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Use case

Find new alpha

From raw order flow to a ranked, backtestable signal — every step in runnable code.

Shown: net taker flow (buys minus sells) summed over each venue’s perpetuals, per minute, 18:00–01:00 UTC on 10 Oct 2025, buys above each venue’s line and sells below it on a square-root scale; the deepest sell minute, 21:13 UTC with $1.9B net sold across the three venues, is orange · built with flow 1m

Featured notebook

Alpha Discovery

We define Directional Alpha as a signal that exhibits a consistent positive or negative relationship with forward returns.

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Or have your agent rerun this notebook and tell you what it finds. Start in Claude · Claude Code · Codex · See the prompt

From notebook to production

01

Pick the metrics

Browse the catalog and pull point-in-time microstructure metrics — no L2 reconstruction, no tick archive to maintain.

02

Run the notebook

Every notebook runs end to end on preview data with a free account, in the browser or through the CLI.

03

Plug it into your stack

Swap the preview key for your own and the same code pulls every year your plan includes through the Python package or the API.

Metrics in this use case

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Related notebook

Intro to Aperiodic Flow Metrics

Find new alpha

This notebook introduces order-flow analytics with the `aperiodic` Python package. We focus on a single large-cap instrument — Binance BTC perpetuals (`perpetual-BTC-USDT:USDT`) — over the exact six-month window from September 1, 2025 through February 28, 2026, using 1-hour observations.

View notebook

Find new alpha

From raw order flow to a ranked, backtestable signal — every step in runnable code.

Run on preview dataBrowse catalog

Shown: minute lows of the 465 Binance perpetuals, 20:40–22:00 UTC on 10 Oct 2025, with BTC’s path traced · built with ohlcv 1m

L2 Order Book Liquidity

L2 (Order Book)

L2 Order Book Liquidity

Total bid/ask depth aggregated over 5, 10, 20, 25 order book levels — instantaneous and interval-averaged.

See it on real data

Raw L2 isn't offered yet. Ask us.

Sample CSVStart in Claude
CodeLive StreamAPI DocsTry It
ask 25bid 25
18152231

Shown: ask_agg_25, ask_agg_20, ask_agg_10, ask_agg_5, bid_agg_5, bid_agg_10, bid_agg_20, bid_agg_25, daily, 1–31 January 2024, from this page's Sample CSV · extreme: 59.815, 14 Jan, ask_agg_25 · built with l2_liquidity

lowhighextreme
ask_agg_25_avg

Avg Ask Aggregate (25 levels)

Average Ask Aggregate across twenty-five levels smooths the deeper sell-side book over the interval.

That makes it a regime measure for how much overhead liquidity the market was consistently showing, not just flashing moment to moment.

bid_agg_25_avg

Avg Bid Aggregate (25 levels)

Average Bid Aggregate across twenty-five levels provides the broad matching view of displayed support.

Stronger readings indicate a deeper cushion of resting liquidity that can absorb pressure before the book thins out materially.

Endpoint
/api/v1/data/l2_liquidity
Category
L2 (Order Book)
Built from
L2 order book (raw not offered yet)
Intervals
1m5m15m30m1h4h1d
Requires Institutional
15s30s
Exchanges
binance-futuresokx-perpshyperliquid-perps

Fields

16
FieldNameDescription
ask_agg_5Ask Aggregate (5 levels)Last total ask quantity across the top 5 levels
ask_agg_10Ask Aggregate (10 levels)Last total ask quantity across the top 10 levels
ask_agg_20Ask Aggregate (20 levels)Last total ask quantity across the top 20 levels
ask_agg_25Ask Aggregate (25 levels)Last total ask quantity across the top 25 levels
bid_agg_5Bid Aggregate (5 levels)Last total bid quantity across the top 5 levels
bid_agg_10Bid Aggregate (10 levels)Last total bid quantity across the top 10 levels
bid_agg_20Bid Aggregate (20 levels)Last total bid quantity across the top 20 levels
bid_agg_25Bid Aggregate (25 levels)Last total bid quantity across the top 25 levels
ask_agg_5_avgAvg Ask Aggregate (5 levels)Average total ask quantity across the top 5 levels in the interval
ask_agg_10_avgAvg Ask Aggregate (10 levels)Average total ask quantity across the top 10 levels in the interval
ask_agg_20_avgAvg Ask Aggregate (20 levels)Average total ask quantity across the top 20 levels in the interval
ask_agg_25_avgAvg Ask Aggregate (25 levels)Average total ask quantity across the top 25 levels in the interval
bid_agg_5_avgAvg Bid Aggregate (5 levels)Average total bid quantity across the top 5 levels in the interval
bid_agg_10_avgAvg Bid Aggregate (10 levels)Average total bid quantity across the top 10 levels in the interval
bid_agg_20_avgAvg Bid Aggregate (20 levels)Average total bid quantity across the top 20 levels in the interval
bid_agg_25_avgAvg Bid Aggregate (25 levels)Average total bid quantity across the top 25 levels in the interval

Example Request

# 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"

# Free preview — no API key required
aperiodic l2_liquidity --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 ./data && echo "Saved to $PWD/data"

Live Stream

Every new row as it is computed, over WebSocket at wss://stream.aperiodic.io/v1/stream. Authenticate with your API key in the X-API-KEY header and subscribe to channels named l2_liquidity.<exchange>.<interval>, on intervals 15s, 30s, 1m, 5m, 15m, 30m, 1h, 4h, 1d (15s and 30s on Institutional Live only). Streaming is included on Live plans.

pip install "aperiodic[stream]"
pypi ↗
import os

from aperiodic import stream

for message in stream(
    api_key=os.environ["APERIODIC_API_KEY"],
    dataset="l2_liquidity",
    exchange="binance-futures",
    interval="1m",
    symbols=["perpetual-BTC-USDT:USDT"],  # omit for every symbol on your plan
):
    print(message.channel, message.snapshot, message.data)

Query Parameters

timestampreqstring
string

Timestamp source. 'exchange' uses the exchange-reported timestamp, 'true' uses actual arrival time at our servers.

exchangetrue
intervalreqstring
string

Aggregation time interval for the data. Sub-minute intervals (15s, 30s) require an Institutional plan.

15s30s1m5m15m30m1h4h1d
exchangereqstring
string

Source exchange for the data.

binance-futuresokx-perpshyperliquid-perps
symbolreqstring
string

Trading pair symbol in the format of Atlas' universal symbology: https://github.com/aperiodic-io/atlas

start_datereqstring<date>
string<date>

Start date for the data range (YYYY-MM-DD format). A metric series starts on the first day of the raw series it is computed from (see /metadata/raw); raw trades and quotes start binance-futures from 2020-01-01, okx-perps from 2019-09-01, hyperliquid-perps from 2025-01-01. Earlier days return no files. How far back you can go depends on the plan: 5 years on Pro, full history on Prime, Prime + Raw and Institutional. An earlier start_date than the plan reaches returns 403.

end_datereqstring<date>
string<date>

End date for the data range (YYYY-MM-DD format). Must be greater than or equal to start_date.

Successful response with download URLs for every existing file covering the range from the series' published start — one per month before 2026-08-01, one per day from 2026-08-01 onwards

Schema
filesobject[]required

Files covering the requested date range, in chronological order. Data before 2026-08-01 is split by month (one file per calendar month, no `day`); data from 2026-08-01 onwards is split by day (one file per calendar day, with `day` set). The changeover falls on a month boundary, so a given month is served entirely one way or the other; a range spanning it returns the earlier months as monthly files followed by a daily file per day.

yearintegerrequired

Year of the data file

monthintegerrequired

Month of the data file (1-12)

dayinteger

Day of the data file (1-31). Present only on daily files, i.e. those covering 2026-08-01 onwards. Absent on monthly files, which cover an entire calendar month.

urlstring<uri>required

Presigned URL for direct file download (valid for 5 minutes). URLs are served from dataset-specific subdomains, e.g. ohlcv.aperiodic.io, trade-metrics.aperiodic.io, l1-metrics.aperiodic.io, l2-metrics.aperiodic.io, derivative-metrics.aperiodic.io.

Example
{
  "files": [
    {
      "year": 2026,
      "month": 6,
      "url": "https://ohlcv.aperiodic.io/binance-futures/1h/BTCUSDT/2026-06.parquet?X-Amz-Expires=300&..."
    },
    {
      "year": 2026,
      "month": 7,
      "url": "https://ohlcv.aperiodic.io/binance-futures/1h/BTCUSDT/2026-07.parquet?X-Amz-Expires=300&..."
    },
    {
      "year": 2026,
      "month": 8,
      "day": 1,
      "url": "https://ohlcv.aperiodic.io/binance-futures/1h/BTCUSDT/2026-08-01.parquet?X-Amz-Expires=300&..."
    },
    {
      "year": 2026,
      "month": 8,
      "day": 2,
      "url": "https://ohlcv.aperiodic.io/binance-futures/1h/BTCUSDT/2026-08-02.parquet?X-Amz-Expires=300&..."
    }
  ]
}

Try It

Prefilled with the shared DEMO-KEY and a free preview slice — send the request to see live data, no account required.

Suggestions shown — any valid value accepted
Suggestions shown — any valid value accepted
Suggestions shown — any valid value accepted
Authentication
GET/api/v1/data/preview/l2_liquidity?timestamp=exchange&interval=5m&exchange=binance-futures&symbol=perpetual-BTC-USDT%3AUSDT&start_date=2025-05-01&end_date=2025-05-31
Response will appear here
Aperiodic

Built from hundreds of terabytes of raw data and distilled into point-in-time metrics you can pull as parquet files. Plus the raw trades, quotes and derivatives data behind them.

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Aperiodic Limited · Registered in Ireland · Company No. 815273 ·
136 Capel Street, Dublin D01 T2C9, Ireland

Shown: minute closes of 847 perpetuals, 20:30–22:30 UTC on 10 Oct 2025, IOTX in orange · built with ohlcv 1m