Hi,
1. I think, the best inputs for our NN are all inputs that influence our desired output. In some case, during training the NN, we can remove any inputs that small correlation with our predicted output. But sure, this is not an easy task to find which parameters are the best for our nets.
To make prediction using HA is possible. You can use a script/indicator/EA to collect your data. Example
input[0] = iCustom(NULL, 0, "Heiken Ashi",0,i+21);
input[1] = iCustom(NULL, 0, "Heiken Ashi",1, i+21);
input[2] = iCustom(NULL, 0, "Heiken Ashi",1, i+21);
input[3] = iCustom(NULL, 0, "Heiken Ashi",1,i+21);
input[4] = iCustom(NULL, 0, "Heiken Ashi",0,i+34);
input[5] = iCustom(NULL, 0, "Heiken Ashi",1, i+34);
input[6] = iCustom(NULL, 0, "Heiken Ashi",2,i+34);
input[7] = iCustom(NULL, 0, "Heiken Ashi",3, i+34);
and the ouput is
output[0] = iHigh(NULL,0,limit-1);
PS: I remove extern color color1/2/3/4 in original Heiken Ashi
Here attached the sample data, for your NN training purpose.
Which one is better HA price or normal price, I am not sure..we need to train the net and test it. I prefer to preprocess data (using normalization) to limit our inputs/ouputs within certain range (e.g, -1 to 1, or 0 to 1).
Precaution: during training NN will measure maximum/minimum value from the pattern data, hence NN may not work in other range of data. Example if you train the NN while the predicted value is between 0.950 to 1.345, the trained net may not work if the actual value is below 0.95 or higher than 1.345.
2. Which pattern we need to choose for our training? I think the range where all patterns are shown. If all your trained pattern is only during bullish market, then your NN may not work during consolidation or bearish. Hence you should train all your data with all possible patterns. 20,000 patterns may be better than 1000 patterns, but 20,000 may not sufficient if not reflecting all conditions and 1,000 patterns may sufficient if it reflecting all conditions.
Arry
1. I think, the best inputs for our NN are all inputs that influence our desired output. In some case, during training the NN, we can remove any inputs that small correlation with our predicted output. But sure, this is not an easy task to find which parameters are the best for our nets.
To make prediction using HA is possible. You can use a script/indicator/EA to collect your data. Example
input[0] = iCustom(NULL, 0, "Heiken Ashi",0,i+21);
input[1] = iCustom(NULL, 0, "Heiken Ashi",1, i+21);
input[2] = iCustom(NULL, 0, "Heiken Ashi",1, i+21);
input[3] = iCustom(NULL, 0, "Heiken Ashi",1,i+21);
input[4] = iCustom(NULL, 0, "Heiken Ashi",0,i+34);
input[5] = iCustom(NULL, 0, "Heiken Ashi",1, i+34);
input[6] = iCustom(NULL, 0, "Heiken Ashi",2,i+34);
input[7] = iCustom(NULL, 0, "Heiken Ashi",3, i+34);
and the ouput is
output[0] = iHigh(NULL,0,limit-1);
PS: I remove extern color color1/2/3/4 in original Heiken Ashi
Here attached the sample data, for your NN training purpose.
Which one is better HA price or normal price, I am not sure..we need to train the net and test it. I prefer to preprocess data (using normalization) to limit our inputs/ouputs within certain range (e.g, -1 to 1, or 0 to 1).
Precaution: during training NN will measure maximum/minimum value from the pattern data, hence NN may not work in other range of data. Example if you train the NN while the predicted value is between 0.950 to 1.345, the trained net may not work if the actual value is below 0.95 or higher than 1.345.
2. Which pattern we need to choose for our training? I think the range where all patterns are shown. If all your trained pattern is only during bullish market, then your NN may not work during consolidation or bearish. Hence you should train all your data with all possible patterns. 20,000 patterns may be better than 1000 patterns, but 20,000 may not sufficient if not reflecting all conditions and 1,000 patterns may sufficient if it reflecting all conditions.
Arry