Python 使用Quandl google finance数据集代码标签将Quandl数据下载到熊猫

我想专门使用Quandl的Google Finance数据库来下载股票价格，以便对策略进行回溯测试。原因是，与Quandl的WIKI和Yahoo数据库相比，google finance拥有针对股票拆分等调整的干净数据。如图所示，最后一个链接将显示调整后的股票拆分：

然而，Quandl的google数据库标签的形式是GOOG/NYSE_IBM或GOOG/NASDAQ_AAPL，这与WIKI/IBM、YAHOO/IBM等标签不同

由于手动添加NYSE或NASDAQ标记在这些交易所上市的股票数量是不可行的，因此在csv或pandas数据框中提供股票列表的情况下，有没有有效的方法从Quandl下载股票数据

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()

---

我曾尝试使用pandas内置的远程数据访问，这在将字符串作为参数的股票符号传递时也会出现问题。此外，任何以矢量化方式执行循环的建议都值得赞赏，而不是迭代执行，并且适用于一般逻辑流。提前谢谢。

没关系，我只是将标签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))

这条线索的功劳是：