def stock_price_eur(symbol):
"""Returns current price of a stock in EUR.
Args:
symbol (str): Identifying symbol of the stock.
Returns:
numpy.float64: Current stock price in EUR.
"""
stock = pdr.get_quote_yahoo(symbol)
currency = stock.currency.values[0]
price = pdr.get_quote_yahoo(f"{currency.upper()}EUR=X").price.values[0]
ratio = price if currency.upper() != "GBP" else price / 100
return stock.price.values[0] * ratio
def market_cap_data(assets):
"""
Helper function to get market cap information from yahoo finance
:param assets: List[str], ticker symbols
:return: np.ndarray
"""
return data.get_quote_yahoo(assets)['marketCap'].values
def track(watchlist):
start_time = time()
while True:
print(pdr.get_quote_yahoo(watchlist)['price'])
elapsed = time() - start_time
if elapsed >= 10:
prompt = input("Would you like to continue? y/n: ")
if prompt == 'n':
break
start_time = time()
elapsed = 0
sleep(1)
def home():
import pandas_datareader as pdr
import csv
import pandas
import json
from pandas_datareader.data import Options
from datetime import date
from sqlalchemy import create_engine
stocks = ['AAPL','GOOGL', 'AMZN', 'FB', 'SSNLF', 'MSFT', 'SNE', 'IBM', 'NTDOY', 'ORCL', 'VMW', 'INTC', 'HPQ', 'DVMT',
'CSCO', 'ADBE', 'SAP', 'LNVGY', 'RHT', 'SYMC', 'TWTR', 'ATNY', 'ACTS', 'ATVI', 'IOTS', 'ADRO', 'AMD', 'ADVS', 'ZBRA',
'XPLR', 'XBIT', 'WIX', 'WBMD', 'VNOM', 'VIA', 'VECO', 'UPLD', 'UBNT', 'UFPT', 'TYME', 'TRIV', 'TRIP', 'TGA', 'TSI',
'TESS', 'AIR', 'ATEN', 'AQN', 'DDD', 'WUBA', 'ASX', 'A', 'AYX', 'AMBR', 'AEE', 'BHE', 'BIO', 'CPL', 'CTS', 'CMI', 'DGI',
'D', 'HEI', 'ASUR', 'AVID', 'EPAY', 'CBAK', 'CPSH', 'CYAN', 'DBVT', 'DELT', 'EDGW', 'XELA', 'FALC', 'SVVC', 'FBIO', 'GEOS',
'QQQC', 'GPRO', 'MRVL', 'MZOR', 'BKFS', 'BA', 'CAJ', 'CRY', 'CUB', 'DGI', 'GME', 'GM', 'GNE', 'ASR', 'PAC', 'MDT',
'MTD', 'MIXT', 'MULE', 'NC', 'NPTN', 'NXPI', 'NSTG', 'UEPS']
stock_df = pdr.get_quote_yahoo(stocks)
#s = Options('AAPL')._get_data_in_date_range(date.today())
#print s
stock_df.index.name = "StockSymbol"
stock_df.to_csv("stock.csv")
company_stock = pandas.read_csv("stock.csv")
companies = pandas.read_csv("companies.csv")
merged = company_stock.merge(companies, on = 'StockSymbol')
merged['Amount'] = merged['last']*(merged['NumberofStocks']).astype(float)
merged.columns = map(str.lower, merged.columns)
merged.rename(columns = {'change_pct':'changepct','last':'lastprice','amount':'totalpricestocks','short_ratio' : 'shortratio'}, inplace = True)
merged.to_csv('new_stock.csv', index=False)
df = pd.read_csv('new_stock.csv')
engine = create_engine('postgresql:///finance')
df.to_sql('j', engine)
return render_template("home.html")
def getSP500Details(start, end):
sp500DF = pd.DataFrame()
#sp500ComponentDf = pd.read_csv(sp500MemberListFile)
sp500ComponentDf = pd.read_csv(sp500MemberListFundamentalFile)
sp500SymbolList = sp500ComponentDf['symbol'].tolist()
successCount = 0
for symbol in sp500SymbolList:
try:
tickerQuote = dr.get_quote_yahoo(symbol)
getDailyPrice([symbol], start, end).to_pickle('{}{}.pkl'.format(symbolPriceDataLocation, symbol))
sp500DF = sp500DF.append(tickerQuote, sort=False)
successCount += 1
except Exception as e:
print('Error {} for symbol {}'.format(e, symbol))
print('Successfully retrieved data for {} stocks'.format(successCount))
print(sp500DF)
sp500DF.to_pickle(sp500PickleLocation)
# conda install -c pandas-datareader
import pandas_datareader as pdr
from time import time, sleep
start_time = time()
while True:
print(pdr.get_quote_yahoo('GOOG')['price'])
elapsed = time() - start_time
print(elapsed)
if elapsed >= 10:
prompt = input("Would you like to continue? y/n: ")
if prompt == 'n':
break
start_time = time()
elapsed = 0
sleep(1)
# x = str(symbol) + '.T'
# # print(x)
# symbols.append(x)
for symbol in df['symbol']:
symbols.append(symbol)
for symbol in symbols:
# 150日間で調べる
start = dt.datetime.now() - dt.timedelta(days=150)
end = dt.datetime.now()
count += 1
print(count)
try:
symbol_df = yf.download(symbol, start, end, interval='1d')
market_capdata = web.get_quote_yahoo(symbol)['marketCap']
market_cap = int(str(market_capdata).split()[1])
print('時価総額は' + str(market_cap))
except:
print('時価総額なし' + str(symbol))
if symbol_df.empty:
continue
print(symbol)
# 標準偏差を計算
try:
# high in the past 81 days
symbol_df['Highest81'] = symbol_df['Adj Close'].rolling(
Part 2
Created on Mon Mar 18 14:20:27 2019
@author: mjmacarty
"""
import pandas as pd
import pandas_datareader as pdr
from time import sleep
# get prices from many symbols
symbols = ['AMZN', 'GOOG', 'NFLX', 'FB', 'GLD', 'SPY']
# sort list, get prices
def get_prices(symbols):
symbols.sort()
return pdr.get_quote_yahoo(symbols)['price']
# program entry point --> gets prices every 5 seconds until broken
def main():
while True:
print(get_prices(symbols))
print("CNTL + C to quit")
sleep(5)
if __name__ == "__main__":
main()
def prices(companies):
companies.sort()
return pdr.get_quote_yahoo(companies)['price']
money.append(x)
#for market capital
y = 0
for j in range(len(session_state.stock_list)):
y = y + (no_shares_cap[j] *
price[session_state.stock_list[j]].iloc[0])
money_cap.append(y)
curr_value = money[len(money) - 1]
#st.write("Curr val",curr_value)
curr_value_cap = money_cap[len(money_cap) - 1]
# getting weights for market capital ratios
market_cap_weights = np.array(
web.get_quote_yahoo(session_state.stock_list, curr_date,
curr_date)['marketCap'])
market_cap_weights = market_cap_weights / np.sum(
market_cap_weights)
for j in range(len(session_state.stock_list)):
money_for_share = curr_value * optimum_weights[j]
no_shares[j] = money_for_share / price[
session_state.stock_list[j]].iloc[0]
#for market capital ratios
money_for_share = curr_value_cap * market_cap_weights[j]
no_shares_cap[j] = money_for_share / price[
session_state.stock_list[j]].iloc[0]
for single_date in (curr_date + timedelta(1) + timedelta(n)
for n in range(29)):
price = yf.download(session_state.stock_list, str(single_date),
def get_stock_prices(list):
for i in list:
res = pdr.get_quote_yahoo(i)["price"]
print(res)
def get_quote(ticker):
data = pdr.get_quote_yahoo(ticker)
return json.loads(data.to_json(orient="index"))
import pandas_datareader as pdr
import csv
import pandas
import json
stocks = ['AAPL','GOOGL', 'AMZN', 'FB', 'SSNLF', 'MSFT', 'SNE', 'IBM', 'NTDOY', 'ORCL', 'VMW', 'INTC', 'HPQ', 'DVMT',
'CSCO', 'ADBE', 'SAP', 'LNVGY', 'RHT', 'SYMC', 'TWTR', 'ATNY', 'ACTS', 'ATVI', 'IOTS', 'ADRO', 'AMD', 'ADVS', 'ZBRA',
'XPLR', 'XBIT', 'WIX', 'WBMD', 'VNOM', 'VIA', 'VECO', 'UPLD', 'UBNT', 'UFPT', 'TYME', 'TRIV', 'TRIP', 'TGA', 'TSI',
'TESS', 'AIR', 'ATEN', 'AQN', 'DDD', 'WUBA', 'ASX', 'A', 'AYX', 'AMBR', 'AEE', 'BHE', 'BIO', 'CPL', 'CTS', 'CMI', 'DGI',
'D', 'HEI', 'ASUR', 'AVID', 'EPAY', 'CBAK', 'CPSH', 'CYAN', 'DBVT', 'DELT', 'EDGW', 'XELA', 'FALC', 'SVVC', 'FBIO', 'GEOS',
'QQQC', 'GPRO', 'MRVL', 'MZOR', 'BKFS', 'BA', 'CAJ', 'CRY', 'CUB', 'DGI', 'GME', 'GM', 'GNE', 'ASR', 'PAC', 'MDT',
'MTD', 'MIXT', 'MULE', 'NC', 'NPTN', 'NXPI', 'NSTG', 'UEPS']
stock_df = pdr.get_quote_yahoo(stocks)
stock_df.index.name = "StockSymbol"
stock_df.to_csv("stock.csv")
company_stock = pandas.read_csv("stock.csv")
companies = pandas.read_csv("companies.csv")
merged = company_stock.merge(companies, on = 'StockSymbol')
merged.to_csv('new.csv', index=False)
import pandas as pd
import numpy as np
from openpyxl import load_workbook
wb = load_workbook('/Users/maria/Job/Python/Ws.xlsx') # Work Book file that I have
ws = wb['Sheet2'] # Work Sheet
column = ws['C'] # Column
tickers_list = [column[x].value for x in range(len(column))]
print(*tickers_list, sep='\n') #επιβεβαιώνω ότι λειτουργεί σωστά και τυπώνει τα tickers από excel
print(len(tickers_list)) #επιβεβαιώνω ότι το μέγεθος των στοιχείων είναι σωστό
#extract the marketcaps
import pandas_datareader as web
tickers = tickers_list[1:]
print(tickers)
market_cap_data= web.get_quote_yahoo(tickers)['marketCap']
print(market_cap_data)
def get_prices(symbols):
print(symbols.sort())
return pdr.get_quote_yahoo(symbols)[
'price'] # live stock data for finance from yahoo about apple inc
def get_stock(stock):
return DataFrame(pdr.get_quote_yahoo(stock, session=session))
def __init__(self, feed_dict, full_analysis):
# initiate common vatiables
self.returns = feed_dict['input']
self.covar = self.returns.cov()
del feed_dict['input']
columns = []
self.inital_weights = []
for col in self.returns.columns:
name = col.split('_')[0]
self.inital_weights.append(get_quote_yahoo(name).marketCap)
columns.append(name)
self.inital_weights = np.array(self.inital_weights)
self.inital_weights = self.inital_weights / np.sum(self.inital_weights)
# save parameters in case replication is desired
self.Parameters = deepcopy(feed_dict)
objective, features, constraints, simulations, methods, tol, maxiter, disp = oc.unfeeder(
feed_dict)
try:
views = []
chg = []
for view in objective['target']:
chg.append(view[0] / 100)
views.append(view[1:])
views = np.array(views)
chg = np.array(chg)
except:
raise AttributeError(
'Black Litterman requires target views to be specified.')
# features
if 'benchmark' in features:
self.benchmark = features['benchmark']
else:
raise AttributeError('Black Litterman requires a benchmark.')
self.periodicity = _embed_periodicity(features['periodicity'])
if methods == 'arithmetic':
self.mean_returns = self.returns.mean() * self.periodicity
self.benchmark_mean = self.benchmark.mean() * self.periodicity
if methods == 'geometric':
self.mean_returns = _geomean(self.returns, self.periodicity)
self.benchmark_mean = _geomean(self.benchmark, self.periodicity)
if 'margin_int_rate' in features:
self.margin_rate = features['margin_int_rate']
else:
self.margin_rate = 0
if 'rf' in features:
self.rf = features['rf']
else:
self.rf = 0
# constraints
if 'min_weights' in constraints:
self.weight_bounds = tuple(
zip(constraints['min_weights'], constraints['max_weights']))
else:
self.weight_bounds = None
constraints = self.get_constraints(constraints)
# return computation
A = (self.benchmark_mean - self.rf) / (
(self.benchmark.std()**2) * self.periodicity)
pi = A * np.matmul(np.mat(self.covar * self.periodicity),
self.inital_weights)
omega = np.matmul(
np.matmul(views, np.mat(self.covar * self.periodicity)), views.T)
e1 = np.mat(
np.mat(self.covar * self.periodicity).I +
np.matmul(np.matmul(views.T,
np.mat(omega).I), views)).I
e2 = np.matmul(np.mat(self.covar * self.periodicity).I,
pi) + np.matmul(np.matmul(views.T,
np.mat(omega).I), chg).T
self.Expected_Returns = np.matmul(e1, e2)
# optimal portfolio
self.OptiParam = minimize(self.optimize_sharpe_ratio,
self.inital_weights,
method='SLSQP',
bounds=self.weight_bounds,
tol=tol,
constraints=constraints,
options={
'maxiter': maxiter,
'disp': disp
})
if not self.OptiParam['success']:
print('Warning!', self.OptiParam['message'])
tanw = self.OptiParam['x']
tanww = pd.DataFrame(tanw).T
tanww.columns = columns
tanr = self.weighted_annual_return(tanw)
margin = np.sum(np.abs(tanw)) - 1
tanra = tanr - self.rf - self.margin_rate * margin
tans = self.weighted_annual_stddev(tanw)
self.Optimal = {
'Weights': tanww,
'Return': tanr,
'AdjReturn': tanra,
'StdDev': tans,
'Sharpe': tanra / tans,
'Series': self.weighted_return(tanw)
}
# efficent frontier
if full_analysis == True:
analyze = np.linspace(max(self.Optimal['Return'] / 4, 0.0001),
min(self.Optimal['Return'] * 4, 0.5),
simulations)
efficent = []
eff = []
for ret in analyze:
e = self.optimize_efficent(ret, constraints, tol, maxiter,
disp)
we = e['x']
wew = pd.DataFrame(we).T
wew.columns = columns
margin = np.sum(np.abs(we)) - 1
re = self.weighted_annual_return(
we) - self.rf - self.margin_rate * margin
se = self.weighted_annual_stddev(we)
efficent.append({
'Weights': wew,
'Returns': re,
'StdDevs': se,
'Sharpe': re / se
})
eff.append(e)
self.EfficentParam = eff
self.EfficentFrontier = efficent
# close
self.ConstituentSharpe = self.get_sharpes(columns)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Part 1
Created on Mon Mar 18 12:17:42 2019
@author: mjmacarty
"""
# open a command line window (terminal) and enter the following:
# pip install pandas-datareader
import pandas as pd
import pandas_datareader as pdr
# read data from one of the APIs
# i.e. pdr.get_data_yahoo('SPY') --> returns dataframe of historical quotes for symbol SPY
pdr.get_data_yahoo("SPY")
# many data sources and variations on data sets, we are interested in a current price:
# get_quote downloads "live" data from the API
print(pdr.get_quote_yahoo('AAPL'))
print(pdr.get_quote_yahoo('AAPL')['price'])
def get_prices(symbols):
symbols.sort()
return pdr.get_quote_yahoo(symbols)['price']
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 3 13:51:10 2020
@author:
"""
import pandas as pd
import pandas_datareader as pdr
print(pdr.get_quote_yahoo("AAPL")['price'])
