def fecth_stock_info(strTickerlist):
strList = strTickerlist.split()
# df_temp = pd.DataFrame(columns=['TickerID', 'TickerShortName', 'TickerType'])
# strList = strList.split()
rows_list = []
result = db.session.query(Ticker.TickerID).group_by(Ticker.TickerID).all()
dbTickerList = []
if result is not None and len(result) > 0:
dbTickerList = [temp[0] for temp in result]
for ticker in strList:
tempdict = {}
if ticker in dbTickerList:
continue
tempdict['TickerID'] = ticker
tempdict['TickerShortName'] = yf.Ticker(ticker).info['shortName']
tempdict['TickerType'] = yf.Ticker(ticker).info['quoteType']
rows_list.append(tempdict)
df_temp = pd.DataFrame(rows_list)
df_temp.to_sql(name="Ticker",
con=db.session.bind,
if_exists='append',
index=False,
dtype={
'TickerID': sqlalchemy.types.VARCHAR(length=20),
'TickerShortName': sqlalchemy.types.VARCHAR(length=80),
'TickerType': sqlalchemy.types.VARCHAR(length=20)
})
def get_prices(from_date, ticker): #y, m, d = from_date #start = datetime.datetime(y, m, d) end = datetime.datetime.now() tic = yf.Ticker(ticker) index = yf.download(ticker, start=from_date, end=end) name = tic.info['shortName'] prices = index['Close'] prices = prices.dropna() return prices, name
def FindTicker(text):
#this regex finds anything that looks like a stock ticker so pretty much anything upper case 1-5 characters long
#it's honestly kind of a nightmare 'cause I don't get regex
temp = re.findall("(?:(?<=\A)|(?<=\s)|(?<=[$]))([A-Z]{1,5})(?=\s|$|[^a-zA-z])", text)
#iterates over temp checking if each ticker is on the blacklist not_tickers if the ticker is not blacklisted it is
#added to tickers
#also
#tests each ticker by making a lookup call to fix_yahoo_finance if the call is not empty it adds the ticker
#it also checks that the ticker is tradable to avoid adding words that people capitalized that happen to be obscure
#non tradeable tickers
for ticker in temp:
if ticker in not_tickers:
temp.remove(ticker)
else:
test = yf.Ticker(ticker)
if test.info != {}:
tickers.add(ticker)
if test.info['tradeable'] == False:
tickers.remove(ticker)
if temp ==[]:
return tickers
#this just removes any duplicates from the temp list
temp = set(temp)
#iterates over temp first making sure that the ticker is valid then looking at the comment the ticker was found in
#and finding any words on the positive and negative lists then using that to add or subtract from the outlook value
for ticker in temp:
if ticker in tickers:
#prints the ticker and the comment it was in so you can check wtf it's doing
print("gonna do shit with " + ticker + ' based on \n' + text)
if ticker not in outlook:
#format is [faith, for, against, symbol, for + against]
outlook[ticker]=[0, 0, 0, ticker, 0]
if ticker in outlook:
for word in negative:
if word in text.lower():
outlook[ticker][0] += -1
outlook[ticker][2] += 1
outlook[ticker][4] += 1
for word in positive:
if word in text.lower():
outlook[ticker][0] += 1
outlook[ticker][1] += 1
outlook[ticker][4] += 1
return tickers, outlook
def get_realtime_data(cls, symbol):
"""
Obtener información en tiempo real de un mercado
:param symbol: símbolo del mercado en yahoo finance
:return: datos en tiempo real en formato JSON
"""
ticker = yf.Ticker(symbol)
info = ticker.info
key = {'_id': symbol}
data = {
'_id': symbol,
'shortName': info['shortName'],
'regularMarketOpen': info['regularMarketOpen'],
'regularMarketDayHigh': info['regularMarketDayHigh'],
'regularMarketDayLow': info['regularMarketDayLow'],
'regularMarketVolume': info['regularMarketVolume'],
}
return data
def get_stocks_returns(_symbol, _start_date, _end_date, _price_label):
_symbol = _symbol
_start_date = _start_date
_end_date = _end_date
_price_label = _price_label
try:
_stock = yf.Ticker(_symbol)
_data = _stock.history(start=_start_date, end=_end_date)
used_data = _data[_price_label]
ret = []
for i in range(len(used_data) - 1):
result = (used_data[i + 1] - used_data[i]) / used_data[i]
if not np.isnan([result]).any():
ret.append(result)
return ret
except:
return []
import fix_yahoo_finance as TickerData
import json # Used to put data in JSON file
# Why do indexes need the carrot top infront of them lol
indices = ["^DJI", "^GSPC", "^IXIC", "^RUT", "CL=F", "GC=F", "SI=F", "EURUSD=X", "^TNX", "^VIX"]
indices_data = []
for index in indices: # Getting info for every index needed
indices_data.append(TickerData.Ticker(index).info)
with open("featuredindicies.json", "w") as outfile: # Basically same script as the other featured one
json.dump(indices_data, outfile, indent=4, sort_keys=True)
import fix_yahoo_finance as TickerData
import os
import sys
import requests
import json
# print("Beginning script for autocomplete")
# I don't know how the person on the stack overflow got this link :)
stocks = requests.get(
"http://d.yimg.com/autoc.finance.yahoo.com/autoc?query={}®ion=1&lang=en"
.format(sys.argv[1])).json()['ResultSet']['Result']
data = []
# First we autocomple for specified string in command line, then we get the info of every stock
for stock in stocks:
data.append(TickerData.Ticker(stock["symbol"]).info)
# Making sure to open the absolute path because ran by swift
with open('../../Data/AutoComplete/autocomplete.json', 'w') as outfile:
json.dump(
data, outfile, indent=4, sort_keys=True
) # Saving autocomplete file, ran with swift so I need the full absolute path
# print("Successfully autocompleted data for {}".format(sys.argv[1]))
def add_company(self, ticker):
s = yf.Ticker(ticker)
self.tick_items[ticker] = s
# -*- coding: utf-8 -*-
# Global imports
import pandas as pd
import fix_yahoo_finance as yf
from update_gsheets import Table
s_name = 'MSFT'
s = yf.Ticker(s_name)
# Dummy classes for Indices and Portfolios. Will fill in later
class Index:
def __repr__(self):
return 'Index object {}'.format(self.name)
def __init__(self, name):
self.name = name
self.tick_items = {}
def add_company(self, ticker):
s = yf.Ticker(ticker)
self.tick_items[ticker] = s
def remove_company(self, ticker):
self.tick_items.pop(ticker)
from Robinhood import Robinhood
import json
import csv
import shelve
#from rh_SMA import SMA
import decimal
import fix_yahoo_finance as yf
import requests
msft = yf.Ticker("DIS190510C00135000")
# get stock info
print(print(msft.info))
# get historical market data
hist = msft.history(period="max")
# show actions (dividends, splits)
msft.actions
# show dividends
msft.dividends
# show splits
msft.splits
'''
'''
def getcloseandopen(trader,stocksymbol):
#stocksymbol string, trader = robinhood object
applequotedata = trader.quote_data(stocksymbol)
import fix_yahoo_finance as TickerData
import json
stocks = ["AAPL", "FB", "GOOG", "MSFT", "GE", "TSLA", "INTC", "IBM"]
stocks_data = []
for stock in stocks:
stocks_data.append(TickerData.Ticker(stock).info)
with open('featuredstocks.json', 'w') as outfile:
json.dump(stocks_data, outfile, indent=4, sort_keys=True)
def download_returns(ticker, period="max"):
if isinstance(period, _pd.DatetimeIndex):
p = {"start": period[0]}
else:
p = {"period": period}
return _yf.Ticker(ticker).history(**p)['Close'].pct_change()
def Generate():
#jinja2 setup stuff
#template file name
template_filename = "display.html"
#output file name
rendered_filename = "index.html"
#these are the vars that are used by Jinja to generate the html document
render_vars = {
#each of these blocks corosponds to one row of the chart in the generated HTML the format for the puts[][][] is
#above
'put1_sym': puts[0][1][3],
'put1_chn': str(yf.Ticker(puts[0][1][3]).info['regularMarketChangePercent'])[:5],
'put1_total': puts[0][1][0],
'put1_up': puts[0][1][1],
'put1_down': puts[0][1][2],
'put2_sym': puts[1][1][3],
'put2_chn': str(yf.Ticker(puts[1][1][3]).info['regularMarketChangePercent'])[:5],
'put2_total': puts[1][1][0],
'put2_up': puts[1][1][1],
'put2_down': puts[1][1][2],
'put3_sym': puts[2][1][3],
'put3_chn': str(yf.Ticker(puts[2][1][3]).info['regularMarketChangePercent'])[:5],
'put3_total': puts[2][1][0],
'put3_up': puts[2][1][1],
'put3_down': puts[2][1][2],
'put4_sym': puts[3][1][3],
'put4_chn': str(yf.Ticker(puts[3][1][3]).info['regularMarketChangePercent'])[:5],
'put4_total': puts[3][1][0],
'put4_up': puts[3][1][1],
'put4_down': puts[3][1][2],
'put5_sym': puts[4][1][3],
'put5_chn': str(yf.Ticker(puts[4][1][3]).info['regularMarketChangePercent'])[:5],
'put5_total': puts[4][1][0],
'put5_up': puts[4][1][1],
'put5_down': puts[4][1][2],
'call1_sym': calls[0][1][3],
'call1_chn': str(yf.Ticker(calls[0][1][3]).info['regularMarketChangePercent'])[:5],
'call1_total': calls[0][1][0],
'call1_up': calls[0][1][1],
'call1_down': calls[0][1][2],
'call2_sym': calls[1][1][3],
'call2_chn': str(yf.Ticker(calls[1][1][3]).info['regularMarketChangePercent'])[:5],
'call2_total': calls[1][1][0],
'call2_up': calls[1][1][1],
'call2_down': calls[1][1][2],
'call3_sym': calls[2][1][3],
'call3_chn': str(yf.Ticker(calls[2][1][3]).info['regularMarketChangePercent'])[:5],
'call3_total': calls[2][1][0],
'call3_up': calls[2][1][1],
'call3_down': calls[2][1][2],
'call4_sym': calls[3][1][3],
'call4_chn': str(yf.Ticker(calls[3][1][3]).info['regularMarketChangePercent'])[:5],
'call4_total': calls[3][1][0],
'call4_up': calls[3][1][1],
'call4_down': calls[3][1][2],
'call5_sym': calls[4][1][3],
'call5_chn': str(yf.Ticker(calls[4][1][3]).info['regularMarketChangePercent'])[:5],
'call5_total': calls[4][1][0],
'call5_up': calls[4][1][1],
'call5_down': calls[4][1][2],
'pop1': popular[0][1][3],
'pop2': popular[1][1][3],
'pop3': popular[2][1][3],
'pop4': popular[3][1][3],
'pop5': popular[4][1][3],
'pop6': popular[5][1][3],
'pop7': popular[6][1][3],
'pop8': popular[7][1][3],
'pop9': popular[8][1][3],
'pop10': popular[9][1][3],
#number of mentions for each popular stock well not quite mentions more of each bad or good word
'val1': popular[0][1][4],
'val2': popular[1][1][4],
'val3': popular[2][1][4],
'val4': popular[3][1][4],
'val5': popular[4][1][4],
'val6': popular[5][1][4],
'val7': popular[6][1][4],
'val8': popular[7][1][4],
'val9': popular[8][1][4],
'val10': popular[9][1][4],
}
#Jinja stuff that I don't understand I borrowed it from a tutorial and it works
script_path = os.path.dirname(os.path.abspath(__file__))
template_file_path = os.path.join(script_path, template_filename)
rendered_file_path = os.path.join(script_path, rendered_filename)
environment = jinja2.Environment(loader=jinja2.FileSystemLoader(script_path))
output_text = environment.get_template(template_filename).render(render_vars).encode("utf-8") #the .encode bit is vital if you want unicode support
#output the generated html to the index.html file
with open(rendered_file_path, "wb") as result_file: #the "wb" option is vital for unicode support
result_file.write(output_text)
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 17 16:34:03 2019
@author: user
"""
import fix_yahoo_finance as yf
msft = yf.Ticker("MSFT")
# get stock info
msft.info
# get historical market data
hist = msft.history(period="max")
# show actions (dividends, splits)
msft.actions
# show dividends
msft.dividends
# show splits
msft.splits
list_status='L',
fields='ts_code,symbol,name,area,industry,list_date')
# save stock information
stock_df = dual_tickers.join(stock_info.set_index('ts_code'),
on='A_Ticker')
stock_df.to_csv(os.path.join(data_dir, 'stock_info.csv'),
index=False,
encoding='utf_8_sig')
period = 10 # in integral year
for tickers in (list(dual_tickers['H_Ticker'].str.slice(start=1)),
list(dual_tickers['A_Ticker'].str.replace('.SH', '.SS'))):
for t in tickers:
try:
ticker = yf.Ticker(t)
hist_data = ticker.history(period=str(period) + 'y')
hist_data = hist_data.reset_index()
hist_data.to_csv(os.path.join(data_dir, '{}.csv'.format(t)),
index=False)
logger.info('Saved daily price of {}.'.format(t))
except ValueError as e:
logger.error('Error occurred while getting {}: {} '.format(
t, str(e)))
# compute hkd cny exchange rate
look_back = datetime.today() - timedelta(days=period * 365)
# get hkd/usd
logger.info('Fetching HKD/USD rates...')
hkd_usd = get_fx('DEXHKUS', look_back.strftime('%Y-%m-%d'))
# get cny/usd
def start(company):
dividends = {}
error = False
try:
data = AV.data(symbol=company, function='DA')
except TypeError:
print(f"{company} caused an Error")
error = True
if error == False:
# pull data
# pull company name
if len(data["high"]) < 2000:
time.sleep(60)
print(company)
try:
data = AV.data(symbol=company, function='DA')
except TypeError:
print(f"{company} caused an Error")
error = True
if len(data["high"]) > 2000:
#check against wrong api requests
data_yahoo = yf.Ticker(company)
shortname = True
try:
name = data_yahoo.info["shortName"]
except KeyError:
shortname = False
if shortname != False:
if " " in name:
name = name.replace(" ", "-")
if "." in name:
name = name.replace(".", "")
if "'" in name:
name = name.replace("'", "")
if "/" in name:
name = name.replace("/", "")
if "(" in name:
name = name.replace("(", "")
if ")" in name:
name = name.replace(")", "")
print(name, company)
currency = data_yahoo.info["currency"]
currency = currency.upper()
market = data_yahoo.info["market"]
highs = data["high"]
lows = data["low"]
closes = data["close"]
dates = data["date"]
index = check_2001(dates)
if index == None:
index = 0
highs = rebuild_lists(highs, index)
lows = rebuild_lists(lows, index)
closes = rebuild_lists(closes, index)
dates = rebuild_lists(dates, index)
dividend_amounts = data_yahoo.dividends
dividend_days = data_yahoo.dividends.keys()
div_index = check_same_beginning(dividend_days, dates)
dividend_amounts = dividend_amounts[div_index:]
dividend_days = dividend_days[div_index:]
temp_list = []
for i in dividend_days:
temp_list.append(str(i)[0:10])
dividend_days = temp_list
days = []
temp_dividends = {}
highs = check_for_null(highs, "first")
lows = check_for_null(lows, "first")
closes = check_for_null(closes, "first")
if currency != "EUR" and currency != "ILS":
print(currency)
with open(
f"currencies/foreign-eur/{currency}EUR.FOREX.csv",
"r") as currency_data:
table = csv.reader(currency_data)
cur_dates = []
cur_close = []
for row in table:
cur_dates.append(row[0])
cur_close.append(row[4])
#print(type(high))
for i in range(len(highs)):
conversion_amount = find_cur_date(
dates[i], cur_dates, cur_close)
high = highs[i]
low = lows[i]
close = closes[i]
high = round(float(high) * float(conversion_amount), 2)
low = round(float(low) * float(conversion_amount), 2)
close = round(
float(close) * float(conversion_amount), 2)
days.append(Day(dates[i], high, low, close))
for n in range(len(dividend_days)):
conversion_amount = find_cur_date(
dividend_days[n], cur_dates, cur_close)
dividend_amount = round(
float(dividend_amounts[n]) *
float(conversion_amount), 2)
temp_dividends[dividend_days[n]] = dividend_amount
if currency == "ILS":
print(currency)
with open(f"currencies/foreign-eur/fx_daily_ILS_EUR.csv",
"r") as currency_data:
table = csv.reader(currency_data)
cur_dates = []
cur_close = []
for row in table:
cur_dates.append(row[0])
cur_close.append(row[4])
#print(type(high))
for i in range(len(highs)):
conversion_amount = find_cur_date(
dates[i], cur_dates, cur_close)
high = highs[i]
low = lows[i]
close = closes[i]
high = round(float(high) * float(conversion_amount), 2)
low = round(float(low) * float(conversion_amount), 2)
close = round(
float(close) * float(conversion_amount), 2)
days.append(Day(dates[i], high, low, close))
for n in range(len(dividend_days)):
conversion_amount = find_cur_date(
dividend_days[n], cur_dates, cur_close)
dividend_amount = round(
float(dividend_amounts[n]) *
float(conversion_amount), 2)
temp_dividends[dividend_days[n]] = dividend_amount
if currency == "EUR":
for i in range(len(highs)):
days.append(Day(dates[i], highs[i], lows[i],
closes[i]))
for n in range(len(dividend_days)):
temp_dividends[dividend_days[n]] = dividend_amounts[n]
dividends[company] = temp_dividends
# create Days with date, high, low
print("step1 done")
calc = DividendCalculator(days)
#start calculations
#
get_prices(calc, company, name, dividends, market, currency)