why the close price is not match with the data from goole finance

like the day 2015/05/11.

2
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 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')
context.apple = sid(24)
context.max_notional = 1000000.1
context.min_notional = -100000.0

# 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
vwap = data[context.apple].vwap(3)
price = data[context.apple].price
notional = context.portfolio.positions[context.apple].amount*price
if price < vwap * 0.995 and notional > context.min_notional:
order(context.apple,-100)
elif price > vwap * 1.005 and notional < context.max_notional:
order(context.apple, +100)
# 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)

# 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.apple].price)
There was a runtime error.
1 response

Feng, check out our FAQ about the data between Quantopian/Google/Yahoo/other vendors: https://www.quantopian.com/faq#data

In particular,

Why is your close price different from other data sources?

Quantopian uses the last traded price as the close price for the security. Depending on the data source, others may use end-of-day (EOD) prices. For example, Yahoo is an EOD datasource. Yahoo and other EOD data providers get their price and volume data from the official exchange record. Quantopian's data is generated by the actual trades, regardless of what exchange the trade was made on. The EOD sources rarely exactly match data derived from intraday data. For instance, the official close for a NYSE stock is the last trade of the day for the stock on NYSE. But if the stock also trades on Chicago, Pacific or another regional exchange, the last trade on one of those exchanges could be our close.

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