Aperiodic Python Client

Python client library for Aperiodic.io — institutional-grade market microstructure, liquidity and order flow metrics with full exchange universe coverage. Turn flow dynamics into alpha in hours, not months. No tick infrastructure to build or maintain.

Access pre-computed derivative and microstructure metrics with parallel downloads for optimal performance.

Installation

pip install aperiodic

Install from source:

git clone https://github.com/aperiodic-io/aperiodic-client.git
cd aperiodic-client
pip install -e .

Authentication

All endpoints require your Aperiodic.io API key passed as api_key="...".

The one exception is preview data: with preview=True the api_key is optional — omit it and the shared public demo key is used automatically.

Symbology

Symbols are expected in Atlas unified symbology — a standardised, exchange-agnostic naming scheme.

• Atlas repo: https://github.com/aperiodic-io/atlas
• Example symbol: perpetual-BTC-USDT:USDT

Quick Start

from datetime import date
from aperiodic import get_metrics

df = get_metrics(
    api_key="your-api-key",
    metric="flow",
    timestamp="true",
    interval="1h",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT", # See https://github.com/aperiodic-io/atlas
    start_date=date(2024, 1, 1),
    end_date=date(2024, 1, 31),
)

print(df.head())
print(df.columns)

Available Functions

get_metrics — Trade & order book metrics

Trade metrics (TradeMetric): "vtwap", "flow", "trade_size", "impact", "range", "updownticks", "run_structure", "returns", "slippage"

L1 order book (L1Metric): "l1_price", "l1_imbalance", "l1_liquidity"

L2 order book (L2Metric): "l2_imbalance", "l2_liquidity"

get_derivative_metrics — Derivative metrics

"basis", "funding", "open_interest", "derivative_price"

Core Parameters

All data endpoints share this shape:

• api_key: Your Aperiodic.io API key. Optional when preview=True — the shared public demo key is used automatically.
• timestamp: "exchange" or "true".
• interval: "1m" | "5m" | "15m" | "30m" | "1h" | "4h" | "1d".
• exchange: "binance-futures" | "okx-perps" | "hyperliquid-perps".
• symbol: Atlas-formatted symbol string (e.g. "perpetual-BTC-USDT:USDT").
• start_date / end_date: Inclusive date boundaries.
• preview: bool = False. When True, routes to the free preview endpoint — no subscription required, but the request must match an exact whitelisted parameter combination (exchange, symbol, interval, timestamp, date range).
• show_progress: show tqdm progress bar (default: True).
• max_concurrent: max parallel file downloads (default: 10).

Examples

Trade metrics

from datetime import date
from aperiodic import get_metrics

flow_df = get_metrics(
    api_key="your-api-key",
    metric="flow",
    timestamp="exchange",
    interval="5m",
    exchange="binance-futures",
    symbol="perpetual-ETH-USDT:USDT", # See https://github.com/aperiodic-io/atlas
    start_date=date(2024, 2, 1),
    end_date=date(2024, 2, 29),
)

L1 / L2 order book metrics

from datetime import date
from aperiodic import get_metrics

l1_df = get_metrics(
    api_key="your-api-key",
    metric="l1_imbalance",
    timestamp="true",
    interval="1m",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT", # See https://github.com/aperiodic-io/atlas
    start_date=date(2024, 3, 1),
    end_date=date(2024, 3, 7),
)

l2_df = get_metrics(
    api_key="your-api-key",
    metric="l2_liquidity",
    timestamp="true",
    interval="1m",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT", # See https://github.com/aperiodic-io/atlas
    start_date=date(2024, 3, 1),
    end_date=date(2024, 3, 7),
)

Derivative metrics

from datetime import date
from aperiodic import get_derivative_metrics

funding_df = get_derivative_metrics(
    api_key="your-api-key",
    metric="funding",
    timestamp="exchange",
    interval="1h",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT", # See https://github.com/aperiodic-io/atlas
    start_date=date(2024, 1, 1),
    end_date=date(2024, 3, 31),
)

Symbol discovery

from aperiodic import get_symbols

symbols = get_symbols(api_key="your-api-key", exchange="binance-futures") # Returns Atlas symbols: https://github.com/aperiodic-io/atlas
perpetuals = [s for s in symbols if s.startswith("perpetual-")]
print(f"Found {len(perpetuals)} perpetual symbols")

Async usage

import asyncio
from datetime import date
from aperiodic import get_metrics_async, get_symbols_async

async def main() -> None:
    symbols = await get_symbols_async(
        api_key="your-api-key",
        exchange="binance-futures",
    )
    for symbol in symbols:
        df = await get_metrics_async(
            api_key="your-api-key",
            metric="l1_liquidity",
            timestamp="true",
            interval="1h",
            exchange="binance-futures",
            symbol=symbol, # See https://github.com/aperiodic-io/atlas
            start_date=date(2024, 1, 1),
            end_date=date(2026, 1, 1),
        )

asyncio.run(main())

Preview (no subscription required)

Anyone can access a curated slice of data via preview=True — no subscription and no API key required. Omit api_key and the client uses the shared public demo key automatically. The request must match the exact parameters (exchange, symbol, interval, timestamp, date range) for one of the whitelisted entries.

Available preview datasets: aperiodic.io/catalog#preview

from datetime import date
from aperiodic import get_ohlcv

# Use the exact parameters listed at https://aperiodic.io/catalog#preview
df = get_ohlcv(
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT",
    interval="5m",
    timestamp="exchange",
    start_date=date(2025, 5, 1),
    end_date=date(2025, 5, 31),
    preview=True,
)

print(df.head())

Raw data (Prime + Raw plan)

Raw trades, top-of-book quotes and derivative ticks for Binance, OKX and Hyperliquid perpetuals, the data the metrics are built from. Same API key and symbols as the metrics. History is one Parquet file per calendar month; from 2026-08-01 there is one file per day.

Datasets (RawDataset): "trades", "quotes", "mark_price", "index_price", "funding_rate", "open_interest", on every venue.

Every file starts with exchange_timestamp (the venue's time) and local_timestamp (when the event reached our capture machine). Timestamps are timezone-aware UTC.

Hyperliquid's derivative feed carries no exchange time, so in its mark_price, index_price, funding_rate and open_interest files exchange_timestamp is modelled from the capture time, and an exchange_timestamp_kind column ("modelled") follows it.

from datetime import date
import aperiodic as ap

# Into one DataFrame, trimmed to the range on exchange_timestamp
trades = ap.get_raw(
    api_key="your-api-key",
    dataset="trades",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT",
    start_date=date(2025, 6, 1),
    end_date=date(2025, 6, 3),
)

# Large ranges: stream the files to disk instead (skips files you already have)
paths = ap.download_raw(
    api_key="your-api-key",
    dataset="quotes",
    exchange="okx-perps",
    symbol="perpetual-BTC-USDT:USDT",
    start_date=date(2024, 1, 1),
    end_date=date(2025, 12, 31),
    output_dir="raw",
)

download_raw writes the bucket's own layout, raw/{dataset}/exchange=…/symbol=…/year=YYYY/month=MM[/day=DD]/data.parquet (: becomes %3A on Windows). Monthly and daily files sit at different depths, so read a folder with a glob and filter on exchange_timestamp:

import polars as pl

lazy = pl.scan_parquet("raw/quotes/**/data.parquet")

• Ranges over 366 days are split into several requests for you.
• Download URLs are valid for one hour; one that has expired is re-requested automatically.
• A plan without raw data raises APIError with status_code=403 and code="raw_not_in_plan".
• get_raw(..., preview=True) returns the free June 2025 file of each venue's BTC perpetual without a key. get_raw_coverage() lists every symbol's first and last day, no key needed.
• download_raw needs CPython (httpx and a filesystem); in Pyodide use get_raw.

Performance Notes

• Downloads are split into monthly parquet files server-side.
• Files are fetched concurrently and concatenated locally.
• Final output is sorted and filtered to your exact requested date range.
• Tune max_concurrent based on your network and compute resources.
• Transient failures — rate limits, upstream 5xx, dropped connections — are retried with exponential backoff before an APIError is raised.

Requirements

• Python 3.11+
• httpx
• polars
• tqdm
• nest-asyncio

License

MIT