Embed Fair Value data
Every way to use our fair-value data elsewhere: as a ready-made block on your own website, as a table in Excel or Google Sheets, as a dataset for your own analysis, or straight inside your AI assistant. Over 35,000 stocks, updated daily, free to use with attribution (CC BY 4.0). No key, no sign-up.
1 · Show it on your own website
The simplest route, no coding: copy the code, paste it into your page, done. It is plain HTML, so it works in WordPress, Wix, Squarespace, Notion or a newsletter, with no script and no changes to your stylesheet.
Fair-value badge for one stock
Shows fair value and the gap to the price. Dropbox as an example. This is how it looks:
Copy this code (click selects everything):
For a SPECIFIC stock: open that stock page and use its “Embed” box, then the badge carries its numbers.
List of undervalued quality stocks
The current top 5 as a compact list:
- Lululemon Athletica Inc.+198%
- CGI Inc+146%
- China Resources Land Ltd+137%
- Gree Electric Appliances Inc of Zhuhai+127%
- Haier Smart Home Co Ltd+121%
Note: the snippet is a snapshot. For permanently fresh numbers, copy the code again now and then, or read the datasets below automatically.
2 · In Excel or Google Sheets
One line into an empty cell and the list appears as a table, refreshable with one click. No download needed.
Google Sheets
=IMPORTDATA("https://www.fairvalue-calculator.com/undervalued-stocks.csv")
Excel
Data → From Web → paste the address above. Excel creates a refreshable connection.
3 · Download the datasets
Five ready-made, daily-fresh lists. Choose the format via the extension: .json or .csv. No sign-up, no key.
Undervalued quality stocks
40 stocksBig names trading below our fair value, with a quality score. Optional sector filter: ?sector=technology
Show fields
| Field | Meaning |
|---|---|
ticker | Exchange ticker symbol |
company | Company name |
upside_pct | Gap from price to our fair value in % (positive = undervalued) |
quality_score | Quality score 0 to 100 (profitability, growth, balance sheet, moat) |
price_local | Current price in the listing currency |
evidence | Confidence tier: low, medium or high |
sector | GICS sector |
country | Listing country |
url | Link to the stock page |
Overvalued stocks
Same calculation, other direction: stocks well above our fair value.
Show fields
| Field | Meaning |
|---|---|
ticker | Exchange ticker symbol |
company | Company name |
upside_pct | Gap from price to our fair value in % (positive = undervalued) |
quality_score | Quality score 0 to 100 (profitability, growth, balance sheet, moat) |
price_local | Current price in the listing currency |
evidence | Confidence tier: low, medium or high |
sector | GICS sector |
country | Listing country |
url | Link to the stock page |
Value traps
Optically cheap but weak quality. The dataset nobody else publishes.
Show fields
| Field | Meaning |
|---|---|
ticker | Exchange ticker symbol |
company | Company name |
upside_pct | Gap from price to our fair value in % (positive = undervalued) |
quality_score | Quality score 0 to 100 (profitability, growth, balance sheet, moat) |
price_local | Current price in the listing currency |
evidence | Confidence tier: low, medium or high |
sector | GICS sector |
country | Listing country |
url | Link to the stock page |
Largest companies by country
520 companies from 42 countriesThe 10 largest listed companies per country by market cap in USD, 20 for the ten largest markets, plus fair value and quality score. One row per company, home listings only. Optional single country: ?country=DE
Show fields
| Field | Meaning |
|---|---|
country | Country code (ISO-2) of the home listing |
country_name | Country name |
rank | Rank within the country by market cap |
company | Company name |
ticker | Ticker |
symbol | Ticker with exchange code, e.g. SAP.XETRA |
exchange | Exchange |
market_cap_usd | Market cap in USD: price x shares outstanding, reproducible from the fields in this row |
market_cap_reported_usd | Market cap as reported by our data vendor (a snapshot, often older) |
market_cap_ratio | Ratio of the two figures, 1.00 = agreement. Rows beyond a factor of 2 are dropped |
shares_outstanding | Share count we calculate with |
currency | Listing currency (price and fair value are in this currency) |
price | Latest closing price in the listing currency |
previous_close | Previous closing price |
change_1d_pct | One-day change in percent |
price_date | Trading day the price belongs to |
website | Company website (use it with any logo service you like) |
fair_value | Our fair value per share, null when we withhold it |
quality_score | Quality score 0 to 100 |
valuation_status | published or withheld |
sector | GICS sector |
as_of | Snapshot date of the dataset |
url | Link to the stock page |
The 150 largest companies worldwide
150 companiesThe same fields without the country grid: the 150 largest listed companies by market cap in USD, across all markets.
Show fields
| Field | Meaning |
|---|---|
country | Country code (ISO-2) of the home listing |
country_name | Country name |
rank | Rank within the country by market cap |
company | Company name |
ticker | Ticker |
symbol | Ticker with exchange code, e.g. SAP.XETRA |
exchange | Exchange |
market_cap_usd | Market cap in USD: price x shares outstanding, reproducible from the fields in this row |
market_cap_reported_usd | Market cap as reported by our data vendor (a snapshot, often older) |
market_cap_ratio | Ratio of the two figures, 1.00 = agreement. Rows beyond a factor of 2 are dropped |
shares_outstanding | Share count we calculate with |
currency | Listing currency (price and fair value are in this currency) |
price | Latest closing price in the listing currency |
previous_close | Previous closing price |
change_1d_pct | One-day change in percent |
price_date | Trading day the price belongs to |
website | Company website (use it with any logo service you like) |
fair_value | Our fair value per share, null when we withhold it |
quality_score | Quality score 0 to 100 |
valuation_status | published or withheld |
sector | GICS sector |
as_of | Snapshot date of the dataset |
url | Link to the stock page |
Provenance: our fair value from multiple models on company-reported fundamentals (regulatory filings) plus third-party market data. Update frequency: daily. Every response carries snapshot_date and updated_at. Where we withhold a valuation, you get null instead of a number.
4 · In your AI assistant (ChatGPT, Claude and others)
Connect Fair Value Calculator to ChatGPT, Claude, Perplexity and others via our MCP server. The assistant can then resolve a stock, fetch a preview and a fair-value verdict, and compare two stocks.
5 · In your own code
curl
curl -s https://www.fairvalue-calculator.com/undervalued-stocks.json
Python (pandas)
import pandas as pd
df = pd.read_json("https://www.fairvalue-calculator.com/undervalued-stocks.json")
print(df["stocks"].apply(pd.Series)[["ticker","upside_pct","quality_score"]].head())
JavaScript
const r = await fetch("https://www.fairvalue-calculator.com/undervalued-stocks.json");
const { stocks } = await r.json();
console.table(stocks.slice(0, 10));
Google Sheets
=IMPORTDATA("https://www.fairvalue-calculator.com/undervalued-stocks.csv")
GitHub
All datasets are also a Git repository to clone and track (including market valuation by sector and country): github.com/petz00711/fvc-open-data
License & citation
Free to use under CC BY 4.0. Attribution is appreciated, a plain-text credit or a link is fine:
Source: Fair Value Calculator (fairvalue-calculator.com), accessed 2026-09-09.
Rate limits protect the service; for higher frequency or a custom cut, write to [email protected].
Research & data
Dated data studies from the Fair Value engine, free to cite with attribution. Every study ships methodology, limitations and a CSV download.