def test_applyFeatures():
dData = datautil.get_data0()
ldfFeatures = ftu.applyFeatures(dData, [featMA], [{'lLookback':2}])
print ldfFeatures
ldfFeatures = ftu.stackSyms(ldfFeatures)
print ldfFeatures
ftu.normFeatures(ldfFeatures,-1,1,False)
ldfData = norObj.get_data( ldtTimestamps, lsSym, lsKeys )
dData = dict(zip(lsKeys, ldfData))
''' Imported functions from qstkfeat.features, NOTE: last function is classification '''
lfcFeatures = [ featMA, featRSI, class_fut_ret ]
''' Default Arguments '''
#ldArgs = [{}] * len(lfcFeatures)
''' Custom Arguments '''
ldArgs = [ {'lLookback':30, 'bRel':True},\
{},\
{}]
''' Generate a list of DataFrames, one for each feature, with the same index/column structure as price data '''
ldfFeatures = ftu.applyFeatures( dData, lfcFeatures, ldArgs )
bPlot = False
if bPlot:
''' Plot feature for XOM '''
for i, fcFunc in enumerate(lfcFeatures[:-1]):
plt.clf()
plt.subplot(211)
plt.title( fcFunc.__name__ )
plt.plot( dfPrice.index, dfPrice['XOM'].values, 'r-' )
plt.subplot(212)
plt.plot( dfPrice.index, ldfFeatures[i]['XOM'].values, 'g-' )
plt.show()
''' Pick Test and Training Points '''
from QSTK.qstkfeat.classes import class_fut_ret import QSTK.qstkfeat.featutil as ftu import sys import time from functions import * if __name__ == '__main__': dDataTrain, dDataTest = get_data0() lfcFeatures,ldArgs = get_features0() ''' Generate a list of DataFrames, one for each feature, with the same index/column structure as price data ''' ldfFeaturesTrain = ftu.applyFeatures( dDataTrain, lfcFeatures, ldArgs, '$SPX') ldfFeaturesTest = ftu.applyFeatures( dDataTest, lfcFeatures, ldArgs, '$SPX') ''' Pick Test and Training Points ''' dtStartTrain = dt.datetime(2008,01,01) dtEndTrain = dt.datetime(2009,12,31) dtStartTest = dt.datetime(2010,01,01) dtEndTest = dt.datetime(2010,12,31) ''' Stack all information into one Numpy array ''' naFeatTrain = ftu.stackSyms( ldfFeaturesTrain, dtStartTrain, dtEndTrain ) naFeatTest = ftu.stackSyms( ldfFeaturesTest, dtStartTest, dtEndTest ) ''' Normalize features, use same normalization factors for testing data as training data ''' ltWeights = ftu.normFeatures( naFeatTrain, -1.0, 1.0, False ) ''' Normalize query points with same weights that come from test data '''
norObj = da.DataAccess('Yahoo')
ldtTimestamps = du.getNYSEdays(dtStart, dtEnd, dt.timedelta(hours=16))
lsKeys = ['open', 'high', 'low', 'close', 'volume']
ldfData = norObj.get_data(ldtTimestamps, lsSym, lsKeys)
dData = dict(zip(lsKeys, ldfData))
''' Imported functions from qstkfeat.features, NOTE: last function is classification '''
lfcFeatures = [featMA, featRSI, class_fut_ret]
''' Default Arguments '''
#ldArgs = [{}] * len(lfcFeatures)
''' Custom Arguments '''
ldArgs = [ {'lLookback':30, 'bRel':True},\
{},\
{}]
''' Generate a list of DataFrames, one for each feature, with the same index/column structure as price data '''
ldfFeatures = ftu.applyFeatures(dData, lfcFeatures, ldArgs)
bPlot = False
if bPlot:
''' Plot feature for XOM '''
for i, fcFunc in enumerate(lfcFeatures[:-1]):
plt.clf()
plt.subplot(211)
plt.title(fcFunc.__name__)
plt.plot(dfPrice.index, dfPrice['XOM'].values, 'r-')
plt.subplot(212)
plt.plot(dfPrice.index, ldfFeatures[i]['XOM'].values, 'g-')
plt.show()
''' Pick Test and Training Points '''
lSplit = int(len(ldtTimestamps) * 0.7)
dtStartTrain = ldtTimestamps[0]
ldArgs = [ {'lLookback':5},{'lLookback':10},{'lLookback':20}, {'lLookback':5,'MR':True},{'lLookback':10,'MR':True},{'lLookback':20,'MR':True},\
{'lLookback':5},{'lLookback':10},{'lLookback':20}, {'lLookback':5,'MR':True},{'lLookback':10,'MR':True},{'lLookback':20,'MR':True},\
{'lLookback':5},{'lLookback':10},{'lLookback':20}, {'lLookback':5,'MR':True},{'lLookback':10,'MR':True},{'lLookback':20,'MR':True},\
{'lLookback':5},{'lLookback':10},{'lLookback':20}, {'lLookback':5,'MR':True},{'lLookback':10,'MR':True},{'lLookback':20,'MR':True},\
{'lLookback':5},{'lLookback':10},{'lLookback':20}, {'lLookback':5,'MR':True},{'lLookback':10,'MR':True},{'lLookback':20,'MR':True},\
{'lLookback':5,'bDown':True},{'lLookback':10,'bDown':True},{'lLookback':20,'bDown':True},{'lLookback':5,'bDown':False},{'lLookback':10,'bDown':False},{'lLookback':20,'bDown':False},{'lLookback':5,'bDown':True,'MR':True},{'lLookback':10,'bDown':True,'MR':True},{'lLookback':20,'bDown':True,'MR':True},{'lLookback':5,'bDown':False,'MR':True},{'lLookback':10,'bDown':False,'MR':True},{'lLookback':20,'bDown':False,'MR':True},\
#{'lLookback':5,'bFast':True},{'lLookback':10,'bFast':True},{'lLookback':20,'bFast':True},{'lLookback':5,'bFast':False},{'lLookback':10,'bFast':False},{'lLookback':20,'bFast':False},{'lLookback':5,'bFast':True,'MR':True},{'lLookback':10,'bFast':True,'MR':True},{'lLookback':20,'bFast':True,'MR':True},{'lLookback':5,'bFast':False,'MR':True},{'lLookback':10,'bFast':False,'MR':True},{'lLookback':20,'bFast':False,'MR':True},\
{'lLookback':5},{'lLookback':10},{'lLookback':20}, {'lLookback':5,'MR':True},{'lLookback':10,'MR':True},{'lLookback':20,'MR':True},\
{'lLookback':5},{'lLookback':10},{'lLookback':20}, {'lLookback':5,'MR':True},{'lLookback':10,'MR':True},{'lLookback':20,'MR':True},\
{'lLookback':5},{'lLookback':10},{'lLookback':20}, {'lLookback':5,'MR':True},{'lLookback':10,'MR':True},{'lLookback':20,'MR':True},\
{},\
{},\
{'i_lookforward':5}
]
''' Generate a list of DataFrames, one for each feature, with the same index/column structure as price data '''
ldfFeaturesTrain = ftu.applyFeatures(dDataTrain, lfcFeatures, ldArgs,
'$SPX')
ldfFeaturesTest = ftu.applyFeatures(dDataTest, lfcFeatures, ldArgs, '$SPX')
''' Pick Test and Training Points '''
dtStartTrain = dt.datetime(2008, 01, 01)
dtEndTrain = dt.datetime(2009, 12, 31)
dtStartTest = dt.datetime(2010, 01, 01)
dtEndTest = dt.datetime(2010, 12, 31)
''' Stack all information into one Numpy array '''
naFeatTrain = ftu.stackSyms(ldfFeaturesTrain, dtStartTrain, dtEndTrain)
naFeatTest = ftu.stackSyms(ldfFeaturesTest, dtStartTest, dtEndTest)
''' Normalize features, use same normalization factors for testing data as training data '''
ltWeights = ftu.normFeatures(naFeatTrain, -1.0, 1.0, False)
''' Normalize query points with same weights that come from test data '''
ftu.normQuery(naFeatTest[:, :-1], ltWeights)
lFeatures = range(0, len(lfcFeatures) - 1)
