update cron job

app/cron_dark_pool_level.py

@@ -9,7 +9,12 @@ import pytz
 from typing import List, Dict
 import sqlite3
 from tqdm import tqdm
+import time
+from collections import defaultdict
+from utils.helper import check_market_hours
 
+utc = pytz.utc
+ny_tz = pytz.timezone("America/New_York")
 
 def save_json(data, symbol):
     def convert_numpy(obj):
@@ -84,6 +89,39 @@ def analyze_dark_pool_levels(trades: List[Dict],
         return {}
 
 
+def today_trend(data):
+    filtered_list = []
+    result = []
+    for item in data:
+        try:
+            # Convert date to NY timezone
+            dt_utc = datetime.fromisoformat(item['date'][:-6]).replace(tzinfo=utc)
+            dt_ny = dt_utc.astimezone(ny_tz)
+            
+            # Define trading hours (9:30 AM - 4:00 PM NY time)
+            market_open = dt_ny.replace(hour=9, minute=30, second=0, microsecond=0)
+            market_close = dt_ny.replace(hour=16, minute=0, second=0, microsecond=0)
+            
+            if market_open <= dt_ny <= market_close:  # Filter valid times
+                filtered_list.append({
+                    'size': item['size'],
+                    'date': dt_ny.strftime("%Y-%m-%d %H:%M")  # Format as HH:MM
+                })
+        except:
+            pass
+
+    filtered_list.sort(key=lambda x: datetime.strptime(x['date'], "%Y-%m-%d %H:%M"))
+    
+    summed_data = defaultdict(float)
+    for entry in filtered_list:
+        try:
+            summed_data[entry['date']] += entry['size']
+        except:
+            pass
+
+    result = [{'date': date, 'totalSize': size} for date, size in summed_data.items()]
+
+    return result
 
 def run():
     con = sqlite3.connect('stocks.db')
@@ -105,34 +143,41 @@ def run():
     total_symbols = stocks_symbols+ etf_symbols
     with open(f"json/dark-pool/feed/data.json", "r") as file:
         raw_data = orjson.loads(file.read())
-    for symbol in tqdm(total_symbols):
-        try:
-            res_list = [item for item in raw_data if isinstance(item, dict) and item['ticker'] == symbol]
-            dark_pool_levels = analyze_dark_pool_levels(
-                trades=res_list,
-                size_threshold=0.8,  # Look for levels with volume in top 20%
-                price_grouping=1.0   # Group prices within $1.00
-            )
 
-            if dark_pool_levels.get('price_level'):  # Ensure there are valid levels
-                top_5_elements = [
-                    {k: v for k, v in item.items() if k not in ['ticker', 'sector', 'assetType']}
-                    for item in sorted(res_list, key=lambda x: float(x.get('premium', 0)), reverse=True)[:5]
-                ]
+    market_status = check_market_hours()
+    if market_status:
+        for symbol in tqdm(total_symbols):
+            try:
+                res_list = [item for item in raw_data if isinstance(item, dict) and item['ticker'] == symbol]
+                
+                trend_list = today_trend(res_list)
+                
+                dark_pool_levels = analyze_dark_pool_levels(
+                    trades=res_list,
+                    size_threshold=0.8,
+                    price_grouping=1.0
+                )
+                
+                if dark_pool_levels.get('price_level'):  # Ensure there are valid levels
+                    top_5_elements = [
+                        {k: v for k, v in item.items() if k not in ['ticker', 'sector', 'assetType']}
+                        for item in sorted(res_list, key=lambda x: float(x.get('premium', 0)), reverse=True)[:5]
+                    ]
 
-                # Add rank to each item
-                for rank, item in enumerate(top_5_elements, 1):
-                    item['rank'] = rank
+                    # Add rank to each item
+                    for rank, item in enumerate(top_5_elements, 1):
+                        item['rank'] = rank
 
-                data_to_save = {
-                    'hottestTrades': top_5_elements,
-                    'priceLevel': dark_pool_levels['price_level'],
-                    'metrics': dark_pool_levels['metrics']
-                }
+                    data_to_save = {
+                        'hottestTrades': top_5_elements,
+                        'priceLevel': dark_pool_levels['price_level'],
+                        'trend': trend_list,
+                        'metrics': dark_pool_levels['metrics']
+                    }
 
-                save_json(data_to_save, symbol)
-        except Exception as e:
-            print(f"Error processing {symbol}: {e}")
+                    save_json(data_to_save, symbol)
+            except Exception as e:
+                print(f"Error processing {symbol}: {e}")
 
 
 

app/primary_cron_job.py

@@ -416,7 +416,7 @@ schedule.every(5).minutes.do(run_threaded, run_list).tag('stock_list_job')
 
 
 
-schedule.every(30).minutes.do(run_threaded, run_dark_pool_level).tag('dark_pool_level_job')
+schedule.every(15).minutes.do(run_threaded, run_dark_pool_level).tag('dark_pool_level_job')
 schedule.every(10).minutes.do(run_threaded, run_dark_pool_flow).tag('dark_pool_flow_job')
 
 schedule.every(2).minutes.do(run_threaded, run_dashboard).tag('dashboard_job')