[Draft] Restructure payouts

src/fetch/buffer_accounting.py

@@ -0,0 +1,39 @@
+"""Functionality for buffer accounting."""
+
+from pandas import DataFrame
+
+BATCH_DATA_COLUMNS = ["solver", "network_fee_eth"]
+SLIPPAGE_COLUMNS = [
+    "solver",
+    "eth_slippage_wei",
+]
+
+BUFFER_ACCOUNTING_COLUMNS = [
+    "solver",
+    "network_fee_eth",
+    "slippage_eth",
+]
+
+
+def compute_buffer_accounting(
+    batch_data: DataFrame, slippage_data: DataFrame
+) -> DataFrame:
+    """Compute buffer accounting per solver"""
+
+    # validate batch rewards and quote rewards columns
+    assert set(BATCH_DATA_COLUMNS).issubset(set(batch_data.columns))
+    assert set(SLIPPAGE_COLUMNS).issubset(set(slippage_data.columns))
+
+    buffer_accounting = batch_data[BATCH_DATA_COLUMNS].merge(
+        slippage_data[SLIPPAGE_COLUMNS], how="outer", on="solver", validate="one_to_one"
+    )
+    buffer_accounting = buffer_accounting.rename(
+        columns={"eth_slippage_wei": "slippage_eth"}
+    )
+
+    # change all types to object to use native python types
+    buffer_accounting = buffer_accounting.astype(object)
+
+    assert set(buffer_accounting.columns) == set(BUFFER_ACCOUNTING_COLUMNS)
+
+    return buffer_accounting

src/fetch/partner_fees.py

@@ -0,0 +1,62 @@
+"""Functionality for partner fees."""
+
+from collections import defaultdict
+
+import numpy as np
+import pandas as pd
+from pandas import DataFrame
+
+from src.config import ProtocolFeeConfig
+
+BATCH_DATA_COLUMNS = ["partner_list", "partner_fee_eth"]
+
+PARTNER_FEES_COLUMNS = ["partner", "partner_fee_eth", "partner_fee_tax"]
+
+
+def compute_partner_fees(batch_data: DataFrame, config: ProtocolFeeConfig) -> DataFrame:
+    """Compute partner fees per integrator"""
+
+    # validate batch rewards and quote rewards columns
+    assert set(BATCH_DATA_COLUMNS).issubset(set(batch_data.columns))
+
+    partner_fee_lists = batch_data[BATCH_DATA_COLUMNS]
+
+    partner_fees = compute_partner_fees_per_partner(partner_fee_lists, config)
+
+    return partner_fees
+
+
+def compute_partner_fees_per_partner(
+    partner_fee_lists: DataFrame, config: ProtocolFeeConfig
+) -> DataFrame:
+    """Aggregate fees from different solvers"""
+
+    partner_fees_dict: defaultdict[str, int] = defaultdict(int)
+    for _, row in partner_fee_lists.iterrows():
+        if row["partner_list"] is None:
+            continue
+
+        # We assume the two lists used below, i.e.,
+        # partner_list and partner_fee_eth,
+        # are "aligned".
+
+        for partner, partner_fee in zip(row["partner_list"], row["partner_fee_eth"]):
+            partner_fees_dict[partner] += int(partner_fee)
+
+    partner_fees_df = pd.DataFrame(
+        list(partner_fees_dict.items()),
+        columns=["partner", "partner_fee_eth"],
+    )
+
+    partner_fees_df["partner_fee_tax"] = np.where(
+        partner_fees_df["partner"] == config.reduced_cut_address,
+        config.partner_fee_reduced_cut,
+        config.partner_fee_cut,
+    )
+
+    # change all types to object to use native python types
+    partner_fees_df = partner_fees_df.astype(object)
+
+    assert set(partner_fees_df.columns) == set(PARTNER_FEES_COLUMNS)
+
+    return partner_fees_df