Why SVM does't work here?

I want to develop an SVM strategy, however it doesn't work.
It's said that" 65 Error Runtime exception: ValueError: This solver needs samples of at least 2 classes in the data, but the data contains only one class: False"
But I double checked that line context.Y has two classes!
Who can tell me why!

import numpy as np
import pandas as pd
import talib
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier

def initialize(context):
context.stock = symbol('SPY')
context.date = None
context.classifier = svm.LinearSVC()

def handle_data(context, data):
todays_date = get_datetime().date()
if todays_date == context.date:
return
context.date = todays_date

high = history(1000,'1d','high')  
low = history(1000,'1d','high')  
close = history(1000,'1d','close_price')  
prices = history(1000,'1d','price')  

atrlst = talib.ATR(high[context.stock],  
                low[context.stock],  
                close[context.stock],  
                timeperiod=14)  
atr = atrlst[-502:-2]  
atr_new = atrlst[-1]  

macdlst = prices.apply(MACD, fastperiod=12, slowperiod=26, signalperiod=9)  
macdlst = np.reshape(np.array(macdlst),len(np.array(macdlst)))  
macd = macdlst[-502:-2]  
macd_new = macdlst[-1]  

rsilst = prices.apply(talib.RSI, timeperiod=14)  
rsilst = np.reshape(np.array(rsilst),len(np.array(rsilst)))  
rsi = rsilst[-502:-2]  
rsi_new = rsilst[-1]  

slowklst, slowdlst = talib.STOCH(high[context.stock],  
                               low[context.stock],  
                               close[context.stock],  
                               fastk_period=5,  
                               slowk_period=3,  
                               slowk_matype=0,  
                               slowd_period=3,  
                               slowd_matype=0)  
slowk = slowklst[-502:-2]  
slowd = slowdlst[-502:-2]  
slowk_new = slowklst[-1]  
slowd_new = slowdlst[-1]  

dataX = np.vstack((atr,macd,rsi,slowk,slowd)).T  
context.X = dataX  

prices_array = np.reshape(np.array(prices),len(np.array(prices)))  
dataY = (np.diff(prices_array)>0)[-501:-1]  
context.Y = dataY  

context.xnew = np.array([atr_new,macd_new,rsi_new,slowk_new,slowd_new])  
log.info(context.Y)  
#Why always Error here!?  
context.classifier.fit(context.X,context.Y)  
signal = context.classifier.predict(context.xnew)  
log.info(signal)  
if signal:  
    order_target_percent(context.stock,1)  
else:  
    order_target_percent(context.stock,0)

def MACD(prices, fastperiod=12, slowperiod=26, signalperiod=9):
macd, signal, hist = talib.MACD(prices,
fastperiod=fastperiod,
slowperiod=slowperiod,
signalperiod=signalperiod)
return macd-signal