Question 1

What is orderbook depth data and how is it different from OHLCV?

Accepted Answer

OHLCV (Open-High-Low-Close-Volume) candles only show price and volume at a surface level. Orderbook depth data reveals the actual bid and ask liquidity sitting at multiple price levels — showing you how much buying and selling pressure exists and how far from the market price. Our dataset provides 10 levels of depth on both sides, measured in cumulative volume and basis-point distance from mid-price. This is the raw material that institutional traders and quantitative researchers use to detect order flow, predict short-term price movements, and model realistic execution costs.

Question 2

Which instruments and timeframe does the dataset cover?

Accepted Answer

The current release covers 24 major crypto perpetual futures sourced from the leading L1 Perpetual Decentralized Exchange: BTC, ETH, SOL, BNB, XRP, DOGE, ADA, AVAX, LINK, DOT, NEAR, SUI, OP, ARB, SEI, TIA, INJ, APT, FIL, LTC, ETC, WIF, XLM, and ATOM. All data is aggregated into 5-minute bars spanning from March 2025 to February 2026 — approximately 12 months of continuous coverage. Each instrument has ~96,000 bars with 47 derived columns per row, including OHLCV, bid/ask volumes, and bid/ask distances at 10 depth levels.

Question 3

Can I use this data for machine learning and deep learning?

Accepted Answer

Absolutely. The dataset is specifically designed for ML workflows. The 47-column schema feeds directly into LSTM networks, Transformer architectures, reinforcement learning environments (like Gymnasium/Stable-Baselines3), and gradient-boosted models (XGBoost, LightGBM). Common derived features include bid-ask imbalance ratios, depth-weighted pressure scores, liquidity concentration metrics, and spread dynamics — all computable in a few lines of Python from our raw columns.

Question 4

Can you create custom datasets with different instruments, timeframes, or depth levels?

Accepted Answer

Yes. We can generate orderbook depth data for any cryptocurrency available on major derivatives DEXs and CEXs, at any candle interval (1-minute, 5-minute, 15-minute, 1-hour, etc.), covering any historical time period. We also support extended depth up to 30 levels. Contact us at imbalancelabs@gmail.com with your requirements for a custom quote.

Question 5

Is this data legally safe to use?

Accepted Answer

Yes. Our datasets are classified as Derived Data — an Aggregated Liquidity and Orderbook Depth Index. All raw order book snapshots have been aggregated across time intervals, normalized, and transformed through statistical computations. The original tick-level data is not recoverable from our product. This classification places it outside the scope of most exchange data redistribution restrictions.

Question 6

What format is the data delivered in?

Accepted Answer

All datasets are delivered as compressed CSV files (.csv.gz), which can be loaded directly by Python pandas, R, DuckDB, Apache Spark, and most data analysis tools. Each file is named by instrument (e.g., BTC_5m_depth10_derived.csv.gz). The full dataset ZIP containing all 24 instruments is approximately 300 MB.

Research & Insights

Does Orderbook Imbalance Predict Crypto Returns? A 24-Asset Rank-IC Study

Crypto Backtesting Explained: What a Backtest Reveals (and the Data It Needs)

Hyperliquid Historical Orderbook Data: How to Get L2 Depth

Crypto Slippage Calculation in Python with L2 Orderbook Data

Why OHLCV Models Fail: Estimating Slippage with DEX L2 Data

Orderbook Imbalance Signals: A Statistical Primer for Crypto Quants

DuckDB vs Pandas for Large-Scale Orderbook Analysis