bugfixing

.gitignore

@@ -16,4 +16,5 @@ fastify/node_modules
 pocketbase/*
 helper.txt
 env/*
-app/ml_models/weights
+app/ml_models/weights
+app/utils/__pycache__

app/cron_dark_pool_flow.py

@@ -75,7 +75,6 @@ def save_to_daily_file(data, directory):
 
 
 
-
 def get_data():
     try:
         response = requests.get(url, headers=headers, params=querystring)

app/cron_sector_flow.py

@@ -53,6 +53,15 @@ def calculate_neutral_premium(data_item):
     
     return safe_round(neutral_premium)
 
+def generate_time_intervals(start_time, end_time):
+    """Generate 1-minute intervals from start_time to end_time."""
+    intervals = []
+    current_time = start_time
+    while current_time <= end_time:
+        intervals.append(current_time.strftime('%Y-%m-%d %H:%M:%S'))
+        current_time += timedelta(minutes=1)
+    return intervals
+
 def get_sector_data():
     try:
         url = "https://api.unusualwhales.com/api/market/sector-etfs"
@@ -60,6 +69,7 @@ def get_sector_data():
         data = response.json().get('data', [])
         res_list = []
         processed_data = []
+
         
         for item in data:
             symbol = item['ticker']
@@ -98,6 +108,13 @@ def get_sector_data():
                 new_item['price'] = round(quote_data.get('price', 0), 2)
                 new_item['changesPercentage'] = round(quote_data.get('changesPercentage', 0), 2)
 
+            #get prem tick data:
+            if symbol != 'SPY':
+                prem_tick_history = get_net_prem_ticks(symbol)
+                #if symbol == 'XLB':
+                #    print(prem_tick_history[10])
+
+                new_item['premTickHistory'] = prem_tick_history
             processed_data.append(new_item)
 
         return processed_data
@@ -105,22 +122,12 @@ def get_sector_data():
         print(e)
         return []
 
-
-def generate_time_intervals(start_time, end_time):
-    """Generate 1-minute intervals from start_time to end_time."""
-    intervals = []
-    current_time = start_time
-    while current_time <= end_time:
-        intervals.append(current_time.strftime('%Y-%m-%d %H:%M:%S'))
-        current_time += timedelta(minutes=1)
-    return intervals
-
 def get_net_prem_ticks(symbol):
     # Fetch data from the API
     url = f"https://api.unusualwhales.com/api/stock/{symbol}/net-prem-ticks"
     response = requests.get(url, headers=headers)
     data = response.json().get('data', [])
-    
+    print(data[0])
     # Sort data by date in descending order
     data = sorted(data, key=lambda x: datetime.fromisoformat(x['date'].replace('Z', '+00:00')), reverse=True)
     
@@ -144,22 +151,41 @@ def get_net_prem_ticks(symbol):
     # Create a dictionary for fast lookups of existing tape_time
     data_dict = {entry['tape_time']: entry for entry in data}
     
-    # Populate data with 1-minute intervals
-    populated_data = []
+    # Initialize aggregated data with cumulative sums
+    aggregated_data = {time: {
+        'net_call_premium': 0,
+        'net_put_premium': 0,
+        'net_call_volume': 0,
+        'net_put_volume': 0,
+        'tape_time': time,
+        'close': None
+    } for time in intervals}
+    
+    # Variable to track cumulative sums
+    cumulative_net_call_premium = 0
+    cumulative_net_put_premium = 0
+    cumulative_net_call_volume = 0
+    cumulative_net_put_volume = 0
+    
+    # Aggregate data for each minute, cumulatively adding values
     for time in intervals:
         if time in data_dict:
-            populated_data.append(data_dict[time])
-        else:
-            populated_data.append({
-                'date': time.split(' ')[0],
-                'net_call_premium': None,
-                'net_call_volume': None,
-                'net_put_premium': None,
-                'net_put_volume': None,
-                'tape_time': time,
-                'close': None
-            })
-    
+            entry = data_dict[time]
+            # Add current values to cumulative sums
+            cumulative_net_call_premium += float(entry.get('net_call_premium', 0))
+            cumulative_net_put_premium += float(entry.get('net_put_premium', 0))
+            cumulative_net_call_volume += float(entry.get('net_call_volume', 0))
+            cumulative_net_put_volume += float(entry.get('net_put_volume', 0))
+        
+        # Set the aggregated values for this minute
+        aggregated_data[time]['net_call_premium'] = cumulative_net_call_premium
+        aggregated_data[time]['net_put_premium'] = cumulative_net_put_premium
+        aggregated_data[time]['net_call_volume'] = cumulative_net_call_volume
+        aggregated_data[time]['net_put_volume'] = cumulative_net_put_volume
+
+    # Populate data with aggregated results
+    populated_data = list(aggregated_data.values())
+
     # Add 'close' values if matches found in price_list
     matched = False
     for entry in populated_data:
@@ -168,18 +194,17 @@ def get_net_prem_ticks(symbol):
                 entry['close'] = price['close']
                 matched = True
                 break  # Exit inner loop once a match is found
+    
 
-    # Return the populated data if matches exist; otherwise, return an empty list
-    print(populated_data)
     return populated_data if matched else []
-
 def main():
-    #sector_data = get_sector_data()
-    sector_data = []
-    net_premium_tick_data = get_net_prem_ticks('XLC')
-
+    '''
+    sector_data = get_sector_data()
     if len(sector_data) > 0:
         save_json(sector_data)
+    '''
+    get_net_prem_ticks('XLB')
+
 
 if __name__ == '__main__':
     main()

app/main.py

@@ -175,9 +175,11 @@ for item in searchbar_data:
         # Look up the symbol in the stock_screener_data_dict
         symbol = item['symbol']
         item['isin'] = stock_screener_data_dict[symbol]['isin']
-    except:
+
+    except Exception as e:
         item['isin'] = None
 
+
 etf_set, crypto_set = set(etf_symbols), set(crypto_symbols)
 
 
@@ -1841,7 +1843,7 @@ async def get_stock(
         return JSONResponse(content=[])
 
     # Check for exact ISIN match first
-    exact_match = next((item for item in searchbar_data if item.get("isin") == query), None)
+    exact_match = next((item for item in searchbar_data if item.get("isin",None) == query), None)
     if exact_match:
         return JSONResponse(content=[exact_match])
 

app/test.py

@@ -5,16 +5,36 @@ import os
 load_dotenv()
 api_key = os.getenv('UNUSUAL_WHALES_API_KEY')
 
-querystring = {"limit":"200"}
-
-url = "https://api.unusualwhales.com/api/darkpool/recent"
-
+url = 'https://api.unusualwhales.com/api/stock/XLB/net-prem-ticks'
 headers = {
-    "Accept": "application/json, text/plain",
-    "Authorization": api_key
+    'Accept': 'application/json',
+    'Authorization': api_key
 }
 
-response = requests.get(url, headers=headers, params=querystring)
+response = requests.get(url, headers=headers)
+data = response.json()['data']
 
-print(len(response.json()['data']))
-print(response.json()['data'][0])
+fields_to_sum = [
+    "net_call_premium",
+    "net_call_volume",
+    "net_put_premium",
+    "net_put_volume"
+]
+
+result = []
+for idx, e in enumerate(data):
+    e['net_call_premium'] = float(e['net_call_premium'])
+    e['net_put_premium'] = float(e['net_put_premium'])
+
+    #e['net_call_volume'] = float(e['net_call_volume'])
+    #e['net_put_volume'] = float(e['net_put_volume'])
+    
+    
+    if idx != 0:
+        for field in fields_to_sum:
+            e[field] += result[idx-1].get(field, 0)
+    
+    result.append(e)
+
+#print(result)
+print(result[-1]['net_put_volume']*result[-1]['net_put_premium']*10**(-6))