diff --git a/.ipynb_checkpoints/main-checkpoint.ipynb b/.ipynb_checkpoints/main-checkpoint.ipynb
new file mode 100644
index 0000000..7daeb9c
--- /dev/null
+++ b/.ipynb_checkpoints/main-checkpoint.ipynb
@@ -0,0 +1,368 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2edb58c0-33a2-4436-8128-05645af9990d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from tms_data_interface import SQLQueryInterface\n",
+    "seq = SQLQueryInterface(schema=\"transactionschema\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0ccc875a-6ff5-4741-9495-93c6871b1027",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['account_data_v1'],\n",
+       " ['account_data_v2'],\n",
+       " ['alert_data_v1'],\n",
+       " ['alert_data_v2'],\n",
+       " ['customer_data_v1'],\n",
+       " ['customer_data_v2'],\n",
+       " ['transaction10m'],\n",
+       " ['transaction60m']]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "seq.execute_raw(\"show tables\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "01502887-b11f-46d2-9a52-e493df19d049",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "    select final.CUSTOMER_NUMBER_main as Focal_id,\n",
+    "        CAST(final.Total_hrc_transaction_amount AS DECIMAL(18, 2)) AS Total_hrc_transaction_amount,\n",
+    "        final.Unique_country_codes,\n",
+    "        final.SEGMENT,\n",
+    "        final.RISK,\n",
+    "        final.SAR_FLAG\n",
+    "    from \n",
+    "    (\n",
+    "        (\n",
+    "            select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n",
+    "                   subquery.Total_hrc_transaction_amount,\n",
+    "                   subquery.Unique_country_codes\n",
+    "            from \n",
+    "            (\n",
+    "                select customer_number as CUSTOMER_NUMBER_1, \n",
+    "                       sum(transaction_amount) as Total_hrc_transaction_amount, \n",
+    "                       array_join(array_agg(DISTINCT benef_cntry_code), ',') AS unique_country_codes\n",
+    "                from \n",
+    "                (\n",
+    "                    select * \n",
+    "                    from {trans_data} trans_table \n",
+    "                    left join {acc_data} acc_table\n",
+    "                    on trans_table.benef_account_number = acc_table.account_number\n",
+    "                ) trans\n",
+    "                where account_number not in ('None')\n",
+    "                group by customer_number\n",
+    "            ) subquery\n",
+    "        ) main \n",
+    "        left join \n",
+    "       (\n",
+    "            select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
+    "                   cd.SEGMENT,\n",
+    "                   cd.RISK,\n",
+    "                   case\n",
+    "                        when ad.SAR_FLAG is NULL then 'N'\n",
+    "                        else ad.SAR_FLAG\n",
+    "                   end as SAR_FLAG    \n",
+    "            from\n",
+    "            (\n",
+    "                select customer_number as CUSTOMER_NUMBER_3, \n",
+    "                       business_segment as SEGMENT,\n",
+    "                       case\n",
+    "                           when RISK_CLASSIFICATION = 1 then 'Low Risk'\n",
+    "                           when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n",
+    "                           when RISK_CLASSIFICATION = 3 then 'High Risk'\n",
+    "                           else 'Unknown Risk'\n",
+    "                       end AS RISK\n",
+    "                from {cust_data}\n",
+    "            ) cd \n",
+    "            left join\n",
+    "            (\n",
+    "                select customer_number as CUSTOMER_NUMBER_4, \n",
+    "                       sar_flag as SAR_FLAG\n",
+    "                from {alert_data}\n",
+    "            ) ad \n",
+    "            on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
+    "        ) as cust_alert\n",
+    "        on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
+    "    ) as final\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c28e15b5-4b09-46a6-849e-7ffd5cefee7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from tms_data_interface import SQLQueryInterface\n",
+    "\n",
+    "class Scenario:\n",
+    "    seq = SQLQueryInterface(schema=\"transactionschema\")\n",
+    "\n",
+    "    def logic(self, **kwargs):\n",
+    "        row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
+    "                                                    cust_data=\"customer_data_v1\",\n",
+    "                                                    acc_data=\"account_data_v1\",\n",
+    "                                                    alert_data=\"alert_data_v1\")\n",
+    "                                       )\n",
+    "        cols = [\"Focal_id\", \"Total_hrc_transaction_amount\", \"Unique_country_codes\",\n",
+    "                \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
+    "        df = pd.DataFrame(row_list, columns = cols)\n",
+    "        return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dc94e713-7267-499a-897f-672209d563c0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "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>Focal_id</th>\n",
+       "      <th>Total_hrc_transaction_amount</th>\n",
+       "      <th>Unique_country_codes</th>\n",
+       "      <th>Segment</th>\n",
+       "      <th>Risk</th>\n",
+       "      <th>SAR_FLAG</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PN739187</td>\n",
+       "      <td>5288386944.18</td>\n",
+       "      <td>None</td>\n",
+       "      <td>SME</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PN573373</td>\n",
+       "      <td>3984448017.83</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Whole Sale Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PN791113</td>\n",
+       "      <td>2203599126.03</td>\n",
+       "      <td>None</td>\n",
+       "      <td>SME</td>\n",
+       "      <td>High Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PN572058</td>\n",
+       "      <td>4424937298.78</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Whole Sale Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PN375785</td>\n",
+       "      <td>496146009.32</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Whole Sale Banking</td>\n",
+       "      <td>High Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10009</th>\n",
+       "      <td>PN759572</td>\n",
+       "      <td>42099733.92</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Ultra High NetWorth</td>\n",
+       "      <td>Medium Risk</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10010</th>\n",
+       "      <td>PN147338</td>\n",
+       "      <td>39374120.63</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Private Banking</td>\n",
+       "      <td>Medium Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10011</th>\n",
+       "      <td>PN632196</td>\n",
+       "      <td>37892236.97</td>\n",
+       "      <td>LK</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>Medium Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10012</th>\n",
+       "      <td>PN100406</td>\n",
+       "      <td>35620658.59</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>High Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10013</th>\n",
+       "      <td>PN452769</td>\n",
+       "      <td>35792822.47</td>\n",
+       "      <td>AM</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10014 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Focal_id Total_hrc_transaction_amount Unique_country_codes  \\\n",
+       "0      PN739187                5288386944.18                 None   \n",
+       "1      PN573373                3984448017.83                 None   \n",
+       "2      PN791113                2203599126.03                 None   \n",
+       "3      PN572058                4424937298.78                 None   \n",
+       "4      PN375785                 496146009.32                 None   \n",
+       "...         ...                          ...                  ...   \n",
+       "10009  PN759572                  42099733.92                   IN   \n",
+       "10010  PN147338                  39374120.63                   IN   \n",
+       "10011  PN632196                  37892236.97                   LK   \n",
+       "10012  PN100406                  35620658.59                   IN   \n",
+       "10013  PN452769                  35792822.47                   AM   \n",
+       "\n",
+       "                   Segment         Risk SAR_FLAG  \n",
+       "0                      SME     Low Risk        N  \n",
+       "1       Whole Sale Banking     Low Risk        Y  \n",
+       "2                      SME    High Risk        N  \n",
+       "3       Whole Sale Banking     Low Risk        N  \n",
+       "4       Whole Sale Banking    High Risk        N  \n",
+       "...                    ...          ...      ...  \n",
+       "10009  Ultra High NetWorth  Medium Risk        Y  \n",
+       "10010      Private Banking  Medium Risk        N  \n",
+       "10011               Others  Medium Risk        N  \n",
+       "10012               Others    High Risk        N  \n",
+       "10013               Others     Low Risk        N  \n",
+       "\n",
+       "[10014 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sen = Scenario()\n",
+    "# sen.logic()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e585bbc-7baf-41a5-8f05-ca25c08e7ca8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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.ipynb b/main.ipynb
index 3277afb..7daeb9c 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -1,33 +1,368 @@
-{
-    "cells": [
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
-            "metadata": {},
-            "outputs": [],
-            "source": "from tms_data_interface import SQLQueryInterface\n\nclass Scenario:\n\tseq = SQLQueryInterface()\n\n\tdef logic(self, **kwargs):\n\t\t# Write your code here\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": "3.8.13"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
-}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2edb58c0-33a2-4436-8128-05645af9990d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from tms_data_interface import SQLQueryInterface\n",
+    "seq = SQLQueryInterface(schema=\"transactionschema\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0ccc875a-6ff5-4741-9495-93c6871b1027",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['account_data_v1'],\n",
+       " ['account_data_v2'],\n",
+       " ['alert_data_v1'],\n",
+       " ['alert_data_v2'],\n",
+       " ['customer_data_v1'],\n",
+       " ['customer_data_v2'],\n",
+       " ['transaction10m'],\n",
+       " ['transaction60m']]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "seq.execute_raw(\"show tables\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "01502887-b11f-46d2-9a52-e493df19d049",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "    select final.CUSTOMER_NUMBER_main as Focal_id,\n",
+    "        CAST(final.Total_hrc_transaction_amount AS DECIMAL(18, 2)) AS Total_hrc_transaction_amount,\n",
+    "        final.Unique_country_codes,\n",
+    "        final.SEGMENT,\n",
+    "        final.RISK,\n",
+    "        final.SAR_FLAG\n",
+    "    from \n",
+    "    (\n",
+    "        (\n",
+    "            select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n",
+    "                   subquery.Total_hrc_transaction_amount,\n",
+    "                   subquery.Unique_country_codes\n",
+    "            from \n",
+    "            (\n",
+    "                select customer_number as CUSTOMER_NUMBER_1, \n",
+    "                       sum(transaction_amount) as Total_hrc_transaction_amount, \n",
+    "                       array_join(array_agg(DISTINCT benef_cntry_code), ',') AS unique_country_codes\n",
+    "                from \n",
+    "                (\n",
+    "                    select * \n",
+    "                    from {trans_data} trans_table \n",
+    "                    left join {acc_data} acc_table\n",
+    "                    on trans_table.benef_account_number = acc_table.account_number\n",
+    "                ) trans\n",
+    "                where account_number not in ('None')\n",
+    "                group by customer_number\n",
+    "            ) subquery\n",
+    "        ) main \n",
+    "        left join \n",
+    "       (\n",
+    "            select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
+    "                   cd.SEGMENT,\n",
+    "                   cd.RISK,\n",
+    "                   case\n",
+    "                        when ad.SAR_FLAG is NULL then 'N'\n",
+    "                        else ad.SAR_FLAG\n",
+    "                   end as SAR_FLAG    \n",
+    "            from\n",
+    "            (\n",
+    "                select customer_number as CUSTOMER_NUMBER_3, \n",
+    "                       business_segment as SEGMENT,\n",
+    "                       case\n",
+    "                           when RISK_CLASSIFICATION = 1 then 'Low Risk'\n",
+    "                           when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n",
+    "                           when RISK_CLASSIFICATION = 3 then 'High Risk'\n",
+    "                           else 'Unknown Risk'\n",
+    "                       end AS RISK\n",
+    "                from {cust_data}\n",
+    "            ) cd \n",
+    "            left join\n",
+    "            (\n",
+    "                select customer_number as CUSTOMER_NUMBER_4, \n",
+    "                       sar_flag as SAR_FLAG\n",
+    "                from {alert_data}\n",
+    "            ) ad \n",
+    "            on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
+    "        ) as cust_alert\n",
+    "        on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
+    "    ) as final\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c28e15b5-4b09-46a6-849e-7ffd5cefee7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from tms_data_interface import SQLQueryInterface\n",
+    "\n",
+    "class Scenario:\n",
+    "    seq = SQLQueryInterface(schema=\"transactionschema\")\n",
+    "\n",
+    "    def logic(self, **kwargs):\n",
+    "        row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
+    "                                                    cust_data=\"customer_data_v1\",\n",
+    "                                                    acc_data=\"account_data_v1\",\n",
+    "                                                    alert_data=\"alert_data_v1\")\n",
+    "                                       )\n",
+    "        cols = [\"Focal_id\", \"Total_hrc_transaction_amount\", \"Unique_country_codes\",\n",
+    "                \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
+    "        df = pd.DataFrame(row_list, columns = cols)\n",
+    "        return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dc94e713-7267-499a-897f-672209d563c0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "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>Focal_id</th>\n",
+       "      <th>Total_hrc_transaction_amount</th>\n",
+       "      <th>Unique_country_codes</th>\n",
+       "      <th>Segment</th>\n",
+       "      <th>Risk</th>\n",
+       "      <th>SAR_FLAG</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PN739187</td>\n",
+       "      <td>5288386944.18</td>\n",
+       "      <td>None</td>\n",
+       "      <td>SME</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PN573373</td>\n",
+       "      <td>3984448017.83</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Whole Sale Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PN791113</td>\n",
+       "      <td>2203599126.03</td>\n",
+       "      <td>None</td>\n",
+       "      <td>SME</td>\n",
+       "      <td>High Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PN572058</td>\n",
+       "      <td>4424937298.78</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Whole Sale Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PN375785</td>\n",
+       "      <td>496146009.32</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Whole Sale Banking</td>\n",
+       "      <td>High Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10009</th>\n",
+       "      <td>PN759572</td>\n",
+       "      <td>42099733.92</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Ultra High NetWorth</td>\n",
+       "      <td>Medium Risk</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10010</th>\n",
+       "      <td>PN147338</td>\n",
+       "      <td>39374120.63</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Private Banking</td>\n",
+       "      <td>Medium Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10011</th>\n",
+       "      <td>PN632196</td>\n",
+       "      <td>37892236.97</td>\n",
+       "      <td>LK</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>Medium Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10012</th>\n",
+       "      <td>PN100406</td>\n",
+       "      <td>35620658.59</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>High Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10013</th>\n",
+       "      <td>PN452769</td>\n",
+       "      <td>35792822.47</td>\n",
+       "      <td>AM</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10014 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Focal_id Total_hrc_transaction_amount Unique_country_codes  \\\n",
+       "0      PN739187                5288386944.18                 None   \n",
+       "1      PN573373                3984448017.83                 None   \n",
+       "2      PN791113                2203599126.03                 None   \n",
+       "3      PN572058                4424937298.78                 None   \n",
+       "4      PN375785                 496146009.32                 None   \n",
+       "...         ...                          ...                  ...   \n",
+       "10009  PN759572                  42099733.92                   IN   \n",
+       "10010  PN147338                  39374120.63                   IN   \n",
+       "10011  PN632196                  37892236.97                   LK   \n",
+       "10012  PN100406                  35620658.59                   IN   \n",
+       "10013  PN452769                  35792822.47                   AM   \n",
+       "\n",
+       "                   Segment         Risk SAR_FLAG  \n",
+       "0                      SME     Low Risk        N  \n",
+       "1       Whole Sale Banking     Low Risk        Y  \n",
+       "2                      SME    High Risk        N  \n",
+       "3       Whole Sale Banking     Low Risk        N  \n",
+       "4       Whole Sale Banking    High Risk        N  \n",
+       "...                    ...          ...      ...  \n",
+       "10009  Ultra High NetWorth  Medium Risk        Y  \n",
+       "10010      Private Banking  Medium Risk        N  \n",
+       "10011               Others  Medium Risk        N  \n",
+       "10012               Others    High Risk        N  \n",
+       "10013               Others     Low Risk        N  \n",
+       "\n",
+       "[10014 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sen = Scenario()\n",
+    "# sen.logic()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e585bbc-7baf-41a5-8f05-ca25c08e7ca8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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..c4f54d8
--- /dev/null
+++ b/main.py
@@ -0,0 +1,120 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+
+
+# In[3]:
+
+
+from tms_data_interface import SQLQueryInterface
+seq = SQLQueryInterface(schema="transactionschema")
+
+
+# In[4]:
+
+
+seq.execute_raw("show tables")
+
+
+# In[5]:
+
+
+query = """
+    select final.CUSTOMER_NUMBER_main as Focal_id,
+        CAST(final.Total_hrc_transaction_amount AS DECIMAL(18, 2)) AS Total_hrc_transaction_amount,
+        final.Unique_country_codes,
+        final.SEGMENT,
+        final.RISK,
+        final.SAR_FLAG
+    from 
+    (
+        (
+            select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
+                   subquery.Total_hrc_transaction_amount,
+                   subquery.Unique_country_codes
+            from 
+            (
+                select customer_number as CUSTOMER_NUMBER_1, 
+                       sum(transaction_amount) as Total_hrc_transaction_amount, 
+                       array_join(array_agg(DISTINCT benef_cntry_code), ',') AS unique_country_codes
+                from 
+                (
+                    select * 
+                    from {trans_data} trans_table 
+                    left join {acc_data} acc_table
+                    on trans_table.benef_account_number = acc_table.account_number
+                ) trans
+                where account_number not in ('None')
+                group by customer_number
+            ) subquery
+        ) main 
+        left join 
+       (
+            select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,
+                   cd.SEGMENT,
+                   cd.RISK,
+                   case
+                        when ad.SAR_FLAG is NULL then 'N'
+                        else ad.SAR_FLAG
+                   end as SAR_FLAG    
+            from
+            (
+                select customer_number as CUSTOMER_NUMBER_3, 
+                       business_segment as SEGMENT,
+                       case
+                           when RISK_CLASSIFICATION = 1 then 'Low Risk'
+                           when RISK_CLASSIFICATION = 2 then 'Medium Risk'
+                           when RISK_CLASSIFICATION = 3 then 'High Risk'
+                           else 'Unknown Risk'
+                       end AS RISK
+                from {cust_data}
+            ) cd 
+            left join
+            (
+                select customer_number as CUSTOMER_NUMBER_4, 
+                       sar_flag as SAR_FLAG
+                from {alert_data}
+            ) ad 
+            on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4
+        ) as cust_alert
+        on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main
+    ) as final
+"""
+
+
+# In[6]:
+
+
+from tms_data_interface import SQLQueryInterface
+
+class Scenario:
+    seq = SQLQueryInterface(schema="transactionschema")
+
+    def logic(self, **kwargs):
+        row_list = self.seq.execute_raw(query.format(trans_data="transaction10m",
+                                                    cust_data="customer_data_v1",
+                                                    acc_data="account_data_v1",
+                                                    alert_data="alert_data_v1")
+                                       )
+        cols = ["Focal_id", "Total_hrc_transaction_amount", "Unique_country_codes",
+                "Segment", "Risk", "SAR_FLAG"]
+        df = pd.DataFrame(row_list, columns = cols)
+        return df
+
+
+# In[7]:
+
+
+# sen = Scenario()
+# sen.logic()
+
+
+# In[ ]:
+
+
+
+