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Sample Algo (VWAP) with variable short/long volumes

hi all, i just tested out something interesting with the sample algo. In the sample algo:

if price < vwap * 0.995 and notional > context.min_notional:

elif price > vwap * 1.005 and notional < context.max_notional:  

I was thinking, if we could track the overall trend of the stock/market, why don't we place "weights" on the long and short volumes, for example, if the trend is predicted to go upwards, we distribute a higher proportion to the long stock and a lesser proportion to the short stock volume. i ran the backtest for 3 years (2008-2011) and got a 170% return for shorting 100 and longing 300.

Anyone has any inputs to implement this "tracker"? I am thinking of ARMA modelling with estimation (i came from an electrical engineering and signal processing background)

cheers all

6 responses

Hi Ivan,

Welcome to Quantopian! Thanks for posting this, could you attach a backtest for more reference? I'd be interesting in seeing your example.

To attach a backtest to a post, you need to run a full backtest and then can click "Add Backtest" in the reply. In general, if you want to share a full backtest at the start of a thread, then on the full backtest results page click the blue "Share Results" button.



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I have used a higher long volume in this case, but i am unsure of using which algorithm to decide the proportion to optimize the returns.

Clone Algorithm
Total Returns
Max Drawdown
Benchmark Returns
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
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
    # For this example, we're going to write a simple momentum script.  When the 
    # stock goes up quickly, we're going to buy; when it goes down quickly, we're
    # going to sell.  Hopefully we'll ride the waves.

    # To run an algorithm in Quantopian, you need two functions: initialize and 
    # handle_data.

def initialize(context):
    # This initialize function sets any data or variables that you'll use in
    # your algorithm.  For instance, you'll want to define the security (or 
    # securities) you want to backtest.  You'll also want to define any 
    # parameters or values you're going to use.

    # In our example, we're looking at Apple.  If you re-type this line 
    # yourself, you'll see the auto-complete that is available for the 
    # security ID.
    context.aapl = sid(24)
    # In these two lines, we set the maximum and minimum we want our algorithm 
    # to go long or short our security.  You don't have to set limits like this
    # when you write an algorithm, but it's good practice.
    context.max_notional = 1000000.1
    context.min_notional = -1000000.0

def handle_data(context, data):
    # This handle_data function is where the real work is done.  Our data is
    # minute-level tick data, and each minute is called a frame.  This function
    # runs on each frame of the data.
    # We've built a handful of useful data transforms for you to use.  In this 
    # line, we're computing the volume-weighted-average-price of the security 
    # defined above, in the context.aapl variable.  For this example, we're 
    # specifying a three-day average.
    vwap = data[context.aapl].vwap(3)
    # We need a variable for the current price of the security to compare to
    # the average.
    price = data[context.aapl].price
    # Another powerful built-in feature of the Quantopian backtester is the
    # portfolio object.  The portfolio object tracks your positions, cash,
    # cost basis of specific holdings, and more.  In this line, we calculate
    # how long or short our position is at this minute.   
    notional = context.portfolio.positions[context.aapl].amount * price
    # This is the meat of the algorithm, placed in this if statement.  If the
    # price of the security is .5% less than the 3-day volume weighted average
    # price AND we haven't reached our maximum short, then we call the order
    # command and sell 100 shares.  Similarly, if the stock is .5% higher than
    # the 3-day average AND we haven't reached our maximum long, then we call
    # the order command and buy 100 shares.     
    if price < vwap * 0.995 and notional > context.min_notional:
    elif price > vwap * 1.005 and notional < context.max_notional:
This backtest was created using an older version of the backtester. Please re-run this backtest to see results using the latest backtester. Learn more about the recent changes.
There was a runtime error.

I'm new to Quantopian. What instrument is your benchmark? (Where do I find what it is without asking you?)

Hi Abraham,

I'd be happy to answer your questions. The benchmark is the S&P500; you can find more information in our FAQ and Help Documentation for references on the function descriptions, syntax details, and overview of important concepts. If you have an individual question for us, you can email us at [email protected].



Thanks Alisa.

While some strategies should be benchmarked against a broad based index, such the S&P500, wouldn't it make more sense to: a. allow selecting a different benchmark, and this particular case to use AAPL as the benchmark? The reason for using AAPL is obvious when one looks at the chart for AAPL over the period in question.

The S&P500 is the current benchmark, but I agree with you, I'd love to see a customizable benchmark. A benchmark that can be tweaked according to the individual algo. It's a request we've heard before and we're listening. This feature is something we're working on but I don't have delivery date.