Python - Pandas Dataframe - 與數據源不匹配

Question

我正在嘗試使用來自雅虎的每月庫存數據來分析模式。出於某種原因，該計劃在特定股票（ATVI）的數據框中吐出的每月回報與實際雅虎網站的回報不匹配。我比較了2015年的月度回報，並列出了平均增加和減少的列數以及每個數字的發生次數。

雅虎鏈接：https://finance.yahoo.com/q/hp?s=ATVI&a=00&b=1&c=2015&d=11&e=31&f=2015&g=m

我的代碼：

from datetime import datetime 
from pandas_datareader import data, wb 
import pandas_datareader.data as web 
import pandas as pd 
from pandas_datareader._utils import RemoteDataError 
import csv 
import sys 
import os 
import time 

class MonthlyChange(object): 
    months = { 0:'JAN', 1:'FEB', 2:'MAR', 3:'APR', 4:'MAY',5:'JUN', 6:'JUL', 7:'AUG', 8:'SEP', 9:'OCT',10:'NOV', 11:'DEC' } 

def __init__(self,month): 
    self.month = MonthlyChange.months[month-1] 
    self.sum_of_pos_changes=0 
    self.sum_of_neg_changes=0 
    self.total_neg=0 
    self.total_pos=0 
def add_change(self,change): 
    if change < 0: 
     self.sum_of_neg_changes+=change 
     self.total_neg+=1 
    elif change > 0: 
     self.sum_of_pos_changes+=change 
     self.total_pos+=1 
def get_data(self): 
    if self.total_pos == 0: 
     return (self.month,0.0,0,self.sum_of_neg_changes/self.total_neg,self.total_neg) 
    elif self.total_neg == 0: 
     return (self.month,self.sum_of_pos_changes/self.total_pos,self.total_pos,0.0,0) 
    else: 
     return (self.month,self.sum_of_pos_changes/self.total_pos,self.total_pos,self.sum_of_neg_changes/self.total_neg,self.total_neg) 


for ticker in ['ATVI']: 

try: 

    data = web.DataReader(ticker.strip('\n'), "yahoo", datetime(2015,01,1), datetime(2015,12,31)) 
    data['ymd'] = data.index 
    year_month = data.index.to_period('M') 
    data['year_month'] = year_month 
    first_day_of_months = data.groupby(["year_month"])["ymd"].min() 
    first_day_of_months = first_day_of_months.to_frame().reset_index(level=0) 
    last_day_of_months = data.groupby(["year_month"])["ymd"].max() 
    last_day_of_months = last_day_of_months.to_frame().reset_index(level=0) 
    fday_open = data.merge(first_day_of_months,on=['ymd']) 
    fday_open = fday_open[['year_month_x','Open']] 
    lday_open = data.merge(last_day_of_months,on=['ymd']) 
    lday_open = lday_open[['year_month_x','Open']] 

    fday_lday = fday_open.merge(lday_open,on=['year_month_x']) 
    monthly_changes = {i:MonthlyChange(i) for i in range(1,13)} 
    for index,ym, openf,openl in fday_lday.itertuples(): 
     month = ym.strftime('%m') 
     month = int(month) 
     diff = (openf-openl)/openf 
     monthly_changes[month].add_change(diff) 

    changes_df = pd.DataFrame([monthly_changes[i].get_data() for i in monthly_changes],columns=["Month","Avg Inc.","Inc","Avg.Dec","Dec"]) 


    print ticker 
    print changes_df 

Answer 1

要獲得日均上/下價格走勢，你可以：

from pandas_datareader.data import DataReader 

data = DataReader('ATVI', "yahoo", datetime(2015, 1, 1), datetime(2015, 12, 31))[['Open', 'Close']] 
open = data.Close.resample('M').first() # get the open of the first day, assign date of last day of month 
close = data.Close.resample('M').last() # get the close of the last day, assign date of last day of month 
returns = close.subtract(open).div(open) # calculate returns 

獲得：

Date 
2014-01-31 -0.052020 
2014-02-28 0.134232 
2014-03-31 0.047131 
2014-04-30 -0.032866 
2014-05-31 0.040561 
2014-06-30 0.081474 
2014-07-31 -0.007539 
2014-08-31 0.049020 
2014-09-30 -0.124263 
2014-10-31 -0.031083 
2014-11-30 0.066503 
2014-12-31 -0.042755 
2015-01-31 0.038251 
2015-02-28 0.103644 
2015-03-31 -0.022366 
2015-04-30 0.017387 
2015-05-31 0.095879 
2015-06-30 -0.046850 
2015-07-31 0.042863 
2015-08-31 0.121865 
2015-09-30 0.108758 
2015-10-31 0.124919 
2015-11-30 0.089384 
2015-12-31 0.003630 
Freq: M, Name: Close, dtype: float64 

要按照月份得到的平均值，您可以：

returns.groupby(returns.index.month).mean() 

獲得：

1 -0.006884 
2  0.118938 
3  0.
4 -0.007739 
5  0.068220 
6  0.017312 
7  0.017662 
8  0.085442 
9 -0.007752 
10 0.046918 
11 0.077943 
12 -0.019563