
# Only correct if at least 2 columns require correction.
|
|
for j in range(len(OHLC)):
|
|
c = OHLC[j]
|
|
ranges = OHLC_correct_ranges[j]
|
|
if ranges is None:
|
|
ranges = []
|
|
for r in ranges:
|
|
if r[2] == 'split':
|
|
m = split ; m_rcp = split_rcp
|
|
else:
|
|
m = split_rcp ; m_rcp = split
|
|
if interday:
|
|
logger.info(f"price-repair-split: Corrected {fix_type} on col={c} range=[{df2.index[r[1]-1].date()}:{df2.index[r[0]].date()}] m={m:.4f}")
|
|
else:
|
|
logger.info(f"price-repair-split: Corrected {fix_type} on col={c} range=[{df2.index[r[1]-1]}:{df2.index[r[0]]}] m={m:.4f}")
|
|
df2.iloc[r[0]:r[1], df2.columns.get_loc(c)] *= m
|
|
if c == 'Close':
|
|
df2.iloc[r[0]:r[1], df2.columns.get_loc('Adj Close')] *= m
|
|
if correct_volume:
|
|
if c == 'Open':
|
|
f_open_fixed[r[0]:r[1]] = True
|
|
elif c == 'Close':
|
|
f_close_fixed[r[0]:r[1]] = True
|
|
f_corrected[r[0]:r[1]] = True
|
|
|
|
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)
|
|
if f_open_and_closed_fixed.any():
|
|
df2.loc[f_open_and_closed_fixed, "Volume"] *= m_rcp
|
|
if f_open_xor_closed_fixed.any():
|
|
df2.loc[f_open_xor_closed_fixed, "Volume"] *= 0.5 * m_rcp
|
|
|
|
df2.loc[f_corrected, 'Repaired?'] = True
|
|
|
|
else:
|
|
idx_first_f = np.where(f)[0][0]
|
|
if appears_suspended and (idx_latest_active is not None and idx_latest_active >= idx_first_f):
|
|
# Suspended midway during data date range.
|
|
# 1: process data before suspension in index-ascending (date-descending) order.
|
|
# 2: process data after suspension in index-descending order. Requires signals to be reversed,
|
|
# then returned ranges to also be reversed, because this logic was originally written for
|
|
# index-ascending (date-descending) order.
|
|
ranges_before = map_signals_to_ranges(f[idx_latest_active:], f_up[idx_latest_active:], f_down[idx_latest_active:])
|
|
if len(ranges_before) > 0:
|
|
# Shift each range back to global indexing
|
|
for i in range(len(ranges_before)):
|
|
r = ranges_before[i]
|
|
ranges_before[i] = (r[0] + idx_latest_active, r[1] + idx_latest_active, r[2])
|
|
f_rev_down = np.flip(np.roll(f_up, -1))
|
|
f_rev_up = np.flip(np.roll(f_down, -1))
|
|
f_rev = f_rev_up | f_rev_down
|
|
ranges_after = map_signals_to_ranges(f_rev[idx_rev_latest_active:], f_rev_up[idx_rev_latest_active:], f_rev_down[idx_rev_latest_active:])
|
|
if len(ranges_after) > 0:
|
|
# Shift each range back to global indexing:
|
|
for i in range(len(ranges_after)):
|
|
r = ranges_after[i]
|
|
ranges_after[i] = (r[0] + idx_rev_latest_active, r[1] + idx_rev_latest_active, r[2])
|
|
# Flip range to normal ordering
|
|
for i in range(len(ranges_after)):
|
|
r = ranges_after[i]
|
|
ranges_after[i] = (n-r[1], n-r[0], r[2])
|
|
ranges = ranges_before ; ranges.extend(ranges_after)
|
|
else:
|
|
ranges = map_signals_to_ranges(f, f_up, f_down)
|
|
if start_min is not None:
|
|
# Prune ranges that are older than start_min
|
|
for i in range(len(ranges)-1, -1, -1):
|
|
r = ranges[i]
|
|
if df.index[r[0]].date() < start_min:
|
|
logger.debug(f'price-repair-split: Pruning range {df.index[r[0]]}->{df.index[r[1]-1]} because too old.')
|
|
del ranges[i]
|
|
for r in ranges:
|
|
if r[2] == 'split':
|
|
m = split ; m_rcp = split_rcp
|
|
else:
|
|
m = split_rcp ; m_rcp = split
|
|
logger.debug(f"price-repair-split: range={r} m={m}")
|
|
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
|
|
df2.iloc[r[0]:r[1], df2.columns.get_loc('Repaired?')] = True
|
|
if r[0] == r[1] - 1:
|
|
if interday:
|
|
msg = f"price-repair-split: Corrected {fix_type} on interval {df2.index[r[0]].date()}"
|
|
else:
|
|
msg = f"price-repair-split: Corrected {fix_type} on interval {df2.index[r[0]]}"
|
|
else:
|
|
# Note: df2 sorted with index descending
|
|
start = df2.index[r[1] - 1]
|
|
end = df2.index[r[0]]
|
|
if interday:
|
|
msg = f"price-repair-split: Corrected {fix_type} across intervals {start.date()} -> {end.date()} (inclusive)"
|
|
else:
|
|
msg = f"price-repair-split: Corrected {fix_type} across intervals {start} -> {end} (inclusive)"
|
|
logger.info(msg)
|
|
|
|
if correct_volume:
|
|
f_na = df2['Volume'].isna()
|
|
if f_na.any():
|
|
df2.loc[~f_na,'Volume'] = df2['Volume'][~f_na].round(0).astype('int')
|
|
else:
|
|
df2['Volume'] = df2['Volume'].round(0).astype('int')
|
|
|
|
return df2
|
|
|
|
def _get_ticker_tz(self, proxy, timeout):
|
|
if self._tz is not None:
|
|
return self._tz
|
|
cache = utils.get_tz_cache()
|
|
tz = cache.lookup(self.ticker)
|
|
|
|
if tz and not utils.is_valid_timezone(tz):
|
|
# Clear from cache and force re-fetch
|
|
cache.store(self.ticker, None)
|
|
tz = None
|
|
|
|
if tz is None:
|
|
tz = self._fetch_ticker_tz(proxy, timeout)
|
|
|
|
if utils.is_valid_timezone(tz):
|
|
# info fetch is relatively slow so cache timezone
|
|
cache.store(self.ticker, tz)
|
|
else:
|
|
tz = None
|
|
|
|
self._tz = tz
|
|
return tz
|
|
|
|
@utils.log_indent_decorator
|
|
def _fetch_ticker_tz(self, proxy, timeout):
|
|
# Query Yahoo for fast price data just to get returned timezone
|
|
|
|
logger = utils.get_yf_logger()
|
|
|
|
params = {"range": "1d", "interval": "1d"}
|
|
|
|
# Getting data from json
|
|
url = f"{self._base_url}/v8/finance/chart/{self.ticker}"
|
|
|
|
try:
|
|
data = self._data.cache_get(url=url, params=params, proxy=proxy, timeout=timeout)
|
|
data = data.json()
|
|
except Exception as e:
|
|
logger.error(f"Failed to get ticker '{self.ticker}' reason: {e}")
|
|
return None
|
|
else:
|
|
error = data.get('chart', {}).get('error', None)
|
|
if error:
|
|
# explicit error from yahoo API
|
|
logger.debug(f"Got error from yahoo api for ticker {self.ticker}, Error: {error}")
|
|
else:
|
|
try:
|
|
return data["chart"]["result"][0]["meta"]["exchangeTimezoneName"]
|
|
except Exception as err:
|
|
logger.error(f"Could not get exchangeTimezoneName for ticker '{self.ticker}' reason: {err}")
|
|
logger.debug("Got response: ")
|
|
logger.debug("-------------")
|
|
logger.debug(f" {data}")
|
|
logger.debug("-------------")
|
|
return None
|
|
|
|
def get_recommendations(self, proxy=None, as_dict=False):
|
|
self._quote.proxy = proxy
|
|
data = self._quote.recommendations
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_calendar(self, proxy=None, as_dict=False):
|
|
self._quote.proxy = proxy
|
|
data = self._quote.calendar
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_major_holders(self, proxy=None, as_dict=False):
|
|
self._holders.proxy = proxy
|
|
data = self._holders.major
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_institutional_holders(self, proxy=None, as_dict=False):
|
|
self._holders.proxy = proxy
|
|
data = self._holders.institutional
|
|
if data is not None:
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_mutualfund_holders(self, proxy=None, as_dict=False):
|
|
self._holders.proxy = proxy
|
|
data = self._holders.mutualfund
|
|
if data is not None:
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_info(self, proxy=None) -> dict:
|
|
self._quote.proxy = proxy
|
|
data = self._quote.info
|
|
return data
|
|
|
|
def get_fast_info(self, proxy=None):
|
|
if self._fast_info is None:
|
|
self._fast_info = FastInfo(self, proxy=proxy)
|
|
return self._fast_info
|
|
|
|
@property
|
|
def basic_info(self):
|
|
warnings.warn("'Ticker.basic_info' is renamed to 'Ticker.fast_info', hopefully purpose is clearer", DeprecationWarning)
|
|
return self.fast_info
|
|
|
|
def get_sustainability(self, proxy=None, as_dict=False):
|
|
self._quote.proxy = proxy
|
|
data = self._quote.sustainability
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_recommendations_summary(self, proxy=None, as_dict=False):
|
|
self._quote.proxy = proxy
|
|
data = self._quote.recommendations
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_analyst_price_target(self, proxy=None, as_dict=False):
|
|
self._analysis.proxy = proxy
|
|
data = self._analysis.analyst_price_target
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_rev_forecast(self, proxy=None, as_dict=False):
|
|
self._analysis.proxy = proxy
|
|
data = self._analysis.rev_est
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_earnings_forecast(self, proxy=None, as_dict=False):
|
|
self._analysis.proxy = proxy
|
|
data = self._analysis.eps_est
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_trend_details(self, proxy=None, as_dict=False):
|
|
self._analysis.proxy = proxy
|
|
data = self._analysis.analyst_trend_details
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_earnings_trend(self, proxy=None, as_dict=False):
|
|
self._analysis.proxy = proxy
|
|
data = self._analysis.earnings_trend
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_earnings(self, proxy=None, as_dict=False, freq="yearly"):
|
|
"""
|
|
:Parameters:
|
|
as_dict: bool
|
|
Return table as Python dict
|
|
Default is False
|
|
freq: str
|
|
"yearly" or "quarterly"
|
|
Default is "yearly"
|
|
proxy: str
|
|
Optional. Proxy server URL scheme
|
|
Default is None
|
|
"""
|
|
self._fundamentals.proxy = proxy
|
|
data = self._fundamentals.earnings[freq]
|
|
if as_dict:
|
|
dict_data = data.to_dict()
|
|
dict_data['financialCurrency'] = 'USD' if 'financialCurrency' not in self._earnings else self._earnings[
|
|
'financialCurrency']
|
|
return dict_data
|
|
return data
|
|
|
|
def get_income_stmt(self, proxy=None, as_dict=False, pretty=False, freq="yearly"):
|
|
"""
|
|
:Parameters:
|
|
as_dict: bool
|
|
Return table as Python dict
|
|
Default is False
|
|
pretty: bool
|
|
Format row names nicely for readability
|
|
Default is False
|
|
freq: str
|
|
"yearly" or "quarterly"
|
|
Default is "yearly"
|
|
proxy: str
|
|
Optional. Proxy server URL scheme
|
|
Default is None
|
|
"""
|
|
self._fundamentals.proxy = proxy
|
|
|
|
data = self._fundamentals.financials.get_income_time_series(freq=freq, proxy=proxy)
|
|
|
|
if pretty:
|
|
data = data.copy()
|
|
data.index = utils.camel2title(data.index, sep=' ', acronyms=["EBIT", "EBITDA", "EPS", "NI"])
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_incomestmt(self, proxy=None, as_dict=False, pretty=False, freq="yearly"):
|
|
return self.get_income_stmt(proxy, as_dict, pretty, freq)
|
|
|
|
def get_financials(self, proxy=None, as_dict=False, pretty=False, freq="yearly"):
|
|
return self.get_income_stmt(proxy, as_dict, pretty, freq)
|
|
|
|
def get_balance_sheet(self, proxy=None, as_dict=False, pretty=False, freq="yearly"):
|
|
"""
|
|
:Parameters:
|
|
as_dict: bool
|
|
Return table as Python dict
|
|
Default is False
|
|
pretty: bool
|
|
Format row names nicely for readability
|
|
Default is False
|
|
freq: str
|
|
"yearly" or "quarterly"
|
|
Default is "yearly"
|
|
proxy: str
|
|
Optional. Proxy server URL scheme
|
|
Default is None
|
|
"""
|
|
self._fundamentals.proxy = proxy
|
|
|
|
data = self._fundamentals.financials.get_balance_sheet_time_series(freq=freq, proxy=proxy)
|
|
|
|
if pretty:
|
|
data = data.copy()
|
|
data.index = utils.camel2title(data.index, sep=' ', acronyms=["PPE"])
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_balancesheet(self, proxy=None, as_dict=False, pretty=False, freq="yearly"):
|
|
return self.get_balance_sheet(proxy, as_dict, pretty, freq)
|
|
|
|
def get_cash_flow(self, proxy=None, as_dict=False, pretty=False, freq="yearly"):
|
|
"""
|
|
:Parameters:
|
|
as_dict: bool
|
|
Return table as Python dict
|
|
Default is False
|
|
pretty: bool
|
|
Format row names nicely for readability
|
|
Default is False
|
|
freq: str
|
|
"yearly" or "quarterly"
|
|
Default is "yearly"
|
|
proxy: str
|
|
Optional. Proxy server URL scheme
|
|
Default is None
|
|
"""
|
|
self._fundamentals.proxy = proxy
|
|
|
|
data = self._fundamentals.financials.get_cash_flow_time_series(freq=freq, proxy=proxy)
|
|
|
|
if pretty:
|
|
data = data.copy()
|
|
data.index = utils.camel2title(data.index, sep=' ', acronyms=["PPE"])
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
def get_cashflow(self, proxy=None, as_dict=False, pretty=False, freq="yearly"):
|
|
return self.get_cash_flow(proxy, as_dict, pretty, freq)
|
|
|
|
def get_dividends(self, proxy=None):
|
|
if self._history is None:
|
|
self.history(period="max", proxy=proxy)
|
|
if self._history is not None and "Dividends" in self._history:
|
|
dividends = self._history["Dividends"]
|
|
return dividends[dividends != 0]
|
|
return []
|
|
|
|
def get_capital_gains(self, proxy=None):
|
|
if self._history is None:
|
|
self.history(period="max", proxy=proxy)
|
|
if self._history is not None and "Capital Gains" in self._history:
|
|
capital_gains = self._history["Capital Gains"]
|
|
return capital_gains[capital_gains != 0]
|
|
return []
|
|
|
|
def get_splits(self, proxy=None):
|
|
if self._history is None:
|
|
self.history(period="max", proxy=proxy)
|
|
if self._history is not None and "Stock Splits" in self._history:
|
|
splits = self._history["Stock Splits"]
|
|
return splits[splits != 0]
|
|
return []
|
|
|
|
def get_actions(self, proxy=None):
|
|
if self._history is None:
|
|
self.history(period="max", proxy=proxy)
|
|
if self._history is not None and "Dividends" in self._history and "Stock Splits" in self._history:
|
|
action_columns = ["Dividends", "Stock Splits"]
|
|
if "Capital Gains" in self._history:
|
|
action_columns.append("Capital Gains")
|
|
actions = self._history[action_columns]
|
|
return actions[actions != 0].dropna(how='all').fillna(0)
|
|
return []
|
|
|
|
def get_shares(self, proxy=None, as_dict=False):
|
|
self._fundamentals.proxy = proxy
|
|
data = self._fundamentals.shares
|
|
if as_dict:
|
|
return data.to_dict()
|
|
return data
|
|
|
|
@utils.log_indent_decorator
|
|
def get_shares_full(self, start=None, end=None, proxy=None):
|
|
logger = utils.get_yf_logger()
|
|
|
|
# Process dates
|
|
tz = self._get_ticker_tz(proxy=proxy, timeout=10)
|
|
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_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_d = end.date()
|
|
if end is None:
|
|
end = dt_now
|
|
if start is None:
|
|
start = end - pd.Timedelta(days=548) # 18 months
|
|
if start >= end:
|
|
logger.error("Start date must be before end")
|
|
return None
|
|
start = start.floor("D")
|
|
end = end.ceil("D")
|
|
|
|
# Fetch
|
|
ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self.ticker}?symbol={self.ticker}"
|
|
shares_url = f"{ts_url_base}&period1={int(start.timestamp())}&period2={int(end.timestamp())}"
|
|
try:
|
|
json_data = self._data.cache_get(url=shares_url, proxy=proxy)
|
|
json_data = json_data.json()
|
|
except (_json.JSONDecodeError, requests.exceptions.RequestException):
|
|
logger.error(f"{self.ticker}: Yahoo web request for share count failed")
|
|
return None
|
|
try:
|
|
fail = json_data["finance"]["error"]["code"] == "Bad Request"
|
|
except KeyError as e:
|
|
fail = False
|
|
if fail:
|
|
logger.error(f"{self.ticker}: Yahoo web request for share count failed")
|
|
return None
|
|
|
|
shares_data = json_data["timeseries"]["result"]
|
|
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"))
|
|
except Exception as e:
|
|
logger.error(f"{self.ticker}: Failed to parse shares count data: {e}")
|
|
return None
|
|
|
|
df.index = df.index.tz_localize(tz)
|
|
df = df.sort_index()
|
|
return df
|
|
|
|
def get_isin(self, proxy=None) -> Optional[str]:
|
|
# *** experimental ***
|
|
if self._isin is not None:
|
|
return self._isin
|
|
|
|
ticker = self.ticker.upper()
|
|
|
|
if "-" in ticker or "^" in ticker:
|
|
self._isin = '-'
|
|
return self._isin
|
|
|
|
q = ticker
|
|
|
|
self._quote.proxy = proxy
|
|
if self._quote.info is None:
|
|
# Don't print error message cause self._quote.info will print one
|
|
return None
|
|
if "shortName" in self._quote.info:
|
|
q = self._quote.info['shortName']
|
|
|
|
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}|'
|
|
if search_str not in data:
|
|
if q.lower() in data.lower():
|
|
search_str = '"|'
|
|
if search_str not in data:
|
|
self._isin = '-'
|
|
return self._isin
|
|
else:
|
|
self._isin = '-'
|
|
return self._isin
|
|
|
|
self._isin = data.split(search_str)[1].split('"')[0].split('|')[0]
|
|
return self._isin
|
|
|
|
def get_news(self, proxy=None):
|
|
if self._news:
|
|
return self._news
|
|
|
|
# Getting data from json
|
|
url = f"{self._base_url}/v1/finance/search?q={self.ticker}"
|
|
data = self._data.cache_get(url=url, proxy=proxy)
|
|
if "Will be right back" in data.text:
|
|
raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
|
|
"Our engineers are working quickly to resolve "
|
|
"the issue. Thank you for your patience.")
|
|
data = data.json()
|
|
|
|
# parse news
|
|
self._news = data.get("news", [])
|
|
return self._news
|
|
|
|
@utils.log_indent_decorator
|
|
def get_earnings_dates(self, limit=12, proxy=None) -> Optional[pd.DataFrame]:
|
|
"""
|
|
Get earning dates (future and historic)
|
|
:param limit: max amount of upcoming and recent earnings dates to return.
|
|
Default value 12 should return next 4 quarters and last 8 quarters.
|
|
Increase if more history is needed.
|
|
|
|
:param proxy: requests proxy to use.
|
|
:return: pandas dataframe
|
|
"""
|
|
if self._earnings_dates and limit in self._earnings_dates:
|
|
return self._earnings_dates[limit]
|
|
|
|
logger = utils.get_yf_logger()
|
|
|
|
page_size = min(limit, 100) # YF caps at 100, don't go higher
|
|
page_offset = 0
|
|
dates = None
|
|
while True:
|
|
url = f"{_ROOT_URL_}/calendar/earnings?symbol={self.ticker}&offset={page_offset}&size={page_size}"
|
|
data = self._data.cache_get(url=url, proxy=proxy).text
|
|
|
|
if "Will be right back" in data:
|
|
raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
|
|
"Our engineers are working quickly to resolve "
|
|
"the issue. Thank you for your patience.")
|
|
|
|
try:
|
|
data = pd.read_html(data)[0]
|
|
except ValueError:
|
|
if page_offset == 0:
|
|
# Should not fail on first page
|
|
if "Showing Earnings for:" in data:
|
|
# Actually YF was successful, problem is company doesn't have earnings history
|
|
dates = utils.empty_earnings_dates_df()
|
|
break
|
|
if dates is None:
|
|
dates = data
|
|
else:
|
|
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
|
|
if len(data) < page_size or len(dates) >= limit:
|
|
dates = dates.iloc[:limit]
|
|
break
|
|
else:
|
|
# do not fetch more than needed next time
|
|
page_size = min(limit - len(dates), page_size)
|
|
|
|
if dates is None or dates.shape[0] == 0:
|
|
err_msg = "No earnings dates found, symbol may be delisted"
|
|
logger.error(f'{self.ticker}: {err_msg}')
|
|
return None
|
|
dates = dates.reset_index(drop=True)
|
|
|
|
# Drop redundant columns
|
|
dates = dates.drop(["Symbol", "Company"], axis=1)
|
|
|
|
# Convert types
|
|
for cn in ["EPS Estimate", "Reported EPS", "Surprise(%)"]:
|
|
dates.loc[dates[cn] == '-', cn] = "NaN"
|
|
dates[cn] = dates[cn].astype(float)
|
|
|
|
# Convert % to range 0->1:
|
|
dates["Surprise(%)"] *= 0.01
|
|
|
|
# Parse earnings date string
|
|
cn = "Earnings Date"
|
|
# - remove AM/PM and timezone from date string
|
|
tzinfo = dates[cn].str.extract('([AP]M[a-zA-Z]*)$')
|
|
dates[cn] = dates[cn].replace(' [AP]M[a-zA-Z]*$', '', regex=True)
|
|
# - split AM/PM from timezone
|
|
tzinfo = tzinfo[0].str.extract('([AP]M)([a-zA-Z]*)', expand=True)
|
|
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")
|
|
# - instead of attempting decoding of ambiguous timezone abbreviation, just use 'info':
|
|
self._quote.proxy = proxy
|
|
tz = self._get_ticker_tz(proxy=proxy, timeout=30)
|
|
dates[cn] = dates[cn].dt.tz_localize(tz)
|
|
|
|
dates = dates.set_index("Earnings Date")
|
|
|
|
self._earnings_dates[limit] = dates
|
|
|
|
return dates
|
|
|
|
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)
|
|
|
|
if self._history_metadata_formatted is False:
|
|
self._history_metadata = utils.format_history_metadata(self._history_metadata)
|
|
self._history_metadata_formatted = True
|
|
|
|
return self._history_metadata
|