API documentation for tradingstrategy.pair.filter_for_base_tokens Python function.

filter_for_base_tokens(pairs, base_token_addresses)[source]#

Filter dataset so that it only contains data for the trading pairs that have a certain base token.

Useful as a preprocess step for creating tradingstrategy.lending.LendingUniverse


client = persistent_test_client

exchange_universe = client.fetch_exchange_universe()

quote_tokens = {
    "0x2791bca1f2de4661ed88a30c99a7a9449aa84174",  # USDC polygon
    "0xc2132d05d31c914a87c6611c10748aeb04b58e8f",  # USDT polygon

pairs_df = client.fetch_pair_universe().to_pandas()

# Find out all volatile pairs traded against USDC and USDT on Polygon
pairs_df = filter_for_chain(pairs_df, ChainId.polygon)
pairs_df = filter_for_stablecoins(pairs_df, StablecoinFilteringMode.only_volatile_pairs)
pairs_df = filter_for_quote_tokens(pairs_df, quote_tokens)

# Create lending universe and trading universe with the cross section of
# - Available assets in the lending protocols
# - Asset we can trade
lending_reserves = client.fetch_lending_reserve_universe()
pairs_df = filter_for_base_tokens(pairs_df, lending_reserves.get_asset_addresses())

pair_universe = PandasPairUniverse(pairs_df, exchange_universe=exchange_universe)

# Lending reserves have around ~320 individual trading pairs on Polygon across different DEXes
assert 1 < pair_universe.get_count() < 1_000

eth_usdc = pair_universe.get_pair_by_human_description((ChainId.polygon, "uniswap-v3", "WETH", "USDC"))
  • quote_token_addresses

    List of Ethereum addresses of the tokens.

    Lowercased, non-checksummed.

  • pairs (DataFrame) –

  • base_token_addresses (Union[List[str], Set[str]]) –


DataFrame with trading pairs filtered to match quote token condition

Return type: