使用简单的交易相关对（对冲），我需要编码一个相关矩阵，就像myfxbook或Oanda上的那样。
重点是我希望能够循环遍历矩阵中的每个值并检查它是否大于85.0左右。
答案 0 :(得分：0)
Q:如何用MQL4计算Pearson的相关性？MQL4直接计算PearsonCorr_r：如果足以使用 double 的精确度，MQL4代码可以实现合理大小的值向量 {{1}的过程}
( X[], Y[] )可以使用例如ZeroMQ消息传递基础结构的分布式处理来请求在MQL4之外执行微积分/独立于本地主机处理。
如果有兴趣，请在 #define RET_OK 0
#define RET_ERROR EMPTY
#define VAL_ERROR EMPTY_VALUE
int PearsonCorr_r( double const &vectorX[], // |-> INPUT X[] = { 1, 3, 5, 5, 6 }
double const &vectorY[], // |-> INPUT Y[] = { 5, 6, 10, 12, 13 }
double &pearson_r // <=| returns RESULT = 0.968
){
double sumX = 0,
meanX = 0,
meanY = 0,
sumY = 0,
sumXY = 0,
sumX2 = 0,
sumY2 = 0;
// deviation_score_x[], // may be re-used for _x^2
// deviation_score_y[], // may be re-used for _y^2
// deviation_score_xy[];
/* =====================================================================
DEVIATION SCORES >>> http://onlinestatbook.com/2/describing_bivariate_data/calculation.html
X[] Y[] x y xy x^2 y^2
1 4 -3 -5 15 9 25
3 6 -1 -3 3 1 9
5 10 1 1 1 1 1
5 12 1 3 3 1 9
6 13 2 4 8 4 16
_______________________________________
SUM 20 45 0 0 30 16 60
MEAN 4 9 0 0 6
r = SUM(xy) / SQRT( SUM( x^2 ) * SUM( y^2 ) )
r = 30 / SQRT( 960 )
r = 0.968
=====================================================================
*/
int vector_maxLEN = MathMin( ArrayRange( vectorX, 0 ),
ArrayRange( vectorY, 0 )
);
if ( vector_maxLEN == 0 ){
pearson_r = VAL_ERROR; // STOR VAL ERROR IN RESULT
return( RET_ERROR ); // FLAG RET_ERROR in JIT/RET
}
for ( int jj = 0; jj < vector_maxLEN; jj++ ){
sumX += vectorX[jj];
sumY += vectorY[jj];
}
meanX = sumX / vector_maxLEN; // DIV!0 FUSED
meanY = sumY / vector_maxLEN; // DIV!0 FUSED
for ( int jj = 0; jj < vector_maxLEN; jj++ ){
// deviation_score_x[ jj] = meanX - vectorX[jj]; //
// deviation_score_y[ jj] = meanY - vectorY[jj];
// deviation_score_xy[jj] = deviation_score_x[jj]
// * deviation_score_y[jj];
// sumXY += deviation_score_x[jj]
// * deviation_score_y[jj];
sumXY += ( meanX - vectorX[jj] ) // PSPACE MOTIVATED MINIMALISTIC WITH CACHE-BENEFITS IN PROCESSING
* ( meanY - vectorY[jj] );
// deviation_score_x[jj] *= deviation_score_x[jj]; // PSPACE MOTIVATED RE-USE, ROW-WISE DESTRUCTIVE, BUT VALUE WAS NEVER USED AGAIN
// sumX2 += deviation_score_x[jj]
// * deviation_score_x[jj];
sumX2 += ( meanX - vectorX[jj] ) // PSPACE MOTIVATED MINIMALISTIC WITH CACHE-BENEFITS IN PROCESSING
* ( meanX - vectorX[jj] );
// deviation_score_y[jj] *= deviation_score_y[jj]; // PSPACE MOTIVATED RE-USE, ROW-WISE DESTRUCTIVE, BUT VALUE WAS NEVER USED AGAIN
// sumY2 += deviation_score_y[jj]
// * deviation_score_y[jj];
sumY2 += ( meanY - vectorY[jj] ) // PSPACE MOTIVATED MINIMALISTIC WITH CACHE-BENEFITS IN PROCESSING
* ( meanY - vectorY[jj] );
}
pearson_r = sumXY
/ MathSqrt( sumX2
* sumY2
); // STOR RET VALUE IN RESULT
return( RET_OK ); // FLAG RET_OK in JIT/RET
中阅读关于分布式流程的other posts（代码示例 - 只是为了了解MQL4方面的情况得到设置 - 可能是found here）
和 MQL4 （ZeroMQ基础架构设置的代码示例可以是found here
因此允许使用MATLAB内置的Pearson相关实现（记得正确地将数据预先格式化为列，并且如果还添加 MATLAB -fusing，则最好） ：
DIV!0同样， [ RHO, PVAL ] = corr( vectorX, vectorY, 'type', 'Pearson' );
% note: double-r in corr()
% # 'Pearson' is default method
语言有一个内置工具：
R最后但并非最不重要的是 corr_r <- cor( vecORmatX, vecORmatY, use = "everything", method = "pearson" )
# "Pearson" is default method
- 作为工具实施，同时 python scipy.stats.stats pearsonr 和 float32 精度：
float64>>> from scipy.stats.stats import pearsonr as pearson_r
>>>
>>> X = np.zeros( (5,), dtype = np.float32 )
>>> Y = np.zeros( (5,), dtype = np.float32 )
>>>
>>> X[0] = 1; X[1] = 3; X[2] = 5; X[3] = 5; X[4] = 6
>>> Y[0] = 5; Y[1] = 6; Y[2] = 10; Y[3] = 12; Y[4] = 13
>>>
>>> pearson_r( X, Y)
(0.94704783, 0.01451040731338055)
>>>
>>> X = np.zeros( (5,), dtype = np.float64 )
>>> Y = np.zeros( (5,), dtype = np.float64 )
>>>
>>> X[0] = 1; X[1] = 3; X[2] = 5; X[3] = 5; X[4] = 6
>>> Y[0] = 5; Y[1] = 6; Y[2] = 10; Y[3] = 12; Y[4] = 13
>>>
>>> pearson_r( X, Y)
(0.94704783738690446, 0.014510403904375592)
>>>
python.scipy.stats.stats.pearsonr(X,Y)