Interactive Guide

The Anatomy of a Trade

An interactive journey through market microstructure: watch orders become prices, feel the spread respond to stress, and discover why these mechanics shape every trade you will ever make.

Start reading

01The Order Book Comes AliveEvery market starts with two opposing lines: buyers and sellers, stacked by price and size. The order book is not just a record — it is the market's living, breathing state.
Imagine a room where buyers line up on the left and sellers on the right. The person at the front of each line sets the best bid and best ask. The gap between them is the spread. When a buyer gets impatient and crosses over to the seller's side at their price, a trade happens.
Live order bookSimulated
One tick every 500 ms
Mid price
A market order hits
103.07102.0599.00
100 s agonow
PriceSize
103.702.12
103.606.99
103.504.24
103.104.72
102.309.20
102.104.96
spread0.10
102.006.64
101.901.53
101.7014.32
101.3012.42
101.108.98
100.907.20
Price levels in the live order book
Total quantity available at this price
Mid
102.05
Spread
0.10
Last trade
—
Order size
10.0
Size10.0
Shown: an illustrative order book, simulated in your browser. Try a large size (10+) and watch the spread blow out.
Try placing a market buy with a large size. Watch how the order eats through the ask side, consuming liquidity level by level. The spread widens. The midprice shifts. This is in its most elemental form.
Biais, Hillion & Spatt (1995) showed that the shape of the order book — not just the best price — contains predictive information about future price dynamics. The distribution of depth across levels reveals strategic positioning before it shows up as obvious price moves.
The book above is simulated. Our measures the depth of real books, and there's a free sample to try.
02The Price of UncertaintyThe spread is not just a cost — it is a risk price. Market makers set it wide when they're nervous, like a store charging more for something they're not sure they can restock.Read
03Below the SurfaceComing soon
04Follow the FlowComing soon
05Every Trade Leaves a MarkComing soon
06The Signal ChainComing soon

Price Range & Distribution

Trades

Price Range & Distribution

Intrabar high-low price range and full distribution statistics (mean, median, std, skewness, kurtosis) for trade prices within each interval.

See it on real data

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

Sample CSV Start in Claude

Shown: range_bps, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 810.3 bps, 9 Jan, 21:00 UTC · built with range

low

high

extreme

range_bps

Range (bps)

Range in basis points expresses the bar high-low spread in a way that is directly comparable across symbols and price levels.

That normalization makes it much more actionable than a raw absolute range when you are screening for unusually active intervals.

price_std

Price Std Dev

Price Std Dev measures how dispersed transaction prices were around the mean within the interval.

Unlike the simple high-low range, it tells you whether activity was broadly scattered or concentrated near a central level.

price_skewness

Price Skewness

Price Skewness captures whether the distribution of trade prices leaned toward the upper or lower tail of the interval.

Positive skew often means occasional bursts to the upside, while negative skew suggests the heavier excursions were downward.

price_kurtosis

Price Kurtosis

Price Kurtosis highlights whether price observations were tightly clustered with a few extreme prints or more evenly spread out.

Elevated kurtosis is often a clue that the interval contained bursts, tails, or jump-like behavior despite a modest average move.

price_cv

Price CV

Price CV scales standard deviation by the mean price, creating a relative dispersion measure.

It is especially helpful when comparing instruments with very different nominal prices or when monitoring how variability changes through time.

A candlestick reduces thousands of trades to four numbers: open, high, low, close. This compression discards the path — and the path contains information that the endpoints don't.

Endpoint: /api/v1/data/range
Category: Trades
Built from: Raw trades →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`range`	Price Range	Highest trade price minus lowest trade price
`range_bps`	Range (bps)	High-low trade price range in basis points of the closing price
`price_mean`	Mean Price	Average trade price in the interval
`price_median`	Median Price	Median trade price in the interval
`price_std`	Price Std Dev	Standard deviation of trade prices in the interval
`price_variance`	Price Variance	Variance of trade prices in the interval
`price_skewness`	Price Skewness	Skewness of the trade-price distribution
`price_kurtosis`	Price Kurtosis	Kurtosis of the trade-price distribution
`price_range_ratio`	Price Range Ratio	Highest trade price divided by lowest trade price
`price_cv`	Price CV	Coefficient of variation of trade price

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 range --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"

Live Stream

Every new row as it is computed, over WebSocket at wss://stream.aperiodic.io/v1/stream. Authenticate with your API key in the X-API-KEY header and subscribe to channels named range.<exchange>.<interval>, on intervals 15s, 30s, 1m, 5m, 15m, 30m, 1h, 4h, 1d (15s and 30s on Institutional Live only). Streaming is included on Live plans.

pip install "aperiodic[stream]"

pypi ↗

import os

from aperiodic import stream

for message in stream(
    api_key=os.environ["APERIODIC_API_KEY"],
    dataset="range",
    exchange="binance-futures",
    interval="1m",
    symbols=["perpetual-BTC-USDT:USDT"],  # omit for every symbol on your plan
):
    print(message.channel, message.snapshot, message.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 an Institutional plan.

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). A metric series starts on the first day of the raw series it is computed from (see /metadata/raw); raw trades and quotes start binance-futures from 2020-01-01, okx-perps from 2019-09-01, hyperliquid-perps from 2025-01-01. Earlier days return no files. How far back you can go depends on the plan: 5 years on Pro, full history on Prime, Prime + Raw and Institutional. An earlier start_date than the plan reaches returns 403.

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 existing file covering the range from the series' published start — 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.

timestamp*

interval*

exchange*

symbol*

Suggestions shown — any valid value accepted

start_date*

Suggestions shown — any valid value accepted

end_date*

Suggestions shown — any valid value accepted

Authentication

X-API-KEY*

GET

/api/v1/data/preview/range?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

Interactive Guide

The Anatomy of a Trade

An interactive journey through market microstructure: watch orders become prices, feel the spread respond to stress, and discover why these mechanics shape every trade you will ever make.

Start reading

01The Order Book Comes AliveEvery market starts with two opposing lines: buyers and sellers, stacked by price and size. The order book is not just a record — it is the market's living, breathing state.
Imagine a room where buyers line up on the left and sellers on the right. The person at the front of each line sets the best bid and best ask. The gap between them is the spread. When a buyer gets impatient and crosses over to the seller's side at their price, a trade happens.
Live order bookSimulated
One tick every 500 ms
Mid price
A market order hits
103.07102.0599.00
100 s agonow
PriceSize
103.702.12
103.606.99
103.504.24
103.104.72
102.309.20
102.104.96
spread0.10
102.006.64
101.901.53
101.7014.32
101.3012.42
101.108.98
100.907.20
Price levels in the live order book
Total quantity available at this price
Mid
102.05
Spread
0.10
Last trade
—
Order size
10.0
Size10.0
Shown: an illustrative order book, simulated in your browser. Try a large size (10+) and watch the spread blow out.
Try placing a market buy with a large size. Watch how the order eats through the ask side, consuming liquidity level by level. The spread widens. The midprice shifts. This is in its most elemental form.
Biais, Hillion & Spatt (1995) showed that the shape of the order book — not just the best price — contains predictive information about future price dynamics. The distribution of depth across levels reveals strategic positioning before it shows up as obvious price moves.
The book above is simulated. Our measures the depth of real books, and there's a free sample to try.
02The Price of UncertaintyThe spread is not just a cost — it is a risk price. Market makers set it wide when they're nervous, like a store charging more for something they're not sure they can restock.Read
03Below the SurfaceComing soon
04Follow the FlowComing soon
05Every Trade Leaves a MarkComing soon
06The Signal ChainComing soon

Price Range & Distribution

Trades

Price Range & Distribution

Intrabar high-low price range and full distribution statistics (mean, median, std, skewness, kurtosis) for trade prices within each interval.

See it on real data

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

Sample CSV Start in Claude

Shown: range_bps, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 810.3 bps, 9 Jan, 21:00 UTC · built with range

low

high

extreme

range_bps

Range (bps)

Range in basis points expresses the bar high-low spread in a way that is directly comparable across symbols and price levels.

That normalization makes it much more actionable than a raw absolute range when you are screening for unusually active intervals.

price_std

Price Std Dev

Price Std Dev measures how dispersed transaction prices were around the mean within the interval.

Unlike the simple high-low range, it tells you whether activity was broadly scattered or concentrated near a central level.

price_skewness

Price Skewness

Price Skewness captures whether the distribution of trade prices leaned toward the upper or lower tail of the interval.

Positive skew often means occasional bursts to the upside, while negative skew suggests the heavier excursions were downward.

price_kurtosis

Price Kurtosis

Price Kurtosis highlights whether price observations were tightly clustered with a few extreme prints or more evenly spread out.

Elevated kurtosis is often a clue that the interval contained bursts, tails, or jump-like behavior despite a modest average move.

price_cv

Price CV

Price CV scales standard deviation by the mean price, creating a relative dispersion measure.

It is especially helpful when comparing instruments with very different nominal prices or when monitoring how variability changes through time.

A candlestick reduces thousands of trades to four numbers: open, high, low, close. This compression discards the path — and the path contains information that the endpoints don't.

Endpoint: /api/v1/data/range
Category: Trades
Built from: Raw trades →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`range`	Price Range	Highest trade price minus lowest trade price
`range_bps`	Range (bps)	High-low trade price range in basis points of the closing price
`price_mean`	Mean Price	Average trade price in the interval
`price_median`	Median Price	Median trade price in the interval
`price_std`	Price Std Dev	Standard deviation of trade prices in the interval
`price_variance`	Price Variance	Variance of trade prices in the interval
`price_skewness`	Price Skewness	Skewness of the trade-price distribution
`price_kurtosis`	Price Kurtosis	Kurtosis of the trade-price distribution
`price_range_ratio`	Price Range Ratio	Highest trade price divided by lowest trade price
`price_cv`	Price CV	Coefficient of variation of trade price

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 range --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"

Live Stream

pip install "aperiodic[stream]"

pypi ↗

import os

from aperiodic import stream

for message in stream(
    api_key=os.environ["APERIODIC_API_KEY"],
    dataset="range",
    exchange="binance-futures",
    interval="1m",
    symbols=["perpetual-BTC-USDT:USDT"],  # omit for every symbol on your plan
):
    print(message.channel, message.snapshot, message.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 an Institutional plan.

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>

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 existing file covering the range from the series' published start — one per month before 2026-08-01, one per day from 2026-08-01 onwards

Schema

filesobject[]required

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

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.

timestamp*

interval*

exchange*

symbol*

Suggestions shown — any valid value accepted

start_date*

Suggestions shown — any valid value accepted

end_date*

Suggestions shown — any valid value accepted

Authentication

X-API-KEY*

GET

/api/v1/data/preview/range?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

Range (bps)

Price Std Dev

Price Skewness

Price Kurtosis

Price CV

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It

Range (bps)

Price Std Dev

Price Skewness

Price Kurtosis

Price CV

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It