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.

Loading notebook…

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

Pick the metrics

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

Run the notebook

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

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

More notebooks for this use case

Also featured

Predicting Short-Term Crypto Returns with Market Microstructure

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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 data Browse 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

L1 Imbalance

L1 (Top of Book)

L1 Imbalance

Bid/ask imbalance, ratio, percentages — both instantaneous (last) and averaged over the interval.

See it on real data

Need the ticks behind this metric? Prime + Raw includes raw quotes.

Sample CSV Start in Claude

Shown: bid_ask_ratio_avg, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 91.55×, 1 Jan, 14:00 UTC · built with l1_imbalance

low

high

extreme

imbalance

Imbalance

Imbalance measures the raw difference between bid-side and ask-side top-of-book size.

It is one of the fastest ways to see which side is presenting more immediate resting interest at the best quotes.

imbalance_ratio

Imbalance Ratio

Imbalance Ratio scales the raw difference into a relative measure that is easier to compare across symbols and market states.

Because it normalizes for total top-of-book size, it often gives a cleaner signal than the raw imbalance alone.

bid_ask_ratio

Bid/Ask Ratio

Bid/Ask Ratio directly compares the best-bid size with the best-ask size.

It is intuitive, portable, and useful when you want to know whether displayed buying depth materially outweighed displayed selling depth.

bid_percentage_avg

Avg Bid %

Average Bid % shows the mean share of top-level liquidity that sat on the bid across the interval.

This smooths through flicker and highlights the prevailing balance of displayed interest instead of a single instant.

ask_percentage_avg

Avg Ask %

Average Ask % is the complementary view, capturing how much of top-level liquidity tended to sit on the ask side.

Monitoring the bid and ask averages together helps reveal whether one side dominated persistently or whether the book spent the interval near equilibrium.

Endpoint: /api/v1/data/l1_imbalance
Category: L1 (Top of Book)
Built from: Raw quotes →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`imbalance`	Imbalance	Last best-bid quantity minus best-ask quantity in the interval
`imbalance_ratio`	Imbalance Ratio	Last normalized difference between best-bid and best-ask quantity
`bid_ask_ratio`	Bid/Ask Ratio	Last best-bid quantity divided by best-ask quantity
`bid_percentage`	Bid %	Last share of top-of-book quantity resting on the bid side
`ask_percentage`	Ask %	Last share of top-of-book quantity resting on the ask side
`imbalance_avg`	Avg Imbalance	Average best-bid quantity minus best-ask quantity in the interval
`imbalance_ratio_avg`	Avg Imbalance Ratio	Average normalized bid-ask quantity imbalance in the interval
`bid_ask_ratio_avg`	Avg Bid/Ask Ratio	Average best-bid quantity divided by best-ask quantity in the interval
`bid_percentage_avg`	Avg Bid %	Average share of top-of-book quantity on the bid side
`ask_percentage_avg`	Avg Ask %	Average share of top-of-book quantity on the ask side

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 l1_imbalance --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 l1_imbalance.<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="l1_imbalance",
    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.

timestamp*

interval*

exchange*

symbol*

Suggestions shown — any valid value accepted

start_date*

Suggestions shown — any valid value accepted

end_date*

Suggestions shown — any valid value accepted

Authentication

X-API-KEY*

GET

/api/v1/data/preview/l1_imbalance?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

Use case

Find new alpha

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

Featured notebook

Alpha Discovery

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

Loading notebook…

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

Pick the metrics

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

Run the notebook

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

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

More notebooks for this use case

Also featured

Predicting Short-Term Crypto Returns with Market Microstructure

Find new alpha

View notebook Related notebook

Intro to Aperiodic Flow Metrics

Find new alpha

View notebook

Find new alpha

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

Run on preview data Browse 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

L1 Imbalance

L1 (Top of Book)

L1 Imbalance

Bid/ask imbalance, ratio, percentages — both instantaneous (last) and averaged over the interval.

See it on real data

Need the ticks behind this metric? Prime + Raw includes raw quotes.

Sample CSV Start in Claude

Shown: bid_ask_ratio_avg, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 91.55×, 1 Jan, 14:00 UTC · built with l1_imbalance

low

high

extreme

imbalance

Imbalance

Imbalance measures the raw difference between bid-side and ask-side top-of-book size.

It is one of the fastest ways to see which side is presenting more immediate resting interest at the best quotes.

imbalance_ratio

Imbalance Ratio

Imbalance Ratio scales the raw difference into a relative measure that is easier to compare across symbols and market states.

Because it normalizes for total top-of-book size, it often gives a cleaner signal than the raw imbalance alone.

bid_ask_ratio

Bid/Ask Ratio

Bid/Ask Ratio directly compares the best-bid size with the best-ask size.

It is intuitive, portable, and useful when you want to know whether displayed buying depth materially outweighed displayed selling depth.

bid_percentage_avg

Avg Bid %

Average Bid % shows the mean share of top-level liquidity that sat on the bid across the interval.

This smooths through flicker and highlights the prevailing balance of displayed interest instead of a single instant.

ask_percentage_avg

Avg Ask %

Average Ask % is the complementary view, capturing how much of top-level liquidity tended to sit on the ask side.

Monitoring the bid and ask averages together helps reveal whether one side dominated persistently or whether the book spent the interval near equilibrium.

Endpoint: /api/v1/data/l1_imbalance
Category: L1 (Top of Book)
Built from: Raw quotes →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`imbalance`	Imbalance	Last best-bid quantity minus best-ask quantity in the interval
`imbalance_ratio`	Imbalance Ratio	Last normalized difference between best-bid and best-ask quantity
`bid_ask_ratio`	Bid/Ask Ratio	Last best-bid quantity divided by best-ask quantity
`bid_percentage`	Bid %	Last share of top-of-book quantity resting on the bid side
`ask_percentage`	Ask %	Last share of top-of-book quantity resting on the ask side
`imbalance_avg`	Avg Imbalance	Average best-bid quantity minus best-ask quantity in the interval
`imbalance_ratio_avg`	Avg Imbalance Ratio	Average normalized bid-ask quantity imbalance in the interval
`bid_ask_ratio_avg`	Avg Bid/Ask Ratio	Average best-bid quantity divided by best-ask quantity in the interval
`bid_percentage_avg`	Avg Bid %	Average share of top-of-book quantity on the bid side
`ask_percentage_avg`	Avg Ask %	Average share of top-of-book quantity on the ask side

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 l1_imbalance --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

pip install "aperiodic[stream]"

pypi ↗

import os

from aperiodic import stream

for message in stream(
    api_key=os.environ["APERIODIC_API_KEY"],
    dataset="l1_imbalance",
    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>

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

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

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.

timestamp*

interval*

exchange*

symbol*

Suggestions shown — any valid value accepted

start_date*

Suggestions shown — any valid value accepted

end_date*

Suggestions shown — any valid value accepted

Authentication

X-API-KEY*

GET

/api/v1/data/preview/l1_imbalance?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

Alpha Discovery

Pick the metrics

Run the notebook

Plug it into your stack

Order flow

Trade sizes

Order-book imbalance

Run structure

Predicting Short-Term Crypto Returns with Market Microstructure

Intro to Aperiodic Flow Metrics

Find new alpha

Imbalance

Imbalance Ratio

Bid/Ask Ratio

Avg Bid %

Avg Ask %

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It

Alpha Discovery

Pick the metrics

Run the notebook

Plug it into your stack

Order flow

Trade sizes

Order-book imbalance

Run structure

Predicting Short-Term Crypto Returns with Market Microstructure

Intro to Aperiodic Flow Metrics

Find new alpha

Imbalance

Imbalance Ratio

Bid/Ask Ratio

Avg Bid %

Avg Ask %

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It