new dataset for weekly traders metrics

scripts/market_metrics.py

@@ -1,7 +1,12 @@
 import numpy as np
 import pandas as pd
+from typing import Tuple
 import time
+from tqdm import tqdm
 from utils import convert_hex_to_int, ROOT_DIR, TMP_DIR
+from get_mech_info import read_all_trades_profitability
+from tools_metrics import prepare_tools
+from predict_kpis import compute_markets_agent_roi
 
 
 def determine_market_status(row):
@@ -59,6 +64,84 @@ def compute_market_metrics(all_trades: pd.DataFrame):
     print(market_metrics.head())
 
 
+def prepare_traders_data() -> Tuple[pd.DataFrame, pd.DataFrame]:
+    """Prepare traders data for weekly metrics computation."""
+    trades = read_all_trades_profitability()
+    trades["creation_timestamp"] = pd.to_datetime(trades["creation_timestamp"])
+    trades["creation_timestamp"] = trades["creation_timestamp"].dt.tz_convert("UTC")
+    trades["creation_date"] = trades["creation_timestamp"].dt.date
+    trades = trades.sort_values(by="creation_timestamp", ascending=True)
+    unique_addresses = trades.trader_address.unique()
+    closed_markets = trades.title.unique()
+    # filter the mech requests done these traders on closed markets
+    try:
+        tools_df = pd.read_parquet(TMP_DIR / "tools.parquet")
+        tools_df = prepare_tools(tools_df)
+    except Exception as e:
+        print(f"Error reading tools parquet file {e}")
+        return None
+    traders_activity = tools_df[
+        tools_df["trader_address"].isin(unique_addresses)
+    ].copy()
+    traders_activity = traders_activity[traders_activity["title"].isin(closed_markets)]
+    traders_activity["request_time"] = pd.to_datetime(
+        traders_activity["request_time"], utc=True
+    )
+    traders_activity = traders_activity.sort_values(by="request_time", ascending=True)
+    traders_activity["request_date"] = traders_activity["request_time"].dt.date
+    return trades, traders_activity
+
+
+def compute_weekly_trader_metrics():
+    trades_data, mechs_data = prepare_traders_data()
+    trades_data["week_start"] = (
+        trades_data["creation_timestamp"].dt.to_period("W").dt.start_time
+    )
+
+    grouped_trades = trades_data.groupby("week_start")
+    contents = []
+    traders = trades_data.trader_address.unique()
+    # Iterate through the groups (each group represents a week)
+    for week, week_data in grouped_trades:
+        print(f"Week: {week}")  # Print the week identifier
+
+        # for all closed markets
+        closed_markets = week_data.title.unique()
+        for trader in tqdm(
+            traders, total=len(traders), desc="Computing metrics for traders"
+        ):
+            # compute all trades done by the trader on those markets, no matter from which week
+            trader_trades = trades_data[
+                (trades_data["trader_address"] == trader)
+                & (trades_data["title"].isin(closed_markets))
+            ]
+            if len(trader_trades) == 0:
+                # no trading activity
+                continue
+
+            # filter mech requests done by the trader on those markets
+            trader_mech_calls = mechs_data.loc[
+                (mechs_data["trader_address"] == trader)
+                & (mechs_data["title"].isin(closed_markets))
+            ]
+            # compute the ROI for that market, that trader and that week
+            try:
+                # Convert the dictionary to DataFrame before appending
+                roi_dict = compute_markets_agent_roi(
+                    trader_trades, trader_mech_calls, trader, "week", week
+                )
+                contents.append(pd.DataFrame([roi_dict]))
+            except ValueError as e:
+                print(f"Skipping ROI calculation: {e}")
+                continue
+    traders_weekly_metrics = pd.concat(contents, ignore_index=True)
+    return traders_weekly_metrics
+
+
 if __name__ == "__main__":
     all_trades = pd.read_parquet(TMP_DIR / "fpmmTrades.parquet")
     compute_market_metrics(all_trades)
+    weekly_metrics_df = compute_weekly_trader_metrics()
+    weekly_metrics_df.to_parquet(
+        ROOT_DIR / "traders_weekly_metrics.parquet", index=False
+    )

scripts/predict_kpis.py

@@ -310,12 +310,12 @@ def compute_markets_agent_roi(
     period_value: datetime,
 ) -> dict:
     # ROI formula net_earnings/total_costs
-    earnings = agent_trades.earnings.sum()
+    total_earnings = agent_trades.earnings.sum()
     total_market_fees = agent_trades.trade_fee_amount.sum()
     total_mech_fees = len(mech_calls) * DEFAULT_MECH_FEE
     total_bet_amount = agent_trades.collateral_amount.sum()
     total_costs = total_bet_amount + total_market_fees + total_mech_fees
-    net_earnings = earnings - total_costs
+    net_earnings = total_earnings - total_costs
     if total_costs == 0:
         raise ValueError(f"Total costs for agent {agent} are zero")
     roi = net_earnings / total_costs
@@ -325,8 +325,10 @@ def compute_markets_agent_roi(
             "week_start": period_value,
             "roi": roi,
             "net_earnings": net_earnings,
+            "earnings": total_earnings,
             "total_bet_amount": total_bet_amount,
             "total_mech_calls": len(mech_calls),
+            "nr_trades": len(agent_trades),
         }
     if period == "day":
         return {
@@ -334,8 +336,10 @@ def compute_markets_agent_roi(
             "creation_date": period_value,
             "roi": roi,
             "net_earnings": net_earnings,
+            "earnings": total_earnings,
             "total_bet_amount": total_bet_amount,
             "total_mech_calls": len(mech_calls),
+            "nr_trades": len(agent_trades),
         }
     raise ValueError(
         f"Invalid period {period} for agent {agent}. Expected 'week' or 'day'."

traders_weekly_metrics.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5af7a128ae02f8213fd6054b18919517539748c5e0d113ae651e48a6b1d4b8fc
+size 214869