generated from user_client2024/58
135 lines
4.2 KiB
Python
135 lines
4.2 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[16]:
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from datetime import datetime, timedelta
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import pandas as pd
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from tms_data_interface import SQLQueryInterface
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query = """
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SELECT
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n.TRADER_ID,
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n.trade_time_window,
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n.net_volume,
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n.order_count, -- Include number of orders
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COALESCE(t.total_trade_volume, 0) AS total_trade_volume,
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CASE
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WHEN COALESCE(t.total_trade_volume, 0) > 0 THEN n.net_volume / t.total_trade_volume
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ELSE 0 -- or another value to indicate no trades
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END AS order_trade_ratio,
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CASE
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WHEN net_volume_all.total_net_volume_all > 0 THEN
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(n.net_volume / net_volume_all.total_net_volume_all) * 100
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ELSE 0
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END AS volume_percentage -- Calculate volume percentage
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FROM (
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-- Step 2: Subquery for net_order_volume
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SELECT
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o.TRADER_ID,
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t.DATE_TIME AS trade_time_window,
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SUM(CASE
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WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME
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WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME
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WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME
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ELSE 0 END
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) AS net_volume,
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COUNT(o.ORDER_ID) AS order_count -- Count the number of orders
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FROM {order_10m} o
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JOIN {trade_data_1b} t
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ON o.TRADER_ID = t.TRADER_ID
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WHERE o.SIDE = 'buy'
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AND o.DATE_TIME BETWEEN t.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t.DATE_TIME
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GROUP BY o.TRADER_ID, t.DATE_TIME
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) AS n
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LEFT JOIN (
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-- Step 6: Subquery for total_trade_volume (opposite side trades after spoofing)
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SELECT
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t.TRADER_ID,
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t.DATE_TIME,
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SUM(t.TRADE_VOLUME) AS total_trade_volume
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FROM (
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-- Step 5: Subquery for relevant_trades
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SELECT t1.*
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FROM {trade_data_1b} t1
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WHERE t1.TRADE_SIDE = 'buy'
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AND EXISTS (
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SELECT 1
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FROM {trade_data_1b} t2
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WHERE t2.TRADER_ID = t1.TRADER_ID
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AND t2.DATE_TIME BETWEEN t1.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t1.DATE_TIME
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)
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) AS t
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GROUP BY t.DATE_TIME, t.TRADER_ID
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) AS t
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ON n.TRADER_ID = t.TRADER_ID AND n.trade_time_window = t.DATE_TIME
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-- New subquery for total net volume for all traders in the same time window
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LEFT JOIN (
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SELECT
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t.DATE_TIME AS trade_time_window,
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SUM(CASE
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WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME
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WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME
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WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME
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ELSE 0 END
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) AS total_net_volume_all
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FROM {order_10m} o
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JOIN {trade_data_1b} t
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ON o.TRADER_ID = t.TRADER_ID
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WHERE o.SIDE = 'buy'
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AND o.DATE_TIME BETWEEN t.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t.DATE_TIME
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GROUP BY t.DATE_TIME
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) AS net_volume_all
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ON n.trade_time_window = net_volume_all.trade_time_window
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ORDER BY n.trade_time_window
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"""
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from tms_data_interface import SQLQueryInterface
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class Scenario:
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seq = SQLQueryInterface(schema="trade_schema")
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def logic(self, **kwargs):
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validation_window = kwargs.get('validation_window')
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spoofing_side = kwargs.get('buy')
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time_window_s = int(validation_window/1000)
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query_start_time = datetime.now()
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print("Query start time :",query_start_time)
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row_list = self.seq.execute_raw(query.format(trade_data_1b="trade_10m_v3",
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order_10m = 'order_10m',
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time_window_s = time_window_s)
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)
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cols = [
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'focal_ID',
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'trade_time_window',
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'net_volume',
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'order_count',
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'total_trade_volume',
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'order_trade_ratio',
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'volume_percentage'
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]
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final_scenario_df = pd.DataFrame(row_list, columns = cols)
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final_scenario_df['Segment'] = 'Default'
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final_scenario_df['SAR_FLAG'] = 'N'
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final_scenario_df['Risk'] = 'Low Risk'
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final_scenario_df.dropna(inplace=True)
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# final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']
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return final_scenario_df
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# In[17]:
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# scenario = Scenario()
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# scenario.logic(validation_window=300000)
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# In[ ]:
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