yahoo finance 404 workaround

machine_learning/kmeans_clustering.py

@@ -8,11 +8,15 @@
 import datetime as dt
 import requests
 from pandas_datareader import data as pdr
-from yahoo_fin import stock_info as si 
+import sys
+import os
+parent_dir = os.path.dirname(os.getcwd())
+sys.path.append(parent_dir)
+import tickers as ti
 
 # Loading the data
 yf.pdr_override()
-stocks = si.tickers_dow()[10:30]  # Shorten the code for readability
+stocks = ti.tickers_dow()  # Shorten the code for readability
 start = dt.datetime(2010, 1, 1)
 now = dt.datetime.now()
 

portfolio_strategies/geometric_brownian_motion.py

@@ -2,8 +2,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import math
-import yahoo_fin.stock_info as si
-from pandas_datareader import DataReader
+import pandas_datareader.data as pdr
 import pandas as pd
 import datetime
 from pylab import rcParams
@@ -20,8 +19,8 @@
 index = '^GSPC'
 
 # Fetch stock and index data from Yahoo Finance
-df = DataReader(stock, 'yahoo', start_date, end_date)
-dfb = DataReader(index, 'yahoo', start_date, end_date)
+df = pdr.get_data_yahoo(stock, start_date, end_date)
+dfb = pdr.get_data_yahoo(index, start_date, end_date)
 
 # Resample the data to monthly frequency
 rts = df.resample('M').last()
@@ -64,7 +63,7 @@ def simulate(self):
 
 # Set the parameters for the Geometric Brownian Motion simulation
 n = 20 # Number of simulations
-initial_price = si.get_live_price(stock) # Use the live stock price as the initial price
+initial_price = df['Adj Close'][-1] # Use the live stock price as the initial price
 drift = .24 # Use a constant drift value
 volatility = np.sqrt(covmat[0,0]) # Use the volatility of the stock
 time_period = 1 / 365 # Daily
@@ -80,7 +79,7 @@ def simulate(self):
 
 # Add the title, legend, and axis labels
 plt.title(f'Geometric Brownian Motion for {stock.upper()}')
-plt.axhline(y=si.get_live_price(stock), color='r')
+plt.axhline(y=initial_price, color='r')
 plt.xlabel('Time')
 plt.ylabel('Price')
 plt.show()

portfolio_strategies/sma_trading_strategy.py

@@ -6,18 +6,17 @@
 import matplotlib.pyplot as plt
 import mplfinance as mpf
 from finta import TA
-from yahoo_fin import stock_info as si
 
 # Define time range 
 num_of_years = 10
 start = dt.date.today() - dt.timedelta(days=365*num_of_years)
 end = dt.datetime.now()
 
 stock = 'NFLX'
-current_price = round(si.get_live_price(stock), 2)
 
 # Download historical stock data
 df = yf.download(stock, start, end, interval='1d')
+current_price = round(df['Adj Close'][-1], 2)
 
 # Calculate Simple Moving Average(SMA)
 short_sma = 20

stock_analysis/capm_analysis.py

@@ -5,7 +5,11 @@
 import datetime
 from socket import gaierror
 from pandas_datareader._utils import RemoteDataError
-from yahoo_fin import stock_info as si
+import sys
+import os
+parent_dir = os.path.dirname(os.getcwd())
+sys.path.append(parent_dir)
+import tickers as ti
 
 # Define risk-free return rate
 risk_free_return = 0.02
@@ -15,7 +19,7 @@
 pd.set_option('display.max_columns', None)
 
 # Get all tickers in NASDAQ stock exchange
-nasdaq_tickers = si.tickers_nasdaq()
+nasdaq_tickers = ti.tickers_nasdaq()
 
 # Define the ticker for index
 index_ticker = '^GSPC'

stock_analysis/intrinsic_value.py

@@ -5,7 +5,7 @@
 from bs4 import BeautifulSoup as bs
 import numpy as np
 import pandas as pd
-from yahoo_fin import stock_info as si
+import pandas_datareader.data as pdr
 
 # Setting pandas options
 pd.set_option('float_format', '{:f}'.format)
@@ -16,7 +16,8 @@
 
 # Parameters
 ticker = 'AAPL'  # The stock ticker to get its intrinsic value
-current_price = si.get_live_price(ticker)
+df = pdr.get_data_yahoo(ticker)
+current_price = df['Adj Close'][-1]
 
 # Retrieving JSON data
 def json_data(url):

stock_analysis/seasonal_stock_analysis.py

@@ -4,35 +4,22 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import yfinance as yf
-from pandas_datareader import data as pdr
-import xlsxwriter
 import requests
-from yahoo_fin import stock_info as si
 import pickle
 import bs4 as bs
+import requests
 
 # You need to change this to a convenient spot on your own hard drive.
 my_path = ""
 threshold = 0.80
 
-# Download a list of the S&P 500 components
-def save_spx_tickers():
-    resp = requests.get("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")
-    soup = bs.BeautifulSoup(resp.text, "lxml")
-    table = soup.find("table", {"class": "wikitable sortable"})
-    tickers = []
-    for row in table.findAll("tr")[1:]:
-        ticker = row.find_all("td")[0].text.strip()
-        tickers.append(ticker)
-    with open("spxTickers.pickle", "wb") as f:
-        pickle.dump(tickers, f)
-    return tickers
-sp500_tickers = save_spx_tickers()
-
-# Make the ticker symbols readable by Yahoo Finance
-sp500_tickers = [item.replace(".", "-") for item in sp500_tickers]
+# Scrape a list of the S&P 500 components
+url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
+html = requests.get(url).text
+sp_df = pd.read_html(html, header=0)[0]
+sp500_tickers = sp_df['Symbol'].tolist()
 
-# Upload a list of the S&P 500 components downloaded from Yahoo.
+# Upload a list of the S&P 500 components downloaded from Wikipedia.
 mylist = []
 mylist2 = []
 df_sp500_tickers = pd.DataFrame(list(zip(sp500_tickers)), columns=["Symbol"])

stock_data/green_line_test.py

@@ -4,7 +4,6 @@
 from pandas_datareader import DataReader
 import matplotlib.pyplot as plt
 from pylab import rcParams 
-import yahoo_fin.stock_info as si
 
 # Set the start date for historical data
 start = dt.datetime(1980,12,1)
@@ -16,12 +15,9 @@
 
 # Continue asking user for a stock ticker until they enter 'quit'
 while stock != 'quit':
-
-    # Get the current stock price
-    price = si.get_live_price(stock)
-
     # Get the historical stock data
     df = DataReader(stock, 'yahoo', start, now)
+    price = df['Adj Close'][-1]
 
     # Remove rows where volume is less than 1000
     df.drop(df[df["Volume"]<1000].index, inplace=True)

stock_data/send_top_movers.py

@@ -4,13 +4,25 @@
 from email.mime.multipart import MIMEMultipart
 from email.mime.base import MIMEBase
 from email import encoders
-import yahoo_fin.stock_info as si
 import time
 import datetime
 import os
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+
+# Get top winner stocks for the day
+def scrape_top_winners():
+    url = 'https://finance.yahoo.com/gainers/'
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, 'html.parser')
+
+    df = pd.read_html(str(soup), attrs={'class': 'W(100%)'})[0]
+    df = df.drop(columns=['52 Week High'])
+    return df
 
 # get top gainers data
-df = si.get_day_gainers()
+df = scrape_top_winners()
 df_filtered = df[df['% Change']>=5]
 
 # get today's date and use it to create a file name