diff --git a/.ipynb_checkpoints/main-checkpoint.ipynb b/.ipynb_checkpoints/main-checkpoint.ipynb
index cf81d87..5098552 100644
--- a/.ipynb_checkpoints/main-checkpoint.ipynb
+++ b/.ipynb_checkpoints/main-checkpoint.ipynb
@@ -2,29 +2,92 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "90c70e46-71a0-44a6-8090-f53aad3193c3",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query start time: 2024-10-13 18:13:45.509982\n",
+      "Query end time: 2024-10-13 18:13:45.944136\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>START_DATE_TIME</th>\n",
+       "      <th>END_DATE_TIME</th>\n",
+       "      <th>FOCAL_ID</th>\n",
+       "      <th>MIN_PRICE</th>\n",
+       "      <th>MAX_PRICE</th>\n",
+       "      <th>PRICE_CHANGE (%)</th>\n",
+       "      <th>PARTICIPANT_VOLUME</th>\n",
+       "      <th>TOTAL_VOLUME</th>\n",
+       "      <th>VOLUME (%)</th>\n",
+       "      <th>PARTICIPANT_VOLUME_PCT</th>\n",
+       "      <th>Segment</th>\n",
+       "      <th>SAR_FLAG</th>\n",
+       "      <th>Risk</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [START_DATE_TIME, END_DATE_TIME, FOCAL_ID, MIN_PRICE, MAX_PRICE, PRICE_CHANGE (%), PARTICIPANT_VOLUME, TOTAL_VOLUME, VOLUME (%), PARTICIPANT_VOLUME_PCT, Segment, SAR_FLAG, Risk]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from datetime import datetime\n",
     "import pandas as pd\n",
     "from tms_data_interface import SQLQueryInterface\n",
     "\n",
-    "# SQL query to aggregate trade data and compute metrics\n",
-    "query = \"\"\"\n",
+    "# SQL query to aggregate trade data and compute metrics using ROWS with optimizations\n",
+    "query_template = \"\"\"\n",
     "WITH trade_data AS (\n",
     "    SELECT \n",
     "        trader_id,\n",
     "        date_time,\n",
     "        trade_price,\n",
     "        trade_volume,\n",
-    "        -- Create a time window for each trade\n",
-    "        date_time - INTERVAL '1 second' * {time_window_s} AS window_start,\n",
-    "        date_time AS window_end\n",
+    "        -- Create a time window for each trade by subtracting time_window_s seconds\n",
+    "        date_add('second', -{time_window_s}, date_time) AS window_start,\n",
+    "        date_time AS window_end,\n",
+    "        trade_side\n",
     "    FROM {trade_10m_v3}\n",
+    "    WHERE date_time BETWEEN date_add('day', -1, current_date) AND current_date -- Limit to the last 1 day of data\n",
+    "    LIMIT 10000 -- Process only a subset of records for testing\n",
     "),\n",
     "\n",
     "aggregated_trades AS (\n",
@@ -32,21 +95,21 @@
     "        td.trader_id,\n",
     "        td.window_start,\n",
     "        td.window_end,\n",
-    "        SUM(CASE WHEN trade_side = 'buy' THEN trade_volume ELSE 0 END) \n",
+    "        SUM(CASE WHEN td.trade_side = 'buy' THEN td.trade_volume ELSE 0 END) \n",
     "            OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS buy_volume,\n",
-    "        SUM(CASE WHEN trade_side = 'sell' THEN trade_volume ELSE 0 END) \n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS buy_volume,\n",
+    "        SUM(CASE WHEN td.trade_side = 'sell' THEN td.trade_volume ELSE 0 END) \n",
     "            OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS sell_volume,\n",
-    "        SUM(trade_volume) OVER (ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS total_volume,\n",
-    "        MAX(trade_price) OVER (ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS highest_price,\n",
-    "        MIN(trade_price) OVER (ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS lowest_price,\n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS sell_volume,\n",
+    "        SUM(td.trade_volume) OVER (ORDER BY td.date_time \n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS total_volume,\n",
+    "        MAX(td.trade_price) OVER (ORDER BY td.date_time \n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS highest_price,\n",
+    "        MIN(td.trade_price) OVER (ORDER BY td.date_time \n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS lowest_price,\n",
     "        COUNT(*) OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS number_of_trades\n",
-    "    FROM {trade_10m_v3} td\n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS number_of_trades\n",
+    "    FROM trade_data td\n",
     ")\n",
     "\n",
     "SELECT \n",
@@ -68,16 +131,18 @@
     "\n",
     "    def logic(self, **kwargs):\n",
     "        validation_window = kwargs.get('validation_window')\n",
-    "        time_window_s = int(validation_window / 1000)\n",
+    "        time_window_s = int(validation_window / 1000)  # Convert milliseconds to seconds\n",
     "        \n",
     "        query_start_time = datetime.now()\n",
     "        print(\"Query start time:\", query_start_time)\n",
     "\n",
-    "        row_list = self.seq.execute_raw(query.format(\n",
+    "        # Execute the optimized query using a time window and limit\n",
+    "        row_list = self.seq.execute_raw(query_template.format(\n",
     "            trade_10m_v3=\"trade_10m_v3\",\n",
     "            time_window_s=time_window_s\n",
     "        ))\n",
     "\n",
+    "        # Define the columns for the resulting DataFrame\n",
     "        cols = [\n",
     "            'START_DATE_TIME',\n",
     "            'END_DATE_TIME',\n",
@@ -90,16 +155,25 @@
     "            'VOLUME (%)',\n",
     "        ]\n",
     "\n",
+    "        # Create DataFrame from query results\n",
     "        final_scenario_df = pd.DataFrame(row_list, columns=cols)\n",
+    "        \n",
+    "        # Calculate the participant's volume percentage\n",
     "        final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME'] / \\\n",
     "            final_scenario_df['TOTAL_VOLUME'] * 100\n",
     "\n",
-    "        # Adding additional columns\n",
+    "        # Add additional columns to the DataFrame\n",
     "        final_scenario_df['Segment'] = 'Default'\n",
     "        final_scenario_df['SAR_FLAG'] = 'N'\n",
     "        final_scenario_df['Risk'] = 'Low Risk'\n",
     "\n",
-    "        return final_scenario_df\n"
+    "        print(\"Query end time:\", datetime.now())\n",
+    "        return final_scenario_df\n",
+    "\n",
+    "\n",
+    "# Instantiate and execute logic\n",
+    "scenario = Scenario()\n",
+    "scenario.logic(validation_window=1000)\n"
    ]
   },
   {
@@ -122,8 +196,9 @@
     }
    ],
    "source": [
-    "#scenario = Scenario()\n",
-    "#scenario.logic()"
+    "# Instantiate and execute logic\n",
+    "scenario = Scenario()\n",
+    "scenario.logic(validation_window=1000)"
    ]
   }
  ],
diff --git a/main.ipynb b/main.ipynb
index cf81d87..db3c9d0 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "90c70e46-71a0-44a6-8090-f53aad3193c3",
    "metadata": {
     "tags": []
@@ -13,18 +13,21 @@
     "import pandas as pd\n",
     "from tms_data_interface import SQLQueryInterface\n",
     "\n",
-    "# SQL query to aggregate trade data and compute metrics\n",
-    "query = \"\"\"\n",
+    "# SQL query to aggregate trade data and compute metrics using ROWS with optimizations\n",
+    "query_template = \"\"\"\n",
     "WITH trade_data AS (\n",
     "    SELECT \n",
     "        trader_id,\n",
     "        date_time,\n",
     "        trade_price,\n",
     "        trade_volume,\n",
-    "        -- Create a time window for each trade\n",
-    "        date_time - INTERVAL '1 second' * {time_window_s} AS window_start,\n",
-    "        date_time AS window_end\n",
+    "        -- Create a time window for each trade by subtracting time_window_s seconds\n",
+    "        date_add('second', -{time_window_s}, date_time) AS window_start,\n",
+    "        date_time AS window_end,\n",
+    "        trade_side\n",
     "    FROM {trade_10m_v3}\n",
+    "    WHERE date_time BETWEEN date_add('day', -1, current_date) AND current_date -- Limit to the last 1 day of data\n",
+    "    LIMIT 10000 -- Process only a subset of records for testing\n",
     "),\n",
     "\n",
     "aggregated_trades AS (\n",
@@ -32,21 +35,21 @@
     "        td.trader_id,\n",
     "        td.window_start,\n",
     "        td.window_end,\n",
-    "        SUM(CASE WHEN trade_side = 'buy' THEN trade_volume ELSE 0 END) \n",
+    "        SUM(CASE WHEN td.trade_side = 'buy' THEN td.trade_volume ELSE 0 END) \n",
     "            OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS buy_volume,\n",
-    "        SUM(CASE WHEN trade_side = 'sell' THEN trade_volume ELSE 0 END) \n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS buy_volume,\n",
+    "        SUM(CASE WHEN td.trade_side = 'sell' THEN td.trade_volume ELSE 0 END) \n",
     "            OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS sell_volume,\n",
-    "        SUM(trade_volume) OVER (ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS total_volume,\n",
-    "        MAX(trade_price) OVER (ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS highest_price,\n",
-    "        MIN(trade_price) OVER (ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS lowest_price,\n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS sell_volume,\n",
+    "        SUM(td.trade_volume) OVER (ORDER BY td.date_time \n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS total_volume,\n",
+    "        MAX(td.trade_price) OVER (ORDER BY td.date_time \n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS highest_price,\n",
+    "        MIN(td.trade_price) OVER (ORDER BY td.date_time \n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS lowest_price,\n",
     "        COUNT(*) OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
-    "                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS number_of_trades\n",
-    "    FROM {trade_10m_v3} td\n",
+    "                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS number_of_trades\n",
+    "    FROM trade_data td\n",
     ")\n",
     "\n",
     "SELECT \n",
@@ -68,16 +71,18 @@
     "\n",
     "    def logic(self, **kwargs):\n",
     "        validation_window = kwargs.get('validation_window')\n",
-    "        time_window_s = int(validation_window / 1000)\n",
+    "        time_window_s = int(validation_window / 1000)  # Convert milliseconds to seconds\n",
     "        \n",
     "        query_start_time = datetime.now()\n",
     "        print(\"Query start time:\", query_start_time)\n",
     "\n",
-    "        row_list = self.seq.execute_raw(query.format(\n",
+    "        # Execute the optimized query using a time window and limit\n",
+    "        row_list = self.seq.execute_raw(query_template.format(\n",
     "            trade_10m_v3=\"trade_10m_v3\",\n",
     "            time_window_s=time_window_s\n",
     "        ))\n",
     "\n",
+    "        # Define the columns for the resulting DataFrame\n",
     "        cols = [\n",
     "            'START_DATE_TIME',\n",
     "            'END_DATE_TIME',\n",
@@ -90,41 +95,104 @@
     "            'VOLUME (%)',\n",
     "        ]\n",
     "\n",
+    "        # Create DataFrame from query results\n",
     "        final_scenario_df = pd.DataFrame(row_list, columns=cols)\n",
+    "        \n",
+    "        # Calculate the participant's volume percentage\n",
     "        final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME'] / \\\n",
     "            final_scenario_df['TOTAL_VOLUME'] * 100\n",
     "\n",
-    "        # Adding additional columns\n",
+    "        # Add additional columns to the DataFrame\n",
     "        final_scenario_df['Segment'] = 'Default'\n",
     "        final_scenario_df['SAR_FLAG'] = 'N'\n",
     "        final_scenario_df['Risk'] = 'Low Risk'\n",
     "\n",
-    "        return final_scenario_df\n"
+    "        print(\"Query end time:\", datetime.now())\n",
+    "        return final_scenario_df\n",
+    "\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "caee5554-5254-4388-bf24-029281d77890",
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "unsupported operand type(s) for /: 'NoneType' and 'int'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m scenario \u001b[38;5;241m=\u001b[39m Scenario()\n\u001b[0;32m----> 2\u001b[0m \u001b[43mscenario\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogic\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[4], line 60\u001b[0m, in \u001b[0;36mScenario.logic\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlogic\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     59\u001b[0m     validation_window \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalidation_window\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 60\u001b[0m     time_window_s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[43mvalidation_window\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m)\n\u001b[1;32m     62\u001b[0m     query_start_time \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow()\n\u001b[1;32m     63\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuery start time:\u001b[39m\u001b[38;5;124m\"\u001b[39m, query_start_time)\n",
-      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'NoneType' and 'int'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query start time: 2024-10-13 18:14:29.793521\n",
+      "Query end time: 2024-10-13 18:14:29.933772\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>START_DATE_TIME</th>\n",
+       "      <th>END_DATE_TIME</th>\n",
+       "      <th>FOCAL_ID</th>\n",
+       "      <th>MIN_PRICE</th>\n",
+       "      <th>MAX_PRICE</th>\n",
+       "      <th>PRICE_CHANGE (%)</th>\n",
+       "      <th>PARTICIPANT_VOLUME</th>\n",
+       "      <th>TOTAL_VOLUME</th>\n",
+       "      <th>VOLUME (%)</th>\n",
+       "      <th>PARTICIPANT_VOLUME_PCT</th>\n",
+       "      <th>Segment</th>\n",
+       "      <th>SAR_FLAG</th>\n",
+       "      <th>Risk</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [START_DATE_TIME, END_DATE_TIME, FOCAL_ID, MIN_PRICE, MAX_PRICE, PRICE_CHANGE (%), PARTICIPANT_VOLUME, TOTAL_VOLUME, VOLUME (%), PARTICIPANT_VOLUME_PCT, Segment, SAR_FLAG, Risk]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "#scenario = Scenario()\n",
-    "#scenario.logic()"
+    "# Instantiate and execute logic\n",
+    "scenario = Scenario()\n",
+    "scenario.logic(validation_window=1000)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1feaf267-88d1-41b8-a8b9-f150b7ff16cd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/main.py b/main.py
index 068be16..621c750 100644
--- a/main.py
+++ b/main.py
@@ -1,25 +1,28 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# In[4]:
+# In[3]:
 
 
 from datetime import datetime
 import pandas as pd
 from tms_data_interface import SQLQueryInterface
 
-# SQL query to aggregate trade data and compute metrics
-query = """
+# SQL query to aggregate trade data and compute metrics using ROWS with optimizations
+query_template = """
 WITH trade_data AS (
     SELECT 
         trader_id,
         date_time,
         trade_price,
         trade_volume,
-        -- Create a time window for each trade
-        date_time - INTERVAL '1 second' * {time_window_s} AS window_start,
-        date_time AS window_end
+        -- Create a time window for each trade by subtracting time_window_s seconds
+        date_add('second', -{time_window_s}, date_time) AS window_start,
+        date_time AS window_end,
+        trade_side
     FROM {trade_10m_v3}
+    WHERE date_time BETWEEN date_add('day', -1, current_date) AND current_date -- Limit to the last 1 day of data
+    LIMIT 10000 -- Process only a subset of records for testing
 ),
 
 aggregated_trades AS (
@@ -27,21 +30,21 @@ aggregated_trades AS (
         td.trader_id,
         td.window_start,
         td.window_end,
-        SUM(CASE WHEN trade_side = 'buy' THEN trade_volume ELSE 0 END) 
+        SUM(CASE WHEN td.trade_side = 'buy' THEN td.trade_volume ELSE 0 END) 
             OVER (PARTITION BY td.trader_id ORDER BY td.date_time 
-                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS buy_volume,
-        SUM(CASE WHEN trade_side = 'sell' THEN trade_volume ELSE 0 END) 
+                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS buy_volume,
+        SUM(CASE WHEN td.trade_side = 'sell' THEN td.trade_volume ELSE 0 END) 
             OVER (PARTITION BY td.trader_id ORDER BY td.date_time 
-                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS sell_volume,
-        SUM(trade_volume) OVER (ORDER BY td.date_time 
-                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS total_volume,
-        MAX(trade_price) OVER (ORDER BY td.date_time 
-                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS highest_price,
-        MIN(trade_price) OVER (ORDER BY td.date_time 
-                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS lowest_price,
+                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS sell_volume,
+        SUM(td.trade_volume) OVER (ORDER BY td.date_time 
+                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS total_volume,
+        MAX(td.trade_price) OVER (ORDER BY td.date_time 
+                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS highest_price,
+        MIN(td.trade_price) OVER (ORDER BY td.date_time 
+                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS lowest_price,
         COUNT(*) OVER (PARTITION BY td.trader_id ORDER BY td.date_time 
-                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS number_of_trades
-    FROM {trade_10m_v3} td
+                  ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS number_of_trades
+    FROM trade_data td
 )
 
 SELECT 
@@ -63,16 +66,18 @@ class Scenario:
 
     def logic(self, **kwargs):
         validation_window = kwargs.get('validation_window')
-        time_window_s = int(validation_window / 1000)
+        time_window_s = int(validation_window / 1000)  # Convert milliseconds to seconds
         
         query_start_time = datetime.now()
         print("Query start time:", query_start_time)
 
-        row_list = self.seq.execute_raw(query.format(
+        # Execute the optimized query using a time window and limit
+        row_list = self.seq.execute_raw(query_template.format(
             trade_10m_v3="trade_10m_v3",
             time_window_s=time_window_s
         ))
 
+        # Define the columns for the resulting DataFrame
         cols = [
             'START_DATE_TIME',
             'END_DATE_TIME',
@@ -85,21 +90,35 @@ class Scenario:
             'VOLUME (%)',
         ]
 
+        # Create DataFrame from query results
         final_scenario_df = pd.DataFrame(row_list, columns=cols)
+        
+        # Calculate the participant's volume percentage
         final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME'] / \
             final_scenario_df['TOTAL_VOLUME'] * 100
 
-        # Adding additional columns
+        # Add additional columns to the DataFrame
         final_scenario_df['Segment'] = 'Default'
         final_scenario_df['SAR_FLAG'] = 'N'
         final_scenario_df['Risk'] = 'Low Risk'
 
+        print("Query end time:", datetime.now())
         return final_scenario_df
 
 
-# In[6]:
 
 
-#scenario = Scenario()
-#scenario.logic()
+
+# In[2]:
+
+
+# Instantiate and execute logic
+scenario = Scenario()
+scenario.logic(validation_window=1000)
+
+
+# In[ ]:
+
+
+