Use Cases
Use average and instantaneous spread metrics to quantify market-making costs and estimate fill quality across instruments.
Detect directional pressure from bid/ask imbalance and imbalance ratio dynamics before price moves occur.
Identify quote stuffing patterns and measure market-maker activity intensity using update frequency metrics.
Compare execution fills against bid/ask VWAP and TWAP to measure adverse selection in your order flow.
Use weighted mid-price, imbalance averages, and depth to build predictive features for HFT and MM strategies.
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.