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L2 Order Book Imbalance

Multi-depth (5, 10, 20, 25 levels) order book imbalance, ratio, and averages.

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imbalance_5

Imbalance (5 levels)

Imbalance across five levels extends top-of-book pressure into the near-touch order book.

It reveals whether directional support is just a quote-level artifact or is backed by additional nearby depth.

imbalance_ratio_25

Imbalance Ratio (25 levels)

The twenty-five-level imbalance ratio normalizes deep-book pressure over a broader slice of resting liquidity.

It is useful for understanding the full displayed environment that larger orders would have to push through.

bid_ask_ratio_25

Bid/Ask Ratio (25 levels)

The twenty-five-level ratio extends that comparison deep enough to capture broader displayed inventory.

It is especially relevant for larger execution and for understanding whether apparent pressure near the touch is reinforced deeper in the ladder.

Order book imbalance is arguably the single most validated short-term predictive signal in the market microstructure literature. The intuition is immediate: if there's substantially more resting size on the bid than the ask, the next price move is more likely upward.

Endpoint

/api/v1/data/l2_imbalance

Category

L2 (Order Book)

Intervals
1m5m15m30m1h4h1d
Requires Institutional
15s30s
Exchanges
binance-futuresokx-perps
Fields24
imbalance_5Imbalance (5 levels)Last top-5 bid quantity minus top-5 ask quantity
imbalance_10Imbalance (10 levels)Last top-10 bid quantity minus top-10 ask quantity
imbalance_20Imbalance (20 levels)Last top-20 bid quantity minus top-20 ask quantity
imbalance_25Imbalance (25 levels)Last top-25 bid quantity minus top-25 ask quantity
imbalance_ratio_5Imbalance Ratio (5 levels)Last normalized difference between top-5 bid and ask quantity
imbalance_ratio_10Imbalance Ratio (10 levels)Last normalized difference between top-10 bid and ask quantity
imbalance_ratio_20Imbalance Ratio (20 levels)Last normalized difference between top-20 bid and ask quantity
imbalance_ratio_25Imbalance Ratio (25 levels)Last normalized difference between top-25 bid and ask quantity
bid_ask_ratio_5Bid/Ask Ratio (5 levels)Last top-5 bid quantity divided by top-5 ask quantity
bid_ask_ratio_10Bid/Ask Ratio (10 levels)Last top-10 bid quantity divided by top-10 ask quantity
bid_ask_ratio_20Bid/Ask Ratio (20 levels)Last top-20 bid quantity divided by top-20 ask quantity
bid_ask_ratio_25Bid/Ask Ratio (25 levels)Last top-25 bid quantity divided by top-25 ask quantity
imbalance_5_avgAvg Imbalance (5 levels)Average top-5 bid quantity minus top-5 ask quantity in the interval
imbalance_10_avgAvg Imbalance (10 levels)Average top-10 bid quantity minus top-10 ask quantity in the interval
imbalance_20_avgAvg Imbalance (20 levels)Average top-20 bid quantity minus top-20 ask quantity in the interval
imbalance_25_avgAvg Imbalance (25 levels)Average top-25 bid quantity minus top-25 ask quantity in the interval
imbalance_ratio_5_avgAvg Imbalance Ratio (5 levels)Average normalized difference between top-5 bid and ask quantity in the interval
imbalance_ratio_10_avgAvg Imbalance Ratio (10 levels)Average normalized difference between top-10 bid and ask quantity in the interval
imbalance_ratio_20_avgAvg Imbalance Ratio (20 levels)Average normalized difference between top-20 bid and ask quantity in the interval
imbalance_ratio_25_avgAvg Imbalance Ratio (25 levels)Average normalized difference between top-25 bid and ask quantity in the interval
bid_ask_ratio_5_avgAvg Bid/Ask Ratio (5 levels)Average top-5 bid quantity divided by top-5 ask quantity in the interval
bid_ask_ratio_10_avgAvg Bid/Ask Ratio (10 levels)Average top-10 bid quantity divided by top-10 ask quantity in the interval
bid_ask_ratio_20_avgAvg Bid/Ask Ratio (20 levels)Average top-20 bid quantity divided by top-20 ask quantity in the interval
bid_ask_ratio_25_avgAvg Bid/Ask Ratio (25 levels)Average top-25 bid quantity divided by top-25 ask quantity in the interval
Example Request
from datetime import date
from aperiodic import get_metrics

# Free preview — no API key required
df = get_metrics(
    metric="l2_imbalance",
    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())

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 a Tier 3 subscription.

15s30s1m5m15m30m1h4h1d
exchangereqstring
string

Source exchange for the data. Not available on this dataset: hyperliquid-perps.

binance-futuresokx-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). Data is partitioned by year and month.

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 file covering the range — 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_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

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Use with AI Agents

Access L2 Order Book Imbalance programmatically via our Python SDK and REST API — optimised for autonomous research workflows.

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Crypto microstructure, liquidity & flow metrics — built from hundreds of terabytes of raw data, distilled into point-in-time metrics you can pull as parquet files.

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