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Flow Metrics

Directional order flow split by taker buy vs. sell, broken into small/medium/large notional segments, with a flow toxicity score.

CodeAPI DocsTry It

volume_delta_notional

Volume Delta Notional

Volume Delta Notional shows the signed difference between aggressive buy and sell notional in the bar, making it one of the cleanest ways to spot net pressure.

Large positive readings usually mean buyers lifted offers with size, while large negative readings point to decisive sell-side aggression.

flow_toxicity_score

Flow Toxicity Score

Flow Toxicity Score combines abnormal notional imbalance with short-horizon realized volatility to highlight bars that were both one-sided and destabilizing.

The metric is designed to surface moments when liquidity providers are most likely trading against informed or urgent flow rather than routine noise.

flow_entropy

Flow Entropy

Flow Entropy measures how evenly buy and sell volume was distributed inside the interval.

High entropy suggests a balanced tug-of-war, while low entropy points to more concentrated and decisive flow in one direction.

taker_buy_sell_ratio

Buy/Sell Ratio

Buy/Sell Ratio compares aggressive buy volume to aggressive sell volume in one simple number.

It gives a fast read on whether the tape leaned meaningfully toward buyers or sellers without requiring any additional transformation.

taker_buy_large_order_percentage

Buy Large Order %

Buy Large Order % isolates how much of total aggressive buy volume came from the largest trade-size bucket.

When this rises alongside positive delta, it often suggests participation from larger and more intentional actors rather than fragmented retail flow.

Order flow tells you direction. Realized volatility tells you uncertainty. Separately, each is informative. Together, they capture something neither can alone: the toxicity of the trading environment for liquidity providers.

Endpoint

/api/v1/data/flow

Category

Trades

Intervals
1m5m15m30m1h4h1d
Requires Institutional
15s30s
Exchanges
binance-futuresokx-perpshyperliquid-perps
Fields36
taker_buy_volumeTaker Buy VolumeTotal buy-side traded quantity in the interval
taker_sell_volumeTaker Sell VolumeTotal sell-side traded quantity in the interval
taker_buy_countTaker Buy CountNumber of buy-side trades in the interval
taker_sell_countTaker Sell CountNumber of sell-side trades in the interval
volume_deltaVolume DeltaBuy-side traded quantity minus sell-side traded quantity
volume_delta_notionalVolume Delta NotionalBuy-side traded notional minus sell-side traded notional
flow_entropyFlow EntropyEntropy of the buy-versus-sell quantity split in the interval
flow_toxicity_scoreFlow Toxicity ScorePast-normalized signed notional flow multiplied by past-normalized short-horizon realized volatility
taker_buy_sell_ratioBuy/Sell RatioBuy-side traded quantity divided by sell-side traded quantity
taker_buy_sell_count_ratioBuy/Sell Count RatioBuy-side trade count divided by sell-side trade count
taker_buy_sell_percentageTaker Buy %Share of traded quantity that came from buy-side trades
taker_buy_sell_count_percentageTaker Buy Count %Share of trades that were buy-side trades
taker_buy_small_order_volumeBuy Small Order VolumeTotal buy-side notional from small trades under $100
taker_buy_small_order_countBuy Small Order CountNumber of buy-side trades under $100
taker_buy_medium_order_volumeBuy Medium Order VolumeTotal buy-side notional from medium trades from $100 to under $1,000
taker_buy_medium_order_countBuy Medium Order CountNumber of buy-side trades from $100 to under $1,000
taker_buy_large_order_volumeBuy Large Order VolumeTotal buy-side notional from large trades of at least $1,000
taker_buy_large_order_countBuy Large Order CountNumber of buy-side trades of at least $1,000
taker_sell_small_order_volumeSell Small Order VolumeTotal sell-side notional from small trades under $100
taker_sell_small_order_countSell Small Order CountNumber of sell-side trades under $100
taker_sell_medium_order_volumeSell Medium Order VolumeTotal sell-side notional from medium trades from $100 to under $1,000
taker_sell_medium_order_countSell Medium Order CountNumber of sell-side trades from $100 to under $1,000
taker_sell_large_order_volumeSell Large Order VolumeTotal sell-side notional from large trades of at least $1,000
taker_sell_large_order_countSell Large Order CountNumber of sell-side trades of at least $1,000
taker_buy_small_order_percentageBuy Small Order %Ratio of small buy-side trade notional to total buy-side traded quantity
taker_buy_medium_order_percentageBuy Medium Order %Ratio of medium buy-side trade notional to total buy-side traded quantity
taker_buy_large_order_percentageBuy Large Order %Ratio of large buy-side trade notional to total buy-side traded quantity
taker_buy_small_order_count_percentageBuy Small Count %Share of buy-side trades that were small trades
taker_buy_medium_order_count_percentageBuy Medium Count %Share of buy-side trades that were medium trades
taker_buy_large_order_count_percentageBuy Large Count %Share of buy-side trades that were large trades
taker_sell_small_order_percentageSell Small Order %Ratio of small sell-side trade notional to total sell-side traded quantity
taker_sell_medium_order_percentageSell Medium Order %Ratio of medium sell-side trade notional to total sell-side traded quantity
taker_sell_large_order_percentageSell Large Order %Ratio of large sell-side trade notional to total sell-side traded quantity
taker_sell_small_order_count_percentageSell Small Count %Share of sell-side trades that were small trades
taker_sell_medium_order_count_percentageSell Medium Count %Share of sell-side trades that were medium trades
taker_sell_large_order_count_percentageSell Large Count %Share of sell-side trades that were large trades
Example Request
from datetime import date
from aperiodic import get_metrics

# Free preview — no API key required
df = get_metrics(
    metric="flow",
    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.

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). 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/flow?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 Flow Metrics programmatically via our Python SDK and REST API — optimised for autonomous research workflows.

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Aperiodic

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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