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LP Mint/Burn Events

Concentrated liquidity LP events from Uniswap V3 and Aerodrome

About This Dataset

Mint, Burn, and Swap events from concentrated liquidity pools. Enables exact reconstruction of the decentralized orderbook for slippage modeling.

Partitions

chain: base, arbitrum, ethereum

Schema

Column	Type	Description
`time_chain`	`timestamp`	Block timestamp (UTC)
`time_local`	`timestamp`	Ingestion timestamp (UTC)
`pool`	`string`	Pool contract address
`event_type`	`string`	Event type: Mint, Burn, or Swap
`tick_lower`	`int64`	Lower tick boundary
`tick_upper`	`int64`	Upper tick boundary
`amount0`	`float64`	Amount of token0
`amount1`	`float64`	Amount of token1
`tx_hash`	`string`	Transaction hash

Use Cases

Reconstruct concentrated liquidity depth at any tick
Model slippage for large DEX trades
Detect LP position changes by smart money wallets
Analyze liquidity provision strategies and JIT liquidity

R2 Path

s3://algotick-data-lake/events/lp_flows/chain={chain}/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/events/lp_flows/chain=base/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=lp_flows&chain=base&date=2026-04-20",
    stream=True,
)

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

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

Get API Key →

Related signals: Whale Activity

Related datasets: DEX Swaps Stablecoin On-Chain Flows

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