I'm trying to use monthly stock data from yahoo to analyze patterns. For some reason, the monthly returns the program is spitting out in a dataframe for a particular stock (ATVI) do not match the returns from the actual yahoo site. I compared monthly returns for the 2015 period and included columns for average increases and decreases as well as the # of occurrences of each.
Yahoo link: https://finance.yahoo.com/q/hp?s=ATVI&a=00&b=1&c=2015&d=11&e=31&f=2015&g=m
My code:
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
To get the average daily up/down price moves, you could:
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
to get:
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
To get the mean by months, you can:
returns.groupby(returns.index.month).mean()
to get:
1 -0.006884
2 0.118938
3 0.012383
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
来源：https://stackoverflow.com/questions/37929068/python-pandas-dataframe-data-not-matching-source