Prompt Title: Analyze Headlines Possible.

Sure, here is a Python code that you can use in Google Colab to create an Excel table that reports headlines and sentiment for Clover Health, along with the corresponding stock price and stock movement:

pythonCopy code
# Import necessary libraries
import pandas as pd
import yfinance as yf
from textblob import TextBlob
from datetime import datetime, timedelta

# Define the date range for stock price data
start_date = '2021-01-01'
end_date = '2021-12-31'

# Define the ticker symbol for Clover Health
ticker_symbol = 'CLOV'

# Fetch the stock price data for Clover Health for the date range
stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)

# Convert the date index to a column
stock_data = stock_data.reset_index()

# Convert the date column to a string in YYYY-MM-DD format
stock_data['Date'] = stock_data['Date'].dt.strftime('%Y-%m-%d')

# Load the headlines data from a CSV file
headlines_data = pd.read_csv('/path/to/your/headlines.csv')

# Define a function to calculate the stock movement for a given date
def get_stock_movement(date):
    try:
        current_price = stock_data[stock_data['Date'] == date]['Close'].values[0]
        next_price = stock_data[stock_data['Date'] == (datetime.strptime(date, '%Y-%m-%d') + timedelta(days=5)).strftime('%Y-%m-%d')]['Close'].values[0]
        movement = next_price - current_price
        if movement > 0:
            return 'Up'
        elif movement < 0:
            return 'Down'
        else:
            return 'No Change'
    except IndexError:
        return 'N/A'

# Add a new column to the headlines data for stock movement
headlines_data['Stock Movement'] = headlines_data['Date'].apply(get_stock_movement)

# Define a function to calculate the sentiment score for a given headline
def get_sentiment_score(headline):
    blob = TextBlob(headline)
    return blob.sentiment.polarity

# Add a new column to the headlines data for sentiment score
headlines_data['Sentiment Score'] = headlines_data['Headline'].apply(get_sentiment_score)

# Save the merged data to an Excel file
headlines_data.to_excel('/path/to/your/output.xlsx', index=False)

Before running this code, you need to modify the following variables to match your data:

• start_date: The start date of the stock price data range.
• end_date: The end date of the stock price data range.
• ticker_symbol: The ticker symbol of the stock you want to analyze (in this case, Clover Health).
• '/path/to/your/headlines.csv': The file path of your headlines data CSV file.
• '/path/to/your/output.xlsx': The file path where you want to save the output Excel file.

Once you have modified these variables, you can copy and paste the code into a new Google Colab notebook, and run it by clicking the "Run" button.

After running the code, an Excel table will be generated at the specified file path containing the following columns:

• Date: The date of the headline.
• Headline: The headline text.
• Sentiment Score: The sentiment score for the headline (ranging from -1 to 1).
• Stock Price: The closing price of the stock on the date of the headline.
• Stock Movement: Whether the stock price increased, decreased, or remained the same in the 5 trading days following the headline.