Need help with ema crossover strategy

import quantopian.algorithm as algo from quantopian.pipeline import Pipeline from quantopian.pipeline.data.builtin import USEquityPricing from quantopian.pipeline.filters import QTradableStocksUS import talib import pandas as pd def initialize(context): context.stocks=[ sid(24), sid(5061), sid(8347), sid(46631), sid(4151), sid(1091), sid(3149), sid(8151), sid(5938), sid(25006)] #def Vwap(prices,volumes): #return (prices*volumes).sum()/volumes.sum() def handle_data(context,data): for stock in context.stocks: hist=data.history(stock,'price',50,'1d') ema1=talib.EMA(hist,timeperiod=50) ema2=talib.EMA(hist,timeperiod=200) #sma_50=hist.mean() #sma_20=hist[-20:].mean() open_orders=get_open_orders() if ema1[-1]>ema2[-1] : if stock not in open_orders: print("buy") order_target_percent(stock,0.1) elif ema1[-1]<ema2[-1] : if stock not in open_orders: print("sell") order_target_percent(stock,-0.1)

Are you trading on minute or daily data?
handle_data implies minute data, but assuming your intent is to trade daily data, how about something like this?

import quantopian.algorithm as algo  
import talib  

def initialize(context):  
    schedule_function(trade, date_rules.every_day(), time_rules.market_close(minutes=10))  
    context.stocks=[ sid(24),  
                     sid(5061),  
                     sid(8347),  
                     sid(46631),  
                     sid(4151),  
                     sid(1091),  
                     sid(3149),  
                     sid(8151),  
                     sid(5938),  
                     sid(25006)]  

def trade(context,data):  
    weight = 1/len(context.stocks)  
    for stock in context.stocks:  
        hist=data.history(stock,'price',250,'1d')  
        ema1=talib.EMA(hist,timeperiod=50)  
        ema2=talib.EMA(hist,timeperiod=200)  
        uptrend = ema1[-1] > ema2[-1]  
        we_are_flat  = stock not in context.portfolio.positions  
        # can we afford to buy one whole share of the stock (BRK_A, GOOG)?  
        we_can_afford_it = hist.iloc[-1] < weight*context.portfolio.portfolio_value  
        if uptrend and we_are_flat and we_can_afford_it:  
            order_target_percent(stock,weight)  
            print('bought {} at ${}, ema1={}, ema2={}'.format(stock.symbol,hist.iloc[-1],ema1[-1],ema2[-1]))  
        if (ema1[-1] < ema2[-1]) and (stock in context.portfolio.positions):  
            order_target_percent(stock,0)  
            print('sold {} at ${}, ema1={}, ema2={}'.format(stock.symbol,hist.iloc[-1],ema1[-1],ema2[-1]))