Question

我可以计算出最后一个交易日52周新高或新低的股票数量。但我需要从csv文件中的第一天开始计算，直到csv中的最后一天。

例：
02-01-2014，10只股票52周高点，45只股票低52周 03-01-2014,23只股票创52周新高56只股票52周低位 04-01-2014,34只股票创52周新高，34只股票创下52周新低。

import pandas as pd
import numpy as np
import csv
import datetime
import matplotlib.pyplot as plt
import talib as ta
import stocklist

now = datetime.datetime.now()

STOCKS = ['Abc','cdf','gg','D','AN','OX']
Stockslen = len(STOCKS)

h_cnt=0
l_cnt=0

#Creating 5 df for data analysis

df_today52w_High = pd.DataFrame(columns=['Stock','Today 52w_High'])
df_today52w_Low = pd.DataFrame(columns=['Stock','Today 52w_Low'])

for x in range (len(STOCKS)):
    print "###############  "
    print STOCKS [x]
    print "###############"
    q_data = pd.read_csv(STOCKS [x]+".csv", index_col='Stock', usecols =[0,1,3,4,5,6,7])

    high = q_data.High
    h=np.array(high)

    date_ = q_data.Date
    dt = np.array(date_)

    open_ = q_data.Open
    o = np.array(open_)

    low = q_data.Low
    l = np.array(low)

    close = q_data.Close
    c = np.array(close)


    if h[-1] == ta.MAX(h,252)[-1]:
        df_today52w_High.loc[len(df_today52w_High)] = [STOCKS[x],1]
        h_cnt += 1
        print h_cnt
    else:
        df_today52w_High.loc[len(df_today52w_High)] = [STOCKS[x],0]


    if l[-1] == ta.MIN(l,252)[-1]:
        df_today52w_Low.loc[len(df_today52w_Low)] = [STOCKS[x],1]
        l_cnt += 1
        print l_cnt
     else:
        df_today52w_Low.loc[len(df_today52w_Low)] = [STOCKS[x],0]




df_new = pd.merge(df_today52w_High,df_today52w_Low,how='outer',on='Stock')

df_new['52w high']= h_cnt
df_new['52w low']= l_cnt

STOCKS中的csv格式如下。我在STOCKS列表中有300只股票。我只是在这里展示一些。

Stock,Date,Time,Open,High,Low,Close,Volume
AAX,2014-01-02,00:00:00,1.0,1.02,1.0,1.01,3251900
AAX,2014-01-03,00:00:00,1.01,1.05,1.01,1.03,8416100
AAX,2014-01-06,00:00:00,1.04,1.05,1.02,1.03,2625200
AAX,2014-01-07,00:00:00,1.03,1.03,1.01,1.01,2539700
AAX,2014-01-08,00:00:00,1.02,1.02,1.0,1.02,2072700
AAX,2014-01-09,00:00:00,1.02,1.02,1.0,1.01,2589600
AAX,2014-01-10,00:00:00,1.01,1.01,1.0,1.01,2057200
AAX,2014-01-13,00:00:00,1.01,1.01,1.0,1.0,1284000
AAX,2014-01-15,00:00:00,1.0,1.01,1.0,1.0,1938100
.
.
AAX,2016-02-29,00:00:00,0.25,0.26,0.24,0.25,63660600
AAX,2016-03-01,00:00:00,0.25,0.26,0.25,0.26,100823200
AAX,2016-03-02,00:00:00,0.27,0.28,0.26,0.28,57543300
AAX,2016-03-03,00:00:00,0.28,0.29,0.27,0.28,113837600
AAX,2016-03-04,00:00:00,0.29,0.3,0.28,0.3,138182600

Answer 1

而不是使用size

的df

writerow

然后，您可以使用if h[y]== ta.MAX(h,20)[y]: csvout = open('52w_h.csv', 'a') csvwrite = csv.writer(csvout) csvwrite.writerow([STOCKS [x][0]]+[dt[y]]+["1"]) csvout.close() else: csvout = open('52w_h.csv', 'a') csvwrite = csv.writer(csvout) csvwrite.writerow([STOCKS [x][0]]+[dt[y]]+["0"]) csvout.close()

对日期进行分组

groupby

输出：

a = pd.read_csv("52w_h.csv")
b = a.groupby('Date')
df_h= b['52wh'].sum()

Python：for循环查找有多少股票触及52周高点和低点

1 个答案: