Use case

Improve your backtest

Stop backtesting fantasy fills. Price in slippage, impact and funding with point-in-time metrics.

Shown: minute lows of the 465 Binance perpetuals, 20:40–22:00 UTC on 10 Oct 2025, with BTC’s path traced · built with ohlcv 1m

Featured notebook

Backtesting with realistic microstructure cost

A price-only backtest quietly assumes you can trade at the mid-price for free. Real fills pay the half-spread, move the book, and miss when liquidity thins out. This notebook builds a microstructure transaction-cost model — half-spread plus market impact — from Aperiodic's L1 price, impact, and slippage metrics, checks it against measured slippage, and shows how different cost assumptions reshape the same simple momentum backtest.

Loading notebook…

Or have your agent rerun this notebook and tell you what it finds. Start in Claude Claude Code Codex See the prompt

From notebook to production

Pick the metrics

Browse the catalog and pull point-in-time microstructure metrics — no L2 reconstruction, no tick archive to maintain.

Run the notebook

Every notebook runs end to end on preview data with a free account, in the browser or through the CLI.

Plug it into your stack

Swap the preview key for your own and the same code pulls every year your plan includes through the Python package or the API.

Metrics in this use case

More notebooks for this use case

Also featured

Funding-aware perpetual backtests

Improve your backtest

A perpetual-futures position does not only earn the price move — it also pays or receives funding at discrete settlement prints. A price-only backtest ignores those cash flows and can misattribute carry to trading alpha, most of all for a persistently long strategy in a rising, positive-funding market. This notebook isolates realised funding and shows exactly where it binds: it is a first-order cost for a directional (long-biased) position and nets out for a direction-neutral one.

View notebook

Improve your backtest

Stop backtesting fantasy fills. Price in slippage, impact and funding with point-in-time metrics.

Run on preview data Browse catalog

Shown: minute lows of the 465 Binance perpetuals, 20:40–22:00 UTC on 10 Oct 2025, with BTC’s path traced · built with ohlcv 1m

Slippage

Trades

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.

See it on real data

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

Sample CSV Start in Claude

Shown: slippage_bps_p95, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 18.8 bps, 5 Jan, 01:00 UTC · built with slippage

low

high

extreme

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
Built from: Raw trades →Raw quotes →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`slippage_mean`	Mean Slippage	Average slippage in price units versus the prevailing quote
`slippage_bps_mean`	Mean Slippage (bps)	Average slippage in basis points versus the prevailing quote
`slippage_bps_std`	Slippage Std Dev (bps)	Standard deviation of slippage in basis points
`slippage_bps_median`	Median Slippage (bps)	Median slippage in basis points
`slippage_bps_p95`	Slippage P95 (bps)	95th percentile slippage in basis points
`slippage_bps_vwap`	VWAP Slippage (bps)	Trade-size-weighted average slippage in basis points
`slippage_bps_buy_mean`	Buy Slippage Mean (bps)	Average slippage in basis points for buy-side trades
`slippage_bps_sell_mean`	Sell Slippage Mean (bps)	Average slippage in basis points for sell-side trades
`slippage_bps_buy_sell_ratio`	Buy/Sell Slippage Ratio	Buy-side average slippage divided by sell-side average slippage
`slippage_bps_sell_buy_ratio`	Sell/Buy Slippage Ratio	Sell-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"

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 slippage.<exchange>.<interval>, on intervals 15s, 30s, 1m, 5m, 15m, 30m, 1h, 4h, 1d (15s and 30s on Institutional Live only). 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="slippage",
    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. Sub-minute intervals (15s, 30s) require an Institutional plan.

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). 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.

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/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

Use case

Improve your backtest

Stop backtesting fantasy fills. Price in slippage, impact and funding with point-in-time metrics.

Shown: minute lows of the 465 Binance perpetuals, 20:40–22:00 UTC on 10 Oct 2025, with BTC’s path traced · built with ohlcv 1m

Featured notebook

Backtesting with realistic microstructure cost

Loading notebook…

Or have your agent rerun this notebook and tell you what it finds. Start in Claude Claude Code Codex See the prompt

From notebook to production

Pick the metrics

Browse the catalog and pull point-in-time microstructure metrics — no L2 reconstruction, no tick archive to maintain.

Run the notebook

Every notebook runs end to end on preview data with a free account, in the browser or through the CLI.

Plug it into your stack

Swap the preview key for your own and the same code pulls every year your plan includes through the Python package or the API.

Metrics in this use case

More notebooks for this use case

Also featured

Funding-aware perpetual backtests

Improve your backtest

View notebook

Improve your backtest

Stop backtesting fantasy fills. Price in slippage, impact and funding with point-in-time metrics.

Run on preview data Browse catalog

Shown: minute lows of the 465 Binance perpetuals, 20:40–22:00 UTC on 10 Oct 2025, with BTC’s path traced · built with ohlcv 1m

Slippage

Trades

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.

See it on real data

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

Sample CSV Start in Claude

Shown: slippage_bps_p95, hourly, 1–31 January 2024, from this page's Sample CSV · extreme: 18.8 bps, 5 Jan, 01:00 UTC · built with slippage

low

high

extreme

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
Built from: Raw trades →Raw quotes →
Intervals: 1m5m15m30m1h4h1d
Requires Institutional: 15s30s
Exchanges: binance-futuresokx-perpshyperliquid-perps

Fields

Field	Name	Description
`slippage_mean`	Mean Slippage	Average slippage in price units versus the prevailing quote
`slippage_bps_mean`	Mean Slippage (bps)	Average slippage in basis points versus the prevailing quote
`slippage_bps_std`	Slippage Std Dev (bps)	Standard deviation of slippage in basis points
`slippage_bps_median`	Median Slippage (bps)	Median slippage in basis points
`slippage_bps_p95`	Slippage P95 (bps)	95th percentile slippage in basis points
`slippage_bps_vwap`	VWAP Slippage (bps)	Trade-size-weighted average slippage in basis points
`slippage_bps_buy_mean`	Buy Slippage Mean (bps)	Average slippage in basis points for buy-side trades
`slippage_bps_sell_mean`	Sell Slippage Mean (bps)	Average slippage in basis points for sell-side trades
`slippage_bps_buy_sell_ratio`	Buy/Sell Slippage Ratio	Buy-side average slippage divided by sell-side average slippage
`slippage_bps_sell_buy_ratio`	Sell/Buy Slippage Ratio	Sell-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"

Live Stream

pip install "aperiodic[stream]"

pypi ↗

import os

from aperiodic import stream

for message in stream(
    api_key=os.environ["APERIODIC_API_KEY"],
    dataset="slippage",
    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. Sub-minute intervals (15s, 30s) require an Institutional plan.

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>

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

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/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

Backtesting with realistic microstructure cost

Pick the metrics

Run the notebook

Plug it into your stack

Slippage

Market impact

Funding rates

Top-of-book prices

Funding-aware perpetual backtests

Improve your backtest

Mean Slippage (bps)

Slippage P95 (bps)

VWAP Slippage (bps)

Buy/Sell Slippage Ratio

Slippage Std Dev (bps)

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It

Backtesting with realistic microstructure cost

Pick the metrics

Run the notebook

Plug it into your stack

Slippage

Market impact

Funding rates

Top-of-book prices

Funding-aware perpetual backtests

Improve your backtest

Mean Slippage (bps)

Slippage P95 (bps)

VWAP Slippage (bps)

Buy/Sell Slippage Ratio

Slippage Std Dev (bps)

Fields

Example Request

Live Stream

API Reference

Query Parameters

Schema

Example

Try It