update insider cron job

app/cron_insider_tracker.py

@@ -3,10 +3,13 @@ import asyncio
 import aiohttp
 import sqlite3
 from datetime import datetime
+from itertools import groupby
+from operator import itemgetter
 from aiofiles import open as async_open
 from tqdm import tqdm
 from dotenv import load_dotenv
 import os
+import time
 
 load_dotenv()
 api_key = os.getenv('FMP_API_KEY')
@@ -17,9 +20,81 @@ async def save_json(data):
         await file.write(ujson.dumps(data))
 
 
+def format_name(name):
+    """
+    Formats a name from "LASTNAME MIDDLE FIRSTNAME" format to "Firstname Middle Lastname"
+    
+    Args:
+        name (str): Name in uppercase format (e.g., "SINGLETON J MATTHEW")
+    
+    Returns:
+        str: Formatted name (e.g., "Matthew J Singleton")
+    """
+    # Split the name into parts
+    parts = name.strip().split()
+    
+    # Handle empty string or single word
+    if not parts:
+        return ""
+    if len(parts) == 1:
+        return parts[0].capitalize()
+    
+    # The first part is the last name
+    lastname = parts[0].capitalize()
+    
+    # The remaining parts are in reverse order
+    other_parts = parts[1:]
+    other_parts.reverse()
+    
+    # Capitalize each part
+    other_parts = [part.capitalize() for part in other_parts]
+    
+    # Join all parts
+    return " ".join(other_parts + [lastname])
+
+def aggregate_transactions(transactions, min_value=100_000):
+
+    # Sort transactions by the keys we want to group by
+    sorted_transactions = sorted(
+        transactions,
+        key=lambda x: (x['reportingName'], x['symbol'], x['transactionType'])
+    )
+    
+    # Group by reportingName, symbol, and transactionType
+    result = []
+    for key, group in groupby(
+        sorted_transactions,
+        key=lambda x: (x['reportingName'], x['symbol'], x['transactionType'])
+    ):
+        group_list = list(group)
+        
+        # Calculate average value
+        avg_value = sum(t['value'] for t in group_list) / len(group_list)
+        
+        # Only include transactions with average value >= min_value
+        if avg_value >= min_value:
+            # Find latest filing date
+            latest_date = max(
+                datetime.strptime(t['filingDate'], '%Y-%m-%d %H:%M:%S')
+                for t in group_list
+            ).strftime('%Y-%m-%d %H:%M:%S')
+            
+            # Create aggregated transaction with formatted name
+            result.append({
+                'reportingName': format_name(key[0]),
+                'symbol': key[1],
+                'transactionType': key[2],
+                'filingDate': latest_date,
+                'avgValue': avg_value
+            })
+    
+    # Sort the final result by filingDate
+    return sorted(result, key=lambda x: x['filingDate'], reverse=True)
+
+
 async def get_data(session, symbols):
     res_list = []
-    for page in range(0, 3):  # Adjust the number of pages as needed
+    for page in range(0, 20):  # Adjust the number of pages as needed
         url = f"https://financialmodelingprep.com/api/v4/insider-trading?page={page}&apikey={api_key}"
         async with session.get(url) as response:
             try:
@@ -48,6 +123,9 @@ async def get_data(session, symbols):
                 print(f"Error while fetching data: {e}")
                 break
 
+    res_list = aggregate_transactions(res_list)
+    
+
     new_data = []
     for item in res_list:
         try:
@@ -79,7 +157,6 @@ async def run():
     async with aiohttp.ClientSession() as session:
         data = await get_data(session, stock_symbols)
         if len(data) > 0:
-            print(data)
             print(f"Fetched {len(data)} records.")
             await save_json(data)