from copy import deepcopy
import matplotlib.pyplot as plt
from time import time
from QSTK.qstkutil.qsdateutil import getNYSEdays
from QSTK.qstkutil.tsutil import returnize1
t0 = time()
startDate = datetime(2012, 9, 1)
endDate = datetime(2012, 9, 12)
symbols = ['AAPL', 'GLD', 'MCD', 'SPY']
forecastLength = 21
numPaths = 1000
origPrices = getPrices(startDate, endDate, symbols, 'close')
# get log returns
origLogRets = deepcopy(origPrices)
for col in origLogRets.columns:
returnize1(origLogRets[col])
origLogRets = np.log(origLogRets)
print 'first 10 rows of origPrices:\n', origPrices[:10]
# print origLogRets
print '\nCorrelation matrix:\n', origLogRets.corr()
print '\nCholesky decomposition:\n', np.linalg.cholesky(origLogRets.corr())
# get correlated future log returns
from copy import deepcopy import matplotlib.pyplot as plt from QSTK.qstkutil.DataAccess import DataAccess from QSTK.qstkutil.qsdateutil import getNYSEdays from QSTK.qstkutil.tsutil import returnize1 startDate = datetime(2010, 8, 30) endDate = datetime(2012, 8, 30) symbols = ['AAPL', 'GLD', 'MCD', '$SPX'] forecastLength = 21 numPaths = 1000 origPrices = getPrices(startDate, endDate, symbols, 'close') # get log returns origLogRets = deepcopy(origPrices) for col in origLogRets.columns: returnize1(origLogRets[col]) origLogRets = np.log(origLogRets) print 'first 10 rows of origPrices:\n', origPrices[:10] # print origLogRets print '\nCorrelation matrix:\n', origLogRets.corr() print '\nCholesky decomposition:\n', np.linalg.cholesky(origLogRets.corr()) # get correlated future log returns meanVec = origLogRets.mean()
startDate = datetime(2008, 1, 1)
endDate = datetime(2009, 12, 30)
lookBackPeriod = 20
numOfStds = 1
marketSymbol = 'SPY'
symbols = 'SP5002012'
bolBenchMark = -2
marketBenchMark = 1
holdingPeriod = 5
numStocksTraded = 100
initialInvestment = 100000
# ----- compute prices and create events -----
prices = getPrices(startDate, endDate, symbols, 'close', isSymbolsList=True, additionalSymbol=marketSymbol)
# prices = getPrices(startDate, endDate, symbols, 'close', additionalSymbol=marketSymbol)
bollingerVals, _, _, _, _ = calculateBollingerValues(prices, lookBackPeriod, numOfStds)
eventFilter, actionDates = createBollingerEventFilter(bollingerVals,
{'type': 'CROSS_WRT_MARKET_FALL', 'bolBenchMark': bolBenchMark,
'marketBenchMark': marketBenchMark, 'marketSymbol': marketSymbol,
'lookBackPeriod': lookBackPeriod, 'numOfStds': numOfStds})
try:
eventprofiler(eventFilter, {'close': prices}, i_lookback=lookBackPeriod, i_lookforward=holdingPeriod,
b_market_neutral=True, b_errorbars=True, s_market_sym=marketSymbol, s_filename='eventPlot.pdf')
except:
print "There aren't enough events for the event profiler."
# ----- trade and evaluate -----
