updating daily data

active_traders.parquet

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+size 18522584

all_trades_profitability.parquet.gz

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+size 12735685

closed_market_metrics.parquet

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+size 138783

daily_info.parquet

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+size 5683511

daily_mech_requests.parquet

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+size 10169

daily_mech_requests_by_pearl_agents.parquet

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+size 4432

error_by_markets.parquet

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+size 10909

errors_by_mech.parquet

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+size 6098

invalid_trades.parquet

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+size 237464

latest_result_DAA_Pearl.parquet

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+size 5291

latest_result_DAA_QS.parquet

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-size 5802
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+size 5826

old_tools_accuracy.csv

@@ -1,13 +1,13 @@
 tool,tool_accuracy,total_requests,min,max
-claude-prediction-offline,59.618459933947506,345180,2025-04-02 05:16:45,2025-06-06 00:13:05
-claude-prediction-online,62.621820624279216,157814,2025-04-02 00:01:00,2025-05-21 18:47:35
-prediction-offline,58.390693147163866,2138983,2025-04-02 00:00:05,2025-06-09 07:28:55
-prediction-offline-sme,59.52069716775599,18360,2025-04-02 00:57:25,2025-06-07 08:53:25
-prediction-online,50.26802218114602,119020,2025-04-02 07:32:50,2025-06-08 22:40:55
-prediction-online-sme,50.509700756330155,66902,2025-04-02 07:33:25,2025-06-09 04:59:30
-prediction-request-rag,49.81549815498155,1355,2025-04-02 14:01:55,2025-06-03 18:59:40
-prediction-request-rag-claude,51.31158029430582,1563,2025-04-02 12:05:10,2025-06-03 17:51:10
-prediction-request-reasoning,52.58601483826535,982999,2025-04-02 07:03:25,2025-06-08 23:46:20
-prediction-request-reasoning-claude,65.74074074074075,1188,2025-04-02 12:25:50,2025-05-28 09:25:40
-prediction-url-cot-claude,57.6890756302521,4760,2025-04-02 05:46:40,2025-05-20 15:21:20
-superforcaster,55.255578133322814,101378,2025-04-02 00:02:10,2025-06-08 21:00:40
+claude-prediction-offline,59.4460280810884,358177,2025-04-02 05:16:45,2025-06-06 00:13:05
+claude-prediction-online,62.54165685897992,161438,2025-04-02 00:01:00,2025-05-21 18:47:35
+prediction-offline,58.46082343489606,2259195,2025-04-02 00:00:05,2025-06-09 07:28:55
+prediction-offline-sme,59.35504135692597,19102,2025-04-02 00:57:25,2025-06-07 08:53:25
+prediction-online,50.531919075962605,127181,2025-04-02 07:32:50,2025-06-08 22:40:55
+prediction-online-sme,50.66788269959309,71270,2025-04-02 07:33:25,2025-06-09 04:59:30
+prediction-request-rag,50.21067415730337,1424,2025-04-02 14:01:55,2025-06-03 18:59:40
+prediction-request-rag-claude,51.951219512195124,1640,2025-04-02 12:05:10,2025-06-03 17:51:10
+prediction-request-reasoning,52.567043388493424,1038432,2025-04-02 07:03:25,2025-06-08 23:46:20
+prediction-request-reasoning-claude,65.94202898550726,1242,2025-04-02 12:25:50,2025-05-28 09:25:40
+prediction-url-cot-claude,57.54314720812183,4925,2025-04-02 05:46:40,2025-05-20 15:21:20
+superforcaster,55.27624465500028,106174,2025-04-02 00:02:10,2025-06-08 21:00:40

pearl_agents.parquet

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+size 46669

retention_activity.parquet.gz

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-size 4676466
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+size 4664359

scripts/market_metrics.py

@@ -80,6 +80,8 @@ def prepare_traders_data() -> Tuple[pd.DataFrame, pd.DataFrame]:
     except Exception as e:
         print(f"Error reading tools parquet file {e}")
         return None
+    # remove the market_creator all category
+    tools_df = tools_df[tools_df["market_creator"] != "all"]
     traders_activity = tools_df[
         tools_df["trader_address"].isin(unique_addresses)
     ].copy()

scripts/predict_kpis.py

@@ -11,7 +11,7 @@ from tqdm import tqdm
 import requests
 import os
 import pickle
-from utils import TMP_DIR, INC_TOOLS, ROOT_DIR
+from utils import TMP_DIR, ROOT_DIR
 from concurrent.futures import ThreadPoolExecutor
 from gnosis_timestamps import get_all_txs_between_blocks_from_trader_address
 from dune_client.types import QueryParameter
@@ -188,7 +188,7 @@ def load_predict_services_file(dune_client: DuneClient):
         print(f"CSV file uploaded successfully!")
 
     except FileNotFoundError:
-        print(f"Error: CSV file not found at {ROOT_DIR / "olas_trader_agents.csv"}")
+        print(f"Error: CSV file not found at {ROOT_DIR} / predict_services.csv")
     except Exception as e:
         print(f"An error occurred during upload: {e}")
 
@@ -278,6 +278,8 @@ def get_mech_requests_on_closed_markets_by_pearl_agents(
     except Exception as e:
         print(f"Error reading tools parquet file {e}")
         return None
+    # remove the market_creator= "all" entries
+    tools_df = tools_df[tools_df["market_creator"] != "all"]
     agents_activity = tools_df[tools_df["trader_address"].isin(unique_addresses)].copy()
     agents_activity = agents_activity[agents_activity["title"].isin(closed_markets)]
     if len(agents_activity) > 0:
@@ -431,6 +433,39 @@ def compute_weekly_avg_roi_pearl_agents(
     return weekly_avg_roi_data
 
 
+def compute_total_roi_pearl_agents(agent_trades, agent_mech_requests) -> pd.DataFrame:
+    closed_markets = agent_trades.title.unique()
+    contents = []
+    agents = agent_trades.trader_address.unique()
+    for agent in agents:
+        # compute all trades done by the agent on those markets, no matter from which week
+        agent_markets_data = agent_trades.loc[
+            (agent_trades["trader_address"] == agent)
+            & (agent_trades["title"].isin(closed_markets))
+        ]
+        if len(agent_markets_data) == 0:
+            # not betting activity
+            continue
+        # filter mech requests done by the agent on that market
+        agent_mech_calls = agent_mech_requests.loc[
+            (agent_mech_requests["trader_address"] == agent)
+            & (agent_mech_requests["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(
+                agent_markets_data, agent_mech_calls, agent, "day", None
+            )
+            contents.append(pd.DataFrame([roi_dict]))
+        except ValueError as e:
+            print(f"Skipping ROI calculation: {e}")
+            continue
+    total_roi_data = pd.concat(contents, ignore_index=True)
+    total_roi_data.to_parquet(ROOT_DIR / "total_roi.parquet")
+
+
 def compute_two_weeks_rolling_avg_roi_pearl_agents(
     agents_trades: pd.DataFrame, agents_mech_requests: pd.DataFrame
 ) -> pd.DataFrame:
@@ -489,10 +524,12 @@ def compute_two_weeks_rolling_avg_roi_pearl_agents(
 if __name__ == "__main__":
     prepare_daa_data()
     prepare_predict_services_dataset()
-    # dune = setup_dune_python_client()
+    dune = setup_dune_python_client()
     # load_predict_services_file(dune_client=dune)
     agents_trades, agents_mech_requests = prepare_agents_data()
-
+    # compute_total_roi_pearl_agents(
+    #     agent_trades=agents_trades, agent_mech_requests=agents_mech_requests
+    # )
     weekly_avg = compute_weekly_avg_roi_pearl_agents(
         agents_trades, agents_mech_requests
     )

scripts/pull_data.py

@@ -136,7 +136,7 @@ def only_new_weekly_analysis():
 
     save_historical_data()
     try:
-        clean_old_data_from_parquet_files("2025-04-29")
+        clean_old_data_from_parquet_files("2025-05-02")
         clean_old_data_from_json_files()
     except Exception as e:
         print("Error cleaning the oldest information from parquet files")

service_map.pkl

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-size 157999
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+size 158519

tools_accuracy.csv

@@ -1,13 +1,13 @@
 tool,tool_accuracy,total_requests,min,max
-claude-prediction-offline,50.274710990949636,19744,2025-05-25 10:30:10,2025-06-29 23:45:05
-claude-prediction-online,49.363139308463246,26735,2025-05-25 00:00:15,2025-06-29 23:42:35
-prediction-offline,58.98738002164555,495321,2025-05-25 00:00:30,2025-06-29 23:44:00
-prediction-offline-sme,60.06413232848768,1491,2025-05-25 00:13:15,2025-06-27 08:12:35
-prediction-online,57.93239712787832,72005,2025-05-25 07:33:20,2025-06-29 23:44:40
-prediction-online-sme,52.55046876031451,56045,2025-05-25 07:05:50,2025-06-29 23:42:35
-prediction-request-rag,39.104435043722226,47847,2025-05-25 14:16:25,2025-06-29 23:42:20
-prediction-request-rag-claude,72.75753093060787,352,2025-05-25 13:21:55,2025-06-29 22:38:00
-prediction-request-reasoning,55.5764699844214,169207,2025-05-25 07:14:15,2025-06-29 23:45:10
-prediction-request-reasoning-claude,77.68147933704856,328,2025-05-25 13:48:00,2025-06-29 22:40:25
-prediction-url-cot-claude,71.20999090232402,321,2025-05-25 00:12:40,2025-06-18 18:07:55
-superforcaster,65.7564104601496,101531,2025-05-25 00:00:20,2025-06-29 23:28:15
+claude-prediction-offline,52.489079123026734,32150,2025-05-25 10:30:10,2025-07-02 22:22:30
+claude-prediction-online,50.97268933846528,39965,2025-05-25 00:00:15,2025-07-02 23:07:45
+prediction-offline,59.3776319899559,631864,2025-05-25 00:00:30,2025-07-02 23:44:40
+prediction-offline-sme,60.25242177383664,1805,2025-05-25 00:13:15,2025-07-01 13:31:30
+prediction-online,58.92259629715118,99979,2025-05-25 07:33:20,2025-07-02 22:20:20
+prediction-online-sme,52.990663647931754,78508,2025-05-25 07:05:50,2025-07-02 23:24:05
+prediction-request-rag,40.00016064652507,61648,2025-05-25 14:16:25,2025-07-02 22:18:30
+prediction-request-rag-claude,67.27216350998684,458,2025-05-25 13:21:55,2025-07-02 06:30:10
+prediction-request-reasoning,55.17073673090758,213737,2025-05-25 07:14:15,2025-07-02 22:26:15
+prediction-request-reasoning-claude,70.78315005527433,408,2025-05-25 13:48:00,2025-07-01 21:23:20
+prediction-url-cot-claude,70.82081675732725,327,2025-05-25 00:12:40,2025-06-18 18:07:55
+superforcaster,65.34915303763684,127148,2025-05-25 00:00:20,2025-07-02 22:13:40

total_roi.parquet

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traders_weekly_metrics.parquet

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+size 3057

unknown_traders.parquet

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validation.parquet

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weekly_mech_calls.parquet

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winning_df.parquet

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