def createKnnLearner(naFeatures, lKnn=30, leafsize=10, method='mean'):
'''
@summary: Creates a quick KNN learner
@param naFeatures: Numpy array of features,
@param fMin: Data frame containing the price information for all of the stocks.
@param fMax: List of feature functions, most likely coming from features.py
@param bAbsolute: If true, min value will be scaled to fMin, max to fMax, if false,
+-1 standard deviations will be scaled to fit between fMin and fMax, i.e. ~69% of the values
@param bIgnoreLast: If true, last column is ignored (assumed to be classification)
@return: None, data is modified in place
'''
cLearner = kdt.kdtknn(k=lKnn, method=method, leafsize=leafsize)
cLearner.addEvidence(naFeatures)
return cLearner
def createKnnLearner(naFeatures, lKnn=30, leafsize=10, method='mean'):
'''
@summary: Creates a quick KNN learner
@param naFeatures: Numpy array of features,
@param fMin: Data frame containing the price information for all of the stocks.
@param fMax: List of feature functions, most likely coming from features.py
@param bAbsolute: If true, min value will be scaled to fMin, max to fMax, if false,
+-1 standard deviations will be scaled to fit between fMin and fMax, i.e. ~69% of the values
@param bIgnoreLast: If true, last column is ignored (assumed to be classification)
@return: None, data is modified in place
'''
cLearner = kdt.kdtknn(k=lKnn, method=method, leafsize=leafsize)
cLearner.addEvidence(naFeatures)
return cLearner
# plot the 3d view
ax = fig.add_subplot(222, projection='3d')
ax.scatter(X1, X2, Y, c=colors, edgecolors='none')
#ax.scatter(X1,X2,Y,c=colors)
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('Y')
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
plt.title('Training Data 3D View', fontsize=12)
##########
# OK, now create and train a learner
#
learner = kdt.kdtknn(k=30, method='mean')
numpoints = X1.shape[0]
dataX = np.zeros([numpoints, 2])
dataX[:, 0] = X1
dataX[:, 1] = X2
trainsize = floor(dataX.shape[0] * .6)
learner.addEvidence(dataX[0:trainsize], dataY=Y[0:trainsize])
steps = 50.0
stepsize = 2.0 / steps
Xtest = np.zeros([steps * steps, 2])
count = 0
for i in np.arange(-1, 1, stepsize):
for j in np.arange(-1, 1, stepsize):
Xtest[count, 0] = i + stepsize / 2
def main():
# symbols = np.loadtxt('./Examples/Features/symbols.txt',dtype='S10',comments='#')
symbols = [
"AA",
"AXP",
"BA",
"BAC",
"CAT",
"CSCO",
"CVX",
"DD",
"DIS",
"GE",
"HD",
"HPQ",
"IBM",
"INTC",
"JNJ",
"JPM",
"KFT",
"KO",
"MCD",
"MMM",
"MRK",
"MSFT",
"PFE",
"PG",
"T",
"TRV",
"UTX",
"VZ",
"WMT",
"XOM",
]
# symbols = ['XOM']
# This is the start and end dates for the entire train and test data combined
alldatastartday = dt.datetime(2007, 1, 1)
alldataendday = dt.datetime(2010, 6, 30)
timeofday = dt.timedelta(hours=16)
timestamps = du.getNYSEdays(alldatastartday, alldataendday, timeofday)
dataobj = da.DataAccess("Norgate")
voldata = dataobj.get_data(timestamps, symbols, "volume", verbose=True)
voldata = (voldata.fillna()).fillna(method="backfill")
close = dataobj.get_data(timestamps, symbols, "close", verbose=True)
close = (close.fillna()).fillna(method="backfill")
featureList = [
featMA,
featMA,
featRSI,
featRSI,
featDrawDown,
featRunUp,
featVolumeDelta,
featVolumeDelta,
featAroon,
classFutRet,
]
featureListArgs = [
{"lLookback": 10, "bRel": True},
{"lLookback": 20},
{"lLookback": 10},
{"lLookback": 20},
{},
{},
{"lLookback": 10},
{"lLookback": 20},
{"bDown": False},
{"lLookforward": 5},
]
# print 'Applying Features'
#
# John Cornwell's featuretest.py was consulted for figuring out the syntax of ftu.applyFeatures() methods and ftu.stackSyms() methods
#
allfeatureValues = ftu.applyFeatures(close, voldata, featureList, featureListArgs)
trainstartday = dt.datetime(2007, 1, 1)
trainendday = dt.datetime(2009, 12, 31)
traintimestamps = du.getNYSEdays(trainstartday, trainendday, timeofday)
# print 'Stack Syms for Training'
trainingData = ftu.stackSyms(allfeatureValues, traintimestamps[0], traintimestamps[-1])
# print 'Norm Features for Training'
scaleshiftvalues = ftu.normFeatures(trainingData, -1.0, 1.0, False)
teststartday = dt.datetime(2010, 1, 1)
testendday = dt.datetime(2010, 6, 30)
testtimestamps = du.getNYSEdays(teststartday, testendday, timeofday)
# print 'Stack Syms for Test'
testData = ftu.stackSyms(allfeatureValues, testtimestamps[0], testtimestamps[-1])
# print 'Norm Features for Test'
ftu.normQuery(testData[:, :-1], scaleshiftvalues)
NUMFEATURES = 9
bestFeatureIndices = []
bestCorrelation = 0.0
fid = open("output.txt", "w")
for iteration in range(NUMFEATURES):
nextFeatureIndexToAdd = -1
for featureIndex in range(NUMFEATURES):
if featureIndex not in bestFeatureIndices:
bestFeatureIndices.append(featureIndex)
fid.write("testing feature set " + str(bestFeatureIndices) + "\n")
print("testing feature set " + str(bestFeatureIndices))
bestFeatureIndices.append(9)
curTrainingData = trainingData[:, bestFeatureIndices]
curTestData = testData[:, bestFeatureIndices]
bestFeatureIndices.remove(9)
kdtlearner = knn.kdtknn(5, "mean", leafsize=100)
kdtlearner.addEvidence(curTrainingData[:, :-1], curTrainingData[:, -1])
testEstimatedValues = kdtlearner.query(curTestData[:, :-1])
testcorrelation = np.corrcoef(testEstimatedValues.T, curTestData[:, -1].T)
curCorrelation = testcorrelation[0, 1]
fid.write("corr coef = %.4f\n" % (curCorrelation))
print("corr coef = %.4f" % (curCorrelation))
if curCorrelation > bestCorrelation:
nextFeatureIndexToAdd = featureIndex
bestCorrelation = curCorrelation
bestFeatureIndices.remove(featureIndex)
if nextFeatureIndexToAdd >= 0:
bestFeatureIndices.append(nextFeatureIndexToAdd)
else:
break
fid.write("best feature set is " + str(bestFeatureIndices) + "\n")
print("best feature set is " + str(bestFeatureIndices))
fid.write("corr coef = %.4f" % (bestCorrelation) + "\n")
print("corr coef = %.4f" % (bestCorrelation))
fid.close()
# plot the 3d view
ax = fig.add_subplot(222,projection='3d')
ax.scatter(X1,X2,Y,c=colors,edgecolors='none')
#ax.scatter(X1,X2,Y,c=colors)
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('Y')
ax.set_xlim3d(-1,1)
ax.set_ylim3d(-1,1)
ax.set_zlim3d(-1,1)
plt.title('Training Data 3D View',fontsize=12)
##########
# OK, now create and train a learner
#
learner = kdt.kdtknn(k=30,method='mean')
numpoints = X1.shape[0]
dataX = np.zeros([numpoints,2])
dataX[:,0] = X1
dataX[:,1] = X2
trainsize = floor(dataX.shape[0] * .6)
learner.addEvidence(dataX[0:trainsize],dataY=Y[0:trainsize])
steps = 50.0
stepsize = 2.0/steps
Xtest = np.zeros([steps*steps,2])
count = 0
for i in np.arange(-1,1,stepsize):
for j in np.arange(-1,1,stepsize):
Xtest[count,0] = i + stepsize/2
