The Scenario
You're a financial advisor. Every quarter you run a fund screener for your model portfolios — 40 mutual funds, evaluated on beta, standard deviation, Sharpe ratio, and Sortino ratio. You have the fund tickers in column A. You've been building this screen the same way for two years: four API calls per fund, manual copy-paste into four columns, 160 individual data entries.
Your junior analyst used to handle this. He got promoted. The screen still needs to run.
The bad version:
- Pull beta from one endpoint for the first fund, paste into column B.
- Pull standard deviation from a second endpoint, paste into column C. Realize you pulled standard deviation for equity, not for the fund's stated benchmark period, and need to re-pull.
- Pull Sharpe and Sortino from a third endpoint, discover that Sortino ratio is not always available for every fund in the dataset, and decide whether to leave blanks or fill in N/A.
Forty funds, four metrics each, and three rounds of fixes for the ones that don't return data cleanly. This is not analysis work. This is data entry work pretending to be analysis work.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Google Sheet. It reads the 40 fund tickers in column A and, through its built-in Twelve Data integration, fetches risk metrics for each fund and writes them into the specified columns.
Fetch risk metrics from Twelve Data for each mutual fund symbol in column A. Write beta into column B, standard deviation into column C, Sharpe ratio into column D, and Sortino ratio into column E. Where a metric is not available for a fund, write N/A rather than leaving the cell blank.
What You Get
- Beta, standard deviation, Sharpe ratio, and Sortino ratio in columns B through E for each fund.
- N/A explicitly written for any metric Twelve Data does not return for a given fund, so blank cells always indicate a pull failure rather than a missing metric.
- Values reflect the most recent available calculation period from Twelve Data.
- Any fund symbol Twelve Data cannot resolve (share class format issues, unlisted funds) flagged in column F.
What If the Data Is Not Quite Ready
If you want ETF-specific risk analytics instead of mutual fund metrics
Fetch ETF risk analytics from Twelve Data for each fund ticker in column A. Write volatility into column B, beta into column C, and 3-year Sharpe ratio into column D. Where the ETF has been listed for fewer than 3 years, write the available period's Sharpe ratio and note the period length in column E.
If you want to normalize standard deviation across funds that use different period lengths
Fetch risk metrics from Twelve Data for each fund in column A. Write beta into column B and standard deviation into column C. In column D, write the period used for the standard deviation calculation (1-year, 3-year, etc.). Flag any fund where the calculation period differs from the majority of the list with PERIOD MISMATCH in column E, so you can identify which funds are not directly comparable.
If your screening criteria require a minimum Sharpe ratio and you want to pre-filter
Fetch Sharpe ratio and Sortino ratio from Twelve Data for each fund in column A. Write Sharpe into column B and Sortino into column C. In column D, write PASSES if Sharpe ratio is above 0.5 and Sortino is above 0.8, REVIEW if one criterion is met and not the other, and FAILS if neither is met. Sort rows so PASSES funds appear first.
Kill-chain: fetch all four metrics, calculate a composite risk-adjusted score, and deliver a ranked screener
Fetch beta, standard deviation, Sharpe ratio, and Sortino ratio from Twelve Data for each fund in column A. Write them into columns B through E. In column F, calculate a composite score: Sharpe ratio plus Sortino ratio minus (beta x 0.5) minus (standard deviation x 0.1). Rank funds from highest to lowest composite score in column G. Highlight the top 10 funds in column H with TOP 10.
Try It
Get the 7-day free trial of SheetXAI and open your fund screener with tickers in column A, then ask it to populate all four risk metrics and rank the results in one pass. Also see pulling ETF composition data and the full Twelve Data overview.