Use Cases
Build directional signals from taker buy/sell imbalance, volume delta, and flow toxicity scores across multiple horizons.
Identify large-order flow clusters via size-segmented metrics to infer informed trading activity.
Estimate Amihud illiquidity, Kyle lambda, and impact-per-notional for realistic transaction cost models in backtests.
Separate continuous realized volatility from jump components using bipower variation and jump ratio metrics.
Evaluate strategy fills against L1-matched slippage benchmarks, broken down by buy/sell side and order size.
Use up/down tick ratios and trade run imbalance to measure short-horizon directional persistence.
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())Get access to our full catalog of market microstructure data.