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L1 & L2 — Order Book

Top-of-book and multi-level order book analytics.

From best bid/ask spread and depth to multi-level imbalance and liquidity dynamics — comprehensive order book data aggregated for high-resolution microstructure research.

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)

L2 — Order Book Datasets

L2 Order Book Imbalance

Tier 3

Multi-depth (5, 10, 20, 25 levels) order book imbalance, ratio, and averages.

Key fields

Imbalance (5 levels)Imbalance (10 levels)Imbalance (20 levels)Imbalance (25 levels)

L2 Order Book Liquidity

Tier 3

Total bid/ask depth aggregated over 5, 10, 20, 25 order book levels — instantaneous and interval-averaged.

Key fields

Ask Aggregate (5 levels)Ask Aggregate (10 levels)Ask Aggregate (20 levels)Ask Aggregate (25 levels)

Use Cases

Spread-based execution cost modelling

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

Deep liquidity analysis

Measure aggregate bid and ask depth at multiple levels to understand resilience and liquidity beyond the top-of-book.

Order book imbalance & directional signals

Detect directional pressure from top-of-book imbalance dynamics and build predictive features from multi-level imbalance ratios that capture it more robustly.

Iceberg & hidden order detection

Detect large resting orders by monitoring depth anomalies and imbalance dynamics across book levels over time.

Market resilience measurement

Quantify how quickly liquidity replenishes after consumption by analyzing depth averages and their variability.

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.

Multi-level feature engineering for execution & alpha models

Combine weighted mid-price, imbalance averages, and bid/ask depth at 5, 10, 20, 25 levels into composite signals for HFT, market-making, and ML-based alpha 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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Institutional-grade market microstructure , liquidity & order flow metrics with full exchange universe coverage.

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