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Order flow

Built from every trade on Binance, OKX and Hyperliquid perps: who crossed the spread, what it cost, and how the price moved.

Shown: an illustrative order book over time, bids below the mid and asks above it.

8 datasets

Flow

Taker buys against sells, by size, with toxicity

Net taker flow moved with BTC's price in the same hour (correlation 0.72) and said almost nothing about the next hour (0.02).

36 fields · Pro

Impact

Kyle's lambda, Amihud, and what size costs

A BTC of taker flow moved the price 1.5× as much at 00:00 UTC as at 11:00.

5 fields · ProBTC now -1.53e-7

Slippage

What taker trades paid against the quote

BTC's p95 slippage was 4.3× higher on its busiest bars than on its quietest.

10 fields · ProBTC now -0.004 bps

Trade size

Small, medium and large trades, and the tails

On BTC, 36% of trades are $1,000 or more, and they carry 97% of the notional.

24 fields · ProBTC now 96.0%

Trade run structure

How long buyers and sellers keep at it

BTC bars where trades rarely changed side moved 2.8× as far as bars where they flipped often.

7 fields · Pro

Up / down ticks

Trades that moved the price up, down, or not

65% of BTC trades printed at the same price as the trade before.

14 fields · Pro

Returns & volatility

Realized vol, jumps and trendiness

BTC's hourly volatility correlated 0.39 with the hour before. Its hourly return correlated -0.02.

6 fields · ProBTC now 0.234%

Price range & distribution

High-low range and the shape inside the bar

A BTC 5-minute bar ranged 1.5× as far on a weekday as at the weekend.

10 fields · ProBTC now 33.0 bps

Fields to start with

volume_delta_notional
Buy-side traded notional minus sell-side traded notional
Flow
flow_toxicity_score
Past-normalized signed notional flow multiplied by past-normalized short-horizon realized volatility
Flow
kyle_like_lambda
Covariance of trade returns and signed trade size divided by signed-size variance
Impact
slippage_bps_mean
Average slippage in basis points versus the prevailing quote
Slippage
large_order_percentage
Ratio of large-trade notional to total traded notional
Trade size
jump_ratio
Share of realized variance not explained by bipower variation
Returns & volatility

What it's used for

Costs in backtests

Charge each simulated trade what real trades paid: slippage against the quote, and Kyle's lambda and Amihud for size.

Model features

Volume delta, toxicity and the mix of trade sizes, bar by bar, without storing every trade.

Volatility and jumps

Realized volatility, bipower variation and the jump ratio, from every trade in the bar.

Execution review

Compare your fills with what every taker paid in the same bar, buys and sells apart.

Every field, with its endpoint and code, is in the Data Catalog.

For the background, start with the microstructure guide.

Aperiodic

Built from hundreds of terabytes of raw data and distilled into point-in-time metrics you can pull as parquet files. Plus the raw trades, quotes and derivatives data behind them.

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Shown: minute closes of 847 perpetuals, 20:30–22:30 UTC on 10 Oct 2025, IOTX in orange · built with ohlcv 1m