Name: Returns & Volatility
Creator: Aperiodic
License: https://aperiodic.io/terms-of-service

Returns & Volatility

Log-return based metrics: variance, realized volatility, bipower variation, jump ratio, autocorrelation, and trendiness.

Code API Docs Try It

realized_vol

Realized Volatility

Realized Volatility aggregates squared log returns into a direct estimate of how much the market actually moved during the interval.

It is one of the core building blocks for short-horizon risk, execution timing, and market-state classification.

bipower_variation

Bipower Variation

Bipower Variation is a jump-robust volatility estimator that emphasizes the continuous part of price variation.

By filtering the influence of outsized moves, it helps distinguish routine trading noise from intervals dominated by discrete jumps.

jump_ratio

Jump Ratio

Jump Ratio estimates how much of realized variance came from jump-like moves rather than from continuous fluctuations.

When it spikes, the interval was driven less by gradual repricing and more by abrupt discontinuities.

ret_autocorr_lag1

Return Autocorr (Lag 1)

Lag-1 return autocorrelation tells you whether successive micro-returns tended to continue or reverse.

Positive readings suggest short-horizon persistence, while negative readings hint at mean reversion and bounce-back behavior.

trendiness

Trendiness

Trendiness compares the net signed move to the total absolute movement inside the interval.

Values near one indicate that most movement lined up in the same direction, while values near zero imply a lot of back-and-forth cancellation.

Does price tend to continue or reverse? This is the most consequential question for any directional strategy. Rather than inferring the answer from backtests of momentum strategies (which conflate the signal with execution, risk management, and survivorship), you can measure it directly from the return series.

Endpoint

/api/v1/data/returns

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/returns?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

Try Free Preview Data

Access a curated slice of real production data with just an account — no credit card or subscription required. Pair it with our research notebooks to get started instantly.

Use with AI Agents

Access Returns & Volatility programmatically via our Python SDK and REST API — optimised for autonomous research workflows.

Get Started

Subscribe to get full API access. Start querying all datasets in minutes.

Returns & Volatility

Log-return based metrics: variance, realized volatility, bipower variation, jump ratio, autocorrelation, and trendiness.

Code API Docs Try It

realized_vol

Realized Volatility

Realized Volatility aggregates squared log returns into a direct estimate of how much the market actually moved during the interval.

It is one of the core building blocks for short-horizon risk, execution timing, and market-state classification.

bipower_variation

Bipower Variation

Bipower Variation is a jump-robust volatility estimator that emphasizes the continuous part of price variation.

By filtering the influence of outsized moves, it helps distinguish routine trading noise from intervals dominated by discrete jumps.

jump_ratio

Jump Ratio

Jump Ratio estimates how much of realized variance came from jump-like moves rather than from continuous fluctuations.

When it spikes, the interval was driven less by gradual repricing and more by abrupt discontinuities.

ret_autocorr_lag1

Return Autocorr (Lag 1)

Lag-1 return autocorrelation tells you whether successive micro-returns tended to continue or reverse.

Positive readings suggest short-horizon persistence, while negative readings hint at mean reversion and bounce-back behavior.

trendiness

Trendiness

Trendiness compares the net signed move to the total absolute movement inside the interval.

Values near one indicate that most movement lined up in the same direction, while values near zero imply a lot of back-and-forth cancellation.

Endpoint

/api/v1/data/returns

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/returns?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

Try Free Preview Data

Access a curated slice of real production data with just an account — no credit card or subscription required. Pair it with our research notebooks to get started instantly.

Use with AI Agents

Access Returns & Volatility programmatically via our Python SDK and REST API — optimised for autonomous research workflows.

Get Started

Subscribe to get full API access. Start querying all datasets in minutes.