BasicTrader.py

# 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 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):
    # The 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 later.
    # It's only called once at the beginning of your algorithm.
    # In our example, we're looking at Apple.
    # If you re-type this line you'll see
    # the auto-complete that is available for security.

    context.security = symbol('AAPL')

# The handle_data function is where the real work is done.
# This function is run either every minute
# (in live trading and minute backtesting mode)
# or every day (in daily backtesting mode).

def handle_data(context, data):
    # We've built a handful of useful data transforms for you to use,
    # such as moving average.
    # To make market decisions, we're calculating the stock's
    # moving average for the last 5 days and its current price.

    average_price = data[context.security].mavg(5)
    current_price = data[context.security].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
    # the current amount of cash in our portfolio.

    cash = context.portfolio.cash

    # Here is the meat of our algorithm.
    # If the current price is 1% above the 5-day average price
    # AND we have enough cash, then we will order.
    # If the current price is below the average price,
    # then we want to close our position to 0 shares.

    if current_price > 1.01*average_price and cash > current_price:
        # Need to calculate how many shares we can buy
        number_of_shares = int(cash/current_price)
        # Place the buy order (positive means buy, negative means sell)
        order(context.security, +number_of_shares)
        log.info("Buying %s" % (context.security.symbol))
    elif current_price < average_price:
        # Sell all of our shares by setting the target position to zero
        order_target(context.security, 0)
        log.info("Selling %s" % (context.security.symbol))

    # You can use the record() method to track any custom signal.
    # The record graph tracks up to five different variables.
    # Here we record the Apple stock price.

    record(stock_price=data[context.security].price)