Spring 2019 Project 6: Manual Strategy - Revision history

Ssung40: /* Part 3: Manual Rule-Based Trader (50 points) */

2019-03-07T06:52:29Z

Part 3: Manual Rule-Based Trader (50 points)

2019-03-05T03:02:40Z

Part 2: Theoretically Optimal Strategy (20 points)

2019-03-05T00:27:27Z

Part 2: Theoretically Optimal Strategy (20 points)

2019-03-04T20:03:07Z

Data Details, Dates and Rules

2019-03-04T20:02:53Z

Revisions

2019-03-04T20:02:37Z

Revisions

2019-03-04T19:56:02Z

Data Details, Dates and Rules

2019-03-04T19:46:28Z

Rubric

2019-03-04T19:43:25Z

Part 2: Theoretically Optimal Strategy (20 points)

2019-03-04T19:37:48Z

Part 3: Manual Rule-Based Trader (50 points)

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 For the report we want a written description, not code, however, it is OK to augment your written description with a pseudocode figure.
-You should tweak your rules as best you can to get the best performance possible during the in sample period (do not peek at out of sample performance).  Use your rule-based strategy to generate an orders dataframe over the in sample period, then run that dataframe through your market simulator to create a chart that includes the following components over the in sample period:
+You should tweak your rules as best you can to get the best performance possible during the in sample period (do not peek at out of sample performance).  Use your rule-based strategy to generate an trades dataframe over the in sample period, then run that dataframe through your market simulator to create a chart that includes the following components over the in sample period:
 * Benchmark (see definition above) normalized to 1.0 at the start: Green line

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 * Benchmark (see definition above) normalized to 1.0 at the start: Green line
 * Value of the theoretically optimal portfolio (normalized to 1.0 at the start): Red line
 Your code should implement testPolicy() as follows:

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      df_trades = ms.testPolicy(symbol = "AAPL", sd=dt.datetime(2010, 1, 1&#41;, ed=dt.datetime(2011,12,31&#41;, sv = 100000&#41;
-You should also report in text:
+The input parameters are:
-* Cumulative return of the benchmark and portfolio
+* symbol: the stock symbol to act on
-* Stdev of daily returns of benchmark and portfolio
+* sd: A datetime object that represents the start date
-* Mean of daily returns of benchmark and portfolio
+* ed: A datetime object that represents the end date
 ==Part 3: Manual Rule-Based Trader (50 points)==

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 * Use only the data provided for this course.  You are not allowed to import external data.
-* Please add in an author function.
+* Please add in an author function to each file.
 * For your report, trade only the symbol JPM. This will enable us to more easily compare results.
 * You may use data from other symbols (such as SPY) to inform your strategy.

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 This assignment is subject to change up until 3 weeks prior to the due date. We do not anticipate changes; any changes will be logged in this section.
-Line colors changed. Added requirements for author function in each file.
+* Line colors changed. Added requirements for author function in each file.
 ==Overview==

← Older revision		Revision as of 20:02, 4 March 2019
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	This assignment is subject to change up until 3 weeks prior to the due date. We do not anticipate changes; any changes will be logged in this section.		This assignment is subject to change up until 3 weeks prior to the due date. We do not anticipate changes; any changes will be logged in this section.
		+
		+	Line colors changed. Added requirements for author function in each file.

	==Overview==		==Overview==

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 * Is the chart correct (dates and equity curve) (-10 points if not)
 * Is the chart correct (dates and equity curve) (-10 points if not)
-* Historic value of benchmark normalized to 1.0 with blue line (-5 if not)
+* Historic value of benchmark normalized to 1.0 with green line (-5 if not)
-* Historic value of portfolio normalized to 1.0 with black line (-5 if not)
+* Historic value of portfolio normalized to 1.0 with red line (-5 if not)
 * Are the reported performance criteria correct ? (-2 points for each item if not)

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 Your code should implement the same API as above for theoretically optimal:
-     df_trades = ms.testPolicy(symbol = "AAPL", sd=dt.datetime(2010, 1, 1&#41;
+     df_trades = ms.testPolicy(symbol = "AAPL", sd=dt.datetime(2010, 1, 1&#41;, ed=dt.datetime(2011,12,31&#41;, sv = 100000&#41;
 ==Part 4: Comparative Analysis (10 points)==