update business cron job

app/cron_business_metrics.py

@@ -12,6 +12,9 @@ import os
 load_dotenv()
 api_key = os.getenv('FMP_API_KEY')
 
+def standardize_strings(string_list):
+    return [string.title() for string in string_list]
+
 def convert_to_dict(data):
     result = {}
     
@@ -28,7 +31,113 @@ async def save_json(data, symbol):
     with open(f"json/business-metrics/{symbol}.json", 'wb') as file:
         file.write(orjson.dumps(data))
 
-def prepare_dataset(data):
+import orjson
+from datetime import datetime
+
+def convert_to_dict(data):
+    result = {}
+    
+    for entry in data:
+        for date, categories in entry.items():
+            if date not in result:
+                result[date] = {}
+            for category, amount in categories.items():
+                result[date][category] = amount
+                
+    return result
+
+def prepare_expense_dataset(symbol):
+    # Define the list of key elements you want to track
+    expense_keys = [
+        'researchAndDevelopmentExpenses',
+        'generalAndAdministrativeExpenses',
+        'sellingAndMarketingExpenses',
+        'operatingExpenses',
+        'costOfRevenue'
+    ]
+    
+    # Open the financial statement data for the symbol
+    with open(f"json/financial-statements/income-statement/quarter/{symbol}.json", 'rb') as file:
+        data = orjson.loads(file.read())
+    # Convert the data into a dictionary
+    
+    # Initialize a dictionary to hold the history and growth for each key
+    expense_data = {}
+    
+    for key in expense_keys:
+        expense_data[key] = []
+        
+        # Prepare the data for the current key
+        for entry in data:
+            date = entry.get('date')
+            value = entry.get(key, 0)  # Default to 0 if the key is missing
+            expense_data[key].append({'date': date, 'value': value})
+        
+        # Sort the list by date
+        expense_data[key] = sorted(expense_data[key], key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'))
+        
+        # Initialize 'valueGrowth' as None for all entries
+        for item in expense_data[key]:
+            item['valueGrowth'] = None
+
+        # Calculate valueGrowth for each item based on the previous date value
+        for i in range(1, len(expense_data[key])):
+            try:
+                current_item = expense_data[key][i]
+                prev_item = expense_data[key][i - 1]
+                growth = round(((current_item['value'] - prev_item['value']) / prev_item['value']) * 100, 2) if prev_item['value'] != 0 else None
+                current_item['valueGrowth'] = growth
+            except:
+                current_item['valueGrowth'] = None
+
+    # Return the results as a dictionary with all keys
+    return expense_data
+
+def prepare_geo_dataset(data):
+    data = convert_to_dict(data)
+    res_list = {}
+    geo_name_list = []
+    geo_history_list = []
+    index = 0
+    for date, info in data.items():
+        value_list = []
+        for name, val in info.items():
+            if index == 0:
+                geo_name_list.append(name)
+            if name in geo_name_list:
+                value_list.append(val)
+        if len(value_list) > 0:
+            geo_history_list.append({'date': date, 'value': value_list})
+        index +=1
+
+    geo_history_list = sorted(geo_history_list, key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'))
+
+    # Initialize 'valueGrowth' as None for all entries
+    for item in geo_history_list:
+        item['valueGrowth'] = [None] * len(item['value'])
+
+    # Calculate valueGrowth for each item based on the previous date value
+    for i in range(1, len(geo_history_list)):  # Start from the second item
+        current_item = geo_history_list[i]
+        prev_item = geo_history_list[i - 1]
+        
+        value_growth = []
+        for cur_value, prev_value in zip(current_item['value'], prev_item['value']):
+            try:
+                growth = round(((cur_value - prev_value) / prev_value) * 100, 2)
+            except:
+                growth = None
+            value_growth.append(growth)
+        
+        current_item['valueGrowth'] = value_growth
+
+    geo_history_list = sorted(geo_history_list, key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'), reverse=True)
+
+    res_list = {'geographic': {'names': standardize_strings(geo_name_list), 'history': geo_history_list}}
+
+    return res_list
+
+def prepare_dataset(data, geo_data, symbol):
     data = convert_to_dict(data)
     res_list = {}
     revenue_name_list = []
@@ -45,10 +154,9 @@ def prepare_dataset(data):
             revenue_history_list.append({'date': date, 'value': value_list})
         index +=1
 
-
     revenue_history_list = sorted(revenue_history_list, key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'))
 
-   # Initialize 'valueGrowth' as None for all entries
+    # Initialize 'valueGrowth' as None for all entries
     for item in revenue_history_list:
         item['valueGrowth'] = [None] * len(item['value'])
 
@@ -59,35 +167,59 @@ def prepare_dataset(data):
         
         value_growth = []
         for cur_value, prev_value in zip(current_item['value'], prev_item['value']):
-            growth = round(((cur_value - prev_value) / prev_value) * 100, 2)
+            try:
+                growth = round(((cur_value - prev_value) / prev_value) * 100, 2)
+            except:
+                growth = None
             value_growth.append(growth)
         
         current_item['valueGrowth'] = value_growth
 
-
     revenue_history_list = sorted(revenue_history_list, key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'), reverse=True)
 
-
     res_list = {'revenue': {'names': revenue_name_list, 'history': revenue_history_list}}
 
+    geo_data = prepare_geo_dataset(geo_data)
+    #operating_expense_data = prepare_expense_dataset(symbol)
+
+
+    #res_list = {**res_list, **geo_data, 'expense': operating_expense_data}
+    res_list = {**res_list, **geo_data}
     return res_list
 
-
 async def get_data(session, total_symbols):
-    for symbol in total_symbols:
+    batch_size = 300  # Process 300 symbols at a time
-        url = f"https://financialmodelingprep.com/api/v4/revenue-product-segmentation?symbol={symbol}&structure=flat&period=quarter&apikey={api_key}"
+    for i in tqdm(range(0, len(total_symbols), batch_size)):
-        try:
+        batch = total_symbols[i:i+batch_size]
-            async with session.get(url) as response:
+        for symbol in batch:
-                if response.status == 200:
+            product_data = []
-                    data = await response.json()
+            geo_data = []
-                    if len(data) > 0:
-                        data = prepare_dataset(data)
-                        await save_json(data, symbol)
 
-        except Exception as e:
+            urls = [f"https://financialmodelingprep.com/api/v4/revenue-product-segmentation?symbol={symbol}&structure=flat&period=quarter&apikey={api_key}",
-            print(f"Error fetching data for {symbol}: {e}")
+                    f"https://financialmodelingprep.com/api/v4/revenue-geographic-segmentation?symbol={symbol}&structure=flat&apikey={api_key}"
-            pass
+                    ]
 
+            for url in urls:
+                try:
+                    async with session.get(url) as response:
+                        if response.status == 200:
+                            data = await response.json()
+                            if "product" in url:
+                                product_data = data
+                            else:
+                                geo_data = data
+                except Exception as e:
+                    print(f"Error fetching data for {symbol}: {e}")
+                    pass
+
+            if len(product_data) > 0 and len(geo_data) > 0:
+                data = prepare_dataset(product_data, geo_data, symbol)
+                await save_json(data, symbol)
+
+        # Wait 60 seconds after processing each batch of 300 symbols
+        if i + batch_size < len(total_symbols):
+            print(f"Processed {i + batch_size} symbols, waiting 60 seconds...")
+            await asyncio.sleep(60)
 
 async def run():
     con = sqlite3.connect('stocks.db')
@@ -95,13 +227,12 @@ async def run():
     cursor.execute("PRAGMA journal_mode = wal")
     cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%'")
     total_symbols = [row[0] for row in cursor.fetchall()]
-    total_symbols = ['AAPL']  # For testing purposes
+    total_symbols = ['TSLA']  # For testing purposes
     con.close()
     
     async with aiohttp.ClientSession() as session:
         await get_data(session, total_symbols)
 
-
 if __name__ == "__main__":
     loop = asyncio.get_event_loop()
     loop.run_until_complete(run())