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Trade Run Structure

Consecutive same-direction trade run lengths, imbalance, flip rate, and price change on direction flip.

CodeAPI DocsTry It

buy_run_max_len

Max Buy Run Length

Max Buy Run Length records the longest uninterrupted sequence of buyer-initiated trades in the interval.

Long runs are often a signature of urgency, persistence, or systematic execution that keeps leaning in one direction.

sell_run_max_len

Max Sell Run Length

Max Sell Run Length is the bearish mirror image, tracking the longest consecutive stretch of sell-initiated prints.

It helps identify intervals where downside pressure arrived as a continuous wave rather than as isolated bursts.

run_imbalance

Run Imbalance

Run Imbalance summarizes whether buy sequences or sell sequences dominated the structure of trading within the bar.

Unlike plain volume imbalance, it captures persistence and sequencing, which can be more informative about trader intent.

flip_rate

Flip Rate

Flip Rate measures how frequently the trade direction switched between buy-led and sell-led prints.

High flip rates usually correspond to choppy, back-and-forth microstructure, while low flip rates indicate more persistent pressure.

price_change_on_flip

Price Change on Flip

Price Change on Flip measures how much the market moved when control passed from one side of the tape to the other.

Large changes on flips can imply that turning points were costly and that reversals happened only after meaningful price concessions.

Here's a microstructure concept that rarely gets discussed outside market-making desks: trade runs — consecutive sequences of same-direction trades. Their statistical properties reveal the hidden structure of order flow in ways that aggregate volume metrics completely miss.

Endpoint

/api/v1/data/run_structure

Category

Trades

Intervals
1m5m15m30m1h4h1d
Requires Institutional
15s30s
Exchanges
binance-futuresokx-perpshyperliquid-perps
Fields7
buy_run_max_lenMax Buy Run LengthLongest consecutive run of buy-side trades
sell_run_max_lenMax Sell Run LengthLongest consecutive run of sell-side trades
buy_run_mean_lenMean Buy Run LengthAverage length of consecutive buy-side runs
sell_run_mean_lenMean Sell Run LengthAverage length of consecutive sell-side runs
run_imbalanceRun ImbalanceNormalized difference between the longest buy run and longest sell run
flip_rateFlip RateShare of trades whose side flipped versus the previous trade
price_change_on_flipPrice Change on FlipAverage absolute price change when trade direction flips
Example Request
from datetime import date
from aperiodic import get_metrics

# Free preview — no API key required
df = get_metrics(
    metric="run_structure",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT",
    interval="5m",
    timestamp="exchange",
    start_date=date(2025, 5, 1),
    end_date=date(2025, 5, 31),
    preview=True,
)

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. 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/run_structure?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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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.

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