Optimize something - Revision history

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What to turn in

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 You are given the following inputs for optimizing a portfolio:
 * A date range to select the historical data to use (specified by a start and end date)
-* Symbols for equities (e.g., GOOG, AAPL, GLD, XOM). Note: You should support any symbol in the data directory.
+* Symbols for equities (e.g., GOOG, AAPL, GLD, XOM). Note: You should support any symbol in the data directory. Your code should support any number of assets >= 2.  Don't hardcode 4.
 Your goal is to find allocations to the symbols that optimize the criteria given above.  Assume 252 trading days in a year and a risk free return of 0.0 per day.  You should implement the following API EXACTLY, if you do not your submission will be penalized.

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 ==Updates==
-* '''Sept. 4, 2018''': Project updated to require maximum Sharpe Ratio (versus older minimize volatility).
+* '''Sept. 4, 2018''':
 ==Overview==

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 ==Updates==
-* '''Sept. 4, 2018''': Project updated to require maximum Sharpe Ration (versus older minimize volatility).
+* '''Sept. 4, 2018''': Project updated to require maximum Sharpe Ratio (versus older minimize volatility).
 ==Overview==

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 * 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.
-* 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']
+* 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']
 Unlimited resubmissions are allowed up to the deadline for the project.

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 Be sure to follow these instructions exactly!
-Via T-Square, submit as attachments (no zip files; refer to schedule for deadline):
+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.

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 * To run the grading script, follow the instructions given in [[ML4T Software Setup]]
-Your solution to the optimization problem should leverage the software you developed in the last assignment.  However '''you cannot call your old file analysis.py,''' you must copy and paste that old code into the single file for this assignment. To test your code, we will be calling <tt>optimize_portfolio()</tt> only.
+Your solution to the optimization problem can leverage the software you developed in the last assignment.  However '''you cannot call your old file analysis.py,''' you must copy and paste that old code into the single file for this assignment. To test your code, we will be calling <tt>optimize_portfolio()</tt> only.
 ==Suggestions==

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 ALL of your code should be present in the single file <tt>optimization.py</tt>
-An example chart.
+An example chart that you might create for assessing your optimizer.
 [[File:comparison_optimal.png]]

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 ==Task==
-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 minimum volatility (i.e., standard deviation of daily return).
+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 should take advantage of routines developed in the portfolio analysis 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 portfolio analysis 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>.
 You are given the following inputs for optimizing a portfolio:
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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, create a plot named <tt>plot.png</tt>
+* gen_plot: If True, optionally create a plot named <tt>plot.png</tt>. We will always call your code with gen_plot = False.
 ALL of your code should be present in the single file <tt>optimization.py</tt>

← Older revision		Revision as of 04:03, 5 September 2018
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		+	==Updates==
		+	* '''Sept. 4, 2018''': Project updated to require maximum Sharpe Ration (versus older minimize volatility).
		+
	==Overview==		==Overview==

← Older revision		Revision as of 04:02, 5 September 2018
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	An older version of this project: [[MC1-Project-2-archive]]		An older version of this project: [[MC1-Project-2-archive]]
−
−	~~==Updates==~~
−	* '''Jan. 26, 2018 - 16:18 ET''': The grading script has been updated to catch certain types of bad return values from optimize_portfolio. Thanks to Daniel Vetter for catching this bug!

	==Task==		==Task==