correction in the computation of num mech calls and roi formula

scripts/metrics.py

@@ -4,6 +4,17 @@ from tqdm import tqdm
 DEFAULT_MECH_FEE = 0.01  # xDAI
 
 
+def compute_total_nr_mech_calls_per_trader(trader_data: pd.DataFrame) -> int:
+    """Function to compute the total number of mech calls for alll markets
+    that the trader bet upon"""
+    nr_mech_calls_per_market = (
+        trader_data.groupby("title")["num_mech_calls"]
+        .max()
+        .reset_index(name="nr_mech_calls_per_market")
+    )
+    return nr_mech_calls_per_market.nr_mech_calls_per_market.sum()
+
+
 def compute_metrics(
     trader_address: str, trader_data: pd.DataFrame, live_metrics: bool = False
 ) -> dict:
@@ -15,22 +26,31 @@ def compute_metrics(
     agg_metrics = {}
     agg_metrics["trader_address"] = trader_address
     total_bet_amounts = trader_data.collateral_amount.sum()
-    total_num_mech_calls = trader_data.num_mech_calls.sum()
+    total_nr_mech_calls_all_markets = compute_total_nr_mech_calls_per_trader(
+        trader_data
+    )
+
     agg_metrics["bet_amount"] = total_bet_amounts
-    agg_metrics["nr_mech_calls"] = total_num_mech_calls
+    agg_metrics["nr_mech_calls"] = total_nr_mech_calls_all_markets
     agg_metrics["staking"] = trader_data.iloc[0].staking
     agg_metrics["nr_trades"] = len(trader_data)
     if live_metrics:
         return agg_metrics
-    total_net_earnings = trader_data.net_earnings.sum()
-    agg_metrics["net_earnings"] = total_net_earnings
-    agg_metrics["earnings"] = trader_data.earnings.sum()
+    total_earnings = trader_data.earnings.sum()
+    agg_metrics["earnings"] = total_earnings
     total_fee_amounts = trader_data.mech_fee_amount.sum()
     total_costs = (
         total_bet_amounts
         + total_fee_amounts
-        + (total_num_mech_calls * DEFAULT_MECH_FEE)
+        + (total_nr_mech_calls_all_markets * DEFAULT_MECH_FEE)
     )
+    total_net_earnings = total_earnings - total_costs
+    previous_net_earnings = trader_data.net_earnings.sum()
+    # if previous_net_earnings > total_net_earnings:
+    #     print(
+    #         f"case for trader {trader_address} where previous net_earnings was higher {previous_net_earnings} > {total_net_earnings} "
+    #     )
+    agg_metrics["net_earnings"] = total_net_earnings
     agg_metrics["roi"] = total_net_earnings / total_costs
     return agg_metrics
 
@@ -62,31 +82,6 @@ def compute_trader_metrics_by_market_creator(
     return metrics
 
 
-def compute_trader_metrics_by_trader_family(
-    trader_address: str, traders_data: pd.DataFrame, trader_family: str = "all"
-) -> dict:
-    """This function computes for a specific time window (week or day) the different metrics:
-    roi, net_earnings, earnings, bet_amount, nr_mech_calls and nr_trades.
-    The global roi of the trader agent by computing the individual net profit and the individual costs values
-    achieved per market and dividing both.
-    It is possible to filter by trader family: quickstart_agent, pearl_agent, non_agent, all
-    """
-    assert "trader_family" in traders_data.columns
-    filtered_traders_data = traders_data.loc[
-        traders_data["trader_address"] == trader_address
-    ]
-    if trader_family != "all":  # compute only for the specific trader family
-        filtered_traders_data = filtered_traders_data.loc[
-            filtered_traders_data["trader_family"] == trader_family
-        ]
-        if len(filtered_traders_data) == 0:
-            # tqdm.write(f"No data. Skipping trader family {trader_family}")
-            return {}  # No Data
-
-    metrics = compute_metrics(trader_address, filtered_traders_data)
-    return metrics
-
-
 def merge_trader_weekly_metrics(
     trader: str, weekly_data: pd.DataFrame, week: str
 ) -> pd.DataFrame:
@@ -232,6 +227,7 @@ def compute_daily_metrics_by_market_creator(
                     merge_trader_daily_metrics(trader, filtered_data, day, live_metrics)
                 )
     print("End computing all daily metrics by market creator")
+    print(f"length of contents = {len(contents)}")
     return pd.concat(contents, ignore_index=True)