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?

544
Total Returns
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Alpha
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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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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