generated from dhairya/scenario_template
System save at 07/10/2024 12:16 by yati
This commit is contained in:
yati
parent
95ca092578
commit
495cc8eb00
139
.ipynb_checkpoints/main-checkpoint.ipynb
Normal file
139
.ipynb_checkpoints/main-checkpoint.ipynb
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{
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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": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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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')\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'] = 'Low 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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172
main.ipynb
172
main.ipynb
@ -1,33 +1,139 @@
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{
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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": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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"metadata": {},
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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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}
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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": "3.8.13"
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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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{
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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": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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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')\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'] = 'Low 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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112
main.py
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112
main.py
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@ -0,0 +1,112 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[ ]:
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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 time_windows AS (
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SELECT
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-- End time is the current trade time
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date_time AS end_time,
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-- Subtract seconds from the end_time using date_add() with negative integer interval
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date_add('second', -{time_window_s}, date_time) AS start_time,
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-- Trade details
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trade_price,
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trade_volume,
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trader_id,
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-- Calculate minimum price within the time window
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MIN(trade_price) OVER (
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ORDER BY date_time
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RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
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) AS min_price,
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-- Calculate maximum price within the time window
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MAX(trade_price) OVER (
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ORDER BY date_time
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RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
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) AS max_price,
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-- Calculate total trade volume within the time window
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SUM(trade_volume) OVER (
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ORDER BY date_time
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RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
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) AS total_volume,
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-- Calculate participant's trade volume within the time window
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SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (
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PARTITION BY trader_id
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ORDER BY date_time
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RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
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) AS participant_volume
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FROM
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{trade_data_1b}
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)
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SELECT
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-- Select the time window details
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start_time,
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end_time,
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-- Select the participant (trader) ID
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trader_id AS "Participant",
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-- Select the calculated min and max prices
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min_price,
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max_price,
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-- Calculate the price change percentage
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(max_price - min_price) / NULLIF(min_price, 0) * 100 AS "Price Change (%)",
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-- Calculate the participant's volume as a percentage of total volume
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(participant_volume / NULLIF(total_volume, 0)) * 100 AS "Volume (%)",
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-- Participant volume
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participant_volume,
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-- Select the total volume within the window
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total_volume AS "Total Volume"
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FROM
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time_windows
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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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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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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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'Focal_id',
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'MIN_PRICE',
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'MAX_PRICE',
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'PRICE_CHANGE_PCT',
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'PARTICIPANT_VOLUME_PCT',
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'PARTICIPANT_VOLUME',
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'TOTAL_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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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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Block a user