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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.
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Shown: an illustrative order book over time, bids below the mid and asks above it.

  1. 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
    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.

  2. 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
  3. 03Below the SurfaceComing soon
  4. 04Follow the FlowComing soon
  5. 05Every Trade Leaves a MarkComing soon
  6. 06The Signal ChainComing soon

Next chapter soon

Chapter 03: Below the Surface

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Open Interest

Derivatives

Open Interest

Open interest values with percentage change, volatility, and OI/price change ratio.

See it on real data

Sample CSVStart in Claude
CodeLive StreamAPI DocsTry It
00h06h12h18h
18152231

Shown: open_interest, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 83154.746, 12 Jan, 19:00 UTC · built with open_interest

lowhighextreme
open_interest

Open Interest

Open Interest tracks the total outstanding derivatives exposure and serves as the balance-sheet backdrop for price action.

Rising open interest means new positions are being opened or expanded, while falling open interest suggests risk is being reduced or closed out.

open_interest_pct_change

Open Interest % Change

Open Interest % Change shows how quickly positioning expanded or contracted over the interval.

This is often more informative than the raw level because it makes sudden positioning events immediately visible.

open_interest_volatility

Open Interest Volatility

Open Interest Volatility measures how unstable outstanding positioning was inside the bar.

High values can indicate frequent leverage adjustments, position recycling, or stress-driven churn rather than steady accumulation.

oi_price_change_ratio

OI/Price Change Ratio

OI/Price Change Ratio compares how much positioning changed relative to the magnitude of the price move.

It helps answer whether price action was accompanied by substantial leverage participation or happened mostly without new positioning.

Open interest (OI) — the total number of outstanding derivative contracts — measures how much leverage the system is carrying. But raw OI is static information. Changes in OI, and their relationship to price, reveal the market's structural state in ways that price alone cannot.

Endpoint
/api/v1/data/open_interest
Category
Derivatives
Intervals
1m5m15m30m1h4h1d
Exchanges
binance-futuresokx-perpshyperliquid-perps

Fields

4
FieldNameDescription
open_interestOpen InterestLast open interest value in the interval
open_interest_pct_changeOpen Interest % ChangePercentage change in open interest from the first value to the last value in the interval
open_interest_volatilityOpen Interest VolatilityStandard deviation of tick-to-tick open-interest percentage changes in the interval
oi_price_change_ratioOI/Price Change RatioOpen-interest return divided by mark-price return over the interval

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 open_interest --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 open_interest.<exchange>.<interval>, on intervals 1m, 5m, 15m, 30m, 1h, 4h, 1d. 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="open_interest",
    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.

1m5m15m30m1h4h1d
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.

Suggestions shown — any valid value accepted
Suggestions shown — any valid value accepted
Suggestions shown — any valid value accepted
Authentication
GET/api/v1/data/preview/open_interest?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

Built from hundreds of terabytes of raw data and 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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Shown: minute closes of 847 perpetuals, 20:30–22:30 UTC on 10 Oct 2025, IOTX in orange · built with ohlcv 1m