MC1-Project-2 - Revision history

Tucker: /* Required, Allowed & Prohibited */

2017-03-02T18:24:59Z

Required, Allowed & Prohibited

Tucker: /* What to turn in */

2017-03-02T18:18:58Z

What to turn in

Tucker: /* Suggestions */

2017-02-16T18:36:11Z

Suggestions

Tucker: /* Updates */

2017-01-26T17:48:27Z

Updates

Tucker: /* What to turn in */

2017-01-26T17:46:15Z

What to turn in

Tucker: /* Updates */

2017-01-25T21:51:43Z

Updates

Tucker: /* What to turn in */

2017-01-25T21:50:34Z

What to turn in

Tucker: /* Updates */

2017-01-25T21:47:01Z

Updates

Tucker: /* Suggestions */

2017-01-23T19:17:39Z

Suggestions

Tucker: /* What to turn in */

2016-09-01T00:40:37Z

What to turn in

@@ Line 153: / Line 153: @@
 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), or on one of the provided virtual images.
+* 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
 * Your code must run in less than 5 seconds on one of the university-provided computers.

@@ Line 130: / Line 130: @@
 * 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 plot comparing the optimal portfolio with SPY as <tt>comparison_optimal.png</tt> 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 as using the following parameters:<br />Start Date: 2008-06-01, End Date: 2009-06-01, Symbols: ['IBM', 'X', 'GLD']
 Unlimited resubmissions are allowed up to the deadline for the project.

@@ Line 57: / Line 57: @@
 ==Suggestions==
-Refer to comments in the provided helper code for pointers regarding how to implement it. In order to specify bounds and constraints when using the <tt>scipy.optmize</tt> module, you'll need to use a special syntax explained here: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
+* Refer to comments in the provided helper code for pointers regarding how to implement it. In order to specify bounds and constraints when using the <tt>scipy.optmize</tt> module, you'll need to use a special syntax explained here: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
-For bounds, you simply need to pass in a sequence of 2-tuples <tt>(&lt;low&gt;, &lt;high&gt;)</tt>. Just remember that you need to supply as many tuples as the number of stocks in your portfolio.
+* For bounds, you simply need to pass in a sequence of 2-tuples <tt>(&lt;low&gt;, &lt;high&gt;)</tt>. Just remember that you need to supply as many tuples as the number of stocks in your portfolio.
-For constraints, it's a little tricky. You need to pass in a sequence of dicts (dictionaries), one dictionary per constraint. Each dictionary must specify the type of constraint (<tt>'eq'</tt> for equality, or <tt>'ineq'</tt> for inequality), and a function that ''returns 0 only when the input satisfies the constraint'' (this is the same input that is supplied to your evaluation function). E.g. to constrain the sum of all values in the input array to be less than 50, you could pass in the following (lambdas are just anonymous functions defined on-the-spot):
+* For constraints, it's a little tricky. You need to pass in a sequence of dicts (dictionaries), one dictionary per constraint. Each dictionary must specify the type of constraint (<tt>'eq'</tt> for equality, or <tt>'ineq'</tt> for inequality), and a function that ''returns 0 only when the input satisfies the constraint'' (this is the same input that is supplied to your evaluation function). E.g. to constrain the sum of all values in the input array to be less than 50, you could pass in the following (lambdas are just anonymous functions defined on-the-spot):
   constraints = ({ 'type': 'ineq', 'fun': lambda inputs: 50.0 - np.sum(inputs) })
 ==Example output 1==

← Older revision		Revision as of 17:48, 26 January 2017
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	* '''2017-01-25''' Symbols and date ranges for chart to create have been updated.		* '''2017-01-25''' Symbols and date ranges for chart to create have been updated.
		+	* '''2017-01-26''' submission process updated -- only one t-square project to submit to.

	==Overview==		==Overview==

@@ Line 124: / Line 124: @@
 Be sure to follow these instructions exactly!
-There are two separate t-square projects to submit.  This is because one will be graded by the auto grader and the other will be graded by our TAs.  Via T-Square, submit as attachments (no zip files; refer to schedule for deadline):
+Via T-Square, 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.

← Older revision		Revision as of 21:51, 25 January 2017
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	==Updates==		==Updates==
		+
		+	* '''2017-01-25''' Symbols and date ranges for chart to create have been updated.

	==Overview==		==Overview==

← Older revision		Revision as of 21:47, 25 January 2017
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	==Updates==		==Updates==
−
−	* '''2016-1-26''' Clarification that you should implement <tt>optimize_portfolio()</tt>
−	* '''2016-1-27''' Revision of which assets and dates to submit for the chart.
−	* '''2016-1-28''' Use of global variables prohibited.

	==Overview==		==Overview==