shashankvemuri

# Imports
from pandas_datareader import data as pdr
from pandas import ExcelWriter
import yfinance as yf
import pandas as pd
import datetime
import time
import requests
yf.pdr_override()

shashankvemuri

# Index Returns
index_df = pdr.get_data_yahoo(index_name, start_date, end_date)
index_df['Percent Change'] = index_df['Adj Close'].pct_change()
index_return = (index_df['Percent Change'] + 1).cumprod()[-1]

# Find top 30% performing stocks (relative to the S&P 500)
for ticker in tickers:
    # Download historical data as CSV for each stock (makes the process faster)
    df = pdr.get_data_yahoo(ticker, start_date, end_date)
    df.to_csv(f'{ticker}.csv')

shashankvemuri

# Checking Minervini conditions of top 30% of stocks in given list
rs_stocks = rs_df['Ticker']
for stock in rs_stocks:    
    try:
        df = pd.read_csv(f'{stock}.csv', index_col=0)
        sma = [50, 150, 200]
        for x in sma:
            df["SMA_"+str(x)] = round(df['Adj Close'].rolling(window=x).mean(), 2)
        
        # Storing required values 

shashankvemuri

print(exportList)

writer = ExcelWriter("ScreenOutput.xlsx")
exportList.to_excel(writer, "Sheet1")
writer.save()

shashankvemuri

import requests
import pandas as pd 
from yahoo_fin import stock_info as si 
from pandas_datareader import DataReader
import numpy as np

tickers = si.tickers_sp500()
recommendations = []

for ticker in tickers:

shashankvemuri

# Imports
from pandas_datareader import data as pdr
from pandas import ExcelWriter
import yfinance as yf
import pandas as pd
import datetime
import time
import requests
yf.pdr_override()

shashankvemuri

# Import libraries
import pandas as pd
from bs4 import BeautifulSoup
import matplotlib.pyplot as plt
from urllib.request import urlopen
from urllib.request import Request
from nltk.sentiment.vader import SentimentIntensityAnalyzer

# Parameters 
n = 3 #the # of article headlines displayed per ticker

shashankvemuri

# Get Data
finviz_url = 'https://finviz.com/quote.ashx?t='
news_tables = {}

for ticker in tickers:
    url = finviz_url + ticker
    req = Request(url=url,headers={'user-agent': 'my-app/0.0.1'}) 
    resp = urlopen(req)    
    html = BeautifulSoup(resp, features="lxml")
    news_table = html.find(id='news-table')

shashankvemuri

# Iterate through the news
parsed_news = []
for file_name, news_table in news_tables.items():
    for x in news_table.findAll('tr'):
        text = x.a.get_text() 
        date_scrape = x.td.text.split()

        if len(date_scrape) == 1:
            time = date_scrape[0]
            

shashankvemuri

# Sentiment Analysis
analyzer = SentimentIntensityAnalyzer()

columns = ['Ticker', 'Date', 'Time', 'Headline']
news = pd.DataFrame(parsed_news, columns=columns)
scores = news['Headline'].apply(analyzer.polarity_scores).tolist()

df_scores = pd.DataFrame(scores)
news = news.join(df_scores, rsuffix='_right')