Merge branch 'main' into remove_dead_code

README.md

@@ -89,6 +89,8 @@ options:
                         Ignore ETH transfers with amount less than this
   --min-transfer-amount-cow-atoms MIN_TRANSFER_AMOUNT_COW_ATOMS
                         Ignore COW transfers with amount less than this
+  --ignore-slippage IGNORE_SLIPPAGE
+                        Ignore slippage computations
 ```
 
 The solver reimbursements are executed each Tuesday with the accounting period of the last 7 days.

src/fetch/dune.py

@@ -88,7 +88,7 @@ def get_vouches(self) -> list[DuneRecord]:
                 query_data=QUERIES["VOUCH_REGISTRY"],
                 params=[
                     QueryParameter.date_type("end_time", self.period.end),
-                    QueryParameter.enum_type("vouch_cte_name", "valid_vouches"),
+                    QueryParameter.enum_type("vouch_cte_name", "named_results"),
                 ],
             )
         )

src/fetch/payouts.py

@@ -388,8 +388,6 @@ def construct_payout_dataframe(
     Namely, reward targets and slippage (coming from Dune)
     with reward and execution data coming from orderbook.
     """
-    # TODO - After CIP-20 phased in, adapt query to return `solver` like all the others
-    slippage_df = slippage_df.rename(columns={"solver_address": "solver"})
     # 1. Assert existence of required columns.
     validate_df_columns(payment_df, slippage_df, reward_target_df, service_fee_df)
 
@@ -401,10 +399,19 @@ def construct_payout_dataframe(
     normalize_address_field(service_fee_df, join_column)
 
     # 3. Merge the three dataframes (joining on solver)
+
+    reward_target_reduced_df_columns = [
+        x for x in list(reward_target_df.columns) if x != "solver_name"
+    ]
+    reward_target_reduced_df = reward_target_df[reward_target_reduced_df_columns]
+    service_fee_reduced_df_columns = [
+        x for x in list(service_fee_df.columns) if x != "solver_name"
+    ]
+    service_fee_reduced_df = service_fee_df[service_fee_reduced_df_columns]
     merged_df = (
         payment_df.merge(slippage_df, on=join_column, how="left")
-        .merge(reward_target_df, on=join_column, how="left")
-        .merge(service_fee_df, on=join_column, how="left")
+        .merge(reward_target_reduced_df, on=join_column, how="left")
+        .merge(service_fee_reduced_df, on=join_column, how="left")
     )
 
     # 4. Add slippage from fees to slippage
@@ -454,7 +461,7 @@ def construct_partner_fee_payments(
 
 
 def construct_payouts(
-    dune: DuneFetcher, orderbook: MultiInstanceDBFetcher
+    dune: DuneFetcher, ignore_slippage_flag: bool, orderbook: MultiInstanceDBFetcher
 ) -> list[Transfer]:
     """Workflow of solver reward payout logic post-CIP27"""
     # pylint: disable-msg=too-many-locals
@@ -480,7 +487,22 @@ def construct_payouts(
         datetime.strptime(time_string, "%Y-%m-%d %H:%M:%S.%f %Z") <= dune.period.start
         for time_string in service_fee_df["expires"]
     ]
-    service_fee_df = service_fee_df[["solver", "service_fee"]]
+    reward_target_df = pandas.DataFrame(dune.get_vouches())
+    # construct slippage df
+    if ignore_slippage_flag:
+        slippage_df_temp = pandas.merge(
+            merged_df[["solver"]],
+            reward_target_df[["solver", "solver_name"]],
+            on="solver",
+            how="inner",
+        )
+        slippage_df = slippage_df_temp.assign(
+            eth_slippage_wei=[0] * slippage_df_temp.shape[0]
+        )
+    else:
+        slippage_df = pandas.DataFrame(dune.get_period_slippage())
+        # TODO - After CIP-20 phased in, adapt query to return `solver` like all the others
+        slippage_df = slippage_df.rename(columns={"solver_address": "solver"})
 
     complete_payout_df = construct_payout_dataframe(
         # Fetch and extend auction data from orderbook.
@@ -493,11 +515,10 @@ def construct_payouts(
             ),
         ),
         # Dune: Fetch Solver Slippage & Reward Targets
-        slippage_df=pandas.DataFrame(dune.get_period_slippage()),
-        reward_target_df=pandas.DataFrame(dune.get_vouches()),
+        slippage_df=slippage_df,
+        reward_target_df=reward_target_df,
         service_fee_df=service_fee_df,
     )
-
     # Sort by solver before breaking this data frame into Transfer objects.
     complete_payout_df = complete_payout_df.sort_values("solver")
 

src/fetch/transfer_file.py

@@ -111,6 +111,7 @@ def auto_propose(
 
     payout_transfers_temp = construct_payouts(
         args.dune,
+        args.ignore_slippage,
         orderbook=MultiInstanceDBFetcher(
             [os.environ["PROD_DB_URL"], os.environ["BARN_DB_URL"]]
         ),

src/utils/script_args.py

@@ -21,6 +21,7 @@ class ScriptArgs:
     consolidate_transfers: bool
     min_transfer_amount_wei: int
     min_transfer_amount_cow_atoms: int
+    ignore_slippage: bool
 
 
 def generic_script_init(description: str) -> ScriptArgs:
@@ -70,6 +71,12 @@ def generic_script_init(description: str) -> ScriptArgs:
         help="Ignore COW transfers with amount less than this",
         default=20000000000000000000,
     )
+    parser.add_argument(
+        "--ignore-slippage",
+        type=bool,
+        help="Flag for ignoring slippage computations",
+        default=False,
+    )
     args = parser.parse_args()
     return ScriptArgs(
         dune=DuneFetcher(
@@ -81,4 +88,5 @@ def generic_script_init(description: str) -> ScriptArgs:
         consolidate_transfers=args.consolidate_transfers,
         min_transfer_amount_wei=args.min_transfer_amount_wei,
         min_transfer_amount_cow_atoms=args.min_transfer_amount_cow_atoms,
+        ignore_slippage=args.ignore_slippage,
     )