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What in the world?

Seems wild. Over leveraged?

Clone Algorithm
23
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Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
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
# Put any initialization logic here.  The context object will be passed to
# the other methods in your algorithm.
def initialize(context):
    pass

# Will be called on every trade event for the securities you specify. 
def handle_data(context, data):
    # Implement your algorithm logic here.

    # data[sid(X)] holds the trade event data for that security.
    # context.portfolio holds the current portfolio state.

    # Place orders with the order(SID, amount) method.

    # TODO: implement your own logic here.
    order(sid(24), 60)
There was a runtime error.
5 responses

Aaron,

1) Your algorithm purchases 60 shares of Apple stock, every minute the market is open, for several years.
2) Apple stock increased in price during that time period.

Quantopian doesn't stop you from ordering more stock than you have money to purchase. So you likely owned billions of dollars in stock, most of that bought with money your account didn't have.

Keep reading the documentation and experimenting. That is the same way we are learning! :)

Cheers,

Tristan

Actually, I am also pretty sure you will also run out of Apple stocks to buy ... :D

Okay, thanks for the feedback. :D

Is there a code that prevents the algorithm from leveraging?

You can use order_target functions to seek to target positions, instead of mechanically ordering X shares each bar.

Also, you can use context.account.leverage to track your algo's leverage. Here's an example:
https://www.quantopian.com/posts/quantopian-open-example-algorithm-to-control-leverage

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