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base.py
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base.py
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#!/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 time as _time
import datetime as _datetime
import requests as _requests
import pandas as _pd
import numpy as _np
import re as _re
try:
from urllib.parse import quote as urlencode
except ImportError:
from urllib import quote as urlencode
from . import utils
import json as _json
# import re as _re
# import sys as _sys
from . import shared
_BASE_URL_ = 'https://query2.finance.yahoo.com'
_SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
_ROOT_URL_ = 'https://finance.yahoo.com'
class TickerBase:
def __init__(self, ticker, session=None):
self.ticker = ticker.upper()
self.session = session
self._history = None
self._base_url = _BASE_URL_
self._scrape_url = _SCRAPE_URL_
self._tz = None
self._fundamentals = False
self._info = None
self._earnings_trend = None
self._sustainability = None
self._recommendations = None
self._analyst_trend_details = None
self._analyst_price_target = None
self._rev_est = None
self._eps_est = None
self._major_holders = None
self._institutional_holders = None
self._mutualfund_holders = None
self._isin = None
self._news = []
self._shares = None
self._calendar = None
self._expirations = {}
self._earnings_dates = None
self._earnings_history = None
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)
def stats(self, proxy=None):
# setup proxy in requests format
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
if self._fundamentals:
return
ticker_url = "{}/{}".format(self._scrape_url, self.ticker)
# get info and sustainability
data = utils.get_json_data_stores(ticker_url, proxy, self.session)["QuoteSummaryStore"]
return data
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=True, raise_errors=False):
"""
: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.
Default is 1900-01-01
end: str
Download end date string (YYYY-MM-DD) or _datetime.
Default is now
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
error message printing to console.
raise_errors: bool
If True, then raise errors as
exceptions instead of printing to console.
"""
if start or period is None or period.lower() == "max":
# Check can get TZ. Fail => probably delisted
tz = self._get_ticker_tz(debug, proxy, timeout)
if tz is None:
# Every valid ticker has a timezone. Missing = problem
err_msg = "No timezone found, symbol certainly delisted"
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if debug:
if raise_errors:
raise Exception('%s: %s' % (self.ticker, err_msg))
else:
print('- %s: %s' % (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:
start = -631159200
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
params["events"] = "div,splits"
# 1) fix weired bug with Yahoo! - returning 60m for 30m bars
if params["interval"] == "30m":
params["interval"] = "15m"
# setup proxy in requests format
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
# Getting data from json
url = "{}/v8/finance/chart/{}".format(self._base_url, self.ticker)
session = self.session or _requests
data = None
try:
data = session.get(
url=url,
params=params,
proxies=proxy,
headers=utils.user_agent_headers,
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
err_msg = "No data found for this date range, symbol may be delisted"
fail = False
if data is None or not type(data) is dict:
fail = True
elif type(data) is dict and 'status_code' in data:
err_msg += "(Yahoo status_code = {})".format(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 \
data["chart"]["result"][0]["meta"]["validRanges"]:
# User provided a bad period. The minimum should be '1d', but sometimes Yahoo accepts '1h'.
err_msg = "Period '{}' is invalid, must be one of {}".format(period, data["chart"]["result"][0]["meta"][
"validRanges"])
fail = True
if fail:
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if debug:
if raise_errors:
raise Exception('%s: %s' % (self.ticker, err_msg))
else:
print('%s: %s' % (self.ticker, err_msg))
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 debug:
if raise_errors:
raise Exception('%s: %s' % (self.ticker, err_msg))
else:
print('%s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
# 2) fix weired bug with Yahoo! - returning 60m for 30m bars
if interval.lower() == "30m":
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()
except Exception:
pass
try:
quotes['Stock Splits'] = quotes2['Dividends'].max()
except Exception:
pass
tz_exchange = data["chart"]["result"][0]["meta"]["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)
if repair:
# Do this before auto/back adjust
quotes = self._fix_unit_mixups(quotes, interval, tz_exchange)
# Auto/back adjust
try:
if auto_adjust:
quotes = utils.auto_adjust(quotes)
elif back_adjust:
quotes = utils.back_adjust(quotes)
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 debug:
if raise_errors:
raise Exception('%s: %s' % (self.ticker, err_msg))
else:
print('%s: %s' % (self.ticker, err_msg))
if rounding:
quotes = _np.round(quotes, data[
"chart"]["result"][0]["meta"]["priceHint"])
quotes['Volume'] = quotes['Volume'].fillna(0).astype(_np.int64)
# actions
dividends, splits = utils.parse_actions(data["chart"]["result"][0])
if start is not None:
startDt = _pd.to_datetime(_datetime.datetime.utcfromtimestamp(start))
if dividends is not None:
dividends = dividends[dividends.index >= startDt]
if splits is not None:
splits = splits[splits.index >= startDt]
if end is not None:
endDt = _pd.to_datetime(_datetime.datetime.utcfromtimestamp(end))
if dividends is not None:
dividends = dividends[dividends.index < endDt]
if splits is not None:
splits = splits[splits.index < endDt]
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)
# 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 params["interval"][-1] in ("m", 'h'):
df.index.name = "Datetime"
else:
df.index.name = "Date"
# If localizing a midnight during DST transition hour when clocks roll back,
# meaning clock hits midnight twice, then use the 2nd (ambiguous=True)
df.index = _pd.to_datetime(df.index.date).tz_localize(tz_exchange, ambiguous=True)
# duplicates and missing rows cleanup
df = df[~df.index.duplicated(keep='first')]
self._history = df.copy()
if not actions:
df = df.drop(columns=["Dividends", "Stock Splits"])
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])
return df
# ------------------------
def _fix_unit_mixups(self, df, interval, tz_exchange):
# Sometimes Yahoo returns few prices in cents/pence instead of $/£
# I.e. 100x bigger
# Easy to detect and fix, just look for outliers = ~100x local median
if df.shape[0] == 0:
return df
if df.shape[0] == 1:
# Need multiple rows to confidently identify outliers
return df
if df.index.tz is None:
df.index = df.index.tz_localize(tz_exchange)
else:
df.index = df.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 = ["Open", "High", "Low", "Close"]
data_cols = [c for c in data_cols if c in df.columns]
median = _ndimage.median_filter(df[data_cols].values, size=(3, 3), mode='mirror')
if (median == 0).any():
raise Exception("median contains zeroes, why?")
ratio = df[data_cols].values / median
# ratio_rounded = (ratio/5).round()*5 # round ratio to nearest 5
ratio_rounded = (ratio / 10).round() * 10 # round ratio to nearest 10
f = ratio_rounded == 100
# Store each mixup:
mixups = {}
for j in range(len(data_cols)):
fj = f[:, j]
if fj.any():
dc = data_cols[j]
for i in _np.where(fj)[0]:
idx = df.index[i]
if idx not in mixups:
mixups[idx] = {"data": df.loc[idx, data_cols], "fields": {dc}}
else:
mixups[idx]["fields"].add(dc)
n_mixups = len(mixups)
if len(mixups) > 0:
# Problem with Yahoo's mixup is they calculate high & low after, so they can be corrupted.
# If interval is weekly then can correct with daily. But if smaller intervals then
# restricted to recent times:
# - daily = hourly restricted to last 730 days
sub_interval = None
td_range = None
if interval == "1wk":
# Correct by fetching week of daily data
sub_interval = "1d"
td_range = _datetime.timedelta(days=7)
elif interval == "1d":
# Correct by fetching day of hourly data
sub_interval = "1h"
td_range = _datetime.timedelta(days=1)
else:
print("WARNING: Have not implemented repair for '{}' interval. Contact developers".format(interval))
return df
# This first pass will correct all errors in Open/Close/Adj Close columns.
# It will also *attempt* to correct Low/High columns, but only if can get price data.
for idx in sorted(list(mixups.keys())):
m = mixups[idx]
# Although only some fields in row exhibit 100x error, normally the other fields are also corrupted,
# so need to recalculate all fields in row.
if td_range is None:
raise Exception("was hoping this wouldn't happen")
start = idx.date()
if sub_interval == "1h" and (_datetime.date.today() - start) > _datetime.timedelta(days=729):
# Don't bother requesting more price data, Yahoo will reject
pass
else:
if sub_interval == "1h":
df_fine = self.history(start=idx.date(), end=idx.date() + td_range, interval=sub_interval,
auto_adjust=False)
else:
df_fine = self.history(start=idx.date() - td_range, end=idx.date() + td_range,
interval=sub_interval, auto_adjust=False)
# First, check whether df_fine has different split-adjustment than df.
# If it is different, then adjust df_fine to match df
good_fields = list(set(data_cols) - m["fields"])
median = df.loc[idx, good_fields].median()
median_fine = _np.median(df_fine[good_fields].values)
ratio = round(median / median_fine, 1)
ratio_rcp = round(median_fine / median, 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_fine[data_cols] *= ratio
elif ratio_rcp > 1:
# data has different split-adjustment than fine-grained data
# Adjust fine-grained to match
df_fine[data_cols] *= 1.0 / ratio_rcp
median_fine = _np.median(df_fine[good_fields].values)
ratio = round(median / median_fine, 1)
ratio_rcp = round(median_fine / median, 1)
if sub_interval != "1h":
# dt_before_week = df_fine.index[df_fine.index.get_loc(idx)-1]
df_last_week = df_fine[df_fine.index < idx]
df_fine = df_fine[df_fine.index >= idx]
if "High" in m["fields"]:
df.loc[idx, "High"] = df_fine["High"].max()
m["fields"].remove("High")
if "Low" in m["fields"]:
df.loc[idx, "Low"] = df_fine["Low"].min()
m["fields"].remove("Low")
if "Open" in m["fields"]:
if sub_interval != "1h" and idx != df_fine.index[0]:
# Exchange closed Monday. In this case, Yahoo sets Open to last week close
df.loc[idx, "Open"] = df_last_week["Close"][-1]
df.loc[idx, "Low"] = min(df.loc[idx, "Open"], df.loc[idx, "Low"])
else:
df.loc[idx, "Open"] = df_fine["Open"].iloc[0]
m["fields"].remove("Open")
if "Close" in m["fields"]:
df.loc[idx, "Close"] = df_fine["Close"].iloc[-1]
m["fields"].remove("Close")
# Assume 'Adj Close' also corrupted, easier than detecting whether true
df.loc[idx, "Adj Close"] = df_fine["Adj Close"].iloc[-1]
if len(m["fields"]) == 0:
del mixups[idx]
# This second pass will *crudely* "fix" any remaining errors in High/Low
# simply by ensuring they don't contradict e.g. Low = 100x High
if len(mixups) > 0:
for idx in sorted(list(mixups.keys())):
m = mixups[idx]
row = df.loc[idx, ["Open", "Close"]]
if "High" in m["fields"]:
df.loc[idx, "High"] = row.max()
m["fields"].remove("High")
if "Low" in m["fields"]:
df.loc[idx, "Low"] = row.min()
m["fields"].remove("Low")
if len(m["fields"]) == 0:
del mixups[idx]
n_fixed = n_mixups - len(mixups)
print("{}: fixed {} currency unit mixups in {} price data".format(self.ticker, n_fixed, interval))
if len(mixups) > 0:
print(" ... and failed to correct {}".format(len(mixups)))
return df
def _get_ticker_tz(self, debug_mode, 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(debug_mode, 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
def _fetch_ticker_tz(self, debug_mode, proxy, timeout):
# Query Yahoo for basic price data just to get returned timezone
params = {"range": "1d", "interval": "1d"}
# setup proxy in requests format
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
# Getting data from json
url = "{}/v8/finance/chart/{}".format(self._base_url, self.ticker)
session = self.session or _requests
try:
data = session.get(url=url, params=params, proxies=proxy, headers=utils.user_agent_headers, timeout=timeout)
data = data.json()
except Exception as e:
if debug_mode:
print("Failed to get ticker '{}' reason: {}".format(self.ticker, e))
return None
else:
error = data.get('chart', {}).get('error', None)
if error:
# explicit error from yahoo API
if debug_mode:
print("Got error from yahoo api for ticker {}, Error: {}".format(self.ticker, error))
else:
try:
return data["chart"]["result"][0]["meta"]["exchangeTimezoneName"]
except Exception as err:
if debug_mode:
print("Could not get exchangeTimezoneName for ticker '{}' reason: {}".format(self.ticker, err))
print("Got response: ")
print("-------------")
print(" {}".format(data))
print("-------------")
return None
def _get_info(self, proxy=None):
# setup proxy in requests format
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
if (self._info is not None) or (self._sustainability is not None) or self._recommendations:
# No need to fetch
return
ticker_url = "{}/{}".format(self._scrape_url, self.ticker)
# get info and sustainability
data = utils.get_json_data_stores(ticker_url, proxy, self.session)['QuoteSummaryStore']
# sustainability
d = {}
try:
if isinstance(data.get('esgScores'), dict):
for item in data['esgScores']:
if not isinstance(data['esgScores'][item], (dict, list)):
d[item] = data['esgScores'][item]
s = _pd.DataFrame(index=[0], data=d)[-1:].T
s.columns = ['Value']
s.index.name = '%.f-%.f' % (
s[s.index == 'ratingYear']['Value'].values[0],
s[s.index == 'ratingMonth']['Value'].values[0])
self._sustainability = s[~s.index.isin(
['maxAge', 'ratingYear', 'ratingMonth'])]
except Exception:
pass
# info (be nice to python 2)
self._info = {}
try:
items = ['summaryProfile', 'financialData', 'quoteType',
'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
for item in items:
if isinstance(data.get(item), dict):
self._info.update(data[item])
except Exception:
pass
# For ETFs, provide this valuable data: the top holdings of the ETF
try:
if 'topHoldings' in data:
self._info.update(data['topHoldings'])
except Exception:
pass
try:
if not isinstance(data.get('summaryDetail'), dict):
# For some reason summaryDetail did not give any results. The price dict
# usually has most of the same info
self._info.update(data.get('price', {}))
except Exception:
pass
try:
# self._info['regularMarketPrice'] = self._info['regularMarketOpen']
self._info['regularMarketPrice'] = data.get('price', {}).get(
'regularMarketPrice', self._info.get('regularMarketOpen', None))
except Exception:
pass
try:
self._info['preMarketPrice'] = data.get('price', {}).get(
'preMarketPrice', self._info.get('preMarketPrice', None))
except Exception:
pass
self._info['logo_url'] = ""
try:
if not 'website' in self._info:
self._info['logo_url'] = 'https://logo.clearbit.com/%s.com' % self._info['shortName'].split(' ')[0].split(',')[0]
else:
domain = self._info['website'].split(
'://')[1].split('/')[0].replace('www.', '')
self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
except Exception:
pass
# events
try:
cal = _pd.DataFrame(
data['calendarEvents']['earnings'])
cal['earningsDate'] = _pd.to_datetime(
cal['earningsDate'], unit='s')
self._calendar = cal.T
self._calendar.index = utils.camel2title(self._calendar.index)
self._calendar.columns = ['Value']
except Exception:
pass
# analyst recommendations
try:
rec = _pd.DataFrame(
data['upgradeDowngradeHistory']['history'])
rec['earningsDate'] = _pd.to_datetime(
rec['epochGradeDate'], unit='s')
rec.set_index('earningsDate', inplace=True)
rec.index.name = 'Date'
rec.columns = utils.camel2title(rec.columns)
self._recommendations = rec[[
'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
except Exception:
pass
# Complementary key-statistics. For now just want 'trailing PEG ratio'
session = self.session or _requests
keys = {"trailingPegRatio"}
if len(keys)>0:
# Simplified the original scrape code for key-statistics. Very expensive for fetching
# just one value, best if scraping most/all:
#
# p = _re.compile(r'root\.App\.main = (.*);')
# url = 'https://finance.yahoo.com/quote/{}/key-statistics?p={}'.format(self.ticker, self.ticker)
# try:
# r = session.get(url, headers=utils.user_agent_headers)
# data = _json.loads(p.findall(r.text)[0])
# key_stats = data['context']['dispatcher']['stores']['QuoteTimeSeriesStore']["timeSeries"]
# for k in keys:
# if k not in key_stats or len(key_stats[k])==0:
# # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
# v = None
# else:
# # Select most recent (last) raw value in list:
# v = key_stats[k][-1]["reportedValue"]["raw"]
# self._info[k] = v
# except Exception:
# raise
# pass
#
# For just one/few variable is faster to query directly:
url = "https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{}?symbol={}".format(self.ticker, self.ticker)
for k in keys:
url += "&type="+k
# Request 6 months of data
url += "&period1={}".format(int((_datetime.datetime.now()-_datetime.timedelta(days=365//2)).timestamp()))
url += "&period2={}".format(int((_datetime.datetime.now()+_datetime.timedelta(days=1)).timestamp()))
json_str = session.get(url=url, proxies=proxy, headers=utils.user_agent_headers).text
json_data = _json.loads(json_str)
key_stats = json_data["timeseries"]["result"][0]
if k not in key_stats:
# Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
v = None
else:
# Select most recent (last) raw value in list:
v = key_stats[k][-1]["reportedValue"]["raw"]
self._info[k] = v
def _get_fundamentals(self, proxy=None):
def cleanup(data):
'''
The cleanup function is used for parsing yahoo finance json financial statement data into a pandas dataframe format.
'''
df = _pd.DataFrame(data).drop(columns=['maxAge'])
for col in df.columns:
df[col] = _np.where(
df[col].astype(str) == '-', _np.nan, df[col])
df.set_index('endDate', inplace=True)
try:
df.index = _pd.to_datetime(df.index, unit='s')
except ValueError:
df.index = _pd.to_datetime(df.index)
df = df.T
df.columns.name = ''
df.index.name = 'Breakdown'
# rename incorrect yahoo key
df.rename(index={'treasuryStock': 'Gains Losses Not Affecting Retained Earnings'}, inplace=True)
df.index = utils.camel2title(df.index)
return df
# setup proxy in requests format
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
if self._fundamentals:
return
ticker_url = "{}/{}".format(self._scrape_url, self.ticker)
# holders
try:
resp = utils.get_html(ticker_url + '/holders', proxy, self.session)
holders = _pd.read_html(resp)
except Exception:
holders = []
if len(holders) >= 3:
self._major_holders = holders[0]
self._institutional_holders = holders[1]
self._mutualfund_holders = holders[2]
elif len(holders) >= 2:
self._major_holders = holders[0]
self._institutional_holders = holders[1]
elif len(holders) >= 1:
self._major_holders = holders[0]
if self._institutional_holders is not None:
if 'Date Reported' in self._institutional_holders:
self._institutional_holders['Date Reported'] = _pd.to_datetime(
self._institutional_holders['Date Reported'])
if '% Out' in self._institutional_holders:
self._institutional_holders['% Out'] = self._institutional_holders[
'% Out'].str.replace('%', '').astype(float) / 100
if self._mutualfund_holders is not None:
if 'Date Reported' in self._mutualfund_holders:
self._mutualfund_holders['Date Reported'] = _pd.to_datetime(
self._mutualfund_holders['Date Reported'])
if '% Out' in self._mutualfund_holders:
self._mutualfund_holders['% Out'] = self._mutualfund_holders[
'% Out'].str.replace('%', '').astype(float) / 100
self._get_info(proxy)
# get fundamentals
fin_data = utils.get_json_data_stores(ticker_url + '/financials', proxy, self.session)
fin_data_quote = fin_data['QuoteSummaryStore']
# generic patterns
self._earnings = {"yearly": utils._pd.DataFrame(), "quarterly": utils._pd.DataFrame()}
self._financials = {}
for name in ["income", "balance-sheet", "cash-flow"]:
self._financials[name] = {"yearly":utils._pd.DataFrame(), "quarterly":utils._pd.DataFrame()}
for name in ["income", "balance-sheet", "cash-flow"]:
annual, qtr = self._create_financials_table(name, proxy)
if annual is not None:
self._financials[name]["yearly"] = annual
if qtr is not None:
self._financials[name]["quarterly"] = qtr
# earnings
if isinstance(fin_data_quote.get('earnings'), dict):
try:
earnings = fin_data_quote['earnings']['financialsChart']
earnings['financialCurrency'] = fin_data_quote['earnings'].get('financialCurrency', 'USD')
self._earnings['financialCurrency'] = earnings['financialCurrency']
df = _pd.DataFrame(earnings['yearly']).set_index('date')
df.columns = utils.camel2title(df.columns)
df.index.name = 'Year'
self._earnings['yearly'] = df
df = _pd.DataFrame(earnings['quarterly']).set_index('date')
df.columns = utils.camel2title(df.columns)
df.index.name = 'Quarter'
self._earnings['quarterly'] = df
except Exception:
pass
# shares outstanding
try:
# keep only years with non None data
available_shares = [shares_data for shares_data in fin_data['QuoteTimeSeriesStore']['timeSeries']['annualBasicAverageShares'] if shares_data]
shares = _pd.DataFrame(available_shares)
shares['Year'] = shares['asOfDate'].agg(lambda x: int(x[:4]))
shares.set_index('Year', inplace=True)
shares.drop(columns=['dataId', 'asOfDate',
'periodType', 'currencyCode'], inplace=True)
shares.rename(
columns={'reportedValue': "BasicShares"}, inplace=True)
self._shares = shares
except Exception:
pass
# Analysis
data = utils.get_json_data_stores(ticker_url + '/analysis', proxy, self.session)["QuoteSummaryStore"]
if isinstance(data.get('earningsTrend'), dict):
try:
analysis = _pd.DataFrame(data['earningsTrend']['trend'])
analysis['endDate'] = _pd.to_datetime(analysis['endDate'])
analysis.set_index('period', inplace=True)
analysis.index = analysis.index.str.upper()
analysis.index.name = 'Period'
analysis.columns = utils.camel2title(analysis.columns)
dict_cols = []
for idx, row in analysis.iterrows():
for colname, colval in row.items():
if isinstance(colval, dict):
dict_cols.append(colname)
for k, v in colval.items():
new_colname = colname + ' ' + \
utils.camel2title([k])[0]
analysis.loc[idx, new_colname] = v
self._earnings_trend = analysis[[
c for c in analysis.columns if c not in dict_cols]]
except Exception:
pass
# Analysis Data/Analyst Forecasts
try:
analysis_data = utils.get_json_data_stores(ticker_url+'/analysis',proxy,self.session)
analysis_data = analysis_data['QuoteSummaryStore']
except Exception as e:
analysis_data = {}
try:
self._analyst_trend_details = _pd.DataFrame(analysis_data['recommendationTrend']['trend'])
except Exception as e:
self._analyst_trend_details = None
try:
self._analyst_price_target = _pd.DataFrame(analysis_data['financialData'], index=[0])[['targetLowPrice','currentPrice','targetMeanPrice','targetHighPrice','numberOfAnalystOpinions']].T
except Exception as e:
self._analyst_price_target = None
earnings_estimate = []
revenue_estimate = []
if len(self._analyst_trend_details) != 0:
for key in analysis_data['earningsTrend']['trend']:
try:
earnings_dict = key['earningsEstimate']
earnings_dict['period'] = key['period']
earnings_dict['endDate'] = key['endDate']
earnings_estimate.append(earnings_dict)
revenue_dict = key['revenueEstimate']
revenue_dict['period'] = key['period']
revenue_dict['endDate'] = key['endDate']
revenue_estimate.append(revenue_dict)
except Exception as e:
pass
self._rev_est = _pd.DataFrame(revenue_estimate)
self._eps_est = _pd.DataFrame(earnings_estimate)
else:
self._rev_est = _pd.DataFrame()
self._eps_est = _pd.DataFrame()
self._fundamentals = True
def _create_financials_table(self, name, proxy):
acceptable_names = ["income", "balance-sheet", "cash-flow"]
if not name in acceptable_names:
raise Exception("name '{}' must be one of: {}".format(name, acceptable_names))
if name == "income":
# Yahoo stores the 'income' table internally under 'financials' key
name = "financials"
ticker_url = "{}/{}".format(self._scrape_url, self.ticker)
data_store = utils.get_json_data_stores(ticker_url+'/'+name, proxy, self.session)
_stmt_annual = None
_stmt_qtr = None
try:
# Developers note: TTM and template stuff allows for reproducing the nested structure
# visible on Yahoo website. But more work needed to make it user-friendly! Ideally
# return a tree data structure instead of Pandas MultiIndex
# So until this is implemented, just return simple tables
_stmt_annual = utils.get_financials_time_series(self.ticker, name, "annual", ticker_url, proxy, self.session)
_stmt_qtr = utils.get_financials_time_series(self.ticker, name, "quarterly", ticker_url, proxy, self.session)
# template_ttm_order, template_annual_order, template_order, level_detail = utils.build_template(data_store["FinancialTemplateStore"])
# TTM_dicts, Annual_dicts = utils.retreive_financial_details(data_store['QuoteTimeSeriesStore'])
# if name == "balance-sheet":
# # Note: balance sheet is the only financial statement with no ttm detail
# _stmt_annual = utils.format_annual_financial_statement(level_detail, Annual_dicts, template_annual_order)
# else:
# _stmt_annual = utils.format_annual_financial_statement(level_detail, Annual_dicts, template_annual_order, TTM_dicts, template_ttm_order)
# Data store doesn't contain quarterly data, so retrieve using different url:
# _qtr_data = utils.get_financials_time_series(self.ticker, name, "quarterly", ticker_url, proxy, self.session)
# _stmt_qtr = utils.format_quarterly_financial_statement(_qtr_data, level_detail, template_order)
except:
pass
return _stmt_annual, _stmt_qtr
def get_recommendations(self, proxy=None, as_dict=False):
self._get_info(proxy)
data = self._recommendations
if as_dict:
return data.to_dict()
return data
def get_calendar(self, proxy=None, as_dict=False):
self._get_info(proxy)
data = self._calendar
if as_dict:
return data.to_dict()
return data
def get_major_holders(self, proxy=None, as_dict=False):
self._get_fundamentals(proxy=proxy)
data = self._major_holders
if as_dict:
return data.to_dict()
return data
def get_institutional_holders(self, proxy=None, as_dict=False):