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| 品牌MT5神经网络EA | 有效期至长期有效 | 最后更新2019-01-10 18:44 |
| 浏览次数330 |
MT5神经网络EA
//+------------------------------------------------------------------+
//| macdNeuroExample.mq5 |
//| wangyu204 |
//| http://www.fxunion.com |
//+------------------------------------------------------------------+
#property copyright wangyu204
#property link http://www.fxunion.com
#property version 1.00
//本EA通过模拟过去训练最佳权重w数据，操作未来
#include
#include
//神经网络权重 每个因子一个权值
input double w0=0.5;
input double w1=0.5;
input double w2=0.5;
input double w3=0.5;
input double w4=0.5;
input double w5=0.5;
input double w6=0.5;
input double w7=0.5;
input double w8=0.5;
input double w9=0.5;
input double w10=0.5;
input double w11=0.5;
input double w12=0.5;
input double w13=0.5;
input double w14=0.5;
input double w15=0.5;
input double w16=0.5;
input double w17=0.5;
input double w18=0.5;
input double w19=0.5;
//全局变量
int iMACD_handle;//macd indicator handle
double iMACD_mainbuf[];//dynamic array for storing indicator values
double iMACD_signalbuf[];//dynamic array for storing indicator values
double inputs[20];//输入因子
double weight[20];//对应输入权重
string my_symbol;
ENUM_TIMEframeS my_timeframe;
double lot_size;
double out;
CTrade m_Trade; //交易相关
CPositionInfo m_Position;//持仓相关
//+------------------------------------------------------------------+
//| Expert initialization function |
//+------------------------------------------------------------------+
int onInit()
{
//---
my_symbol=Symbol();
my_timeframe=PERIOD_CURRENT;
lot_size=SymbolInfoDouble(my_symbol,SYMBOL_VOLUME_MIN);
iMACD_handle=iMACD(my_symbol,my_timeframe,12,26,9,PRICE_CLOSE);
if(iMACD_handle==INVALID_HANDLE)
{
Print( Failed to get the indicator handle
return(-1);
}
ChartIndicatorAdd(ChartID(),0,iMACD_handle);
ArraySetAsSeries(iMACD_mainbuf,true);//设置数组序列
ArraySetAsSeries(iMACD_signalbuf,true);
//place weights into the array
weight[0]=w0;
weight[1]=w1;
weight[2]=w2;
weight[3]=w3;
weight[4]=w4;
weight[5]=w5;
weight[6]=w6;
weight[7]=w7;
weight[8]=w8;
weight[9]=w9;
weight[10]=w10;
weight[11]=w11;
weight[12]=w12;
weight[13]=w13;
weight[14]=w14;
weight[15]=w15;
weight[16]=w16;
weight[17]=w17;
weight[18]=w18;
weight[19]=w19;
return(0);
}
//+------------------------------------------------------------------+
//| Expert deinitialization function |
//+------------------------------------------------------------------+
void onDeinit(const int reason)
{
//---
IndicatorRelease(iMACD_handle);//delete the indicator handle and deallocate the memory space
ArrayFree(iMACD_mainbuf);//free dynamic array
ArrayFree(iMACD_signalbuf);
}
//+------------------------------------------------------------------+
//| Expert tick function |
//+------------------------------------------------------------------+
void onTick()
{
//---
int err1=0,err2=0;
err1=CopyBuffer(iMACD_handle,0,2,ArraySize(inputs)/2,iMACD_mainbuf);
err2=CopyBuffer(iMACD_handle,0,2,ArraySize(inputs)/2,iMACD_signalbuf);
if(err1 0 || err2 0)
{
Print( Failed to copy data from the indicator buffer
return;
}
//input标准化
double d1=-1.0;
double d2=1.0;
double x_min=MathMin(iMACD_mainbuf[ArrayMinimum(iMACD_mainbuf)],iMACD_signalbuf[ArrayMinimum(iMACD_signalbuf)]);
double x_max=MathMax(iMACD_mainbuf[ArrayMaximum(iMACD_mainbuf)],iMACD_signalbuf[ArrayMaximum(iMACD_signalbuf)]);
for(int i=0;i
{
inputs[i*2]=(iMACD_mainbuf[i]-x_min)*(d2-d1)/(x_max-x_min)+d1;
inputs[i*2+1]=(iMACD_signalbuf[i]-x_min)*(d2-d1)/(x_max-x_min)+d1;
}
//out
out=calculateNeuron(inputs,weight);
if(out 0)
{
if(m_Position.Select(my_symbol))
{
if(m_Position.PositionType()==POSITION_TYPE_SELL){m_Trade.PositionClose(my_symbol);}
if(m_Position.PositionType()==POSITION_TYPE_BUY){return;}
}
m_Trade.Buy(lot_size,my_symbol);
}
if(out =0)
{
if(m_Position.Select(my_symbol))
{
if(m_Position.PositionType()==POSITION_TYPE_BUY){m_Trade.PositionClose(my_symbol);}
if(m_Position.PositionType()==POSITION_TYPE_SELL){return;}
}
m_Trade.Sell(lot_size,my_symbol);
}
}
//+------------------------------------------------------------------+
double calculateNeuron(double x[],double w[])
double NET=0.0;
for(int i=0;i
{
NET+=x[i]*w[i];
}
NET*=0.1;//additional coefficient d=0.1
return(activateNeuron(NET));
//激励函数
double activateNeuron(double x)
double out;
out=(exp(x)-exp(-x))/(exp(x)+exp(-x));
return(out);
本文标签:MT5
