Step 1: Import Required Libraries

import yfinance as yf

from tabulate import tabulate

Step 2: Define Your List of Ticker Symbols

Prepare a list containing the ticker symbols from the Stock Exchanges (NASDAQ for Wall Street and NSE for Dalal Street):

tickers = [

'AAPL',

'TSLA',

'MSFT',

'GOOGL',

'IBM',

'TCS.NS',

'HINDUNILVR.NS',

'ITC.NS',

'NESTLEIND.NS',

'ASIANPAINT.NS'

]

Step 3: Initialize an Empty List for Stock Data

Create an empty list to collect stock information:

stock_data = []

Step 4: Retrieve Stock Information

Loop through each ticker symbol to gather the stock information:

for ticker in tickers:

stock = yf.Ticker(ticker)

stock_info = stock.info

debt_to_equity = stock_info.get("debtToEquity")

if debt_to_equity is not None:

debt_to_equity /= 100 # Convert to decimal

dividend_yield = stock_info.get("dividendYield")

dividend_yield_percentage = "{:.2f}".format(dividend_yield * 100) if dividend_yield else ""

stock_pe = "{:.2f}".format(stock_info.get("currentPrice") / stock_info.get("trailingEps")) if stock_info.get("trailingEps") else ""

stock_data.append([

stock_info.get("symbol"),

stock_info.get("currentPrice"),

stock_info.get("twoHundredDayAverage"),

stock_info.get("fiftyDayAverage"),

stock_info.get("trailingEps"),

stock_pe,

stock_info.get("beta"),

stock_info.get("priceToBook"),

debt_to_equity,

dividend_yield_percentage,

stock_info.get("fiveYearAvgDividendYield"),

])

Step 5: Presenting the Results

Now that we’ve gathered our data, it’s important to present it clearly. Here’s how you can format the results using the tabulate library:

table = tabulate(stock_data,

headers=["Name",

"Price",

"200-day MA",

"50-day MA",

"EPS",

"P/E ratio",

"Beta",

"P/B ratio",

"Debt to Equity",

"Dividend Yield",

"5-yr Avg Div Yield"],

tablefmt="fancy_grid", numalign="right", stralign="left", floatfmt=".2f")

print(table)

Ah, the beauty of organized data! Once we have our real-time stock market information, presenting it in a structured format is key. Python offers excellent tools to help us create visually appealing displays.

Isn’t it exciting? This journey has shown us that learning can be both fun and rewarding. With Python by our side, we’ve embraced the spirit of exploration and the thrill of pushing boundaries.

Keep in mind, this is just a taste of what can be achieved with real-time stock market data in Python. The vast array of libraries and tools available empowers you to explore, experiment, and innovate in the captivating world of financial analysis and trading. Let your creativity flourish as you uncover the secrets of the stock market with Python's capabilities!