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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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From notebook to production

  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

Find new alpha

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.

View notebook
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Find new alpha

From raw order flow to a ranked, backtestable signal — every step in runnable code.

Run on preview data
Browse catalog
Returns & Volatility

Returns & Volatility

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

CodeAPI DocsTry It

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

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

Category

Trades

Intervals
1m5m15m30m1h4h1d
Requires Institutional
15s30s
Exchanges
binance-futuresokx-perpshyperliquid-perps
Built from
Raw trades →
Fields6
logret_varLog-Return VarianceVariance of log returns within the interval
realized_volRealized VolatilitySquare root of the sum of squared log returns in the interval
bipower_variationBipower VariationJump-robust volatility estimate built from adjacent absolute log returns
jump_ratioJump RatioShare of realized variance not explained by bipower variation
ret_autocorr_lag1Return Autocorr (Lag 1)Lag-one autocorrelation of log returns within the interval
trendinessTrendinessAbsolute net log return divided by total absolute log return
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 returns --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/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
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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Registered in Ireland · Company No. 815273

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