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System save at 07/10/2024 12:00 by qatest

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qatest 2024-10-07 06:30:24 +00:00
parent 9d8d767fdc
commit d39722b82b
3 changed files with 300 additions and 380 deletions
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234
.ipynb_checkpoints/main-checkpoint.ipynb
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@ -7,145 +7,113 @@
"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()\n" "seq.execute_raw(\"show tables\")\n",
] "\n",
},
{
"cell_type": "code",
"execution_count": null,
"id": "399856f9-dd38-4239-a35e-9a878c5391a6",
"metadata": {},
"outputs": [],
"source": [
"seq.execute_raw(\"show tables\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea68418d-e898-451b-8c9a-2f379b91dd6b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4d51f70-feaa-4565-8894-7fc5893f258e",
"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.SEGMENT,\n", " -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
" final.RISK,\n", " date_add('second', -{time_window_s}, date_time) AS start_time,\n",
" final.SAR_FLAG\n", "\n",
" from \n", " -- Trade details\n",
" (\n", " trade_price,\n",
" (\n", " trade_volume,\n",
" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n", " trader_id,\n",
" subquery.Credit_transaction_amount,\n", "\n",
" subquery.Total_no_of_credit_transactions,\n", " -- Calculate minimum price within the time window\n",
" case\n", " MIN(trade_price) OVER (\n",
" when subquery.Debit_transaction_amount is NULL then 0\n", " ORDER BY date_time \n",
" else Debit_transaction_amount\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" end as Debit_transaction_amount,\n", " ) AS min_price,\n",
" case\n", "\n",
" when subquery.Total_no_of_debit_transactions is NULL then 0\n", " -- Calculate maximum price within the time window\n",
" else Total_no_of_debit_transactions\n", " MAX(trade_price) OVER (\n",
" end as Total_no_of_debit_transactions\n", " ORDER BY date_time \n",
" from \n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" (\n", " ) AS max_price,\n",
" (\n", "\n",
" select customer_number as CUSTOMER_NUMBER_1, \n", " -- Calculate total trade volume within the time window\n",
" sum(transaction_amount) as Credit_transaction_amount, \n", " SUM(trade_volume) OVER ( \n",
" count(*) as Total_no_of_credit_transactions\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",
" select * \n", "\n",
" from {trans_data} trans_table left join {acc_data} acc_table\n", " -- Calculate participant's trade volume within the time window\n",
" on trans_table.benef_account_number = acc_table.account_number\n", " SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
" )\n", " PARTITION BY trader_id \n",
" where account_number not in ('None')\n", " ORDER BY date_time \n",
" group by 1\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) credit left join\n", " ) AS participant_volume\n",
" (\n", " FROM\n",
" select customer_number as CUSTOMER_NUMBER_2, \n", " {trade_data_1b}\n",
" sum(transaction_amount) as Debit_transaction_amount, \n", ")\n",
" count(*) as Total_no_of_debit_transactions\n", "SELECT\n",
" from \n", " -- Select the time window details\n",
" (\n", " start_time,\n",
" select * \n", " end_time,\n",
" from {trans_data} trans_table left join {acc_data} acc_table\n", "\n",
" on trans_table.orig_account_number = acc_table.account_number\n", " -- Select the participant (trader) ID\n",
" )\n", " trader_id AS \"Participant\",\n",
" where account_number not in ('None')\n", "\n",
" group by 1\n", " -- Select the calculated min and max prices\n",
" ) debit on credit.CUSTOMER_NUMBER_1 = debit.CUSTOMER_NUMBER_2 \n", " min_price,\n",
" ) subquery\n", " max_price,\n",
" ) main left join \n", "\n",
" (\n", " -- Calculate the price change percentage\n",
" select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n", " (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
" subquery.SEGMENT,\n", "\n",
" subquery.RISK,\n", " -- Calculate the participant's volume as a percentage of total volume\n",
" case\n", " (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
" when subquery.SAR_FLAG is NULL then 'N'\n", "\n",
" else subquery.SAR_FLAG\n", " -- Participant volume\n",
" end as SAR_FLAG \n", " participant_volume,\n",
" from\n", "\n",
" (\n", " -- Select the total volume within the window\n",
" (\n", " total_volume AS \"Total Volume\"\n",
" select customer_number as CUSTOMER_NUMBER_3, \n", "FROM\n",
" business_segment as SEGMENT,\n", " time_windows\n",
" case\n", "\"\"\"\n",
" when RISK_CLASSIFICATION = 1 then 'Low Risk'\n", "\n",
" when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n", "\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",
"\"\"\" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea234072-0b05-4cde-8f12-7ac5c27e08ce",
"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()\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=\"test_the_table\",\n", " validation_window = kwargs.get('validation_window')\n",
" cust_data=\"final_customer_data_v1\",\n", " time_window_s = int(validation_window/1000)\n",
" acc_data=\"final_account_data_v1\",\n", " query_start_time = datetime.now()\n",
" alert_data=\"final_alert_data_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\", \"Total_no_of_credit_transactions\",\n", " cols = [\n",
" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n", " 'START_DATE_TIME',\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n", " 'END_DATE_TIME',\n",
" df = pd.DataFrame(row_list, columns = cols)\n", " 'Focal_id',\n",
" return df" " 'MIN_PRICE',\n",
" 'MAX_PRICE',\n",
" '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\n",
"\n"
] ]
} }
], ],

234
main.ipynb
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@ -7,145 +7,113 @@
"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()\n" "seq.execute_raw(\"show tables\")\n",
] "\n",
},
{
"cell_type": "code",
"execution_count": null,
"id": "399856f9-dd38-4239-a35e-9a878c5391a6",
"metadata": {},
"outputs": [],
"source": [
"seq.execute_raw(\"show tables\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea68418d-e898-451b-8c9a-2f379b91dd6b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4d51f70-feaa-4565-8894-7fc5893f258e",
"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.SEGMENT,\n", " -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
" final.RISK,\n", " date_add('second', -{time_window_s}, date_time) AS start_time,\n",
" final.SAR_FLAG\n", "\n",
" from \n", " -- Trade details\n",
" (\n", " trade_price,\n",
" (\n", " trade_volume,\n",
" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n", " trader_id,\n",
" subquery.Credit_transaction_amount,\n", "\n",
" subquery.Total_no_of_credit_transactions,\n", " -- Calculate minimum price within the time window\n",
" case\n", " MIN(trade_price) OVER (\n",
" when subquery.Debit_transaction_amount is NULL then 0\n", " ORDER BY date_time \n",
" else Debit_transaction_amount\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" end as Debit_transaction_amount,\n", " ) AS min_price,\n",
" case\n", "\n",
" when subquery.Total_no_of_debit_transactions is NULL then 0\n", " -- Calculate maximum price within the time window\n",
" else Total_no_of_debit_transactions\n", " MAX(trade_price) OVER (\n",
" end as Total_no_of_debit_transactions\n", " ORDER BY date_time \n",
" from \n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" (\n", " ) AS max_price,\n",
" (\n", "\n",
" select customer_number as CUSTOMER_NUMBER_1, \n", " -- Calculate total trade volume within the time window\n",
" sum(transaction_amount) as Credit_transaction_amount, \n", " SUM(trade_volume) OVER ( \n",
" count(*) as Total_no_of_credit_transactions\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",
" select * \n", "\n",
" from {trans_data} trans_table left join {acc_data} acc_table\n", " -- Calculate participant's trade volume within the time window\n",
" on trans_table.benef_account_number = acc_table.account_number\n", " SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
" )\n", " PARTITION BY trader_id \n",
" where account_number not in ('None')\n", " ORDER BY date_time \n",
" group by 1\n", " RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) credit left join\n", " ) AS participant_volume\n",
" (\n", " FROM\n",
" select customer_number as CUSTOMER_NUMBER_2, \n", " {trade_data_1b}\n",
" sum(transaction_amount) as Debit_transaction_amount, \n", ")\n",
" count(*) as Total_no_of_debit_transactions\n", "SELECT\n",
" from \n", " -- Select the time window details\n",
" (\n", " start_time,\n",
" select * \n", " end_time,\n",
" from {trans_data} trans_table left join {acc_data} acc_table\n", "\n",
" on trans_table.orig_account_number = acc_table.account_number\n", " -- Select the participant (trader) ID\n",
" )\n", " trader_id AS \"Participant\",\n",
" where account_number not in ('None')\n", "\n",
" group by 1\n", " -- Select the calculated min and max prices\n",
" ) debit on credit.CUSTOMER_NUMBER_1 = debit.CUSTOMER_NUMBER_2 \n", " min_price,\n",
" ) subquery\n", " max_price,\n",
" ) main left join \n", "\n",
" (\n", " -- Calculate the price change percentage\n",
" select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n", " (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
" subquery.SEGMENT,\n", "\n",
" subquery.RISK,\n", " -- Calculate the participant's volume as a percentage of total volume\n",
" case\n", " (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
" when subquery.SAR_FLAG is NULL then 'N'\n", "\n",
" else subquery.SAR_FLAG\n", " -- Participant volume\n",
" end as SAR_FLAG \n", " participant_volume,\n",
" from\n", "\n",
" (\n", " -- Select the total volume within the window\n",
" (\n", " total_volume AS \"Total Volume\"\n",
" select customer_number as CUSTOMER_NUMBER_3, \n", "FROM\n",
" business_segment as SEGMENT,\n", " time_windows\n",
" case\n", "\"\"\"\n",
" when RISK_CLASSIFICATION = 1 then 'Low Risk'\n", "\n",
" when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n", "\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",
"\"\"\" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea234072-0b05-4cde-8f12-7ac5c27e08ce",
"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()\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=\"test_the_table\",\n", " validation_window = kwargs.get('validation_window')\n",
" cust_data=\"final_customer_data_v1\",\n", " time_window_s = int(validation_window/1000)\n",
" acc_data=\"final_account_data_v1\",\n", " query_start_time = datetime.now()\n",
" alert_data=\"final_alert_data_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\", \"Total_no_of_credit_transactions\",\n", " cols = [\n",
" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n", " 'START_DATE_TIME',\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n", " 'END_DATE_TIME',\n",
" df = pd.DataFrame(row_list, columns = cols)\n", " 'Focal_id',\n",
" return df" " 'MIN_PRICE',\n",
" 'MAX_PRICE',\n",
" '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\n",
"\n"
] ]
} }
], ],

212
main.py
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@ -4,127 +4,111 @@
# In[ ]: # In[ ]:
from datetime import datetime, timedelta
import pandas as pd
from tms_data_interface import SQLQueryInterface from tms_data_interface import SQLQueryInterface
seq = SQLQueryInterface()
# In[ ]:
seq.execute_raw("show tables") seq.execute_raw("show tables")
# In[ ]:
import pandas as pd
# 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.SEGMENT,
final.RISK,
final.SAR_FLAG
from
(
(
select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
subquery.Credit_transaction_amount,
subquery.Total_no_of_credit_transactions,
case
when subquery.Debit_transaction_amount is NULL then 0
else Debit_transaction_amount
end as Debit_transaction_amount,
case
when subquery.Total_no_of_debit_transactions is NULL then 0
else Total_no_of_debit_transactions
end as Total_no_of_debit_transactions
from
(
(
select customer_number as CUSTOMER_NUMBER_1,
sum(transaction_amount) as Credit_transaction_amount,
count(*) as Total_no_of_credit_transactions
from
(
select *
from {trans_data} trans_table left join {acc_data} acc_table
on trans_table.benef_account_number = acc_table.account_number
)
where account_number not in ('None')
group by 1
) credit left join
(
select customer_number as CUSTOMER_NUMBER_2,
sum(transaction_amount) as Debit_transaction_amount,
count(*) as Total_no_of_debit_transactions
from
(
select *
from {trans_data} trans_table left join {acc_data} acc_table
on trans_table.orig_account_number = acc_table.account_number
)
where account_number not in ('None')
group by 1
) debit on credit.CUSTOMER_NUMBER_1 = debit.CUSTOMER_NUMBER_2
) subquery
) main left join
(
select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,
subquery.SEGMENT,
subquery.RISK,
case
when subquery.SAR_FLAG is NULL then 'N'
else subquery.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
) subquery
) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main
) final
"""
-- Subtract seconds from the end_time using date_add() with negative integer interval
date_add('second', -{time_window_s}, date_time) AS start_time,
# In[ ]: -- Trade details
trade_price,
trade_volume,
trader_id,
-- Calculate minimum price within the time window
MIN(trade_price) OVER (
ORDER BY date_time
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
) AS min_price,
-- Calculate maximum price within the time window
MAX(trade_price) OVER (
ORDER BY date_time
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
) AS max_price,
-- Calculate total trade volume within the time window
SUM(trade_volume) OVER (
ORDER BY date_time
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
) AS total_volume,
-- Calculate participant's trade volume within the time window
SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (
PARTITION BY trader_id
ORDER BY date_time
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
) AS participant_volume
FROM
{trade_data_1b}
)
SELECT
-- Select the time window details
start_time,
end_time,
-- Select the participant (trader) ID
trader_id AS "Participant",
-- Select the calculated min and max prices
min_price,
max_price,
-- Calculate the price change percentage
(max_price - min_price) / NULLIF(min_price, 0) * 100 AS "Price Change (%)",
-- Calculate the participant's volume as a percentage of total volume
(participant_volume / NULLIF(total_volume, 0)) * 100 AS "Volume (%)",
-- Participant volume
participant_volume,
-- Select the total volume within the window
total_volume AS "Total Volume"
FROM
time_windows
"""
from tms_data_interface import SQLQueryInterface from tms_data_interface import SQLQueryInterface
class Scenario: class Scenario:
seq = SQLQueryInterface() seq = SQLQueryInterface(schema="trade_schema")
def logic(self, **kwargs):
def logic(self, **kwargs): validation_window = kwargs.get('validation_window')
row_list = self.seq.execute_raw(query.format(trans_data="test_the_table", time_window_s = int(validation_window/1000)
cust_data="final_customer_data_v1", query_start_time = datetime.now()
acc_data="final_account_data_v1", print("Query start time :",query_start_time)
alert_data="final_alert_data_v1") 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", "Total_no_of_credit_transactions", )
"Debit_transaction_amount", "Total_no_of_debit_transactions", cols = [
"Segment", "Risk", "SAR_FLAG"] 'START_DATE_TIME',
df = pd.DataFrame(row_list, columns = cols) 'END_DATE_TIME',
return df 'Focal_id',
'MIN_PRICE',
'MAX_PRICE',
'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