def generatePriceConfidence(self, ticker, predictedPE, fileDf):
print("\nPrice Confidence Summary for Ticker: " + ticker)
mrktData = web.get_quote_yahoo(ticker)
mrktData.columns = [
'P/E', 'Change Percent', 'Current Price', 'Short Ratio', 'Time'
]
mrktPrice = mrktData.ix[ticker]['Current Price']
print mrktData
upperPredPrice = 0.0
lowerPredPrice = 0.0
mrktEPS = float(fileDf.ix[ticker][CONST_EPS])
mrktMonVolatility = float(fileDf.ix[ticker][CONST_MON_VOLATILITY][:-1])
mrktYrVolatility = mrktMonVolatility * sqrt(12)
predictedPrice = predictedPE * mrktEPS
print("\nEPS (ttm) = %0.2f" % mrktEPS)
print("Volatility (Month) = %0.2f" % mrktMonVolatility + "%")
print("Volatility (Year) = %0.2f" % mrktYrVolatility + "%")
print("Market Price = %0.2f" % mrktPrice)
print("Predicted Price (Mean) = %0.2f" % predictedPrice)
confIntVolatility = 1.96 * mrktYrVolatility
upperPredPrice = predictedPrice * (1 + confIntVolatility / 100)
lowerPredPrice = predictedPrice * (1 - confIntVolatility / 100)
print("\tConf Int \t95% or 1.96σ")
print("Volatility (Year) \t %0.2f" % confIntVolatility + "%")
print("Upper Price \t\t %0.2f" % upperPredPrice)
print("Lower Price \t\t %0.2f" % lowerPredPrice)
print("\nValuation Summary for Ticker: " + ticker)
if mrktPrice >= upperPredPrice:
print("\tOver Valued")
valPercentage = ((mrktPrice - upperPredPrice) / mrktPrice) * 100
print("\tDownside of %0.2f" % valPercentage +
"% to Upper Predicted Price")
valPercentage = ((mrktPrice - predictedPrice) / mrktPrice) * 100
print("\tDownside of %0.2f" % valPercentage +
"% to Mean Predicted Price")
elif mrktPrice < lowerPredPrice:
print("\tUnder Valued")
valPercentage = ((lowerPredPrice - mrktPrice) / mrktPrice) * 100
print("\tUpside of %0.2f" % valPercentage +
"% to Lower Predicted Price")
valPercentage = ((predictedPrice - mrktPrice) / mrktPrice) * 100
print("\tUpside of %0.2f" % valPercentage +
"% to Mean Predicted Price")
elif mrktPrice >= predictedPrice:
print("\tFairly Valued")
valPercentage = ((mrktPrice - predictedPrice) / mrktPrice) * 100
print("\tDownside of %0.2f" % valPercentage +
"% to Mean Predicted Price")
elif mrktPrice < predictedPrice:
print("\tFairly Valued")
valPercentage = ((predictedPrice - mrktPrice) / mrktPrice) * 100
print("\tUpside of %0.2f" % valPercentage +
"% to Mean Predicted Price")
else:
print("Oops ! Something went wrong. Cannot value Ticker : " +
ticker)
def snapshot_yahoo_pandas(symbols):
'''
Get a simple snapshot from yahoo, return dataframe
__________________________________________________
Return
pandas.DataFrame with symbols as index
and columns = [change_pct, time, last, short_ratio, PE]
'''
if isinstance(symbols, str):
symbols = [symbols]
return get_quote_yahoo(symbols)
def stock(symbol):
"""
gets last traded price from google for given security
"""
import pandas.io.data as pd
df = pd.get_quote_yahoo(symbol)
#print(df)
cols = ['PE', 'change_pct', 'last', 'short_ratio', 'time']
result = pd.DataFrame(df, columns=cols)
return result.iloc[0]['last']
def stock(symbol):
"""
gets last traded price from google for given security
"""
import pandas.io.data as pd
df = pd.get_quote_yahoo(symbol)
# print(df)
cols = ["PE", "change_pct", "last", "short_ratio", "time"]
result = pd.DataFrame(df, columns=cols)
return result.iloc[0]["last"]
def stock(symbol):
"""
gets last traded price from google for given security
"""
import pandas.io.data as pd
df = pd.get_quote_yahoo(symbol)
#print(df)
cols = ['PE', 'change_pct', 'last', 'short_ratio', 'time']
result = pd.DataFrame(df, columns=cols)
return result.iloc[0]['last']
def generatePriceConfidence(self, ticker, predictedPE, fileDf):
print("\nPrice Confidence Summary for Ticker: " + ticker)
mrktData = web.get_quote_yahoo(ticker)
mrktData.columns = ['P/E', 'Change Percent', 'Current Price', 'Short Ratio', 'Time']
mrktPrice = mrktData.ix[ticker]['Current Price']
print mrktData
upperPredPrice = 0.0
lowerPredPrice = 0.0
mrktEPS = float(fileDf.ix[ticker][CONST_EPS])
mrktMonVolatility = float(fileDf.ix[ticker][CONST_MON_VOLATILITY][:-1])
mrktYrVolatility = mrktMonVolatility * sqrt(12)
predictedPrice = predictedPE * mrktEPS
print("\nEPS (ttm) = %0.2f" % mrktEPS)
print("Volatility (Month) = %0.2f" % mrktMonVolatility + "%")
print("Volatility (Year) = %0.2f" % mrktYrVolatility + "%")
print("Market Price = %0.2f" % mrktPrice)
print("Predicted Price (Mean) = %0.2f" % predictedPrice)
confIntVolatility = 1.96 * mrktYrVolatility
upperPredPrice = predictedPrice * (1 + confIntVolatility/100)
lowerPredPrice = predictedPrice * (1 - confIntVolatility/100)
print("\tConf Int \t95% or 1.96σ")
print("Volatility (Year) \t %0.2f" % confIntVolatility + "%")
print("Upper Price \t\t %0.2f" % upperPredPrice)
print("Lower Price \t\t %0.2f" % lowerPredPrice)
print("\nValuation Summary for Ticker: " + ticker)
if mrktPrice >= upperPredPrice:
print("\tOver Valued")
valPercentage = ((mrktPrice - upperPredPrice)/mrktPrice) * 100
print("\tDownside of %0.2f" % valPercentage + "% to Upper Predicted Price")
valPercentage = ((mrktPrice - predictedPrice)/mrktPrice) * 100
print("\tDownside of %0.2f" % valPercentage + "% to Mean Predicted Price")
elif mrktPrice < lowerPredPrice:
print("\tUnder Valued")
valPercentage = ((lowerPredPrice - mrktPrice)/mrktPrice) * 100
print("\tUpside of %0.2f" % valPercentage + "% to Lower Predicted Price")
valPercentage = ((predictedPrice - mrktPrice)/mrktPrice) * 100
print("\tUpside of %0.2f" % valPercentage + "% to Mean Predicted Price")
elif mrktPrice >= predictedPrice:
print("\tFairly Valued")
valPercentage = ((mrktPrice - predictedPrice)/mrktPrice) * 100
print("\tDownside of %0.2f" % valPercentage + "% to Mean Predicted Price")
elif mrktPrice < predictedPrice:
print("\tFairly Valued")
valPercentage = ((predictedPrice - mrktPrice)/mrktPrice) * 100
print("\tUpside of %0.2f" % valPercentage + "% to Mean Predicted Price")
else:
print("Oops ! Something went wrong. Cannot value Ticker : " +ticker)
def run(self, frequency):
while True:
self.start_time = datetime.datetime.now()
log.info("Scraping Data at %s" % self.start_time)
if self.symbols:
self.curr_quotes = web.get_quote_yahoo(self.symbols)
self.time = datetime.datetime.utcnow()
if self.forex_codes:
self.curr_forex = self.get_forex(self.forex_codes)
log.debug(self.curr_forex)
log.debug(self.curr_quotes)
log.debug("Difference between times: %s" %
(self.time - self.start_time).total_seconds())
self.save_current_data()
sleep(frequency -
(datetime.datetime.now() - self.start_time).total_seconds())
def run(self, frequency):
while True:
self.start_time = datetime.datetime.now()
log.info("Scraping Data at %s" % self.start_time)
if self.symbols:
self.curr_quotes = web.get_quote_yahoo(self.symbols)
self.time = datetime.datetime.utcnow()
if self.forex_codes:
self.curr_forex = self.get_forex(self.forex_codes)
log.debug(self.curr_forex)
log.debug(self.curr_quotes)
log.debug("Difference between times: %s"
% (self.time - self.start_time).total_seconds())
self.save_current_data()
sleep(frequency -
(datetime.datetime.now() - self.start_time).total_seconds() )
def test_get_quote_string(self):
_yahoo_codes.update({'MarketCap': 'j1'})
df = web.get_quote_yahoo('GOOG')
self.assertFalse(pd.isnull(df['MarketCap'][0]))
def test_get_quote_string(self):
df = web.get_quote_yahoo('GOOG')
def get_quote(self, symbol): """Retrieve the Quote from Yahoo using pandas""" quote = web.get_quote_yahoo(symbol) if 0 == quote['last'][0]: raise SymbolNotInDB(symbol) return quote
def test_get_quote(self):
df = web.get_quote_yahoo(pd.Series(["GOOG", "AAPL", "GOOG"]))
assert_series_equal(df.ix[0], df.ix[2])
def get_near_stock_price(self, above_below=2, call=True, put=False,
month=None, year=None):
"""
Cuts the data frame opt_df that is passed in to only take
options that are near the current stock price.
Parameters
----------
above_below: number, int, optional (default=2)
The number of strike prices above and below the stock price that
should be taken
call: bool
Tells the function weather or not it should be using
self.calls
put: bool
Tells the function weather or not it should be using
self.puts
month: number, int, optional(default=None)
The month the options expire. This should be either 1 or 2
digits.
year: number, int, optional(default=None)
The year the options expire. This sould be a 4 digit int.
Returns
-------
chopped: DataFrame
The resultant DataFrame chopped down to be 2 * above_below + 1 rows
desired. If there isn't data as far out as the user has asked for
then
"""
price = float(get_quote_yahoo([self.symbol])['last'])
if call:
try:
if month:
m1 = month if len(str(month)) == 2 else '0' + str(month)
name = 'calls' + str(m1) + str(year)[2:]
df_c = self.__getattribute__(name)
else:
df_c = self.calls
except AttributeError:
df_c = self.get_call_data(month, year)
# NOTE: For some reason the put commas in all values >1000. We remove
# them here
df_c.Strike = df_c.Strike.astype(str).apply(lambda x: \
x.replace(',', ''))
# Now make sure Strike column has dtype float
df_c.Strike = df_c.Strike.astype(float)
start_index = np.where(df_c['Strike'] > price)[0][0]
get_range = range(start_index - above_below,
start_index + above_below + 1)
chop_call = df_c.ix[get_range, :]
chop_call = chop_call.dropna()
chop_call = chop_call.reset_index()
if put:
try:
if month:
m1 = month if len(str(month)) == 2 else '0' + str(month)
name = 'puts' + str(m1) + str(year)[2:]
df_p = self.__getattribute__(name)
else:
df_p = self.puts
except AttributeError:
df_p = self.get_put_data(month, year)
# NOTE: For some reason the put commas in all values >1000. We remove
# them here
df_p.Strike = df_p.Strike.astype(str).apply(lambda x: \
x.replace(',', ''))
# Now make sure Strike column has dtype float
df_p.Strike = df_p.Strike.astype(float)
start_index = np.where(df_p.Strike > price)[0][0]
get_range = range(start_index - above_below,
start_index + above_below + 1)
chop_put = df_p.ix[get_range, :]
chop_put = chop_put.dropna()
chop_put = chop_put.reset_index()
if call and put:
return [chop_call, chop_put]
else:
if call:
return chop_call
else:
return chop_put
def get_quote(self, symbol):
"""Retrieve the Quote from Yahoo using pandas"""
quote = web.get_quote_yahoo(symbol)
if 0 == quote['last'][0]:
raise SymbolNotInDB(symbol)
return quote
def test_get_quote_string(self):
df = web.get_quote_yahoo('GOOG')
def get_stock_prices(self):
dataframe = get_quote_yahoo([stock for stock in self.stocks_owned])
for stock in self.stocks_owned:
self.stocks_owned[stock]['price'] = dataframe.ix[stock]['last']
def test_get_quote_stringlist(self):
df = web.get_quote_yahoo(['GOOG', 'AAPL', 'GOOG'])
assert_series_equal(df.ix[0], df.ix[2])
def test_get_quote_stringlist(self):
df = web.get_quote_yahoo(["GOOG", "AAPL", "GOOG"])
assert_series_equal(df.ix[0], df.ix[2])
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 4 22:07:12 2014
@author: jmalinchak
"""
#from pandas.io.data import DataReader
#from datetime import datetime
#msft = DataReader("MSFT", "yahoo")
#print(msft.bid)
import pandas.io.data as pd
df = pd.get_quote_yahoo('HPQ')
#print(df)
cols = ['PE', 'change_pct', 'last', 'short_ratio', 'time']
result = pd.DataFrame(df, columns=cols)
print(result.iloc[0]['last'])
#data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],
# 'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions', 'Lions', 'Lions'],
# 'wins': [11, 8, 10, 15, 11, 6, 10, 4],
# 'losses': [5, 8, 6, 1, 5, 10, 6, 12]}
#
#football = pd.DataFrame(data, columns=['year', 'team', 'wins', 'losses'])
#print(football)
#
#from_csv = pd.read_csv('C:\\Documents and Settings\\jmalinchak\\My Documents\\My Python\\Active\inputs\\Options AAPL 2015-01-17 20141203221727.csv')
#from_csv.head()
#print(from_csv)
import datetime
# We will look at stock prices over the past year, starting at January 1, 2016
start = datetime.datetime(2016,1,1)
end = datetime.date.today()
# Let's get Apple stock data; Apple's ticker symbol is AAPL
# First argument is the series we want, second is the source ("yahoo" for Yahoo! Finance), third is the start date, fourth is the end date
apple = web.DataReader("AAPL", "yahoo", start, end)
type(apple)
!pip install pandas_datareader
import pandas_datareader.data as web
amzn = web.get_quote_yahoo('AMZN')
amzn
### API KEY Quandl:
import quandl
import datetime
quandl.ApiConfig.api_key =
def quandl_stocks(symbol, start_date=(2000, 1, 1), end_date=None):
"""
symbol is a string representing a stock symbol, e.g. 'AAPL'
def test_get_quote_stringlist(self):
df = web.get_quote_yahoo(['GOOG', 'AAPL', 'GOOG'])
assert_series_equal(df.ix[0], df.ix[2])
if len(df_optionpricescurrent) == 0:
if showresults == 1:
print('@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ got df_optionpricescurrent here')
df_optionpricescurrent = pp.options_to_dataframe(symbol,expirationdate_string,0)
df_optionpricescurrent['optiontype'] = df_optionpricescurrent['optionsymbol'].to_frame(name='optionsymbol').applymap(optiontype)
dict_of_df_optionpricescurrent[symbol+'|'+expirationdate_string] = df_optionpricescurrent
else:
print('>>>>>>>>>>>>>>>>>>>> already have df_optionpricescurrent')
continue
df_stockpricecurrent = dict_of_df_stockpricecurrent.get(symbol,dict_empty)
if len(df_stockpricecurrent) == 0:
#print('@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ got df_stockpricecurrent here')
df_stockpricecurrent = pdata.get_quote_yahoo(symbol)
dict_of_df_stockpricecurrent[symbol] = df_stockpricecurrent
else:
continue
#if showresults == 1:
#print('>>>>>>>>>>>>>>>>>>>> already have df_stockpricecurrent')
stockpricelast = df_stockpricecurrent['last'].iloc[0]
stockpricetime = df_stockpricecurrent['time'].iloc[0]
print('symbol: '+symbol)
#if showresults == 1:
# print('stockpricelast',stockpricelast)
filepath = os.path.join(myselectedcandidatesfolder,filename)
df_00 = pd.read_csv(filepath)
df_00['captureopen'] = df_00.sbid - df_00.bask
def test_get_quote_series(self):
df = web.get_quote_yahoo(pd.Series(['GOOG', 'AAPL', 'GOOG']))
assert_series_equal(df.ix[0], df.ix[2])
def get_stock_prices(self):
dataframe = get_quote_yahoo([stock for stock in self.stocks_owned])
for stock in self.stocks_owned:
self.stocks_owned[stock]['price'] = dataframe.ix[stock]['last']
def test_get_quote_string(self):
_yahoo_codes.update({'MarketCap': 'j1'})
df = web.get_quote_yahoo('GOOG')
self.assertFalse(pd.isnull(df['MarketCap'][0]))
def test_get_quote_string(self):
df = web.get_quote_yahoo('GOOG')
df2 = web.get_quote_yahoo('GOOG')
assert_frame_equal(df, df2)
def test_get_quote(self):
df = web.get_quote_yahoo(pd.Series(['GOOG', 'AAPL', 'GOOG']))
assert_series_equal(df.ix[0], df.ix[2])
df_optionpricescurrent = dict_of_df_optionpricescurrent.get(symbol+'|'+expirationdate_string,dict_empty)
if len(df_optionpricescurrent) == 0:
print('@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ got df_optionpricescurrent - -',symbol+'|'+expirationdate_string)
df_optionpricescurrent = pp.options_to_dataframe(symbol,expirationdate_string,0)
df_optionpricescurrent['optiontype'] = df_optionpricescurrent['optionsymbol'].to_frame(name='optionsymbol').applymap(optiontype)
dict_of_df_optionpricescurrent[symbol+'|'+expirationdate_string] = df_optionpricescurrent
else:
print('>>>>>>>>>>>>>>>>>>>> already have df_optionpricescurrent - -',symbol+'|'+expirationdate_string)
#continue
df_stockpricecurrent = dict_of_df_stockpricecurrent.get(symbol,dict_empty)
if len(df_stockpricecurrent) == 0:
print('@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ got df_stockpricecurrent here')
df_stockpricecurrent = pdata.get_quote_yahoo(symbol)
dict_of_df_stockpricecurrent[symbol] = df_stockpricecurrent
else:
#continue
#if showresults == 1:
print('>>>>>>>>>>>>>>>>>>>> already have df_stockpricecurrent - '+symbol)
stockpricelast = df_stockpricecurrent['last'].iloc[0]
stockpricetime = df_stockpricecurrent['time'].iloc[0]
print('symbol: '+symbol)
#if showresults == 1:
# print('stockpricelast',stockpricelast)
filepath = os.path.join(myselectedcandidatesfolder,filename)
print(filepath)
df_00 = pd.read_csv(filepath)
