diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..94c2fe7
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,213 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0f8357f-babc-4b20-bc31-e2b675e3ad4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "from tms_data_interface import SQLQueryInterface\n",
+    " \n",
+    "query = \"\"\"\n",
+    "\n",
+    "WITH time_windows AS (\n",
+    "\n",
+    "    SELECT\n",
+    "\n",
+    "        -- End time is the current trade time\n",
+    "\n",
+    "        date_time AS end_time,\n",
+    " \n",
+    "        -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
+    "\n",
+    "        date_add('second', -{time_window_s}, date_time) AS start_time,\n",
+    " \n",
+    "        -- Trade details\n",
+    "\n",
+    "        trade_price,\n",
+    "\n",
+    "        trade_volume,\n",
+    "\n",
+    "        trader_id,\n",
+    " \n",
+    "        -- Calculate minimum price within the time window\n",
+    "\n",
+    "        MIN(trade_price) OVER (\n",
+    "\n",
+    "            ORDER BY date_time \n",
+    "\n",
+    "            RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
+    "\n",
+    "        ) AS min_price,\n",
+    " \n",
+    "        -- Calculate maximum price within the time window\n",
+    "\n",
+    "        MAX(trade_price) OVER (\n",
+    "\n",
+    "            ORDER BY date_time \n",
+    "\n",
+    "            RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
+    "\n",
+    "        ) AS max_price,\n",
+    " \n",
+    "        -- Calculate total trade volume within the time window\n",
+    "\n",
+    "        SUM(trade_volume) OVER ( \n",
+    "\n",
+    "            ORDER BY date_time \n",
+    "\n",
+    "            RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
+    "\n",
+    "        ) AS total_volume,\n",
+    " \n",
+    "        -- Calculate participant's trade volume within the time window\n",
+    "\n",
+    "        SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
+    "\n",
+    "            PARTITION BY trader_id \n",
+    "\n",
+    "            ORDER BY date_time \n",
+    "\n",
+    "            RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
+    "\n",
+    "        ) AS participant_volume\n",
+    "\n",
+    "    FROM\n",
+    "\n",
+    "        {trade_data_1b}\n",
+    "\n",
+    ")\n",
+    "\n",
+    "SELECT\n",
+    "\n",
+    "    -- Select the time window details\n",
+    "\n",
+    "    start_time,\n",
+    "\n",
+    "    end_time,\n",
+    " \n",
+    "    -- Select the participant (trader) ID\n",
+    "\n",
+    "    trader_id AS \"Participant\",\n",
+    " \n",
+    "    -- Select the calculated min and max prices\n",
+    "\n",
+    "    min_price,\n",
+    "\n",
+    "    max_price,\n",
+    " \n",
+    "    -- Calculate the price change percentage\n",
+    "\n",
+    "    (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
+    " \n",
+    "    -- Calculate the participant's volume as a percentage of total volume\n",
+    "\n",
+    "    (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
+    " \n",
+    "    --  Participant volume\n",
+    "\n",
+    "    participant_volume,\n",
+    " \n",
+    "    -- Select the total volume within the window\n",
+    "\n",
+    "    total_volume AS \"Total Volume\"\n",
+    "\n",
+    "FROM\n",
+    "\n",
+    "    time_windows\n",
+    "\n",
+    "\"\"\"\n",
+    " \n",
+    " \n",
+    "from tms_data_interface import SQLQueryInterface\n",
+    " \n",
+    "class Scenario:\n",
+    "\n",
+    "    seq = SQLQueryInterface(schema=\"trade_schema\")\n",
+    "\n",
+    "    def logic(self, **kwargs):\n",
+    "\n",
+    "        validation_window = kwargs.get('validation_window')\n",
+    "\n",
+    "        time_window_s = int(validation_window/1000)\n",
+    "\n",
+    "        query_start_time = datetime.now()\n",
+    "\n",
+    "        print(\"Query start time :\",query_start_time)\n",
+    "\n",
+    "        row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
+    "\n",
+    "                                                    time_window_s = time_window_s)\n",
+    "\n",
+    "                                       )\n",
+    "\n",
+    "        cols = [\n",
+    "\n",
+    "        'START_DATE_TIME',\n",
+    "\n",
+    "        'END_DATE_TIME',\n",
+    "\n",
+    "        'Focal_id',\n",
+    "\n",
+    "        'MIN_PRICE',\n",
+    "\n",
+    "        'MAX_PRICE',\n",
+    "\n",
+    "        'PRICE_CHANGE_PCT',\n",
+    "\n",
+    "        'PARTICIPANT_VOLUME_PCT',\n",
+    "\n",
+    "        'PARTICIPANT_VOLUME',\n",
+    "\n",
+    "        'TOTAL_VOLUME',\n",
+    "\n",
+    "        ]\n",
+    "\n",
+    "        final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
+    "\n",
+    "        final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\\\n",
+    "\n",
+    "        final_scenario_df['TOTAL_VOLUME'] * 100\n",
+    "\n",
+    "        final_scenario_df['Segment'] = 'Default'\n",
+    "\n",
+    "        final_scenario_df['SAR_FLAG'] = 'N'\n",
+    "\n",
+    "        final_scenario_df['Risk'] = 'Low Risk'\n",
+    "\n",
+    "        final_scenario_df.dropna(inplace=True)\n",
+    "\n",
+    "        # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
+    "\n",
+    "        return final_scenario_df\n",
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..94c2fe7
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,213 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0f8357f-babc-4b20-bc31-e2b675e3ad4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "from tms_data_interface import SQLQueryInterface\n",
+    " \n",
+    "query = \"\"\"\n",
+    "\n",
+    "WITH time_windows AS (\n",
+    "\n",
+    "    SELECT\n",
+    "\n",
+    "        -- End time is the current trade time\n",
+    "\n",
+    "        date_time AS end_time,\n",
+    " \n",
+    "        -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
+    "\n",
+    "        date_add('second', -{time_window_s}, date_time) AS start_time,\n",
+    " \n",
+    "        -- Trade details\n",
+    "\n",
+    "        trade_price,\n",
+    "\n",
+    "        trade_volume,\n",
+    "\n",
+    "        trader_id,\n",
+    " \n",
+    "        -- Calculate minimum price within the time window\n",
+    "\n",
+    "        MIN(trade_price) OVER (\n",
+    "\n",
+    "            ORDER BY date_time \n",
+    "\n",
+    "            RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
+    "\n",
+    "        ) AS min_price,\n",
+    " \n",
+    "        -- Calculate maximum price within the time window\n",
+    "\n",
+    "        MAX(trade_price) OVER (\n",
+    "\n",
+    "            ORDER BY date_time \n",
+    "\n",
+    "            RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
+    "\n",
+    "        ) AS max_price,\n",
+    " \n",
+    "        -- Calculate total trade volume within the time window\n",
+    "\n",
+    "        SUM(trade_volume) OVER ( \n",
+    "\n",
+    "            ORDER BY date_time \n",
+    "\n",
+    "            RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
+    "\n",
+    "        ) AS total_volume,\n",
+    " \n",
+    "        -- Calculate participant's trade volume within the time window\n",
+    "\n",
+    "        SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
+    "\n",
+    "            PARTITION BY trader_id \n",
+    "\n",
+    "            ORDER BY date_time \n",
+    "\n",
+    "            RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
+    "\n",
+    "        ) AS participant_volume\n",
+    "\n",
+    "    FROM\n",
+    "\n",
+    "        {trade_data_1b}\n",
+    "\n",
+    ")\n",
+    "\n",
+    "SELECT\n",
+    "\n",
+    "    -- Select the time window details\n",
+    "\n",
+    "    start_time,\n",
+    "\n",
+    "    end_time,\n",
+    " \n",
+    "    -- Select the participant (trader) ID\n",
+    "\n",
+    "    trader_id AS \"Participant\",\n",
+    " \n",
+    "    -- Select the calculated min and max prices\n",
+    "\n",
+    "    min_price,\n",
+    "\n",
+    "    max_price,\n",
+    " \n",
+    "    -- Calculate the price change percentage\n",
+    "\n",
+    "    (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
+    " \n",
+    "    -- Calculate the participant's volume as a percentage of total volume\n",
+    "\n",
+    "    (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
+    " \n",
+    "    --  Participant volume\n",
+    "\n",
+    "    participant_volume,\n",
+    " \n",
+    "    -- Select the total volume within the window\n",
+    "\n",
+    "    total_volume AS \"Total Volume\"\n",
+    "\n",
+    "FROM\n",
+    "\n",
+    "    time_windows\n",
+    "\n",
+    "\"\"\"\n",
+    " \n",
+    " \n",
+    "from tms_data_interface import SQLQueryInterface\n",
+    " \n",
+    "class Scenario:\n",
+    "\n",
+    "    seq = SQLQueryInterface(schema=\"trade_schema\")\n",
+    "\n",
+    "    def logic(self, **kwargs):\n",
+    "\n",
+    "        validation_window = kwargs.get('validation_window')\n",
+    "\n",
+    "        time_window_s = int(validation_window/1000)\n",
+    "\n",
+    "        query_start_time = datetime.now()\n",
+    "\n",
+    "        print(\"Query start time :\",query_start_time)\n",
+    "\n",
+    "        row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
+    "\n",
+    "                                                    time_window_s = time_window_s)\n",
+    "\n",
+    "                                       )\n",
+    "\n",
+    "        cols = [\n",
+    "\n",
+    "        'START_DATE_TIME',\n",
+    "\n",
+    "        'END_DATE_TIME',\n",
+    "\n",
+    "        'Focal_id',\n",
+    "\n",
+    "        'MIN_PRICE',\n",
+    "\n",
+    "        'MAX_PRICE',\n",
+    "\n",
+    "        'PRICE_CHANGE_PCT',\n",
+    "\n",
+    "        'PARTICIPANT_VOLUME_PCT',\n",
+    "\n",
+    "        'PARTICIPANT_VOLUME',\n",
+    "\n",
+    "        'TOTAL_VOLUME',\n",
+    "\n",
+    "        ]\n",
+    "\n",
+    "        final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
+    "\n",
+    "        final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\\\n",
+    "\n",
+    "        final_scenario_df['TOTAL_VOLUME'] * 100\n",
+    "\n",
+    "        final_scenario_df['Segment'] = 'Default'\n",
+    "\n",
+    "        final_scenario_df['SAR_FLAG'] = 'N'\n",
+    "\n",
+    "        final_scenario_df['Risk'] = 'Low Risk'\n",
+    "\n",
+    "        final_scenario_df.dropna(inplace=True)\n",
+    "\n",
+    "        # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
+    "\n",
+    "        return final_scenario_df\n",
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..f220144
--- /dev/null
+++ b/main.py
@@ -0,0 +1,14 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+from tms_data_interface import SQLQueryInterface
+
+class Scenario:
+	seq = SQLQueryInterface()
+
+	def logic(self, **kwargs):
+		# Write your code here
+