我正在尝试使用pdr.get_data_yahoo(ticker,start,end)从雅虎财务“调整关闭”数据
我能够将数据提取到数据框中,但无法按代码符号进行排序
我创建了一个来自csv文件的股票代码列表。 并使用get_data_yahoo方法获取每个符号的数据。
但是我无法将数据放入按代码分类的列中,而是将数据连接到1列中,或者我只能从列表中提取1个符号。
import fix_yahoo_finance as fyf
from pandas_datareader import data as pdr
import csv
import datetime
import pandas as pd
import numpy as np
import pandas_datareader as dr
#Stock tickers from csv
#Csv file data looks like this.........................
Symbol
0 MOQ.AX
1 ONT.AX
2 14D.AX
3 1ST.AX
4 T3D.AX
#----------------------------
Asx_Stocks = pd.read_csv('ASXListedCompaniesAX1.csv',usecols = ['Symbol'],
squeeze=True)
df_symbol = pd.DataFrame(Asx_Stocks)
print(df_symbol.head())
#df looks like this--------
Symbol
0 MOQ.AX
1 ONT.AX
2 14D.AX
3 1ST.AX
4 T3D.AX
#-----------------------------
#set symbols to list
symbols = df_symbol['Symbol'].tolist()
symbol is a list of the tickers formatted for australian stock exchange
(ASX)
e.g....
['MOQ.AX', 'ONT.AX', '14D.AX', '1ST.AX', 'T3D.AX', 'TGP.AX'........]
#set start and end parameters
start = datetime.datetime(2018, 1, 1)
end = datetime.datetime(2019, 1, 1);
#loop through symbols from list and fetch adj close data
for symbol in symbols:
f = pdr.get_data_yahoo(symbol, start, end)['Adj Close']
print(f)
我希望每行有一个数据框,作为按收盘价排序的收市价,例如以日期作为索引列
AAA DJRE GOLD IJR NDQ PMGOLD QCB VAP VAS VCF \
0 50.15 20.63 170.50 105.25 16.34 18.13 8.27 79.74 72.13 47.25
1 50.14 20.62 170.86 104.57 16.07 18.27 8.27 81.00 72.03 47.25
2 50.15 20.53 169.29 104.66 16.11 18.22 8.08 80.39 71.19 47.21
我得到的是............
[*********************100%***********************] 1 of 1 downloaded
Date
2018-01-01 0.230
2018-01-02 0.230
2018-01-03 0.230
2018-01-04 0.230
2018-01-07 0.230
2018-01-08 0.230
2018-01-09 0.220
2018-01-10 0.220
2018-01-11 0.230
[*********************100%***********************] 1 of 1 downloaded
etc.... continues on many times so data is downloading
我想我得到了一系列单独的数据框,其中包含每个股票的价格数据
但是我需要将它串联到一个数据帧中。
我相信……........
# concatenate all dataframes into one final dataframe
dfs = [out put from looping through each ticker]
#perform the joins on DATE column as key and drop null values
df = reduce(lambda left,right: pd.merge(left,right,on='DATE',
how='outer'), dfs).dropna() df.head(5)
我不确定如何正确实施
答案 0 :(得分:0)
使用fix-yahoo-finance取得了很多成功。这是github page。
before(:each) do
http_login
@job = Job.create(:client_id => 1, :title => "Title", :date => Time.now, :number_of_workers => 10, :venue => "Somewhere")
end