Python：for循环查找有多少股票达到52周的高点和低点答案

【问题标题】：Python: for loop to find how many stocks hits 52 weeks high and lowPython：for循环查找有多少股票达到52周的高点和低点
【发布时间】：2016-04-02 15:15:12
【问题描述】：

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 文件的第一天到 csv 的最后一天计算。

示例：
02-01-2014 , 10 只股票 52 周高点和 45 只股票 52 周低点
2014 年 3 月 1 日，23 只股票 52 周高点和 56 只股票 52 周低点
2014 年 4 月 1 日，34 只股票 52 周高点和 34 只股票 52 周低点。

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。我这里只展示一些。

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

【问题讨论】：

标签： python csv finance

【解决方案1】：

用writerow代替df

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()

然后你可以使用groupby对日期进行分组

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

输出：

2016-04-06 160
2016-04-07 170
2016-04-08 142

【讨论】：