Getting undervalued large caps data for trading.

In general, large-cap stocks tend to be less volatile and less risky than small-cap stocks and usually preferred by investors. According to Investopedia big-cap (large-cap) stocks have a market cap of $10 billion or more.
Undervalued stocks trade below their assumed value and the strategy of investing in value stocks whose prices are undervalued is the confidence that the market has "gotten it wrong" and the price of the stock will finally recover, leading to abundant gains for the investor.

Undervalued large caps.

So we are talking about stocks considered underpriced based on fundamental analysis, often paying a relatively high dividend to shareholders and having a low price to equity (P/E) ratio. And criteria for final output are high market cap, price to equity below specified threshold and Cash return on capital invested (CROCI) above specified threshold. The PE ratio is calculated by dividing the company's stock price by its earnings per share. Cash return on capital invested is calculated by dividing the earnings before interest, taxes, depreciation and amortization by the total capital invested.

In our case we will use Python to access trading statistics from Mboum Finance Official API which provides undervalued large caps stocks data. To use necessary API you need to get "your-api-key" directly from Mboum Finance, or using, the way I recommend. Output data is in JSON format which can be easily converted to any suitable form for further visualisation and analysis, undervalued large caps data will definetely help to provide stable market profits.

Python code to get undervalued large caps:

Undervalued large caps: Large cap stocks that are potentially undervalued.

import requests

url = ""

querystring = {"start":"0"}

headers = {
"X-RapidAPI-Key": "your-api-key",
"X-RapidAPI-Host": ""

response = requests.request("GET", url, headers=headers, params=querystring)


Undervalued large caps output (shown partly):

Undervalued large caps output.

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