If you’re choosing data for a finance project , getting a CSV is only the first step. What does “close” mean? Which date does a dividend filter use? Is a fundamental ratio current or a snapshot?
One source worth evalauting is London Strategic Edge, which offers free access to market data with your own API key. Available data includes price candles, company fundamentals, dividends, splits, financial statements and economc series. Check coverage and history for your particular instruments before committing to it.
Disclosure: I maintain an unofficial, MIT-licensed adapter for this API. I’m not affiliated with the provider and take no payment.
Here is a compact data quality checklist from working on that adapter:
| Data | What to check before using it |
|---|---|
| Daily prices | The provider’s daily candles include extended trading. The close can differ from the regular-session closing price. |
| Volume | Treat it as indicative. Our August checks found inconsistent agreement with another vendor; validate it before using it for turnover or liquidity research. |
| Fundamentals | Inspect updated_at. Price and valuation fields belong to that snapshot. |
| Dividends | Date filters select effective_date (the ex-date). Declaration, record, and payment dates are separate fields. |
| Splits | Check events and the price-adjustment convention before comparing per-share values across dates. |
For a concrete starting exercise, I’d inspect IBM s daily prices and dividend events over a short historical window. Compare the close with a second source using the same session definition, inspect the dividend dates and check for splits before calculating returns.
If you already use an AI assistant with MCP tools ,this is a sample request you can adapt:
Inspect IBM daily candles, dividends, and splits for June 2026. Show the returned fields and dates, report any truncation, and explain the session and adjustment caveats. Flag anything the data does not establish.
My adapter, lse-data-mcp, lets an asssistant do that initial inspection. It runs locally, uses your own key and exposes 15 read-only tools. It cannot place trades and does not cache or persist market-data responses. It is still 0.x beta.
For the actual research dataset, use the provider ‘s SDK/API or exports directly, record extraction parameters and package versions and retain inputs where the licence permits. The MCP adapter returns bounded samples, with a maximum of 5000 rows per call and a separate response-size cap.
Free access does not mean open data: the adapter’s MIT licence covers its code, not the underlying data. The provider’s terms restrict redistribution, so check them before sharing a dataset or replication package.
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