Hi there, I'm trying to get a basic algorithm for EMA crossovers.

Basically, I want to have 3 data series:
- 10-Day Exponential Moving Average
- 20-Day Exponential Moving Average
- 30-Day Exponential Moving Average

Orders for short/long should be based on the 10-day being above/below the 20-day AND 30-day moving average.
I.e. if the 10-day EMA crosses the 20-day and 30-day EMAs, then order 100 shares of TCK.A (teck resources class A).

Can anyone help?

I've attached someone else's previous EMA build but I cannot work my way through it to even backtest!

import talib

def initialize(context):
set_symbol_lookup_date('2003-01-01')
context.stocks = symbols('fb','tsla','aapl','gild','uri','tck.a','scty','intc','msft','nvda','cog', 'bp', 'ste', 'xom')
set_benchmark(symbol('qqq'))
set_commission(commission.PerTrade(cost=4.95))
context.dayCount = 0
context.date = None
context.shorts = 0
context.cancelCounter=0

def handle_data(context, data):

todays_date = get_datetime().date()  

if todays_date==context.date:  
    return  

if todays_date!=context.date:  
    context.dayCount+=1  
    context.cancelCounter+=1  
    context.date = todays_date  

if context.cancelCounter==5:  
    end_of_day(context, data)  
    context.cancelCounter=0  

if has_orders(context,data) and context.dayCount>1:  
    print('has open orders - doing nothing!')  
    return  

if context.dayCount == 1:  
    buyAll(context,data)  

for stock in context.stocks:  

    hist30 = history(bar_count=30, frequency='1d', field='price')  
    series30 = hist30[stock]  
    ema_result30 = talib.EMA(series30, timeperiod=30)  
    ema_result10 = talib.EMA(series30, timeperiod=10)  
    ema30 = ema_result30[-1]  
    ema10 = ema_result10[-1]  

    getCash(context,data)  

    if ema30 > ema10 and context.portfolio.positions[stock].amount > 0 and context.dayCount>2:  
        log.info('{0}:  Selling {1} shares, EMA10:  {2}, EMA30:  {3}'.format(stock.symbol,context.portfolio.positions[stock].amount,ema10,ema30))  
        order_target(stock, 0, style=LimitOrder(ema10))  

    if has_orders(context,data) and context.dayCount>7:  
        print('has open orders - doing nothing!')  
        return  

    elif ema30 < ema10 and context.portfolio.positions[stock].amount == 0 and context.dayCount>2 and data[stock].price<context.freeCash:  
        getCash(context,data)  
        shares = round((context.freeCash*0.95)/data[stock].price,0)  
        log.info('{0}:  Buying {1} shares, EMA10:  {2}, EMA30:  {3}'.format(stock.symbol,shares,ema10,ema30))  
        order_target(stock, shares, style=LimitOrder(ema10))  

    getCash(context,data)  

    if context.portfolio.positions[stock].amount < 0:  
        context.shorts+=1  

getCash(context,data)  
record(Cash=context.freeCash)  
record(Shorts=context.shorts)  
context.shorts=0  

def has_orders(context,data):
# Return true if there are pending orders.
has_orders = False
for sec in data:
orders = get_open_orders(sec)
if orders:
for oo in orders:
message = 'Open order for {amount} shares in {stock}'
message = message.format(amount=oo.amount, stock=sec)
log.info(message)

        has_orders = True  
return has_orders  

def getCash(context,data):
if context.portfolio.cash < (context.portfolio.portfolio_value-context.portfolio.positions_value):
context.freeCash = context.portfolio.cash-5
else:
context.freeCash = (context.portfolio.portfolio_value-context.portfolio.positions_value)-5

def end_of_day(context, data):
#cancle any order at the end of day. Do it ourselves so we can see slow moving stocks.
open_orders = get_open_orders()

if open_orders:  

    log.info("")  
    log.info("* EOD: Stoping Orders *")  
    log.info("")  

if open_orders:  

    for security, orders in open_orders.iteritems():  
        for oo in orders:  
            log.info("X CANCLED {0:s} with {1:,d} / {2:,d} filled"\  
                                 .format(security.symbol,  
                                         oo.filled,  
                                         oo.amount))  
            cancel_order(oo)

def buyAll(context,data):
for stock in context.stocks:
order_value(stock, 1000)
log.info('{0}: Buying {1}shares'.format(stock.symbol,round(1000/data[stock].price,0)))