def question_3():
startDate = datetime(2008, 1, 1)
endDate = datetime(2009, 12, 31)
timeStamps = getNYSEdays(startDate, endDate, timedelta(hours=16))
dataReader = DataAccess('Yahoo')
symbolsListName = 'sp5002012'
symbols = dataReader.get_symbols_from_list(symbolsListName)
symbols.append('SPY')
data = fillNA(dataReader.get_data(timeStamps, symbols, 'actual_close')) # read data and filled na
# create events and event-profile
events = find_crossing_threshold_events(data, threshold=7)
eventprofiler(events, {'close': data}, i_lookback=20, i_lookforward=20, s_filename = join(rootDir, symbolsListName + '_threshold7.pdf'),
b_market_neutral=True, b_errorbars=True, s_market_sym='SPY')
def graphStrategies(eventMaker, startDate, endDate, symbols, marketSymbol, fieldName, isSymbolsList, holdingPeriodsToTry,
outputDir='.'):
"""
run eventprofiler and produce graphs for given the strategy
@param eventMaker: a function that takes prices as its sole argument
@return: nothing. save graphs (xxxdays.pdf) to outputDir
"""
prices = pd.concat([getPrices(startDate, endDate, symbols, fieldName, isSymbolsList=isSymbolsList),
getPrices(startDate, endDate, [marketSymbol], fieldName)], axis=1)
events = eventMaker(prices)
# make holdingPeriodsToTry iterable
if not isinstance(holdingPeriodsToTry, Iterable):
holdingPeriodsToTry = [holdingPeriodsToTry]
for holdingPeriod in holdingPeriodsToTry:
eventprofiler(deepcopy(events), {'close': prices}, i_lookback=holdingPeriod, i_lookforward=holdingPeriod,
s_filename=os.path.join(outputDir, str(holdingPeriod) + 'days.pdf'),
b_market_neutral=True, b_errorbars=True, s_market_sym=marketSymbol)
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 -----
orders = makeOrdersAccordingToEvents(eventFilter, holdingPeriod, numStocksTraded, saveIntermediateResults=True)
portValues = marketsim(orders, initialInvestment)
# calculateMetrics(portValues, saveIntermediateResults=True)
analyze(portValues, '$SPX', plotFname='performancePlot.pdf', verbosity=0)
plt.show()
__author__ = 'jjin'
from datetime import datetime
from pprint import pprint
from utilities import getPrices, createBollingerEventFilter, calculateBollingerValues
from QSTK.qstkstudy.EventProfiler import eventprofiler
if __name__ == '__main__':
startDate = datetime(2008, 1, 1)
endDate = datetime(2009, 12, 31)
lookBackPeriod = 20
numOfStds = 1
marketSymbol = 'SPY'
# symbols = ['AAPL', 'GOOG', 'IBM', 'MSFT']
symbols = 'SP5002012'
# ----- compute prices and Bollinger stuff -----
prices = getPrices(startDate, endDate, symbols, 'close', isSymbolsList=True, additionalSymbol=marketSymbol)
bollingerVals, _, _, _, _ = calculateBollingerValues(prices, lookBackPeriod, numOfStds)
eventFilter, actionDates = createBollingerEventFilter(bollingerVals,
{'type': 'CROSS_WRT_MARKET_FALL', 'bolBenchMark': -2,
'marketBenchMark': 2, 'marketSymbol': marketSymbol,
'lookBackPeriod': lookBackPeriod, 'numOfStds': numOfStds})
pprint(actionDates)
eventprofiler(eventFilter, {'close': prices}, i_lookback=lookBackPeriod, i_lookforward=1,
s_filename = None, b_market_neutral=True, b_errorbars=True, s_market_sym=marketSymbol)