Python基于皮尔逊系数实现股票预测（多线程）

  1 # -*- coding: utf-8 -*-
  2 """
  3 Created on Tue Dec  4 08:53:08 2018
  4 
  5 @author: zhen
  6 """
  7 from dtw import fastdtw
  8 import matplotlib.pyplot as plt
  9 import numpy as np
 10 import pandas as pd
 11 import threading
 12 import time
 13 from datetime import datetime
 14 
 15 def normalization(x): # np.std:计算矩阵的标准差（方差的算术平方根）
 16     return (x - np.mean(x)) / np.std(x)
 17 
 18 def corrcoef(a,b):
 19     corrc = np.corrcoef(a,b) # 计算皮尔逊相关系数，用于度量两个变量之间的相关性，其值介于-1到1之间
 20     corrc = corrc[0,1]
 21     return (16 * ((1 - corrc) / (1 + corrc)) ** 1) # ** 表示乘方
 22         
 23 startTimeStamp = datetime.now() # 获取当前时间
 24 # 加载数据
 25 filename = ‘C:/Users/zhen/.spyder-py3/sh000300_2017.csv‘
 26 # 获取第一，二列的数据
 27 all_date = pd.read_csv(filename,usecols=[0, 1], dtype = ‘str‘)
 28 all_date = np.array(all_date)
 29 data = all_date[:, 0]
 30 times = all_date[:, 1]
 31 
 32 data_points = pd.read_csv(filename,usecols=[3])
 33 data_points = np.array(data_points)
 34 data_points = data_points[:,0] #数据
 35 
 36 topk = 10 #只显示top-10
 37 baselen = 100 # 假设在50到150之间变化
 38 basebegin = 365
 39 basedata = data[basebegin]+‘ ‘+times[basebegin]+‘~‘+data[basebegin+baselen-1]+‘ ‘+times[basebegin+baselen-1]
 40 length = len(data_points) #数据长度
 41 
 42 # 定义自定义线程类
 43 class Thread_Local(threading.Thread):
 44     def __init__(self, thread_id, name, counter):
 45         threading.Thread.__init__(self)
 46         self.thread_id = thread_id
 47         self.name = name
 48         self.counter = counter
 49         self.__running = threading.Event() # 标识停止线程
 50         self.__running.set() # 设置为True
 51         
 52     def run(self):
 53         print("starting %s" % self.name)
 54         split_data(self, self.counter) # 执行代码逻辑
 55         
 56     def stop(self):
 57         self.__running.clear()
 58         
 59 # 分割片段并执行匹配，多线程
 60 def split_data(self, split_len):
 61     base = data_points[basebegin:basebegin+split_len]  # 获取初始要匹配的数据
 62     subseries = []
 63     dateseries = []
 64     for j in range(0, length): 
 65         if (j < (basebegin - split_len) or j > (basebegin + split_len - 1)) and j <length - split_len:
 66             subseries.append(data_points[j:j+split_len])
 67             dateseries.append(j) #开始位置
 68     search(self, subseries, base, dateseries)  # 调用模式匹配
 69 
 70 # 定义结果变量
 71 result = []  
 72 base_list = []
 73 date_list = []
 74 def search(self, subseries, base, dateseries):
 75      # 片段搜索
 76     listdistance = []
 77     for i in range(0, len(subseries)):
 78         tt = np.array(subseries[i])
 79         # dist, cost, acc, path = fastdtw(base, tt, dist=‘euclidean‘)
 80         # listdistance.append(dist)
 81         distance = corrcoef(base, tt)
 82         listdistance.append(distance)
 83     # 排序
 84     index = np.argsort(listdistance, kind=‘quicksort‘) #排序，返回排序后的索引序列
 85     result.append(subseries[index[0]])
 86     print("result length is %d" % len(result))
 87     base_list.append(base)
 88     date_list.append(dateseries[index[0]])
 89     # 关闭线程
 90     self.stop()
 91     
 92 # 变换数据（收缩或扩展），生成50到150之间的数据，间隔为10
 93 loc = 0
 94 for split_len in range(round(0.5 * baselen), round(1.5 * baselen), 10):
 95     # 执行匹配
 96    thread = Thread_Local(1, "Thread" + str(loc), split_len)
 97    loc += 1
 98    # 开启线程
 99    thread.start()
100 
101 boo = 1
102 
103 while(boo > 0):
104     if(len(result) < 10):
105         if(boo % 100 == 0):
106             print("has running %d s" % boo)
107         boo += 1
108         time.sleep(1)
109     else:
110         boo = 0
111         
112  # 片段搜索
113 listdistance = []
114 for i in range(0, len(result)):
115     tt = np.array(result[i])
116     distance = corrcoef(base_list[i], tt)
117     listdistance.append(distance)
118 # 最终排序   
119 index = np.argsort(listdistance, kind=‘quicksort‘) #排序，返回排序后的索引序列
120 print("closed Main Thread")
121 endTimeStamp = datetime.now()
122 # 结果集对比
123 plt.figure(0)
124 plt.plot(normalization(base_list[index[0]]),label= basedata,linewidth=‘2‘)
125 length = len(result[index[0]])
126 begin = data[date_list[index[0]]] + ‘ ‘ + times[date_list[index[0]]]
127 end = data[date_list[index[0]] + length - 1] + ‘ ‘ + times[date_list[index[0]] + length - 1]
128 label = begin + ‘~‘ + end
129 plt.plot(normalization(result[index[0]]), label=label, linewidth=‘2‘)  
130 plt.legend(loc=‘upper left‘)
131 plt.title(‘normal similarity search‘)
132 plt.show()
133 print(‘run time‘, (endTimeStamp-startTimeStamp).seconds, "s")