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Microsoft 163.33%

Stock 3

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3
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Backtest from to with initial capital
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
"""
This algorithm trades once per week by going 100% long in AAPL.

For more teaching examples, check out the Quantopian Lecture Series:
https://www.quantopian.com/lectures
and the Tutorials:
https://www.quantopian.com/tutorials

Please direct any questions, feedback, or corrections to [email protected]
"""

import quantopian.algorithm as algo
import quantopian.optimize as opt


def initialize(context):

    # Reference to the AAPL security.
    context.att = sid(5061)

    # Rebalance every day, one hour and a half after market open.
    algo.schedule_function(
        rebalance,
        algo.date_rules.every_day(),
        algo.time_rules.market_open(hours=1, minutes=30)
    )


def rebalance(context, data):

    # Target a 100% long allocation of our portfolio in AAPL.
    objective = opt.TargetWeights({context.att: 1.0})

    # The Optimize API allows you to define portfolio constraints, which can be
    # useful when you have a more complex objective. In this algorithm, we
    # don't have any constraints, so we pass an empty list.
    constraints = []

    # order_optimal_portfolio uses `objective` and `constraints` to find the
    # "best" portfolio weights (as defined by your objective) that meet all of
    # your constraints. Since our objective is just "target 100% in AAPL", and
    # we have no constraints, this will maintain 100% of our portfolio in AAPL.
    algo.order_optimal_portfolio(objective, constraints)
There was a runtime error.