VaultHistoricalRead

Documentation for eth_defi.vault.base.VaultHistoricalRead Python class.

class VaultHistoricalRead

Bases: object

Vault share price and fee structure at the point of time.

Attributes summary

vault
block_number
timestamp
share_price
total_assets
total_supply
performance_fee
management_fee
errors
vault_poll_frequency
max_deposit
max_redeem
deposits_open
redemption_open
trading
available_liquidity
utilisation

Methods summary

__init__(vault, block_number, timestamp, ...)
export()	Convert historical read for a Parquet/DataFrame export.
is_almost_equal(other[, epsilon])	Check if the read statistics match.
migrate_parquet_schema(existing_table)	Migrate an existing Parquet table to the current schema.
to_pyarrow_schema()	Get parquet schema for writing this data.
write_uncleaned_parquet(df, path[, compression])	Write a DataFrame to the uncleaned parquet using proper PyArrow types.

__init__(vault, block_number, timestamp, share_price, total_assets, total_supply, performance_fee, management_fee, errors, vault_poll_frequency=None, max_deposit=None, max_redeem=None, deposits_open=None, redemption_open=None, trading=None, available_liquidity=None, utilisation=None)

Parameters

• vault (eth_defi.vault.base.VaultBase) –

• block_number (int) –

• timestamp (datetime.datetime) –

• share_price (Optional[decimal.Decimal]) –

• total_assets (Optional[decimal.Decimal]) –

• total_supply (Optional[decimal.Decimal]) –

• performance_fee (Optional[float]) –

• management_fee (Optional[float]) –

• errors (Optional[list[str]]) –

• vault_poll_frequency (Optional[str]) –

• max_deposit (Optional[decimal.Decimal]) –

• max_redeem (Optional[decimal.Decimal]) –

• deposits_open (Optional[bool]) –

• redemption_open (Optional[bool]) –

• trading (Optional[bool]) –

• available_liquidity (Optional[decimal.Decimal]) –

• utilisation (Optional[float]) –

Return type

None

export()

Convert historical read for a Parquet/DataFrame export.

The returned dict conforms to RawVaultPriceRow.

Return type

eth_defi.vault.base.RawVaultPriceRow

is_almost_equal(other, epsilon=0.001)

Check if the read statistics match.

• Throttle with epsilon relative difference to get rid of small increment rows

Parameters

• epsilon (float) – Write changes with 10 BPS granularity

• other (Optional[eth_defi.vault.base.VaultHistoricalRead]) –

Return type

bool

static migrate_parquet_schema(existing_table)

Migrate an existing Parquet table to the current schema.

When new columns are added to to_pyarrow_schema(), existing parquet files still have the old schema. This function adds missing columns as null arrays so incremental scans can write new data without losing columns.

Non-canonical columns (e.g. account_pnl, leader_fraction added by native protocol merges) are preserved so that the EVM scanner does not destroy data written by Hyperliquid/GRVT/Lighter.

Parameters

existing_table (pyarrow.Table) – Table read from an older parquet file.

Returns

Table with all canonical columns present (missing ones filled with nulls), legacy columns removed, extra columns preserved.

Return type

pyarrow.Table

classmethod to_pyarrow_schema()

Get parquet schema for writing this data.

• Write multiple chains, multiple vaults, to a single Parquet file

• Column semantics are documented in RawVaultPriceRow

Return type

pyarrow.Schema

static write_uncleaned_parquet(df, path, compression='zstd')

Write a DataFrame to the uncleaned parquet using proper PyArrow types.

Native protocol merge functions (Hyperliquid, GRVT, Lighter) must use this instead of pandas.DataFrame.to_parquet() to avoid type promotion (e.g. timestamp[ms] → timestamp[us]) that breaks migrate_parquet_schema() on the next EVM scan run.

Columns present in the canonical schema are cast to their canonical types. Extra columns (from native protocols) are kept with their pandas-inferred types. No pandas index is written.

Parameters

• df (pandas.DataFrame) – Combined DataFrame to write.

• path (pathlib.Path) – Output parquet file path.

• compression (str) – Parquet compression codec.