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Overview

  • Dataset ID: 0302
  • Table: blockdb0302_token_to_token_prices_swap_prints_v1
  • Description: Executed swap prints (realized token_in → token_out price & sizes) per on-chain swap event. No liquidity/quotes.
This dataset captures the realized execution price from actual swap events, providing a direct view of what prices were achieved in practice. Unlike L1/L2/L3 pricing layers that model liquidity depth, swap prints represent historical execution outcomes with no forward-looking quotes.

Columns

ColumnTypeDescription
idBIGINTSurrogate identity for the swap print record (auto-incremented).
pool_uidBYTEAForeign key to blockdb0203_liquidity_pools_v1.uid, identifying the AMM pool where the swap occurred.
block_numberBIGINTCanonical block height where the swap event 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 swap 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 swap execution.
amount_inNUMERIC(78,18)Amount of token_in executed in the swap (decimals-adjusted, direction-consistent).
amount_outNUMERIC(78,18)Amount of token_out received in the swap (decimals-adjusted, direction-consistent).
exec_priceNUMERIC(78,18)Realized execution price: token_out per 1 token_in (decimals-adjusted).
_tracing_idBYTEADeterministic BlockDB lineage identifier for the swap print 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 swap print.
_updated_atTIMESTAMPTZLast time the record was modified (usually on replay/backfill).

Use Cases

  • Execution price analysis: Analyze realized prices from actual swap executions to understand slippage and execution quality
  • Historical price reconstruction: Build price time series from executed swaps without relying on liquidity models
  • MEV and arbitrage detection: Identify price discrepancies by comparing swap prints across pools and chains
  • Trade execution benchmarking: Compare actual execution prices against theoretical prices from depth curves
  • Market microstructure research: Study price formation and execution patterns in AMM pools
  • Backtesting strategies: Use historical swap prints to validate trading strategies with real execution data