Машинное обучение: что нужно знать о создании стратегий для торговли на бирже

int detect(double* sig)
{
  if(sig[1]<sig[2] && sig[4]<sig[0] && sig[0]<sig[5] && sig[5]<sig[3] && sig[10]<sig[11] && sig[11]<sig[7] && sig[7]<sig[8] && sig[8]<sig[9] && sig[9]<sig[6])
      return 1; 
  if(sig[4]<sig[1] && sig[1]<sig[2] && sig[2]<sig[5] && sig[5]<sig[3] && sig[3]<sig[0] && sig[7]<sig[8] && sig[10]<sig[6] && sig[6]<sig[11] && sig[11]<sig[9])
      return 1;
  if(sig[1]<sig[4] && eqF(sig[4]-sig[5]) && sig[5]<sig[2] && sig[2]<sig[3] && sig[3]<sig[0] && sig[10]<sig[7] && sig[8]<sig[6] && sig[6]<sig[11] && sig[11]<sig[9])
      return 1;
  if(sig[1]<sig[4] && sig[4]<sig[5] && sig[5]<sig[2] && sig[2]<sig[0] && sig[0]<sig[3] && sig[7]<sig[8] && sig[10]<sig[11] && sig[11]<sig[9] && sig[9]<sig[6])
      return 1;
  if(sig[1]<sig[2] && sig[4]<sig[5] && sig[5]<sig[3] && sig[3]<sig[0] && sig[10]<sig[7] && sig[7]<sig[8] && sig[8]<sig[6] && sig[6]<sig[11] && sig[11]<sig[9])
      return 1;
  ....
  return 0;
}

int predict(double* sig)
{
  if(-27.99*sig[0] + 1.24*sig[1] - 3.54*sig[2] > -21.50)
    return 100;
  else
    return -100;
}

library('deepnet', quietly = T) 
library('caret', quietly = T)

# called by Zorro for training
neural.train = function(model,XY) 
{
  XY <- as.matrix(XY)
  X <- XY[,-ncol(XY)] # predictors
  Y <- XY[,ncol(XY)]  # target
  Y <- ifelse(Y > 0,1,0) # convert -1..1 to 0..1
  Models[[model]] <<- sae.dnn.train(X,Y,
      hidden = c(20,20,20), 
      activationfun = "tanh", 
      learningrate = 0.5, 
      momentum = 0.5, 
      learningrate_scale = 1.0, 
      output = "sigm", 
      sae_output = "linear", 
      numepochs = 100, 
      batchsize = 100,
      hidden_dropout = 0, 
      visible_dropout = 0)
}

# called by Zorro for prediction
neural.predict = function(model,X) 
{
  if(is.vector(X)) X <- t(X) # transpose horizontal vector
  return(nn.predict(Models[[model]],X))
}

# called by Zorro for saving the models
neural.save = function(name)
{
  save(Models,file=name) # save trained models
}

# called by Zorro for initialization
neural.init = function()
{
  set.seed(365)
  Models <<- vector("list")
}

# quick OOS test for experimenting with the settings
Test = function() 
{
  neural.init()
  XY <<- read.csv('C:/Project/Zorro/Data/signals0.csv',header = F)
  splits <- nrow(XY)*0.8
  XY.tr <<- head(XY,splits) # training set
  XY.ts <<- tail(XY,-splits) # test set
  neural.train(1,XY.tr)
  X <<- XY.ts[,-ncol(XY.ts)]
  Y <<- XY.ts[,ncol(XY.ts)]
  Y.ob <<- ifelse(Y > 0,1,0)
  Y <<- neural.predict(1,X)
  Y.pr <<- ifelse(Y > 0.5,1,0)
  confusionMatrix(Y.pr,Y.ob) # display prediction accuracy
}