Step-By-Step operating recommendations

There you can find basic recommendations, how to set up your trading system for getting excellent forecasting results, and build profitable models. Basic steps of operating is:

1. Creating model

2. Learning model

3. Estimating model's performance

4. Using model for getting forecasting signals on new data.

If model's performance seems to be not too high for you, that means that you have to try to create another model, with different parameters' set. The most important part is to define 'optimized' model for selected ticker symbol. For example, the model with No Filtration Level shows greater results than 2nd Filtration Level, or, Neural Network larger structure gives worse results than smaller structure because the data used in learning, is not sufficient for such a large NN structure.

The way how to get optimized model, is to create several models with different parameters for selected symbol, learn them all, and compare the performance of each model. The model with highest performance is your 'optimized' model, which may be used in further forecasting. For example, you can create 5 models for IBM, changing filtration level, or, trading strategy, or, desired signals frequency (see Creating Model chapter for detailed explanations of model's parameters), with different modes' names, such as "IBM_INVEST_FILTR3_FREQ5", "IBM_MIDDLE_FILTRNO_FREQ3" etc. This way, you will have the description of created model in its name. You also always can see detailed model's parameters by clicking FILE/INFORMATION ABOUT ACTIVE NEURAL MODEL menu item, under main menu.

For estimating the performance of the model, i usually learn it, and then get forecasting results on out-of-sample period. It is the period which was extracted from learning process, so the data for that period is unknown for the system. If the accuracy of signals is acceptable, and, profitability is high, i reset the out of sample period, randomize the model, and relearn it from the beginning. Then, the model is ready for using.

Once the model is learned, it may be used for getting forecasting signals. Press UPDATE button daily, and, new price bar will appear on stock chart. Then, click MAKE PREDICTION ON ENTIRE DATASET menu, and you will have forecasting signal at the last added bar, and at CURRENT POSITION INFO tab. There the recommendation will appear, for the next day open. It may show HOLD for being out of trade if you are in opened position already, NONE for keeping tracking in the next several days if you're not in opened position yet, SELL for closing previously opened LONG position or for opening SHORT position tomorrow, BUY for opening LONG position at the next market open, or for closing previously opened SHORT position.

One last point-you dont have not relearn your model after each update, as the structure of NN changes every time when learning process starts. So, i would recommend to use optimized model for the period of %15-%20 of initial training period, then, estimate model's performance again, and if it is still up-to-date, and can give adequate forecasting signals, continue using it. If the accuracy goes lower after some period of updating, just relearn it, so that the new data passed to the system by updating procedure, has reflection in the model. For example, if i create the model with 1 year historical period stock quotes, learn it, then, i may use it for 1-2 months, for getting forecasting, and being in high accuracy level and total net profit. If the accuracy decreases after 2 months of using, i may click AFTERLEARN MODEL, so new data will be used in afterlearning. The model is not randomized in this case. Or, i may select RANDOMIZE MODEL, RESET OUT OF SAMPLE PERIOD, and start total learning.