messari.defillama package

Submodules

messari.defillama.defillama module

This module is meant to contain the DeFiLlama class

class messari.defillama.defillama.DeFiLlama

Bases: messari.dataloader.DataLoader

This class is a wrapper around the DeFi Llama API

Methods

`get_chain_tvl_timeseries`(chains_in[, ...])	Retrive timeseries TVL for a given chain
`get_current_tvl`(asset_slugs)	Retrive current protocol tvl for an asset
`get_global_tvl_timeseries`([start_date, end_date])	Returns timeseries TVL from total of all Defi Llama supported protocols
`get_protocol_tvl_timeseries`(asset_slugs[, ...])	Returns times TVL of a protocol with token amounts as a pandas DataFrame.
`get_protocols`()	Returns basic information on all listed protocols, their current TVL and the changes to it in the last hour/day/week
`get_response`(endpoint_url[, params, headers])	Gets response from endpoint and checks for HTTP errors when requesting data.
`set_api_dict`(api_dict)	Sets a new dictionary to be used as an API key pair
`set_taxonomy_dict`(taxonomy_dict)	Sets a new dictionary to be used for taxonomy translations
`translate`(input_slugs)	Wrapper around messari.utils.validate_input, validate input & check if it's supported by DeFi Llama

get_chain_tvl_timeseries(chains_in: Union[str, List], start_date: Optional[Union[str, datetime.datetime]] = None, end_date: Optional[Union[str, datetime.datetime]] = None) → pandas.core.frame.DataFrame

Retrive timeseries TVL for a given chain

Parameters

chains_in: str, list

Single asset slug string or list of asset slugs (i.e. bitcoin)

start_date: str, datetime.datetime: Optional start date to set filter for tvl timeseries (“YYYY-MM-DD”)
end_date: str, datetime.datetime: Optional end date to set filter for tvl timeseries (“YYYY-MM-DD”)

Returns

DataFrame: DataFrame containing timeseries tvl data for each chain

get_current_tvl(asset_slugs: Union[str, List]) → Dict

Retrive current protocol tvl for an asset

Parameters

asset_slugs: str, list: Single asset slug string or list of asset slugs (i.e. bitcoin)

Returns

DataFrame: Pandas Series for tvl indexed by each slug {slug: tvl, …}

get_global_tvl_timeseries(start_date: Optional[Union[str, datetime.datetime]] = None, end_date: Optional[Union[str, datetime.datetime]] = None) → pandas.core.frame.DataFrame

Returns timeseries TVL from total of all Defi Llama supported protocols

Parameters

start_date: str, datetime.datetime

Optional start date to set filter for tvl timeseries (“YYYY-MM-DD”)

end_date: str, datetime.datetime: Optional end date to set filter for tvl timeseries (“YYYY-MM-DD”)

Returns

DataFrame: DataFrame containing timeseries tvl data for every protocol

get_protocol_tvl_timeseries(asset_slugs: Union[str, List], start_date: Optional[Union[str, datetime.datetime]] = None, end_date: Optional[Union[str, datetime.datetime]] = None) → pandas.core.frame.DataFrame

Returns times TVL of a protocol with token amounts as a pandas DataFrame. Returned DataFrame is indexed by df[protocol][chain][asset].

Parameters

asset_slugs: str, list

Single asset slug string or list of asset slugs (i.e. bitcoin)

start_date: str, datetime.datetime: Optional start date to set filter for tvl timeseries (“YYYY-MM-DD”)
end_date: str, datetime.datetime: Optional end date to set filter for tvl timeseries (“YYYY-MM-DD”)

Returns

DataFrame: pandas DataFrame of protocol TVL, indexed by df[protocol][chain][asset] to look at total tvl across all chains, index with chain=’all’ to look at total tvl across all tokens of a chain, asset=’totalLiquidityUSD’ tokens can be indexed by asset=’tokenName’ or by asset=’tokenName_usd’

get_protocols() → pandas.core.frame.DataFrame

Returns basic information on all listed protocols, their current TVL and the changes to it in the last hour/day/week

Returns

DataFrame: DataFrame with one column per DeFi Llama supported protocol

messari.defillama.helpers module

This module is dedicated to helpers for the DeFiLlama class

messari.defillama.helpers.format_df(df_in: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

format a typical DF from DL, replace date & drop duplicates

Parameters

df_in: pd.DataFrame: input DataFrame

Returns

DataFrame: formated pandas DataFrame

Module contents

Module to handle initialization, imports, for DeFiLlama class