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230
.ipynb_checkpoints/main-checkpoint.ipynb
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@ -3,149 +3,115 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "393f184e-6bc6-44d0-bf4f-89610f05c1dc", "id": "a4895b40-8a5a-41d8-b3dc-d2590fc04c3c",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1599a219-9bf4-49d5-aed4-db58f6810c26",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from datetime import datetime, timedelta\n",
"import pandas as pd\n",
"from tms_data_interface import SQLQueryInterface\n", "from tms_data_interface import SQLQueryInterface\n",
"seq = SQLQueryInterface(schema = \"qa_schema\")\n", "\n",
"seq.execute_raw(\"show tables\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7df9028-84c1-47dd-82e6-161238532686",
"metadata": {},
"outputs": [],
"source": [
"query = \"\"\"\n", "query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n", "WITH time_windows AS (\n",
" final.Credit_transaction_amount,\n", " SELECT\n",
" final.Total_no_of_credit_transactions,\n", " -- End time is the current trade time\n",
" final.Debit_transaction_amount,\n", " date_time AS end_time,\n",
" final.Total_no_of_debit_transactions,\n", "\n",
" final.Wash_Ratio,\n", " -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
" final.SEGMENT,\n", " date_add('second', -{time_window_s}, date_time) AS start_time,\n",
" final.RISK,\n", "\n",
" final.SAR_FLAG\n", " -- Trade details\n",
" from \n", " trade_price,\n",
" (\n", " trade_volume,\n",
" (\n", " trader_id,\n",
" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n", "\n",
" subquery.Credit_transaction_amount,\n", " -- Calculate minimum price within the time window\n",
" subquery.Total_no_of_credit_transactions,\n", " MIN(trade_price) OVER (\n",
" case\n", " ORDER BY date_time \n",
" when subquery.Debit_transaction_amount is NULL then 0\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" else Debit_transaction_amount\n", " ) AS min_price,\n",
" end as Debit_transaction_amount,\n", "\n",
" case\n", " -- Calculate maximum price within the time window\n",
" when subquery.Total_no_of_debit_transactions is NULL then 0\n", " MAX(trade_price) OVER (\n",
" else Total_no_of_debit_transactions\n", " ORDER BY date_time \n",
" end as Total_no_of_debit_transactions,\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" case\n", " ) AS max_price,\n",
" when subquery.Debit_transaction_amount = 0\n", "\n",
" or subquery.Debit_transaction_amount is NULL then 0\n", " -- Calculate total trade volume within the time window\n",
" else subquery.Credit_transaction_amount / subquery.Debit_transaction_amount\n", " SUM(trade_volume) OVER ( \n",
" end as Wash_Ratio\n", " ORDER BY date_time \n",
" from \n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" (\n", " ) AS total_volume,\n",
" (\n", "\n",
" select customer_number as CUSTOMER_NUMBER_1, \n", " -- Calculate participant's trade volume within the time window\n",
" sum(transaction_amount) as Credit_transaction_amount, \n", " SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
" count(*) as Total_no_of_credit_transactions\n", " PARTITION BY trader_id \n",
" from \n", " ORDER BY date_time \n",
" (\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" select * \n", " ) AS participant_volume\n",
" from {trans_data} as trans_table left join {acc_data} as acc_table\n", " FROM\n",
" on trans_table.benef_account_number = acc_table.account_number\n", " {trade_data_1b}\n",
" )\n", ")\n",
" where account_number not in ('None')\n", "SELECT\n",
" group by 1\n", " -- Select the time window details\n",
" ) credit left join\n", " start_time,\n",
" (\n", " end_time,\n",
" select customer_number as CUSTOMER_NUMBER_2, \n", "\n",
" sum(transaction_amount) as Debit_transaction_amount, \n", " -- Select the participant (trader) ID\n",
" count(*) as Total_no_of_debit_transactions\n", " trader_id AS \"Participant\",\n",
" from \n", "\n",
" (\n", " -- Select the calculated min and max prices\n",
" select * \n", " min_price,\n",
" from {trans_data} as trans_table left join {acc_data} as acc_table\n", " max_price,\n",
" on trans_table.orig_account_number = acc_table.account_number\n", "\n",
" )\n", " -- Calculate the price change percentage\n",
" where account_number not in ('None')\n", " (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
" group by 1\n", "\n",
" ) debit on credit.CUSTOMER_NUMBER_1 = debit.CUSTOMER_NUMBER_2 \n", " -- Calculate the participant's volume as a percentage of total volume\n",
" ) subquery\n", " (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
" ) main left join \n", "\n",
" (\n", " -- Participant volume\n",
" select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n", " participant_volume,\n",
" subquery.SEGMENT,\n", "\n",
" subquery.RISK,\n", " -- Select the total volume within the window\n",
" case\n", " total_volume AS \"Total Volume\"\n",
" when subquery.SAR_FLAG is NULL then 'N'\n", "FROM\n",
" else subquery.SAR_FLAG\n", " time_windows\n",
" end as SAR_FLAG \n",
" from\n",
" (\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 left join\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_4, \n",
" sar_flag as SAR_FLAG\n",
" from {alert_data}\n",
" ) ad on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
" ) subquery\n",
" ) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
" ) final\n",
"\"\"\"\n", "\"\"\"\n",
"\n" "\n",
] "\n",
},
{
"cell_type": "code",
"execution_count": null,
"id": "361ae64c-dc41-456d-93e4-f7d8b211dce4",
"metadata": {},
"outputs": [],
"source": [
"from tms_data_interface import SQLQueryInterface\n", "from tms_data_interface import SQLQueryInterface\n",
"\n", "\n",
"class Scenario:\n", "class Scenario:\n",
" seq = SQLQueryInterface(schema =\"qa_schema\")\n", " seq = SQLQueryInterface(schema=\"trade_schema\")\n",
"\n",
" def logic(self, **kwargs):\n", " def logic(self, **kwargs):\n",
" row_list = self.seq.execute_raw(query.format(trans_data=\"qa_table_test\",\n", " validation_window = kwargs.get('validation_window')\n",
" cust_data=\"customer_v1\",\n", " time_window_s = int(validation_window/1000)\n",
" acc_data=\"account_v1\",\n", " query_start_time = datetime.now()\n",
" alert_data=\"alert_v1\")\n", " print(\"Query start time :\",query_start_time)\n",
" row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
" time_window_s = time_window_s)\n",
" )\n", " )\n",
" cols = [\"Focal_id\", \"Credit_transaction_amount\",\n", " cols = [\n",
" \"Total_no_of_credit_transactions\",\n", " 'START_DATE_TIME',\n",
" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n", " 'END_DATE_TIME',\n",
" \"Wash_Ratio\",\n", " 'Focal_id',\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n", " 'MIN_PRICE',\n",
" df = pd.DataFrame(row_list, columns = cols)\n", " 'MAX_PRICE',\n",
" return df" " 'PRICE_CHANGE_PCT',\n",
" 'PARTICIPANT_VOLUME_PCT',\n",
" 'PARTICIPANT_VOLUME',\n",
" 'TOTAL_VOLUME',\n",
" ]\n",
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
" final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\\\n",
" final_scenario_df['TOTAL_VOLUME'] * 100\n",
" final_scenario_df['Segment'] = 'Default'\n",
" final_scenario_df['SAR_FLAG'] = 'N'\n",
" final_scenario_df['Risk'] = 'Low Risk'\n",
" final_scenario_df.dropna(inplace=True)\n",
" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
" return final_scenario_df"
] ]
} }
], ],

230
main.ipynb
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@ -3,149 +3,115 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "393f184e-6bc6-44d0-bf4f-89610f05c1dc", "id": "a4895b40-8a5a-41d8-b3dc-d2590fc04c3c",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1599a219-9bf4-49d5-aed4-db58f6810c26",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from datetime import datetime, timedelta\n",
"import pandas as pd\n",
"from tms_data_interface import SQLQueryInterface\n", "from tms_data_interface import SQLQueryInterface\n",
"seq = SQLQueryInterface(schema = \"qa_schema\")\n", "\n",
"seq.execute_raw(\"show tables\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7df9028-84c1-47dd-82e6-161238532686",
"metadata": {},
"outputs": [],
"source": [
"query = \"\"\"\n", "query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n", "WITH time_windows AS (\n",
" final.Credit_transaction_amount,\n", " SELECT\n",
" final.Total_no_of_credit_transactions,\n", " -- End time is the current trade time\n",
" final.Debit_transaction_amount,\n", " date_time AS end_time,\n",
" final.Total_no_of_debit_transactions,\n", "\n",
" final.Wash_Ratio,\n", " -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
" final.SEGMENT,\n", " date_add('second', -{time_window_s}, date_time) AS start_time,\n",
" final.RISK,\n", "\n",
" final.SAR_FLAG\n", " -- Trade details\n",
" from \n", " trade_price,\n",
" (\n", " trade_volume,\n",
" (\n", " trader_id,\n",
" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n", "\n",
" subquery.Credit_transaction_amount,\n", " -- Calculate minimum price within the time window\n",
" subquery.Total_no_of_credit_transactions,\n", " MIN(trade_price) OVER (\n",
" case\n", " ORDER BY date_time \n",
" when subquery.Debit_transaction_amount is NULL then 0\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" else Debit_transaction_amount\n", " ) AS min_price,\n",
" end as Debit_transaction_amount,\n", "\n",
" case\n", " -- Calculate maximum price within the time window\n",
" when subquery.Total_no_of_debit_transactions is NULL then 0\n", " MAX(trade_price) OVER (\n",
" else Total_no_of_debit_transactions\n", " ORDER BY date_time \n",
" end as Total_no_of_debit_transactions,\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" case\n", " ) AS max_price,\n",
" when subquery.Debit_transaction_amount = 0\n", "\n",
" or subquery.Debit_transaction_amount is NULL then 0\n", " -- Calculate total trade volume within the time window\n",
" else subquery.Credit_transaction_amount / subquery.Debit_transaction_amount\n", " SUM(trade_volume) OVER ( \n",
" end as Wash_Ratio\n", " ORDER BY date_time \n",
" from \n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" (\n", " ) AS total_volume,\n",
" (\n", "\n",
" select customer_number as CUSTOMER_NUMBER_1, \n", " -- Calculate participant's trade volume within the time window\n",
" sum(transaction_amount) as Credit_transaction_amount, \n", " SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
" count(*) as Total_no_of_credit_transactions\n", " PARTITION BY trader_id \n",
" from \n", " ORDER BY date_time \n",
" (\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" select * \n", " ) AS participant_volume\n",
" from {trans_data} as trans_table left join {acc_data} as acc_table\n", " FROM\n",
" on trans_table.benef_account_number = acc_table.account_number\n", " {trade_data_1b}\n",
" )\n", ")\n",
" where account_number not in ('None')\n", "SELECT\n",
" group by 1\n", " -- Select the time window details\n",
" ) credit left join\n", " start_time,\n",
" (\n", " end_time,\n",
" select customer_number as CUSTOMER_NUMBER_2, \n", "\n",
" sum(transaction_amount) as Debit_transaction_amount, \n", " -- Select the participant (trader) ID\n",
" count(*) as Total_no_of_debit_transactions\n", " trader_id AS \"Participant\",\n",
" from \n", "\n",
" (\n", " -- Select the calculated min and max prices\n",
" select * \n", " min_price,\n",
" from {trans_data} as trans_table left join {acc_data} as acc_table\n", " max_price,\n",
" on trans_table.orig_account_number = acc_table.account_number\n", "\n",
" )\n", " -- Calculate the price change percentage\n",
" where account_number not in ('None')\n", " (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
" group by 1\n", "\n",
" ) debit on credit.CUSTOMER_NUMBER_1 = debit.CUSTOMER_NUMBER_2 \n", " -- Calculate the participant's volume as a percentage of total volume\n",
" ) subquery\n", " (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
" ) main left join \n", "\n",
" (\n", " -- Participant volume\n",
" select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n", " participant_volume,\n",
" subquery.SEGMENT,\n", "\n",
" subquery.RISK,\n", " -- Select the total volume within the window\n",
" case\n", " total_volume AS \"Total Volume\"\n",
" when subquery.SAR_FLAG is NULL then 'N'\n", "FROM\n",
" else subquery.SAR_FLAG\n", " time_windows\n",
" end as SAR_FLAG \n",
" from\n",
" (\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 left join\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_4, \n",
" sar_flag as SAR_FLAG\n",
" from {alert_data}\n",
" ) ad on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
" ) subquery\n",
" ) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
" ) final\n",
"\"\"\"\n", "\"\"\"\n",
"\n" "\n",
] "\n",
},
{
"cell_type": "code",
"execution_count": null,
"id": "361ae64c-dc41-456d-93e4-f7d8b211dce4",
"metadata": {},
"outputs": [],
"source": [
"from tms_data_interface import SQLQueryInterface\n", "from tms_data_interface import SQLQueryInterface\n",
"\n", "\n",
"class Scenario:\n", "class Scenario:\n",
" seq = SQLQueryInterface(schema =\"qa_schema\")\n", " seq = SQLQueryInterface(schema=\"trade_schema\")\n",
"\n",
" def logic(self, **kwargs):\n", " def logic(self, **kwargs):\n",
" row_list = self.seq.execute_raw(query.format(trans_data=\"qa_table_test\",\n", " validation_window = kwargs.get('validation_window')\n",
" cust_data=\"customer_v1\",\n", " time_window_s = int(validation_window/1000)\n",
" acc_data=\"account_v1\",\n", " query_start_time = datetime.now()\n",
" alert_data=\"alert_v1\")\n", " print(\"Query start time :\",query_start_time)\n",
" row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
" time_window_s = time_window_s)\n",
" )\n", " )\n",
" cols = [\"Focal_id\", \"Credit_transaction_amount\",\n", " cols = [\n",
" \"Total_no_of_credit_transactions\",\n", " 'START_DATE_TIME',\n",
" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n", " 'END_DATE_TIME',\n",
" \"Wash_Ratio\",\n", " 'Focal_id',\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n", " 'MIN_PRICE',\n",
" df = pd.DataFrame(row_list, columns = cols)\n", " 'MAX_PRICE',\n",
" return df" " 'PRICE_CHANGE_PCT',\n",
" 'PARTICIPANT_VOLUME_PCT',\n",
" 'PARTICIPANT_VOLUME',\n",
" 'TOTAL_VOLUME',\n",
" ]\n",
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
" final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\\\n",
" final_scenario_df['TOTAL_VOLUME'] * 100\n",
" final_scenario_df['Segment'] = 'Default'\n",
" final_scenario_df['SAR_FLAG'] = 'N'\n",
" final_scenario_df['Risk'] = 'Low Risk'\n",
" final_scenario_df.dropna(inplace=True)\n",
" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
" return final_scenario_df"
] ]
} }
], ],

208
main.py
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@ -4,131 +4,109 @@
# In[ ]: # In[ ]:
from datetime import datetime, timedelta
import pandas as pd import pandas as pd
# In[ ]:
from tms_data_interface import SQLQueryInterface from tms_data_interface import SQLQueryInterface
seq = SQLQueryInterface(schema = "qa_schema")
seq.execute_raw("show tables")
# In[ ]:
query = """ query = """
select final.CUSTOMER_NUMBER_main as Focal_id, WITH time_windows AS (
final.Credit_transaction_amount, SELECT
final.Total_no_of_credit_transactions, -- End time is the current trade time
final.Debit_transaction_amount, date_time AS end_time,
final.Total_no_of_debit_transactions,
final.Wash_Ratio, -- Subtract seconds from the end_time using date_add() with negative integer interval
final.SEGMENT, date_add('second', -{time_window_s}, date_time) AS start_time,
final.RISK,
final.SAR_FLAG -- Trade details
from trade_price,
( trade_volume,
( trader_id,
select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
subquery.Credit_transaction_amount, -- Calculate minimum price within the time window
subquery.Total_no_of_credit_transactions, MIN(trade_price) OVER (
case ORDER BY date_time
when subquery.Debit_transaction_amount is NULL then 0 RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
else Debit_transaction_amount ) AS min_price,
end as Debit_transaction_amount,
case -- Calculate maximum price within the time window
when subquery.Total_no_of_debit_transactions is NULL then 0 MAX(trade_price) OVER (
else Total_no_of_debit_transactions ORDER BY date_time
end as Total_no_of_debit_transactions, RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
case ) AS max_price,
when subquery.Debit_transaction_amount = 0
or subquery.Debit_transaction_amount is NULL then 0 -- Calculate total trade volume within the time window
else subquery.Credit_transaction_amount / subquery.Debit_transaction_amount SUM(trade_volume) OVER (
end as Wash_Ratio ORDER BY date_time
from RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
( ) AS total_volume,
(
select customer_number as CUSTOMER_NUMBER_1, -- Calculate participant's trade volume within the time window
sum(transaction_amount) as Credit_transaction_amount, SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (
count(*) as Total_no_of_credit_transactions PARTITION BY trader_id
from ORDER BY date_time
( RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
select * ) AS participant_volume
from {trans_data} as trans_table left join {acc_data} as acc_table FROM
on trans_table.benef_account_number = acc_table.account_number {trade_data_1b}
) )
where account_number not in ('None') SELECT
group by 1 -- Select the time window details
) credit left join start_time,
( end_time,
select customer_number as CUSTOMER_NUMBER_2,
sum(transaction_amount) as Debit_transaction_amount, -- Select the participant (trader) ID
count(*) as Total_no_of_debit_transactions trader_id AS "Participant",
from
( -- Select the calculated min and max prices
select * min_price,
from {trans_data} as trans_table left join {acc_data} as acc_table max_price,
on trans_table.orig_account_number = acc_table.account_number
) -- Calculate the price change percentage
where account_number not in ('None') (max_price - min_price) / NULLIF(min_price, 0) * 100 AS "Price Change (%)",
group by 1
) debit on credit.CUSTOMER_NUMBER_1 = debit.CUSTOMER_NUMBER_2 -- Calculate the participant's volume as a percentage of total volume
) subquery (participant_volume / NULLIF(total_volume, 0)) * 100 AS "Volume (%)",
) main left join
( -- Participant volume
select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust, participant_volume,
subquery.SEGMENT,
subquery.RISK, -- Select the total volume within the window
case total_volume AS "Total Volume"
when subquery.SAR_FLAG is NULL then 'N' FROM
else subquery.SAR_FLAG time_windows
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
) subquery
) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main
) final
""" """
# In[ ]:
from tms_data_interface import SQLQueryInterface from tms_data_interface import SQLQueryInterface
class Scenario: class Scenario:
seq = SQLQueryInterface(schema ="qa_schema") seq = SQLQueryInterface(schema="trade_schema")
def logic(self, **kwargs): def logic(self, **kwargs):
row_list = self.seq.execute_raw(query.format(trans_data="qa_table_test", validation_window = kwargs.get('validation_window')
cust_data="customer_v1", time_window_s = int(validation_window/1000)
acc_data="account_v1", query_start_time = datetime.now()
alert_data="alert_v1") print("Query start time :",query_start_time)
row_list = self.seq.execute_raw(query.format(trade_data_1b="trade_10m_v3",
time_window_s = time_window_s)
) )
cols = ["Focal_id", "Credit_transaction_amount", cols = [
"Total_no_of_credit_transactions", 'START_DATE_TIME',
"Debit_transaction_amount", "Total_no_of_debit_transactions", 'END_DATE_TIME',
"Wash_Ratio", 'Focal_id',
"Segment", "Risk", "SAR_FLAG"] 'MIN_PRICE',
df = pd.DataFrame(row_list, columns = cols) 'MAX_PRICE',
return df 'PRICE_CHANGE_PCT',
'PARTICIPANT_VOLUME_PCT',
'PARTICIPANT_VOLUME',
'TOTAL_VOLUME',
]
final_scenario_df = pd.DataFrame(row_list, columns = cols)
final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\
final_scenario_df['TOTAL_VOLUME'] * 100
final_scenario_df['Segment'] = 'Default'
final_scenario_df['SAR_FLAG'] = 'N'
final_scenario_df['Risk'] = 'Low Risk'
final_scenario_df.dropna(inplace=True)
# final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']
return final_scenario_df