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L2 — Order Book

Multi-level order book depth, liquidity, and imbalance.

Order book analytics aggregated across 5, 10, 20, and 25 levels — capturing depth, imbalance, and liquidity dynamics at each granularity without storing terabytes of raw snapshots.

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

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 signals

Build predictive features from multi-level imbalance ratios that capture directional pressure more robustly than L1 alone.

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.

Cross-level depth feature engineering

Combine bid/ask depth at 5, 10, 20, 25 levels into composite signals for ML-based execution and alpha models.

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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