updating scripts and files. New daily mech requests

active_traders.parquet

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+size 78737

all_trades_profitability.parquet.gz

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+size 10304115

closed_market_metrics.parquet

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+size 135945

closed_markets_div.parquet

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+size 85582

daily_info.parquet

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+size 2726173

daily_mech_requests.parquet

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+oid sha256:a074e073d5cb0200eac4032a2b5663a4286c41a5ed6f0d675ee2e146aa284c36
+size 10572

error_by_markets.parquet

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+oid sha256:186fbeade1cf969fccf481d45883379cc23ccfffa2861c9fdb721be04ac0865a
+size 12610

invalid_trades.parquet

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+size 769743

retention_activity.parquet.gz

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+size 7882734

scripts/daily_data.py

@@ -1,5 +1,5 @@
 import logging
-from utils import measure_execution_time, ROOT_DIR, TMP_DIR
+from utils import measure_execution_time, ROOT_DIR, TMP_DIR, transform_to_datetime
 from profitability import (
     analyse_all_traders,
     label_trades_by_staking,
@@ -8,7 +8,6 @@ import pandas as pd
 from nr_mech_calls import (
     create_unknown_traders_df,
     compute_daily_mech_calls,
-    transform_to_datetime,
 )
 from markets import check_current_week_data
 from staking import generate_retention_activity_file

scripts/gnosis_timestamps.py

@@ -134,7 +134,7 @@ def compute_request_time(tools_df: pd.DataFrame) -> pd.DataFrame:
         tools_df.at[i, "request_time"] = timestamp
     # creating other time fields
     tools_df["request_month_year"] = pd.to_datetime(
-        tools_df["request_time"]
+        tools_df["request_time"], utc=True
     ).dt.strftime("%Y-%m")
     tools_df["request_month_year_week"] = (
         pd.to_datetime(tools_df["request_time"])

scripts/{num_mech_calls.py → mech_calls_file_generator.py}

@@ -30,7 +30,7 @@ def compute_total_mech_calls():
     try:
         print("Reading tools file")
         tools = pd.read_parquet(TMP_DIR / "tools.parquet")
-        tools["request_time"] = pd.to_datetime(tools["request_time"])
+        tools["request_time"] = pd.to_datetime(tools["request_time"], utc=True)
         tools["request_date"] = tools["request_time"].dt.date
         tools = tools.sort_values(by="request_time", ascending=True)
         tools["month_year_week"] = (

scripts/nr_mech_calls.py

@@ -1,7 +1,6 @@
 import pandas as pd
-from utils import ROOT_DIR, DEFAULT_MECH_FEE, TMP_DIR, transform_to_datetime
+from utils import ROOT_DIR, DEFAULT_MECH_FEE
 from tqdm import tqdm
-
 from typing import Dict, Any
 from collections import defaultdict
 from tools import IRRELEVANT_TOOLS
@@ -138,7 +137,7 @@ def compute_daily_mech_calls(
     fpmmTrades["creation_timestamp"] = pd.to_datetime(fpmmTrades["creationTimestamp"])
     fpmmTrades["creation_date"] = fpmmTrades["creation_timestamp"].dt.date
     fpmmTrades = fpmmTrades.sort_values(by="creation_timestamp", ascending=True)
-    tools["request_time"] = pd.to_datetime(tools["request_time"])
+    tools["request_time"] = pd.to_datetime(tools["request_time"], utc=True)
     tools["request_date"] = tools["request_time"].dt.date
     tools = tools.sort_values(by="request_time", ascending=True)
     all_mech_calls = []
@@ -240,7 +239,7 @@ def compute_mech_calls_based_on_timestamps(
     fpmmTrades["creation_timestamp"] = pd.to_datetime(fpmmTrades["creationTimestamp"])
     fpmmTrades["creation_date"] = fpmmTrades["creation_timestamp"].dt.date
     fpmmTrades = fpmmTrades.sort_values(by="creation_timestamp", ascending=True)
-    tools["request_time"] = pd.to_datetime(tools["request_time"])
+    tools["request_time"] = pd.to_datetime(tools["request_time"], utc=True)
     tools["request_date"] = tools["request_time"].dt.date
     tools = tools.sort_values(by="request_time", ascending=True)
     all_mech_calls = []
@@ -258,11 +257,4 @@ def compute_mech_calls_based_on_timestamps(
 
 
 if __name__ == "__main__":
-    # update_trade_nr_mech_calls(non_agents=True)
-    tools = pd.read_parquet(TMP_DIR / "tools.parquet")
-    fpmmTrades = pd.read_parquet(TMP_DIR / "fpmmTrades.parquet")
-    fpmmTrades["creationTimestamp"] = fpmmTrades["creationTimestamp"].apply(
-        lambda x: transform_to_datetime(x)
-    )
-    result = compute_mech_calls_based_on_timestamps(fpmmTrades=fpmmTrades, tools=tools)
-    result.to_parquet(TMP_DIR / "result_df.parquet", index=False)
+    print("Nothing to do")

scripts/profitability.py

@@ -37,11 +37,11 @@ from utils import (
     DEFAULT_MECH_FEE,
     TMP_DIR,
     measure_execution_time,
+    transform_to_datetime,
 )
 from staking import label_trades_by_staking
 from nr_mech_calls import (
     create_unknown_traders_df,
-    transform_to_datetime,
     compute_mech_calls_based_on_timestamps,
 )
 

scripts/pull_data.py

@@ -135,7 +135,7 @@ def only_new_weekly_analysis():
 
     save_historical_data()
     try:
-        clean_old_data_from_parquet_files("2024-12-09")
+        clean_old_data_from_parquet_files("2024-12-11")
     except Exception as e:
         print("Error cleaning the oldest information from parquet files")
         print(f"reason = {e}")

scripts/tools_metrics.py

@@ -57,9 +57,9 @@ def get_tool_winning_rate_by_market(
 
 
 def prepare_tools(tools: pd.DataFrame) -> pd.DataFrame:
-    tools["request_time"] = pd.to_datetime(tools["request_time"])
+    tools["request_time"] = pd.to_datetime(tools["request_time"], utc=True)
     tools = tools.sort_values(by="request_time", ascending=True)
-
+    tools["request_date"] = tools["request_time"].dt.date
     tools["request_month_year_week"] = (
         pd.to_datetime(tools["request_time"])
         .dt.to_period("W")
@@ -80,8 +80,18 @@ def compute_tools_based_datasets():
         tools_df = pd.read_parquet(TMP_DIR / "tools.parquet")
         tools_df = prepare_tools(tools_df)
     except Exception as e:
-        print(f"Error reading old tools parquet file {e}")
+        print(f"Error reading tools parquet file {e}")
         return None
+
+    # daily mech requests
+    daily_mech_req_per_tool = (
+        tools_df.groupby(["request_date", "tool", "market_creator"])["request_id"]
+        .count()
+        .reset_index(name="total_mech_requests")
+    )
+    daily_mech_req_per_tool.to_parquet(
+        ROOT_DIR / "daily_mech_requests.parquet", index=False
+    )
     # error by markets
     error_by_markets = get_error_data_by_market(tools_df=tools_df, inc_tools=INC_TOOLS)
     error_by_markets.to_parquet(ROOT_DIR / "error_by_markets.parquet", index=False)
@@ -89,7 +99,11 @@ def compute_tools_based_datasets():
         tools_df = pd.read_parquet(TMP_DIR / "tools.parquet")
         tools_df = prepare_tools(tools_df)
     except Exception as e:
-        print(f"Error reading old tools parquet file {e}")
+        print(f"Error reading tools parquet file {e}")
         return None
     winning_df = get_tool_winning_rate_by_market(tools_df, inc_tools=INC_TOOLS)
     winning_df.to_parquet(ROOT_DIR / "winning_df.parquet", index=False)
+
+
+if __name__ == "__main__":
+    compute_tools_based_datasets()

service_map.pkl

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tools_accuracy.csv

@@ -1,13 +1,13 @@
 tool,tool_accuracy,total_requests,min,max
-claude-prediction-offline,57.42090202393503,86317,2024-12-09 00:03:40,2025-02-08 22:53:10
-claude-prediction-online,58.005167004982475,54190,2024-12-09 00:00:15,2025-02-09 01:41:10
-prediction-offline,59.210526315789465,161576,2024-12-09 00:00:05,2025-02-09 02:15:50
-prediction-offline-sme,50.1501708841095,28967,2024-12-09 00:03:25,2025-02-08 15:31:00
-prediction-online,50.49545829892651,4844,2024-12-09 00:40:50,2025-02-08 17:36:10
-prediction-online-sme,49.05218317358893,4695,2024-12-09 00:05:50,2025-02-08 17:01:45
-prediction-request-rag,47.74678111587983,932,2024-12-09 00:13:15,2025-02-07 19:02:55
-prediction-request-rag-claude,46.95187165775401,935,2024-12-09 00:30:50,2025-02-08 16:07:25
-prediction-request-reasoning,52.584455511407555,111242,2024-12-09 00:01:10,2025-02-08 18:21:40
-prediction-request-reasoning-claude,62.217659137576995,974,2024-12-09 02:09:10,2025-02-08 16:02:55
-prediction-url-cot-claude,53.17820658342792,1762,2024-12-09 00:05:25,2025-02-08 19:16:35
-superforcaster,53.81277795856384,9219,2024-12-09 00:16:25,2025-02-08 22:33:45
+claude-prediction-offline,58.01688764497995,84322,2024-12-11 00:00:35,2025-02-10 23:47:05
+claude-prediction-online,58.75600044654485,53746,2024-12-11 00:00:10,2025-02-11 17:08:10
+prediction-offline,59.28727182107034,160510,2024-12-11 00:00:40,2025-02-11 02:05:50
+prediction-offline-sme,50.16101880992462,27326,2024-12-11 00:02:20,2025-02-10 21:58:55
+prediction-online,49.9157184997893,4746,2024-12-11 00:25:45,2025-02-10 19:09:45
+prediction-online-sme,49.02548725637181,4669,2024-12-11 01:28:25,2025-02-10 19:18:30
+prediction-request-rag,46.76089517078916,849,2024-12-11 00:24:25,2025-02-10 17:56:20
+prediction-request-rag-claude,47.38067520372526,859,2024-12-11 11:10:05,2025-02-10 12:20:15
+prediction-request-reasoning,52.30039975061429,109068,2024-12-11 00:00:20,2025-02-10 20:05:45
+prediction-request-reasoning-claude,62.05016357688113,917,2024-12-11 00:25:45,2025-02-10 17:44:25
+prediction-url-cot-claude,53.30948121645797,1677,2024-12-11 00:25:45,2025-02-10 16:54:15
+superforcaster,53.6015574302401,9246,2024-12-11 00:25:55,2025-02-10 23:53:40

unknown_traders.parquet

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weekly_mech_calls.parquet

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winning_df.parquet

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