data.portfolios

Data for analyzing a portfolio of different tokens.

Examples

from datetime import datetime
from web3cat.data import PortfolioData

addresses = [
    "0x78605Df79524164911C144801f41e9811B7DB73D",
    "0xBF72Da2Bd84c5170618Fbe5914B0ECA9638d5eb5",
    "0x8EB8a3b98659Cce290402893d0123abb75E3ab28"
]
tokens = ["USDC", "WETH", "ETH", "WBTC"]
base_tokens = ["USDC", "WETH"]
portfolio_data = PortfolioData(
    tokens=tokens,
    base_tokens=base_tokens,
    addresses = addresses,
    start = datetime(2022, 6, 1),
    end = datetime(2022, 9, 1),
    numpoints = 4
)

# Portfolio value in USDC by address for USDC, WETH, ETH and WBTC holdings
portfolio_data.breakdown_by_address("USDC")

# Portfolio value in WETH by address for USDC, WETH, ETH and WBTC holdings
portfolio_data.breakdown_by_address("WETH")

# Portfolio value in USDC by token for USDC, WETH, ETH and WBTC holdings
portfolio_data.breakdown_by_token("USDC")

# Portfolio value in WETH by token for USDC, WETH, ETH and WBTC holdings
portfolio_data.breakdown_by_token("WETH")

# Balance and price data
portfolio_data.balances_and_prices

# Balances of a one specific token
portfolio_data.balances("USDC")

class web3cat.data.portfolios.PortfolioData(tokens: List[str], base_tokens: List[str], addresses: List[str], start: int | datetime.datetime, end: int | datetime.datetime, numpoints: int, **kwargs)

Bases: DataCore

Portfolio data (a group of tokens for a group of addresses)

Parameters:

tokens – A list of tokens to track
base_tokens – A list of tokens for aggregation by price
addresses – A list of addresses to track
start – start of the data
end – endof the data
numpoints – number of points between start and end

property tokens: List[ERC20Meta]: Tracked tokens metas.

property base_tokens: List[ERC20Meta]: Base tokens metas.

balances(base_token: str) → DataFrame

Breakdown of a specific token holdings by the owner address.

Note

Unlike breakdown_by_address() it doesn’t convert other tokens. For example, with basetoken='USDC' ETH holdings will be just ignored.

Parameters:: base_token – token for holdings
Return type:: A Dataframe with fields

Field	Type	Description
`timestamp`	`numpy.int64`	Timestamp for the snapshot of the balance
`date`	`datetime.datetime`	Date for the timestamp
`block_number`	`int`	Number of the block
`<address N>`	`numpy.float64`	Balance of the address of `base_token`
`total`	`numpy.float64`	Total portfolio balance of `base_token`

breakdown_by_address(base_token: str) → DataFrame

Breakdown of token holdings by the owner address.

Parameters:: base_token – token for denomination of holdings
Return type:: A Dataframe with fields

Field	Type	Description
`timestamp`	`numpy.int64`	Timestamp for the snapshot of the balance
`date`	`datetime.datetime`	Date for the timestamp
`block_number`	`int`	Number of the block
`<address N>`	`numpy.float64`	Balance of the address in `base_token`
`total`	`numpy.float64`	Total portfolio balance in `base_token`

breakdown_by_token(base_token: str) → DataFrame

Breakdown of token holdings by the owner address.

Parameters:: base_token – token for denomination of holdings
Return type:: A Dataframe with fields

Field	Type	Description
`timestamp`	`numpy.int64`	Timestamp for the snapshot of the balance
`date`	`datetime.datetime`	Date for the timestamp
`block_number`	`int`	Number of the block
`<token> (<base_token>)`	`numpy.float64`	Value of token in `base_token`
`total`	`numpy.float64`	Total portfolio balance in `base_token`

property balances_and_prices: DataFrame

Balances and prices data.

Field	Type	Description
`timestamp`	`numpy.int64`	Timestamp for the snapshot of the balance
`date`	`datetime.datetime`	Date for the timestamp
`block_number`	`int`	Number of the block
`address`	`str`	Address of tokens owner
`<token>`	`numpy.float64`	Value of token in `base_token`
`<token> / <base_token>`	`numpy.float64`	Price of token in terms of base token
`total`	`numpy.float64`	Total portfolio balance in `base_token`