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Trades

Flow Metrics

Name: Flow Metrics
Creator: Aperiodic
License: https://aperiodic.io/terms-of-service

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

See it on real data

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

Sample CSV Start in Claude

Shown: taker_buy_large_order_count_percentage, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 45.2%, 3 Jan, 12:00 UTC · built with flow

low

high

extreme

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 notional 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 notional to aggressive sell notional 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 notional 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
Built from: Raw trades →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`taker_buy_volume`	Taker Buy Volume	Total buy-side traded notional in the interval
`taker_sell_volume`	Taker Sell Volume	Total sell-side traded notional in the interval
`taker_buy_count`	Taker Buy Count	Number of buy-side trades in the interval
`taker_sell_count`	Taker Sell Count	Number of sell-side trades in the interval
`volume_delta`	Volume Delta	Buy-side traded quantity minus sell-side traded quantity
`volume_delta_notional`	Volume Delta Notional	Buy-side traded notional minus sell-side traded notional
`flow_entropy`	Flow Entropy	Entropy of the buy-versus-sell notional split in the interval
`flow_toxicity_score`	Flow Toxicity Score	Past-normalized signed notional flow multiplied by past-normalized short-horizon realized volatility
`taker_buy_sell_ratio`	Buy/Sell Ratio	Buy-side traded notional divided by sell-side traded notional
`taker_buy_sell_count_ratio`	Buy/Sell Count Ratio	Buy-side trade count divided by sell-side trade count
`taker_buy_sell_percentage`	Taker Buy %	Share of traded notional that came from buy-side trades
`taker_buy_sell_count_percentage`	Taker Buy Count %	Share of trades that were buy-side trades
`taker_buy_small_order_volume`	Buy Small Order Volume	Total buy-side notional from small trades under $100
`taker_buy_small_order_count`	Buy Small Order Count	Number of buy-side trades under $100
`taker_buy_medium_order_volume`	Buy Medium Order Volume	Total buy-side notional from medium trades from $100 to under $1,000
`taker_buy_medium_order_count`	Buy Medium Order Count	Number of buy-side trades from $100 to under $1,000
`taker_buy_large_order_volume`	Buy Large Order Volume	Total buy-side notional from large trades of at least $1,000
`taker_buy_large_order_count`	Buy Large Order Count	Number of buy-side trades of at least $1,000
`taker_sell_small_order_volume`	Sell Small Order Volume	Total sell-side notional from small trades under $100
`taker_sell_small_order_count`	Sell Small Order Count	Number of sell-side trades under $100
`taker_sell_medium_order_volume`	Sell Medium Order Volume	Total sell-side notional from medium trades from $100 to under $1,000
`taker_sell_medium_order_count`	Sell Medium Order Count	Number of sell-side trades from $100 to under $1,000
`taker_sell_large_order_volume`	Sell Large Order Volume	Total sell-side notional from large trades of at least $1,000
`taker_sell_large_order_count`	Sell Large Order Count	Number of sell-side trades of at least $1,000
`taker_buy_small_order_percentage`	Buy Small Order %	Ratio of small buy-side trade notional to total buy-side traded notional
`taker_buy_medium_order_percentage`	Buy Medium Order %	Ratio of medium buy-side trade notional to total buy-side traded notional
`taker_buy_large_order_percentage`	Buy Large Order %	Ratio of large buy-side trade notional to total buy-side traded notional
`taker_buy_small_order_count_percentage`	Buy Small Count %	Share of buy-side trades that were small trades
`taker_buy_medium_order_count_percentage`	Buy Medium Count %	Share of buy-side trades that were medium trades
`taker_buy_large_order_count_percentage`	Buy Large Count %	Share of buy-side trades that were large trades
`taker_sell_small_order_percentage`	Sell Small Order %	Ratio of small sell-side trade notional to total sell-side traded notional
`taker_sell_medium_order_percentage`	Sell Medium Order %	Ratio of medium sell-side trade notional to total sell-side traded notional
`taker_sell_large_order_percentage`	Sell Large Order %	Ratio of large sell-side trade notional to total sell-side traded notional
`taker_sell_small_order_count_percentage`	Sell Small Count %	Share of sell-side trades that were small trades
`taker_sell_medium_order_count_percentage`	Sell Medium Count %	Share of sell-side trades that were medium trades
`taker_sell_large_order_count_percentage`	Sell Large Count %	Share of sell-side trades that were large trades

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 flow --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 flow.<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="flow",
    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/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

Try Free Preview Data

Access a curated slice of real production data with just an account — no credit card or subscription required. Pair it with our research notebooks to get started instantly.

Use with AI Agents

Access Flow Metrics programmatically via our Python SDK and REST API — optimised for autonomous research workflows.

Get Started

Subscribe to get full API access. Start querying all datasets in minutes.

Back to Flow in the library Browse the catalog

Trades

Flow Metrics

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

See it on real data

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

Sample CSV Start in Claude

Shown: taker_buy_large_order_count_percentage, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 45.2%, 3 Jan, 12:00 UTC · built with flow

low

high

extreme

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 notional 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 notional to aggressive sell notional 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 notional 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.

Endpoint: /api/v1/data/flow
Category: Trades
Built from: Raw trades →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`taker_buy_volume`	Taker Buy Volume	Total buy-side traded notional in the interval
`taker_sell_volume`	Taker Sell Volume	Total sell-side traded notional in the interval
`taker_buy_count`	Taker Buy Count	Number of buy-side trades in the interval
`taker_sell_count`	Taker Sell Count	Number of sell-side trades in the interval
`volume_delta`	Volume Delta	Buy-side traded quantity minus sell-side traded quantity
`volume_delta_notional`	Volume Delta Notional	Buy-side traded notional minus sell-side traded notional
`flow_entropy`	Flow Entropy	Entropy of the buy-versus-sell notional split in the interval
`flow_toxicity_score`	Flow Toxicity Score	Past-normalized signed notional flow multiplied by past-normalized short-horizon realized volatility
`taker_buy_sell_ratio`	Buy/Sell Ratio	Buy-side traded notional divided by sell-side traded notional
`taker_buy_sell_count_ratio`	Buy/Sell Count Ratio	Buy-side trade count divided by sell-side trade count
`taker_buy_sell_percentage`	Taker Buy %	Share of traded notional that came from buy-side trades
`taker_buy_sell_count_percentage`	Taker Buy Count %	Share of trades that were buy-side trades
`taker_buy_small_order_volume`	Buy Small Order Volume	Total buy-side notional from small trades under $100
`taker_buy_small_order_count`	Buy Small Order Count	Number of buy-side trades under $100
`taker_buy_medium_order_volume`	Buy Medium Order Volume	Total buy-side notional from medium trades from $100 to under $1,000
`taker_buy_medium_order_count`	Buy Medium Order Count	Number of buy-side trades from $100 to under $1,000
`taker_buy_large_order_volume`	Buy Large Order Volume	Total buy-side notional from large trades of at least $1,000
`taker_buy_large_order_count`	Buy Large Order Count	Number of buy-side trades of at least $1,000
`taker_sell_small_order_volume`	Sell Small Order Volume	Total sell-side notional from small trades under $100
`taker_sell_small_order_count`	Sell Small Order Count	Number of sell-side trades under $100
`taker_sell_medium_order_volume`	Sell Medium Order Volume	Total sell-side notional from medium trades from $100 to under $1,000
`taker_sell_medium_order_count`	Sell Medium Order Count	Number of sell-side trades from $100 to under $1,000
`taker_sell_large_order_volume`	Sell Large Order Volume	Total sell-side notional from large trades of at least $1,000
`taker_sell_large_order_count`	Sell Large Order Count	Number of sell-side trades of at least $1,000
`taker_buy_small_order_percentage`	Buy Small Order %	Ratio of small buy-side trade notional to total buy-side traded notional
`taker_buy_medium_order_percentage`	Buy Medium Order %	Ratio of medium buy-side trade notional to total buy-side traded notional
`taker_buy_large_order_percentage`	Buy Large Order %	Ratio of large buy-side trade notional to total buy-side traded notional
`taker_buy_small_order_count_percentage`	Buy Small Count %	Share of buy-side trades that were small trades
`taker_buy_medium_order_count_percentage`	Buy Medium Count %	Share of buy-side trades that were medium trades
`taker_buy_large_order_count_percentage`	Buy Large Count %	Share of buy-side trades that were large trades
`taker_sell_small_order_percentage`	Sell Small Order %	Ratio of small sell-side trade notional to total sell-side traded notional
`taker_sell_medium_order_percentage`	Sell Medium Order %	Ratio of medium sell-side trade notional to total sell-side traded notional
`taker_sell_large_order_percentage`	Sell Large Order %	Ratio of large sell-side trade notional to total sell-side traded notional
`taker_sell_small_order_count_percentage`	Sell Small Count %	Share of sell-side trades that were small trades
`taker_sell_medium_order_count_percentage`	Sell Medium Count %	Share of sell-side trades that were medium trades
`taker_sell_large_order_count_percentage`	Sell Large Count %	Share of sell-side trades that were large trades

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 flow --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="flow",
    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/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

Try Free Preview Data

Access a curated slice of real production data with just an account — no credit card or subscription required. Pair it with our research notebooks to get started instantly.

Use with AI Agents

Access Flow Metrics programmatically via our Python SDK and REST API — optimised for autonomous research workflows.

Get Started

Subscribe to get full API access. Start querying all datasets in minutes.

Volume Delta Notional

Flow Toxicity Score

Flow Entropy

Buy/Sell Ratio

Buy Large Order %

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It

Try Free Preview Data

Use with AI Agents

Get Started

Volume Delta Notional

Flow Toxicity Score

Flow Entropy

Buy/Sell Ratio

Buy Large Order %

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It

Try Free Preview Data

Use with AI Agents

Get Started