AssetIdentifier#

API documentation for tradeexecutor.state.identifier.AssetIdentifier Python class in Trading Strategy framework.

class AssetIdentifier[source]#

Bases: object

Identify a blockchain asset for trade execution.

This is pass-by-copy (as opposite to pass-by-reference) asset identifier we use across the persistent state. Because we copy a lot of information about asset, not just its id, this makes data reads and diagnosing problems simpler.

As internal token_ids and pair_ids may be unstable, trading pairs and tokens are explicitly referred by their smart contract addresses when a strategy decision moves to the execution. We duplicate data here to make sure we have a persistent record that helps to diagnose the issues.

Setting custom data:

Both AssetIdentifier and TradingPairIdentifier offer AssetIdentifier.other_data allowing you to set custom attributes.
Most useful of them is to categorise tokens with different tags, see get_tags() and set_tags().
You must set these attributes in create_trading_universe function.
For more usage examples see test_custom_labels.

A custom asset tagging example:

def create_trading_universe(
    ts: datetime.datetime,
    client: Client,
    execution_context: ExecutionContext,
    universe_options: UniverseOptions,
) -> TradingStrategyUniverse:

    assert universe_options.start_at

    pairs = [
        (ChainId.polygon, "uniswap-v3", "WETH", "USDC", 0.0005)
    ]

    dataset = load_partial_data(
        client,
        execution_context=unit_test_execution_context,
        time_bucket=TimeBucket.d1,
        pairs=pairs,
        universe_options=default_universe_options,
        start_at=universe_options.start_at,
        end_at=universe_options.end_at,
    )

    strategy_universe = TradingStrategyUniverse.create_single_pair_universe(dataset)

    # IMPORTANT
    # Warm up must be called before any tags are set
    strategy_universe.warm_up_data()

    # Set custom labels on the token WETH on the trading pair
    weth_usdc = strategy_universe.get_pair_by_human_description(pairs[0])
    weth_usdc.base.set_tags({"L1", "EVM", "bluechip"})

    assert strategy_universe.data_universe.pairs.pair_map is not None, "Cache data structure missing?"

    weth_usdc = strategy_universe.get_pair_by_human_description(pairs[0])
    assert len(weth_usdc.base.get_tags()) > 0

    return strategy_universe

Then how to read tag data and use it in trade decision making:

def decide_trades(input: StrategyInput) -> list[TradeExecution]:
    # Show how to read pair and asset labels in decide_trade()
    for pair in input.strategy_universe.iterate_pairs():
        if "L1" in pair.get_tags():
            # Do some trading logic for L1 tokens only
            pass

    return []

__init__(chain_id, address, token_symbol, decimals, internal_id=None, info_url=None, underlying=None, type=None, liquidation_threshold=None, other_data=<factory>)#

Parameters:

chain_id (int) –
address (str) –
token_symbol (str) –
decimals (int) –
internal_id (Optional[int]) –
info_url (Optional[str]) –
underlying (Optional[AssetIdentifier]) –
type (Optional[AssetType]) –
liquidation_threshold (float | None) –
other_data (Optional[dict]) –

Return type:

None

Methods

`__init__`(chain_id, address, token_symbol, ...)
`convert_to_decimal`(raw_amount)
`convert_to_raw_amount`(amount)	Return any amount in token native units.
`from_dict`(kvs, *[, infer_missing])
`from_json`(s, *[, parse_float, parse_int, ...])
`get_buy_tax`()	Get buy tax associated with this token if any.
`get_identifier`()	Assets are identified by their smart contract address.
`get_pricing_asset`()	Get the asset that delivers price for this asset.
`get_sell_tax`()	Get sell tax associated with this token if any.
`get_tags`()	Return list of tags associated with this asset.
`is_credit`()	Is this a credit asset that accrue interest for us
`is_debt`()	Is this a credit asset that accrue interest for us
`is_interest_accruing`()	Will this token gain on-chain interest thru rebase
`is_stablecoin`()	Do we think this asset reprents a stablecoin
`schema`(*[, infer_missing, only, exclude, ...])
`set_tags`(tags)	Set tags for this asset.
`to_dict`([encode_json])
`to_json`(*[, skipkeys, ensure_ascii, ...])

Attributes

`checksum_address`	Ethereum madness.
`info_url`	Info page URL for this asset
`internal_id`	How this asset is referred in the internal database
`liquidation_threshold`	Aave liquidation threhold for this asset
`type`	What kind of asset is this
`underlying`	The underlying asset for aTokens, vTokens and such
`chain_id`	See https://chainlist.org/
`address`	Smart contract address of the asset.
`token_symbol`	The ticker symbol of this token.
`decimals`	How many tokens this decimals.
`other_data`	User storeable properties.

chain_id: int#: See https://chainlist.org/

address: str#: Smart contract address of the asset. Always lowercase.

token_symbol: str#: The ticker symbol of this token.

decimals: int#: How many tokens this decimals. Must be always set and non-negative.

internal_id: Optional[int] = None#: How this asset is referred in the internal database

info_url: Optional[str] = None#: Info page URL for this asset

underlying: Optional[AssetIdentifier] = None#: The underlying asset for aTokens, vTokens and such

type: Optional[AssetType] = None#

What kind of asset is this

Legacy data will default to None.

liquidation_threshold: float | None = None#

Aave liquidation threhold for this asset

Set on aTokens that are used as collateral.

other_data: Optional[dict]#

User storeable properties.

You can add any of your own metadata on the assets here.

Be wary of the life cycle of the instances. The life time of the class instances tied to the trading universe that is recreated for every strategy cycle.