deduplicate_pairs_by_volume#

API documentation for tradingstrategy.utils.token_filter.deduplicate_pairs_by_volume Python function.

deduplicate_pairs_by_volume(pairs_df)[source]#

Deduplicate trading pairs.

  • For each base token, we might have several trading pairs with different quote tokens (WETH, USDC) and different DEXes (uniswap-v2, uniswap-v3)

  • In this function, we deduplicate the incoming trading pairs so that we pick one with the best volume (volume 24h USD, whatever was recorded at the time of creating the pairs dataset).

  • Note that a smarter method of doing this is to check trading fee + liquidity, but for that we may need to download the liquidity universe - this method using “some past” record of volume is simpler

Example:

chain_id = ChainId.ethereum
category = "Meme"
client = Client.create_live_client(api_key=os.environ['TRADING_STRATEGY_API_KEY'])

coingecko_universe = CoingeckoUniverse.load()
logger.info("Coingecko universe is %s", coingecko_universe)

pairs_df = client.fetch_pair_universe().to_pandas()
category_df = categorise_pairs(coingecko_universe, pairs_df)

# Get all trading pairs that are memecoin, across all coingecko data
mask = category_df["category"] == category
meme_pair_ids = category_df[mask]["pair_id"]

logger.info("Memecoin pairs across all chain %s", len(meme_pair_ids))

# From these pair ids, see what trading pairs we have on Ethereum mainnet
pairs_df = pairs_df[pairs_df["pair_id"].isin(meme_pair_ids) & (pairs_df["chain_id"] == chain_id.value)]
logger.info("Ethereum filtered memecoins %s", len(pairs_df))

pairs_universe = PandasPairUniverse(pairs_df)

logger.info("Example pairs:")
for pair in list(pairs_universe.iterate_pairs())[0:10]:
    logger.info("   Pair: %s", pair)
# SHIB - WETH (0x811beed0119b4afce20d2583eb608c6f7af1954f) at exchange 1 on ethereum>
# SHIB - USDT (0x773dd321873fe70553acc295b1b49a104d968cc8) at exchange 1 on ethereum>
# LEASH - WETH (0x874376be8231dad99aabf9ef0767b3cc054c60ee) at exchange 1 on ethereum>
# LEASH - DAI (0x761d5dca312484036de12ba22b660a2e5b1aa211) at exchange 1 on ethereum>

# Deduplicate trading pairs

# - Choose the best pair with the best volume
pairs_df = deduplicate_pairs_by_volume(pairs_df)
pairs_universe = PandasPairUniverse(pairs_df)

logger.info("Example of deduplicated pairs:")
for pair in list(pairs_universe.iterate_pairs())[0:10]:
    logger.info("   Pair: %s", pair)
# INFO:__main__:   Pair: <Pair #37836 SHIB - WETH (0x24d3dd4a62e29770cf98810b09f89d3a90279e7a) at exchange 22 on ethereum>
# INFO:__main__:Example of deduplicated pairs:
# INFO:__main__:   Pair: <Pair #3018988 PEPE - WETH (0x11950d141ecb863f01007add7d1a342041227b58) at exchange 3681 on ethereum>
# INFO:__main__:   Pair: <Pair #3047249 TURBO - WETH (0x8107fca5494375fc743a9fc4d4844353a1af3d94) at exchange 3681 on ethereum>
# INFO:__main__:   Pair: <Pair #3842242 Neiro - WETH (0x15153da0e9e13cfc167b3d417d3721bf545479bb) at exchange 3681 on ethereum>
# INFO:__main__:   Pair: <Pair #3376429 MEME - WETH (0x70cf99553471fe6c0d513ebfac8acc55ba02ab7b) at exchange 3681 on ethereum>

logger.info(
    "Total %d pairs to trade on %s for category %s",
    len(pairs_df),
    chain_id.name,
    category,
)
Returns:

The returning pairs DataFrame is sorted by volume

Parameters:

pairs_df (DataFrame) –

Return type:

DataFrame