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A Python file to help me get quick statistics on the stock market. It's always evolving.
Raw
investing.py
"""
Investing Quick Analytics
Author: Elijah Lopez
Version: 1.10.3
Created: April 3rd 2020
Updated: September 28th 2020
"""
import calendar
from contextlib import suppress
import csv
from datetime import datetime, timedelta
import io
import json
import math
# noinspection PyUnresolvedReferences
from pprint import pprint
# 3rd party libraries
from bs4 import BeautifulSoup
from fuzzywuzzy import process
import pandas as pd
import requests
import yfinance as yf
NASDAQ_TICKERS_URL = 'https://old.nasdaq.com/screening/companies-by-name.aspx?letter=0&exchange=nasdaq&render=download'
NYSE_TICKERS_URL = 'https://old.nasdaq.com/screening/companies-by-name.aspx?letter=0&exchange=nyse&render=download'
AMEX_TICKERS_URL = 'https://old.nasdaq.com/screening/companies-by-name.aspx?letter=0&exchange=amex&render=download'
PREMARKET_FUTURES = 'https://ca.investing.com/indices/indices-futures'
GLOBAL_DATA = {}
US_COMPANY_LIST = []
SORTED_INFO_CACHE = {} # for when its past 4 PM
GENERIC_HEADERS = {
'accept': 'text/html,application/xhtml+xml',
'user-agent': 'Mozilla/5.0'
}
# NOTE: something for later https://www.alphavantage.co/
def load_cache(filename='investing_cache.json'):
global GLOBAL_DATA
with suppress(FileNotFoundError):
with open(filename) as f:
GLOBAL_DATA = json.load(f)
def save_cache(filename='investing_cache.json'):
def default(obj):
if isinstance(obj, set):
return list(obj)
raise TypeError
with open(filename, 'w') as f:
json.dump(GLOBAL_DATA, f, indent=4, default=default)
def get_dow_tickers():
if ('DOW' in GLOBAL_DATA and
datetime.strptime(GLOBAL_DATA['DOW']['UPDATED'], '%m/%d/%Y').date() == datetime.today().date()):
return set(GLOBAL_DATA['DOW']['TICKERS'])
resp = requests.get('https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average')
soup = BeautifulSoup(resp.text, 'html.parser')
table = soup.find('table', {'id': 'constituents'}).find('tbody')
rows = table.find_all('tr')
_dow_tickers = {row.find_all('td')[1].find_all('a')[-1].text.strip() for row in rows if row.find_all('td')}
GLOBAL_DATA['DOW'] = {'UPDATED': datetime.today().strftime('%m/%d/%Y'), 'TICKERS': _dow_tickers}
save_cache()
return _dow_tickers
def get_sp500_tickers():
global GLOBAL_DATA
if 'S&P500' in GLOBAL_DATA and datetime.strptime(GLOBAL_DATA['S&P500']['UPDATED'],
'%m/%d/%Y').date() == datetime.today().date():
return set(GLOBAL_DATA['S&P500']['TICKERS'])
resp = requests.get('http://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
soup = BeautifulSoup(resp.text, 'html.parser')
table = soup.find('table', {'id': 'constituents'})
_sp500_tickers = {row.find('td').text.strip() for row in table.findAll('tr')[1:]}
GLOBAL_DATA['S&P500'] = {'UPDATED': datetime.today().strftime('%m/%d/%Y'), 'TICKERS': _sp500_tickers}
save_cache()
return _sp500_tickers
def get_tsx_tickers():
global GLOBAL_DATA
if 'TSX' in GLOBAL_DATA and datetime.strptime(GLOBAL_DATA['TSX']['UPDATED'],
'%m/%d/%Y').date() == datetime.today().date():
return set(GLOBAL_DATA['TSX']['TICKERS'])
base_url = 'http://eoddata.com/stocklist/TSX/'
_tsx_tickers = []
for i in range(26):
resp = requests.get(base_url + chr(65 + i) + '.html')
soup = BeautifulSoup(resp.text, 'html.parser')
table = soup.find('table', {'class': 'quotes'})
_tsx_tickers += {row.find('a').text.strip().upper() + '.TO' for row in table.findAll('tr')[1:]}
GLOBAL_DATA['TSX'] = {'UPDATED': datetime.today().strftime('%m/%d/%Y'), 'TICKERS': _tsx_tickers}
save_cache()
return _tsx_tickers
def get_nyse_arca_tickers():
global GLOBAL_DATA
if 'NYSEARCA' in GLOBAL_DATA and datetime.strptime(GLOBAL_DATA['NYSEARCA']['UPDATED'], '%m/%d/%Y').date() == datetime.today().date():
return set(GLOBAL_DATA['NYSE']['TICKERS'])
base_url = 'https://www.nyse.com/api/quotes/filter'
post_data = {'instrumentType': 'EXCHANGE_TRADED_FUND', 'pageNumber': 1, 'sortColumn': 'NORMALIZED_TICKER',
'sortOrder': 'ASC', 'maxResultsPerPage': 5000, 'filterToken': ''}
r = requests.post('https://www.nyse.com/api/quotes/filter', json=post_data)
_arca_tickers = {item['normalizedTicker'] for item in r.json()}
GLOBAL_DATA['NYSEARCA'] = {'UPDATED': datetime.today().strftime('%m/%d/%Y'), 'TICKERS': _arca_tickers}
save_cache()
return _arca_tickers
def tickers_from_csv(url, name=''):
if name and name in GLOBAL_DATA and datetime.strptime(GLOBAL_DATA[name]['UPDATED'],
'%m/%d/%Y').date() == datetime.today().date():
return set(GLOBAL_DATA[name]['TICKERS'])
s = requests.get(url).content
_tickers = set(pd.read_csv(io.StringIO(s.decode('utf-8')))['Symbol'])
GLOBAL_DATA[name] = {'UPDATED': datetime.today().strftime('%m/%d/%Y'), 'TICKERS': _tickers}
save_cache()
return _tickers
def get_tickers(market):
# TODO: add ETFs to the markets
market = market.upper()
# OPTIONS: CUSTOM, ALL, US, NYSE, NASDAQ, S&P500, DOW/DJIA, TSX/CA, Mortgage REITs
tickers_to_download = set()
if market in {'S&P500', 'S&P 500'}: tickers_to_download = tickers_to_download.union(get_sp500_tickers())
if market in {'DOW', 'DJIA'}: tickers_to_download = tickers_to_download.union(get_dow_tickers())
if market in {'NASDAQ', 'US', 'ALL'}:
tickers_to_download = tickers_to_download.union(tickers_from_csv(NASDAQ_TICKERS_URL, name='NASDAQ'))
if market in {'NYSE', 'US', 'ALL'}:
tickers_to_download = tickers_to_download.union(tickers_from_csv(NYSE_TICKERS_URL, name='NYSE'))
if market in {'AMEX', 'US', 'ALL'}:
tickers_to_download = tickers_to_download.union(tickers_from_csv(AMEX_TICKERS_URL, name='AMEX'))
if market in {'NYSEARCA', 'US', 'ALL'}:
tickers_to_download = tickers_to_download.union(get_nyse_arca_tickers())
if market in {'TSX', 'CA', 'ALL'}: tickers_to_download = tickers_to_download.union(get_tsx_tickers())
elif market == 'Mortgage REITs':
tickers_to_download = {'NLY', 'STWD', 'AGNC', 'TWO', 'PMT', 'MITT', 'NYMT', 'MFA',
'IVR', 'NRZ', 'TRTX', 'RWT', 'DX', 'XAN', 'WMC'}
elif market == 'OIL': tickers_to_download = {'DNR', 'PVAC', 'ROYT', 'SWN', 'CPE', 'CEQP', 'PAA', 'PUMP', 'PBF'}
elif market in {'AUTO', 'AUTOMOBILE', 'CARS'}:
tickers_to_download = {'TSLA', 'GM', 'F', 'RACE', 'FCAU', 'HMC', 'NIO', 'TTM', 'TM'}
elif market in {'INDEXFUTURES', 'INDEX_FUTURES'}: tickers_to_download = {'YM=F', 'NQ=F', 'RTY=F', 'ES=F'}
elif market == 'TANKERS':
tickers_to_download = {'EURN', 'TNK', 'TK', 'TNP', 'DSX', 'NAT', 'STNG', 'SFL', 'DHT', 'CPLP', 'DSSI', 'FRO', 'INSW', 'NNA', 'SBNA'}
elif market in {'UTILS', 'UTILITIES'}:
tickers_to_download = {'PCG', 'ELLO', 'AT', 'ELP', 'ES', 'EDN', 'IDA', 'HNP', 'GPJA', 'NEP', 'SO', 'CEPU', 'AES', 'ETR', 'KEP', 'OGE', 'EIX', 'NEE', 'TVC', 'TAC', 'EE', 'CIG', 'PNW', 'EMP', 'EBR.B', 'CPL', 'DTE', 'POR', 'EAI', 'NRG', 'CWEN', 'KEN', 'AGR', 'BEP', 'ORA', 'EAE', 'PPX', 'AZRE', 'ENIC', 'FE', 'CVA', 'BKH', 'ELJ', 'EZT', 'HE', 'VST', 'ELU', 'ELC', 'TVE', 'AQN', 'PAM', 'AEP', 'ENIA', 'EAB', 'PPL', 'CNP', 'D', 'PNM', 'EBR', 'FTS'}
elif market == 'CUSTOM': pass
# TODO: add more sectors
# TODO: add foreign markets
return tickers_to_download
def get_company_name_from_ticker(ticker: str):
global US_COMPANY_LIST
if ticker.count('.TO'):
ticker = ticker.replace('.TO', '')
r = requests.get(f'https://www.tsx.com/json/company-directory/search/tsx/{ticker}')
results = {}
for s in r.json()['results']:
s['name'] = s['name'].upper()
results[s['symbol']] = s
best_match = process.extractOne(ticker, list(results.keys()))[0]
return results[best_match]['name']
else:
if not US_COMPANY_LIST:
r = requests.get('https://api.iextrading.com/1.0/ref-data/symbols')
US_COMPANY_LIST = {s['symbol']: s for s in r.json()}
best_match = process.extractOne(ticker, list(US_COMPANY_LIST.keys()))[0]
# noinspection PyTypeChecker
return US_COMPANY_LIST[best_match]['name']
def get_ticker_info(ticker: str, round_values=True) -> dict:
# TODO: test pre-market values
data_latest = yf.download(ticker, interval='1m', period='1d', threads=3, prepost=True, progress=False,
group_by='ticker')
data_last_close = yf.download(ticker, interval='1m', period='5d', threads=3, progress=False)['Close']
latest_price = float(data_latest.tail(1)['Close'])
closing_price = float(data_last_close.tail(1))
timestamp = data_latest.last_valid_index()
if closing_price == latest_price:
_today = datetime.today()
_today = datetime(_today.year, _today.month, _today.day, 15, 59)
_today -= timedelta(days=1)
while _today.strftime('%Y-%m-%d %H:%M:%S-04:00') not in data_last_close:
_today -= timedelta(days=1)
closing_price = data_last_close[_today.strftime('%Y-%m-%d %H:%M:%S-04:00')]
# as_of = f'Market Open: {timestamp["Datetime"]}'
# else:
# as_of = f'After hours: {timestamp["Datetime"]}'
change = latest_price - closing_price
if round_values:
change = round(change, 4)
percent_change = round(change/closing_price * 100, 2)
latest_price = round(latest_price, 4)
closing_price = round(closing_price, 4)
else: percent_change = change/closing_price * 100
name = get_company_name_from_ticker(ticker)
try:
dividend = get_latest_dividend(ticker)
pd_ratio = round(latest_price/dividend, 2)
except Exception as e:
dividend = 0
pd_ratio = 10 ** 10 # basically infinity
info = {'name': name, 'price': latest_price, 'last_close_price': closing_price, 'price:dividend': pd_ratio,
'change': change, 'percent_change': percent_change, 'timestamp': timestamp, 'symbol': ticker, 'last_dividend': dividend}
return info
def get_data(tickers: set, start_date=None, end_date=None, period='3mo', group_by='ticker', interval='1d',
show_progress=True):
# http://www.datasciencemadesimple.com/union-and-union-all-in-pandas-dataframe-in-python-2/
# global_data['yf']
# new format
_key = ' '.join(tickers) + f' {start_date} {end_date} {period} {group_by}'
if 'yf' not in GLOBAL_DATA: GLOBAL_DATA['yf'] = {}
if _key not in GLOBAL_DATA['yf']:
_data = yf.download(tickers, start_date, end_date, period=period, group_by=group_by, threads=3,
progress=show_progress, interval=interval)
return _data
def parse_info(_data, ticker, start_date, end_date, start_price_key='Open'):
"""
start_price_key: can be 'Open' or 'Close'
TODO: change parse_info keys to snake_case
"""
start_price = _data[ticker][start_price_key][start_date]
end_price = _data[ticker]['Close'][end_date]
change = end_price - start_price
percent_change = change / start_price
start_volume = round(_data[ticker]['Volume'][start_date])
end_volume = round(_data[ticker]['Volume'][end_date])
avg_volume = (start_volume + end_volume) / 2
return {'Start': start_price, 'End': end_price, 'Change': change, 'Percent Change': percent_change,
'Open Volume': start_volume, 'Close Volume': end_volume, 'Avg Volume': avg_volume}
def get_parsed_data(_data=None, tickers: list = None, market='ALL', sort_key='Percent Change',
of='day', start_date: datetime = None, end_date: datetime = None):
"""
returns the parsed trading data sorted by percent change
:param _data: if you are doing a lot of parsing but None is recommended unless you are dealing with >= 1 month
:param tickers: specify if you have your own custom tickers list, otherwise market is used to get the list
:param market: the market if data is None
:param of: one of {'day', 'mtd', 'ytd', '1m', '1yr'}
:param sort_key: one of {'Start', 'End', 'Change', 'Percent Change', 'Open Volume', 'Close Volume', 'Avg Volume'}
if None, a dict with the tickers as keys is returned instead of a list
:param start_date: if of == 'custom' specify this values
:param end_date: if of == 'custom' specify this value
"""
of = of.lower()
_today = datetime.today()
todays_date = _today.date()
if tickers is None: tickers = get_tickers(market)
if _today.hour >= 16 and of == 'day':
# TODO: cache pre-market as well
# key format will be
with suppress(KeyError):
return SORTED_INFO_CACHE[of][str(todays_date)][','.join(tickers)]
if of == 'custom':
assert start_date and end_date
if _data is None: _data = get_data(tickers, start_date=start_date, end_date=end_date)
start_date, end_date = _data.first_valid_index(), _data.last_valid_index()
parsed_info = {}
for ticker in tickers:
info = parse_info(_data, ticker, start_date, end_date)
if not math.isnan(info['Start']): parsed_info[ticker] = info
elif of in {'day', '1d'}:
_data = get_data(tickers, period='5d', interval='1m') # ALWAYS USE LATEST DATA
market_day = _data.last_valid_index().date() == todays_date
if not market_day or (_today.hour * 60 + _today.minute >= 645): # >= 10:45 AM
# movers of the latest market day
recent_day = _data.last_valid_index()
parsed_info = {}
for ticker in tickers:
info = parse_info(_data, ticker, recent_day, recent_day)
if not math.isnan(info['Start']): parsed_info[ticker] = info
else: # movers of the second last market day
yest = _data.tail(2).first_valid_index() # assuming interval = 1d
parsed_info = {}
for ticker in tickers:
info = parse_info(_data, ticker, yest, yest)
if not math.isnan(info['Start']): parsed_info[ticker] = info
# TODO: custom day amount
elif of in {'mtd', 'month_to_date', 'monthtodate'}:
start_date = todays_date.replace(day=1)
if _data is None: _data = get_data(tickers, start_date=start_date, end_date=_today)
while start_date not in _data.index and start_date < todays_date:
start_date += timedelta(days=1)
if start_date >= todays_date: raise RuntimeError('No market days this month')
parsed_info = {}
for ticker in tickers:
info = parse_info(_data, ticker, start_date, todays_date)
if not math.isnan(info['Start']):
parsed_info[ticker] = info
elif of in {'month', '1m', 'm'}:
start_date = todays_date - timedelta(days=30)
if _data is None: _data = get_data(tickers, start_date=start_date, end_date=_today)
while start_date not in _data.index:
start_date += timedelta(days=1)
parsed_info = {}
for ticker in tickers:
info = parse_info(_data, ticker, start_date, todays_date)
if not math.isnan(info['Start']):
parsed_info[ticker] = info
# TODO: x months
elif of in {'ytd', 'year_to_date', 'yeartodate'}:
if _data is None:
_data = get_data(tickers, start_date=_today.replace(day=1, month=1), end_date=_today)
start_date = _data.first_valid_index() # first market day of the year
else:
start_date = _today.replace(day=1, month=1).date() # Jan 1st
while start_date not in _data.index: start_date += timedelta(days=1) # find first market day of the year
end_date = _data.last_valid_index()
parsed_info = {}
for ticker in tickers:
info = parse_info(_data, ticker, start_date, end_date)
if not math.isnan(info['Start']): parsed_info[ticker] = info
elif of in {'year', '1yr', 'yr', 'y'}:
if _data is None:
_data = get_data(tickers, start_date=_today - timedelta(days=365), end_date=_today)
start_date = _data.first_valid_index() # first market day of the year
else:
start_date = _today.date() - timedelta(days=365)
_data = get_data(tickers, start_date=_today.replace(day=1, month=1), end_date=_today)
end_date = _data.last_valid_index()
parsed_info = {}
for ticker in tickers:
info = parse_info(_data, ticker, start_date, end_date)
if not math.isnan(info['Start']): parsed_info[ticker] = info
# TODO: x years
else: parsed_info = {} # invalid of
if sort_key is None: return parsed_info
sorted_info = sorted(parsed_info.items(), key=lambda item: item[1][sort_key])
if _today.hour >= 16 and of == 'day':
if of not in SORTED_INFO_CACHE: SORTED_INFO_CACHE[of] = {}
if str(todays_date) not in SORTED_INFO_CACHE[of]: SORTED_INFO_CACHE[of][str(todays_date)] = {}
SORTED_INFO_CACHE[of][str(todays_date)][','.join(tickers)] = sorted_info
return sorted_info
def winners(sorted_info=None, tickers: list = None, market='ALL', of='day', start_date=None, end_date=None, show=5):
# sorted_info is the return of get_parsed_data with non-None sort_key
if sorted_info is None:
sorted_info = get_parsed_data(tickers=tickers, market=market, of=of, start_date=start_date, end_date=end_date)
return list(reversed(sorted_info[-show:]))
def losers(sorted_info=None, tickers: list = None, market='ALL', of='day', start_date=None, end_date=None, show=5):
# sorted_info is the return of get_parsed_data with non-None sort_key
if sorted_info is None:
sorted_info = get_parsed_data(tickers=tickers, market=market, of=of, start_date=start_date, end_date=end_date)
return sorted_info[:show]
# noinspection PyUnresolvedReferences,PyTypeChecker
def winners_and_losers(_data=None, tickers=None, market='ALL', of='day', start_date=None, end_date=None, show=5,
console_output=True, csv_output=''):
sorted_info = get_parsed_data(_data, tickers, market, of=of, start_date=start_date, end_date=end_date)
if console_output:
bulls = ''
bears = ''
length = min(show, len(sorted_info))
for i in range(length):
better_stock = sorted_info[-i - 1]
worse_stock = sorted_info[i]
open_close1 = f'{round(better_stock[1]["Start"], 2)}, {round(better_stock[1]["End"], 2)}'
open_close2 = f'{round(worse_stock[1]["Start"], 2)}, {round(worse_stock[1]["End"], 2)}'
bulls += f'\n{better_stock[0]} [{open_close1}]: {round(better_stock[1]["Percent Change"] * 100, 2)}%'
bears += f'\n{worse_stock[0]} [{open_close2}]: {round(worse_stock[1]["Percent Change"] * 100, 2)}%'
header1 = f'TOP {length} WINNERS of ({of})'
header2 = f'TOP {length} LOSERS ({of})'
line = '-' * len(header1)
print(f'{line}\n{header1}\n{line}{bulls}')
line = '-' * len(header2)
print(f'{line}\n{header2}\n{line}{bears}')
if csv_output:
with open(csv_output, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['TICKER'] + list(sorted_info[0][1].keys()))
for ticker in sorted_info:
writer.writerow([ticker[0]] + list(ticker[1].values()))
return sorted_info
def top_movers(_data=None, tickers=None, market='ALL', of='day', start_date=None, end_date=None, show=5,
console_output=True, csv_output=''):
return winners_and_losers(_data=_data, tickers=tickers, market=market, of=of, start_date=start_date,
end_date=end_date, show=show, console_output=console_output, csv_output=csv_output)
def create_headers(subdomain):
hdrs = {'authority': 'finance.yahoo.com',
'method': 'GET',
'path': subdomain,
'scheme': 'https',
'accept': 'text/html,application/xhtml+xml',
'accept-encoding': 'gzip, deflate, br',
'accept-language': 'en-US,en;q=0.9',
'cache-control': 'no-cache',
'cookie': 'cookies',
'dnt': '1',
'pragma': 'no-cache',
'sec-fetch-mode': 'navigate',
'sec-fetch-site': 'same-origin',
'sec-fetch-user': '?1',
'upgrade-insecure-requests': '1',
'user-agent': 'Mozilla/5.0'}
return hdrs
def get_latest_dividend(ticker: str) -> float:
url = f'https://finance.yahoo.com/quote/{ticker}/history'
hdr = create_headers(f'{ticker}/history')
soup = BeautifulSoup(requests.get(url, headers=hdr).text, 'html.parser')
soup = soup.find('tbody')
dividend = soup.find('strong').text
return float(dividend)
def price_to_earnings(ticker, return_dict: dict = None, return_price=False):
# uses latest EPS (diluted)
pe = -999999
if ticker.endswith('.TO'):
ticker = '.'.join(ticker.split('.')[:-1])
url = f'https://www.marketwatch.com/investing/stock/{ticker}/financials?country=ca'
else:
url = f'https://www.marketwatch.com/investing/stock/{ticker}/financials'
try:
text_soup = BeautifulSoup(requests.get(url, headers=GENERIC_HEADERS).text, 'html.parser')
except requests.TooManyRedirects:
return pe
try:
price = float(text_soup.find('p', {'class': 'bgLast'}).text.replace(',', ''))
titles = text_soup.findAll('td', {'class': 'rowTitle'})
for title in titles:
if 'EPS (Diluted)' in title.text:
eps = [td.text for td in title.findNextSiblings(attrs={'class': 'valueCell'}) if td.text]
try: latest_eps = float(eps[-1])
except ValueError: latest_eps = -float(eps[-1][1:-1])
pe = price / latest_eps
break
except AttributeError: price = -1
if return_dict is not None: return_dict[ticker] = (pe, price) if return_price else pe
if return_price: return pe, price
return pe
def tickers_by_pe(tickers: set, output_to_csv='', console_output=True):
"""
Returns the tickers by price-earnings ratio (remove negatives)
:param tickers:
:param output_to_csv:
:param console_output:
:return:
"""
pes = {}
# TODO: thread
for ticker in tickers:
pe = price_to_earnings(ticker)
if pe > 0: pes[ticker] = pe
pes = sorted(pes.items(), key=lambda item: item[1])
if console_output:
header = 'TOP 5 TICKERS BY P/E'
line = '-' * len(header)
print(f'{header}\n{line}')
for i, (ticker, pe) in enumerate(pes):
if i == 5: break
print(f'{ticker}: {round(pe, 2)}')
if output_to_csv:
with open(output_to_csv, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['TICKER', 'Price-earnings'])
for ticker in pes:
writer.writerow(ticker)
return pes
def sort_by_volume(tickers):
pass
def index_futures():
resp = requests.get(PREMARKET_FUTURES, headers=GENERIC_HEADERS)
soup = BeautifulSoup(resp.text, 'html.parser')
quotes = soup.find('tbody').findAll('tr')
return_obj = {}
for quote in quotes:
index_name = quote.find('a').text.upper()
nums = quote.findAll('td')[3:]
price = nums[0].text
change = nums[3].text
percent_change = nums[4].text
return_obj[index_name] = {'name': index_name, 'price': price, 'change': change, 'percent_change': percent_change}
return return_obj
load_cache() # IGNORE. This loads cache from investing_cache.json if the file exists
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