Ich versuche, monatliche Bestandsdaten von Yahoo zum Analysieren von Mustern zu verwenden. Aus irgendeinem Grund stimmen die monatlichen Renditen, die das Programm in einem Datenrahmen für einen bestimmten Bestand (ATVI) ausgibt, nicht mit den Renditen der tatsächlichen Yahoo-Site überein. Ich habe die monatlichen Renditen für den Zeitraum 2015 verglichen und Spalten für durchschnittliche Zu- und Abnahmen sowie für die Anzahl der Vorkommen von beiden angegeben.

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

Mein 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