Trades
Shown: net taker flow (buys minus sells) summed over each venue’s perpetuals, per minute, 18:00–01:00 UTC on 10 Oct 2025, buys above each venue’s line and sells below it on a square-root scale; the deepest sell minute, 21:13 UTC with $1.9B net sold across the three venues, is orange · built with flow 1m
Trades Datasets
Directional order flow split by taker buy vs. sell, broken into small/medium/large notional segments, with a flow toxicity score.
Trade notional split into small (< $100), medium ($100–$1,000), and large (≥ $1,000) segments, with summary statistics for individual trade sizes.
Intrabar high-low price range and full distribution statistics (mean, median, std, skewness, kurtosis) for trade prices within each interval.
How much each unit of trading volume moves the price, measured via Amihud illiquidity, Kyle lambda, impact per notional, and large-trade reversal.
Uptick and downtick count, volume, ratios and percentages based on price movement direction.
Consecutive same-direction trade run lengths, imbalance, flip rate, and price change on direction flip.
Log-return based metrics: variance, realized volatility, bipower variation, jump ratio, autocorrelation, and trendiness.
Highlighted Metrics
volume_delta_notionalVolume Delta Notional shows the signed difference between aggressive buy and sell notional in the bar, making it one of the cleanest ways to spot net pressure.
Large positive readings usually mean buyers lifted offers with size, while large negative readings point to decisive sell-side aggression.
flow_toxicity_scoreFlow Toxicity Score combines abnormal notional imbalance with short-horizon realized volatility to highlight bars that were both one-sided and destabilizing.
The metric is designed to surface moments when liquidity providers are most likely trading against informed or urgent flow rather than routine noise.
flow_entropyFlow Entropy measures how evenly buy and sell notional was distributed inside the interval.
High entropy suggests a balanced tug-of-war, while low entropy points to more concentrated and decisive flow in one direction.
taker_buy_sell_ratioBuy/Sell Ratio compares aggressive buy notional to aggressive sell notional in one simple number.
It gives a fast read on whether the tape leaned meaningfully toward buyers or sellers without requiring any additional transformation.
taker_buy_large_order_percentageBuy Large Order % isolates how much of total aggressive buy notional came from the largest trade-size bucket.
When this rises alongside positive delta, it often suggests participation from larger and more intentional actors rather than fragmented retail flow.
large_order_volumeLarge Order Volume tracks how much notional traded in the largest size bucket during the interval.
It is useful because the presence of size often changes the meaning of otherwise ordinary moves in price or total volume.
large_order_percentageLarge Order % shows what share of total traded notional came from the largest bucket rather than from all trades equally.
This helps normalize across busy and quiet sessions, making it easier to identify genuine changes in participant mix.
medium_order_percentageMedium Order % often acts as the overlooked middle layer between retail-like fragmentation and outright block activity.
Changes here can matter because many transitions in market participation appear first in the medium bucket before they spill into the large one.
small_order_count_percentageSmall Order Count % measures how much of the tape was made up of small individual executions.
It is especially revealing when trade count is high but small-order share dominates, since that can point to reactive retail flow or quote-chasing behavior.
n_tradesTrade Count is the raw number of matched trades in the interval, and it remains one of the best gauges of tape activity.
It helps distinguish quiet directional moves from crowded periods where many separate participants are trying to transact at once.
range_bpsRange in basis points expresses the bar high-low spread in a way that is directly comparable across symbols and price levels.
That normalization makes it much more actionable than a raw absolute range when you are screening for unusually active intervals.
price_stdPrice Std Dev measures how dispersed transaction prices were around the mean within the interval.
Unlike the simple high-low range, it tells you whether activity was broadly scattered or concentrated near a central level.
price_skewnessPrice Skewness captures whether the distribution of trade prices leaned toward the upper or lower tail of the interval.
Positive skew often means occasional bursts to the upside, while negative skew suggests the heavier excursions were downward.
price_kurtosisPrice Kurtosis highlights whether price observations were tightly clustered with a few extreme prints or more evenly spread out.
Elevated kurtosis is often a clue that the interval contained bursts, tails, or jump-like behavior despite a modest average move.
price_cvPrice CV scales standard deviation by the mean price, creating a relative dispersion measure.
It is especially helpful when comparing instruments with very different nominal prices or when monitoring how variability changes through time.
amihud_likeAmihud Illiquidity measures how much price moved for a given amount of traded notional, providing a classic impact-per-volume lens.
Higher values imply that relatively small amounts of trading were able to move the market, which is usually a sign of thin or fragile liquidity.
kyle_like_lambdaKyle Lambda estimates the sensitivity of price changes to signed order flow, translating imbalance into impact.
When it rises, the same net buying or selling pressure tends to push price farther, suggesting a more impressionable market.
impact_per_notionalImpact per Notional asks a practical question: how much movement did each unit of traded value cause?
It helps traders benchmark execution cost in a way that feels intuitive and transferable across strategies.
large_trade_reversalLarge Trade Reversal measures how often and how strongly price snaps back after a large trade moves through the market.
Strong reversal behavior often suggests the initiating trade consumed liquidity but failed to represent durable information.
directional_impact_asymmetryDirectional Impact Asymmetry compares whether buy-side and sell-side aggression moved price by the same amount.
Asymmetry can reveal one-sided fragility, such as a market that lifts easily but resists downside, or the reverse.
uptick_downtick_volume_ratioThis ratio compares the notional traded on upticks with the notional traded on downticks.
It gives a tape-based view of directional pressure that is grounded in actual price changes rather than trade classification alone.
uptick_downtick_count_ratioThe count ratio focuses on how frequently prices were moving up versus down, regardless of trade size.
That makes it a useful companion to the volume ratio when you want to know whether direction came from many small pushes or a few heavy prints.
uptick_volume_percentageUptick Volume % shows what share of traded volume occurred while price was moving upward.
It is an intuitive measure for spotting intervals where positive price progression was supported by meaningful participation rather than by sparse prints.
unchanged_volume_percentageUnchanged Volume % captures how much volume traded without moving the last price.
A high reading can indicate heavier matching at stable quotes, stronger passive replenishment, or a temporary equilibrium between buyers and sellers.
uptick_count_percentageUptick Count % expresses the share of price-changing events that moved upward during the interval.
Because it ignores size, it is especially good at showing whether the tape rhythm itself was persistently positive or negative.
buy_run_max_lenMax Buy Run Length records the longest uninterrupted sequence of buyer-initiated trades in the interval.
Long runs are often a signature of urgency, persistence, or systematic execution that keeps leaning in one direction.
sell_run_max_lenMax Sell Run Length is the bearish mirror image, tracking the longest consecutive stretch of sell-initiated prints.
It helps identify intervals where downside pressure arrived as a continuous wave rather than as isolated bursts.
run_imbalanceRun Imbalance summarizes whether buy sequences or sell sequences dominated the structure of trading within the bar.
Unlike plain volume imbalance, it captures persistence and sequencing, which can be more informative about trader intent.
flip_rateFlip Rate measures how frequently the trade direction switched between buy-led and sell-led prints.
High flip rates usually correspond to choppy, back-and-forth microstructure, while low flip rates indicate more persistent pressure.
price_change_on_flipPrice Change on Flip measures how much the market moved when control passed from one side of the tape to the other.
Large changes on flips can imply that turning points were costly and that reversals happened only after meaningful price concessions.
realized_volRealized Volatility aggregates squared log returns into a direct estimate of how much the market actually moved during the interval.
It is one of the core building blocks for short-horizon risk, execution timing, and market-state classification.
bipower_variationBipower Variation is a jump-robust volatility estimator that emphasizes the continuous part of price variation.
By filtering the influence of outsized moves, it helps distinguish routine trading noise from intervals dominated by discrete jumps.
jump_ratioJump Ratio estimates how much of realized variance came from jump-like moves rather than from continuous fluctuations.
When it spikes, the interval was driven less by gradual repricing and more by abrupt discontinuities.
ret_autocorr_lag1Lag-1 return autocorrelation tells you whether successive micro-returns tended to continue or reverse.
Positive readings suggest short-horizon persistence, while negative readings hint at mean reversion and bounce-back behavior.
trendinessTrendiness compares the net signed move to the total absolute movement inside the interval.
Values near one indicate that most movement lined up in the same direction, while values near zero imply a lot of back-and-forth cancellation.
slippage_bps_meanMean Slippage in basis points captures the average execution shortfall relative to the chosen benchmark.
It offers a clean first-pass estimate of how expensive crossing the spread and consuming liquidity was in practice.
slippage_bps_p95The 95th percentile of slippage focuses on the bad tail rather than on the average experience.
It is especially useful for execution planning because desks often care more about occasional painful outcomes than about typical ones.
slippage_bps_vwapVWAP Slippage compares executions with the interval volume-weighted market price, anchoring cost to where activity actually traded.
This often provides a more realistic benchmark for larger or more patient trading styles than a single last price snapshot.
slippage_bps_buy_sell_ratioBuy/Sell Slippage Ratio asks whether lifting liquidity was more expensive than hitting it, or vice versa.
Asymmetry here can reveal directional stress, inventory pressure, or a book that is materially less resilient on one side.
slippage_bps_stdSlippage Std Dev measures how variable execution quality was from trade to trade.
Two markets can have the same mean cost but very different predictability, and this metric tells you which one is more stable.
Market Microstructure Context
Every trade has a buyer and a seller, but one of them pushed — they were in a hurry. Order flow tracks who is pushing and how hard. When a lot of urgent buyers show up at the same time prices are swinging wildly, that's when being a market maker gets expensive and dangerous.
Every trade nudges the price. Bigger trades nudge more. Impact measures how much price moves per dollar traded. In thin markets that nudge is large, so investors demand higher returns to take the risk of getting in and out. That's why less liquid assets often have higher expected returns.
Not all trades carry the same information. Barclay & Warner (1993) found that medium-sized orders — not the largest — account for a disproportionate share of cumulative price impact. Informed traders deliberately size down to avoid detection. This means aggregate volume alone is insufficient: you need to decompose flow by direction, size, and concentration to understand what it is actually telling you.
Kyle's lambda and Amihud illiquidity approach the same question from opposite ends: how much does trading move the price? Lambda measures information content per unit of signed flow; Amihud measures return per dollar of volume. Where they agree, the market is behaving consistently. Where they diverge is where the diagnosis gets interesting — rising Amihud without rising lambda points to exogenous volatility, not informed flow. Rising lambda alone means someone in the order flow knows something the quote does not yet reflect.
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
curl -fsSL https://raw.githubusercontent.com/aperiodic-io/cli/main/install.sh | bash# Install the Aperiodic CLI into ~/.local/bin
mkdir -p "$HOME/.local/bin"
curl -fsSL https://raw.githubusercontent.com/aperiodic-io/cli/main/install.sh | INSTALL_DIR="$HOME/.local/bin" bash
export PATH="$HOME/.local/bin:$PATH"
# Fetch the preview slice as Parquet into ./data — no API key needed
aperiodic flow --preview \
--exchange binance-futures \
--symbol perpetual-BTC-USDT:USDT \
--interval 5m \
--timestamp exchange \
--start-date 2025-05-01 \
--end-date 2025-05-31 \
--output-dir ./data && echo "Saved to $PWD/data"Get access to our full catalog of market microstructure data.
Shown: minute lows of the 465 Binance perpetuals, 20:40–22:00 UTC on 10 Oct 2025, with BTC’s path traced · built with ohlcv 1m