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神经网络实验（第 3 部分）：实际应用

神经网络实验（第 3 部分）：实际应用

MetaTrader 5 — EA交易 | 22 五月 2023, 10:55
1 034 0
Roman Poshtar
Roman Poshtar

概述

在系列的前几篇文章中，重访几何，和智能神经网络优化，我分享了我对神经网络的观察和实验。此外，我还对生成的 EA 进行了优化，并提供了一些有关它们操作的解释。 尽管如此，我几乎没有触及所获得结果的实际应用的主题。 在本文中，我将修复这个不幸的遗漏。

我将展示所获得结果的实际应用，并重点介绍一种允许我们扩展 EA 功能的新算法。 与往常一样，我只会用到 MetaTrader 5 工具，而不使用任何第三方软件。 本文很可能类似于分步指导。我将尝试以最易于访问和最简单的方式解释所有内容。


1. 应用思路

在优化前两个系统时，我收获了一些结果，其中包含盈利因子、或复杂准则的最佳值，以及感知器和神经网络的一组权重。 对获得的结果的测试展现出相当的可容忍值。 如此改进的主要思路是将所有优化结果组合到一个 EA 当中，并令它们同时工作。 正如您可能想象的那样，保持 10 个图表并运行 10 个 EA 不是很方便。 此外，这将令我们能够以扩展（全面）的方式查看结果，例如同时使用 10-20 个参数。


2. 货币对。 优化和前瞻测试范围。 设置

以下是所有优化和测试参数：

  • 外汇；
  • EURUSD；
  • H1；
  • 指标: 2 个 TEMA 指标，周期分别为 1 和 24。 我忍痛放弃 MA，因为 TEMA 指标在多次测试中都摆明效果更好。
  • 止损和止盈相应改为 600 和 60；
  • “仅开盘价”和“最复杂准则”优化和测试模式。 与“最大盈利”相比，“最大复杂准则”模式显示出更稳定和可盈利结果；
  • 优化范围 3 年。 2018.12.09 - 2021.12.09。 3 年并非是一个可靠的准则。 您可以自行试验此参数；
  • 前向验证测试范围为 1 年。 2021.12.09 - 2022.12.09；
  • 在所有前向验证测试中，同时用到了 20 个优化结果；
  • EA 优化采用“快速（基于遗传算法）”感知器；
  • EA 优化采用 DeepNeuralNetwork.mqh “慢速完整算法” 函数库；
  • 初始本金 10,000；
  • 杠杆 1:500。

3. 基于感知器的 EA

 根据大量观察，事实证明，带有感知器的 EA 不需要 200 的权重深度。 20 足矣。 因此，感知器代码本身和优化参数发生了变化。 现在我们自 0 增量为 1 直到 20 优化权重。

 还引入了 “Param” 参数，该参数负责向感知器的正侧或负侧回撤的深度。 此参数会影响交易数量及其准确性。 交易数量减少了，而准确性却提高了。

 每个系统用到 2 个 EA。 第一个用于优化，而第二个直接用于操作。 我决定通过订单注释来安置订单划分，因为我相信这是最简单、最方便的方式。 唯一的订单索引是样品本身中的序列号。 该样本是包含所获得的优化结果的存档。  MaxSeries 参数用于限制同时操作的数量。

for(int i=0; i<=(ArraySize(EURUSD)/6)-1; i++){
 comm=IntegerToString(i);
 x1=(int)StringToInteger(EURUSD[i][0]);
 x2=(int)StringToInteger(EURUSD[i][1]);
 x3=(int)StringToInteger(EURUSD[i][2]);
 x4=(int)StringToInteger(EURUSD[i][3]);
 
 Param=(int)StringToInteger(EURUSD[i][4]);

//SELL++++++++++++++++++++++++++++++++++++++++++++++++
if (CalculateSeries(Magic)<MaxSeries && (perceptron1()<-Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment+" En_"+comm)==0) && (ind_In1[1]>ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenSell(symbolS1.Name(), LotsXSell, TakeProfit, StopLoss, EAComment+" En_"+comm);
}

//BUY++++++++++++++++++++++++++++++++++++++++++++++++
if (CalculateSeries(Magic)<MaxSeries && (perceptron1()>Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment+" En_"+comm)==0) && (ind_In1[1]<ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenBuy(symbolS1.Name(), LotsXBuy, TakeProfit, StopLoss, EAComment+" En_"+comm);
}

}

 新的感知器代码:

1 个感知器 4 角度 SL TP 和 1 个感知器 4 角度

double perceptron1() 
  {
   double w1 = x1 - 10.0;
   double w2 = x2 - 10.0;
   double w3 = x3 - 10.0;
   double w4 = x4 - 10.0;
   
   double a1 = (((ind_In1[1]-ind_In1[6])/Point())/6);
   double a2 = (((ind_In1[1]-ind_In1[11])/Point())/11);
   double a3 = (((ind_In2[1]-ind_In2[6])/Point())/6);
   double a4 = (((ind_In2[1]-ind_In2[11])/Point())/11);   
   
   return (w1 * a1 + w2 * a2 + w3 * a3 + w4 * a4);
  }

1 个感知器 8 角度 SL TP 和 1 个感知器 8 角度

double perceptron1() 
  {
   double w1 = x1 - 10.0;
   double w2 = x2 - 10.0;
   double w3 = x3 - 10.0;
   double w4 = x4 - 10.0;
   
   double v1 = y1 - 10.0;
   double v2 = y2 - 10.0;
   double v3 = y3 - 10.0;
   double v4 = y4 - 10.0;  
   
   double a1 = (((ind_In1[1]-ind_In1[6])/Point())/6);
   double a2 = (((ind_In1[1]-ind_In1[11])/Point())/11);
   double a3 = (((ind_In2[1]-ind_In2[6])/Point())/6);
   double a4 = (((ind_In2[1]-ind_In2[11])/Point())/11);   
   
   double b1 = (((ind_In1[1]-ind_In1[11])/Point())/11);
   double b2 = (((ind_In2[1]-ind_In1[11])/Point())/11);
   double b3 = (((ind_In1[1]-ind_In2[11])/Point())/11);
   double b4 = (((ind_In2[1]-ind_In2[11])/Point())/11);
   
   return (w1 * a1 + w2 * a2 + w3 * a3 + w4 * a4   +   v1 * b1 + v2 * b2 + v3 * b3 + v4 * b4);
  }

2 个感知器 а 4 角度 SL TP 和 2 个感知器а 4 角度

double perceptron1() 
  {
   double w1 = x1 - 10.0;
   double w2 = x2 - 10.0;
   double w3 = x3 - 10.0;
   double w4 = x4 - 10.0;
   
   double a1 = (((ind_In1[1]-ind_In1[6])/Point())/6);
   double a2 = (((ind_In1[1]-ind_In1[11])/Point())/11);
   double a3 = (((ind_In2[1]-ind_In2[6])/Point())/6);
   double a4 = (((ind_In2[1]-ind_In2[11])/Point())/11);   
   
   return (w1 * a1 + w2 * a2 + w3 * a3 + w4 * a4);
  }

double perceptron2() 
  {
   double v1 = y1 - 10.0;
   double v2 = y2 - 10.0;
   double v3 = y3 - 10.0;
   double v4 = y4 - 10.0;  
   
   double b1 = (((ind_In1[1]-ind_In1[11])/Point())/11);
   double b2 = (((ind_In2[1]-ind_In1[11])/Point())/11);
   double b3 = (((ind_In1[1]-ind_In2[11])/Point())/11);
   double b4 = (((ind_In2[1]-ind_In2[11])/Point())/11);
   
   return (v1 * b1 + v2 * b2 + v3 * b3 + v4 * b4);
  }

 入场激活码：

1 个感知器 4 角度 SL TP 和 1 个感知器 4 角度

//SELL++++++++++++++++++++++++++++++++++++++++++++++++
if ((perceptron1()<-Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment)==0) && (ind_In1[1]>ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenSell(symbolS1.Name(), LotsXSell, TakeProfit, StopLoss, EAComment);
}

//BUY++++++++++++++++++++++++++++++++++++++++++++++++
if ((perceptron1()>Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment)==0) && (ind_In1[1]<ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenBuy(symbolS1.Name(), LotsXBuy, TakeProfit, StopLoss, EAComment);
}

1 个感知器 8 角度 SL TP 和 1 个感知器 8 角度

//SELL++++++++++++++++++++++++++++++++++++++++++++++++
if ((perceptron1()<-Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment)==0) && (ind_In1[1]>ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenSell(symbolS1.Name(), LotsXSell, TakeProfit, StopLoss, EAComment);
}

//BUY++++++++++++++++++++++++++++++++++++++++++++++++
if ((perceptron1()>Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment)==0) && (ind_In1[1]<ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenBuy(symbolS1.Name(), LotsXBuy, TakeProfit, StopLoss, EAComment);
}

2 个感知器 а 4 角度 SL TP 和 2 个感知器а 4 角度

//SELL++++++++++++++++++++++++++++++++++++++++++++++++
if ((perceptron1()<-Param) && (perceptron2()<-Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment)==0) && (ind_In1[1]>ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenSell(symbolS1.Name(), LotsXSell, TakeProfit, StopLoss, EAComment);
}

//BUY++++++++++++++++++++++++++++++++++++++++++++++++
if ((perceptron1()>Param) && (perceptron2()>Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment)==0) && (ind_In1[1]<ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenBuy(symbolS1.Name(), LotsXBuy, TakeProfit, StopLoss, EAComment);
}

 优化设置：

1 个感知器 4 角度 SL TP 和 1 个感知器 4 角度

优化设置 1

1 个感知器 8 角度 SL TP 和 1 个感知器 8 角度

优化设置

2 个感知器 а 4 角度 SL TP 和 2 个感知器а 4 角度

优化设置


3.1 EA 1 个感知器 4 角度 SL TP

此 EA 修改依据止损和止盈离场。 策略 1 感知器和 4 个 TEMA 指标的倾角。 执行优化 10 次。 倾角的结构和优化原理可以在第一篇文章中找到。 在这里重复它们是没有意义的。

优化结果：

优化结果


优化结果

复杂准则的大量结果 99.99。 盈利因子处于 4-8 的高水平。

 接下来，将得到的结果导出到 Excel。 保留前 100 个最佳结果，并删除其它所有结果。 删除 х1、х2、х3、х4 和 Param 之外的所有列。  文件保存为 CSV （以逗号作为分隔符）。为了清楚起见，我将文件命名为 EURUSD 。 我们可在 EA 代码当中将此格式作为文本数组加载。  它应该如下图所示。

优化结果

通过 MetaEditor 菜单将文件插入到代码中。

优化结果

获取带有优化结果的现成文本数组。

string EURUSD[][6]=
  {
   {"19","1","3","6","1100"},
   {"20","1","4","6","1000"},
   {"20","0","4","4","1200"},
   {"19","0","6","4","1100"},
   {"19","1","5","4","1100"},
   {"17","0","7","4","1100"},
   {"19","1","3","8","1000"},
   {"20","0","4","3","1300"},
   {"17","0","7","0","1400"}
  };

编译并执行前向验证测试。 

前向验证测试

结果不错。 我们可以稳步上升贯穿全年。


3.2 EA 1 感知器 4 角度

此 EA 修改版未用 止损和止盈。平仓由感知器回调执行。

//SELL++++++++++++++++++++++++++++++++++++++++++++++++
if ((perceptron1()<-Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment)==0) && (ind_In1[1]>ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenSell(symbolS1.Name(), LotsXSell, 0, 0, EAComment);
}

if ((perceptron1()>0) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment)>0)){
ClosePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL);
}

//BUY++++++++++++++++++++++++++++++++++++++++++++++++
if ((perceptron1()>Param) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment)==0) && (ind_In1[1]<ind_In2[1]) && (SpreadS1<=MaxSpread)){
  OpenBuy(symbolS1.Name(), LotsXBuy, 0, 0, EAComment);
}

if ((perceptron1()<0) && (CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment)>0)){
ClosePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY);
}

优化结果：

优化结果


优化结果

复杂准则的结果略低于前一种情况。 盈利因子处于 2-2.5 的水平。

前向验证测试结果：

前向验证测试结果

余额曲线重复了上一次结果，但回撤更深。


3.3 EA 1 个感知器 8 角度 SL TP

此 EA 修改依据止损和止盈离场。 策略 1 感知器和 8 个 TEMA 指标的倾角。

对于此 EA，我们需要准备一个 Excel 文件，如下所示。 在此，我们优化 х1, х2, х3, х4, y1, y2, y3, y4 和 Param 参数。

优化结果

优化结果：

优化结果


优化结果

复杂准则结果很高。 盈利因子处于 2.5-3 的水平。

前向验证测试结果：

前向验证测试结果

余额曲线不是很稳定。 回撤也相当大。 不过，结果是正面的。


3.4 EA 1 感知器 8 角度

未用止损和止盈 。 由感知器的逆向信号执行平仓。策略 1 感知器和 8 个 TEMA 指标的倾角。

优化结果：

优化结果


优化结果

复杂准则结果很高。 盈利因子处于 2.5-3 的水平。

前向验证测试结果：

前向验证测试结果

余额曲线很稳定。 账面大幅增加贯穿全年。 回撤也不深。


3.5 EA 2 个感知器 8 角度 SL TP

未用止损和止盈。 策略 2 感知器，第一个有 4 个倾角，第二个有 4 个倾角，这与之前不同。

优化结果：

优化结果


优化结果

复杂准则的结果处于 99.99 水平。 结果的盈利因子几乎同样是 4.3。

前向验证测试结果：

前向验证测试结果

锯齿余额线。 盈利贯穿全年。


3.6 EA 2 感知器 8 角度

无止损和止盈。 策略 2 感知器，第一个有 4 个倾角，第二个有 4 个倾角，这与之前不同。 平仓由感知器逆向信号执行。

优化结果：

优化结果


优化结果

复杂准则的结果处于 99.8。 结果的盈利因子范围在 2.8-3.2。

前向验证测试结果：

前向验证测试结果

锯齿余额线，不稳定。 盈利贯穿全年。 年底时有大幅回撤。 


4. 基于 DeepNeuralNetwork.mqh 函数库的 EA

在本文中，我将使用 4 个基于 DeepNeuralNetwork.mqh 函数库的 EA —角度 4-4-3 SL TP，和角度 8-4-3 SL TP，应用止损和止盈平仓，以及角度 4-4-3，和角度 8-4-3，其中收盘信号来自神经网络。  所有  EA 都采用倾角作为策略。 此处不用在实验第二部分中介绍的形状。

止损和止盈代码:

//SELL++++++++++++++++++++++++++++++++++++++++++++++++
if ((CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment+" En_"+comm1)==0) && (yValues[1]>LL) && (SpreadS1<=MaxSpread)){
  if(CalculateSeries(Magic)<MaxSeries){
  OpenSell(symbolS1.Name(), LotsXSell, TP, SL, EAComment+" En_"+comm1);
  }
}

//BUY++++++++++++++++++++++++++++++++++++++++++++++++
if ((CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment+" En_"+comm1)==0) && (yValues[0]>LL) && (SpreadS1<=MaxSpread)){
  if(CalculateSeries(Magic)<MaxSeries){
  OpenBuy(symbolS1.Name(), LotsXBuy, TP, SL, EAComment+" En_"+comm1);
  }
}

来自神经网络的平仓代码：

//SELL++++++++++++++++++++++++++++++++++++++++++++++++
if ((CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment+" En_"+comm1)==0) && (yValues[1]>LL) && (SpreadS1<=MaxSpread)){
  ClosePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment+" En_"+comm1);
  if(CalculateSeries(Magic)<MaxSeries){
  OpenSell(symbolS1.Name(), LotsXSell, TP, SL, EAComment+" En_"+comm1);
  }
}

//BUY++++++++++++++++++++++++++++++++++++++++++++++++
if ((CalculatePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment+" En_"+comm1)==0) && (yValues[0]>LL) && (SpreadS1<=MaxSpread)){
  ClosePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment+" En_"+comm1);
  if(CalculateSeries(Magic)<MaxSeries){
  OpenBuy(symbolS1.Name(), LotsXBuy, TP, SL, EAComment+" En_"+comm1);
  }
}

//CLOSE ALL++++++++++++++++++++++++++++++++++++++++++
if (yValues[2]>LL){
  ClosePositions(symbolS1.Name(), Magic, POSITION_TYPE_SELL, EAComment+" En_"+comm1);
  ClosePositions(symbolS1.Name(), Magic, POSITION_TYPE_BUY, EAComment+" En_"+comm1);
}

现在，我们应用一个更复杂的方案。 我们在每套当中有 3 个智能交易系统。 第一个用于优化，第二个用于将获得的结果转换为文本数组，而第三个用于测试和处理结果数组。

您也许还记得，在优化过程中，EA 会在 C\Users\Your 用户名 \AppData\Roaming\MetaQuotes\Terminal\Common\Files 处创建一个含有一组优化权重的 CSV 文件。 将 CSV 优化报告文件复制到该文件夹。

在货币对图表上启动 Angle EA 4-4-3 convert。

智能系统

EA 转换参数:

  • Param – 复杂准则值，低于该值的结果不会复制到数组中。我设置 80；
  • OptimizationFileName1 – CSV 优化报告文件
  • OptimizationFileName2 – CSV 在优化期间由EA 创建的 CSV 文件，包含神经网络权重；
  • OptimizationFileName3 – 要插入到 EA 中的数组文件。 该文件将自动创建。

您可以在日志中观看该过程。

日志

将所获文件插入到 Angle EA 4-4-3 交易系统的代码当中： 

string Result[][37]=
  {
   {"17293","0.8","-0.1","-0.2","1.0","0.9","0.6","0.4","1.0","0.6","-0.4","0.9","-0.5","0.1","-0.5","-0.5","0.9","-0.1","-0.8","0.4","0.0","-0.1","0.1","0.2","-0.4","-0.7","-0.6","-0.9","-0.8","-0.9","-0.7","-0.5","0.4","0.4","0.8","-0.6"},
   {"18030","0.6","0.2","-0.4","0.9","-1.0","-0.9","-0.9","0.4","-0.9","-0.8","0.4","0.9","0.2","-0.8","0.9","-0.1","-0.6","0.3","0.5","-0.4","0.7","0.6","-0.4","-0.1","0.4","-0.8","0.4","0.9","-0.2","0.0","0.4","-0.6","-0.4","-0.7","0.7"},
   {"13128","0.7","-0.3","0.5","-0.5","-0.5","-0.1","0.8","0.0","0.6","0.9","-0.2","0.8","1.0","0.7","-0.7","-0.2","0.5","0.5","-0.6","0.5","-0.9","-0.5","-0.5","0.5","-0.3","0.5","0.8","0.2","-0.5","-0.2","0.1","-0.1","-0.4","-0.7","0.1"},
   {"10688","0.3","0.0","0.2","-0.1","0.6","0.1","0.1","-0.2","-1.0","0.3","0.2","0.5","-0.8","0.7","0.4","-0.5","-0.4","-0.3","-0.3","-0.9","-0.2","0.0","0.1","0.9","0.3","-0.9","-0.2","-0.2","0.1","0.9","0.8","0.1","0.4","0.8","0.6"},
   {"8356","0.8","0.8","0.7","0.2","0.0","-0.4","0.5","-0.8","0.0","0.9","0.2","-0.1","1.0","0.6","0.2","-0.8","-0.1","-0.5","-0.3","0.0","0.7","-0.5","-0.3","0.0","0.9","-1.0","-0.2","-0.6","-0.7","-0.5","-0.8","0.5","-0.3","-0.1","0.8"},
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  };


4.1 Angle 4-4-3 SL TP EA

该 EA 使用 止损和止盈离场。 策略 4 倾角TEMA 指标。

优化结果：

优化结果


优化结果

如您所见，有很多好的结果。 盈利因子范围在 1.6-5 范围内。 有 27 个复杂准则值超过 80。

前向验证测试结果：

前向验证测试结果

不幸的是，EA 未能通过前向验证测试。 结果呈负面，且不稳定。


4.2 Angle 4-4-3 EA

该 EA 采用神经网络离场。 策略 4 倾角  TEMA 指标。

优化结果：

优化结果


优化结果

如您所见，对于复杂准则，超过 80 的优良结果仅有 6 个。 盈利因子处于 1.6-1.9 范围。

前向验证测试结果：

前向验证测试结果

依据神经网络信号收盘，EA 全年均呈现盈利。 结果比采用止损和止盈更稳定。


4.3 Angle 8-4-3 SL TP EA

该 EA 使用 止损和止盈离场。 策略 8 倾角 TEMA 指标。

优化结果：

优化结果


优化结果

与神经网络 4-4-3 相比，结果的盈利因子较低。有 13 个复杂准则结果超过 80。

前向验证测试结果：

前向验证测试结果

正如预期的那样，结果与上一个采用止损和止盈的结果相似。 前向验证测试则失败。


4.4 Angle 8-4-3 EA

该 EA 采用神经网络离场。 策略 8 倾角 TEMA 指标。

优化结果：

优化结果


优化结果

仅有 3 个复杂准则的结果超过 80。 与之前的结果相比，盈利因子处于较低水平。

前向验证测试结果：

前向验证测试结果

前向验证的结果并不令人满意。 我们看到本金逐渐亏损。


结束语

从前向验证测试结果可以看出，尽管运行 6 个月后盈利能力有所下降，但没有一个基于感知器的 EA 在年内出现负增长。 这意味着至少每 6 个月需要优化一次。

至于基于 DeepNeuralNetwork.mqh 函数库的 EA，一切更为复杂。 结果也不如预期。 也许策略本身也会影响事物，有必要将其它东西传递给神经网络。

在大多数情况下，盈利能力可以通过优化序列的盈利因子来跟踪。 这为我们提供了额外的思考。

对于未来，我想指出 2 个任务。 有必要基于其它货币对和时间帧内验证获得的最佳结果。

交易次数没有我们想要的那么多，但并没有人禁止使用其它多货币对，并基于这些系统创建投资组合。 反过来，这不可避免的要协同劳力成本的优化。

如果您有任何疑问，请在论坛上提问，或通过私信与我联系。 我始终乐意为您提供帮助。



本文由MetaQuotes Ltd译自俄文
原文地址： https://www.mql5.com/ru/articles/11949

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