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L1 — Top of Book

Millisecond-resolution best bid/ask analytics.

Top-of-book quote data distilled into interval aggregates: spread, depth, imbalance, mid-price dynamics, and update frequencies — enabling high-resolution microstructure research without raw tick storage.

L1 — Top of Book Datasets

L1 Price

Tier 2

Top-of-book ask/bid prices, amounts, midprice, weighted midprice, TWAP/VWAP, and update frequencies.

Key fields

Mid PriceWeighted Mid PriceBid Price TWAPAsk Price VWAP

L1 Imbalance

Tier 3

Bid/ask imbalance, ratio, percentages — both instantaneous (last) and averaged over the interval.

Key fields

ImbalanceImbalance RatioBid/Ask RatioAvg Imbalance

L1 Liquidity

Tier 3

Spread (absolute and bps), depth, dollar depth — instantaneous and interval-averaged.

Key fields

Spread (bps)Total DepthTotal Dollar DepthAvg Spread (bps)

Use Cases

Spread-based execution cost modelling

Use average and instantaneous spread metrics to quantify market-making costs and estimate fill quality across instruments.

Informed trader detection via imbalance

Detect directional pressure from bid/ask imbalance and imbalance ratio dynamics before price moves occur.

Quote update frequency analysis

Identify quote stuffing patterns and measure market-maker activity intensity using update frequency metrics.

VWAP/TWAP quote benchmarking

Compare execution fills against bid/ask VWAP and TWAP to measure adverse selection in your order flow.

High-frequency strategy feature engineering

Use weighted mid-price, imbalance averages, and depth to build predictive features for HFT and MM strategies.

Get started in minutes

pip install aperiodic
pypi ↗
example.py
from datetime import date
from aperiodic import get_metrics

df = get_metrics(
    api_key="your-api-key",
    metric="flow",
    timestamp="exchange",
    interval="1h",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT",
    start_date=date(2024, 1, 1),
    end_date=date(2024, 1, 31),
)

print(df.head())

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