DMelt:Finance/Market Prediction

Table of contents

Market prediction

In Section Neural Network we discussed how to build a neural net based on backpropogation algorithm. Now we will show how to use this neural network approach to make predictions of financial market market.

Using Joone

Let us consider Joone Neural net for forecasting market predictions using a generated time series in the form cos(x)*sin(x)+Gaussian noise.

Here is the output image that shows the original time series and the predicted trends (n red).

As you can see, the neural net correctly predicts the expected behavior.

Market prediction

Now we consider another practical example. First, we will fetch data from Yahoo and store this data in a file: YahooFinance.egb. We will gather training data for the last 2 years, stopping 60 days short of today.

This script also prints input (10 values) and output (one value): A single entry out of 491 will look as this:

[0.00384, 0.00178, 0.006807,
-0.00833, 0.00795, 0.014900,
-0.00409, 0.00220, -0.00312,
-0.00934]  
ideal= [-0.01494]

Now let us build a neural net with 10 inputs, one output and 20 hidden layers. We will train this network for 1 minute and then save the trained network in a file. The script code is given below. <ifauthr>

</ifauth>

Now we read data from YahooFinance again, this time using last 30 days. Then we read trained neural network and perform the prediction based on the last 2 year trend. We compare the actual and predicted value, and print likelihood of our correct prediction:

The output of the execution of this script will look something like:

Fetching data from Yahoo .. 
Print prediction..
Day  1.0 :actual= -0.0113 ( -1 ) predict=-0.0041 ( -1 ),diff= 0.00723281045594
Day  2.0 :actual= -0.0108 ( -1 ) predict=0.0056 ( 1 ),diff= 0.016323498302
Day  3.0 :actual= 0.0070 ( 1 ) predict=0.0068 ( 1 ),diff= 0.000142392841989
Day  4.0 :actual= 0.0320 ( 1 ) predict=0.0026 ( 1 ),diff= 0.0294366145542
Day  5.0 :actual= 0.0226 ( 1 ) predict=-0.0030 ( -1 ),diff= 0.0256697221103
Day  6.0 :actual= 0.0055 ( 1 ) predict=0.0132 ( 1 ),diff= 0.00767318237177
Day  7.0 :actual= -0.0080 ( -1 ) predict=0.0118 ( 1 ),diff= 0.0198059904802
Day  8.0 :actual= -0.0057 ( -1 ) predict=0.0092 ( 1 ),diff= 0.0148906339578
Day  9.0 :actual= 0.0176 ( 1 ) predict=0.0020 ( 1 ),diff= 0.0156139264763
Day  10.0 :actual= -0.0038 ( -1 ) predict=0.0031 ( 1 ),diff= 0.00693045712649
Day  11.0 :actual= 0.0156 ( 1 ) predict=-0.0033 ( -1 ),diff= 0.0188739030904
Day  12.0 :actual= -0.0018 ( -1 ) predict=0.0065 ( 1 ),diff= 0.00835930166595
Day  13.0 :actual= 0.0022 ( 1 ) predict=0.0024 ( 1 ),diff= 0.000244326149596
Day  14.0 :actual= 0.0065 ( 1 ) predict=-0.0063 ( -1 ),diff= 0.0127469148732
Day  15.0 :actual= 0.0003 ( 1 ) predict=0.0071 ( 1 ),diff= 0.00687984465776
Day  16.0 :actual= 0.0034 ( 1 ) predict=-0.0029 ( -1 ),diff= 0.00624265349531
Day  17.0 :actual= -0.0158 ( -1 ) predict=0.0020 ( 1 ),diff= 0.0177893230083
Day  18.0 :actual= 0.0032 ( 1 ) predict=-0.0046 ( -1 ),diff= 0.00781173668604
Day  19.0 :actual= -0.0172 ( -1 ) predict=0.0009 ( 1 ),diff= 0.0180277119123
Day  20.0 :actual= 0.0514 ( 1 ) predict=-0.0004 ( -1 ),diff= 0.0518078026793
Day  21.0 :actual= -0.0046 ( -1 ) predict=0.0060 ( 1 ),diff= 0.010602114572
Day  22.0 :actual= 0.0057 ( 1 ) predict=0.0051 ( 1 ),diff= 0.000595736696516
Day  23.0 :actual= 0.0154 ( 1 ) predict=0.0064 ( 1 ),diff= 0.00904954951323
Day  24.0 :actual= 0.0124 ( 1 ) predict=0.0152 ( 1 ),diff= 0.00284543760372
Day  25.0 :actual= -0.0018 ( -1 ) predict=-0.0040 ( -1 ),diff=
0.00223833948343
Day  26.0 :actual= 0.0092 ( 1 ) predict=0.0017 ( 1 ),diff= 0.0074896027783
Day  27.0 :actual= 0.0129 ( 1 ) predict=0.0012 ( 1 ),diff= 0.0116513741193
Day  28.0 :actual= 0.0149 ( 1 ) predict=0.0018 ( 1 ),diff= 0.0131001498602
Day  29.0 :actual= -0.0089 ( -1 ) predict=0.0062 ( 1 ),diff= 0.0151645127572
Day  30.0 :actual= -0.0006 ( -1 ) predict=-0.0028 ( -1 ),diff=
0.00226212010508
Day  31.0 :actual= -0.0020 ( -1 ) predict=-0.0015 ( -1 ),diff=
0.000477980160665
Day  32.0 :actual= -0.0142 ( -1 ) predict=-0.0026 ( -1 ),diff= 0.0116349624231
Direction correct: 25.0 / 32.0
Directional Accuracy: 63.1250 %