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Single trade in AAPL should give AAPL's return

I modified the sample app to just buy and hold a 1 position of AAPL. I got a single digit return, which is nowhere near what AAPL has returned since 2009. Is this a bug?

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Total Returns
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Alpha
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Beta
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Sharpe
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Benchmark Returns
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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 = 100000.1
  context.min_notional = -100000.0

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)
  #low = data[context.aapl].low
  #high = data[context.aapl].high
  # 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 ojbect 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 < low * 1.005 and notional <= 0:
  #  order(context.aapl,+100)
  #elif price > high * .995 and notional > 0:
  #  order(context.aapl,-100)
  if notional == 0:
            order(context.aapl, +100)
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.
2 responses

Wait sorry, nevermind. I just figured it out. The notional is set to 100K, and the algorithm return is based off of that.

Hi Gary, thanks for the question. I just wanted to make a small clarification. The returns are based on the starting capital base, which is the dollar amount you set in the backtest controls. The default is $1M. In the code above, the notional value was for a single position, and was originally intended to avoid over concentration in a single position. The notional value is used only in the algorithm itself, not in the calculation of the returns.

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