This is a modified version of a program from a tutorial that extracts data from all of the stocks in the S&P 500 and picks stocks that match the criteria you specify.
The issue is that when I run the program List index out of range [stock symbol] pops up and those stocks are skipped and aren't added to the final CSV file.
Example:
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
I'm not really sure what the issue is, I would greatly appreciate it if someone would explain it to me! Also, I am not applying any of the specifying criteria yet and am just trying to get all of the stock data into the CSV file. Make sure to create a database named stock_data if you try the program. Thanks!
My code:
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')
CodePudding user response：
I have done the error shooting on your behalf. As a conclusion, I see that you have not checked the contents of the acquisition of the individual indicator data. They are being added to the dictionary format and empty data frames as they are in index and named series. I believe that is the root cause of the error.
- Specifying the last data and retrieving the values
- iloc is not used.
- 52*5 lookbacks for 253 data
In addition, when additional indicators are acquired for the acquired issue data, there are cases where they can be acquired for the same issue, and cases where they cannot. (The cause is unknown.) Therefore, it may be necessary to change the method of processing pe_ratio and peg_ratio after obtaining them in advance.
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
Note
Some stocks are missing because additional indicators could not be obtained.
(tickers = sp_list[:10] tested on the first 10)