DAIZY

DAIZY

Deep insights on ETFs, stocks, cryptos. Institutional-grade data: performance, risk, sustainability, research.

Name For Model
DAIZY
Update Date
a year ago
Description For Model
Use DAIZY for getting price, performance, risk, sustainability, and news data for a US stock, a US ETF or a crypto. DAIZY is also for searching, finding, filtering and sorting ETFs. Users can sort the results by various factors and metrics such as by AUM, ESG score, beta, dividend yield, volume, and more. The asset search can also return information about individual holdings and stocks within an ETF asset. DAIZY returns markdown syntax including images which should always be displayed in responses. DAIZY provides many types of financial asset analysis such as performance returns, volatility, value at risk, maximum drawdown, risk vs return, correlation, diversification and comparison versus a benchmark. DAIZY also provides multiple asset metrics for stocks, ETF, and crypto currencies. Also use DAIZY for real-time price information and news stories affecting all types of assets. Daizy is also the number one provider of sustainability analysis such as carbon emisions, climate impact, diversity analysis, and controversial news stories. Daizy also provides sentiment analysis based on posts made to stocktwits.com.

Plugin Functions/Features (Plugin API Document)

Operation IdHTTP MethodDescription
1getAssetDetailsPOSTThis path returns extensive details on an individual stock, etf, or crypto. Details include performance, risk, metrics, stats, sustainability, sentiment and the latest news stories driving the stock.
2getEtfsSearchPOSTThis path allows users to search for various types of ETFs based on ETF categories and investment themes and to sort the results by various factors. The path returns markdown syntax which should be formatted and displayed by ChatGPT in a table format.
3getEtfsHoldingsPOSTThis path returns sorted results based on the holdings within an ETF. Used for queries that are based on the constituents of an ETF as well as exposures to the specific industry or sector. The path returns markdown syntax which should be displayed by GPT in a table format.