Average Volume and Floating Indicators

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posted Sep 16, 2014

2 things I'm having trouble with in this alg and 1 question::
1) Average Volume, is this the right way to do it.
2) I'm getting an error on the boolean indicators, open_long_position and open_short_position. Im trying to avoid taking another position if I have one already.
3) For set_universe, is it possible to filter the stock selection based on prices, i.e., stocks between 1 and 10 dollars for instance, or does that have to be done as in if statement in the handle_data loop?

# Put any initialization logic here.  The context object will be passed to  
# the other methods in your algorithm.  
def initialize(context):  
    set_universe(universe.DollarVolumeUniverse(98, 100))

# Will be called on every trade event for the securities you specify.  
def handle_data(context, data):  
    #track the volume history for 3 days  
    volume_history = history(bar_count=50, frequency='1d', field='volume')  
    # average_volume = sum(volume_history)/50 (getting an error here)  
    #track the price history for 3 days  
    price_history = history(bar_count=4, frequency='1d', field='price')  
    # get close price history  
    for stock in data:  
        # Moving Averages - http://www.forexonlinetradingsystems.info/simple-systems/triple-moving-averages-crossover  
        slow_ma = data[stock].mavg(50)  
        medium_ma = data[stock].mavg(25)  
        fast_ma = data[stock].mavg(10)  
        # volume_1 = volume_history[-1]  
        if open_long_position is None:  
            open_long_position = False  
        if open_short_position is None:  
            open_short_position = False

        # Open Long Position  
        if fast_ma>medium_ma and fast_ma>slow_ma and context.portfolio.positions[stock]==0:  
            order_target(stock, 100)  
            open_long_position = true  
            log.info("Open Long Position: " + str(stock))  
        # Close Long Position  
        if fast_ma<=medium_ma and context.portfolio.positions[stock]>0 and open_long_position==True and open_short_position==False:  
            order_target(stock, 0)  
            open_long_position = false  
            log.info("Close Long Position: " + str(stock))  
        # Open Short Position  
        if fast_ma<medium_ma and fast_moving_average<slow_moving_average and context.portfolio.positions[stock]==0:  
            order_target(stock, -100)  
            open_short_position = true  
            log.info("Open Short Position: " + str(stock))  
        # Cover Short Position  
        if fast_ma>=medium_ma and context.portfolio.positions[stock]>0 and open_short_position==True and open_long_position==False:  
            order_target(stock, 0)  
            open_short_position = false  
            log.info("Cover Short Position: " + str(stock))

        record(SMA=slow_ma,MMA=medium_ma,FMA=fast_ma)

2 responses

Alisa Deychman

Sep 18, 2014

Hi Don,

This backtest is a step in the right direction. It's not quite there yet, but we're making progress!

To answer your questions and describe some of the changes I made,

Average Volume - The history call returns a pandas dataframe where the rows are the date and the columns are labeled by security. The cells are the volumes per security on each date. As a result, this piece of code was throwing an error: sum(volume_history). Instead, you need to index into the stock to select the appropriate column. I fixed this in your code by moving this call to handle_data on line 31, average_volume = sum(volume_history[stock])/50. Do you know you're not using this variable? Was this intentional?
open long and short positions - The open_long_position and open_short_positions needs to be created as a context variable in initialize, to save state between bars of the backtest. I also updated this section to make the backtest compile.
Filter stocks based on price - Within your 'for stock in data' loop in handle_data you can query for the stock price to create your stock selection
calculating moving average - Since you're already using history, I changed the syntax to use panda's mean function to calculate the moving average. This is a faster function and improves performance instead of using the built-in Quantopian .mavg() call.
open_long_position - The open long position on line 42 was not getting triggered. To access the number of shares owned of a stock, you need to use 'context.portfolio.positions[stock].amount'
Closing positions - This is perhaps the most important one. When do you want to close your long and short positions? If you enter a long position on line 42, then open_long_position = True. In that same bar it will hit line 47 and submit a second, asynchronous order to close the position. It also looks like the short position was not getting closed. In the interim, I added a section on line 37 to close all positions at the end of day.

For testing purposes, I narrowed down the set_universe size from 2% to 0.1%. Let me know how you'd like to close the positions for #6 and we'll continue developing the strategy.

Cheers,
Alisa

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Don Sevcik

Sep 19, 2014

This is great, thanks for the reply! I wanted to close a long position when fast_ma=medium_ma instead of closing at the end of the day. I used the booleans so that an Open Long would not be closed for an Open Short and vice versa. The context setup makes sense, I will use this. As far as #3, I figured that was the answer, to do it in the loop and filter by price.

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