generated from dhairya/scenario_template
System save at 11/10/2024 11:55 by user_client2024
This commit is contained in:
user_client2024
parent
511222ac72
commit
05d1197ef0
223
main.ipynb
223
main.ipynb
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"cells": [
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"cells": [
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"ename": "IndentationError",
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"evalue": "expected an indented block after function definition on line 6 (1665995538.py, line 8)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;36m Cell \u001b[0;32mIn[1], line 8\u001b[0;36m\u001b[0m\n\u001b[0;31m import pandas as pd\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after function definition on line 6\n"
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]
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}
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],
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"source": [
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"class Scenario:\n",
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"\tseq = SQLQueryInterface()\n",
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"\n",
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"\tdef logic(self, **kwargs):\n",
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"\t\t# from datetime import datetime, timedelta\n",
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"import pandas as pd\n",
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"query = \"\"\"\n",
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"WITH trade_data AS (\n",
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" SELECT \n",
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" trader_id,\n",
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" date_time,\n",
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" trade_price,\n",
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" trade_volume,\n",
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" -- Create a time window for each trade\n",
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" date_time - INTERVAL '1 second' * {time_window_s} AS window_start,\n",
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" date_time AS window_end\n",
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" FROM {trade_data_1b}\n",
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"),\n",
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"\n",
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"aggregated_trades AS (\n",
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" SELECT \n",
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" td.trader_id,\n",
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" td.window_start,\n",
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" td.window_end,\n",
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" SUM(CASE WHEN trade_side = 'buy' THEN trade_volume ELSE 0 END) \n",
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" OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS buy_volume,\n",
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" SUM(CASE WHEN trade_side = 'sell' THEN trade_volume ELSE 0 END) \n",
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" OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS sell_volume,\n",
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" SUM(trade_volume) OVER (ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS total_volume,\n",
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" MAX(trade_price) OVER (ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS highest_price,\n",
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" MIN(trade_price) OVER (ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS lowest_price,\n",
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" COUNT(*) OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS number_of_trades\n",
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" FROM trade_data td\n",
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")\n",
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"\n",
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"SELECT \n",
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" window_start AS start_time,\n",
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" window_end AS end_time,\n",
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" trader_id AS \"Participant\",\n",
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" lowest_price AS min_price,\n",
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" highest_price AS max_price,\n",
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" (highest_price - lowest_price) / NULLIF(lowest_price, 0) * 100 AS \"Price Change (%)\",\n",
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" buy_volume AS participant_volume,\n",
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" total_volume,\n",
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" (buy_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\"\n",
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"FROM aggregated_trades\n",
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"WHERE buy_volume > 0 OR sell_volume > 0\n",
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"\"\"\"\n",
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"\n",
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"class Scenario:\n",
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" seq = SQLQueryInterface(schema=\"internal\")\n",
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"\n",
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" def logic(self, **kwargs):\n",
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" validation_window = kwargs.get('validation_window')\n",
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" time_window_s = int(validation_window / 1000)\n",
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" query_start_time = datetime.now()\n",
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" print(\"Query start time :\", query_start_time)\n",
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"\n",
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" row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_data_2b\",\n",
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" time_window_s=time_window_s)\n",
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" )\n",
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"\n",
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" cols = [\n",
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" 'START_DATE_TIME',\n",
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" 'END_DATE_TIME',\n",
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" 'PARTICIPANT',\n",
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" 'MIN_PRICE',\n",
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" 'MAX_PRICE',\n",
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" 'PRICE_CHANGE (%)',\n",
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" 'PARTICIPANT_VOLUME',\n",
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" 'TOTAL_VOLUME',\n",
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" 'VOLUME (%)',\n",
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" ]\n",
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"\n",
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" final_scenario_df = pd.DataFrame(row_list, columns=cols)\n",
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" final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME'] / \\\n",
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" final_scenario_df['TOTAL_VOLUME'] * 100\n",
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"\n",
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" # Adding additional columns\n",
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" final_scenario_df['Segment'] = 'Default'\n",
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" final_scenario_df['SAR_FLAG'] = 'N'\n",
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" final_scenario_df['Risk'] = 'Low Risk'\n",
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"\n",
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" return final_scenario_df\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "90c70e46-71a0-44a6-8090-f53aad3193c3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"import pandas as pd\n",
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"# SQL query to aggregate trade data and compute metrics\n",
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"query = \"\"\"\n",
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"WITH trade_data AS (\n",
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" SELECT \n",
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" trader_id,\n",
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" date_time,\n",
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" trade_price,\n",
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" trade_volume,\n",
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" -- Create a time window for each trade\n",
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" date_time - INTERVAL '1 second' * {time_window_s} AS window_start,\n",
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" date_time AS window_end\n",
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" FROM {trade_data_1b}\n",
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"),\n",
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"\n",
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"aggregated_trades AS (\n",
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" SELECT \n",
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" td.trader_id,\n",
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" td.window_start,\n",
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" td.window_end,\n",
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" SUM(CASE WHEN trade_side = 'buy' THEN trade_volume ELSE 0 END) \n",
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" OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS buy_volume,\n",
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" SUM(CASE WHEN trade_side = 'sell' THEN trade_volume ELSE 0 END) \n",
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" OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS sell_volume,\n",
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" SUM(trade_volume) OVER (ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS total_volume,\n",
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" MAX(trade_price) OVER (ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS highest_price,\n",
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" MIN(trade_price) OVER (ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS lowest_price,\n",
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" COUNT(*) OVER (PARTITION BY td.trader_id ORDER BY td.date_time \n",
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" RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS number_of_trades\n",
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" FROM trade_data td\n",
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")\n",
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"\n",
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"SELECT \n",
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" window_start AS start_time,\n",
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" window_end AS end_time,\n",
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" trader_id AS \"Participant\",\n",
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" lowest_price AS min_price,\n",
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" highest_price AS max_price,\n",
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" (highest_price - lowest_price) / NULLIF(lowest_price, 0) * 100 AS \"Price Change (%)\",\n",
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" buy_volume AS participant_volume,\n",
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" total_volume,\n",
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" (buy_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\"\n",
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"FROM aggregated_trades\n",
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"WHERE buy_volume > 0 OR sell_volume > 0\n",
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"\"\"\"\n",
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"\n",
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"class Scenario:\n",
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" seq = SQLQueryInterface(schema=\"internal\")\n",
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"\n",
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" def logic(self, **kwargs):\n",
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" validation_window = kwargs.get('validation_window')\n",
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" time_window_s = int(validation_window / 1000)\n",
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" \n",
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" query_start_time = datetime.now()\n",
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" print(\"Query start time:\", query_start_time)\n",
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"\n",
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" row_list = self.seq.execute_raw(query.format(\n",
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" trade_data_1b=\"trade_data_2b\",\n",
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" time_window_s=time_window_s\n",
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" ))\n",
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"\n",
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" cols = [\n",
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" 'START_DATE_TIME',\n",
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" 'END_DATE_TIME',\n",
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" 'PARTICIPANT',\n",
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" 'MIN_PRICE',\n",
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" 'MAX_PRICE',\n",
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" 'PRICE_CHANGE (%)',\n",
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" 'PARTICIPANT_VOLUME',\n",
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" 'TOTAL_VOLUME',\n",
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" 'VOLUME (%)',\n",
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" ]\n",
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"\n",
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" final_scenario_df = pd.DataFrame(row_list, columns=cols)\n",
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" final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME'] / \\\n",
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" final_scenario_df['TOTAL_VOLUME'] * 100\n",
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"\n",
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" # Adding additional columns\n",
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" final_scenario_df['Segment'] = 'Default'\n",
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" final_scenario_df['SAR_FLAG'] = 'N'\n",
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" final_scenario_df['Risk'] = 'Low Risk'\n",
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"\n",
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" return final_scenario_df\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "caee5554-5254-4388-bf24-029281d77890",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": "from tms_data_interface import SQLQueryInterface\n\nclass Scenario:\n\tseq = SQLQueryInterface()\n\n\tdef logic(self, **kwargs):\n\t\t# Write your code here\n"
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"source": []
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}
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}
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],
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],
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"metadata": {
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"metadata": {
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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"version": "3.11.8"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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194
main.py
194
main.py
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#!/usr/bin/env python
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#!/usr/bin/env python
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# coding: utf-8
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# coding: utf-8
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# In[ ]:
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# In[1]:
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from tms_data_interface import SQLQueryInterface
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from tms_data_interface import SQLQueryInterface
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@ -10,5 +10,195 @@ class Scenario:
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seq = SQLQueryInterface()
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seq = SQLQueryInterface()
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def logic(self, **kwargs):
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def logic(self, **kwargs):
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# Write your code here
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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',
|
||||||
|
'TOTAL_VOLUME',
|
||||||
|
'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
|
||||||
|
|
||||||
|
# Adding additional columns
|
||||||
|
final_scenario_df['Segment'] = 'Default'
|
||||||
|
final_scenario_df['SAR_FLAG'] = 'N'
|
||||||
|
final_scenario_df['Risk'] = 'Low Risk'
|
||||||
|
|
||||||
|
return final_scenario_df
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# In[2]:
|
||||||
|
|
||||||
|
|
||||||
|
from datetime import datetime
|
||||||
|
import pandas as pd
|
||||||
|
from tms_data_interface import SQLQueryInterface
|
||||||
|
|
||||||
|
# SQL query to aggregate trade data and compute metrics
|
||||||
|
query = """
|
||||||
|
WITH trade_data AS (
|
||||||
|
SELECT
|
||||||
|
trader_id,
|
||||||
|
date_time,
|
||||||
|
trade_price,
|
||||||
|
trade_volume,
|
||||||
|
-- Create a time window for each trade
|
||||||
|
date_time - INTERVAL '1 second' * {time_window_s} AS window_start,
|
||||||
|
date_time AS window_end
|
||||||
|
FROM {trade_data_1b}
|
||||||
|
),
|
||||||
|
|
||||||
|
aggregated_trades AS (
|
||||||
|
SELECT
|
||||||
|
td.trader_id,
|
||||||
|
td.window_start,
|
||||||
|
td.window_end,
|
||||||
|
SUM(CASE WHEN trade_side = 'buy' THEN trade_volume ELSE 0 END)
|
||||||
|
OVER (PARTITION BY td.trader_id ORDER BY td.date_time
|
||||||
|
RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS buy_volume,
|
||||||
|
SUM(CASE WHEN trade_side = 'sell' THEN trade_volume ELSE 0 END)
|
||||||
|
OVER (PARTITION BY td.trader_id ORDER BY td.date_time
|
||||||
|
RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS sell_volume,
|
||||||
|
SUM(trade_volume) OVER (ORDER BY td.date_time
|
||||||
|
RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS total_volume,
|
||||||
|
MAX(trade_price) OVER (ORDER BY td.date_time
|
||||||
|
RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS highest_price,
|
||||||
|
MIN(trade_price) OVER (ORDER BY td.date_time
|
||||||
|
RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS lowest_price,
|
||||||
|
COUNT(*) OVER (PARTITION BY td.trader_id ORDER BY td.date_time
|
||||||
|
RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS number_of_trades
|
||||||
|
FROM trade_data td
|
||||||
|
)
|
||||||
|
|
||||||
|
SELECT
|
||||||
|
window_start AS start_time,
|
||||||
|
window_end AS end_time,
|
||||||
|
trader_id AS "Participant",
|
||||||
|
lowest_price AS min_price,
|
||||||
|
highest_price AS max_price,
|
||||||
|
(highest_price - lowest_price) / NULLIF(lowest_price, 0) * 100 AS "Price Change (%)",
|
||||||
|
buy_volume AS participant_volume,
|
||||||
|
total_volume,
|
||||||
|
(buy_volume / NULLIF(total_volume, 0)) * 100 AS "Volume (%)"
|
||||||
|
FROM aggregated_trades
|
||||||
|
WHERE buy_volume > 0 OR sell_volume > 0
|
||||||
|
"""
|
||||||
|
|
||||||
|
class Scenario:
|
||||||
|
seq = SQLQueryInterface(schema="internal")
|
||||||
|
|
||||||
|
def logic(self, **kwargs):
|
||||||
|
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_data_2b",
|
||||||
|
time_window_s=time_window_s
|
||||||
|
))
|
||||||
|
|
||||||
|
cols = [
|
||||||
|
'START_DATE_TIME',
|
||||||
|
'END_DATE_TIME',
|
||||||
|
'PARTICIPANT',
|
||||||
|
'MIN_PRICE',
|
||||||
|
'MAX_PRICE',
|
||||||
|
'PRICE_CHANGE (%)',
|
||||||
|
'PARTICIPANT_VOLUME',
|
||||||
|
'TOTAL_VOLUME',
|
||||||
|
'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
|
||||||
|
|
||||||
|
# Adding additional columns
|
||||||
|
final_scenario_df['Segment'] = 'Default'
|
||||||
|
final_scenario_df['SAR_FLAG'] = 'N'
|
||||||
|
final_scenario_df['Risk'] = 'Low Risk'
|
||||||
|
|
||||||
|
return final_scenario_df
|
||||||
|
|
||||||
|
|
||||||
|
# In[ ]:
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user