vault.vaultdb

Documentation for eth_defi.vault.vaultdb Python module.

Describe vault database pickle format.

Functions

has_good_fee_data(vault_row)

Check if the vault row has good fee data.

read_default_vault_prices()

Read the default raw vault prices database.

Classes

VaultDatabase

Metadata for all vaults across all chains we know about.

VaultRow

Vault info gathered during the vault discovery from the chain.
DEFAULT_VAULT_DATABASE = PosixPath('/home/runner/.tradingstrategy/vaults/vault-metadata-db.pickle')

Where we store the vault metadata database by default

DEFAULT_RAW_PRICE_DATABASE = PosixPath('/home/runner/.tradingstrategy/vaults/cleaned-vault-prices-1h.parquet')

Where we store cleaned prices - with columns share_price and raw_share_prcie

DEFAULT_UNCLEANED_PRICE_DATABASE = PosixPath('/home/runner/.tradingstrategy/vaults/vault-prices-1h.parquet')

Where we store the raw scan data prior to cleaning

DEFAULT_READER_STATE_DATABASE = PosixPath('/home/runner/.tradingstrategy/vaults/vault-reader-state-1h.pickle')

The state per vault for reading vault prices, and disabled vaults

See

VaultReaderData

See eth_defi.erc_4626.vault.VaultReaderState

alias of dict[eth_defi.vault.base.VaultSpec, dict]

class VaultRow

Bases: TypedDict

Vault info gathered during the vault discovery from the chain.

  • Avaulable as VaultDb pickle

  • Human readable entry

  • Machine readable data in _detection_data

__init__(*args, **kwargs)
__new__(**kwargs)
clear() → None.  Remove all items from D.
copy() → a shallow copy of D
fromkeys(value=None, /)

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
pop(k[, d]) → v, remove specified key and return the corresponding value.

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
VaultDatabaseOld

Legacy pickle format

alias of dict[eth_defi.vault.base.VaultSpec, eth_defi.vault.vaultdb.VaultRow]

has_good_fee_data(vault_row)

Check if the vault row has good fee data.

Parameters

vault_row (eth_defi.vault.vaultdb.VaultRow) –

Return type

bool

class VaultDatabase

Bases: object

Metadata for all vaults across all chains we know about.

  • The pickle format dump for vault-db.pickle

  • Does not include share price/TVL data, only metadata

Manually checking the contents:

from eth_defi.vault.vaultdb import VaultDatabase
from eth_defi.vault.base import VaultSpec

vault_db = VaultDatabase.read()
# Choose D2 HYPE++ on Arbitrum
spec = VaultSpec(chain_id=42161, vault_address="0x75288264FDFEA8ce68e6D852696aB1cE2f3E5004")
vault_metadata = vault_db.rows[spec]
print(vault_metadata)
rows: dict[eth_defi.vault.base.VaultSpec, eth_defi.vault.vaultdb.VaultRow]

Correctly detected vaults.

(chain id, address) -> vault info mapping for detected vaults

leads: dict[eth_defi.vault.base.VaultSpec, eth_defi.erc_4626.discovery_base.PotentialVaultMatch]

(chain id, address) -> vault info mapping for ongoing scans on which we are still unsure

Persistent to continue scan

last_scanned_block: dict[int, int]

Chain id -> block number.

static read(path=PosixPath('/home/runner/.tradingstrategy/vaults/vault-metadata-db.pickle'))

Load the picked file.

Example:

from eth_defi.vault.vaultdb import VaultDatabase

# Load from the default location on local filesystem
vault_db = VaultDatabase.read()

print(f"We have data for {vault_db.get_lead_count()} potential vaults")
Parameters

path (pathlib.Path | io.BufferedIOBase) –

Return type

eth_defi.vault.vaultdb.VaultDatabase

write(path=PosixPath('/home/runner/.tradingstrategy/vaults/vault-metadata-db.pickle'))

Do an atomic write to avoid corrupted data.

Parameters

path (pathlib.Path) –

get_chain_start_block(chain_id, default_start_block=1)

Get the block to start scanning from for a given chain id.

  • Used to for iterative scanning

Parameters

chain_id (int) –

Return type

int

get_existing_leads_by_chain(chain_id)

Get existing leads for a given chain id.

  • Used to for iterative scanning

Parameters

chain_id (int) –

Return type

dict[eth_typing.evm.HexAddress, eth_defi.erc_4626.discovery_base.PotentialVaultMatch]

limit_to_single_vault(vault_spec)

Limit results to a single vault.

Used for diagnostics.

Parameters

vault_spec (eth_defi.vault.base.VaultSpec) –

Return type

eth_defi.vault.vaultdb.VaultDatabase

static to_dataframe(rows)

Convert selected vault rows to a Pandas DataFrame for diagnostics.

Parameters

rows (Iterable[eth_defi.vault.vaultdb.VaultRow]) –

Return type

pandas.core.frame.DataFrame

keys()

Iterable human readable vault (chain, address) tuples.

Return type

Iterable[eth_defi.vault.base.VaultSpec]

values()

Iterable human readable rows.

Return type

Iterable[eth_defi.vault.vaultdb.VaultRow]

items()

Iterable human readable rows.

Return type

Iterable[tuple[eth_typing.evm.HexAddress, eth_defi.vault.vaultdb.VaultRow]]

get(key, default=None)

Get vault row by spec.

Parameters

key (eth_defi.vault.base.VaultSpec) –

Return type

eth_defi.vault.vaultdb.VaultRow | None

__init__(rows=<factory>, leads=<factory>, last_scanned_block=<factory>)
Parameters
Return type

None

read_default_vault_prices()

Read the default raw vault prices database.

  • Use the default cleaned price data file

Return type

pandas.core.frame.DataFrame