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utils.py
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utils.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 datetime as _datetime
from typing import Dict, Union
import pytz as _tz
import requests as _requests
import re as _re
import pandas as _pd
import numpy as _np
import sys as _sys
import os as _os
import appdirs as _ad
import sqlite3 as _sqlite3
import atexit as _atexit
from threading import Lock
from pytz import UnknownTimeZoneError
try:
import ujson as _json
except ImportError:
import json as _json
user_agent_headers = {
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
def is_isin(string):
return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))
def get_all_by_isin(isin, proxy=None, session=None):
if not (is_isin(isin)):
raise ValueError("Invalid ISIN number")
from .base import _BASE_URL_
session = session or _requests
url = "{}/v1/finance/search?q={}".format(_BASE_URL_, isin)
data = session.get(url=url, proxies=proxy, headers=user_agent_headers)
try:
data = data.json()
ticker = data.get('quotes', [{}])[0]
return {
'ticker': {
'symbol': ticker['symbol'],
'shortname': ticker['shortname'],
'longname': ticker['longname'],
'type': ticker['quoteType'],
'exchange': ticker['exchDisp'],
},
'news': data.get('news', [])
}
except Exception:
return {}
def get_ticker_by_isin(isin, proxy=None, session=None):
data = get_all_by_isin(isin, proxy, session)
return data.get('ticker', {}).get('symbol', '')
def get_info_by_isin(isin, proxy=None, session=None):
data = get_all_by_isin(isin, proxy, session)
return data.get('ticker', {})
def get_news_by_isin(isin, proxy=None, session=None):
data = get_all_by_isin(isin, proxy, session)
return data.get('news', {})
def empty_df(index=None):
if index is None:
index = []
empty = _pd.DataFrame(index=index, data={
'Open': _np.nan, 'High': _np.nan, 'Low': _np.nan,
'Close': _np.nan, 'Adj Close': _np.nan, 'Volume': _np.nan})
empty.index.name = 'Date'
return empty
def empty_earnings_dates_df():
empty = _pd.DataFrame(
columns=["Symbol", "Company", "Earnings Date",
"EPS Estimate", "Reported EPS", "Surprise(%)"])
return empty
def get_html(url, proxy=None, session=None):
session = session or _requests
html = session.get(url=url, proxies=proxy, headers=user_agent_headers).text
return html
def get_json_data_stores(url, proxy=None, session=None):
'''
get_json_data_stores returns a python dictionary of the data stores in yahoo finance web page.
'''
session = session or _requests
html = session.get(url=url, proxies=proxy, headers=user_agent_headers).text
json_str = html.split('root.App.main =')[1].split(
'(this)')[0].split(';\n}')[0].strip()
data = _json.loads(json_str)['context']['dispatcher']['stores']
# return data
new_data = _json.dumps(data).replace('{}', 'null')
new_data = _re.sub(
r'{[\'|\"]raw[\'|\"]:(.*?),(.*?)}', r'\1', new_data)
return _json.loads(new_data)
def build_template(data):
'''
build_template returns the details required to rebuild any of the yahoo finance financial statements in the same order as the yahoo finance webpage. The function is built to be used on the "FinancialTemplateStore" json which appears in any one of the three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
Returns:
- template_annual_order: The order that annual figures should be listed in.
- template_ttm_order: The order that TTM (Trailing Twelve Month) figures should be listed in.
- template_order: The order that quarterlies should be in (note that quarterlies have no pre-fix - hence why this is required).
- level_detail: The level of each individual line item. E.g. for the "/financials" webpage, "Total Revenue" is a level 0 item and is the summation of "Operating Revenue" and "Excise Taxes" which are level 1 items.
'''
template_ttm_order = [] # Save the TTM (Trailing Twelve Months) ordering to an object.
template_annual_order = [] # Save the annual ordering to an object.
template_order = [] # Save the ordering to an object (this can be utilized for quarterlies)
level_detail = [] # Record the level of each line item of the income statement ("Operating Revenue" and "Excise Taxes" sum to return "Total Revenue" we need to keep track of this)
for key in data['template']:
# Loop through the json to retreive the exact financial order whilst appending to the objects
template_ttm_order.append('trailing{}'.format(key['key']))
template_annual_order.append('annual{}'.format(key['key']))
template_order.append('{}'.format(key['key']))
level_detail.append(0)
if 'children' in key:
for child1 in key['children']: # Level 1
template_ttm_order.append('trailing{}'.format(child1['key']))
template_annual_order.append('annual{}'.format(child1['key']))
template_order.append('{}'.format(child1['key']))
level_detail.append(1)
if 'children' in child1:
for child2 in child1['children']: # Level 2
template_ttm_order.append('trailing{}'.format(child2['key']))
template_annual_order.append('annual{}'.format(child2['key']))
template_order.append('{}'.format(child2['key']))
level_detail.append(2)
if 'children' in child2:
for child3 in child2['children']: # Level 3
template_ttm_order.append('trailing{}'.format(child3['key']))
template_annual_order.append('annual{}'.format(child3['key']))
template_order.append('{}'.format(child3['key']))
level_detail.append(3)
if 'children' in child3:
for child4 in child3['children']: # Level 4
template_ttm_order.append('trailing{}'.format(child4['key']))
template_annual_order.append('annual{}'.format(child4['key']))
template_order.append('{}'.format(child4['key']))
level_detail.append(4)
if 'children' in child4:
for child5 in child4['children']: # Level 5
template_ttm_order.append('trailing{}'.format(child5['key']))
template_annual_order.append('annual{}'.format(child5['key']))
template_order.append('{}'.format(child5['key']))
level_detail.append(5)
return template_ttm_order, template_annual_order, template_order, level_detail
def retreive_financial_details(data):
'''
retreive_financial_details returns all of the available financial details under the "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
Returns:
- TTM_dicts: A dictionary full of all of the available Trailing Twelve Month figures, this can easily be converted to a pandas dataframe.
- Annual_dicts: A dictionary full of all of the available Annual figures, this can easily be converted to a pandas dataframe.
'''
TTM_dicts = [] # Save a dictionary object to store the TTM financials.
Annual_dicts = [] # Save a dictionary object to store the Annual financials.
for key in data['timeSeries']: # Loop through the time series data to grab the key financial figures.
try:
if len(data['timeSeries'][key]) > 0:
time_series_dict = {}
time_series_dict['index'] = key
for each in data['timeSeries'][key]: # Loop through the years
if each == None:
continue
else:
time_series_dict[each['asOfDate']] = each['reportedValue']
# time_series_dict["{}".format(each['asOfDate'])] = data['timeSeries'][key][each]['reportedValue']
if 'trailing' in key:
TTM_dicts.append(time_series_dict)
elif 'annual' in key:
Annual_dicts.append(time_series_dict)
except Exception as e:
pass
return TTM_dicts, Annual_dicts
def format_annual_financial_statement(level_detail, annual_dicts, annual_order, ttm_dicts=None, ttm_order=None):
'''
format_annual_financial_statement formats any annual financial statement
Returns:
- _statement: A fully formatted annual financial statement in pandas dataframe.
'''
Annual = _pd.DataFrame.from_dict(annual_dicts).set_index("index")
Annual = Annual.reindex(annual_order)
Annual.index = Annual.index.str.replace(r'annual', '')
# Note: balance sheet is the only financial statement with no ttm detail
if (ttm_dicts not in [[], None]) and (ttm_order not in [[], None]):
TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index")
TTM = TTM.reindex(ttm_order)
# Add 'TTM' prefix to all column names, so if combined we can tell
# the difference between actuals and TTM (similar to yahoo finance).
TTM.columns = ['TTM ' + str(col) for col in TTM.columns]
TTM.index = TTM.index.str.replace(r'trailing', '')
_statement = Annual.merge(TTM, left_index=True, right_index=True)
else:
_statement = Annual
_statement.index = camel2title(_statement.T)
_statement['level_detail'] = level_detail
_statement = _statement.set_index([_statement.index, 'level_detail'])
_statement = _statement[sorted(_statement.columns, reverse=True)]
_statement = _statement.dropna(how='all')
return _statement
def format_quarterly_financial_statement(_statement, level_detail, order):
'''
format_quarterly_financial_statements formats any quarterly financial statement
Returns:
- _statement: A fully formatted quarterly financial statement in pandas dataframe.
'''
_statement = _statement.reindex(order)
_statement.index = camel2title(_statement.T)
_statement['level_detail'] = level_detail
_statement = _statement.set_index([_statement.index, 'level_detail'])
_statement = _statement[sorted(_statement.columns, reverse=True)]
_statement = _statement.dropna(how='all')
_statement.columns = _pd.to_datetime(_statement.columns).date
return _statement
def get_financials_time_series(ticker, name, timescale, ticker_url, proxy=None, session=None):
acceptable_names = ["financials", "balance-sheet", "cash-flow"]
if not name in acceptable_names:
raise Exception("name '{}' must be one of: {}".format(name, acceptable_names))
acceptable_timestamps = ["annual", "quarterly"]
if not timescale in acceptable_timestamps:
raise Exception("timescale '{}' must be one of: {}".format(timescale, acceptable_timestamps))
session = session or _requests
financials_data = get_json_data_stores(ticker_url + '/' + name, proxy, session)
# Step 1: get the keys:
def _finditem1(key, obj):
values = []
if isinstance(obj, dict):
if key in obj.keys():
values.append(obj[key])
for k, v in obj.items():
values += _finditem1(key, v)
elif isinstance(obj, list):
for v in obj:
values += _finditem1(key, v)
return values
keys = _finditem1("key", financials_data['FinancialTemplateStore'])
# Step 2: construct url:
ts_url_base = "https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{0}?symbol={0}".format(
ticker)
if len(keys) == 0:
raise Exception("Fetching keys failed")
url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
# Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
start_dt = _datetime.datetime(2016, 12, 31)
end = (_datetime.datetime.now() + _datetime.timedelta(days=366))
url += "&period1={}&period2={}".format(int(start_dt.timestamp()), int(end.timestamp()))
# Step 3: fetch and reshape data
json_str = session.get(url=url, proxies=proxy, headers=user_agent_headers).text
json_data = _json.loads(json_str)
data_raw = json_data["timeseries"]["result"]
# data_raw = [v for v in data_raw if len(v) > 1] # Discard keys with no data
for d in data_raw:
del d["meta"]
# Now reshape data into a table:
# Step 1: get columns and index:
timestamps = set()
data_unpacked = {}
for x in data_raw:
for k in x.keys():
if k == "timestamp":
timestamps.update(x[k])
else:
data_unpacked[k] = x[k]
timestamps = sorted(list(timestamps))
dates = _pd.to_datetime(timestamps, unit="s")
df = _pd.DataFrame(columns=dates, index=data_unpacked.keys())
for k, v in data_unpacked.items():
if df is None:
df = _pd.DataFrame(columns=dates, index=[k])
df.loc[k] = {_pd.Timestamp(x["asOfDate"]): x["reportedValue"]["raw"] for x in v}
df.index = df.index.str.replace("^" + timescale, "", regex=True)
# Reorder table to match order on Yahoo website
df = df.reindex([k for k in keys if k in df.index])
df = df[sorted(df.columns, reverse=True)]
return df
def camel2title(o):
return [_re.sub("([a-z])([A-Z])", r"\g<1> \g<2>", i).title() for i in o]
def _parse_user_dt(dt, exchange_tz):
if isinstance(dt, int):
# Should already be epoch, test with conversion:
_datetime.datetime.fromtimestamp(dt)
else:
# Convert str/date -> datetime, set tzinfo=exchange, get timestamp:
if isinstance(dt, str):
dt = _datetime.datetime.strptime(str(dt), '%Y-%m-%d')
if isinstance(dt, _datetime.date) and not isinstance(dt, _datetime.datetime):
dt = _datetime.datetime.combine(dt, _datetime.time(0))
if isinstance(dt, _datetime.datetime) and dt.tzinfo is None:
# Assume user is referring to exchange's timezone
dt = _tz.timezone(exchange_tz).localize(dt)
dt = int(dt.timestamp())
return dt
def auto_adjust(data):
df = data.copy()
ratio = df["Close"] / df["Adj Close"]
df["Adj Open"] = df["Open"] / ratio
df["Adj High"] = df["High"] / ratio
df["Adj Low"] = df["Low"] / ratio
df.drop(
["Open", "High", "Low", "Close"],
axis=1, inplace=True)
df.rename(columns={
"Adj Open": "Open", "Adj High": "High",
"Adj Low": "Low", "Adj Close": "Close"
}, inplace=True)
df = df[["Open", "High", "Low", "Close", "Volume"]]
return df[["Open", "High", "Low", "Close", "Volume"]]
def back_adjust(data):
""" back-adjusted data to mimic true historical prices """
df = data.copy()
ratio = df["Adj Close"] / df["Close"]
df["Adj Open"] = df["Open"] * ratio
df["Adj High"] = df["High"] * ratio
df["Adj Low"] = df["Low"] * ratio
df.drop(
["Open", "High", "Low", "Adj Close"],
axis=1, inplace=True)
df.rename(columns={
"Adj Open": "Open", "Adj High": "High",
"Adj Low": "Low"
}, inplace=True)
return df[["Open", "High", "Low", "Close", "Volume"]]
def parse_quotes(data):
timestamps = data["timestamp"]
ohlc = data["indicators"]["quote"][0]
volumes = ohlc["volume"]
opens = ohlc["open"]
closes = ohlc["close"]
lows = ohlc["low"]
highs = ohlc["high"]
adjclose = closes
if "adjclose" in data["indicators"]:
adjclose = data["indicators"]["adjclose"][0]["adjclose"]
quotes = _pd.DataFrame({"Open": opens,
"High": highs,
"Low": lows,
"Close": closes,
"Adj Close": adjclose,
"Volume": volumes})
quotes.index = _pd.to_datetime(timestamps, unit="s")
quotes.sort_index(inplace=True)
return quotes
def parse_actions(data):
dividends = _pd.DataFrame(
columns=["Dividends"], index=_pd.DatetimeIndex([]))
splits = _pd.DataFrame(
columns=["Stock Splits"], index=_pd.DatetimeIndex([]))
if "events" in data:
if "dividends" in data["events"]:
dividends = _pd.DataFrame(
data=list(data["events"]["dividends"].values()))
dividends.set_index("date", inplace=True)
dividends.index = _pd.to_datetime(dividends.index, unit="s")
dividends.sort_index(inplace=True)
dividends.columns = ["Dividends"]
if "splits" in data["events"]:
splits = _pd.DataFrame(
data=list(data["events"]["splits"].values()))
splits.set_index("date", inplace=True)
splits.index = _pd.to_datetime(splits.index, unit="s")
splits.sort_index(inplace=True)
splits["Stock Splits"] = splits["numerator"] / \
splits["denominator"]
splits = splits[["Stock Splits"]]
return dividends, splits
def set_df_tz(df, interval, tz):
if df.index.tz is None:
df.index = df.index.tz_localize("UTC")
df.index = df.index.tz_convert(tz)
return df
def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
# Yahoo bug fix. If market is open today then Yahoo normally returns
# todays data as a separate row from rest-of week/month interval in above row.
# Seems to depend on what exchange e.g. crypto OK.
# Fix = merge them together
n = quotes.shape[0]
if n > 1:
dt1 = quotes.index[n - 1]
dt2 = quotes.index[n - 2]
if quotes.index.tz is None:
dt1 = dt1.tz_localize("UTC")
dt2 = dt2.tz_localize("UTC")
dt1 = dt1.tz_convert(tz_exchange)
dt2 = dt2.tz_convert(tz_exchange)
if interval == "1d":
# Similar bug in daily data except most data is simply duplicated
# - exception is volume, *slightly* greater on final row (and matches website)
if dt1.date() == dt2.date():
# Last two rows are on same day. Drop second-to-last row
quotes = quotes.drop(quotes.index[n - 2])
else:
if interval == "1wk":
last_rows_same_interval = dt1.year == dt2.year and dt1.week == dt2.week
elif interval == "1mo":
last_rows_same_interval = dt1.month == dt2.month
elif interval == "3mo":
last_rows_same_interval = dt1.year == dt2.year and dt1.quarter == dt2.quarter
else:
last_rows_same_interval = False
if last_rows_same_interval:
# Last two rows are within same interval
idx1 = quotes.index[n - 1]
idx2 = quotes.index[n - 2]
if _np.isnan(quotes.loc[idx2, "Open"]):
quotes.loc[idx2, "Open"] = quotes["Open"][n - 1]
# Note: nanmax() & nanmin() ignores NaNs
quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"][n - 1], quotes["High"][n - 2]])
quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"][n - 1], quotes["Low"][n - 2]])
quotes.loc[idx2, "Close"] = quotes["Close"][n - 1]
if "Adj High" in quotes.columns:
quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"][n - 1], quotes["Adj High"][n - 2]])
if "Adj Low" in quotes.columns:
quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"][n - 1], quotes["Adj Low"][n - 2]])
if "Adj Close" in quotes.columns:
quotes.loc[idx2, "Adj Close"] = quotes["Adj Close"][n - 1]
quotes.loc[idx2, "Volume"] += quotes["Volume"][n - 1]
quotes = quotes.drop(quotes.index[n - 1])
return quotes
def safe_merge_dfs(df_main, df_sub, interval):
# Carefully merge 'df_sub' onto 'df_main'
# If naive merge fails, try again with reindexing df_sub:
# 1) if interval is weekly or monthly, then try with index set to start of week/month
# 2) if still failing then manually search through df_main.index to reindex df_sub
if df_sub.shape[0] == 0:
raise Exception("No data to merge")
df_sub_backup = df_sub.copy()
data_cols = [c for c in df_sub.columns if c not in df_main]
if len(data_cols) > 1:
raise Exception("Expected 1 data col")
data_col = data_cols[0]
def _reindex_events(df, new_index, data_col_name):
if len(new_index) == len(set(new_index)):
# No duplicates, easy
df.index = new_index
return df
df["_NewIndex"] = new_index
# Duplicates present within periods but can aggregate
if data_col_name == "Dividends":
# Add
df = df.groupby("_NewIndex").sum()
df.index.name = None
elif data_col_name == "Stock Splits":
# Product
df = df.groupby("_NewIndex").prod()
df.index.name = None
else:
raise Exception("New index contains duplicates but unsure how to aggregate for '{}'".format(data_col_name))
if "_NewIndex" in df.columns:
df = df.drop("_NewIndex", axis=1)
return df
df = df_main.join(df_sub)
f_na = df[data_col].isna()
data_lost = sum(~f_na) < df_sub.shape[0]
if not data_lost:
return df
# Lost data during join()
# Backdate all df_sub.index dates to start of week/month
if interval == "1wk":
new_index = _pd.PeriodIndex(df_sub.index, freq='W').to_timestamp()
elif interval == "1mo":
new_index = _pd.PeriodIndex(df_sub.index, freq='M').to_timestamp()
elif interval == "3mo":
new_index = _pd.PeriodIndex(df_sub.index, freq='Q').to_timestamp()
else:
new_index = None
if new_index is not None:
new_index = new_index.tz_localize(df.index.tz, ambiguous=True)
df_sub = _reindex_events(df_sub, new_index, data_col)
df = df_main.join(df_sub)
f_na = df[data_col].isna()
data_lost = sum(~f_na) < df_sub.shape[0]
if not data_lost:
return df
# Lost data during join(). Manually check each df_sub.index date against df_main.index to
# find matching interval
df_sub = df_sub_backup.copy()
new_index = [-1] * df_sub.shape[0]
for i in range(df_sub.shape[0]):
dt_sub_i = df_sub.index[i]
if dt_sub_i in df_main.index:
new_index[i] = dt_sub_i
continue
# Found a bad index date, need to search for near-match in df_main (same week/month)
fixed = False
for j in range(df_main.shape[0] - 1):
dt_main_j0 = df_main.index[j]
dt_main_j1 = df_main.index[j + 1]
if (dt_main_j0 <= dt_sub_i) and (dt_sub_i < dt_main_j1):
fixed = True
if interval.endswith('h') or interval.endswith('m'):
# Must also be same day
fixed = (dt_main_j0.date() == dt_sub_i.date()) and (dt_sub_i.date() == dt_main_j1.date())
if fixed:
dt_sub_i = dt_main_j0
break
if not fixed:
last_main_dt = df_main.index[df_main.shape[0] - 1]
diff = dt_sub_i - last_main_dt
if interval == "1mo" and last_main_dt.month == dt_sub_i.month:
dt_sub_i = last_main_dt
fixed = True
elif interval == "3mo" and last_main_dt.year == dt_sub_i.year and last_main_dt.quarter == dt_sub_i.quarter:
dt_sub_i = last_main_dt
fixed = True
elif interval == "1wk":
if last_main_dt.week == dt_sub_i.week:
dt_sub_i = last_main_dt
fixed = True
elif (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < _datetime.timedelta(weeks=1)):
# With some specific start dates (e.g. around early Jan), Yahoo
# messes up start-of-week, is Saturday not Monday. So check
# if same week another way
dt_sub_i = last_main_dt
fixed = True
elif interval == "1d" and last_main_dt.day == dt_sub_i.day:
dt_sub_i = last_main_dt
fixed = True
elif interval == "1h" and last_main_dt.hour == dt_sub_i.hour:
dt_sub_i = last_main_dt
fixed = True
elif interval.endswith('m') or interval.endswith('h'):
td = _pd.to_timedelta(interval)
if (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < td):
dt_sub_i = last_main_dt
fixed = True
new_index[i] = dt_sub_i
df_sub = _reindex_events(df_sub, new_index, data_col)
df = df_main.join(df_sub)
f_na = df[data_col].isna()
data_lost = sum(~f_na) < df_sub.shape[0]
if data_lost:
## Not always possible to match events with trading, e.g. when released pre-market.
## So have to append to bottom with nan prices.
## But should only be impossible with intra-day price data.
if interval.endswith('m') or interval.endswith('h'):
f_missing = ~df_sub.index.isin(df.index)
df_sub_missing = df_sub[f_missing]
keys = {"Adj Open", "Open", "Adj High", "High", "Adj Low", "Low", "Adj Close",
"Close"}.intersection(df.columns)
df_sub_missing[list(keys)] = _np.nan
df = _pd.concat([df, df_sub_missing], sort=True)
else:
raise Exception("Lost data during merge despite all attempts to align data (see above)")
return df
def fix_Yahoo_dst_issue(df, interval):
if interval in ["1d", "1w", "1wk"]:
# These intervals should start at time 00:00. But for some combinations of date and timezone,
# Yahoo has time off by few hours (e.g. Brazil 23:00 around Jan-2022). Suspect DST problem.
# The clue is (a) minutes=0 and (b) hour near 0.
# Obviously Yahoo meant 00:00, so ensure this doesn't affect date conversion:
f_pre_midnight = (df.index.minute == 0) & (df.index.hour.isin([22, 23]))
dst_error_hours = _np.array([0] * df.shape[0])
dst_error_hours[f_pre_midnight] = 24 - df.index[f_pre_midnight].hour
df.index += _pd.TimedeltaIndex(dst_error_hours, 'h')
return df
def is_valid_timezone(tz: str) -> bool:
try:
_tz.timezone(tz)
except UnknownTimeZoneError:
return False
return True
class ProgressBar:
def __init__(self, iterations, text='completed'):
self.text = text
self.iterations = iterations
self.prog_bar = '[]'
self.fill_char = '*'
self.width = 50
self.__update_amount(0)
self.elapsed = 1
def completed(self):
if self.elapsed > self.iterations:
self.elapsed = self.iterations
self.update_iteration(1)
print('\r' + str(self), end='')
_sys.stdout.flush()
print()
def animate(self, iteration=None):
if iteration is None:
self.elapsed += 1
iteration = self.elapsed
else:
self.elapsed += iteration
print('\r' + str(self), end='')
_sys.stdout.flush()
self.update_iteration()
def update_iteration(self, val=None):
val = val if val is not None else self.elapsed / float(self.iterations)
self.__update_amount(val * 100.0)
self.prog_bar += ' %s of %s %s' % (
self.elapsed, self.iterations, self.text)
def __update_amount(self, new_amount):
percent_done = int(round((new_amount / 100.0) * 100.0))
all_full = self.width - 2
num_hashes = int(round((percent_done / 100.0) * all_full))
self.prog_bar = '[' + self.fill_char * \
num_hashes + ' ' * (all_full - num_hashes) + ']'
pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
pct_string = '%d%%' % percent_done
self.prog_bar = self.prog_bar[0:pct_place] + \
(pct_string + self.prog_bar[pct_place + len(pct_string):])
def __str__(self):
return str(self.prog_bar)
# ---------------------------------
# TimeZone cache related code
# ---------------------------------
class _KVStore:
"""Simpel Sqlite backed key/value store, key and value are strings. Should be thread safe."""
def __init__(self, filename):
self._cache_mutex = Lock()
with self._cache_mutex:
self.conn = _sqlite3.connect(filename, timeout=10, check_same_thread=False)
self.conn.execute('pragma journal_mode=wal')
self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT) without rowid')
self.conn.commit()
_atexit.register(self.close)
def close(self):
if self.conn is not None:
with self._cache_mutex:
self.conn.close()
self.conn = None
def get(self, key: str) -> Union[str, None]:
"""Get value for key if it exists else returns None"""
item = self.conn.execute('select value from "kv" where key=?', (key,))
if item:
return next(item, (None,))[0]
def set(self, key: str, value: str) -> None:
with self._cache_mutex:
self.conn.execute('replace into "kv" (key, value) values (?,?)', (key, value))
self.conn.commit()
def bulk_set(self, kvdata: Dict[str, str]):
records = tuple(i for i in kvdata.items())
with self._cache_mutex:
self.conn.executemany('replace into "kv" (key, value) values (?,?)', records)
self.conn.commit()
def delete(self, key: str):
with self._cache_mutex:
self.conn.execute('delete from "kv" where key=?', (key,))
self.conn.commit()
class _TzCacheException(Exception):
pass
class _TzCache:
"""Simple sqlite file cache of ticker->timezone"""
def __init__(self):
self._tz_db = None
self._setup_cache_folder()
def _setup_cache_folder(self):
if not _os.path.isdir(self._db_dir):
try:
_os.makedirs(self._db_dir)
except OSError as err:
raise _TzCacheException("Error creating TzCache folder: '{}' reason: {}"
.format(self._db_dir, err))
elif not (_os.access(self._db_dir, _os.R_OK) and _os.access(self._db_dir, _os.W_OK)):
raise _TzCacheException("Cannot read and write in TzCache folder: '{}'"
.format(self._db_dir, ))
def lookup(self, tkr):
return self.tz_db.get(tkr)
def store(self, tkr, tz):
if tz is None:
self.tz_db.delete(tkr)
elif self.tz_db.get(tkr) is not None:
raise Exception("Tkr {} tz already in cache".format(tkr))
else:
self.tz_db.set(tkr, tz)
@property
def _db_dir(self):
global _cache_dir
return _os.path.join(_cache_dir, "py-yfinance")
@property
def tz_db(self):
# lazy init
if self._tz_db is None:
self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
self._migrate_cache_tkr_tz()
return self._tz_db
def _migrate_cache_tkr_tz(self):
"""Migrate contents from old ticker CSV-cache to SQLite db"""
fp = _os.path.join(self._db_dir, "tkr-tz.csv")
if not _os.path.isfile(fp):
return None
df = _pd.read_csv(fp, index_col="Ticker")
self.tz_db.bulk_set(df.to_dict()['Tz'])
_os.remove(fp)
class _TzCacheDummy:
"""Dummy cache to use if tz cache is disabled"""
def lookup(self, tkr):
return None
def store(self, tkr, tz):
pass
@property
def tz_db(self):
return None
def get_tz_cache():
"""
Get the timezone cache, initializes it and creates cache folder if needed on first call.
If folder cannot be created for some reason it will fall back to initialize a
dummy cache with same interface as real cash.
"""
# as this can be called from multiple threads, protect it.
with _cache_init_lock:
global _tz_cache
if _tz_cache is None:
try:
_tz_cache = _TzCache()
except _TzCacheException as err:
print("Failed to create TzCache, reason: {}".format(err))
print("TzCache will not be used.")
print("Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
_tz_cache = _TzCacheDummy()
return _tz_cache
_cache_dir = _ad.user_cache_dir()
_cache_init_lock = Lock()
_tz_cache = None
def set_tz_cache_location(cache_dir: str):
"""
Sets the path to create the "py-yfinance" cache folder in.
Useful if the default folder returned by "appdir.user_cache_dir()" is not writable.
Must be called before cache is used (that is, before fetching tickers).
:param cache_dir: Path to use for caches
:return: None
"""
global _cache_dir, _tz_cache
assert _tz_cache is None, "Time Zone cache already initialized, setting path must be done before cache is created"
_cache_dir = cache_dir