Adding weekly data

active_traders.parquet

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all_trades_profitability.parquet.gz

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closed_market_metrics.parquet

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closed_markets_div.parquet

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+size 80767

daily_info.parquet

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daily_mech_requests.parquet

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+size 10871

error_by_markets.parquet

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errors_by_mech.parquet

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+size 6005

gnosis_txs_dataset.parquet

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invalid_trades.parquet

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retention_activity.parquet.gz

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scripts/daa.py

@@ -0,0 +1,94 @@
+import pandas as pd
+from datetime import datetime, timedelta, UTC
+from web3_utils import ROOT_DIR
+from utils import measure_execution_time
+from get_mech_info import fetch_block_number, get_last_block_number
+from tqdm import tqdm
+import requests
+from concurrent.futures import ThreadPoolExecutor
+from gnosis_timestamps import get_all_txs_between_blocks_from_trader_address
+
+DATETIME_60_DAYS_AGO = datetime.now(UTC) - timedelta(days=60)
+
+
+def get_block_number_from_datetime(datetime_sample) -> int:
+    """Function to get the block number from a datetime sample"""
+    print(f"Datetime sample = {datetime_sample}")
+    five_seconds = timedelta(seconds=5)
+    block_number = fetch_block_number(
+        int(datetime_sample.timestamp()),
+        int((datetime_sample + five_seconds).timestamp()),
+    )
+    block_number = block_number.get("number", "")
+    if block_number.isdigit():
+        return int(block_number)
+
+
+def safe_get_txs(session, trader_address, market_creator: str, start_block, end_block):
+    try:
+        df = get_all_txs_between_blocks_from_trader_address(
+            session, trader_address, market_creator, start_block, end_block
+        )
+        if df is None:
+            return pd.DataFrame()  # Return empty DataFrame if None
+        return df
+    except Exception as e:
+        print(f"Error getting transactions for {trader_address}: {e}")
+        return pd.DataFrame()  # Return empty DataFrame on error
+
+
+@measure_execution_time
+def prepare_gnosis_txs_dataset():
+    """Function to extract the gnosis txs from the Olas traders"""
+
+    # To read current all trades profitability
+    all_trades = pd.read_parquet(ROOT_DIR / "all_trades_profitability.parquet")
+
+    # to filter only traders of the trader_type Olas
+    olas_traders = all_trades[all_trades["staking"] != "non_Olas"]
+
+    # To extract the list of unique trader addresses from olas_traders and their corresponding market creator
+    olas_traders = olas_traders[["trader_address", "market_creator"]].drop_duplicates()
+    olas_traders = olas_traders.reset_index(drop=True)
+    print(f"Number of unique traders = {len(olas_traders)}")
+    print(olas_traders.market_creator.value_counts())
+
+    # Get the starting block number
+    starting_block = get_block_number_from_datetime(DATETIME_60_DAYS_AGO)
+    print(f"Starting block = {starting_block}")
+    # Get the ending block number
+    ending_block = get_last_block_number()
+    print(f"Ending block = {ending_block}")
+    print(f"Number of blocks to fetch = {ending_block - starting_block}")
+    # Parallelize getting the transactions from the trader addresses
+    results = []
+    session = requests.Session()
+    # Use this session for all requests
+    with ThreadPoolExecutor(max_workers=10) as executor:
+        results = list(
+            tqdm(
+                executor.map(
+                    lambda x: safe_get_txs(
+                        session,
+                        x.trader_address,
+                        x.market_creator,
+                        starting_block,
+                        ending_block,
+                    ),
+                    olas_traders.itertuples(index=False),
+                ),
+                total=len(olas_traders),
+            )
+        )
+    results = [r for r in results if isinstance(r, pd.DataFrame)]
+    return pd.concat(results, ignore_index=True)
+
+
+if __name__ == "__main__":
+    # Measure the execution time of the function
+    txs_dataset = prepare_gnosis_txs_dataset()
+    # Save the dataset to a parquet file
+    txs_dataset.to_parquet(
+        ROOT_DIR / "gnosis_txs_dataset.parquet", index=False, compression="gzip"
+    )
+    print(f"Number of transactions = {len(txs_dataset)}")

scripts/gnosis_timestamps.py

@@ -2,8 +2,9 @@ from web3 import Web3
 import os
 import requests
 import time
+import random
 import pickle
-from datetime import datetime, timezone
+from datetime import datetime, timezone, UTC, timedelta
 from functools import partial
 import pandas as pd
 import pytz
@@ -11,6 +12,7 @@ from tqdm import tqdm
 from utils import ROOT_DIR, TMP_DIR, measure_execution_time
 from concurrent.futures import ThreadPoolExecutor
 
+
 GNOSIS_API_INTERVAL = 0.2  # 5 calls in 1 second
 GNOSIS_URL = "https://api.gnosisscan.io/api"
 GNOSIS_API_KEY = os.environ.get("GNOSIS_API_KEY", None)
@@ -33,6 +35,65 @@ def transform_timestamp_to_datetime(timestamp):
     return dt
 
 
+def get_all_txs_between_blocks_from_trader_address(
+    session, trader_address: str, market_creator: str, starting_block, ending_block
+) -> pd.DataFrame:
+    """Function to get all the transactions from a trader address between two blocks"""
+    # https://docs.gnosisscan.io/api-endpoints/accounts
+    params = {
+        "module": "account",
+        "action": "txlist",
+        "address": trader_address,
+        "startblock": starting_block,
+        "endblock": ending_block,
+        "page": 1,
+        "offset": 5000,
+        "sort": "asc",
+        "apikey": GNOSIS_API_KEY,
+    }
+    max_retries = 5
+    for attempt in range(max_retries):
+        try:
+            response = session.get(GNOSIS_URL, params=params)
+            tx_list = response.json()["result"]
+            # time.sleep(GNOSIS_API_INTERVAL)
+            if len(tx_list) == 0:
+                print("No transactions found")
+                return pd.DataFrame()
+            # Extract only blockNumber and timeStamp
+            filtered_tx_list = [
+                {
+                    "blockNumber": tx["blockNumber"],
+                    "timeStamp": tx["timeStamp"],
+                    "trader_address": trader_address,
+                    "market_creator": market_creator,
+                    "hash": tx["hash"],
+                }
+                for tx in tx_list
+                if "blockNumber" in tx and "timeStamp" in tx
+            ]
+            df = pd.DataFrame.from_records(filtered_tx_list)
+            # Convert the timestamp to datetime
+            if not df.empty:
+                df["tx_datetime"] = df["timeStamp"].apply(
+                    lambda x: transform_timestamp_to_datetime(int(x))
+                )
+                # Convert the block number to int
+                df["blockNumber"] = df["blockNumber"].astype(int)
+            return df
+        except Exception as e:
+            if attempt < max_retries - 1:
+                sleep_time = (2**attempt) + random.random()
+                print(
+                    f"Attempt {attempt+1} failed. Retrying in {sleep_time:.2f} seconds..."
+                )
+                time.sleep(sleep_time)
+            else:
+                raise
+    print(f"Error getting transactions for {trader_address}: {e}")
+    return pd.DataFrame()
+
+
 def get_tx_hash(trader_address, request_block):
     """Function to get the transaction hash from the address and block number"""
     params = {

scripts/pull_data.py

@@ -135,7 +135,7 @@ def only_new_weekly_analysis():
 
     save_historical_data()
     try:
-        clean_old_data_from_parquet_files("2025-03-14")
+        clean_old_data_from_parquet_files("2025-03-18")
         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 206368

tools_accuracy.csv

@@ -1,13 +1,13 @@
 tool,tool_accuracy,total_requests,min,max
-claude-prediction-offline,60.77737447065941,66120,2025-03-14 05:58:40,2025-05-13 21:55:05
-claude-prediction-online,62.836010376993976,36234,2025-03-14 00:00:35,2025-05-13 23:07:50
-prediction-offline,55.20349988804492,290295,2025-03-14 00:00:05,2025-05-14 21:35:50
-prediction-offline-sme,52.338530066815146,3143,2025-03-14 00:00:55,2025-05-13 20:16:20
-prediction-online,45.43903752917939,11138,2025-03-14 07:18:15,2025-05-14 20:52:40
-prediction-online-sme,47.43523760507806,5829,2025-03-14 08:27:05,2025-05-14 17:20:40
-prediction-request-rag,45.07042253521127,284,2025-03-14 11:52:15,2025-05-11 21:35:05
-prediction-request-rag-claude,50.602409638554214,249,2025-03-14 07:14:25,2025-05-12 17:47:40
-prediction-request-reasoning,52.409874974303385,107018,2025-03-14 07:02:40,2025-05-14 22:49:50
-prediction-request-reasoning-claude,67.38197424892704,233,2025-03-14 13:27:10,2025-05-13 16:42:35
-prediction-url-cot-claude,60.547667342799194,986,2025-03-14 00:24:30,2025-05-13 20:03:25
-superforcaster,52.55152109911678,16304,2025-03-14 00:28:25,2025-05-14 21:22:30
+claude-prediction-offline,59.6009936584839,64811,2025-03-18 00:03:55,2025-05-17 23:32:30
+claude-prediction-online,62.110541112367045,34503,2025-03-18 00:04:30,2025-05-17 23:03:00
+prediction-offline,55.13734075734771,285021,2025-03-18 00:00:25,2025-05-18 23:42:20
+prediction-offline-sme,49.43551146082792,2923,2025-03-18 00:34:45,2025-05-17 18:47:55
+prediction-online,46.3707616288976,11738,2025-03-18 07:37:50,2025-05-18 21:55:40
+prediction-online-sme,47.47508866745483,5921,2025-03-18 07:38:35,2025-05-18 21:20:20
+prediction-request-rag,45.348837209302324,258,2025-03-18 08:06:20,2025-05-17 20:43:25
+prediction-request-rag-claude,50.65502183406113,229,2025-03-18 13:32:45,2025-05-16 15:02:00
+prediction-request-reasoning,52.527100088764556,111531,2025-03-18 07:00:10,2025-05-18 23:45:00
+prediction-request-reasoning-claude,68.80733944954129,218,2025-03-18 14:11:20,2025-05-17 18:24:25
+prediction-url-cot-claude,59.32203389830508,944,2025-03-18 08:06:50,2025-05-17 22:14:10
+superforcaster,52.69106237399963,16369,2025-03-18 00:00:45,2025-05-18 21:15:50

unknown_traders.parquet

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weekly_mech_calls.parquet

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winning_df.parquet

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