-
-
Notifications
You must be signed in to change notification settings - Fork 2.2k
/
utils.py
370 lines (296 loc) · 11.5 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
#!/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
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
from threading import Lock
mutex = Lock()
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=[]):
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(url, proxy=None, session=None):
session = session or _requests
html = session.get(url=url, proxies=proxy, headers=user_agent_headers).text
if "QuoteSummaryStore" not in html:
html = session.get(url=url, proxies=proxy).text
if "QuoteSummaryStore" not in html:
return {}
json_str = html.split('root.App.main =')[1].split(
'(this)')[0].split(';\n}')[0].strip()
data = _json.loads(json_str)[
'context']['dispatcher']['stores']['QuoteSummaryStore']
# add data about Shares Outstanding for companies' tickers if they are available
try:
data['annualBasicAverageShares'] = _json.loads(
json_str)['context']['dispatcher']['stores'][
'QuoteTimeSeriesStore']['timeSeries']['annualBasicAverageShares']
except Exception:
pass
# 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 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 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
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)
# Simple file cache of ticker->timezone:
_cache_dp = None
def get_cache_dirpath():
if _cache_dp is None:
dp = _os.path.join(_ad.user_cache_dir(), "py-yfinance")
else:
dp = _os.path.join(_cache_dp, "py-yfinance")
return dp
def set_tz_cache_location(dp):
global _cache_dp
_cache_dp = dp
def cache_lookup_tkr_tz(tkr):
fp = _os.path.join(get_cache_dirpath(), "tkr-tz.csv")
if not _os.path.isfile(fp):
return None
mutex.acquire()
df = _pd.read_csv(fp, index_col="Ticker", on_bad_lines="skip")
mutex.release()
if tkr in df.index:
return df.loc[tkr,"Tz"]
else:
return None
def cache_store_tkr_tz(tkr,tz):
dp = get_cache_dirpath()
fp = _os.path.join(dp, "tkr-tz.csv")
mutex.acquire()
if not _os.path.isdir(dp):
_os.makedirs(dp)
if (not _os.path.isfile(fp)) and (tz is not None):
df = _pd.DataFrame({"Tz":[tz]}, index=[tkr])
df.index.name = "Ticker"
df.to_csv(fp)
else:
df = _pd.read_csv(fp, index_col="Ticker", on_bad_lines="skip")
if tz is None:
# Delete if in cache:
if tkr in df.index:
df.drop(tkr).to_csv(fp)
else:
if tkr in df.index:
raise Exception("Tkr {} tz already in cache".format(tkr))
df.loc[tkr,"Tz"] = tz
df.to_csv(fp)
mutex.release()