filter_scams#

API documentation for tradingstrategy.utils.token_extra_data.filter_scams Python function.

filter_scams(pairs_df, client, min_token_sniffer_score=65, drop_token_tax=False, min_volume_24h_usd=0, risk_score_threshold=0, verbose=True)[source]#

Filter out scam tokens in pairs dataset and print some stdout diagnostics.

  • Loads token extra metadata from the server for the trading pairs

  • This includes TokenSniffer scores

  • Zero means zero TokenSniffer score. Nan means the TokenSniffer data was not available on the server likely due to low liquidity/trading pair no longer functional.

Note

Deprecated. Use load_token_metadata() instead.

Example:

# Scam filter using TokenSniffer
pairs_df = filter_scams(pairs_df, client, min_token_sniffer_score=Parameters.min_token_sniffer_score)
pairs_df = pairs_df.sort_values("volume", ascending=False)

print("Top pair matches (including benchmark pairs):")
for _, pair in pairs_df.head(10).iterrows():
    print(f"   Pair: {pair.base_token_symbol} - {pair.quote_token_symbol} ({pair.exchange_slug})")

uni_v2 = pairs_df.loc[pairs_df["exchange_slug"] == "uniswap-v2"]
uni_v3 = pairs_df.loc[pairs_df["exchange_slug"] == "uniswap-v3"]
print(f"Pairs on Uniswap v2: {len(uni_v2)}, Uniswap v3: {len(uni_v3)}")

dataset = load_partial_data(
    client=client,
    time_bucket=Parameters.candle_time_bucket,
    pairs=pairs_df,
    execution_context=execution_context,
    universe_options=universe_options,
    liquidity=True,
    liquidity_time_bucket=TimeBucket.d1,
)
Parma drop_token_tax:

Discard tokens with token tax features, as by Tokensniffer data

Parameters:

  • min_token_sniffer_score – Do not load data from the server unless score is higher

  • min_volume_24h_usd –

    Ignore pairs with less than this 24h volume.

    Set to zero to load historical scams.

  • verbose – Print out data about scams

  • pairs_df (DataFrame) –

  • client (Client) –

Return type:

DataFrame