This is a slightly modified version of the sample algo, where instead of buying when the stock goes up, you buy when the stock goes down, vice versa for selling. It bets the whole account in each trade, so no scaling in or scaling out. I've added commissions as well, to reduce the outrageous return.

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)
#context.aapl = sid(8229)

set_commission(commission.PerShare(cost=0.01))

# 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(5)
#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,+context.portfolio.cash/price)
#elif price > high * .995 and notional > 0 and price > context.portfolio.positions[context.aapl].cost_basis/context.portfolio.positions[context.aapl].amount:
#  order(context.aapl,-context.portfolio.positions[context.aapl].amount)

if price < vwap * .97 and notional <= 0:
order(context.aapl,+context.portfolio.cash/price)
elif price > vwap * 1.04 and notional > 0:
order(context.aapl,-context.portfolio.positions[context.aapl].amount)
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.
7 responses

Apple is a wonderful stock ;)

That's a neat exercise, thanks!

Dan

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actually tried this out on a couple of other stocks. not too shabby heh.

Hmm. I copied your source code, and got completely different results.

Hi Ken - did you run the same start and end dates?

What you can do is reply to this thread, click "add backtest" and share your backtest code/result with this thread - we can figure out the difference that way.

Dan

Hi Dan. Thanks. That was it - non-matching end date. After July, 2012, this algorithm falls apart. Still, very interesting. Love the platform!

The choice of apple as the default stock was canny :)

I think this is leverage... how to remove.. the leverage part...?