140 lines
5.1 KiB
Plaintext
140 lines
5.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "776ef599-d5a9-4665-b8e5-b07f219fb9c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"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 time_windows AS (\n",
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" SELECT\n",
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" -- End time is the current trade time\n",
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" date_time AS end_time,\n",
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"\n",
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" -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
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" date_add('second', -{time_window_s}, date_time) AS start_time,\n",
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"\n",
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" -- Trade details\n",
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" trade_price,\n",
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" trade_volume,\n",
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" trader_id,\n",
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"\n",
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" -- Calculate minimum price within the time window\n",
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" MIN(trade_price) OVER (\n",
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" ORDER BY date_time \n",
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" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
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" ) AS min_price,\n",
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"\n",
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" -- Calculate maximum price within the time window\n",
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" MAX(trade_price) OVER (\n",
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" ORDER BY date_time \n",
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" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
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" ) AS max_price,\n",
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"\n",
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" -- Calculate total trade volume within the time window\n",
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" SUM(trade_volume) OVER ( \n",
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" ORDER BY date_time \n",
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" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
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" ) AS total_volume,\n",
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"\n",
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" -- Calculate participant's trade volume within the time window\n",
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" SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
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" PARTITION BY trader_id \n",
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" ORDER BY date_time \n",
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" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
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" ) AS participant_volume\n",
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" FROM\n",
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" {trade_data_1b}\n",
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")\n",
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"SELECT\n",
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" -- Select the time window details\n",
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" start_time,\n",
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" end_time,\n",
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"\n",
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" -- Select the participant (trader) ID\n",
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" trader_id AS \"Participant\",\n",
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"\n",
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" -- Select the calculated min and max prices\n",
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" min_price,\n",
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" max_price,\n",
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"\n",
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" -- Calculate the price change percentage\n",
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" (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
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"\n",
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" -- Calculate the participant's volume as a percentage of total volume\n",
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" (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
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"\n",
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" -- Participant volume\n",
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" participant_volume,\n",
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"\n",
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" -- Select the total volume within the window\n",
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" total_volume AS \"Total Volume\"\n",
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"FROM\n",
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" time_windows\n",
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"\"\"\"\n",
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"\n",
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"\n",
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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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" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
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" def logic(self, **kwargs):\n",
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" validation_window = kwargs.get('validation_window', 300000)\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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" row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
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" time_window_s = time_window_s)\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_PCT',\n",
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" 'PARTICIPANT_VOLUME_PCT',\n",
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" 'PARTICIPANT_VOLUME',\n",
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" 'TOTAL_VOLUME',\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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" 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'] = 'Medium Risk'\n",
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" final_scenario_df.dropna(inplace=True)\n",
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" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\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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"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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