generated from dhairya/scenario_template
205 lines
6.6 KiB
Python
205 lines
6.6 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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from tms_data_interface import SQLQueryInterface
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class Scenario:
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seq = SQLQueryInterface()
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def logic(self, **kwargs):
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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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WITH trade_data AS (
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SELECT
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trader_id,
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date_time,
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trade_price,
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trade_volume,
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-- Create a time window for each trade
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date_time - INTERVAL '1 second' * {time_window_s} AS window_start,
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date_time AS window_end
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FROM {trade_data_1b}
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),
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aggregated_trades AS (
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SELECT
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td.trader_id,
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td.window_start,
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td.window_end,
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SUM(CASE WHEN trade_side = 'buy' THEN trade_volume ELSE 0 END)
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OVER (PARTITION BY td.trader_id ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS buy_volume,
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SUM(CASE WHEN trade_side = 'sell' THEN trade_volume ELSE 0 END)
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OVER (PARTITION BY td.trader_id ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS sell_volume,
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SUM(trade_volume) OVER (ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS total_volume,
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MAX(trade_price) OVER (ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS highest_price,
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MIN(trade_price) OVER (ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS lowest_price,
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COUNT(*) OVER (PARTITION BY td.trader_id ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS number_of_trades
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FROM trade_data td
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)
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SELECT
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window_start AS start_time,
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window_end AS end_time,
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trader_id AS "Participant",
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lowest_price AS min_price,
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highest_price AS max_price,
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(highest_price - lowest_price) / NULLIF(lowest_price, 0) * 100 AS "Price Change (%)",
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buy_volume AS participant_volume,
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total_volume,
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(buy_volume / NULLIF(total_volume, 0)) * 100 AS "Volume (%)"
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FROM aggregated_trades
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WHERE buy_volume > 0 OR sell_volume > 0
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"""
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class Scenario:
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seq = SQLQueryInterface(schema="internal")
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def logic(self, **kwargs):
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validation_window = kwargs.get('validation_window')
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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_data_2b",
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time_window_s=time_window_s)
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)
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cols = [
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'START_DATE_TIME',
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'END_DATE_TIME',
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'PARTICIPANT',
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'MIN_PRICE',
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'MAX_PRICE',
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'PRICE_CHANGE (%)',
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'PARTICIPANT_VOLUME',
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'TOTAL_VOLUME',
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'VOLUME (%)',
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]
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final_scenario_df = pd.DataFrame(row_list, columns=cols)
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final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME'] / \
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final_scenario_df['TOTAL_VOLUME'] * 100
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# Adding additional columns
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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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return final_scenario_df
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# In[2]:
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from datetime import datetime
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import pandas as pd
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from tms_data_interface import SQLQueryInterface
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# SQL query to aggregate trade data and compute metrics
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query = """
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WITH trade_data AS (
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SELECT
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trader_id,
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date_time,
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trade_price,
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trade_volume,
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-- Create a time window for each trade
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date_time - INTERVAL '1 second' * {time_window_s} AS window_start,
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date_time AS window_end
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FROM {trade_data_1b}
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),
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aggregated_trades AS (
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SELECT
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td.trader_id,
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td.window_start,
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td.window_end,
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SUM(CASE WHEN trade_side = 'buy' THEN trade_volume ELSE 0 END)
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OVER (PARTITION BY td.trader_id ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS buy_volume,
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SUM(CASE WHEN trade_side = 'sell' THEN trade_volume ELSE 0 END)
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OVER (PARTITION BY td.trader_id ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS sell_volume,
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SUM(trade_volume) OVER (ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS total_volume,
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MAX(trade_price) OVER (ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS highest_price,
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MIN(trade_price) OVER (ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS lowest_price,
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COUNT(*) OVER (PARTITION BY td.trader_id ORDER BY td.date_time
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RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS number_of_trades
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FROM trade_data td
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)
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SELECT
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window_start AS start_time,
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window_end AS end_time,
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trader_id AS "Participant",
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lowest_price AS min_price,
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highest_price AS max_price,
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(highest_price - lowest_price) / NULLIF(lowest_price, 0) * 100 AS "Price Change (%)",
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buy_volume AS participant_volume,
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total_volume,
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(buy_volume / NULLIF(total_volume, 0)) * 100 AS "Volume (%)"
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FROM aggregated_trades
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WHERE buy_volume > 0 OR sell_volume > 0
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"""
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class Scenario:
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seq = SQLQueryInterface(schema="internal")
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def logic(self, **kwargs):
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validation_window = kwargs.get('validation_window')
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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(
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trade_data_1b="trade_data_2b",
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time_window_s=time_window_s
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))
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cols = [
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'START_DATE_TIME',
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'END_DATE_TIME',
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'PARTICIPANT',
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'MIN_PRICE',
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'MAX_PRICE',
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'PRICE_CHANGE (%)',
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'PARTICIPANT_VOLUME',
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'TOTAL_VOLUME',
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'VOLUME (%)',
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]
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final_scenario_df = pd.DataFrame(row_list, columns=cols)
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final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME'] / \
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final_scenario_df['TOTAL_VOLUME'] * 100
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# Adding additional columns
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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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return final_scenario_df
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# In[ ]:
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