d-找出結算日在陣列中的位置

import talib

import numpy as np

import tushare as ts

import matplotlib.pyplot as plt

import mpl_finance as mpf

import pandas as pd

#data = ts.get_k_data('399300', index=True, start='2017-01-01', end='2017-06-31')

data=pd.read_csv('2017_tx.csv',usecols=['date','open','close','high','low','volume'])

data=data.head(60)

#TA-lib wants numpy arrays of "double" floats as inputs. You want to convert it to double float data.-->np.array(data['close'],dtype=float)

fig = plt.figure(figsize=(17, 10))

'''

listA=['ab','23','yu','ki','ug'] #所有的成交日

listB=['ab','yu' ] #摩台結算日

retA = [listA.index(i) for i in listA if i in listB]

data_x = pd.read_csv("E:/Tianchi/result/features.csv",usecols=[2,3,4])#pd.dataframe

data_y = pd.read_csv("E:/Tianchi/result/features.csv",usecols=[5])

train_data = np.array(data_x)#np.ndarray()

train_x_list=train_data.tolist()#list

'''

#--------算出結算日在 日期陣列裡的位置 ------------

deadline_data = pd.read_csv('deadline_day.csv',usecols=['MTX']) #讀取摩台指 結算日之資料

test3 = np.array(deadline_data['MTX']).tolist()

test2 = np.array(data['date']).tolist() #首先使用np.array()函数把DataFrame转化为np.ndarray()，再利用tolist()函数把np.ndarray()转为list

#for j in test2

# print(j)

retA = [test2.index(i) for i in test2 if i in test3] #retA 結算日在 日期陣列裡的位置

print(retA)

#--------END OF 算出結算日在 日期陣列裡的位置 ------------

ax = fig.add_axes([0,0.2,1,0.5])

#------劃直線------------

fruits =retA

for x2 in fruits:

plt.axvline(x=x2, ymin=0, ymax = 20, linewidth=2, color='B')

#------劃直線------------

ax2 = fig.add_axes([0,0,1,0.2])

mpf.candlestick2_ochl(ax, data['open'], data['close'], data['high'], data['low'],

width=0.5, colorup='r', colordown='g', alpha=0.6)

ax.set_xticks(range(0, len(data['date']), 10))

ax.plot(sma_10, label='10 日均线')

ax.plot(sma_30, label='30 日均线')

ax.legend(loc='upper left')

ax.grid(True)

mpf.volume_overlay(ax2, data['open'], data['close'], data['volume'], colorup='r', colordown='g', width=0.5, alpha=0.8)

ax2.set_xticks(range(0, len(data['date']), 10))

ax2.set_xticklabels(data['date'][::10], rotation=30)

ax2.grid(True)

#plt.show()

plt.show()