Prompt
I'm building a finance app and need market data with historical prices
Latest observation
For a finance app, you’ll usually want a market data API that provides:
- Real-time or delayed quotes
- Historical OHLCV prices (open, high, low, close, volume)
- Corporate actions like splits/dividends
- Coverage for your needed assets: stocks, ETFs, indices, forex, crypto, options, etc.
- Reliable licensing for commercial use
- Reasonable rate limits and bulk download options
Good options to consider
1) Polygon.io
- Strong for US stocks, options, forex, and crypto
- Good historical intraday and daily data
- Popular for fintech apps
- Paid tiers; commercial-friendly
2) Alpha Vantage
- Easy to start with
- Free tier available, but rate-limited
- Good for prototyping and basic historical data
- Less ideal for production-scale apps
3) Tiingo
- Good historical equity data
- Clean API
- Reasonably priced
- Good for daily and some intraday use cases
4) IEX Cloud
- Useful for US equities
- API is developer-friendly
- Check current product availability/pricing, as plans can change
5) Twelve Data
- Broad asset coverage
- Historical data for stocks, forex, crypto, ETFs, indices
- Easy API integration
6) Nasdaq Data Link (Quandl)
- Strong for certain datasets
- Good if you need specialized financial/economic data
- Historical pricing available through many datasets
7) Marketstack / Financial Modeling Prep
- Simple APIs
- Useful for MVPs and prototypes
- Verify depth and data quality for production needs
What to check before choosing
- Historical depth: how many years of data?
- Intraday granularity: 1-min, 5-min, hourly, daily?
- Adjusted prices: split/dividend adjusted?
- Latency: real-time vs delayed
- Coverage: exchanges and asset classes
- Commercial rights: can you display data to users?
- Rate limits and cost
- Data quality and survivorship bias handling
If you want a practical recommendation
- MVP / prototype: Alpha Vantage or Twelve Data
- Production app for US market data: Polygon.io or Tiingo
- Specialized/alternative datasets: Nasdaq Data Link
If you want, I can also help you:
- compare 3–5 providers for your exact use case, or
- show example API calls and a data model for storing historical prices.
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 Circuit. 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.