从雅虎财务python一次下载多个股票

Question

我对使用熊猫数据阅读器的雅虎财务功能有疑问.我现在使用了几个月的股票代码清单,并按以下几行执行:

import pandas_datareader as pdr
import datetime

stocks = ["stock1","stock2",....]
start = datetime.datetime(2012,5,31)
end = datetime.datetime(2018,3,1)

f = pdr.DataReader(stocks, 'yahoo',start,end)

从昨天开始我收到错误"IndexError:list index out of range",只有当我试图获得多个股票时才出现.

最近几天有什么变化我必须考虑或者你有更好的解决方案吗？

Answer 1

如果您阅读Pandas DataReader的文档,他们会立即对多个数据源API发布折旧,其中一个是Yahoo! 金融.

v0.6.0(2018年1月24日)

立即弃用Yahoo! ,Google选项和报价以及EDGAR.这些API背后的终点已发生根本变化,现有读者需要完全重写.在大多数雅虎的情况下! 数据已删除端点.PDR希望恢复这些功能,欢迎提取请求.

这可能是你获得IndexError(或任何其他通常不存在的错误)的原因的罪魁祸首.

但是,还有另一个Python包,其目标是修复对Yahoo!的支持.为Pandas DataReader提供资金,你可以在这里找到这个包:

https://pypi.python.org/pypi/fix-yahoo-finance

根据他们的文件:

雅虎 金融已退役其历史数据API,导致许多依赖它的程序停止工作.

fix-yahoo-finance通过从Yahoo!抓取数据来解决问题.融资使用在相同的格式返回一个数据帧的熊猫/面板pandas_datareader的 get_data_yahoo().

通过基本上"劫持"的pandas_datareader.data.get_data_yahoo() 方法,fix-yahoo-finance的植入很容易,只需要导入fix_yahoo_finance你的代码.

您需要添加的是:

from pandas_datareader import data as pdr
import fix_yahoo_finance as yf

yf.pdr_override() 

stocks = ["stock1","stock2", ...]
start = datetime.datetime(2012,5,31)
end = datetime.datetime(2018,3,1)

f = pdr.get_data_yahoo(stocks, start=start, end=end)

或者甚至不需要Pandas DataReader:

import fix_yahoo_finance as yf

stocks = ["stock1","stock2", ...]
start = datetime.datetime(2012,5,31)
end = datetime.datetime(2018,3,1)
data = yf.download(stocks, start=start, end=end)

Answer 2

您可以将新的Python YahooFinancials模块与pandas一起使用。YahooFinancials的构建良好，可以通过散列每个Yahoo Finance网页中存在的数据存储对象来获取其数据，因此它速度很快，并且不依赖旧的停产api也不像刮板那样依赖Web驱动程序。数据以JSON形式返回，您可以通过传递股票/指数行情清单来初始化YahooFinancials类，从而一次提取任意数量的股票。

$ pip install yahoofinancials

用法示例：

from yahoofinancials import YahooFinancials
import pandas as pd

# Select Tickers and stock history dates
ticker = 'AAPL'
ticker2 = 'MSFT'
ticker3 = 'INTC'
index = '^NDX'
freq = 'daily'
start_date = '2012-10-01'
end_date = '2017-10-01'


# Function to clean data extracts
def clean_stock_data(stock_data_list):
    new_list = []
    for rec in stock_data_list:
        if 'type' not in rec.keys():
            new_list.append(rec)
    return new_list

# Construct yahoo financials objects for data extraction
aapl_financials = YahooFinancials(ticker)
mfst_financials = YahooFinancials(ticker2)
intl_financials = YahooFinancials(ticker3)
index_financials = YahooFinancials(index)

# Clean returned stock history data and remove dividend events from price history
daily_aapl_data = clean_stock_data(aapl_financials
                                     .get_historical_stock_data(start_date, end_date, freq)[ticker]['prices'])
daily_msft_data = clean_stock_data(mfst_financials
                                     .get_historical_stock_data(start_date, end_date, freq)[ticker2]['prices'])
daily_intl_data = clean_stock_data(intl_financials
                                     .get_historical_stock_data(start_date, end_date, freq)[ticker3]['prices'])
daily_index_data = index_financials.get_historical_stock_data(start_date, end_date, freq)[index]['prices']
stock_hist_data_list = [{'NDX': daily_index_data}, {'AAPL': daily_aapl_data}, {'MSFT': daily_msft_data},
                        {'INTL': daily_intl_data}]


# Function to construct data frame based on a stock and it's market index
def build_data_frame(data_list1, data_list2, data_list3, data_list4):
    data_dict = {}
    i = 0
    for list_item in data_list2:
        if 'type' not in list_item.keys():
            data_dict.update({list_item['formatted_date']: {'NDX': data_list1[i]['close'], 'AAPL': list_item['close'],
                                                            'MSFT': data_list3[i]['close'],
                                                            'INTL': data_list4[i]['close']}})
            i += 1
    tseries = pd.to_datetime(list(data_dict.keys()))
    df = pd.DataFrame(data=list(data_dict.values()), index=tseries,
                      columns=['NDX', 'AAPL', 'MSFT', 'INTL']).sort_index()
    return df

一次多个股票数据的示例（返回每个股票行情的JSON对象列表）：

from yahoofinancials import YahooFinancials

tech_stocks = ['AAPL', 'MSFT', 'INTC']
bank_stocks = ['WFC', 'BAC', 'C']

yahoo_financials_tech = YahooFinancials(tech_stocks)
yahoo_financials_banks = YahooFinancials(bank_stocks)

tech_cash_flow_data_an = yahoo_financials_tech.get_financial_stmts('annual', 'cash')
bank_cash_flow_data_an = yahoo_financials_banks.get_financial_stmts('annual', 'cash')

banks_net_ebit = yahoo_financials_banks.get_ebit()
tech_stock_price_data = tech_cash_flow_data.get_stock_price_data()
daily_bank_stock_prices = yahoo_financials_banks.get_historical_stock_data('2008-09-15', '2017-09-15', 'daily')

JSON输出示例：

码：

yahoo_financials = YahooFinancials('WFC')
print(yahoo_financials.get_historical_stock_data("2017-09-10", "2017-10-10", "monthly"))

JSON返回：

{
    "WFC": {
        "prices": [
            {
                "volume": 260271600,
                "formatted_date": "2017-09-30",
                "high": 55.77000045776367,
                "adjclose": 54.91999816894531,
                "low": 52.84000015258789,
                "date": 1506830400,
                "close": 54.91999816894531,
                "open": 55.15999984741211
            }
        ],
        "eventsData": [],
        "firstTradeDate": {
            "date": 76233600,
            "formatted_date": "1972-06-01"
        },
        "isPending": false,
        "timeZone": {
            "gmtOffset": -14400
        },
        "id": "1mo15050196001507611600"
    }
}