update market movers

app/cron_market_movers.py

@@ -82,6 +82,11 @@ async def get_quote_of_stocks(ticker_list):
             df = await response.json()
     return df
 
+def add_rank(data):
+    for key in data:
+        for index, item in enumerate(data[key], start=1):
+            item['rank'] = index
+    return data
     
 async def get_gainer_loser_active_stocks():
 
@@ -218,7 +223,9 @@ async def get_gainer_loser_active_stocks():
     # Iterate through time periods, categories, and symbols
     for time_period in data.keys():
         for category in data[time_period].keys():
-            for stock_data in data[time_period][category]:
+            # Add rank and process symbols
+            for index, stock_data in enumerate(data[time_period][category], start=1):
+                stock_data['rank'] = index  # Add rank field
                 symbol = stock_data["symbol"]
                 unique_symbols.add(symbol)
 
@@ -230,16 +237,17 @@ async def get_gainer_loser_active_stocks():
     latest_quote = await get_quote_of_stocks(unique_symbols_list)
     # Updating values in the data list based on matching symbols from the quote list
     for time_period in data.keys():
-        for category in data[time_period].keys():
+        # Only proceed if the time period is "1D"
-            for stock_data in data[time_period][category]:
+        if time_period == "1D":
-                symbol = stock_data["symbol"]
+            for category in data[time_period].keys():
-                quote_stock = next((item for item in latest_quote if item["symbol"] == symbol), None)
+                for stock_data in data[time_period][category]:
-                if quote_stock:
+                    symbol = stock_data["symbol"]
-                    stock_data['price'] = quote_stock['price']
+                    quote_stock = next((item for item in latest_quote if item["symbol"] == symbol), None)
-                    stock_data['changesPercentage'] = quote_stock['changesPercentage']
+                    if quote_stock:
-                    stock_data['marketCap'] = quote_stock['marketCap']
+                        stock_data['price'] = quote_stock['price']
-                    stock_data['volume'] = quote_stock['volume']
+                        stock_data['changesPercentage'] = quote_stock['changesPercentage']
-
+                        stock_data['marketCap'] = quote_stock['marketCap']
+                        stock_data['volume'] = quote_stock['volume']
 
     return data 
 
@@ -310,17 +318,20 @@ try:
     #Filter out tickers
     symbols = [symbol for symbol in symbols if symbol != "STEC"]
 
-    data = asyncio.run(get_historical_data())
+    
-    with open(f"json/mini-plots-index/data.json", 'w') as file:
-        ujson.dump(data, file)
 
     data = asyncio.run(get_gainer_loser_active_stocks())
     with open(f"json/market-movers/data.json", 'w') as file:
         ujson.dump(data, file)
+    '''
+    data = asyncio.run(get_historical_data())
+    with open(f"json/mini-plots-index/data.json", 'w') as file:
+        ujson.dump(data, file)
 
     data = asyncio.run(get_pre_post_market_movers(symbols))
     with open(f"json/market-movers/pre-post-data.json", 'w') as file:
         ujson.dump(data, file)
+    '''
 
     con.close()
 except Exception as e:

app/market_movers.py

@@ -48,7 +48,7 @@ class Past_Market_Movers:
                 start_date = current_date
         return start_date.strftime("%Y-%m-%d")
 
-    def run(self, time_periods=[7,30,90,180]):
+    def run(self, time_periods=[7,20,252,756,1260]):
         performance_data = []
         query_template = """
             SELECT date, close, volume FROM "{ticker}" WHERE date >= ?
@@ -75,9 +75,10 @@ class Past_Market_Movers:
                     if not df.empty:
                         fundamental_data = pd.read_sql_query(query_fundamental_template, self.con, params=(ticker,))
                         avg_volume = df['volume'].mean()
-                        if avg_volume > 1E6 and df['close'].mean() > 1:
+                        market_cap = int(fundamental_data['marketCap'].iloc[0])
+                        if avg_volume > 1E6 and df['close'].mean() > 1 and market_cap >=50E6:
                             changes_percentage = ((df['close'].iloc[-1] - df['close'].iloc[0]) / df['close'].iloc[0]) * 100
-                            performance_data.append((ticker, fundamental_data['name'].iloc[0], df['close'].iloc[-1], changes_percentage, avg_volume, int(fundamental_data['marketCap'].iloc[0])))
+                            performance_data.append((ticker, fundamental_data['name'].iloc[0], df['close'].iloc[-1], changes_percentage, avg_volume, market_cap))
                 except:
                     pass
 
@@ -97,18 +98,22 @@ class Past_Market_Movers:
                 gainer_json['1W'] = gainer_data
                 loser_json['1W'] = loser_data
                 active_json['1W'] = active_data
-            elif time_period == 30:
+            elif time_period == 20:
                 gainer_json['1M'] = gainer_data
                 loser_json['1M'] = loser_data
                 active_json['1M'] = active_data
-            elif time_period == 90:
+            elif time_period == 252:
-                gainer_json['3M'] = gainer_data
+                gainer_json['1Y'] = gainer_data
-                loser_json['3M'] = loser_data
+                loser_json['1Y'] = loser_data
-                active_json['3M'] = active_data
+                active_json['1Y'] = active_data
-            elif time_period == 180:
+            elif time_period == 756:
-                gainer_json['6M'] = gainer_data
+                gainer_json['3Y'] = gainer_data
-                loser_json['6M'] = loser_data
+                loser_json['3Y'] = loser_data
-                active_json['6M'] = active_data
+                active_json['3Y'] = active_data
+            elif time_period == 1260:
+                gainer_json['5Y'] = gainer_data
+                loser_json['5Y'] = loser_data
+                active_json['5Y'] = active_data
 
         return gainer_json, loser_json, active_json