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

Impact Metrics

How much each unit of trading volume moves the price, measured via Amihud illiquidity, Kyle lambda, impact per notional, and large-trade reversal.

Code API Docs Try It

amihud_like

Amihud Illiquidity

Amihud Illiquidity measures how much price moved for a given amount of traded notional, providing a classic impact-per-volume lens.

Higher values imply that relatively small amounts of trading were able to move the market, which is usually a sign of thin or fragile liquidity.

kyle_like_lambda

Kyle Lambda

Kyle Lambda estimates the sensitivity of price changes to signed order flow, translating imbalance into impact.

When it rises, the same net buying or selling pressure tends to push price farther, suggesting a more impressionable market.

impact_per_notional

Impact per Notional

Impact per Notional asks a practical question: how much movement did each unit of traded value cause?

It helps traders benchmark execution cost in a way that feels intuitive and transferable across strategies.

large_trade_reversal

Large Trade Reversal

Large Trade Reversal measures how often and how strongly price snaps back after a large trade moves through the market.

Strong reversal behavior often suggests the initiating trade consumed liquidity but failed to represent durable information.

directional_impact_asymmetry

Directional Impact Asymmetry

Directional Impact Asymmetry compares whether buy-side and sell-side aggression moved price by the same amount.

Asymmetry can reveal one-sided fragility, such as a market that lifts easily but resists downside, or the reverse.

Market impact — the price displacement caused by trading — is the central cost of active investing. Two foundational academic measures capture it from different angles, and both reveal something the other misses.

Endpoint

/api/v1/data/impact

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.

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/impact?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 Impact 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.

Impact Metrics

How much each unit of trading volume moves the price, measured via Amihud illiquidity, Kyle lambda, impact per notional, and large-trade reversal.

Code API Docs Try It

amihud_like

Amihud Illiquidity

Amihud Illiquidity measures how much price moved for a given amount of traded notional, providing a classic impact-per-volume lens.

Higher values imply that relatively small amounts of trading were able to move the market, which is usually a sign of thin or fragile liquidity.

kyle_like_lambda

Kyle Lambda

Kyle Lambda estimates the sensitivity of price changes to signed order flow, translating imbalance into impact.

When it rises, the same net buying or selling pressure tends to push price farther, suggesting a more impressionable market.

impact_per_notional

Impact per Notional

Impact per Notional asks a practical question: how much movement did each unit of traded value cause?

It helps traders benchmark execution cost in a way that feels intuitive and transferable across strategies.

large_trade_reversal

Large Trade Reversal

Large Trade Reversal measures how often and how strongly price snaps back after a large trade moves through the market.

Strong reversal behavior often suggests the initiating trade consumed liquidity but failed to represent durable information.

directional_impact_asymmetry

Directional Impact Asymmetry

Directional Impact Asymmetry compares whether buy-side and sell-side aggression moved price by the same amount.

Asymmetry can reveal one-sided fragility, such as a market that lifts easily but resists downside, or the reverse.

Endpoint

/api/v1/data/impact

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

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/impact?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 Impact 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.