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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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  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.

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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
Flow Metrics

Flow Metrics

Directional order flow split by taker buy vs. sell, broken into small/medium/large notional segments, with a flow toxicity score.

CodeAPI DocsTry It

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

volume_delta_notional

Volume Delta Notional

Volume Delta Notional shows the signed difference between aggressive buy and sell notional in the bar, making it one of the cleanest ways to spot net pressure.

Large positive readings usually mean buyers lifted offers with size, while large negative readings point to decisive sell-side aggression.

flow_toxicity_score

Flow Toxicity Score

Flow Toxicity Score combines abnormal notional imbalance with short-horizon realized volatility to highlight bars that were both one-sided and destabilizing.

The metric is designed to surface moments when liquidity providers are most likely trading against informed or urgent flow rather than routine noise.

flow_entropy

Flow Entropy

Flow Entropy measures how evenly buy and sell notional was distributed inside the interval.

High entropy suggests a balanced tug-of-war, while low entropy points to more concentrated and decisive flow in one direction.

taker_buy_sell_ratio

Buy/Sell Ratio

Buy/Sell Ratio compares aggressive buy notional to aggressive sell notional in one simple number.

It gives a fast read on whether the tape leaned meaningfully toward buyers or sellers without requiring any additional transformation.

taker_buy_large_order_percentage

Buy Large Order %

Buy Large Order % isolates how much of total aggressive buy notional came from the largest trade-size bucket.

When this rises alongside positive delta, it often suggests participation from larger and more intentional actors rather than fragmented retail flow.

Endpoint

/api/v1/data/flow

Category

Trades

Intervals
1m5m15m30m1h4h1d
Requires Institutional
15s30s
Exchanges
binance-futuresokx-perpshyperliquid-perps
Built from
Raw trades →
Fields36
taker_buy_volumeTaker Buy VolumeTotal buy-side traded notional in the interval
taker_sell_volumeTaker Sell VolumeTotal sell-side traded notional in the interval
taker_buy_countTaker Buy CountNumber of buy-side trades in the interval
taker_sell_countTaker Sell CountNumber of sell-side trades in the interval
volume_deltaVolume DeltaBuy-side traded quantity minus sell-side traded quantity
volume_delta_notionalVolume Delta NotionalBuy-side traded notional minus sell-side traded notional
flow_entropyFlow EntropyEntropy of the buy-versus-sell notional split in the interval
flow_toxicity_scoreFlow Toxicity ScorePast-normalized signed notional flow multiplied by past-normalized short-horizon realized volatility
taker_buy_sell_ratioBuy/Sell RatioBuy-side traded notional divided by sell-side traded notional
taker_buy_sell_count_ratioBuy/Sell Count RatioBuy-side trade count divided by sell-side trade count
taker_buy_sell_percentageTaker Buy %Share of traded notional that came from buy-side trades
taker_buy_sell_count_percentageTaker Buy Count %Share of trades that were buy-side trades
taker_buy_small_order_volumeBuy Small Order VolumeTotal buy-side notional from small trades under $100
taker_buy_small_order_countBuy Small Order CountNumber of buy-side trades under $100
taker_buy_medium_order_volumeBuy Medium Order VolumeTotal buy-side notional from medium trades from $100 to under $1,000
taker_buy_medium_order_countBuy Medium Order CountNumber of buy-side trades from $100 to under $1,000
taker_buy_large_order_volumeBuy Large Order VolumeTotal buy-side notional from large trades of at least $1,000
taker_buy_large_order_countBuy Large Order CountNumber of buy-side trades of at least $1,000
taker_sell_small_order_volumeSell Small Order VolumeTotal sell-side notional from small trades under $100
taker_sell_small_order_countSell Small Order CountNumber of sell-side trades under $100
taker_sell_medium_order_volumeSell Medium Order VolumeTotal sell-side notional from medium trades from $100 to under $1,000
taker_sell_medium_order_countSell Medium Order CountNumber of sell-side trades from $100 to under $1,000
taker_sell_large_order_volumeSell Large Order VolumeTotal sell-side notional from large trades of at least $1,000
taker_sell_large_order_countSell Large Order CountNumber of sell-side trades of at least $1,000
taker_buy_small_order_percentageBuy Small Order %Ratio of small buy-side trade notional to total buy-side traded notional
taker_buy_medium_order_percentageBuy Medium Order %Ratio of medium buy-side trade notional to total buy-side traded notional
taker_buy_large_order_percentageBuy Large Order %Ratio of large buy-side trade notional to total buy-side traded notional
taker_buy_small_order_count_percentageBuy Small Count %Share of buy-side trades that were small trades
taker_buy_medium_order_count_percentageBuy Medium Count %Share of buy-side trades that were medium trades
taker_buy_large_order_count_percentageBuy Large Count %Share of buy-side trades that were large trades
taker_sell_small_order_percentageSell Small Order %Ratio of small sell-side trade notional to total sell-side traded notional
taker_sell_medium_order_percentageSell Medium Order %Ratio of medium sell-side trade notional to total sell-side traded notional
taker_sell_large_order_percentageSell Large Order %Ratio of large sell-side trade notional to total sell-side traded notional
taker_sell_small_order_count_percentageSell Small Count %Share of sell-side trades that were small trades
taker_sell_medium_order_count_percentageSell Medium Count %Share of sell-side trades that were medium trades
taker_sell_large_order_count_percentageSell Large Count %Share of sell-side trades that were large trades
Example Request
# Install the Aperiodic CLI into ~/.local/bin
mkdir -p "$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 flow --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/flow?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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