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Trades

Deep microstructure signals from every raw trade.

Comprehensive trade-level analytics — order flow, size distribution, market impact, price volatility, tick structure, and execution slippage — all aggregated from full trade-by-trade history.

Trades Datasets

Flow Metrics

Tier 1

Taker buy/sell volume, count, ratios, percentages, and size-segmented order flow (small / medium / large).

Key fields

Volume DeltaFlow Toxicity ScoreBuy/Sell RatioBuy Large Order Volume

Trade Size

Tier 1

Size-segmented order volume/count, distribution statistics (mean, median, std, skewness, kurtosis, percentiles).

Key fields

Large Order VolumeLarge Order %Mean Trade SizeTrade Size Skewness

Price Range & Distribution

Tier 1

Price high/low range, basis points, mean, median, std, variance, skewness, kurtosis, range ratio, CV.

Key fields

Range (bps)Mean PricePrice Std DevPrice Skewness

Impact Metrics

Tier 2

Market impact metrics including Amihud illiquidity, Kyle lambda, impact per notional, and directional asymmetry.

Key fields

Amihud IlliquidityKyle LambdaImpact per NotionalLarge Trade Reversal

Up / Down Tick Metrics

Tier 2

Uptick and downtick count, volume, ratios and percentages based on price movement direction.

Key fields

Uptick VolumeDowntick VolumeUptick/Downtick Volume RatioUptick Count %

Trade Run Structure

Tier 2

Consecutive same-direction trade run lengths, imbalance, flip rate, and price change on direction flip.

Key fields

Max Buy Run LengthMax Sell Run LengthRun ImbalanceFlip Rate

Returns & Volatility

Tier 2

Log-return based metrics: variance, realized volatility, bipower variation, jump ratio, autocorrelation, and trendiness.

Key fields

Realized VolatilityBipower VariationJump RatioReturn Autocorr (Lag 1)

Slippage

Tier 2

Per-trade execution slippage vs. prevailing L1 quote (ASOF join). For buys: trade_price − ask_price. For sells: bid_price − trade_price.

Key fields

Mean Slippage (bps)Slippage Std Dev (bps)Slippage P95 (bps)VWAP Slippage (bps)

Use Cases

Order flow alpha signals

Build directional signals from taker buy/sell imbalance, volume delta, and flow toxicity scores across multiple horizons.

Institutional accumulation detection

Identify large-order flow clusters via size-segmented metrics to infer informed trading activity.

Market impact modelling

Estimate Amihud illiquidity, Kyle lambda, and impact-per-notional for realistic transaction cost models in backtests.

Jump detection & volatility decomposition

Separate continuous realized volatility from jump components using bipower variation and jump ratio metrics.

Execution quality & slippage benchmarking

Evaluate strategy fills against L1-matched slippage benchmarks, broken down by buy/sell side and order size.

Tick structure & directional momentum

Use up/down tick ratios and trade run imbalance to measure short-horizon directional persistence.

Get started in minutes

pip install aperiodic
pypi ↗
example.py
from 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())

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