def fetch_price(producer, symbol):
"""
helper function to get stock data and send to kafka
:param producer: instance of kafka producer
:param symbol: symbol of specific stock
:return: None
"""
logger.debug('Start to fetch stock price for %s', symbol)
try:
ticker_info = Ticker(symbol).info
price = ticker_info.get('preMarketPrice')
last_trade_time = datetime.utcfromtimestamp(
ticker_info.get('preMarketTime')).strftime("%Y-%m-%dT%H:%M:%SZ")
print(last_trade_time)
payload = (
'[{"StockSymbol":"%s","LastTradePrice":%s,"LastTradeDateTime":"%s"}]'
% (symbol, price, last_trade_time)).encode('utf-8')
logger.debug('Retrieved stock info %s', payload)
producer.send(topic=topic_name,
value=payload,
timestamp_ms=int(time.time()))
logger.debug('Sent stock price for %s to kafka', symbol)
except KafkaTimeoutError as timeout_error:
logger.warning(
'Failed to send stock price for %s to kafka, cauase by %s',
(symbol, timeout_error))
except Exception:
logger.warning('Failed to get stock price for %s', symbol)
def main():
gran = [('2y', '60m'), ('60d', '15m'), ('30d', '5m'), ('7d', '1m')]
config = ConfigParser()
config.read('/opt/market/market.ini')
stocks = config.get('general', 'stocks')
db = "postgres://[email protected]:5432/stocks"
session = market.session.Session(db)
for stock in stocks.split():
print(stock)
if session.check_table(stock):
continue
dfs = list()
ticker = Ticker(stock)
for period, interval in gran:
df = ticker.history(period=period, interval=interval)
dfs.append(df)
df = pd.concat(dfs)
df = market.df.deduplicate(df)
session.push_df(dataframe=df, name=stock)
def historicaltest():
if request.method == 'GET':
obj = ''
try:
obj = Ticker('msft')
except Exception as ex:
print(ex)
val = ''
try:
val = obj.history(period='max', interval='1d')
val = val.reset_index()
except Exception as ex:
print(ex)
data = dict()
data['date'] = val['Date'].astype(str).tolist()
data['open'] = val['Open'].tolist()
data['high'] = val['High'].tolist()
data['low'] = val['Low'].tolist()
data['close'] = val['Close'].tolist()
data['volume'] = val['Volume'].tolist()
for i in range(0, len(data['date'])):
year = data['date'][i][:4]
month = data['date'][i][5:7]
day = data['date'][i][8:]
dt = datetime.datetime(int(year), int(month), int(day))
timestamp = int(dt.replace(tzinfo=timezone.utc).timestamp())
data['date'][i] = timestamp
outputdata = jsonify(data)
outputdata.headers.add("Access-Control-Allow-Origin", "*")
return outputdata
def getStock(ticker, time):
# get current prices
c = Ticker(ticker)
t = c.history(time)
ans = {}
# return based on change
a = t.to_dict()["Open"].keys()
days = [str(i.to_pydatetime()).split(" ")[0] for i in a]
try:
c = c.info
mc = intword(c['marketCap'], format="%0.3f")
inf = ticker + ": " + c["longName"] + "!Sector: " + \
c['sector'] + "!Market Cap: $" + mc
except:
inf = ticker[1:] + " Index"
if t["Close"][-1] > t["Close"][-2]:
ans["price"] = "{: .2f}".format(t["Close"][-1])
ans["change"] = "+{:.2f}%".format(
(1 - (t["Close"][-2] / t["Close"][-1])) * 100)
ans["change"] += " " * (7 - len(ans["change"])) + "⬆"
ans["history"] = inf + "|" + str(list(zip(days, t["Close"])))
else:
ans["price"] = "{: .2f}".format(t["Close"][-1])
ans["change"] = "-{:.2f}%".format(
((t["Close"][-2] / t["Close"][-1]) - 1) * 100)
ans["change"] += " " * (7 - len(ans["change"])) + "⬇"
ans["history"] = inf + "|" + str(list(zip(days, t["Close"])))
print(ticker)
return ans
def build_moex_df(first_month, last_month):
moex = Ticker('IMOEX.ME').history(start=first_month[0], end=last_month[1],
interval='1mo')
moex = moex.reset_index().drop(columns=['Date', 'Volume',
'Dividends', 'Stock Splits'])
moex.rename(columns=lambda x: 'MOEX_%s' % x.lower(), inplace=True)
moex['date_block_num'] = np.arange(2, 35)
return moex
def calculate_previous_ema(ticker: yf.Ticker,
time_period="1mo",
days_previous=1) -> float:
time_period_days = len(ticker.history(period=time_period))
stock_history = ticker.history(period=time_period)
return stock_history.iloc[time_period_days - days_previous - 1].to_dict(
)['Close'] * (2.5 / (1 + time_period_days)) + calculate_sma(
ticker, time_period) * (1 - (2.5 / (1 + time_period_days)))
def download_hist(stock_to_download):
try:
ticker_obj = Ticker(stock_to_download)
hist = ticker_obj.history(period="max")
hist.to_csv("data/stocks/" + stock_to_download + ".csv")
except:
error = sys.exc_info()[0]
logger.debug("Unable to download historical stock data for: %s %s",
stock_to_download, str(error))
raise
def __get_history(self):
from yfinance import Ticker
from pandas import DataFrame
from datetime import datetime, timedelta, date
# Get stock data from yfinance module from 60 days before 01/01 to today
stock_data = Ticker(self.stock)
df = DataFrame(stock_data.history(start=str(datetime.now().date().replace(month=1, day=1) - timedelta(60)),
end=str(date.today())))
return df
def run(self):
company = Ticker(self.ticker)
if (company == None):
return
# Get prices using yfinance package.
quotes = company.history(period="max")
# Save results to csv.
with self.output().open("w") as out_file:
quotes.to_csv(out_file, index=False, compression="gzip")
def _set_info_data(self, yf_ticker: yf.Ticker) -> None:
info = yf_ticker.get_info()
# Check if this is an equity. If not, raise error, since only equity is implemented.
if not info['quoteType'] in self.supported_quote_type:
raise Exception(
f'"{self.symbol}" is "quoteType" == "{info["quoteType"]}". This "quoteType" is not implemented, '
f'yet.')
self.recommendation = info[
'recommendationKey'] if 'recommendationKey' in info else 'N/A'
self.volume = info['volume']
prices_to_convert = {
'current_price':
info['preMarketPrice']
if info['preMarketPrice'] else info['regularMarketPrice'],
'price_52w_high':
info['fiftyTwoWeekHigh'],
'price_52w_low':
info['fiftyTwoWeekLow']
}
if info['quoteType'] == 'EQUITY':
self.percent_change_52w = info['52WeekChange']
prices_converted = self._convert_to_local_currency(prices_to_convert)
self.current_price = prices_converted['current_price']
self.price_52w_high = prices_converted['price_52w_high']
self.price_52w_low = prices_converted['price_52w_low']
self.market_capitalization = self.volume * self.current_price
def get_stock_info_from_cache(symbol):
"Return element from cache if available, if not create"
if STOCK_INFO_CACHE.get(symbol) != None:
return STOCK_INFO_CACHE.get(symbol)
stock = Ticker(symbol)
#stock.refresh()
STOCK_INFO_CACHE.add(symbol, stock)
return stock
def previous_high(ticker: yf.Ticker, time_period: str) -> float:
high = 0
stock_history = ticker.history(time_period)
for i in range(0, len(stock_history) - 2):
temp_high = stock_history.iloc[i].to_dict()['High']
if temp_high > high:
high = temp_high
return high
def companyinfo():
if request.method == 'POST':
stock = ''
try:
stock = str(json.loads(request.data)["stock"])
except Exception as ex:
print(ex)
try:
stock = str(request.form["stock"])
except Exception as ex:
print(ex)
obj = ''
try:
obj = Ticker(stock)
except Exception as ex:
print(ex)
val = ''
try:
val = obj.info
except Exception as ex:
print(ex)
data = dict()
try:
data["PEGratio"] = val["pegRatio"]
data["futurePEGratio"] = val["pegRatio"]
except Exception as ex:
print(ex)
try:
data["futurePEratio"] = val["forwardPE"]
data["PEratio"] = val["forwardPE"]
except Exception as ex:
print(ex)
try:
data["beta"] = val["beta"]
except Exception as ex:
print(ex)
try:
val = si.get_quote_table(stock)
except Exception as ex:
print(ex)
try:
data["PEratio"] = val["PE Ratio (TTM)"]
except Exception as ex:
print(ex)
outputdata = jsonify(data)
outputdata.headers.add("Access-Control-Allow-Origin", "*")
outputdata.headers.add("Access-Control-Allow-Headers", "*")
outputdata.headers.add("Access-Control-Allow-Methods", "*")
return outputdata
def main():
config = ConfigParser()
config.read('/opt/market/market.ini')
stocks = config.get('general', 'stocks')
db = "postgres://[email protected]:5432/stocks"
session = market.session.Session(db)
for stock in stocks.split():
print(stock)
if not session.check_table(stock):
continue
ticker = Ticker(stock)
df = ticker.history(period='15m', interval='1m')
session.push_df(dataframe=df, name=stock)
def historical():
if request.method == 'POST':
data = json.loads(request.data)
stock = str(data['stock'])
period = str(data['period'])
interval = str(data['interval'])
obj = ''
try:
obj = Ticker(stock)
except Exception as ex:
print(ex)
val = ''
try:
val = obj.history(period=period, interval=interval)
val = val.reset_index()
except Exception as ex:
print(ex)
data = dict()
data['date'] = val['Date'].astype(str).tolist()
data['open'] = val['Open'].tolist()
data['high'] = val['High'].tolist()
data['low'] = val['Low'].tolist()
data['close'] = val['Close'].tolist()
data['volume'] = val['Volume'].tolist()
for i in range(0, len(data['date'])):
year = data['date'][i][:4]
month = data['date'][i][5:7]
day = data['date'][i][8:]
dt = datetime.datetime(int(year), int(month), int(day))
timestamp = int(dt.replace(tzinfo=timezone.utc).timestamp())
data['date'][i] = timestamp
outputdata = jsonify(data)
#outputdata.headers.add("Access-Control-Allow-Origin", "*")
#outputdata.headers.add("Access-Control-Allow-Headers", "*")
#outputdata.headers.add("Access-Control-Allow-Methods", "*")
return outputdata
class EquityYFinance(Asset):
def __init__(self, ticker):
from yfinance import Ticker
super().__init__(ticker, None)
self.y = Ticker(ticker)
self.h = self.y.history(period="max")
def price(self, t: Date) -> float:
return self.h.Close[t]
def coupon(self, t: Date) -> float:
return self.h.Dividends[t]
def calculate_sma(ticker: yf.Ticker,
time_period="1mo",
interval="1d") -> float:
stock_history = ticker.history(period=time_period, interval=interval)
summation = 0
time_period_days = 0
for i in range(0, len(stock_history) - 1):
summation += stock_history.iloc[i].to_dict()['Close']
time_period_days += 1
if time_period_days > 0:
return summation / time_period_days
return sys.maxsize
def get_info_data(tickername):
"""
:param tickername: краткое название биржевого инструмента
:return: возвращает список из [тикера, название компании,
описание компании, ссылка на лого]
"""
info = {}
try:
info = Ticker(tickername).info
except (IndexError, ):
info = None
return info
def get_ticker(name: str) -> Ticker:
ticker = Ticker(name)
info = ticker.info
if not info.get("ask"):
if name[-3:] != ".HE":
if '.' in name:
# user probably wrote the name wrong
return get_ticker(f'{name.replace(".", "-")}')
# if not, maybe they forgot the helsinki börs (.he)
return get_ticker(f"{name}.HE")
return False
# print(ticker.dividends)
return ticker
def run(self):
company = Ticker(self.ticker)
if (company == None):
return
# Get dividends using yfinance package.
dividends = company.dividends.to_frame().reset_index("Date")
# Since there may be more than one dividends payment within a year I need to group dividends by year.
divByYear = (dividends.groupby(
dividends["Date"].dt.year).sum().reset_index("Date"))
# Save results to csv.
with self.output().open("w") as out_file:
divByYear.to_csv(out_file, index=False, compression="gzip")
def processHist(ticker: yf.Ticker):
hist = {}
info = {}
data = ticker.history(period='max', interval='5d')
data = data.where(pd.notnull(data), None)
try:
for item in data.T.iteritems():
stuff = dict(item[1])
try:
hist[item[0].value] = stuff
except Exception as e:
hist[item[0]] = stuff
except Exception as e:
hist['error'] = e
return hist
def get_sp500_recommendations(self, stocks=['all'], print_ratings=False):
import pandas as pd
from yfinance import Ticker
# Call the get_sp500_tickers to get a list of tickers
if stocks == ['all']:
tickers = self.__get_sp500_tickers()
else:
tickers = []
for stock in stocks:
tickers.append(Ticker(stock))
# Get a list of analyst ratings for each ticker in tickers
length = len(tickers)
i = 1
recs = []
symbols = []
print('Getting analyst ratings...')
for ticker in tickers:
print('\tGetting ratings for ',
ticker.ticker,
' (',
i,
'/',
length,
')',
sep='')
symbols.append(ticker.ticker)
try:
recs.append(ticker.recommendations)
except:
recs.append('No ratings found')
i += 1
print('Done!')
# Get the numerical and average rating for each recommendation
numerical_ratings, avg_ratings = self.__rating_to_num(
recs, print_ratings)
# Create data frame with all calculated info
sp500_ratings_df = pd.DataFrame()
sp500_ratings_df['Tickers'] = symbols
sp500_ratings_df['Analyst Ratings'] = recs
sp500_ratings_df['Numerical Ratings'] = numerical_ratings
sp500_ratings_df['Average Rating'] = avg_ratings
sp500_ratings_df = sp500_ratings_df.sort_values(by='Average Rating')
return sp500_ratings_df
def update_stock_records():
'''Update all stock records from Yahoo API.'''
symbols = db.Stocks.distinct('Symbol')
for sym in symbols:
try:
stock = Ticker(sym).history(period='1y')
db.Stocks.update_one({StockColumn.Symbol.name: sym}, {
'$set': {
StockColumn.Records.name:
convert_dataframe_to_document(stock)
}
})
except:
db.Stocks.update_one({StockColumn.Symbol.name: sym},
{'$set': {
StockColumn.Records.name: []
}})
def yfinance(cls,
stock_ticker: str,
stock_max: float,
stock_min: float,
short: bool = True) -> list:
"""Checks whether the current price of the stock has increased or decreased.
Args:
stock_ticker: Stock ticker value.
stock_max: Maximum value after which a notification has to be triggered.
stock_min: Minimum value below which a notification has to be triggered.
short: Boolean flag to send a short summary vs long description.
Returns:
list:
A list of configured notification message and the price.
"""
raw_details = Ticker(ticker=stock_ticker).info
price = round(float(raw_details['currentPrice']), 2)
if price < stock_min or price > stock_max:
if short:
if price < stock_min:
return [
f'{stock_ticker} [${price}] is currently less than {int(stock_min)}\n',
price
]
elif price > stock_max:
return [
f'{stock_ticker} [${price}] is currently more than {int(stock_max)}\n',
price
]
msg = f"The current price of {raw_details.get('longName')} is: ${price}"
day_list = f"\nToday's change list: {[raw_details.get('previousClose'), raw_details.get('open')]}"
if price < stock_min:
return [
f'{stock_ticker} is currently less than ${stock_min}\n{msg}{day_list}',
price
]
elif price > stock_max:
return [
f'{stock_ticker} is currently more than ${stock_max}\n{msg}{day_list}',
price
]
def __get_sp500_tickers():
import requests
import bs4 as bs
from yfinance import Ticker
# Get a list of SP500 tickers from wikipedia
print('Getting S&P500 tickers...')
resp = requests.get(
'http://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
soup = bs.BeautifulSoup(resp.text, 'lxml')
table = soup.find('table', {'class': 'wikitable sortable'})
tickers = []
for row in table.findAll('tr')[1:]:
ticker = row.findAll('td')[0].text
tickers.append(Ticker(ticker.strip()))
print('Done!\n')
return tickers
def update(n_clicks, value, input_stock):
try:
if n_clicks is not None:
ticker = input_stock
else:
ticker = 'AAPL'
df = web.DataReader(ticker, 'yahoo', start, end)
except RemoteDataError:
raise PreventUpdate
fig = go.Figure(go.Candlestick(
x=df.index,
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close']
))
fig.update_layout(xaxis_rangeslider_visible='slider' in value)
return fig, Ticker(ticker).info['shortName']
def __init__(self,
symbol: str,
start: str = str((datetime.now() - timedelta(days=365))),
end: str = str(datetime.now())):
"""
The constructor for the Stock class.
Parameters:
symbol (str): A stock ticker symbol.
start (str): A date in yyyy-mm-dd format. This data is the beginning of a period of time to query stock data.
end (str): A date in yyyy-mm-dd format. This data is the beginning of a period of time to query stock data.
"""
# Enforce capitalization
self._ticker = symbol.upper().split(" ")[0]
# Retrieval the financial data
self._history = None
self.start_date = start.split(" ")[0]
self.end_date = end.split(" ")[0]
self.stock_data = Ticker(self.ticker)
self.calculate_stats()
def _download_one(ticker,
start=None,
end=None,
auto_adjust=False,
back_adjust=False,
actions=False,
period="max",
interval="1d",
prepost=False,
proxy=None,
rounding=False):
return Ticker(ticker).history(period=period,
interval=interval,
start=start,
end=end,
prepost=prepost,
actions=actions,
auto_adjust=auto_adjust,
back_adjust=back_adjust,
proxy=proxy,
rounding=rounding,
many=True)
from yfinance import Ticker
msft = Ticker("MSFT")
# get stock info
infoData = msft.info
print(infoData)
#
# # show actions (dividends, splits)
# msft.actions
#
# # show dividends
# msft.dividends
#
# # show splits
# msft.splits
#
# # show financials
# msft.financials
# msft.quarterly_financials
#
# # show major holders
# msft.major_holders
#
# # show institutional holders
# msft.institutional_holders
#
# # show balance sheet
# msft.balance_sheet
# msft.quarterly_balance_sheet
def get_price(ticker_symbol):
stock = Ticker(ticker_symbol)
data = stock.info
return {'ask': data["ask"], 'bid': data["bid"]}
