def load_data():
symbols = ['XOM', 'AAPL', 'MSFT', 'JNJ', 'GE', 'GOOG', 'CVX', 'PG', 'WFC']
cap = {'^GSPC':14.90e12, 'XOM':403.02e9, 'AAPL':392.90e9, 'MSFT':283.60e9, 'JNJ':243.17e9, 'GE':236.79e9, 'GOOG':292.72e9, 'CVX':231.03e9, 'PG':214.99e9, 'WFC':218.79e9}
n = len(symbols)
prices_out, caps_out = [], []
for s in symbols:
print("Reading symbol %s" % s)
q = QuoteSeries.loadfromfile(s, 'data/black_litterman/%s.csv' % s)
prices = q.getprices()[-500:]
prices_out.append(prices)
caps_out.append(cap[s])
return symbols, prices_out, caps_out
def getquotesfromstring(symbol, string):
linecount = 0
data = []
for line in string.split("\\n"):
linecount += 1
entries = line.split(",")
if len(entries) != 7 or linecount == 1: continue
quote = Quote(datetime.strptime(entries[0], '%Y-%m-%d'),
float(entries[1]), float(entries[2]), float(entries[3]),
float(entries[4]), int(entries[5]))
data.append(quote)
reversedIterator = reversed(data)
return QuoteSeries(symbol, list(reversedIterator))
def load_data():
symbols = ['XOM', 'AAPL', 'MSFT', 'JNJ', 'GE', 'GOOG', 'CVX', 'PG', 'WFC']
cap = {
'^GSPC': 14.90e12,
'XOM': 403.02e9,
'AAPL': 392.90e9,
'MSFT': 283.60e9,
'JNJ': 243.17e9,
'GE': 236.79e9,
'GOOG': 292.72e9,
'CVX': 231.03e9,
'PG': 214.99e9,
'WFC': 218.79e9
}
n = len(symbols)
prices_out, caps_out = [], []
for s in symbols:
print("Reading symbol %s" % s)
q = QuoteSeries.loadfromfile(s, 'data/black_litterman/%s.csv' % s)
prices = q.getprices()[-500:]
prices_out.append(prices)
caps_out.append(cap[s])
return symbols, prices_out, caps_out
# Copyright (c) 2012 Quantitative & Financial, All rights reserved
# www.quantandfinancial.com
import datasources.google as google
from structures.quote import QuoteSeries
import numpy
from math import log, exp
from datetime import datetime
# Definition of enumerators
call, put, european, american = 100, 101, 102, 103
#google.getquotesfromweb('IVV').savetofile('data/ivv.csv')
prices = QuoteSeries.loadfromfile('IVV', 'data/ivv_2012_11_05.csv').getprices()
prices = prices[-250:] # We will use last 250 trading days
# Calculation of daily continuous (logaritmic) returns
returns = []
for i in range(0, len(prices)-1):
r = log(prices[i] / prices[i-1])
returns.append(r)
# Calculation of daily and annualized volatility from daily returns
volat_d = numpy.std(returns) # Daily volatility
volat = volat_d * 250**.5 # Annualized volatility
# Calculation inputs
side = call # Option side
style = american # Option style
price = prices[-1] # Current instrument price (147.31, as of 2012/11/05)
