generated from dhairya/scenario_template
152 lines
6.7 KiB
Plaintext
152 lines
6.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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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_10m_v3}\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_10m_v3} 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=\"trade_schema\")\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_10m_v3=\"trade_10m_v3\",\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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" 'FOCAL_ID',\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": 6,
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"id": "caee5554-5254-4388-bf24-029281d77890",
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"metadata": {},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "unsupported operand type(s) for /: 'NoneType' and 'int'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m scenario \u001b[38;5;241m=\u001b[39m Scenario()\n\u001b[0;32m----> 2\u001b[0m \u001b[43mscenario\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogic\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
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"Cell \u001b[0;32mIn[4], line 60\u001b[0m, in \u001b[0;36mScenario.logic\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlogic\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 59\u001b[0m validation_window \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalidation_window\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 60\u001b[0m time_window_s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[43mvalidation_window\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m)\n\u001b[1;32m 62\u001b[0m query_start_time \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow()\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuery start time:\u001b[39m\u001b[38;5;124m\"\u001b[39m, query_start_time)\n",
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"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'NoneType' and 'int'"
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]
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}
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],
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"source": [
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"#scenario = Scenario()\n",
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"#scenario.logic()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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