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Data Catalog

Browse our curated market data catalog across exchanges and asset types, and the raw trades, quotes and derivatives data behind it.

Preview data — no subscription. Try it free with DEMO-KEY↓
6 raw · 19 datasets

Raw data

The ticks the metrics are built from, as Parquet filesHow raw data works →

Raw L2 order books aren't offered yet. Ask us.

Trades

Derivatives

Market Data

L1 (Top of Book)

L2 (Order Book)

Preview Data

No account needed

A fixed slice of every dataset is free to download: the shared key DEMO-KEY over cURL, --preview in the CLI or preview=True in Python. Pick one for a command you can paste and run. A subscription unlocks full history and every exchange, symbol, interval and parameter; extended trials are available on request.

Raw data

The 2025-06 file of each venue's BTC perpetual, for every dataset the venue serves.

DatasetBinanceperpetual-BTC-USDT:USDTOKXperpetual-BTC-USDT:USDTHyperliquidperpetual-BTC-USDC:USDC
trades
quotes
mark_price
index_price
funding_rate
open_interest

Metrics

Every metric, over one slice: binance-futures · perpetual-BTC-USDT:USDT · 5m · exchange timestamps · 2025-05-01 → 2025-05-31

Trades

Derivatives

Market Data

L1 (Top of Book)

L2 (Order Book)

trades · binance-futures · perpetual-BTC-USDT:USDT · 2025-06

Open dataset
terminal
# 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"

# Download the 2025-06 trades file into ./raw — no API key needed
aperiodic raw trades --preview \
  --exchange binance-futures \
  --symbol perpetual-BTC-USDT:USDT \
  --output-dir ./raw && echo "Saved to $PWD/raw"
Slippage

Slippage

How much each trade pays above the best ask (buys) or receives below the best bid (sells) relative to the prevailing quote, aggregated per interval.

CodeAPI DocsTry It

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

slippage_bps_mean

Mean Slippage (bps)

Mean Slippage in basis points captures the average execution shortfall relative to the chosen benchmark.

It offers a clean first-pass estimate of how expensive crossing the spread and consuming liquidity was in practice.

slippage_bps_p95

Slippage P95 (bps)

The 95th percentile of slippage focuses on the bad tail rather than on the average experience.

It is especially useful for execution planning because desks often care more about occasional painful outcomes than about typical ones.

slippage_bps_vwap

VWAP Slippage (bps)

VWAP Slippage compares executions with the interval volume-weighted market price, anchoring cost to where activity actually traded.

This often provides a more realistic benchmark for larger or more patient trading styles than a single last price snapshot.

slippage_bps_buy_sell_ratio

Buy/Sell Slippage Ratio

Buy/Sell Slippage Ratio asks whether lifting liquidity was more expensive than hitting it, or vice versa.

Asymmetry here can reveal directional stress, inventory pressure, or a book that is materially less resilient on one side.

slippage_bps_std

Slippage Std Dev (bps)

Slippage Std Dev measures how variable execution quality was from trade to trade.

Two markets can have the same mean cost but very different predictability, and this metric tells you which one is more stable.

Endpoint

/api/v1/data/slippage

Category

Trades

Intervals
1m5m15m30m1h4h1d
Requires Institutional
15s30s
Exchanges
binance-futuresokx-perpshyperliquid-perps
Built from
Raw trades →Raw quotes →
Fields10
slippage_meanMean SlippageAverage slippage in price units versus the prevailing quote
slippage_bps_meanMean Slippage (bps)Average slippage in basis points versus the prevailing quote
slippage_bps_stdSlippage Std Dev (bps)Standard deviation of slippage in basis points
slippage_bps_medianMedian Slippage (bps)Median slippage in basis points
slippage_bps_p95Slippage P95 (bps)95th percentile slippage in basis points
slippage_bps_vwapVWAP Slippage (bps)Trade-size-weighted average slippage in basis points
slippage_bps_buy_meanBuy Slippage Mean (bps)Average slippage in basis points for buy-side trades
slippage_bps_sell_meanSell Slippage Mean (bps)Average slippage in basis points for sell-side trades
slippage_bps_buy_sell_ratioBuy/Sell Slippage RatioBuy-side average slippage divided by sell-side average slippage
slippage_bps_sell_buy_ratioSell/Buy Slippage RatioSell-side average slippage divided by buy-side average slippage
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 slippage --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
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Authentication
GET/api/v1/data/preview/slippage?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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