#!/usr/bin/env python # -*- coding: utf-8 -*- # # yfinance - market data downloader # https://github.com/ranaroussi/yfinance # # Copyright 2017-2019 Ran Aroussi # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import print_function import datetime as _datetime from io import StringIO import json as _json import logging import time as _time import warnings from typing import Optional from urllib.parse import quote as urlencode import dateutil as _dateutil import numpy as np import pandas as pd import requests from . import shared, utils, cache from .data import YfData from .scrapers.analysis import Analysis from .scrapers.fundamentals import Fundamentals from .scrapers.holders import Holders from .scrapers.quote import Quote, FastInfo from .const import _BASE_URL_, _ROOT_URL_ class TickerBase: def __init__(self, ticker, session=None): self.ticker = ticker.upper() self.session = session self._history = None self._history_metadata = None self._history_metadata_formatted = False self._base_url = _BASE_URL_ self._tz = None self._isin = None self._news = [] self._shares = None self._earnings_dates = {} self._earnings = None self._financials = None # accept isin as ticker if utils.is_isin(self.ticker): self.ticker = utils.get_ticker_by_isin(self.ticker, None, session) self._data: YfData = YfData(session=session) self._analysis = Analysis(self._data, self.ticker) self._holders = Holders(self._data, self.ticker) self._quote = Quote(self._data, self.ticker) self._fundamentals = Fundamentals(self._data, self.ticker) self._fast_info = None # Limit recursion depth when repairing prices self._reconstruct_start_interval = None @utils.log_indent_decorator def history(self, period="1mo", interval="1d", start=None, end=None, prepost=False, actions=True, auto_adjust=True, back_adjust=False, repair=False, keepna=False, proxy=None, rounding=False, timeout=10, debug=None, # deprecated raise_errors=False) -> pd.DataFrame: """ :Parameters: period : str Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max Either Use period parameter or use start and end interval : str Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo Intraday data cannot extend last 60 days start: str Download start date string (YYYY-MM-DD) or _datetime, inclusive. Default is 99 years ago E.g. for start="2020-01-01", the first data point will be on "2020-01-01" end: str Download end date string (YYYY-MM-DD) or _datetime, exclusive. Default is now E.g. for end="2023-01-01", the last data point will be on "2022-12-31" prepost : bool Include Pre and Post market data in results? Default is False auto_adjust: bool Adjust all OHLC automatically? Default is True back_adjust: bool Back-adjusted data to mimic true historical prices repair: bool Detect currency unit 100x mixups and attempt repair. Default is False keepna: bool Keep NaN rows returned by Yahoo? Default is False proxy: str Optional. Proxy server URL scheme. Default is None rounding: bool Round values to 2 decimal places? Optional. Default is False = precision suggested by Yahoo! timeout: None or float If not None stops waiting for a response after given number of seconds. (Can also be a fraction of a second e.g. 0.01) Default is 10 seconds. debug: bool If passed as False, will suppress message printing to console. DEPRECATED, will be removed in future version raise_errors: bool If True, then raise errors as Exceptions instead of logging. """ logger = utils.get_yf_logger() if debug is not None: if debug: 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)") logger.setLevel(logging.ERROR) else: 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)") logger.setLevel(logging.CRITICAL) start_user = start end_user = end if start or period is None or period.lower() == "max": # Check can get TZ. Fail => probably delisted tz = self._get_ticker_tz(proxy, timeout) if tz is None: # Every valid ticker has a timezone. Missing = problem err_msg = "No timezone found, symbol may be delisted" shared._DFS[self.ticker] = utils.empty_df() shared._ERRORS[self.ticker] = err_msg if raise_errors: raise Exception(f'{self.ticker}: {err_msg}') else: logger.error(f'{self.ticker}: {err_msg}') return utils.empty_df() if end is None: end = int(_time.time()) else: end = utils._parse_user_dt(end, tz) if start is None: if interval == "1m": start = end - 604800 # Subtract 7 days else: max_start_datetime = pd.Timestamp.utcnow().floor("D") - _datetime.timedelta(days=99 * 365) start = int(max_start_datetime.timestamp()) else: start = utils._parse_user_dt(start, tz) params = {"period1": start, "period2": end} else: period = period.lower() params = {"range": period} params["interval"] = interval.lower() params["includePrePost"] = prepost # 1) fix weired bug with Yahoo! - returning 60m for 30m bars if params["interval"] == "30m": params["interval"] = "15m" # if the ticker is MUTUALFUND or ETF, then get capitalGains events params["events"] = "div,splits,capitalGains" params_pretty = dict(params) tz = self._get_ticker_tz(proxy, timeout) for k in ["period1", "period2"]: if k in params_pretty: params_pretty[k] = str(pd.Timestamp(params[k], unit='s').tz_localize("UTC").tz_convert(tz)) logger.debug(f'{self.ticker}: Yahoo GET parameters: {str(params_pretty)}') # Getting data from json url = f"{self._base_url}/v8/finance/chart/{self.ticker}" data = None get_fn = self._data.get if end is not None: end_dt = pd.Timestamp(end, unit='s').tz_localize("UTC") dt_now = pd.Timestamp.utcnow() data_delay = _datetime.timedelta(minutes=30) if end_dt + data_delay <= dt_now: # Date range in past so safe to fetch through cache: get_fn = self._data.cache_get try: data = get_fn( url=url, params=params, proxy=proxy, timeout=timeout ) if "Will be right back" in data.text or data is None: 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() except Exception: pass # Store the meta data that gets retrieved simultaneously try: self._history_metadata = data["chart"]["result"][0]["meta"] except Exception: self._history_metadata = {} intraday = params["interval"][-1] in ("m", 'h') err_msg = "No price data found, symbol may be delisted" if start or period is None or period.lower() == "max": err_msg += f' ({params["interval"]} ' if start_user is not None: err_msg += f'{start_user}' elif not intraday: err_msg += f'{pd.Timestamp(start, unit="s").tz_localize("UTC").tz_convert(tz).date()}' else: err_msg += f'{pd.Timestamp(start, unit="s").tz_localize("UTC").tz_convert(tz)}' err_msg += ' -> ' if end_user is not None: err_msg += f'{end_user})' elif not intraday: err_msg += f'{pd.Timestamp(end, unit="s").tz_localize("UTC").tz_convert(tz).date()})' else: err_msg += f'{pd.Timestamp(end, unit="s").tz_localize("UTC").tz_convert(tz)})' else: err_msg += f' (period={period})' fail = False if data is None or type(data) is not dict: fail = True elif type(data) is dict and 'status_code' in data: err_msg += f"(Yahoo status_code = {data['status_code']})" fail = True elif "chart" in data and data["chart"]["error"]: err_msg = data["chart"]["error"]["description"] fail = True elif "chart" not in data or data["chart"]["result"] is None or not data["chart"]["result"]: fail = True elif period is not None and "timestamp" not in data["chart"]["result"][0] and period not in \ self._history_metadata["validRanges"]: # User provided a bad period. The minimum should be '1d', but sometimes Yahoo accepts '1h'. err_msg = f"Period '{period}' is invalid, must be one of {self._history_metadata['validRanges']}" fail = True if fail: shared._DFS[self.ticker] = utils.empty_df() shared._ERRORS[self.ticker] = err_msg if raise_errors: raise Exception(f'{self.ticker}: {err_msg}') else: logger.error(f'{self.ticker}: {err_msg}') if self._reconstruct_start_interval is not None and self._reconstruct_start_interval == interval: self._reconstruct_start_interval = None return utils.empty_df() # parse quotes try: quotes = utils.parse_quotes(data["chart"]["result"][0]) # Yahoo bug fix - it often appends latest price even if after end date if end and not quotes.empty: endDt = pd.to_datetime(_datetime.datetime.utcfromtimestamp(end)) if quotes.index[quotes.shape[0] - 1] >= endDt: quotes = quotes.iloc[0:quotes.shape[0] - 1] except Exception: shared._DFS[self.ticker] = utils.empty_df() shared._ERRORS[self.ticker] = err_msg if raise_errors: raise Exception(f'{self.ticker}: {err_msg}') else: logger.error(f'{self.ticker}: {err_msg}') if self._reconstruct_start_interval is not None and self._reconstruct_start_interval == interval: self._reconstruct_start_interval = None return shared._DFS[self.ticker] logger.debug(f'{self.ticker}: yfinance received OHLC data: {quotes.index[0]} -> {quotes.index[-1]}') # 2) fix weired bug with Yahoo! - returning 60m for 30m bars if interval.lower() == "30m": logger.debug(f'{self.ticker}: resampling 30m OHLC from 15m') quotes2 = quotes.resample('30T') quotes = pd.DataFrame(index=quotes2.last().index, data={ 'Open': quotes2['Open'].first(), 'High': quotes2['High'].max(), 'Low': quotes2['Low'].min(), 'Close': quotes2['Close'].last(), 'Adj Close': quotes2['Adj Close'].last(), 'Volume': quotes2['Volume'].sum() }) try: quotes['Dividends'] = quotes2['Dividends'].max() quotes['Stock Splits'] = quotes2['Stock Splits'].max() except Exception: pass # Select useful info from metadata quote_type = self._history_metadata["instrumentType"] expect_capital_gains = quote_type in ('MUTUALFUND', 'ETF') tz_exchange = self._history_metadata["exchangeTimezoneName"] # Note: ordering is important. If you change order, run the tests! quotes = utils.set_df_tz(quotes, params["interval"], tz_exchange) quotes = utils.fix_Yahoo_dst_issue(quotes, params["interval"]) quotes = utils.fix_Yahoo_returning_live_separate(quotes, params["interval"], tz_exchange) intraday = params["interval"][-1] in ("m", 'h') if not prepost and intraday and "tradingPeriods" in self._history_metadata: tps = self._history_metadata["tradingPeriods"] if not isinstance(tps, pd.DataFrame): self._history_metadata = utils.format_history_metadata(self._history_metadata, tradingPeriodsOnly=True) tps = self._history_metadata["tradingPeriods"] quotes = utils.fix_Yahoo_returning_prepost_unrequested(quotes, params["interval"], tps) logger.debug(f'{self.ticker}: OHLC after cleaning: {quotes.index[0]} -> {quotes.index[-1]}') # actions dividends, splits, capital_gains = utils.parse_actions(data["chart"]["result"][0]) if not expect_capital_gains: capital_gains = None if splits is not None: splits = utils.set_df_tz(splits, interval, tz_exchange) if dividends is not None: dividends = utils.set_df_tz(dividends, interval, tz_exchange) if capital_gains is not None: capital_gains = utils.set_df_tz(capital_gains, interval, tz_exchange) if start is not None: if not quotes.empty: startDt = quotes.index[0].floor('D') if dividends is not None: dividends = dividends.loc[startDt:] if capital_gains is not None: capital_gains = capital_gains.loc[startDt:] if splits is not None: splits = splits.loc[startDt:] if end is not None: endDt = pd.Timestamp(end, unit='s').tz_localize(tz) if dividends is not None: dividends = dividends[dividends.index < endDt] if capital_gains is not None: capital_gains = capital_gains[capital_gains.index < endDt] if splits is not None: splits = splits[splits.index < endDt] # Prepare for combine intraday = params["interval"][-1] in ("m", 'h') if not intraday: # If localizing a midnight during DST transition hour when clocks roll back, # meaning clock hits midnight twice, then use the 2nd (ambiguous=True) quotes.index = pd.to_datetime(quotes.index.date).tz_localize(tz_exchange, ambiguous=True, nonexistent='shift_forward') if dividends.shape[0] > 0: dividends.index = pd.to_datetime(dividends.index.date).tz_localize(tz_exchange, ambiguous=True, nonexistent='shift_forward') if splits.shape[0] > 0: splits.index = pd.to_datetime(splits.index.date).tz_localize(tz_exchange, ambiguous=True, nonexistent='shift_forward') # Combine df = quotes.sort_index() if dividends.shape[0] > 0: df = utils.safe_merge_dfs(df, dividends, interval) if "Dividends" in df.columns: df.loc[df["Dividends"].isna(), "Dividends"] = 0 else: df["Dividends"] = 0.0 if splits.shape[0] > 0: df = utils.safe_merge_dfs(df, splits, interval) if "Stock Splits" in df.columns: df.loc[df["Stock Splits"].isna(), "Stock Splits"] = 0 else: df["Stock Splits"] = 0.0 if expect_capital_gains: if capital_gains.shape[0] > 0: df = utils.safe_merge_dfs(df, capital_gains, interval) if "Capital Gains" in df.columns: df.loc[df["Capital Gains"].isna(), "Capital Gains"] = 0 else: df["Capital Gains"] = 0.0 logger.debug(f'{self.ticker}: OHLC after combining events: {quotes.index[0]} -> {quotes.index[-1]}') df = df[~df.index.duplicated(keep='first')] # must do before repair if isinstance(repair, str) and repair=='silent': 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.") repair = True if repair: # Do this before auto/back adjust logger.debug(f'{self.ticker}: checking OHLC for repairs ...') df = self._fix_unit_mixups(df, interval, tz_exchange, prepost) df = self._fix_bad_stock_split(df, interval, tz_exchange) # Must repair 100x and split errors before price reconstruction df = self._fix_zeroes(df, interval, tz_exchange, prepost) df = self._fix_missing_div_adjust(df, interval, tz_exchange) df = df.sort_index() # Auto/back adjust try: if auto_adjust: df = utils.auto_adjust(df) elif back_adjust: df = utils.back_adjust(df) except Exception as e: if auto_adjust: err_msg = "auto_adjust failed with %s" % e else: err_msg = "back_adjust failed with %s" % e shared._DFS[self.ticker] = utils.empty_df() shared._ERRORS[self.ticker] = err_msg if raise_errors: raise Exception('%s: %s' % (self.ticker, err_msg)) else: logger.error('%s: %s' % (self.ticker, err_msg)) if rounding: df = np.round(df, data["chart"]["result"][0]["meta"]["priceHint"]) df['Volume'] = df['Volume'].fillna(0).astype(np.int64) if intraday: df.index.name = "Datetime" else: df.index.name = "Date" self._history = df.copy() # missing rows cleanup if not actions: df = df.drop(columns=["Dividends", "Stock Splits", "Capital Gains"], errors='ignore') if not keepna: mask_nan_or_zero = (df.isna() | (df == 0)).all(axis=1) 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): if df.empty: return df 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. if df.empty: return df # 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 ratio_rcp = 1.0/ratio ratio_rcp_rounded = (ratio_rcp / 20).round() * 20 # round ratio to nearest 20 f_rcp = (ratio_rounded == 100) | (ratio_rcp_rounded == 100) f_either = f | f_rcp if not f_either.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_either[:, 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[data_cols].to_numpy() == tag) & f for i in range(f.shape[0]): fi = f[i, :] if not fi.any(): continue idx = df2.index[i] for c in ['Open', '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() f_rcp = (df2[data_cols].to_numpy() == tag) & f_rcp for i in range(f_rcp.shape[0]): fi = f_rcp[i, :] if not fi.any(): continue idx = df2.index[i] for c in ['Open', 'Close']: j = data_cols.index(c) if fi[j]: df2.loc[idx, c] = df.loc[idx, c] * 100.0 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_either = df2[data_cols].to_numpy() == tag for j in range(len(data_cols)): fj = f_either[:, 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. 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. if df.empty: return df 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. if df.empty: return df 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. if df.empty: return df 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): if df.empty: return df 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']].to_numpy() f_zero = price_data == 0.0 else: _1d_change_x = np.full((n, 4), 1.0) price_data = df2[OHLC].to_numpy() f_zero = price_data == 0.0 if f_zero.any(): price_data[f_zero] = 1.0 # Update: if a VERY large dividend is paid out, then can be mistaken for a 1:2 stock split. # Fix = use adjusted prices adj = df2['Adj Close'].to_numpy() / df2['Close'].to_numpy() for j in range(price_data.shape[1]): price_data[:,j] *= adj _1d_change_x[1:] = price_data[1:, ] / price_data[:-1, ] f_zero_num_denom = f_zero | np.roll(f_zero, 1, axis=0) if f_zero_num_denom.any(): _1d_change_x[f_zero_num_denom] = 1.0 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 # Update: if any 100x changes are soon after a stock split, so could be confused with split error, then abort threshold_days = 30 f_splits = df['Stock Splits'].to_numpy() != 0.0 if change in [100.0, 0.01] and f_splits.any(): indices_A = np.where(f_splits)[0] indices_B = np.where(f)[0] if not len(indices_A) or not len(indices_B): return None gaps = indices_B[:, None] - indices_A # Because data is sorted in DEscending order, need to flip gaps gaps *= -1 f_pos = gaps > 0 if f_pos.any(): gap_min = gaps[f_pos].min() gap_td = utils._interval_to_timedelta(interval) * gap_min if isinstance(gap_td, _dateutil.relativedelta.relativedelta): threshold = _dateutil.relativedelta.relativedelta(days=threshold_days) else: threshold = _datetime.timedelta(days=threshold_days) if gap_td < threshold: logger.info(f'price-repair-split: 100x changes are too soon after stock split events, aborting') 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 c = cache.get_tz_cache() tz = c.lookup(self.ticker) if tz and not utils.is_valid_timezone(tz): # Clear from cache and force re-fetch c.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 c.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(StringIO(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