def testcode():
prices = defineData(startDate = '01-01-2008', stopDate = '31-12-2009', \
symList = ['AAPL']) # in-sample dates
# optimizeY(prices) # UNCOMMENT to optimize Ybuy/Ysell
Ybuy = 0.01
Ysell = 0.01
holdTime = 21
X, Y, dates = getXY(prices, Ybuy, Ysell, holdTime) # X is an ndarray, Y is
# a 1darray
# optimizeParam(X, Y, dates) # UNCOMMENT to optimize leaf size of RTclassLearner
# or numher of bags of BagClassLearner
##### create a learner
learner = rt.RTclassLearner(leaf_size=75, verbose=False) # create learner
split = int(0.6 * len(Y)) # divide into training and cross-val sets
trainX = X[:split - 1, :]
trainY = Y[:split - 1]
trainDates = dates[:split - 1]
crossValX = X[split:, :]
crossValY = Y[split:]
crossValDates = dates[split:]
#####Training on the entire in-sample data (USE WITH CAUTION)
# learner.add_Evidence(X, Y)# train it
# pred = learner.query(X)# get the predictions
# accuracy = np.sum(pred == Y) / float( len(Y) )
# print("In sample results")
# print ('Error [%]: ', round(100 - 100 * accuracy, 4) )
# # Generate order from in-sample predictions
# signal = pd.Series(pred, index = dates)
# order = tradingStrategy(signal)
# createOrder(order, 'ML_based')
# cumReturn, portVals = testcode_marketsim('ML_based', verbose = False)
#
# # Plot entire in-sample data, training indicators and their predictions (USE WITH CAUTION)
# plt.figure(figsize = (11,13))
# plt.subplot(411)
# plt.plot(prices / prices[0], color = 'k', label = 'benchmark')
# plt.plot(portVals / portVals[0], color = 'g', label = 'ML-based')
# plt.xticks(rotation=30)
# plt.ylabel('normalized')
# plt.title('cumulative return = {} %'.format(round(cumReturn * 100)))
# plotVline(order)
# lg = plt.legend(loc = 'best')
# lg.draw_frame(False)
#
# plt.subplot(412)
# plt.plot(signal / 2)
# plt.xlim((prices.index[0], prices.index[-1]))
# plt.xticks(rotation=30)
#
# plt.subplot(413)
# plt.scatter(X[Y == 0, 1], X[Y == 0, 2], color = 'k', label = 'hold')
# plt.scatter(X[Y == 1, 1], X[Y == 1, 2], color = 'g', label = 'buy')
# plt.scatter(X[Y == -1, 1], X[Y == -1, 2], color = 'r', label = 'sell')
# plt.title('in-sample data and labels before training')
# lg = plt.legend(loc = 'best')
# lg.draw_frame(True)
#
# plt.subplot(414)
# plt.scatter(X[pred == 0, 1], X[pred == 0, 2], color = 'k', label = 'hold')
# plt.scatter(X[pred == 1, 1], X[pred == 1, 2], color = 'g', label = 'buy')
# plt.scatter(X[pred == -1, 1], X[pred == -1, 2], color = 'r', label = 'sell')
# plt.xlabel('Momentum indicator'); plt.ylabel('Crossover indicator')
# plt.title('in-sample data and predictions after training')
# lg = plt.legend(loc = 'best')
# lg.draw_frame(True)
#
######out of sample testing after training on training set
testPrices = defineData('01-01-2010', '31-12-2011') # out-of-sample dates
testX, testY, testDates = getXY(testPrices, Ybuy, Ysell, holdTime) # testX
# is an ndarray, testY is a 1darray
cumRetTest = 0.0
accTest = 0.0
reps = 1 # number of configuratiokns of RTclassLearners whose predictions
# to average over
start = time.time()
for j in range(reps):
learner.add_Evidence(trainX, trainY) # train it
pred = learner.query(testX) # get the predictions
accTest += np.sum(pred == testY) / float(len(testY)) # accuracy
# Generate order from out of sample predictions
signal = pd.Series(pred, index=testDates)
order = tradingStrategy(signal)
createOrder(order, 'ML_based')
cumReturn, portVals = testcode_marketsim('ML_based', verbose=False)
cumRetTest += cumReturn
stop = time.time()
print("Test sample results")
print('Error [%]: ', round(100 - 100 * accTest / reps, 4))
print('Cumulative return [%]: ', round(cumRetTest * 100 / reps, 4))
print('Elapsed time [s]: ', round(stop - start, 2))
plt.plot(prices[:split - 1] / prices.values[0], label='train')
plt.plot(prices[split:] / prices.values[0], label='cross-val')
plt.plot(testPrices / prices.values[0], label='test')
lg = plt.legend(loc='best')
lg.draw_frame(False)
plt.xticks(rotation=30)
def optimizeParam(X, Y, dates):
"""
@Summary: Optimize leaf size of RTclassLearner or number of bags of
BagClassLearner by plotting averaged error and cumulative return
for training & cross-val datasets vs. leaf size / number of bags
@param X: ndarray, of technical indicators. Examples in rows, features in
columns
@param Y: 1darray, of the labels -1 (sell), 0 (hold), and 1 (buy) based on
future returns after a holding time period
@returns nothing
"""
numParam = 1
tryParam = np.linspace(75, 75,
num=numParam) # values of leaf size / number
# of bags to iterate over
fname = 'ML_based' # name of orders file
cumRetTrain = np.zeros(numParam)
cumRetCrossVal = np.zeros(numParam)
accTrain = np.zeros(len(tryParam))
accCrossVal = np.zeros(len(tryParam))
reps = 1000 # number of configurations of RTclassLearners or
#BagClassLearners whose predictions to average over
split = int(0.6 * len(Y)) # divide into train and cross-val sets
trainX = X[:split - 1, :]
trainY = Y[:split - 1]
trainDates = dates[:split - 1]
crossValX = X[split:, :]
crossValY = Y[split:]
crossValDates = dates[split:]
start = time.time()
for i in range(
numParam): # initialize either RTclassLearner/BagClassLearner
learner = rt.RTclassLearner(leaf_size=tryParam[i],
verbose=False) # create learner
# learner = bl.BagClassLearner(learner = rt.RTclassLearner, kwargs = {"leaf_size":75}, \
# bags = int(tryParam[i]), boost = False, verbose = False) # create BagClassLearner
print tryParam[i] # print parameter values during computation
for j in range(reps):
learner.add_Evidence(trainX, trainY) # train on trainX, trainY
# training sample querying
pred = learner.query(trainX) # get the predictions
accTrain[i] += np.sum(pred == trainY) / float(
len(trainY)) # accuracy
signal = pd.Series(
pred, index=trainDates) # create Series from predictions
order = tradingStrategy(signal)
createOrder(order, fname) # write order to disk
cumReturn, portVals = testcode_marketsim(fname, verbose=False)
cumRetTrain[i] += cumReturn
# cross-validation sample querying
pred = learner.query(crossValX) # get the predictions
accCrossVal[i] += np.sum(pred == crossValY) / float(len(crossValY))
signal = pd.Series(pred, index=crossValDates)
order = tradingStrategy(signal)
createOrder(order, fname)
cumReturn, portVals = testcode_marketsim(fname, verbose=False)
cumRetCrossVal[i] += cumReturn
stop = time.time()
print 'Time to calculate error vs the selected parameter: {} s'.format( \
round( stop - start, 2))
plt.figure(figsize=(6, 8))
plt.subplot(211)
plt.plot(tryParam, cumRetTrain * 100 / reps, '.-', label='training')
plt.plot(tryParam, cumRetCrossVal * 100 / reps, '.-', label='cross-val')
plt.ylabel('cumulative return [%]')
plt.title('RT Learner (Ybuy = Ysell = 1 %)') # CHECK Ybuy/Ysell value here
lg = plt.legend(loc='best')
lg.draw_frame(False)
print('Cumulative return [%]: ', round(cumRetCrossVal[-1] * 100 / reps, 4))
plt.subplot(212)
plt.plot(tryParam, 100 - 100 * accTrain / reps, '.-', label='training')
plt.plot(tryParam, 100 - 100 * accCrossVal / reps, '.-', label='cross-val')
plt.xlabel('leaf size')
# CHECK leaf size OR number of bags
plt.ylabel('error')
lg = plt.legend(loc='best')
lg.draw_frame(False)
print('Error [%]: ', round(100 - 100 * accCrossVal[-1] / reps, 4))
def optimizeY(prices):
"""
@Summary: Optimize Ybuy/Ysell parameters by plotting averaged error &
cumulative return for training/cross-val datasets vs. Ybuy
(assuming Ybuy = Ysell)
@param prices: Series, contains adj. close price with date indices
@returns nothing
"""
numParam = 13
tryParam = np.logspace(-4, -1, num=numParam) # values of Ybuy = Ysell
# to iterate over
fname = 'ML_based' # name of orders file
cumRetTrain = np.zeros(numParam)
cumRetCrossVal = np.zeros(numParam)
accTrain = np.zeros(numParam)
accCrossVal = np.zeros(numParam)
learner = rt.RTclassLearner(leaf_size=5, verbose=False) # create learner
reps = 100 # number of RTclassLearners whose predictions to average over
start = time.time()
for i in range(numParam): # X is a ndarray, Y is a 1darray
X, Y, dates = getXY(prices, Ybuy=tryParam[i], Ysell=tryParam[i])
split = int(0.6 * len(Y)) # divide into train and cross-val sets
trainX = X[:split - 1, :]
trainY = Y[:split - 1]
trainDates = dates[:split - 1]
crossValX = X[split:, :]
crossValY = Y[split:]
crossValDates = dates[split:]
print tryParam[i] # print parameter values during computation
for j in range(reps):
learner.add_Evidence(trainX, trainY) # train on trainX, trainY
# training sample querying
pred = learner.query(trainX) # get the predictions
accTrain[i] += np.sum(pred == trainY) / float(
len(trainY)) # accuracy
signal = pd.Series(
pred, index=trainDates) # create Series from predictions
order = tradingStrategy(signal)
createOrder(order, fname) # write order to disk
cumReturn, portVals = testcode_marketsim(fname, verbose=False)
cumRetTrain[i] += cumReturn
# cross-validation sample querying
pred = learner.query(crossValX) # get the predictions
accCrossVal[i] += np.sum(pred == crossValY) / float(len(crossValY))
signal = pd.Series(pred, index=crossValDates)
order = tradingStrategy(signal)
createOrder(order, fname)
cumReturn, portVals = testcode_marketsim(fname, verbose=False)
cumRetCrossVal[i] += cumReturn
stop = time.time()
print 'Time to calculate cumulative return & error vs the selected parameters: {} s' \
.format(stop - start)
plt.figure(figsize=(6, 8))
plt.subplot(211)
plt.semilogx(tryParam * 100,
cumRetTrain * 100 / reps,
'.-',
label='training')
plt.semilogx(tryParam * 100,
cumRetCrossVal * 100 / reps,
'.-',
label='cross-val')
plt.ylabel('cumulative return [%]')
plt.title('Random Decision Tree (leaf size = 5)')
lg = plt.legend(loc='best')
lg.draw_frame(False)
plt.subplot(212)
plt.semilogx(100 * tryParam,
100 - 100 * accTrain / reps,
'.-',
label='training')
plt.semilogx(100 * tryParam,
100 - 100 * accCrossVal / reps,
'.-',
label='cross-val')
plt.xlabel('Ybuy or Ysell [% change]')
plt.ylabel('error [%]')
lg = plt.legend(loc='best')
lg.draw_frame(False)
split = int(0.6 * len(Y)) # divide into training and cross-val sets
trainX = X[:split - 1, :]
trainY = Y[:split - 1]
trainDates = dates[:split - 1]
crossValX = X[split:, :]
crossValY = Y[split:]
crossValDates = dates[split:]
learner.addEvidence(X, Y) # Training on the entire in-sample data
pred = learner.query(X) # get the predictions
accuracy = np.sum(pred == Y) / float(len(Y))
print("In sample results")
print ('Error [%]: ', round(100 - 100 * accuracy, 4))
signal = pd.Series(pred, index=dates) # Generate order from in-sample predictions
order = tradingStrategy(signal)
createOrder(order, 'ML_Strat')
cumReturn, portVals = testcode_marketsim('ML_Strat', verbose=False)
# Plot entire in-sample data, training indicators and their predictions
plt.figure(figsize=(11, 13))
plt.subplot(411)
plt.plot(prices / prices[0], color='k', label='benchmark')
plt.plot(portVals / portVals[0], color='g', label='ML-based')
plt.xticks(rotation=30)
plt.ylabel('normalized')
plt.title('cumulative return = {} %'.format(round(cumReturn * 100)))
plotVline(order)
lg = plt.legend(loc='best')
lg.draw_frame(False)