AMD 453.02%

Stock 1

5
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
 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(351)

# 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.
1 response

As they say, hindsight is 20-20.