Manual strategy - Revision history

Greg at 04:21, 31 October 2018

2018-10-31T04:21:25Z

2018-10-31T02:01:35Z

Template

2018-05-03T18:23:06Z

Required, Allowed & Prohibited

2018-04-05T16:11:21Z

Part 4: Comparative Analysis (10 points)

2018-04-05T15:51:16Z

Rubric

2018-04-05T15:50:34Z

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

2018-04-05T15:49:59Z

Part 2: Theoretically Optimal Strategy (20 points)

2018-04-05T15:49:00Z

Data Details, Dates and Rules

2018-04-05T15:48:02Z

Template

2018-04-05T15:47:22Z

Part 2: Best Possible Strategy (20 points)

@@ Line 165: / Line 165: @@
 ==What to turn in==
-Turn your project in via t-square.
+Turn your project in via Canvas.
 * Your report as <tt>report.pdf</tt>

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 * <tt>indicators.py</tt> Your code that implements your indicators as functions that operate on dataframes.  The "main" code in indicators.py should generate the charts that illustrate your indicators in the report.
-* <tt>marketsimcode.py</tt> An improved version of your marketsim code that accepts a "trades" data frame (instead of a file).  More info on the trades data frame below.
+* <tt>marketsimcode.py</tt> An improved version of your marketsim code that accepts a "trades" data frame (instead of a file).  More info on the trades data frame below.  It is OK not to submit this file if you have subsumed its functionality into one of your other code files.
 * <tt>ManualStrategy.py</tt> Code implementing a ManualStrategy object (your manual strategy).  It should implement testPolicy() which returns a trades data frame (see below). The main part of this code should call marketsimcode as necessary to generate the plots used in the report.
 * <tt>TheoreticallyOptimalStrategy.py</tt> Code implementing a TheoreticallyOptimalStrategy object (details below).  It should implement testPolicy() which returns a trades data frame (see below). The main part of this code should call marketsimcode as necessary to generate the plots used in the report.

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 * Your code may use standard Python libraries.
 * You may use the NumPy, SciPy, matplotlib and Pandas libraries.  Be sure you are using the correct versions.
 * Code provided by the instructor, or allowed by the instructor to be shared.
 * A herring.
@@ Line 235: / Line 234: @@
 * Any other method of reading data besides util.py
 * Any libraries not listed in the "allowed" section above.
-* Any code you did not write yourself (except for the 5 line rule in the "allowed" section).
+* Any code you did not write yourself.
 ==Legacy==

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 * Both should be normalized to 1.0 at the start.
-Create a table that summarizes the performance of the stock, and the manual strategy for both in sample and out of sample periods.  Do your best to explain any differences.
+Create a table that summarizes the performance of the stock, and the manual strategy for both in sample and out of sample periods.  Explain WHY these differences occur.
 ==Hints==

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 * Does the submitted code <tt>indicators.py</tt> properly reflect the indicators provided in the report (-20 points if not)
-Best possible (up to 20 points potential deductions):
+Theoretically optimal (up to 20 points potential deductions):
 * Is the methodology described correct and convincing? (-10 points if not)
 * Is the chart correct (dates and equity curve) (-10 points if not)

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 We expect that your rule-based strategy should outperform the benchmark over the in sample period.
-Your code should implement the same API as above for best possible:
+Your code should implement the same API as above for theoretically optimal:
   df_trades = ms.testPolicy(symbol = "AAPL", sd=dt.datetime(2010,1,1), ed=dt.datetime(2011,12,31), sv = 100000)

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 * Benchmark (see definition above) normalized to 1.0 at the start: Blue line
-* Value of the best possible portfolio (normalized to 1.0 at the start): Black line
+* Value of the theoretically optimal portfolio (normalized to 1.0 at the start): Black line
 You should also report in text:
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 Your code should implement testPolicy() as follows:
-  df_trades = bps.testPolicy(symbol = "AAPL", sd=dt.datetime(2010,1,1), ed=dt.datetime(2011,12,31), sv = 100000)
+  df_trades = tos.testPolicy(symbol = "AAPL", sd=dt.datetime(2010,1,1), ed=dt.datetime(2011,12,31), sv = 100000)
 The input parameters are:

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 * There is no limit on leverage.
 * Transaction costs for ManualStrategy: Commission: $9.95, Impact: 0.005.
-* Transaction costs for BestPossibleStrategy: Commission: $0.00, Impact: 0.00.
+* Transaction costs for TheoreticallyOptimalStrategy: Commission: $0.00, Impact: 0.00.
 ==Part 1: Technical Indicators (20 points)==

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 At least one of the indicators you use should be completely different from the ones presented in our lectures. (i.e. something other than SMA, Bollinger Bands, RSI).
-==Part 2: Best Possible Strategy (20 points)==
+==Part 2: Theoretically Optimal Strategy (20 points)==
 Assume that you can see the future, but that you are constrained by the portfolio size and order limits as specified above.  Create a set of trades that represents the best a strategy could possibly do during the in sample period. The reason we're having you do this is so that you will have an idea of an upper bound on performance.