diff --git a/backend/utils/performance_data_source.py b/backend/utils/performance_data_source.py
index 30e7bfd8..ca108370 100755
--- a/backend/utils/performance_data_source.py
+++ b/backend/utils/performance_data_source.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 import pandas as pd
+
 from hummingbot.core.data_type.common import TradeType
 from hummingbot.strategy_v2.models.base import RunnableStatus
 from hummingbot.strategy_v2.models.executors import CloseType
@@ -36,7 +37,7 @@ def load_trade_fill(self):
     def load_controllers(self):
         controllers = self.checkpoint_data["controllers"].copy()
         controllers = pd.DataFrame(controllers)
-        controllers["config"] = controllers["config"].apply(lambda x: json.loads(x))
+        controllers["config"] = controllers["config"].apply(lambda x: json.loads(x) if isinstance(x, str) else x)
         controllers["datetime"] = pd.to_datetime(controllers.timestamp, unit="s")
         return controllers
 
@@ -105,6 +106,8 @@ def get_executors_with_orders(executors_df: pd.DataFrame, orders: pd.DataFrame):
         df = (executors_df[["id", "order_ids"]]
               .rename(columns={"id": "executor_id", "order_ids": "order_id"})
               .explode("order_id"))
+        if "id" in orders.columns:
+            orders.rename(columns={"id": "client_order_id"}, inplace=True)
         exec_with_orders = df.merge(orders, left_on="order_id", right_on="client_order_id", how="inner")
         exec_with_orders = exec_with_orders[exec_with_orders["last_status"].isin(["SellOrderCompleted",
                                                                                   "BuyOrderCompleted"])]
diff --git a/frontend/pages/performance/bot_performance/app.py b/frontend/pages/performance/bot_performance/app.py
index 1a99ed99..f5a9f47d 100755
--- a/frontend/pages/performance/bot_performance/app.py
+++ b/frontend/pages/performance/bot_performance/app.py
@@ -10,7 +10,7 @@
     display_performance_summary_table,
     display_tables_section,
 )
-from frontend.visualization.performance_etl import display_etl_section
+from frontend.visualization.performance_etl import display_postgres_etl_section, display_sqlite_etl_section
 
 
 async def main():
@@ -19,7 +19,14 @@ async def main():
     backend_api = get_backend_api_client()
 
     st.subheader("🔫 DATA SOURCE")
-    checkpoint_data = display_etl_section(backend_api)
+    selected_db = st.selectbox("Select DB connector", ["PostgreSQL", "SQLite"])
+    if selected_db == "SQLite":
+        checkpoint_data = display_sqlite_etl_section(backend_api)
+    else:
+        checkpoint_data = await display_postgres_etl_section()
+        if checkpoint_data is None:
+            st.warning("Unable to retrieve data. Ensure the PostgreSQL database is accessible and contains relevant information.")
+            st.stop()
     data_source = PerformanceDataSource(checkpoint_data)
     st.divider()
 
diff --git a/frontend/visualization/performance_etl.py b/frontend/visualization/performance_etl.py
index 659267e0..d61561f6 100644
--- a/frontend/visualization/performance_etl.py
+++ b/frontend/visualization/performance_etl.py
@@ -1,11 +1,56 @@
 import json
+import os
 
+import pandas as pd
 import streamlit as st
+from dotenv import load_dotenv
+from sqlalchemy import create_engine
 
 from backend.services.backend_api_client import BackendAPIClient
 
 
-def display_etl_section(backend_api: BackendAPIClient):
+async def display_postgres_etl_section():
+    load_dotenv()
+    col1, col2, col3, col4, col5 = st.columns(5)
+
+    with col1:
+        username = st.text_input("Username", os.getenv("POSTGRES_USERNAME", "postgres"))
+    with col2:
+        password = st.text_input("Password", os.getenv("POSTGRES_PASSWORD", "postgres"), type="password")
+    with col3:
+        host = st.text_input("Host", os.getenv("POSTGRES_HOST", "localhost"))
+    with col4:
+        port = st.number_input("Port", os.getenv("POSTGRES_PORT", 5432))
+    with col5:
+        database = st.text_input("Database", os.getenv("POSTGRES_DATABASE", "postgres"))
+    if st.button("Fetch data"):
+        db_url = f"postgresql://{username}:{password}@{host}:{port}/{database}"
+        try:
+            engine = create_engine(
+                db_url,
+                pool_size=5,
+                max_overflow=10,
+                pool_timeout=30,
+                pool_recycle=1800,
+                connect_args={"connect_timeout": 10}
+            )
+            with engine.connect() as connection:
+                raw_connection = connection.connection
+                checkpoint_data = {
+                    "executors": pd.read_sql_query("SELECT * FROM \"Executors\"", raw_connection).to_dict('records'),
+                    "orders": pd.read_sql_query("SELECT * FROM \"Order\"", raw_connection).to_dict('records'),
+                    "trade_fill": pd.read_sql_query("SELECT * FROM \"TradeFill\"", raw_connection).to_dict('records'),
+                    "controllers": pd.read_sql_query("SELECT * FROM \"Controllers\"", raw_connection).to_dict('records')
+                }
+            return checkpoint_data
+        except Exception as e:
+            print(f"Error fetching data: {e}")
+            return None
+    else:
+        st.stop()
+
+
+def display_sqlite_etl_section(backend_api: BackendAPIClient):
     db_paths = backend_api.list_databases()
     dbs_dict = backend_api.read_databases(db_paths)
     healthy_dbs = [db["db_path"].replace("sqlite:///", "") for db in dbs_dict if db["healthy"]]