使用 Pandas 和 Yahoo_fin 时出现列表索引超出范围错误
List index out of range error when using Pandas and Yahoo_fin
这是从标准普尔 500 指数中的所有股票中提取数据并挑选符合您指定标准的股票的教程中程序的修改版本。
问题是，当我 运行 程序列表索引超出范围 [stock symbol] 弹出时，这些股票将被跳过，不会添加到最终的 CSV 文件中。
示例：
list index out of range for ABMD
list index out of range for ABT
list index out of range for ADBE
list index out of range for ADI
我不太确定是什么问题，如果有人能给我解释一下，我将不胜感激！此外，我还没有应用任何指定标准，只是试图将所有股票数据放入 CSV 文件中。如果您尝试该程序，请确保创建一个名为 stock_data 的数据库。谢谢！
我的代码：
import pandas_datareader as web
import pandas as pd
from yahoo_fin import stock_info as si
import datetime as dt
dow_list = si.tickers_dow()
sp_list = si.tickers_sp500()
tickers = sp_list
'''tickers = list(set(tickers))
tickers.sort()'''
start = dt.datetime.now() - dt.timedelta(days=365)
end = dt.datetime.now()
sp500_df = web.DataReader('^GSPC', 'yahoo', start, end)
sp500_df['Pct Change'] = sp500_df['Adj Close'].pct_change()
sp500_return = (sp500_df['Pct Change'] + 1).cumprod()[-1]
return_list = []
final_df = pd.DataFrame(columns=['Ticker', 'Latest_Price', 'Score', 'PE_Ratio', 'PEG_Ratio', 'SMA_150', 'SMA_200', '52_Week_Low', '52_Week_High'])
counter = 0
for ticker in tickers:
df = web.DataReader(ticker, 'yahoo', start, end)
df.to_csv(f'stock_data/{ticker}.csv')
df['Pct Change'] = df['Adj Close'].pct_change()
stock_return = (df['Pct Change'] + 1).cumprod()[-1]
returns_compared = round((stock_return / sp500_return), 2)
return_list.append(returns_compared)
counter += 1
if counter == 100:
break
best_performers = pd.DataFrame(list(zip(tickers, return_list)), columns=['Ticker', 'Returns Compared'])
best_performers['Score'] = best_performers['Returns Compared'].rank(pct=True) * 100
best_performers = best_performers[best_performers['Score'] >= best_performers['Score'].quantile(0)] #picks stocks in top 25 percentile
for ticker in best_performers['Ticker']:
try:
df = pd.read_csv(f'stock_data/{ticker}.csv', index_col=0)
moving_averages = [150, 200]
for ma in moving_averages:
df['SMA_' + str(ma)] = round(df['Adj Close'].rolling(window=ma).mean(), 2)
latest_price = df['Adj Close'][-1]
pe_ratio = float(si.get_quote_table(ticker)['PE Ratio (TTM)'])
peg_ratio = float(si.get_stats_valuation(ticker)[1][4])
moving_average_150 = df['SMA_150'][-1]
moving_average_200 = df['SMA_200'][-1]
low_52week = round(min(df['Low'][-(52*5):]), 2)
high_52week = round(min(df['High'][-(52 * 5):]), 2)
score = round(best_performers[best_performers['Ticker'] == ticker]['Score'].tolist()[0])
condition_1 = latest_price > moving_average_150 > moving_average_200
condition_2 = latest_price >= (1.3 * low_52week)
condition_3 = latest_price >= (0.75 * high_52week)
condition_4 = pe_ratio < 25
condition_5 = peg_ratio < 2
final_df = final_df.append({'Ticker': ticker,
'Latest_Price': latest_price,
'Score': score,
'PE_Ratio': pe_ratio,
'PEG_Ratio': peg_ratio,
'SMA_150': moving_average_150,
'SMA_200': moving_average_200,
'52_Week_Low': low_52week,
'52_Week_High': high_52week}, ignore_index=True)
except Exception as e:
print(f"{e} for {ticker}")
final_df.sort_values(by='Score', ascending=False)
pd.set_option('display.max_columns', 10)
print(final_df)
final_df.to_csv('final.csv')
我已经帮您排错了。作为结论，我看到你没有检查个别指标数据的获取内容。
它们被添加到字典格式和空数据框中，因为它们在索引和命名系列中。我相信这是错误的根本原因。
- 指定最后的数据并检索值
- 未使用 iloc。
- 253 条数据的 52*5 次回顾
另外，在针对取得的问题数据取得追加指标的情况下，对于同一问题，有时可以取得，有时不能取得。 （原因不明。）因此，可能需要在事先获得pe_ratio和peg_ratio后更改处理方法。
for ticker in best_performers['Ticker']:
#print(ticker)
try:
df = pd.read_csv(f'stock_data/{ticker}.csv')#, index_col=0
moving_averages = [150, 200]
for ma in moving_averages:
df['SMA_' + str(ma)] = round(df['Adj Close'].rolling(window=ma).mean(), 2)
latest_price = df['Adj Close'][-1:].values[0]
pe_ratio = float(si.get_quote_table(ticker)['PE Ratio (TTM)'])
moving_average_150 = df['SMA_150'][-1:].values[0]
moving_average_200 = df['SMA_200'][-1:].values[0]
low_52week = round(min(df['Low'][-(52*1):]), 2)
high_52week = round(min(df['High'][-(52*1):]), 2)
#print(low_52week, high_52week)
score = round(best_performers[best_performers['Ticker'] == ticker]['Score'].tolist()[0])
#print(score)
#print(ticker, latest_price,score,pe_ratio,moving_average_200,low_52week,high_52week)
final_df = final_df.append({'Ticker': ticker,
'Latest_Price': latest_price,
'Score': score,
'PE_Ratio': pe_ratio,
'SMA_150': moving_average_150,
'SMA_200': moving_average_200,
'52_Week_Low': low_52week,
'52_Week_High': high_52week}, ignore_index=True)
#print(final_df)
except Exception as e:
print(f"{e} for {ticker}")
final_df
Ticker Latest_Price Score PE_Ratio SMA_150 SMA_200 52_Week_Low 52_Week_High
0 A 123.839996 40 31.42 147.26 150.31 123.06 126.75
1 AAP 218.250000 70 22.23 220.66 216.64 190.79 202.04
2 AAPL 165.070007 80 29.42 161.85 158.24 150.10 154.12
3 ABC 161.899994 90 21.91 132.94 129.33 132.00 137.79
4 ADBE 425.470001 10 42.46 552.19 571.99 407.94 422.38
备注
一些股票因无法获得额外指标而缺失。
（tickers = sp_list[:10] 测试前 10 个）
这是从标准普尔 500 指数中的所有股票中提取数据并挑选符合您指定标准的股票的教程中程序的修改版本。
问题是，当我 运行 程序列表索引超出范围 [stock symbol] 弹出时，这些股票将被跳过，不会添加到最终的 CSV 文件中。
示例：
list index out of range for ABMD
list index out of range for ABT
list index out of range for ADBE
list index out of range for ADI
我不太确定是什么问题，如果有人能给我解释一下，我将不胜感激！此外，我还没有应用任何指定标准，只是试图将所有股票数据放入 CSV 文件中。如果您尝试该程序，请确保创建一个名为 stock_data 的数据库。谢谢！
我的代码：
import pandas_datareader as web
import pandas as pd
from yahoo_fin import stock_info as si
import datetime as dt
dow_list = si.tickers_dow()
sp_list = si.tickers_sp500()
tickers = sp_list
'''tickers = list(set(tickers))
tickers.sort()'''
start = dt.datetime.now() - dt.timedelta(days=365)
end = dt.datetime.now()
sp500_df = web.DataReader('^GSPC', 'yahoo', start, end)
sp500_df['Pct Change'] = sp500_df['Adj Close'].pct_change()
sp500_return = (sp500_df['Pct Change'] + 1).cumprod()[-1]
return_list = []
final_df = pd.DataFrame(columns=['Ticker', 'Latest_Price', 'Score', 'PE_Ratio', 'PEG_Ratio', 'SMA_150', 'SMA_200', '52_Week_Low', '52_Week_High'])
counter = 0
for ticker in tickers:
df = web.DataReader(ticker, 'yahoo', start, end)
df.to_csv(f'stock_data/{ticker}.csv')
df['Pct Change'] = df['Adj Close'].pct_change()
stock_return = (df['Pct Change'] + 1).cumprod()[-1]
returns_compared = round((stock_return / sp500_return), 2)
return_list.append(returns_compared)
counter += 1
if counter == 100:
break
best_performers = pd.DataFrame(list(zip(tickers, return_list)), columns=['Ticker', 'Returns Compared'])
best_performers['Score'] = best_performers['Returns Compared'].rank(pct=True) * 100
best_performers = best_performers[best_performers['Score'] >= best_performers['Score'].quantile(0)] #picks stocks in top 25 percentile
for ticker in best_performers['Ticker']:
try:
df = pd.read_csv(f'stock_data/{ticker}.csv', index_col=0)
moving_averages = [150, 200]
for ma in moving_averages:
df['SMA_' + str(ma)] = round(df['Adj Close'].rolling(window=ma).mean(), 2)
latest_price = df['Adj Close'][-1]
pe_ratio = float(si.get_quote_table(ticker)['PE Ratio (TTM)'])
peg_ratio = float(si.get_stats_valuation(ticker)[1][4])
moving_average_150 = df['SMA_150'][-1]
moving_average_200 = df['SMA_200'][-1]
low_52week = round(min(df['Low'][-(52*5):]), 2)
high_52week = round(min(df['High'][-(52 * 5):]), 2)
score = round(best_performers[best_performers['Ticker'] == ticker]['Score'].tolist()[0])
condition_1 = latest_price > moving_average_150 > moving_average_200
condition_2 = latest_price >= (1.3 * low_52week)
condition_3 = latest_price >= (0.75 * high_52week)
condition_4 = pe_ratio < 25
condition_5 = peg_ratio < 2
final_df = final_df.append({'Ticker': ticker,
'Latest_Price': latest_price,
'Score': score,
'PE_Ratio': pe_ratio,
'PEG_Ratio': peg_ratio,
'SMA_150': moving_average_150,
'SMA_200': moving_average_200,
'52_Week_Low': low_52week,
'52_Week_High': high_52week}, ignore_index=True)
except Exception as e:
print(f"{e} for {ticker}")
final_df.sort_values(by='Score', ascending=False)
pd.set_option('display.max_columns', 10)
print(final_df)
final_df.to_csv('final.csv')
我已经帮您排错了。作为结论，我看到你没有检查个别指标数据的获取内容。 它们被添加到字典格式和空数据框中，因为它们在索引和命名系列中。我相信这是错误的根本原因。
- 指定最后的数据并检索值
- 未使用 iloc。
- 253 条数据的 52*5 次回顾
另外，在针对取得的问题数据取得追加指标的情况下，对于同一问题，有时可以取得，有时不能取得。 （原因不明。）因此，可能需要在事先获得pe_ratio和peg_ratio后更改处理方法。
for ticker in best_performers['Ticker']:
#print(ticker)
try:
df = pd.read_csv(f'stock_data/{ticker}.csv')#, index_col=0
moving_averages = [150, 200]
for ma in moving_averages:
df['SMA_' + str(ma)] = round(df['Adj Close'].rolling(window=ma).mean(), 2)
latest_price = df['Adj Close'][-1:].values[0]
pe_ratio = float(si.get_quote_table(ticker)['PE Ratio (TTM)'])
moving_average_150 = df['SMA_150'][-1:].values[0]
moving_average_200 = df['SMA_200'][-1:].values[0]
low_52week = round(min(df['Low'][-(52*1):]), 2)
high_52week = round(min(df['High'][-(52*1):]), 2)
#print(low_52week, high_52week)
score = round(best_performers[best_performers['Ticker'] == ticker]['Score'].tolist()[0])
#print(score)
#print(ticker, latest_price,score,pe_ratio,moving_average_200,low_52week,high_52week)
final_df = final_df.append({'Ticker': ticker,
'Latest_Price': latest_price,
'Score': score,
'PE_Ratio': pe_ratio,
'SMA_150': moving_average_150,
'SMA_200': moving_average_200,
'52_Week_Low': low_52week,
'52_Week_High': high_52week}, ignore_index=True)
#print(final_df)
except Exception as e:
print(f"{e} for {ticker}")
final_df
Ticker Latest_Price Score PE_Ratio SMA_150 SMA_200 52_Week_Low 52_Week_High
0 A 123.839996 40 31.42 147.26 150.31 123.06 126.75
1 AAP 218.250000 70 22.23 220.66 216.64 190.79 202.04
2 AAPL 165.070007 80 29.42 161.85 158.24 150.10 154.12
3 ABC 161.899994 90 21.91 132.94 129.33 132.00 137.79
4 ADBE 425.470001 10 42.46 552.19 571.99 407.94 422.38
备注
一些股票因无法获得额外指标而缺失。
（tickers = sp_list[:10] 测试前 10 个）