Institutional-Grade Order Flow, Liquidity & Derivative Metrics
How It Works
Raw Data
Aggregates
Why Aperiodic
Local Timestamped
Metrics with both exchange & local timestamps.
Full Coverage
10+ exchanges (soon) — every instrument. No survivorship bias.
5+ years history
Immutable data, no periodic re-calculations.
Your Crypto Data Lake
Parquet files, no rate limits. Download in 2 commands.
Start Exploring — No Subscription Required
Sign up and start working with real market data immediately. Run interactive notebooks or query preview data via the API — then upgrade when you need the full catalog.
Slippage
How much each trade pays above the best ask (buys) or receives below the best bid (sells) relative to the prevailing quote, aggregated per interval.
Key fields
Returns & Volatility
Log-return based metrics: variance, realized volatility, bipower variation, jump ratio, autocorrelation, and trendiness.
Key fields
L1 Liquidity
Spread (absolute and bps), depth, dollar depth — instantaneous and interval-averaged.
Key fields
L1 Imbalance
Bid/ask imbalance, ratio, percentages — both instantaneous (last) and averaged over the interval.
Key fields
L2 Order Book Liquidity
Total bid/ask depth aggregated over 5, 10, 20, 25 order book levels — instantaneous and interval-averaged.
Key fields
Derivative Price
Snapshot of mark, index, and last trade prices with interval percentage changes and pairwise divergence ratios.
Key fields
Open Interest
Open interest values with percentage change, volatility, and OI/price change ratio.
Key fields
A practitioner-oriented introduction to market microstructure: how orders become prices, which frictions define execution quality, and which metrics matter.
The order book is the market’s immediate state
Think of the order book as two lines at a checkout: one side wants to buy, the other wants to sell. The price is set by whoever is at the front of each line. When buyers start piling up faster than sellers, the price moves up — and you can see that coming before it happens.
The spread is a risk price, not just a cost
The spread is the gap between what buyers offer and what sellers ask. Market makers set it wide when they’re nervous — like a store charging more for something they’re not sure they can restock. A widening spread is an early warning that something is off, before prices visibly react.
Depth determines resilience under pressure
Depth is how many people are standing in line behind the front. A long line means a big order won’t shift the price much. A short line means one large trade can send the price flying. A book that looks full at the top but empty underneath is the most dangerous — it feels safe until it isn’t.
Trusted by Professional Traders
Use Cases
Order flow alpha signals
Build directional signals from taker buy/sell imbalance, volume delta, and flow toxicity scores across multiple horizons.
Systematic strategy backtesting
Build and validate momentum, mean-reversion, and carry strategies on reliable price history without data leakage.
Funding rate regime analysis
Detect crowded positioning and carry opportunities by monitoring funding rate levels and predicted-vs-realized spread dynamics.
Deep liquidity analysis
Measure aggregate bid and ask depth at multiple levels to understand resilience and liquidity beyond the top-of-book.
Spread-based execution cost modelling
Use average and instantaneous spread metrics to quantify market-making costs and estimate fill quality across instruments.
Market impact modelling
Estimate Amihud illiquidity, Kyle lambda, and impact-per-notional for realistic transaction cost models in backtests.
Order book imbalance signals
Build predictive features from multi-level imbalance ratios that capture directional pressure more robustly than L1 alone.
Open interest divergence signals
Identify leverage build-up and potential liquidation cascades from OI/price divergences before they materialize.
Institutional accumulation detection
Identify large-order flow clusters via size-segmented metrics to infer informed trading activity.
High-frequency strategy feature engineering
Use weighted mid-price, imbalance averages, and depth to build predictive features for HFT and MM strategies.
Aperiodic is designed for autonomous research workflows. Structured API responses, Python SDK, and machine-readable documentation make it the ideal data source for AI-powered quant research.
Python SDK
pip install aperiodic — one-line access to every dataset
llms.txt
Machine-readable docs your agent can consume in one fetch
OpenAPI Spec
Auto-generate API calls from a full OpenAPI 3.0 specification
Claude Skills
Pre-built skills for market analysis, factor research, and backtesting
Get started in minutes
pip install aperiodicfrom 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())Order Flow & Liquidity Metrics Without the Infrastructure
Professional traders and quant researchers use Aperiodic to go from raw exchange data to actionable metrics in minutes — not months.