Can We Upgrade a Trading System with a Neural Net?

Dr Michael Bowleshttp://www.linkedin.com/in/mikebowles

mike@mbowles.com

Program

Produce a Simple Trading System Try 2 neural nets for culling trades One works the other doesn't Test whether model selection techniques

would have chosen correctly.

1230

1235

1240

1245

1250

1255

1260

1265

1270

1275

1280

1285

1290

1230123512401245125012551260126512701275128012851290

6 :0 0 6 :3 0 7 :0 0 7 :3 0 8 :0 0 8 :3 0

E-Mini Nasdaq 100 (Continuous) (NQ #F)

2 0 0 9

Clos e Las t T op P ric e(High,10) Las t B ottom P ric e(Low,11)

T rading S trategy #2

Simple Trading System

Buy when price crosses above last high Sell when price crosses below last low

*Charts are Drawn with NeuroShell Trading Software Package

Simple System Performance

1120

1140

1160

1180

1200

1220

1240

1260

1280

1300

0

100

200

300

400

500

D e c 2 8 J a n 4 J a n 1 1 J a n 1 8 J a n 2 5 F e b 1 F e b 8 F e b 1 5

E-Mini Nasdaq 100 (Continuous) (NQ #F)

2 0 0 8

T rading S tra tegy #2

S y s tem E quity (T rading S tra tegy #2)

Optimize over 6 weeks. Trade for 2 weeks

Q1 Performance

Period # Return1 7.10%2 -5.20%3 6.70%4 -3.10%5 9.90%6 -3.00%7 5.10%

*In this table a “Period” is 2 weeks

Per 2 WeeksAvg Rtn: 2.50%

Rtn s.d.: 6.07%

YearlySharpe's: 2.06

Can Statistical Learning Discard Some Losing Trades?

Give a NN some traditional indicators Train it to reject losing trades 1st try:

2 input (traditional momentum indicators) 1 to 3 hidden neurons Compare >0 or <0 for accept reject

How does it work?

1170

1175

1180

1185

1190

1195

1200

1205

1170

1175

1180

1185

1190

1195

1200

1205

4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 6 1 7 1 9 2 1 F e b 1 9 1

E-Mini Nasdaq 100 (Continuous) (NQ #F)

2 0 0 9

T rading S trategy #2

T rading S trategy

Q1 Performance (Basically the Same)

Period # Return1 4.90%2 -3.40%3 4.00%4 -5.10%5 5.50%6 4.10%7 2.40%

Per 2 WeeksAvg Rtn: 1.77%

Rtn s.d.: 4.25%

YearlySharpe's: 2.08

*Period = 2 weeks

Try Something Else

4 inputs (traditional momentum indicators) 2 hidden neurons Selectable tanh or Gaussian activation

functions for hiddens GA for weights and activation function

selection

Q1 Performance (Good News / Bad News)

Period # Return1 7.80%2 -4.30%3 5.90%4 -0.50%5 7.10%6 7.30%7 5.30%

Per 2 WeeksAvg Rtn: 4.09%

Rtn s.d.: 4.64%

YearlySharpe's: 4.40

*Period = 2 weeks

Predict the Better Performing Model?

How to judge models BEFORE looking at the OOS data

Have a look at linear regression

2-Input Model Results(Linear Terms)

Estimate Std.Error t value Pr(>|t|)(Intercept) -0.78735 1.54228 -0.511 0.610x1 -0.14016 1.86259 -0.075 0.940x2 0.03526 0.10061 0.350 0.726

Multiple R-squared: 0.0004033AIC = 2910.813

2-Input Model Results(Polynomial Terms)

Estimate Std. Error t value Pr(>|t|)(Intercept) -0.787343 1.549424 -0.508 0.612x1 -0.160997 2.283883 -0.070 0.944x2 0.035802 0.103914 0.345 0.731x3 0.132438 0.850780 0.156 0.876x4 0.042197 0.157572 0.268 0.789x5 0.001034 0.004287 0.241 0.810

Multiple R-squared: 0.001049AIC = 2916.614

4-Input Model Results(Linear Terms)

Estimate StdError t value Pr(>|t|) (Intercept) -0.7929 1.5289 -0.519 0.60438 x1BB.MACD -3.1617 2.4588 -1.286 0.19948 x1BB.Price 0.7592 0.7233 1.050 0.29472 x1TT.MACD 6.8499 2.5562 2.680 0.00777 **x1TT.Price -0.9122 0.5645 -1.616 0.10712 Multiple R-squared: 0.02418AIC = 2907.399

4-Input Model Results(Polynomial Terms)

Estimate StdError t value Pr(>|t|) (Intercept) -0.7896 1.5283 -0.517 0.60581 x1BB.MACD -2.8592 2.8054 -1.019 0.30896 x1BB.Price 0.4084 0.7980 0.512 0.60920 x1TT.MACD 6.8554 2.8108 2.439 0.01532 * x1TT.Price -1.0843 0.7179 -1.511 0.13199 x2X1x1 -3.3184 2.1810 -1.521 0.12921 x2X1x2 1.5070 1.1713 1.287 0.19927 x2X1x3 11.1495 4.1630 2.678 0.00782 **x2X1x4 -2.0146 0.8542 -2.358 0.01901 * x2X2x2 -0.1230 0.1511 -0.814 0.41623 x2X2x3 -2.6644 1.1330 -2.352 0.01935 * x2X2x4 0.4220 0.2220 1.901 0.05825 . x2X3x3 -7.5425 2.6674 -2.828 0.00501 **x2X3x4 2.9110 1.0361 2.810 0.00529 **x2X4x4 -0.2644 0.1136 -2.327 0.02066 * Multiple R-squared: 0.05708AIC = 2916.835

Conclusion

Demonstrated NN Successfully Upgrades Trade System Statistics

Demonstrated Criteria for Model / Input Selection

Some References

Dunis C., Laws J., Naim P, Applied Quantitative methods for trading and investment John Wiley & Sons, 2003 Ch 4. Forecasting and Trading Currency Volatility:An Application of Recurrent

Neural RegressionCh5. Implementing Neural Networks, Classification Trees, and Rule Induction

Classification Techniques: An Application to Credit Risk

Franses P.H., van Dijk D, Non-linear time series models in empirical finance, CambridgeUniversity Press, 2000Ch 5. Artificial neural networks for returns