import numpy as np

a = [10, 11, 12, 13, 14]
b = [10, 12, 11, 11, 12]
c = [6, 9, -8, 0, 1]

corr1 = np.corrcoef(a, b)
print(corr1[1,0])
corr2 = np.corrcoef(a, c)
print(corr2[1,0])

2021/02/10,906,915,905,914,2677000000
2021/02/12,910,914,902,910,3371500000
2021/02/15,926,944,924,943,3315900000
2021/02/16,955,959,942,947,2673800000
2021/02/17,946,948,935,936,2365200000
2021/02/18,939,948,920,922,2997000000
2021/02/19,915,919,909,911,2833900000
2021/02/22,932,943,931,936,3182100000
2021/02/24,929,937,921,921,3191300000
2021/02/25,950,963,943,957,3063000000
2021/02/26,950,952,909,913,5239900000
2021/03/01,924,942,924,937,1922500000
2021/03/02,941,947,929,938,2153100000
2021/03/03,936,957,936,957,2786100000
2021/03/04,957,964,942,953,2482400000
2021/03/05,965,967,946,967,2417800000
2021/03/08,984,1003,975,982,3167600000
2021/03/09,997,999,979,995,2824800000
2021/03/10,968,995,968,990,3594900000
2021/03/11,999,1014,994,1000,2865800000
2021/03/12,1007,1008,996,1002,3294000000
2021/03/15,1005,1016,1004,1014,2113000000
2021/03/16,1012,1035,1008,1027,2581500000
2021/03/17,1010,1012,997,1007,3222500000
2021/03/18,1016,1037,1014,1028,3015600000
2021/03/22,1044,1062,1039,1057,2573700000
2021/03/23,1053,1068,1049,1056,2476700000
2021/03/24,1045,1046,1011,1013,3031600000
2021/03/25,1031,1052,1028,1040,2509100000
2021/03/26,1059,1066,1050,1056,2274600000

# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd

def full_up_shape(term):
    # 上昇
    up = range(0, term)
    normalized = list(map(lambda v: v/float(term), up))
    return normalized

def flat_up_shape(term):
    # 急上昇
    half = int(term/2)
    flat = [0]*half
    up = range(1, half+1)
    flat[len(flat):len(flat)] = up
    return list(map(lambda v: v/float(half), flat))

def normalize_close_value(df):
    close_value = df['Close'].to_list()
    min_value = min(close_value)
    max_value = max(close_value)
    return list(map(lambda v: (v-min_value)/(max_value-min_value), close_value))

def calc_corr_coeff(v1, v2):
    corr = np.corrcoef(v1, v2)
    return corr[1, 0]
    
def main():
    term = 30
    path = '1802.csv'
    df = pd.read_csv(path, header=None,
                     names=['Date','Open','High','Low','Close','Volume'], encoding='UTF-8')
    
    df['Date'] = pd.to_datetime(df['Date'])
    df = df.set_index("Date")
    df = df.tail(term)
    
    close_value = normalize_close_value(df)
    
    f_full_up = full_up_shape(term)
    corr1 = calc_corr_coeff(close_value, f_full_up)
    
    f_flat_up = flat_up_shape(term)
    corr2 = calc_corr_coeff(close_value, f_flat_up)
    
    print(corr1, corr2)

if __name__ == '__main__':
    main()

0.9302408236379394 0.9169569543912299

data/   ※6
    1000/
        1001.csv
        ...
    2000/
        ...
    9000/
        9001.csv
        ...

1383,ベルグアース
1413,桧家Ｈ
1419,タマホーム
1489,日経高配指数
1543,パラジウム信託
1651,ダイワ 高配４０
1670,Maxｓｐ三菱
1675,ETFSパラジ
1766,東建コーポ
1787,ナカボテック
1801,大成建
1808,長谷工
1811,銭高組
1812,鹿島
1814,大末建
1852,浅沼組
1860,戸田建
1887,日本国土開発
1904,大成温調
1928,積ハウス
...

# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

DATA_PATH = "../data/"

def get_close_value(df):
    close_value = df['Close'].to_list()
    return close_value

def read_csv(code, term):
    code_dir = code[0]+"000"
    path = DATA_PATH + code_dir + "/" + code + ".csv"
    df = pd.read_csv(path, header=None,
                     names=['Date','Open','High','Low','Close','Volume'], encoding='UTF-8')
    df['Date'] = pd.to_datetime(df['Date'])
    df = df.set_index("Date")
    df = df.tail(term)
    return df
        
def visualize(term, codes, names):
    x = np.array(list(range(0, term)))
    pages = int(len(codes)/20)
    if len(codes)%20 != 0: pages = pages + 1
    ncols = 5
    nrows = 4
    i = 0
    for page in range(0, pages):
        fig, axes = plt.subplots(
            nrows=nrows, ncols=ncols, sharex=False)
        for j in range(0, ncols):
            for k in range(0, nrows):
                axes[k,j].tick_params(labelbottom=False,
                                      labelleft=False,
                                      labelright=False,
                                      labeltop=False)
        for j in range(0, nrows*ncols):
            if i>=len(codes): break
            df = read_csv(str(codes[i]), term)
            close_value = get_close_value(df)
            row = j // ncols
            col = j % ncols
            title =str(codes[i])+' '+names[i][:4]
            axes[row,col].plot(x, np.array(close_value))
            axes[row,col].set_title(title)
            i = i + 1
        fig.tight_layout()
        filename = 'chart_thumbs'+str(page)+'.png'
        plt.savefig(filename) 
    
def main():
    term = 30
    filename = 'list.txt'
    df = pd.read_csv(filename, header=None,
                     names=['code','name'], encoding='UTF-8')
    codes = df['code'].to_list()
    names = df['name'].to_list()
    if len(codes)>0:
        visualize(term, codes, names)

if __name__ == '__main__':
    main()