Spring 2019 Project 2: Optimize Something - Revision history

Ssung40: /* Required, Allowed & Prohibited */

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Required, Allowed & Prohibited

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Task

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Task

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Required, Allowed & Prohibited

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What to turn in

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Task

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Overview

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Overview

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Overview

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Required, Allowed & Prohibited

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 Required:
 * Your project must be coded in Python 2.7.x.
-* Your code must run on one of the university-provided computers (e.g. buffet02.cc.gatech.edu).
+* Your code must run on one of the university-provided computers (e.g. buffet01.cc.gatech.edu).
 * Use the code for reading in historical data provided in util.py. Only use the API methods provided here to read in stock data. Do NOT modify this file. For grading, we will use our own unmodified version.
 * Your code must run in less than 5 seconds on one of the university-provided computers.

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 * ed: A datetime object that represents the end date
 * syms: A list of symbols that make up the portfolio (note that your code should support any symbol in the data directory)
-* gen_plot: If True, optionally create a plot named <tt>plot.png</tt>. We will always call your code with gen_plot = False.
+* gen_plot: If True, optionally create a plot named <tt>plot.png</tt>. Autograder will always call your code with gen_plot = False.
 ALL of your code should be present in the single file <tt>optimization.py</tt>

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 Implement a Python function named <tt>optimize_portfolio()</tt> in the file <tt>optimization.py</tt> that can find the optimal allocations for a given set of stocks. You should optimize for maximum Sharpe Ratio.
-The function should accept as input a list of symbols as well as start and end dates and return a list of floats (as a one-dimensional numpy array) that represents the allocations to each of the equities. You can take advantage of routines developed in the optional assess portfolio project to compute daily portfolio value and statistics.  You should cut-and-paste your code for the functions that did this from the last project  into <tt>optimization.py</tt>.
+The function should accept as input a list of symbols as well as start and end dates and return a list of floats (as a one-dimensional numpy array) that represents the allocations to each of the equities. You can take advantage of routines developed in the optional assess portfolio project to compute daily portfolio value and statistics.  You should cut-and-paste your code for the functions that did this into <tt>optimization.py</tt>.
 You are given the following inputs for optimizing a portfolio:

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 * Your project must be coded in Python 2.7.x.
 * Your code must run on one of the university-provided computers (e.g. buffet02.cc.gatech.edu).
-* Use the code for reading in historical data provided in util.py
+* Use the code for reading in historical data provided in util.py. Only use the API methods provided here to read in stock data. Do NOT modify this file. For grading, we will use our own unmodified version.
 * Your code must run in less than 5 seconds on one of the university-provided computers.

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 Via Canvas, submit as attachments (no zip files; refer to schedule for deadline):
-* Your code as <tt>optimization.py</tt> (only the function <tt>optimize_portfolio()</tt> will be tested).  Make sure that all necessary code is in that file.
+* Your code as <tt>optimization.py</tt> (only the function <tt>optimize_portfolio()</tt> will be tested).  Make sure that all necessary code is in that file. Please submit the code (.py) into the assignment "Project 2: Optimize Something (Code)" only
-* A report <tt>report.pdf</tt> that includes a chart comparing the optimal portfolio with SPY using the following parameters:<br />Start Date: 2008-06-01, End Date: 2009-06-01, Symbols: ['IBM', 'X', 'GLD', 'JPM']
+* A report <tt>report.pdf</tt> that includes a chart comparing the optimal portfolio with SPY using the following parameters:<br />Start Date: 2008-06-01, End Date: 2009-06-01, Symbols: ['IBM', 'X', 'GLD', 'JPM']. Please submit the report (.pdf) into the assignment "Project 2: Optimize Something (Report) only
 Unlimited resubmissions are allowed up to the deadline for the project.

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 In this project you will use what you learned about optimizers to optimize a portfolio.  That means that you will find how much of a portfolio's funds should be allocated to each stock so as to optimize it's performance.  We can optimize for many different metrics.  In this version of the assignment we will maximize Sharpe Ratio.
-You can leverage the functions in the now deprecated (optional) assess portfolio project that assessed the value of a portfolio with a given set of allocations.
+You can leverage the functions in the now deprecated (optional) [[assess portfolio]] project that assessed the value of a portfolio with a given set of allocations.
 An older version of this project: [[MC1-Project-2-archive]]

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 * Any use of global variables.
 * Any libraries not listed in the "allowed" section above.
-* Calls to code in any other local files besides util.py (e.g. don't depend on analysis.py being present).
+* Calls to code in any other local files besides util.py
 * Use of any code other than util.py to read in data.
 * Use of Python's os module.
 * Any code you did not write yourself (except for the 5 line rule in the "allowed" section).
 * Camels and other dromedaries.