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Trade Size

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

CodeAPI DocsTry It

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 volume 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.

Not all trades carry the same information. A 50tradeanda50 trade and a 50tradeanda50,000 trade on the same instrument represent fundamentally different market participants with different motives, horizons, and information sets. Separating them is one of the most powerful decompositions in empirical microstructure.

Endpoint

/api/v1/data/trade_size

Category

Trades

Intervals
1m5m15m30m1h4h1d
Requires Prime
1s

Requires timestamp=true

Exchanges
binance-futuresokx-perpshyperliquid-perps
Fields24
small_order_volumeSmall Order VolumeTotal traded notional from small trades under $100
small_order_countSmall Order CountNumber of trades under $100
medium_order_volumeMedium Order VolumeTotal traded notional from medium trades from $100 to under $1,000
medium_order_countMedium Order CountNumber of trades from $100 to under $1,000
large_order_volumeLarge Order VolumeTotal traded notional from large trades of at least $1,000
large_order_countLarge Order CountNumber of trades of at least $1,000
volumeTotal VolumeTotal traded quantity in the interval
n_tradesTrade CountNumber of trades in the interval
small_order_percentageSmall Order %Ratio of small-trade notional to total traded quantity
medium_order_percentageMedium Order %Ratio of medium-trade notional to total traded quantity
large_order_percentageLarge Order %Ratio of large-trade notional to total traded quantity
small_order_count_percentageSmall Order Count %Share of trades that were small trades
medium_order_count_percentageMedium Order Count %Share of trades that were medium trades
large_order_count_percentageLarge Order Count %Share of trades that were large trades
trade_amount_meanMean Trade SizeAverage trade quantity
trade_amount_medianMedian Trade SizeMedian trade quantity
trade_amount_stdTrade Size Std DevStandard deviation of trade quantity
trade_amount_varianceTrade Size VarianceVariance of trade quantity
trade_amount_skewnessTrade Size SkewnessSkewness of the trade-quantity distribution
trade_amount_kurtosisTrade Size KurtosisKurtosis of the trade-quantity distribution
trade_amount_minMin Trade SizeSmallest trade quantity in the interval
trade_amount_maxMax Trade SizeLargest trade quantity in the interval
trade_amount_range_ratioTrade Size Range RatioLargest trade quantity divided by smallest trade quantity
trade_amount_cvTrade Size CVCoefficient of variation of trade quantity
Example Request
from datetime import date
from aperiodic import get_metrics
df = get_metrics(
    api_key="YOUR_API_KEY",
    metric="trade_size",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT",
    interval="1d",
    start_date=date(2024, 1, 1),
    end_date=date(2024, 3, 1),
)
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.

1m5m15m30m1h4h1d
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 each monthly file

Schema
filesobject[]required

Array of file information for each month in the requested date range

yearintegerrequired

Year of the data file

monthintegerrequired

Month of the data file (1-12)

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": 2024,
      "month": 1,
      "url": "https://ohlcv.aperiodic.io/binance-futures/1h/BTCUSDT/2024-01.parquet?X-Amz-Expires=300&..."
    },
    {
      "year": 2024,
      "month": 2,
      "url": "https://ohlcv.aperiodic.io/binance-futures/1h/BTCUSDT/2024-02.parquet?X-Amz-Expires=300&..."
    }
  ]
}
Try It
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Authentication
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GET/api/v1/data/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

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