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From raw order flow to a ranked, backtestable signal — every step in runnable code.

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Alpha Discovery

We define Directional Alpha as a signal that exhibits a consistent positive or negative relationship with forward returns.

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  1. 01

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Intro to Aperiodic Flow Metrics

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This notebook introduces order-flow analytics with the `aperiodic` Python package. We focus on a single large-cap instrument — Binance BTC perpetuals (`perpetual-BTC-USDT:USDT`) — over the exact six-month window from September 1, 2025 through February 28, 2026, using 1-hour observations.

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Find new alpha

From raw order flow to a ranked, backtestable signal — every step in runnable code.

Run on preview data
Browse catalog
Trade Size

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

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

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

Intervals
1m5m15m30m1h4h1d
Requires Institutional
15s30s
Exchanges
binance-futuresokx-perpshyperliquid-perps
Built from
Raw trades →
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 $1,000
volumeTotal VolumeTotal traded notional in the interval
n_tradesTrade CountNumber of trades in the interval
small_order_percentageSmall Order %Ratio of small-trade notional to total traded notional
medium_order_percentageMedium Order %Ratio of medium-trade notional to total traded notional
large_order_percentageLarge Order %Ratio of large-trade notional to total traded notional
small_order_count_percentageSmall Order Count %Share of trades that were small trades
medium_order_count_percentageMedium Order Count %Share of trades that were medium trades
large_order_count_percentageLarge Order Count %Share of trades that were large trades
trade_amount_meanMean Trade SizeAverage trade notional
trade_amount_medianMedian Trade SizeMedian trade notional
trade_amount_stdTrade Size Std DevStandard deviation of trade notional
trade_amount_varianceTrade Size VarianceVariance of trade notional
trade_amount_skewnessTrade Size SkewnessSkewness of the trade-notional distribution
trade_amount_kurtosisTrade Size KurtosisKurtosis of the trade-notional distribution
trade_amount_minMin Trade SizeSmallest trade notional in the interval
trade_amount_maxMax Trade SizeLargest trade notional in the interval
trade_amount_range_ratioTrade Size Range RatioLargest trade notional divided by smallest trade notional
trade_amount_cvTrade Size CVCoefficient of variation of trade notional
Example Request
# Install the Aperiodic CLI into ~/.local/bin
mkdir -p "$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"

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.

Suggestions shown — any valid value accepted
Suggestions shown — any valid value accepted
Suggestions shown — any valid value accepted
Authentication
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
Aperiodic

Crypto microstructure, liquidity & flow metrics — built from hundreds of terabytes of raw data, distilled into point-in-time metrics you can pull as parquet files. Plus the raw trades, quotes and derivatives data behind them.

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Registered in Ireland · Company No. 815273

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