ChatGPT Plugin

Aperiodic

RWA & crypto market data

Brand: Aperiodic

Category: Data & Analytics
Primary Subcategory: Trading & Live Market Data Platforms

Integration details

Description

Aperiodic is point-in-time market data for perpetual futures on Binance, OKX and Hyperliquid: crypto, and real-world assets such as tokenized stocks, ETFs, commodities and FX, including Hyperliquid HIP-3 markets. It covers 19 metrics datasets (order flow, L1/L2 book liquidity, slippage, price impact, derivatives and OHLCV) and the raw data behind them: trades, top-of-book quotes, mark and index price, and funding rates. With Aperiodic, ChatGPT can: - Find datasets and symbols, and see how far back each series goes. - Read field definitions and schemas. - Compute statistics, correlations and resampled series on Aperiodic's side, without code. - Return signed Parquet links for research and backtests; your code downloads the files, so the rows never enter the conversation. - Set up a live metrics feed: the WebSocket URL, the subscription message and a Python client. Who it is for: quantitative researchers, traders and analysts who backtest, study execution costs and liquidity, or research trading signals. Limitations: all tools are read-only; it does not place trades, move funds or give investment advice. Without signing in, tools work on a sample (Binance BTC-USDT perpetual, 5-minute bars, May 2025; raw trades for June 2025); signing in gives the datasets and history your Aperiodic account includes. Loading Parquet files needs code execution. The live feed runs in your own code with an Aperiodic API key, not through ChatGPT. Raw L2 order books are not offered.

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Data & Analytics
Primary Subcategory: Trading & Live Market Data Platforms
Secondary Subcategories: None listed
Brand: Aperiodic
Access: Account optional
First tracked: 2026-11-11
Tool count: 8
Market: US

AgentDiscoChatGPT Plugin Discovery Score

ChatGPT organic discovery is live

Not discoverable in ChatGPT yet

ChatGPT now suggests Plugins on its own when they match a user's request, but not every Plugin is being surfaced yet.We haven’t seen Aperiodic suggested yet. Sign up and we’ll track it, and your Discoverability Score will appear here once ChatGPT starts surfacing it.

ChatGPT recommends Airtable when a user asks it to update a CRM. — ChatGPT surfaces Airtable because it matches the user's request.

Primary Subcategory

Competing in ChatGPT Trading & Live Market Data Platforms

View Category

Listed in alphabetical order

Primary Subcategory

0xArchiveComing soonGet notified

ABG Market DataComing soonGet notified

AlgoVaultComing soonGet notified

AlpacaComing soonGet notified

Alpaca LiveComing soonGet notified

AperiodicComing soonGet notified

Tools

8 tools agents can invoke

How your own code connects to the real-time WebSocket feed of metrics datasets (raw data is not streamed): its URL, the subscribe frame for a dataset, exchange, interval and symbols, your plan's limits and a Python client. Never the stream itself, which your code consumes outside MCP. The stream authenticates with an API key in X-API-KEY, not an MCP OAuth sign-in. With no dataset, lists what streams and which plans include it.
live_feed
Field definitions, intervals, exchanges with their start dates, the plan needed and the free preview window for one dataset.
describe_dataset
The full text of one search result: a dataset with all its fields, a category, or the plans guide.
fetch
Symbol ids on one exchange matching every word of the query, at most 50: crypto and RWA perpetuals alike. Ids look like "perpetual-BTC-USDT:USDT", "perpetual-TSLA-USDT:USDT" for a tokenized stock on Binance, or "perpetual-XYZ:TSLA-USDC:USDC" for a Hyperliquid HIP-3 market (dex before the colon). Matching is on the id, so search by ticker (e.g. "TSLA"), not by company name. The free preview symbol is perpetual-BTC-USDT:USDT on binance-futures.
find_symbols
A handle to one slice of a dataset, not the data: signed Parquet URLs (valid one hour), schema, row counts, per-column min/max/nulls from the Parquet footers, 5 sample rows and a Polars snippet. Load the files in your own code; never paste them into the conversation. Without code, use summarize. Every argument but dataset defaults to the free preview, which needs no API key.
get_data
Every Aperiodic dataset, metrics and raw: id, category, the plan that unlocks it, a one-line description, field count, and whether this caller gets full access or the free preview. Start here.
list_datasets
Keyword search over every dataset, its fields, the categories and the plans guide. Returns ids, titles and URLs; read one with fetch.
search
Statistics worked out on our side, for when you can't run code: per-column count, mean, std, quantiles and range ("stats"), a correlation matrix ("correlation"), or the series resampled to at most 500 points ("resample"). Same slice arguments and plan rules as get_data. Decodes only the columns you name (up to 12, numeric; default the first 6).
summarize

FAQs

Aperiodic ChatGPT Plugin FAQ

How the directory, categories and Discoverability Score work.

Read the methodology

How do I improve Aperiodic's ChatGPT Plugin discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

What are Aperiodic alternatives on ChatGPT?

As of 2026-11-11, Aperiodic competes with 0xArchive, ABG Market Data, AlgoVault, Alpaca, Alpaca Live, Alpaca Paper Trading, Alphascope, Ask Gina and 32 more in ChatGPT Trading & Live Market Data Platforms, ranked by public Discoverability Score.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.