Use case

Improve your backtest

Stop backtesting fantasy fills. Price in slippage, impact and funding with point-in-time metrics.

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

Featured notebook

Backtesting with realistic microstructure cost

A price-only backtest quietly assumes you can trade at the mid-price for free. Real fills pay the half-spread, move the book, and miss when liquidity thins out. This notebook builds a microstructure transaction-cost model — half-spread plus market impact — from Aperiodic's L1 price, impact, and slippage metrics, checks it against measured slippage, and shows how different cost assumptions reshape the same simple momentum backtest.

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

Also featured

Funding-aware perpetual backtests

Improve your backtest

A perpetual-futures position does not only earn the price move — it also pays or receives funding at discrete settlement prints. A price-only backtest ignores those cash flows and can misattribute carry to trading alpha, most of all for a persistently long strategy in a rising, positive-funding market. This notebook isolates realised funding and shows exactly where it binds: it is a first-order cost for a directional (long-biased) position and nets out for a direction-neutral one.

View notebook

Improve your backtest

Stop backtesting fantasy fills. Price in slippage, impact and funding with point-in-time metrics.

Run on preview data Browse catalog

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

Trade Size

Trades

Trade Size

Trade notional split into small (< $100), medium ($100–$1,000), and large (≥ $1,000) segments, with summary statistics for individual trade sizes.

See it on real data

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

Sample CSV Start in Claude

Shown: n_trades, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 1,637,788, 9 Jan, 21:00 UTC · built with trade_size

low

high

extreme

large_order_volume

Large Order Volume

Large Order Volume tracks how much notional traded in the largest size bucket during the interval.

It is useful because the presence of size often changes the meaning of otherwise ordinary moves in price or total volume.

large_order_percentage

Large Order %

Large Order % shows what share of total traded notional came from the largest bucket rather than from all trades equally.

This helps normalize across busy and quiet sessions, making it easier to identify genuine changes in participant mix.

medium_order_percentage

Medium Order %

Medium Order % often acts as the overlooked middle layer between retail-like fragmentation and outright block activity.

Changes here can matter because many transitions in market participation appear first in the medium bucket before they spill into the large one.

small_order_count_percentage

Small Order Count %

Small Order Count % measures how much of the tape was made up of small individual executions.

It is especially revealing when trade count is high but small-order share dominates, since that can point to reactive retail flow or quote-chasing behavior.

n_trades

Trade Count

Trade Count is the raw number of matched trades in the interval, and it remains one of the best gauges of tape activity.

It helps distinguish quiet directional moves from crowded periods where many separate participants are trying to transact at once.

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

Fields

Field	Name	Description
`small_order_volume`	Small Order Volume	Total traded notional from small trades under $100
`small_order_count`	Small Order Count	Number of trades under $100
`medium_order_volume`	Medium Order Volume	Total traded notional from medium trades from $100 to under $1,000
`medium_order_count`	Medium Order Count	Number of trades from $100 to under $1,000
`large_order_volume`	Large Order Volume	Total traded notional from large trades of at least $1,000
`large_order_count`	Large Order Count	Number of trades of at least $1,000
`volume`	Total Volume	Total traded notional in the interval
`n_trades`	Trade Count	Number of trades in the interval
`small_order_percentage`	Small Order %	Ratio of small-trade notional to total traded notional
`medium_order_percentage`	Medium Order %	Ratio of medium-trade notional to total traded notional
`large_order_percentage`	Large Order %	Ratio of large-trade notional to total traded notional
`small_order_count_percentage`	Small Order Count %	Share of trades that were small trades
`medium_order_count_percentage`	Medium Order Count %	Share of trades that were medium trades
`large_order_count_percentage`	Large Order Count %	Share of trades that were large trades
`trade_amount_mean`	Mean Trade Size	Average trade notional
`trade_amount_median`	Median Trade Size	Median trade notional
`trade_amount_std`	Trade Size Std Dev	Standard deviation of trade notional
`trade_amount_variance`	Trade Size Variance	Variance of trade notional
`trade_amount_skewness`	Trade Size Skewness	Skewness of the trade-notional distribution
`trade_amount_kurtosis`	Trade Size Kurtosis	Kurtosis of the trade-notional distribution
`trade_amount_min`	Min Trade Size	Smallest trade notional in the interval
`trade_amount_max`	Max Trade Size	Largest trade notional in the interval
`trade_amount_range_ratio`	Trade Size Range Ratio	Largest trade notional divided by smallest trade notional
`trade_amount_cv`	Trade Size CV	Coefficient of variation of trade notional

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 trade_size --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 trade_size.<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="trade_size",
    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/trade_size?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

Improve your backtest

Stop backtesting fantasy fills. Price in slippage, impact and funding with point-in-time metrics.

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

Featured notebook

Backtesting with realistic microstructure cost

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

Also featured

Funding-aware perpetual backtests

Improve your backtest

View notebook

Improve your backtest

Stop backtesting fantasy fills. Price in slippage, impact and funding with point-in-time metrics.

Run on preview data Browse catalog

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

Trade Size

Trades

Trade Size

Trade notional split into small (< $100), medium ($100–$1,000), and large (≥ $1,000) segments, with summary statistics for individual trade sizes.

See it on real data

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

Sample CSV Start in Claude

Shown: n_trades, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 1,637,788, 9 Jan, 21:00 UTC · built with trade_size

low

high

extreme

large_order_volume

Large Order Volume

Large Order Volume tracks how much notional traded in the largest size bucket during the interval.

It is useful because the presence of size often changes the meaning of otherwise ordinary moves in price or total volume.

large_order_percentage

Large Order %

Large Order % shows what share of total traded notional came from the largest bucket rather than from all trades equally.

This helps normalize across busy and quiet sessions, making it easier to identify genuine changes in participant mix.

medium_order_percentage

Medium Order %

Medium Order % often acts as the overlooked middle layer between retail-like fragmentation and outright block activity.

Changes here can matter because many transitions in market participation appear first in the medium bucket before they spill into the large one.

small_order_count_percentage

Small Order Count %

Small Order Count % measures how much of the tape was made up of small individual executions.

It is especially revealing when trade count is high but small-order share dominates, since that can point to reactive retail flow or quote-chasing behavior.

n_trades

Trade Count

Trade Count is the raw number of matched trades in the interval, and it remains one of the best gauges of tape activity.

It helps distinguish quiet directional moves from crowded periods where many separate participants are trying to transact at once.

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

Fields

Field	Name	Description
`small_order_volume`	Small Order Volume	Total traded notional from small trades under $100
`small_order_count`	Small Order Count	Number of trades under $100
`medium_order_volume`	Medium Order Volume	Total traded notional from medium trades from $100 to under $1,000
`medium_order_count`	Medium Order Count	Number of trades from $100 to under $1,000
`large_order_volume`	Large Order Volume	Total traded notional from large trades of at least $1,000
`large_order_count`	Large Order Count	Number of trades of at least $1,000
`volume`	Total Volume	Total traded notional in the interval
`n_trades`	Trade Count	Number of trades in the interval
`small_order_percentage`	Small Order %	Ratio of small-trade notional to total traded notional
`medium_order_percentage`	Medium Order %	Ratio of medium-trade notional to total traded notional
`large_order_percentage`	Large Order %	Ratio of large-trade notional to total traded notional
`small_order_count_percentage`	Small Order Count %	Share of trades that were small trades
`medium_order_count_percentage`	Medium Order Count %	Share of trades that were medium trades
`large_order_count_percentage`	Large Order Count %	Share of trades that were large trades
`trade_amount_mean`	Mean Trade Size	Average trade notional
`trade_amount_median`	Median Trade Size	Median trade notional
`trade_amount_std`	Trade Size Std Dev	Standard deviation of trade notional
`trade_amount_variance`	Trade Size Variance	Variance of trade notional
`trade_amount_skewness`	Trade Size Skewness	Skewness of the trade-notional distribution
`trade_amount_kurtosis`	Trade Size Kurtosis	Kurtosis of the trade-notional distribution
`trade_amount_min`	Min Trade Size	Smallest trade notional in the interval
`trade_amount_max`	Max Trade Size	Largest trade notional in the interval
`trade_amount_range_ratio`	Trade Size Range Ratio	Largest trade notional divided by smallest trade notional
`trade_amount_cv`	Trade Size CV	Coefficient of variation of trade notional

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 trade_size --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="trade_size",
    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/trade_size?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

Backtesting with realistic microstructure cost

Pick the metrics

Run the notebook

Plug it into your stack

Slippage

Market impact

Funding rates

Top-of-book prices

Funding-aware perpetual backtests

Improve your backtest

Large Order Volume

Large Order %

Medium Order %

Small Order Count %

Trade Count

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It

Backtesting with realistic microstructure cost

Pick the metrics

Run the notebook

Plug it into your stack

Slippage

Market impact

Funding rates

Top-of-book prices

Funding-aware perpetual backtests

Improve your backtest

Large Order Volume

Large Order %

Medium Order %

Small Order Count %

Trade Count

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It