Question

这是我的数据

             Last
Date                  
2019-02-19  277.850006
2019-02-20  278.410004
2019-02-21  277.420013
2019-02-22  279.140015
2019-02-25  279.940002

我正在使用此功能获取每日数据，效果很好。

def corr_window (data, cdw, dyf, corr_level):

    '''
    data = historical data
    cdw = corralation days window
    dyf = number of days forward
    corr_level = desirable correlation level
    '''
    mylabels = ['Dax', str(dyf)+' days forward']
    s=[]
    data2= data[-cdw:]
    data[-cdw:].plot(title='Dax last {} trading Days'.format(str(cdw)))
    for i in range(len(data)-cdw*2):
        if (pearsonr(data[i:i+cdw],data2)[0]) > corr_level:
            s.append((data.iloc[i+cdw+dyf]['Last']/data.iloc[i+cdw]['Last'])-1)
            fig, ax = plt.subplots(1, 1)
            data[i:i+cdw+dyf].plot(title="Correlation:"+str(pearsonr(data[i:i+cdw],data2)[0]),ax=ax)
            data[i+cdw:i+cdw+dyf].plot(color = 'red',label='Days forward', ax=ax)
            ax.legend(labels=mylabels, loc=0)
            plt.tight_layout();
    return print(f'Average Return after {dyf} days is {round(np.mean(s)*100,2)}% \nfor {len(s)} occurrences\
    ----> {np.round(sum(1 for x in s if x>0)/len(s)*100,1)}% positive returns\n')

当尝试将数据移动到分辨率时，我使用：

data.index = pd.to_datetime(data.Date + ' ' + data.Time)
data['Date'] = pd.to_datetime(data.Date)
data['Time'] = pd.to_datetime(data['Time'], format=' %H:%M:%S.%f').dt.time

我的数据如下：

                    Date        Time        Last
2019-03-01 20:51:00 2019-03-01  20:51:00.0  11628.5
2019-03-01 20:54:00 2019-03-01  20:54:00.0  11627.5
2019-03-01 20:57:00 2019-03-01  20:57:00.0  11633.5
2019-03-01 21:00:00 2019-03-01  21:00:00.0  11633.0
2019-03-01 21:03:00 2019-03-01  21:03:00.0  11629.5

在我的小数据上运行上述功能时，出现此错误：

ValueError: view limit minimum -24654.425000000003 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units

Answer 1

我看了一下，但它似乎对我来说很好用：
（唯一的调整是添加了 .values.ravel()，因为 scipy.pearsonr 似乎不喜欢数据帧...）

你能提供一个（不）工作的例子吗？

import pandas as pd
import numpy  as np
from scipy.stats import pearsonr
import matplotlib.pyplot as plt

data = pd.DataFrame([['2019-03-01 20:51:00', '11628.5'],
                     ['2019-03-01 20:54:00', '11627.5'],
                     ['2019-03-01 20:57:00', '11633.5'],
                     ['2019-03-01 21:00:00', '11633.0'],
                     ['2019-03-01 21:03:00', '11629.5']], 
                    columns=['index', 'Last'])

data.index = pd.to_datetime(data.pop('index'))
data['Last'] = data['Last'].astype(float)

# make the dataframe a bit longer...
data = data.append(data)


# --------------------------

def corr_window (data, cdw, dyf, corr_level):

    '''
    data = historical data
    cdw = corralation days window
    dyf = number of days forward
    corr_level = desirable correlation level
    '''
    mylabels = ['Dax', str(dyf)+' days forward']
    s=[]
    data2= data[-cdw:]
    data[-cdw:].plot(title='Dax last {} trading Days'.format(str(cdw)))
    for i in range(len(data)-cdw*2):
        if (pearsonr(data[i:i+cdw].values.ravel(),data2.values.ravel())[0]) > corr_level:
            s.append((data.iloc[i+cdw+dyf]['Last']/data.iloc[i+cdw]['Last'])-1)
            fig, ax = plt.subplots(1, 1)
            data[i:i+cdw+dyf].plot(title="Correlation:"+str(pearsonr(data[i:i+cdw].values.ravel(),data2.values.ravel())[0]),ax=ax)
            data[i+cdw:i+cdw+dyf].plot(color = 'red',label='Days forward', ax=ax)
            ax.legend(labels=mylabels, loc=0)
            plt.tight_layout();
    return print(f'Average Return after {dyf} days is {round(np.mean(s)*100,2)}% \nfor {len(s)} occurrences\
    ----> {np.round(sum(1 for x in s if x>0)/len(s)*100,1)}% positive returns\n')



corr_window(data, 2, 0, -1)

如何解决视图下限最小值小于1且是无效的Matplotlib日期值

1 个答案: