使用Quandl代码标记向熊猫下载Quandl数据

Question

我想特别使用Quandl的Google数据库下载股票价格，以测试策略。原因是，与Quandl的WIKI和Yahoo数据库相比，谷歌金融的数据是干净的，包括股票经拆分调整后的数据等等。如图所示，最后一个链接将显示调整后的股票分拆：

https://www.quandl.com/WIKI/AAPL-Apple-Inc-AAPL

https://www.quandl.com/YAHOO/AAPL-AAPL-Apple-Inc

AAPL-Apple-Inc-AAPL

然而，Quandl的google数据库标记以GOOG/NYSE_IBM或GOOG/NASDAQ_AAPL的形式出现，与WIKI/IBM、YAHOO/IBM等标记不同。

由于手工添加在这些交易所上市的股票数量的纽约证交所或纳斯达克标签是不可行的，是否有一种有效的方法从Quandl下载股票数据，列出csv或熊猫的股票列表？

这是我的代码FWIW：

nyseList = pd.read_csv('dowjonesIA.csv')  # read csv
masterList = pd.DataFrame(nyseList.Ticker)  # save symbols only into another df

 for index, rows in masterList.iterrows():
     ticker = masterList.loc[index]  # this will not work for passing element
     stock = Quandl.get(ticker, trim_start="2000-01-01", trim_end="2015-01-01")
#stock = Quandl.get("GOOG/NASDAQ_AAPL", trim_start="2000-01-01", trim_end="2015-01-01")  #this is the actual format that works

 # lags data for signal
 stock['diff'] = (stock.Open - stock.Close.shift(1))/stock.Close.shift(1)

 lowerBound = -0.08
 upperBound = 0.08

#generate signal based on 8% rule
stock['signal'] = np.where(stock['diff'] >= upperBound, 1.0, np.where (stock['diff'] <= lowerBound, -1.0, 0.0))

initialCapital = 100000.0
accountLimit = 0.05

#calculate size based on account risk and price
stock['position'] = (stock.signal*initialCapital*accountLimit)/stock.Open

#shows if there is a position open
stock['open trade'] = np.where(stock['position'] > 0, 1.0, np.where(stock['position'] < 0, -1.0, 0.0))

#determine profit/loss
stock['pnl'] = (stock.position*stock.Close) - (stock.position*stock.Open)

#sums up results to starting acct capital
stock['equity curve'] = initialCapital + stock.pnl.cumsum()

print(stock.head(20))  # is dataframe

# plots test results
stock['equity curve'].plot()
plt.show()

我尝试过使用内置于远程数据访问中的熊猫，当将字符串作为args的股票符号传递时也会出现问题。此外，任何以向量化方式执行循环的建议都是值得赞赏的，而不是以迭代的方式执行，并且适用于一般的逻辑流。提前谢谢。

Answer 1

没关系，我只是将标记作为字符串附加到股票符号字符串中。这种格式将适用于：

masterList = pd.Dataframe('GOOG/NYSE_' + nyseList['Ticker'].astype(str))

归功于这个线程：Append string to the start of each value in a said column of a pandas dataframe (elegantly)