Prompt
What should I use for historical financial data?
Latest observation
It depends on what you need the data for. A good choice usually comes down to asset class, frequency, coverage, quality, and budget.
Common options
1) Free / low-cost
- Stooq – good for daily historical equities, ETFs, indices, FX; easy and free.
- Alpha Vantage – convenient API, free tier, but rate-limited.
- Yahoo Finance – widely used for quick prototyping; not ideal for production reliability.
- FRED – excellent for macro/interest rate/US economic series.
Best for: prototypes, personal projects, academic work.
2) Professional market data vendors
- Polygon.io – strong for US equities/options/crypto; good API.
- Tiingo – good balance of cost and usability, equities and some fundamentals.
- Quandl/Nasdaq Data Link – broad alternative datasets, some free and paid.
- IEX Cloud – useful for US equities, though coverage is not as broad as some competitors.
- Bloomberg / Refinitiv / FactSet / S&P Capital IQ – enterprise-grade, expensive, very comprehensive.
Best for: trading tools, research, production systems, compliance-sensitive use.
3) Asset-class specific
- Equities: Polygon, Tiingo, IEX Cloud, Bloomberg
- Options: Polygon, ORATS, Cboe-related datasets, Bloomberg
- Crypto: Kaiko, CoinAPI, CryptoCompare
- FX / macro: FRED, Oanda, Refinitiv, Bloomberg
- Futures / rates: CME data, Refinitiv, Bloomberg, Quandl datasets
What to look for
- Adjusted vs unadjusted prices: critical for splits/dividends.
- Survivorship bias: important for backtesting.
- Corporate actions coverage: splits, dividends, symbol changes.
- Data granularity: daily vs intraday vs tick.
- History depth: how far back it goes.
- Licensing: especially if you’ll redistribute or use commercially.
- API reliability: if the data feeds an app or trading system.
Practical recommendation
- If you’re just getting started: use Stooq or Yahoo Finance.
- If you want a good API for equities: Tiingo or Polygon.io.
- If you need macro/interest rate data: FRED.
- If you’re doing serious backtesting: use a paid vendor with adjusted data and corporate-action support.
If you want, I can recommend the best source based on your exact use case—for example:
- stocks vs crypto vs forex
- daily vs intraday
- free vs paid
- research vs live trading
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.