Classification Trader Hints - Revision history

Tucker: /* Hints */

2017-12-06T20:26:56Z

Hints

Tucker at 18:26, 22 November 2017

2017-11-22T18:26:07Z

Tucker: /* Hints */

2017-11-22T03:57:11Z

Hints

Tucker: /* ML Trader */

2017-11-22T03:54:03Z

ML Trader

Tucker: /* Manual Rule-Based Trader */

2017-11-22T03:49:44Z

Manual Rule-Based Trader

Tucker: /* Best Possible Strategy */

2017-11-22T03:49:33Z

Best Possible Strategy

Tucker: /* Technical Indicators */

2017-11-22T03:49:21Z

Technical Indicators

Tucker: /* Q & A */

2017-11-22T03:49:08Z

Q & A

Tucker: /* Overview */

2017-11-22T03:48:53Z

Overview

Tucker: /* ML Trader */

2017-07-28T17:29:17Z

ML Trader

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 * Indicator design hints:
 ** For your X values: Identify and implement at least 3 technical features that you believe may be predictive of future return.
 * Train a classification learner on in sample training data:
 ** For your Y values: Use future N day return (not future price).  Then classify that return as LONG, SHORT or CASH.  You're trying to predict a relative change that you can use to invest with.

← Older revision		Revision as of 18:26, 22 November 2017
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−
	==Overview==		==Overview==

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 ** Use a cascade of if statements conditioned on the indicators to identify whether a SHORT condition is met.
 ** The conditions for LONG and SHORT should be mutually exclusive.
-** If neither LONG or SHORT is triggered, the result should be DO NOTHING.
+** If neither LONG or SHORT is triggered, the result should be CASH.
 ** For debugging purposes, you may find it helpful to plot the value of the rule-based output (-1, 0, 1) versus the stock price.
 * Train a classification learner on in sample training data:
-** For your Y values: Use future 21 day return (not future price).  Then classify that return as LONG, SHORT or DO NOTHING.  You're trying to predict a relative change that you can use to invest with.
+** For your Y values: Use future N day return (not future price).  Then classify that return as LONG, SHORT or CASH.  You're trying to predict a relative change that you can use to invest with.
 ** For debugging purposes, you may find it helpful to plot the value of the training classification data (-1, 0, 1) versus the stock price in one color.
 ** For debugging purposes, you may find it helpful to plot the value of the training classification output (-1, 0, 1) versus the stock price in another color.  Ideally, these two lines should be very similar.
-'''Choosing Technical Features -- Your X Values'''
+Your code should classify based on N day change in price.  You need to build a new Y that reflects the N day change and aligns with the current date.  Here's pseudo code for the calculation of Y
-You should have already successfully coded the Bollinger Band feature:
+  ret = (price[t+N]/price[t]) - 1.0
   if ret > YBUY:
       Y[t] = +1 # LONG
@@ Line 68: / Line 48: @@
       Y[t] = -1 # SHORT
   else:
-      Y[t] = 0
+      Y[t] = 0 # CASH
-If you select Y in this manner and use it for training, your learner will classify 21 day returns.
+If you select Y in this manner and use it for training, your learner will classify N day returns.
 ==Legacy==

@@ Line 9: / Line 9: @@
 * +1: LONG
-* 0: DO NOTHING
+* 0: CASH
 * -1: SHORT
-The X data for each sample (day) are simply the values of your indicators for the stock -- you should have 3 to 5 of them.  The Y data (or classifications) will be based on 21 day return.  You should classify the example as a +1 or "LONG" if the 21 day return exceeds a certain value, let's call it YBUY for the moment.  You should classify the example as a -1 or "SHORT" if the 21 day return is below a certain value we'll call YSELL.   In all other cases the sample should be classified as a 0 or "DO NOTHING."  Note that it is very important that you train your learner with these classification values (not the 21 day returns).
+The X data for each sample (day) are simply the values of your indicators for the stock -- you should have 3 to 5 of them.  The Y data (or classifications) will be based on N day return (your choice for N).  You should classify the example as a +1 or "LONG" if the N day return exceeds a certain value, let's call it YBUY for the moment.  You should classify the example as a -1 or "SHORT" if the N day return is below a certain value we'll call YSELL.   In all other cases the sample should be classified as a 0 or "CASH."  Note that it is very important that you train your learner with these classification values (not the N day returns).
 Note that your X values are calculated each day from the current day's (and earlier) data, but the Y value (classification) is calculated using data from the future.  You may tweak various parameters of your learner to maximize return (more on that below).  Train and test your learning strategy over the in sample period.
 '''Important note:''' You must set the leaf_size parameter of your decision tree learner to 5 or larger.  This requirement is intended to avoid a degenerate overfit solution to this problem.
 You should tweak the parameters of your learner to maximize performance during the in sample period.  Here is a partial list of things you can tweak:

← Older revision		Revision as of 03:49, 22 November 2017
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	You will utilize your Random Tree learner to train and test a learning trading algorithm. Here are some ideas (gathered from a previous project) that you might find helpful if you are going to use a classification or regression learner for your trader.		You will utilize your Random Tree learner to train and test a learning trading algorithm. Here are some ideas (gathered from a previous project) that you might find helpful if you are going to use a classification or regression learner for your trader.
−
−	~~==Manual Rule-Based Trader==~~
−
−	~~As you ramp up for your learning trader, try this exercise.~~
−
−	Devise a set of rules using the indicators you created above. Your rules should be designed to trigger a "long" or "short" entry for a 21 trading day hold. In other words, once an entry is initiated, you must remain in the position for 21 trading days.
−
−	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 file over the in sample period, then run that file 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: Black line
−	* Value of the rule-based portfolio (normalized to 1.0 at the start): Blue line
−	* Vertical green lines indicating LONG entry points.
−	* Vertical red lines indicating SHORT entry points.
−
−	~~Note that each red or green vertical line should be at least 21 days from the preceding line. We expect that your rule-based strategy should outperform the benchmark over the in sample period.~~

	==ML Trader==		==ML Trader==

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 ==Overview==
-In this project you will develop trading strategies using Technical Analysis, and test them using your market simulator. You will then utilize your Random Tree learner to train and test a learning trading algorithm.  Here are some ideas (gathered from a previous project) that you might find helpful if you are going to use a classification or regression learner for your trader.
+You will utilize your Random Tree learner to train and test a learning trading algorithm.  Here are some ideas (gathered from a previous project) that you might find helpful if you are going to use a classification or regression learner for your trader.
 ==Q & A==