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.

Scroll to begin▼

Chapter 01

The Order Book Comes Alive

Every 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 Book

MID 99.45SPR 0.10

101.103.40

101.005.18

100.904.29

100.306.89

99.7011.50

99.503.91

spread 0.10

99.401.09

98.709.69

98.505.69

98.303.18

98.202.63

98.103.38

Price levels in the live order book

Total quantity available at this price

Size:10.0

Try a large size (10+) and watch the spread blow out

Mid Price

BuySell

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.

Chapter 02

The Price of Uncertainty

The 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.

Huang & Stoll (1997) decomposed the spread into three components: adverse selection (the risk of trading against someone who knows more), inventory holding (the risk of being stuck with a position), and order processing (the cost of doing business). Drag the slider below and watch all three shift in real time.

Spread Stress TestNormal

BID

11.0 bps

ASK

Market Uncertainty25%

Spread Decomposition (Huang & Stoll)

Adverse selection 38%Inventory 24%Processing 39%

Active Market Makers

7 of 8 still quoting

Notice how market makers withdraw as uncertainty rises. In crisis conditions, only one or two remain — and they demand a massive spread to compensate. A 2 bps average spread that was actually 0.5 bps for 55 seconds and 8 bps for 5 seconds tells a fundamentally different story than a steady 2 bps. The itself predicts future return volatility.

A widening spread is a leading indicator — it signals increased uncertainty about fair value before the price itself visibly reacts. Spread compression signals confident competition among liquidity providers and a regime where execution is cheaper and mean reversion is more reliable.

Next Chapter Soon

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Trade Size

Trade notional split into small (< $100), medium ($100–$1,000), and large (≥ $1,000) segments, with summary statistics for individual trade sizes.

Code API Docs Try 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.

Not all trades carry the same information. A $50 trade and a$ 50,000 trade on the same instrument represent fundamentally different market participants with different motives, horizons, and information sets. Separating them is one of the most powerful decompositions in empirical microstructure.

Endpoint

/api/v1/data/trade_size

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.

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/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

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.

Scroll to begin▼

Chapter 01

The Order Book Comes Alive

Every 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.

Live Order Book

MID 99.45SPR 0.10

101.103.40

101.005.18

100.904.29

100.306.89

99.7011.50

99.503.91

spread 0.10

99.401.09

98.709.69

98.505.69

98.303.18

98.202.63

98.103.38

Price levels in the live order book

Total quantity available at this price

Size:10.0

Try a large size (10+) and watch the spread blow out

Mid Price

BuySell

Chapter 02

The Price of Uncertainty

The 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.

Spread Stress TestNormal

BID

11.0 bps

ASK

Market Uncertainty25%

Spread Decomposition (Huang & Stoll)

Adverse selection 38%Inventory 24%Processing 39%

Active Market Makers

7 of 8 still quoting

Next Chapter Soon

Register to be notified when the next chapter is available

Trade Size

Trade notional split into small (< $100), medium ($100–$1,000), and large (≥ $1,000) segments, with summary statistics for individual trade sizes.

Code API Docs Try 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

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

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/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