Quantopian - Dual Moving averages clone w/ major indices

Dual Moving averages clone w/ major indices

First backtest I have posted, this site is awesome! Instead of using this strategy w/ individual stocks I chose major market etfs (i.e. spy, dia, qqqq). I changed the long moving average to 100 days from 50, left the short moving average at 10. I added one line that adds commissions per shares traded (not sure how realistic this is, I don't trade in real life) I really like the way this performs during major downturns in SP500.

Clone Algorithm

Backtest from to with initial capital ( data)

Cumulative performance:

Custom data:

Week

Month

All

Total Returns

Alpha

Beta

Sharpe

Sortino

Information Ratio

Benchmark Returns

Volatility

Max Drawdown

Returns	1 Month	3 Month	6 Month	12 Month

Alpha	1 Month	3 Month	6 Month	12 Month

Beta	1 Month	3 Month	6 Month	12 Month

Sharpe	1 Month	3 Month	6 Month	12 Month

Sortino	1 Month	3 Month	6 Month	12 Month

Information Ratio	1 Month	3 Month	6 Month	12 Month

Volatility	1 Month	3 Month	6 Month	12 Month

Max Drawdown	1 Month	3 Month	6 Month	12 Month


def initialize(context):
    # Define which stocks we want to work with. I'm starting with just one, but you can add up to 10.
    context.sids = [sid(8554), sid(2174), sid(19920)]
    context.N = len(context.sids)

    # This section initializes a dictionary that will later track whether we invested in a particular stock
    context.invested = {}
    for cur_sid in context.sids:
        context.invested[cur_sid] = False
    # Adds commission per trade
    # Add commission per trade
    # Add commission per trade
    set_commission(commission.PerShare(cost=.10))
def handle_data(context, data):
    # Uncomment the line below if you want to log the sids that you're testing. Be careful, it's a lot of
    # log lines if you're doing a full backtest!
    # log.info(data.keys())
    
    # allocate part of our portfolio for each sid in our list    
    cash_per_sid = context.portfolio.starting_cash / context.N
    
    # This loop goes over the specified sids
    for cur_sid in context.sids:
        # skip sids that have no data (like Facebook pre-IPO)
        if not (cur_sid) in data:
            continue
        
        # buy if short moving average crossed long moving average going up       
        if (data[cur_sid].mavg(10) > data[cur_sid].mavg(100)) and not context.invested[cur_sid]:
            order_in_cash(data, cur_sid, cash_per_sid)
            context.invested[cur_sid] = True
        # sell if short moving average crossed long moving average going down  
        elif (data[cur_sid].mavg(10) < data[cur_sid].mavg(100)) and context.invested[cur_sid]:
            # log the fact that we're closing our position, and the conditions that triggered it
            log.info('Selling SID %i by %i shares. mavg(10) is %i, mavg(100) is %i' %(cur_sid, context.portfolio.positions[cur_sid].amount, data[cur_sid].mavg(10), data[cur_sid].mavg(100)))
            # sell the amount we invested
            order(cur_sid, -context.portfolio.positions[cur_sid].amount)
            context.invested[cur_sid] = False
    
def order_in_cash(data, security, cash):
    # This function calculates how many shares to buy given the price and cash available
    shares = cash // data[security].price
    # log.debug("security: %i, cash: %f, shares: %i"% (security, cash, shares))
    if shares < 0: 
        shares += 1 
    # to fix python's flooring of negative integer division. 
    # log the fact that we're going long, and the conditions that triggered it
    log.info('Ordering %i shares of sid %i. mavg(10) is %i, mavg(100) is %i' %(shares, security, data[security].mavg(10), data[security].mavg(100)))
    order(security, shares)

This backtest is using an old benchmark. Please re-run this backtest to see results using the new benchmark. Learn more about the updated benchmark.

Andrew O'Shea

I noticed I need to fix some of the comments to accurately represent changes I made.

John Fawcett

@Andrew, great share!

The commissions rate you have is on the high side for per share commissions - you can find prices between $0.005 and $0.001 per share from direct market access brokers like Interactive Brokers. The commission model lets you specify a per trade commission as well, which you would see at a retail broker. That cost would be more like $7-$10 per trade. If you assume 100 share trades, your current per share commission actually resembles the cost of a retail broker.

Andrew O'Shea

@John Thanks for the Info and kind words.

I ran another full backtest with sight modifications to the above algo; is there anyway I can post it in this thread instead of making a new post/newthread?

John Fawcett

@Andrew, sorry! not yet. It comes up a lot. As a work around, people have posted a comment and pasted the algo code in using the "fence posts" that you can insert to mark a code block (triple back quotes before and after your code, code sample button above will insert them for you).

Dan Dunn

You can post the code in a reply, but you can't post a backtest in a reply - at least not yet.

Grant Kiehne

Hello Fawce,

To get the $0.005 to $0.001 per share commission from direct market access brokers, what are the typical minimums, restrictions, etc.? For example, does this assume a certain daily/monthly/yearly number of shares traded? What is the implication for the amount of capital required to be "in the game"? I understand that Quantopian is planning/hoping to set up live trading with real money. I think that it would be helpful to provide some initial guidance on the barriers to entry. Realistically, how much capital would need to be ventured?

Adam Turner

Hi Andrew,

Could you explain the "context.invested" code?

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Dual Moving averages clone w/ major indices

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