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

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

208
main.py
View File

@ -4,131 +4,109 @@
# In[ ]:
from datetime import datetime, timedelta
import pandas as pd
# In[ ]:
from tms_data_interface import SQLQueryInterface
seq = SQLQueryInterface(schema = "qa_schema")
seq.execute_raw("show tables")
# In[ ]:
query = """
select final.CUSTOMER_NUMBER_main as Focal_id,
final.Credit_transaction_amount,
final.Total_no_of_credit_transactions,
final.Debit_transaction_amount,
final.Total_no_of_debit_transactions,
final.Wash_Ratio,
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,
case
when subquery.Debit_transaction_amount = 0
or subquery.Debit_transaction_amount is NULL then 0
else subquery.Credit_transaction_amount / subquery.Debit_transaction_amount
end as Wash_Ratio
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} as trans_table left join {acc_data} as 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} as trans_table left join {acc_data} as 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
WITH time_windows AS (
SELECT
-- End time is the current trade time
date_time AS end_time,
-- Subtract seconds from the end_time using date_add() with negative integer interval
date_add('second', -{time_window_s}, date_time) AS start_time,
-- 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
"""
# In[ ]:
from tms_data_interface import SQLQueryInterface
class Scenario:
seq = SQLQueryInterface(schema ="qa_schema")
seq = SQLQueryInterface(schema="trade_schema")
def logic(self, **kwargs):
row_list = self.seq.execute_raw(query.format(trans_data="qa_table_test",
cust_data="customer_v1",
acc_data="account_v1",
alert_data="alert_v1")
validation_window = kwargs.get('validation_window')
time_window_s = int(validation_window/1000)
query_start_time = datetime.now()
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",
"Total_no_of_credit_transactions",
"Debit_transaction_amount", "Total_no_of_debit_transactions",
"Wash_Ratio",
"Segment", "Risk", "SAR_FLAG"]
df = pd.DataFrame(row_list, columns = cols)
return df
cols = [
'START_DATE_TIME',
'END_DATE_TIME',
'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