如何通过yahoo导入熊猫的多个股票价格？
如何通过yahoo导入熊猫的多个股票价格？
提问于 2020-05-26 02:49:40
因此，我正在尝试使用pandas和panadas datareader获取多个股票价格。如果我只尝试导入一个报价器，它会运行得很好，但如果我使用多个报价器，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) 尽管我得到了错误：
ValueError: Wrong number of items passed 2, placement implies 1那么我怎么才能绕过它，只允许通过1只股票。到目前为止，我已经尝试使用quandl和谷歌，这也不起作用。我也尝试过pdr.get_data_yahoo，但我得到了同样的结果。我也尝试过yf.download()，但仍然遇到同样的问题。有没有人有什么办法来解决这个问题呢？谢谢。
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回答 3
Stack Overflow用户
发布于 2020-05-26 03:07:15
我在你的代码中尝试了3只股票，它返回了所有3只股票的数据，我不确定我是否理解了你面临的问题？
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)输出：
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 25201400.0Stack Overflow用户
发布于 2020-05-26 03:52:09
我认为误差来自移动平均线和d'sma50‘=np.round(d’‘Close’..rolling(window=2).mean()，decimals=2)这条线。因为d代表3只股票，所以我认为你必须分离每只股票并分别计算移动平均线。
编辑:我只尝试了两只股票(英航和AMD)，但这不是最好的解决方案，因为我总是重复每一行。我只是Python的初学者，但这可能会帮助你找到问题的解决方案PS :最后一行不太好用(这是打印P&L和Return)
“
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)))Stack Overflow用户
发布于 2020-09-18 22:29:21
似乎pandas数据阅读器中有一个bug。我通过初始化一个符号，然后在实例化的对象上设置symbols属性来解决这个问题。这样做之后，可以很好地调用下面的tmp上的read()。
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()页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/62008825
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