如何通过雅虎导入pandas的多个股票价格？

Question

所以我试图使用 pandas 和 panadas 数据阅读器来获得多个股票价格。 如果我只尝试导入一个代码，它将运行良好，但如果我使用多个，则会出现错误。 代码是：

import pandas as pd
import pandas_datareader as web
import datetime as dt
stocks = ['BA', 'AMD']
start = dt.datetime(2018, 1, 1)
end = dt.datetime(2020, 1, 1)
d = web.DataReader(stocks, 'yahoo', start, end) 

虽然我得到了错误：

ValueError: Wrong number of items passed 2, placement implies 1

那么我该如何绕过它，只允许通过 1 只股票。 到目前为止，我已经尝试使用 quandl 和 google，但它们也不起作用。 我也尝试过pdr.get_data_yahoo但我得到了相同的结果。 我也尝试过yf.download()并且仍然遇到同样的问题。 有没有人有任何想法来解决这个问题？ 谢谢你。

编辑：完整代码：

import pandas as pd
import pandas_datareader as web
import datetime as dt
import yfinance as yf
import numpy as np
stocks = ['BA', 'AMD', 'AAPL']
start = dt.datetime(2018, 1, 1)
end = dt.datetime(2020, 1, 1)
d = web.DataReader(stocks, 'yahoo', start, end)
d['sma50'] = np.round(d['Close'].rolling(window=2).mean(), decimals=2)
d['sma200'] = np.round(d['Close'].rolling(window=14).mean(), decimals=2)
d['200-50'] = d['sma200'] - d['sma50']
_buy = -2
d['Crossover_Long'] = np.where(d['200-50'] < _buy, 1, 0)
d['Crossover_Long_Change']=d.Crossover_Long.diff()
d['buy'] = np.where(d['Crossover_Long_Change'] == 1, 'buy', 'n/a')
d['sell'] = np.where(d['Crossover_Long_Change'] == -1, 'sell', 'n/a')
pd.set_option('display.max_rows', 5093)
d.drop(['High', 'Low', 'Close', 'Volume', 'Open'], axis=1, inplace=True)
d.dropna(inplace=True)
#make 2 dataframe
d.set_index(d['Adj Close'], inplace=True)
buy_price = d.index[d['Crossover_Long_Change']==1]
sell_price = d.index[d['Crossover_Long_Change']==-1]
d['Crossover_Long_Change'].value_counts()
profit_loss = (sell_price - buy_price)*10
commision = buy_price*.01
position_value = (buy_price + commision)*10
percent_return = (profit_loss/position_value)*100
percent_rounded = np.round(percent_return, decimals=2)
prices = { 
    "Buy Price" : buy_price,
    "Sell Price" : sell_price,
    "P/L" : profit_loss,
    "Return": percent_rounded
}
df = pd.DataFrame(prices)
print('The return was {}%, and profit or loss was ${} '.format(np.round(df['Return'].sum(), decimals=2), 
                                                               np.round(df['P/L'].sum(), decimals=2)))
d

Answer 1

我在您的代码中尝试了 3 只股票，它返回所有 3 只股票的数据，不确定我是否理解您面临的问题？

import pandas as pd
import pandas_datareader as web
import datetime as dt
stocks = ['BA', 'AMD', 'AAPL']
start = dt.datetime(2018, 1, 1)
end = dt.datetime(2020, 1, 1)
d = web.DataReader(stocks, 'yahoo', start, end)
print(d)

Output：

Attributes   Adj Close                              Close                         ...        Open                            Volume
Symbols             BA        AMD        AAPL          BA        AMD        AAPL  ...          BA        AMD        AAPL         BA          AMD        AAPL
Date                                                                              ...
2018-01-02  282.886383  10.980000  166.353714  296.839996  10.980000  172.259995  ...  295.750000  10.420000  170.160004  2978900.0   44146300.0  25555900.0
2018-01-03  283.801239  11.550000  166.324722  297.799988  11.550000  172.229996  ...  295.940002  11.610000  172.529999  3211200.0  154066700.0  29517900.0
2018-01-04  282.724396  12.120000  167.097290  296.670013  12.120000  173.029999  ...  297.940002  12.100000  172.539993  4171700.0  109503000.0  22434600.0
2018-01-05  294.322296  11.880000  168.999741  308.839996  11.880000  175.000000  ...  296.769989  12.190000  173.440002  6177700.0   63808900.0  23660000.0
2018-01-08  295.570740  12.280000  168.372040  310.149994  12.280000  174.350006  ...  308.660004  12.010000  174.350006  4124900.0   63346000.0  20567800.0
...                ...        ...         ...         ...        ...         ...  ...         ...        ...         ...        ...          ...         ...
2019-12-24  331.030457  46.540001  282.831299  333.000000  46.540001  284.269989  ...  339.510010  46.099998  284.690002  4120100.0   44432200.0  12119700.0
2019-12-26  327.968689  46.630001  288.442780  329.920013  46.630001  289.910004  ...  332.700012  46.990002  284.820007  4593400.0   57562800.0  23280300.0
2019-12-27  328.187408  46.180000  288.333313  330.140015  46.180000  289.799988  ...  330.200012  46.849998  291.119995  4124000.0   36581300.0  36566500.0
2019-12-30  324.469513  45.520000  290.044617  326.399994  45.520000  291.519989  ...  330.500000  46.139999  289.459991  4525500.0   41149700.0  36028600.0
2019-12-31  323.833313  45.860001  292.163818  325.760010  45.860001  293.649994  ...  325.410004  45.070000  289.929993  4958800.0   31673200.0  25201400.0

Answer 2

我认为错误来自您的移动平均线和 d['sma50'] = np.round(d['Close'].rolling(window=2).mean(), decimals=2) 行，因为 d 代表 3 只股票，我认为你必须分开每只股票并分别计算移动平均线

编辑：我只为两只股票（BA和AMD）尝试了一些东西，但这不是最好的解决方案，因为我总是在每一行都重复自己。 我只是 Python 的初学者，但也许这会帮助您找到解决问题的方法 PS：最后一行效果不佳（这是 P&L 和 Return 的打印）

"

import pandas as pd
import pandas_datareader as web
import datetime as dt
stock1 = ['BA']
stock2=['AMD']
start = dt.datetime(2018, 1, 1)
end = dt.datetime(2020, 1, 1)
d1 = web.DataReader(stock1, 'yahoo', start, end)
d2 = web.DataReader(stock2, 'yahoo', start, end)
d1['sma50'] = np.round(d1['Close'].rolling(window=2).mean(), decimals=2)
d2['sma50'] = np.round(d2['Close'].rolling(window=2).mean(), decimals=2)
d1['sma200'] = np.round(d1['Close'].rolling(window=14).mean(), decimals=2)
d2['sma200'] = np.round(d2['Close'].rolling(window=14).mean(), decimals=2)
d1['200-50'] = d1['sma200'] - d1['sma50']
d2['200-50'] = d2['sma200'] - d2['sma50']
_buy = -2
d1['Crossover_Long'] = np.where(d1['200-50'] < _buy, 1, 0)
d2['Crossover_Long'] = np.where(d2['200-50'] < _buy, 1, 0)
d1['Crossover_Long_Change']=d1.Crossover_Long.diff()
d2['Crossover_Long_Change']=d2.Crossover_Long.diff()
d1['buy'] = np.where(d1['Crossover_Long_Change'] == 1, 'buy', 'n/a')
d2['buy'] = np.where(d2['Crossover_Long_Change'] == 1, 'buy', 'n/a')
d1['sell_BA'] = np.where(d1['Crossover_Long_Change'] == -1, 'sell', 'n/a')
d2['sell_AMD'] = np.where(d2['Crossover_Long_Change'] == -1, 'sell', 'n/a')
pd.set_option('display.max_rows', 5093)
d1.drop(['High', 'Low', 'Close', 'Volume', 'Open'], axis=1, inplace=True)
d2.drop(['High', 'Low', 'Close', 'Volume', 'Open'], axis=1, inplace=True)
d2.dropna(inplace=True)
d1.dropna(inplace=True)
d1.set_index("Adj Close",inplace=True)
d2.set_index("Adj Close",inplace=True)
buy_price_BA = np.array(d1.index[d1['Crossover_Long_Change']==1])
buy_price_AMD = np.array(d2.index[d2['Crossover_Long_Change']==1])
sell_price_BA = np.array(d1.index[d1['Crossover_Long_Change']==-1])
sell_price_AMD = np.array(d2.index[d2['Crossover_Long_Change']==-1])
d1['Crossover_Long_Change'].value_counts()
d2['Crossover_Long_Change'].value_counts()
profit_loss_BA = (sell_price_BA - buy_price_BA)*10
profit_loss_AMD = (sell_price_AMD - buy_price_AMD)*10
commision_BA = buy_price_BA*.01
commision_AMD = buy_price_AMD*.01
position_value_BA = (buy_price_BA + commision_BA)*10
position_value_AMD = (buy_price_AMD + commision_AMD)*10
percent_return_BA = np.round(((profit_loss_BA/position_value_BA)*100),decimals=2)
percent_return_AMD = np.round(((profit_loss_AMD/position_value_AMD)*100),decimals=2)
prices_BA = { 
    "Buy Price BA" : [buy_price_BA],
    "Sell Price BA" : [sell_price_BA],
    "P/L BA" : [profit_loss_BA],
    "Return BA": [percent_return_BA]}
df = pd.DataFrame(prices_BA)
print('The return was {}%, and profit or loss was ${} '.format(np.round(df['Return BA'].sum(), decimals=2), 
                                                           np.round(df['P/L BA'].sum(), decimals=2)))
prices_AMD = { 
    "Buy Price AMD" : [buy_price_AMD],
    "Sell Price AMD" : [sell_price_AMD],
    "P/L AMD" : [profit_loss_AMD],
    "Return AMD": [percent_return_AMD]}
df = pd.DataFrame(prices_AMD)
print('The return was {}%, and profit or loss was ${} '.format(np.round(df['Return AMD'].sum(), decimals=2), 
                                                               np.round(df['P/L AMD'].sum(), decimals=2)))

Answer 3

pandas 数据读取器中似乎存在错误。 我通过使用一个符号初始化然后在实例化的 object 上设置符号属性来解决它。 之后，在下面的 tmp 上调用 read() 就可以了。

import pandas_datareader as pdr
all_symbols = ['ibb', 'xly', 'fb', 'exx1.de']

tmp = pdr.yahoo.daily.YahooDailyReader(symbols=all_symbols[0])
# this is a work-around, pdr is broken...
tmp.symbols = all_symbols
data = tmp.read()