update cron job

app/cron_var.py

@@ -4,9 +4,11 @@ import numpy as np
 import ujson
 import asyncio
 import sqlite3
+import os
 from tqdm import tqdm
 
 async def save_json(symbol, data):
+    os.makedirs("json/var", exist_ok=True)  # Ensure directory exists
     with open(f"json/var/{symbol}.json", 'w') as file:
         ujson.dump(data, file)
 
@@ -39,10 +41,10 @@ def compute_var(df):
     df = df.dropna()
     # Calculate VaR at 95% confidence level
     confidence_level = 0.95
-    var = abs(np.percentile(df['Returns'], 100 * (1 - confidence_level)))
-    var_N_days = round(var * np.sqrt(5)*100,2) # N days
+    var = np.percentile(df['Returns'], 100 * (1 - confidence_level))
+    var_N_days = round(var * np.sqrt(len(df)) * 100, 2)  # N days: the length of df represents the N days
 
-    return -var_N_days #{'rating': risk_rating, 'var': -var_N_days, 'outlook': outlook}
+    return var_N_days  # Positive value represents a loss
 
 async def run():
     start_date = "2015-01-01"
@@ -70,12 +72,15 @@ async def run():
     total_symbols = stocks_symbols + etf_symbols + crypto_symbols
 
     for symbol in tqdm(total_symbols):
+        try:
             if symbol in etf_symbols:  
                 query_con = etf_con
             elif symbol in crypto_symbols:  
                 query_con = crypto_con
             elif symbol in stocks_symbols:  
                 query_con = con
+            else:
+                continue
 
             query_template = """
                     SELECT
@@ -93,10 +98,10 @@ async def run():
 
             # Group by year and month
             monthly_groups = df.groupby(df['date'].dt.to_period('M'))
-
             history = []
-        try:
+
             for period, group in monthly_groups:
+                if len(group) >=19:  # Check if the month has at least 19 data points
                     var_data = compute_var(group)
                     history.append({'date': str(period), 'var': var_data})