Use case

Execute smarter

Know when liquidity is deep, when spreads are wide, and when flow turns toxic.

Shown: the median perpetual’s book depth across the top 25 levels, as a share of its 20:00–20:45 average, per venue, 18:00–01:00 UTC on 10 Oct 2025; minutes under half of normal take the strong tone, and the lowest of all, OKX’s 8.5% at 22:06, is orange · built with l2_liquidity 1m

Featured notebook

Timing execution around liquidity

Execution cost is not constant through the day. Spreads widen, depth thins, and order flow turns adverse together around stressed periods. For a moderate-frequency order, when you execute can matter more than shaving microseconds off the route. This notebook measures when liquidity is deep, spreads are narrow, and flow is least toxic, then ranks the hours by modelled execution cost and cross-checks that ranking against measured slippage.

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

Related notebook

Intro to Aperiodic L1 Price Metrics

Execute smarter

This notebook explores top-of-book (L1) pricing with Aperiodic's `l1_price` metric for Binance BTC perpetuals from September 1, 2025 through February 28, 2026 at 5-minute granularity.

View notebook

Execute smarter

Know when liquidity is deep, when spreads are wide, and when flow turns toxic.

Explore real-time streaming Book a call (Institutional)

Or create a free account to run the notebooks on preview data.

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

Impact Metrics

Trades

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.

See it on real data

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

Sample CSV Start in Claude

Shown: kyle_like_lambda, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 2.66e-5, 5 Jan, 01:00 UTC · built with impact

low

high

extreme

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
Category: Trades
Built from: Raw trades →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`amihud_like`	Amihud Illiquidity	Absolute interval return divided by total traded notional
`kyle_like_lambda`	Kyle Lambda	Covariance of trade returns and signed trade size divided by signed-size variance
`impact_per_notional`	Impact per Notional	Absolute interval return divided by absolute signed notional flow
`large_trade_reversal`	Large Trade Reversal	Average five-trade-ahead return after trades in the top 10% by notional
`directional_impact_asymmetry`	Directional Impact Asymmetry	Average buy-trade price impact minus average sell-trade price impact

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 impact --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 impact.<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="impact",
    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: 4 years on Pro, 5 years on Prime and Prime + Raw, full history on Institutional. An earlier start_date 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/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

Use case

Execute smarter

Know when liquidity is deep, when spreads are wide, and when flow turns toxic.

Featured notebook

Timing execution around liquidity

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

Related notebook

Intro to Aperiodic L1 Price Metrics

Execute smarter

This notebook explores top-of-book (L1) pricing with Aperiodic's `l1_price` metric for Binance BTC perpetuals from September 1, 2025 through February 28, 2026 at 5-minute granularity.

View notebook

Execute smarter

Know when liquidity is deep, when spreads are wide, and when flow turns toxic.

Explore real-time streaming Book a call (Institutional)

Or create a free account to run the notebooks on preview data.

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

Impact Metrics

Trades

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.

See it on real data

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

Sample CSV Start in Claude

Shown: kyle_like_lambda, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 2.66e-5, 5 Jan, 01:00 UTC · built with impact

low

high

extreme

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
Category: Trades
Built from: Raw trades →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`amihud_like`	Amihud Illiquidity	Absolute interval return divided by total traded notional
`kyle_like_lambda`	Kyle Lambda	Covariance of trade returns and signed trade size divided by signed-size variance
`impact_per_notional`	Impact per Notional	Absolute interval return divided by absolute signed notional flow
`large_trade_reversal`	Large Trade Reversal	Average five-trade-ahead return after trades in the top 10% by notional
`directional_impact_asymmetry`	Directional Impact Asymmetry	Average buy-trade price impact minus average sell-trade price impact

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 impact --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="impact",
    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/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

Timing execution around liquidity

Pick the metrics

Run the notebook

Plug it into your stack

Spreads & top-of-book liquidity

Multi-level depth

Market impact

Flow toxicity

Intro to Aperiodic L1 Price Metrics

Execute smarter

Amihud Illiquidity

Kyle Lambda

Impact per Notional

Large Trade Reversal

Directional Impact Asymmetry

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It

Timing execution around liquidity

Pick the metrics

Run the notebook

Plug it into your stack

Spreads & top-of-book liquidity

Multi-level depth

Market impact

Flow toxicity

Intro to Aperiodic L1 Price Metrics

Execute smarter

Amihud Illiquidity

Kyle Lambda

Impact per Notional

Large Trade Reversal

Directional Impact Asymmetry

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It