Use new service fee query (#433)

This PR updates the query id that is used for computing whether a
service fee should be applied. Some minor simplifications in the code
are needed.

This PR should be merged only after this dune-queries has been properly
reviewed: cowprotocol/dune-queries#66

src/fetch/dune.py

@@ -116,6 +116,9 @@ def get_service_fee_status(self) -> list[DuneRecord]:
         return self._get_query_results(
             query=self._parameterized_query(
                 query_data=QUERIES["SERVICE_FEE_STATUS"],
-                params=[],
+                params=[
+                    QueryParameter.date_type("start_time", self.period.start),
+                    QueryParameter.date_type("end_time", self.period.end),
+                ],
             )
         )

src/fetch/payouts.py

@@ -5,7 +5,7 @@
 import logging
 import math
 from dataclasses import dataclass
-from datetime import datetime, timedelta
+from datetime import timedelta
 from fractions import Fraction
 from typing import Callable
 
@@ -437,13 +437,6 @@ def construct_payout_dataframe(
         config.reward_config.reward_token_address.address
     )
 
-    # 7. Missing service fee is treated as new solver
-    if any(merged_df["service_fee"].isna()):
-        missing_solvers = merged_df["solver"].loc[merged_df["service_fee"].isna()]
-        logging.warning(
-            f"Solvers {missing_solvers} without service fee info. Using 0%. "
-            f"Check service fee query."
-        )
     merged_df["service_fee"] = merged_df["service_fee"].fillna(Fraction(0, 1))  # type: ignore
 
     return merged_df
@@ -518,10 +511,10 @@ def construct_payouts(
 
     service_fee_df = pandas.DataFrame(dune.get_service_fee_status())
     service_fee_df["service_fee"] = [
-        (datetime.strptime(time_string, "%Y-%m-%d %H:%M:%S.%f %Z") <= dune.period.start)
-        * config.reward_config.service_fee_factor
-        for time_string in service_fee_df["expires"]
+        service_fee_flag * config.reward_config.service_fee_factor
+        for service_fee_flag in service_fee_df["service_fee"]
     ]
+
     reward_target_df = pandas.DataFrame(dune.get_vouches())
     # construct slippage df
     if ignore_slippage_flag or (not config.buffer_accounting_config.include_slippage):

src/queries.py

@@ -40,7 +40,7 @@ def with_params(self, params: list[QueryParameter]) -> QueryBase:
     ),
     "SERVICE_FEE_STATUS": QueryData(
         name="CIP-48 Service fee status",
-        q_id=4017925,
+        q_id=4298142,
     ),
     "PERIOD_SLIPPAGE": QueryData(
         name="Solver Slippage for Period",