OHLCV Candles Are a Liquidity Illusion
- ✕Traditional candles assume infinite liquidity at every price
- ✕Raw L2/L3 feeds produce terabytes of microstructural noise
- ✕Clock drift across WebSocket channels destroys time alignment
- ✕GPU-class servers required just to parse and normalize
- ✕Raw redistribution violates exchange Terms of Service
Mathematically Derived Depth Intelligence
- ✓Time-aligned 5-minute bars — no clock drift, no asynchrony
- ✓10-level depth: cumulative volumes + distance from mid-price
- ✓Pre-computed imbalance metrics — instant feature engineering
- ✓Derived Data classification — legally clean intellectual property
- ✓Dual format: CSV + Apache Parquet (Spark / DuckDB / Pandas)
Every Row. Every Field. Documented.
Each row = one 5-minute aggregated orderbook snapshot
| Column | Type | Description |
|---|---|---|
| timestamp_utc | DateTime | ISO 8601 UTC timestamp |
| instrument_symbol | String | Trading pair (e.g., BTC-USDT) |
| open_price | Float | Mid-price at bar open |
| high_price | Float | Highest mid-price in bar |
| low_price | Float | Lowest mid-price in bar |
| close_price | Float | Mid-price at bar close |
| interval_traded_volume | Float | Taker flow volume proxy |
| bid_volume_level_1..10 | Float | Cumulative passive bid volume |
| ask_volume_level_1..10 | Float | Cumulative passive ask volume |
| bid_distance_level_1..10 | Float | Distance from mid-price (bps) |
| ask_distance_level_1..10 | Float | Distance from mid-price (bps) |
Built For Quantitative Minds
Quant Researchers
Build order flow features for LSTM, Transformer, and RL models without months of data engineering.
Execution Algos
Backtest TWAP, VWAP, and iceberg strategies against real depth profiles. Estimate slippage pre-deployment.
Market Makers
Study bid-ask dynamics, quote density, and liquidity provision patterns across 25 instruments.
Academics
Institutional-quality microstructure data without exchange partnerships or Bloomberg terminals.
One-Time Purchase. No Subscriptions.
Full dataset delivered instantly after payment. CSV + Parquet included.
- ✓ Pick any 1 instrument (12+ months)
- ✓ CSV + Parquet formats
- ✓ Non-commercial, internal use
- ✓ Personal license (single user)
- ✓ Jupyter Notebook included
- ✓ Full dataset (25 instruments, 12+ months)
- ✓ CSV + Parquet formats
- ✓ Non-commercial, internal research
- ✓ Personal license (single user)
- ✓ Jupyter Notebook (EDA starter kit)
- ✓ Everything in Academic
- ✓ Full team internal use (up to 10 users)
- ✓ Live strategy feeding permitted
- ✓ Priority email support
- ✓ Commercial use within your org
- ✓ Everything in Professional
- ✓ Unrestricted commercial integration
- ✓ Redistribution rights
- ✓ Custom instrument / timeframe requests
- ✓ API access (programmatic delivery)
- ✓ Dedicated technical onboarding
Try Before You Buy
7-day sample of all 25 instruments. Parquet + CSV + Jupyter Notebook included.
Legal Disclaimer
The datasets distributed by Imbalance Labs constitute an Aggregated Liquidity and Orderbook Depth Index — a proprietary, mathematically derived analytical product. All raw order book data has been independently collected, aggregated across time intervals, normalized to mid-price reference frames, and transformed through statistical computations (cumulative volume aggregation, basis-point distance normalization).
This product is classified as Derived Data under standard market data licensing frameworks. It does not constitute, reproduce, or redistribute any raw, unmodified exchange data stream. The original tick-level order book snapshots are not included, recoverable, or reverse-engineerable from this dataset.
Imbalance Labs is not affiliated with, endorsed by, or officially connected to Binance, Coinbase, or any cryptocurrency exchange. All exchange names and trademarks are the property of their respective owners.
This data is provided for research, backtesting, and analytical purposes only. It does not constitute financial advice, trading signals, or investment recommendations. Users assume full responsibility for any trading or investment decisions made using this data.