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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.

Pre-executed results — free to run on preview data, or use the CLI with DEMO-KEY.

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From notebook to production

  1. 01

    Pick the metrics

    Browse the catalog and pull point-in-time microstructure metrics — no L2 reconstruction, no tick archive to maintain.

  2. 02

    Run the notebook

    Every notebook runs end to end on preview data with a free account, in the browser or through the CLI.

  3. 03

    Plug it into your stack

    Swap the preview key for your own and the same code pulls the full history through the Python package or the API.

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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.

View notebook
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11_ _00011 111__ __00 000001111_ ..+cbba+ .++abc c+. +a bc+ .+a c. .abc .+ac+. . .+ab .+abcc+. ++. _0000111_ _000111_ _ 0111 _0001 _00111 _0 1_ _00011_ 0111_ _000 11_ _00001 111_ _000 0001111_ .++cba++.. .+abc+. . +abc +. .+bc + .+ab+ .+abc. +. .+a .+abcc+. +. . 001111_ _0 00111_ _00 11 _00011_ _00111 _00 1 _00011_ 0111_ _000 11_ _00001 111_ _0000 01111_ _0.+ccba+...+a .+abc++. .+abc+. +abc+ . +abc+ .+abc+ +. .+a .+abcc+. . .+ 111_ _0000 11_ _00011 _000111_ 0011_ _000 _ _00111_ 00111_ _000 111_ _0001 11_ _0001 111__ _000ccba+..+aabc abc+. .+abc+. .+ab+. .+ac+. .+abc+ +. .+a .+abcc+. ..+a 1_ _000111 _ _000111_ _000111_ 0 011_ _0001 _ _000111_ 00111_ _00 111_ _0001 11_ _00001 11_ _00001ba+..+abcc+. +. .+ .+bc+. .+abc+. .+abc+. .+abc+ c+. .+ .+abc++. .++ab _ _0001111_ _000111_ 000111_ _0 111_ _0001 1 _000111_ _00111_ _0 0111_ _000 11_ __0001 1_ _000011 .+abc+. +. .+a +abc+. .+abc+. .+abc+. . .+ab abc+. .+abc+. +. .+a _000111_ _ 00111_ _00 111_ _0001 1_ _000111 _000111_ 000111_ _0 0111_ 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+abc+. .+abc+.. c+. .++ab .+aabc++. c+. . .+abc+. .+ac+. .abc. ._00111 _001 000111_ 0001111_ _ _000001111 111__ _00 00001111_ _000111_ _ 111_ _00011 00111 _001 .+bc+. .+abc+. . .++ ..+abbc .++abbc+.. c+.. . .+bc+. .abc+ abc. .a0011_ _00111 000111_ 00 0111_ _00 000011111_ 11__ _000 00001111_ _000111_ _ 111_ _00011 0011_ _0011 ac+. .+ abc+. ..+aab ..+aabcc+ ..+aabcc+. cc+. . .+ab+. +abc. bc+ +ab011_ _0011_ 0111 _0001 11_ _00001 001111__ __ __0000 00001111_ _000111_ _ 111_ _00111 011_ 00111 c. .+bc c+. .+ .+aabcc+ .++aabcc++. .++abbc++. bc+. .+abc. .+ac+. . c+ +ac+ 0011_ _001 _ _000111_ _ _000001 000011111__ 11111__ __ _0000111_ 000111_ _00 00011_ _0 _0011 000bc. .+ac+ .+abc+ c+.. ..+ .. .+aa .+abc+. .+ac+. ac+. +ac 111 _00111 _000111_ __00001111 0011111__ 11111_ _0 _0000111_ _00111_ _00 _0011_ 00 _0011 0011+. .abc. .+ab+. . ..+ .++aa ..+ .+aa .+abc+. .+ac+. bc. .ab+ 1_ _00111_ 00111_ _00 000011111_ 11111__ 1111__ __0 __000111_ _00111_ _00 _0011_ _00 0011_ _011_. +ab+. .+ac+. .+abb 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.+bc. .+ .+b+ _0011_ _001 _ _0000111 011111__ 111111111__ _____00000 __000111_ _0011_ _001 0011 0011 0011 _011_c. .ab+ +ac+. ..+aab ...+ ccc+++++... ..+a .+abc+. .abc. .a +ac. . 0011 00011 _0000111__ 111__ __ 1111____ _____0000000 __000111_ 00011_ _001 011_ _001_ _011_ 001_ . .ab+ b+. .+ .++abbcc ..+++aa ++....... ..+a .+abc+ +ab+. +a .+b+ + 011 001_ _0 000111_ _ __000011111 00011111111 _________ 000111111_ _00011_ _ 1_ _0011 _0 0011 _001_ ac. .+bc c+. . ccc++++.... abcc+. .+bc. . +. .+b+. c. +ac. 01_ 001_ _01 00111_ _00 000011111__ 1111111___ __000000000 001111111_ _00111_ _ _ 0011_ _0 001_ 0011 _ c. +ab+. . .+ .. ++.... abcc+. .ab+. . + .ac+ + .ac. 1_ 001_ _011 011_ _0001 0011111__ 1111__ 00000000000 001111111_ _00111 _ _ _0011 00 011 _001_ 0 . .+bc+ .++a ..++ . abcc+. .+ac+. + . +bc. . +b+ .

Find new alpha

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

Run on preview data
Browse catalog
Impact Metrics

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.

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

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

Intervals
1m5m15m30m1h4h1d
Requires Institutional
15s30s
Exchanges
binance-futuresokx-perpshyperliquid-perps
Built from
Raw trades →
Fields5
amihud_likeAmihud IlliquidityAbsolute interval return divided by total traded notional
kyle_like_lambdaKyle LambdaCovariance of trade returns and signed trade size divided by signed-size variance
impact_per_notionalImpact per NotionalAbsolute interval return divided by absolute signed notional flow
large_trade_reversalLarge Trade ReversalAverage five-trade-ahead return after trades in the top 10% by notional
directional_impact_asymmetryDirectional Impact AsymmetryAverage 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"

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/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
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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