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Cross-Venue Orderbook Imbalance

Aggregated buy/sell pressure imbalance across venues for BTC, ETH, SOL

About This Dataset

Cross-venue orderbook imbalance computed from multiple data sources. Captures aggressive flow, absorption patterns, and momentum signals. Updated every 1–5 seconds.

Partitions

asset: btc, eth, sol

Schema

Column	Type	Description
`time_chain`	`timestamp`	Measurement timestamp (UTC)
`time_local`	`timestamp`	Ingestion timestamp (UTC)
`asset`	`string`	Asset symbol
`imbalance`	`float64`	Imbalance score (-1 to 1)
`bid_depth_usd`	`float64`	Total bid depth within 1% (USD)
`ask_depth_usd`	`float64`	Total ask depth within 1% (USD)

Use Cases

Detect short-term directional pressure
Build orderbook-based trading signals
Study imbalance vs forward returns correlation
Combine with funding rate for confluence signals

R2 Path

s3://algotick-data-lake/states/imbalance/asset={asset}/year=YYYY/month=MM/day=DD/node={node}/data.parquet

Replace YYYY, MM, DD with the target date. Data is collected from node=eu-central.

Query with DuckDB

import duckdb

df = duckdb.sql("""
    SELECT *
    FROM read_parquet(
        's3://algotick-data-lake/states/imbalance/asset=btc/year=2026/month=04/day=20/node=eu-central/data.parquet'
    )
    LIMIT 100
""").df()

print(f"Rows: {len(df)}, Columns: {list(df.columns)}")

Download via API

import requests

resp = requests.get(
    "https://algotick.dev/v2/history/raw?dataset=imbalance&coin=BTC&date=2026-04-20",
    stream=True,
)

with open("imbalance.parquet", "wb") as f:
    for chunk in resp.iter_content(8192):
        f.write(chunk)

# Then query locally
import duckdb
df = duckdb.sql("SELECT * FROM 'imbalance.parquet' LIMIT 100").df()
print(df)

Get API Key →

Related signals: Whale Activity Composite Signal

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