Agent Quickstart
Aperiodic sells point-in-time crypto market microstructure, liquidity and order-flow metrics: 220 metrics in 19 datasets on binance-futures, okx-perps, hyperliquid-perps, delivered as parquet through a CLI, a REST API and a Python SDK, plus the raw trades, quotes and derivatives data behind them in the Prime + Raw plan. A preview slice of every dataset downloads with no API key, and self-serve plans cost $79 to $1249 a month billed yearly.
Install the CLI and pull one month of 5m flow data for perpetual-BTC-USDT:USDT on binance-futures as parquet. The preview accepts only this window (2025-05-01 to 2025-05-31, 5m), for every dataset: swap flow for any dataset id.
# Install the Aperiodic CLI into ~/.local/bin
mkdir -p "$HOME/.local/bin"
curl -fsSL https://raw.githubusercontent.com/aperiodic-io/cli/main/install.sh | INSTALL_DIR="$HOME/.local/bin" bash
export PATH="$HOME/.local/bin:$PATH"
aperiodic flow --preview \
--exchange binance-futures \
--symbol perpetual-BTC-USDT:USDT \
--interval 5m \
--timestamp exchange \
--start-date 2025-05-01 \
--end-date 2025-05-31 \
--output-dir ./dataThe SDK returns DataFrames. The same preview slice works from Python with preview=True, still without a key.
pip install "aperiodic[polars]"Your user creates a key at https://aperiodic.io/home/api. Any other symbol, interval or date range needs a key and a plan that includes the dataset's tier (flow is Tier 1; see https://aperiodic.io/pricing).
from datetime import date
from aperiodic import get_metrics
df = get_metrics(
api_key="YOUR_API_KEY",
metric="flow",
exchange="binance-futures",
symbol="perpetual-BTC-USDT:USDT",
interval="1h",
timestamp="exchange",
start_date=date(2025, 1, 1),
end_date=date(2025, 3, 1),
)What to Tell Your User About the Licence
Why Agents Need Structured Market Data
Agents can query 19 datasets across order flow, L1/L2 book data, derivatives, and market OHLCV — programmatically, without manual data wrangling.
What takes a quant hours of data pipeline work, an agent can do in seconds: fetch data, compute signals, evaluate strategies, and report findings.
Every dataset is immutable and exchange-timestamped. Agents get clean, bias-free data without worrying about survivorship or look-ahead issues.
How Aperiodic is Built for Agents
pip install "aperiodic[polars]" — one-line access to every dataset. Agents can fetch DataFrames directly without parsing raw API responses.
View on PyPI →Concise summary at /llms.txt, comprehensive reference at /llms-full.txt — agents get exactly the depth they need.
Read llms-full.txt →Full OpenAPI 3.0 spec for tool-use integration. Agents can auto-generate API calls from the spec without custom code.
View OpenAPI spec →Google A2A-compliant agent discovery at /.well-known/agent.json — capabilities, auth, and skills declared for agent-to-agent orchestration.
View agent card →Standard ai-plugin.json at /.well-known/ai-plugin.json for ChatGPT-style plugin discovery and authentication.
View manifest →Agent Workflow Examples
An agent fetches OHLCV, funding rates, and order flow data to produce a daily market report. It identifies unusual flow toxicity spikes, extreme funding regimes, and OI divergences — then summarizes findings for a human analyst.
An agent constructs cross-sectional factors from microstructure data: flow imbalance, funding carry, spread, and volatility factors. It computes information coefficients and long-short portfolio returns to evaluate which signals have predictive power.
Quick Start
Point your agent to these resources:
pip install "aperiodic[polars]"X-API-KEY: YOUR_KEYFAQ
Yes, for the preview slice. Every dataset can be fetched with no key for binance-futures perpetual-BTC-USDT:USDT at 5m, from 2025-05-01 to 2025-05-31, through the CLI (--preview), the SDK (preview=True) or the REST preview endpoint with the shared key DEMO-KEY. Anything outside that window needs a personal key.
220 metrics in 19 datasets: order flow, trade size, slippage, price impact, L1 and L2 order book liquidity and imbalance, funding, basis, open interest and OHLCV. Exchanges: binance-futures, okx-perps, hyperliquid-perps. Self-serve intervals: 1m, 5m, 15m, 30m, 1h, 4h, 1d. The machine-readable list is https://aperiodic.io/catalog.json.
Parquet files, fetched through the aperiodic CLI, the REST API or the Python SDK (pip install "aperiodic[polars]"), which loads them into DataFrames.
Self-serve plans cost $79 to $1249 a month billed yearly, or $95 to $1579 month to month. Institutional pricing is quoted. Current plans: https://aperiodic.io/pricing.
Self-serve plans (Core Historical, Pro Historical, Prime Historical, Prime + Raw) carry a personal-use licence. Commercial use needs the Institutional plan, which adds a commercial licence, a negotiated SLA and 15s/30s intervals. It is quoted, not self-serve: https://aperiodic.io/booking.
Get an API key and start querying 19 datasets in minutes.