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Overview

  • Dataset ID: 0401
  • Table: blockdb0401_token_to_token_prices_l1_v1
  • Description: Level-1 executable size and average price at ±100 bps from mid (per direction).
This dataset provides liquiidty depth on the surface of the book. If you need even more granularity, you can check Token-to-Token Prices L2 or Token-to-Token Prices L3.

Columns

ColumnTypeDescription
idBIGINTSurrogate identity for the L1 quote record (auto-incremented).
pool_uidBIGINTForeign key to blockdb0203_liquidity_pools_v1.uid, identifying the AMM pool that produced the quote.
block_numberBIGINTCanonical block height where the quote snapshot was observed.
block_timeTIMESTAMPTZUTC timestamp of the originating block for easy temporal joins.
tx_indexINTEGERZero-based transaction index inside the block; pairs with log_index to pinpoint provenance.
log_indexINTEGERLog index emitted by the pool event, ensuring uniqueness with tx_index and pool_uid.
token_inBYTEA20-byte address of the input token (direction of trade); decimals are normalized in price/size fields.
token_outBYTEA20-byte address of the output token for this direction of liquidity consumption.
current_priceNUMERIC(78,18)Mid price at snapshot (token_out per 1 token_in), decimals-adjusted.
offset_bpsSMALLINTAbsolute distance from mid expressed in basis points (default 100 for ±1%).
size_inNUMERIC(78,18)Amount of token_in (gross, pre-fee) required to push price to the offset boundary.
size_outNUMERIC(78,18)Amount of token_out received when executing size_in at the offset boundary (pre-fee).
target_priceNUMERIC(78,18)Resulting mid price after consuming size_in liquidity to the offset (fees excluded).
_tracing_idBYTEADeterministic BlockDB lineage identifier for the quote record.
_genesis_tracing_idsBYTEA[]Tracing IDs for the raw artifacts (pool, block, event) that seeded this record.
_parent_tracing_idsBYTEA[]Lineage references to derived upstream records used in computation (e.g., pool state snapshots).
_created_atTIMESTAMPTZTimestamp when BlockDB materialized the quote.
_updated_atTIMESTAMPTZLast time the record was modified (usually on replay/backfill).

Use Cases

  • Micro-depth & slippage at +/-1% for execution sizing and risk.
  • Routing heuristics and price-impact screens across pools/chains.
  • Strategy backtests with stable, comparable L1 signals.
  • Monitoring liquidity degradation around events.