generated from user_client2024/58
System save at 14/10/2024 12:07 by user_client2024
This commit is contained in:
user_client2024
parent
539f6bd9bb
commit
c65df70254
@ -2,9 +2,11 @@
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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": 16,
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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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"metadata": {
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"tags": []
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"from datetime import datetime, timedelta\n",
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"from datetime import datetime, timedelta\n",
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@ -12,71 +14,83 @@
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"from tms_data_interface import SQLQueryInterface\n",
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"\n",
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"query = \"\"\"\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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" SELECT\n",
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" n.TRADER_ID,\n",
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" -- End time is the current trade time\n",
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" n.trade_time_window,\n",
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" date_time AS end_time,\n",
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" n.net_volume,\n",
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" n.order_count, -- Include number of orders\n",
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" COALESCE(t.total_trade_volume, 0) AS total_trade_volume,\n",
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" CASE \n",
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" WHEN COALESCE(t.total_trade_volume, 0) > 0 THEN n.net_volume / t.total_trade_volume\n",
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" ELSE 0 -- or another value to indicate no trades\n",
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" END AS order_trade_ratio,\n",
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" CASE \n",
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" WHEN net_volume_all.total_net_volume_all > 0 THEN \n",
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" (n.net_volume / net_volume_all.total_net_volume_all) * 100 \n",
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" ELSE 0 \n",
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" END AS volume_percentage -- Calculate volume percentage\n",
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"FROM (\n",
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" -- Step 2: Subquery for net_order_volume\n",
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" SELECT \n",
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" o.TRADER_ID,\n",
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" t.DATE_TIME AS trade_time_window,\n",
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" SUM(CASE \n",
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" WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME\n",
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" WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME\n",
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" WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME\n",
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" ELSE 0 END\n",
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" ) AS net_volume,\n",
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" COUNT(o.ORDER_ID) AS order_count -- Count the number of orders\n",
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" FROM {order_10m} o\n",
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" JOIN {trade_data_1b} t\n",
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" ON o.TRADER_ID = t.TRADER_ID\n",
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" WHERE o.SIDE = 'buy'\n",
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" AND o.DATE_TIME BETWEEN t.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t.DATE_TIME\n",
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" GROUP BY o.TRADER_ID, t.DATE_TIME\n",
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") AS n\n",
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"LEFT JOIN (\n",
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" -- Step 6: Subquery for total_trade_volume (opposite side trades after spoofing)\n",
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" SELECT \n",
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" t.TRADER_ID,\n",
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" t.DATE_TIME,\n",
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" SUM(t.TRADE_VOLUME) AS total_trade_volume\n",
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" FROM (\n",
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" -- Step 5: Subquery for relevant_trades\n",
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" SELECT t1.*\n",
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" FROM {trade_data_1b} t1\n",
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" WHERE t1.TRADE_SIDE = 'buy'\n",
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" AND EXISTS (\n",
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" SELECT 1\n",
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" FROM {trade_data_1b} t2\n",
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" WHERE t2.TRADER_ID = t1.TRADER_ID\n",
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" AND t2.DATE_TIME BETWEEN t1.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t1.DATE_TIME\n",
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" )\n",
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" ) AS t\n",
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" GROUP BY t.DATE_TIME, t.TRADER_ID\n",
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") AS t \n",
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"ON n.TRADER_ID = t.TRADER_ID AND n.trade_time_window = t.DATE_TIME\n",
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"\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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"-- New subquery for total net volume for all traders in the same time window\n",
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" date_add('second', -{time_window_s}, date_time) AS start_time,\n",
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"LEFT JOIN (\n",
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" SELECT \n",
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" t.DATE_TIME AS trade_time_window,\n",
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" SUM(CASE \n",
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" WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME\n",
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" WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME\n",
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" WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME\n",
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" ELSE 0 END\n",
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" ) AS total_net_volume_all\n",
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" FROM {order_10m} o\n",
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" JOIN {trade_data_1b} t\n",
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" ON o.TRADER_ID = t.TRADER_ID\n",
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" WHERE o.SIDE = 'buy'\n",
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" AND o.DATE_TIME BETWEEN t.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t.DATE_TIME\n",
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" GROUP BY t.DATE_TIME\n",
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") AS net_volume_all\n",
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"ON n.trade_time_window = net_volume_all.trade_time_window\n",
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"\n",
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"\n",
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" -- Trade details\n",
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"ORDER BY n.trade_time_window\n",
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" trade_price,\n",
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"limit 1000\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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"\n",
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"\n",
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"\n",
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@ -86,26 +100,24 @@
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" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
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" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
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" def logic(self, **kwargs):\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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" validation_window = kwargs.get('validation_window')\n",
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" spoofing_side = kwargs.get('buy')\n",
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" time_window_s = int(validation_window/1000)\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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" query_start_time = datetime.now()\n",
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" print(\"Query start time :\",query_start_time)\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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" row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
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" order_10m = 'order_10m',\n",
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" time_window_s = time_window_s)\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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" cols = [\n",
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" 'START_DATE_TIME',\n",
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" 'TRADER_ID',\n",
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" 'END_DATE_TIME',\n",
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" 'trade_time_window',\n",
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" 'Focal_id',\n",
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" 'net_volume',\n",
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" 'MIN_PRICE',\n",
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" 'order_count',\n",
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" 'MAX_PRICE',\n",
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" 'total_trade_volume',\n",
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" 'PRICE_CHANGE_PCT',\n",
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" 'order_trade_ratio',\n",
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" 'PARTICIPANT_VOLUME_PCT',\n",
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" 'volume_percentage'\n",
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" 'PARTICIPANT_VOLUME',\n",
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" 'TOTAL_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 = 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['Segment'] = 'Default'\n",
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" final_scenario_df['SAR_FLAG'] = 'N'\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['Risk'] = 'Low Risk'\n",
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" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\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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" 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": null,
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"id": "b5c4307f-bc35-47e2-b680-fd1df2168d6c",
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Query start time : 2024-10-14 06:23:07.242919\n"
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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(validation_window=300000)"
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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": "36b1b24a-aeca-4d22-a2b3-6e04aca31695",
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"metadata": {},
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"outputs": [],
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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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416
main.ipynb
416
main.ipynb
@ -2,7 +2,7 @@
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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": 2,
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"execution_count": 16,
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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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"metadata": {
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"tags": []
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"tags": []
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@ -14,72 +14,83 @@
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"from tms_data_interface import SQLQueryInterface\n",
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"\n",
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"query = \"\"\"\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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" SELECT\n",
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" n.TRADER_ID,\n",
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" -- End time is the current trade time\n",
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" n.trade_time_window,\n",
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" date_time AS end_time,\n",
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" n.net_volume,\n",
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" n.order_count, -- Include number of orders\n",
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" COALESCE(t.total_trade_volume, 0) AS total_trade_volume,\n",
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" CASE \n",
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" WHEN COALESCE(t.total_trade_volume, 0) > 0 THEN n.net_volume / t.total_trade_volume\n",
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" ELSE 0 -- or another value to indicate no trades\n",
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" END AS order_trade_ratio,\n",
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" CASE \n",
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" WHEN net_volume_all.total_net_volume_all > 0 THEN \n",
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" (n.net_volume / net_volume_all.total_net_volume_all) * 100 \n",
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" ELSE 0 \n",
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" END AS volume_percentage -- Calculate volume percentage\n",
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"FROM (\n",
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" -- Step 2: Subquery for net_order_volume\n",
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" SELECT \n",
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" o.TRADER_ID,\n",
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" t.DATE_TIME AS trade_time_window,\n",
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" SUM(CASE \n",
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" WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME\n",
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" WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME\n",
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" WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME\n",
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" ELSE 0 END\n",
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" ) AS net_volume,\n",
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" COUNT(o.ORDER_ID) AS order_count -- Count the number of orders\n",
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" FROM {order_10m} o\n",
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" JOIN {trade_data_1b} t\n",
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" ON o.TRADER_ID = t.TRADER_ID\n",
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" WHERE o.SIDE = 'buy'\n",
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" AND o.DATE_TIME BETWEEN t.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t.DATE_TIME\n",
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" GROUP BY o.TRADER_ID, t.DATE_TIME\n",
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") AS n\n",
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"LEFT JOIN (\n",
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" -- Step 6: Subquery for total_trade_volume (opposite side trades after spoofing)\n",
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" SELECT \n",
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" t.TRADER_ID,\n",
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" t.DATE_TIME,\n",
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" SUM(t.TRADE_VOLUME) AS total_trade_volume\n",
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" FROM (\n",
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" -- Step 5: Subquery for relevant_trades\n",
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" SELECT t1.*\n",
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" FROM {trade_data_1b} t1\n",
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" WHERE t1.TRADE_SIDE = 'buy'\n",
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" AND EXISTS (\n",
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" SELECT 1\n",
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" FROM {trade_data_1b} t2\n",
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" WHERE t2.TRADER_ID = t1.TRADER_ID\n",
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" AND t2.DATE_TIME BETWEEN t1.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t1.DATE_TIME\n",
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" )\n",
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" ) AS t\n",
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" GROUP BY t.DATE_TIME, t.TRADER_ID\n",
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") AS t \n",
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"ON n.TRADER_ID = t.TRADER_ID AND n.trade_time_window = t.DATE_TIME\n",
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"\n",
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"\n",
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" -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
|
"-- New subquery for total net volume for all traders in the same time window\n",
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||||||
" date_add('second', -{time_window_s}, date_time) AS start_time,\n",
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"LEFT JOIN (\n",
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" SELECT \n",
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" t.DATE_TIME AS trade_time_window,\n",
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" SUM(CASE \n",
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||||||
|
" WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME\n",
|
||||||
|
" WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME\n",
|
||||||
|
" WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME\n",
|
||||||
|
" ELSE 0 END\n",
|
||||||
|
" ) AS total_net_volume_all\n",
|
||||||
|
" FROM {order_10m} o\n",
|
||||||
|
" JOIN {trade_data_1b} t\n",
|
||||||
|
" ON o.TRADER_ID = t.TRADER_ID\n",
|
||||||
|
" WHERE o.SIDE = 'buy'\n",
|
||||||
|
" AND o.DATE_TIME BETWEEN t.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t.DATE_TIME\n",
|
||||||
|
" GROUP BY t.DATE_TIME\n",
|
||||||
|
") AS net_volume_all\n",
|
||||||
|
"ON n.trade_time_window = net_volume_all.trade_time_window\n",
|
||||||
"\n",
|
"\n",
|
||||||
" -- Trade details\n",
|
"ORDER BY n.trade_time_window\n",
|
||||||
" trade_price,\n",
|
"limit 1000\n",
|
||||||
" trade_volume,\n",
|
|
||||||
" trader_id,\n",
|
|
||||||
"\n",
|
|
||||||
" -- Calculate minimum price within the time window\n",
|
|
||||||
" MIN(trade_price) OVER (\n",
|
|
||||||
" ORDER BY date_time \n",
|
|
||||||
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
|
|
||||||
" ) AS min_price,\n",
|
|
||||||
"\n",
|
|
||||||
" -- Calculate maximum price within the time window\n",
|
|
||||||
" MAX(trade_price) OVER (\n",
|
|
||||||
" ORDER BY date_time \n",
|
|
||||||
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
|
|
||||||
" ) AS max_price,\n",
|
|
||||||
"\n",
|
|
||||||
" -- Calculate total trade volume within the time window\n",
|
|
||||||
" SUM(trade_volume) OVER ( \n",
|
|
||||||
" ORDER BY date_time \n",
|
|
||||||
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
|
|
||||||
" ) AS total_volume,\n",
|
|
||||||
"\n",
|
|
||||||
" -- Calculate participant's trade volume within the time window\n",
|
|
||||||
" SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
|
|
||||||
" PARTITION BY trader_id \n",
|
|
||||||
" ORDER BY date_time \n",
|
|
||||||
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
|
|
||||||
" ) AS participant_volume\n",
|
|
||||||
" FROM\n",
|
|
||||||
" {trade_data_1b}\n",
|
|
||||||
")\n",
|
|
||||||
"SELECT\n",
|
|
||||||
" -- Select the time window details\n",
|
|
||||||
" start_time,\n",
|
|
||||||
" end_time,\n",
|
|
||||||
"\n",
|
|
||||||
" -- Select the participant (trader) ID\n",
|
|
||||||
" trader_id AS \"Participant\",\n",
|
|
||||||
"\n",
|
|
||||||
" -- Select the calculated min and max prices\n",
|
|
||||||
" min_price,\n",
|
|
||||||
" max_price,\n",
|
|
||||||
"\n",
|
|
||||||
" -- Calculate the price change percentage\n",
|
|
||||||
" (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
|
|
||||||
"\n",
|
|
||||||
" -- Calculate the participant's volume as a percentage of total volume\n",
|
|
||||||
" (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
|
|
||||||
"\n",
|
|
||||||
" -- Participant volume\n",
|
|
||||||
" participant_volume,\n",
|
|
||||||
"\n",
|
|
||||||
" -- Select the total volume within the window\n",
|
|
||||||
" total_volume AS \"Total Volume\"\n",
|
|
||||||
"FROM\n",
|
|
||||||
" time_windows\n",
|
|
||||||
" limit 1000\n",
|
|
||||||
"\"\"\"\n",
|
"\"\"\"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
@ -89,26 +100,24 @@
|
|||||||
" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
|
" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
|
||||||
" def logic(self, **kwargs):\n",
|
" def logic(self, **kwargs):\n",
|
||||||
" validation_window = kwargs.get('validation_window')\n",
|
" validation_window = kwargs.get('validation_window')\n",
|
||||||
|
" spoofing_side = kwargs.get('buy')\n",
|
||||||
" time_window_s = int(validation_window/1000)\n",
|
" time_window_s = int(validation_window/1000)\n",
|
||||||
" query_start_time = datetime.now()\n",
|
" query_start_time = datetime.now()\n",
|
||||||
" print(\"Query start time :\",query_start_time)\n",
|
" print(\"Query start time :\",query_start_time)\n",
|
||||||
" row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
|
" row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
|
||||||
|
" order_10m = 'order_10m',\n",
|
||||||
" time_window_s = time_window_s)\n",
|
" time_window_s = time_window_s)\n",
|
||||||
" )\n",
|
" )\n",
|
||||||
" cols = [\n",
|
" cols = [\n",
|
||||||
" 'START_DATE_TIME',\n",
|
" 'TRADER_ID',\n",
|
||||||
" 'END_DATE_TIME',\n",
|
" 'trade_time_window',\n",
|
||||||
" 'Focal_id',\n",
|
" 'net_volume',\n",
|
||||||
" 'MIN_PRICE',\n",
|
" 'order_count',\n",
|
||||||
" 'MAX_PRICE',\n",
|
" 'total_trade_volume',\n",
|
||||||
" 'PRICE_CHANGE_PCT',\n",
|
" 'order_trade_ratio',\n",
|
||||||
" 'PARTICIPANT_VOLUME_PCT',\n",
|
" 'volume_percentage'\n",
|
||||||
" 'PARTICIPANT_VOLUME',\n",
|
|
||||||
" 'TOTAL_VOLUME',\n",
|
|
||||||
" ]\n",
|
" ]\n",
|
||||||
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
|
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
|
||||||
" final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\\\n",
|
|
||||||
" final_scenario_df['TOTAL_VOLUME'] * 100\n",
|
|
||||||
" final_scenario_df['Segment'] = 'Default'\n",
|
" final_scenario_df['Segment'] = 'Default'\n",
|
||||||
" final_scenario_df['SAR_FLAG'] = 'N'\n",
|
" final_scenario_df['SAR_FLAG'] = 'N'\n",
|
||||||
" final_scenario_df['Risk'] = 'Low Risk'\n",
|
" final_scenario_df['Risk'] = 'Low Risk'\n",
|
||||||
@ -119,14 +128,261 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 17,
|
||||||
"id": "b5c4307f-bc35-47e2-b680-fd1df2168d6c",
|
"id": "b5c4307f-bc35-47e2-b680-fd1df2168d6c",
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
"outputs": [],
|
"tags": []
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Query start time : 2024-10-14 06:23:07.242919\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<div>\n",
|
||||||
|
"<style scoped>\n",
|
||||||
|
" .dataframe tbody tr th:only-of-type {\n",
|
||||||
|
" vertical-align: middle;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe tbody tr th {\n",
|
||||||
|
" vertical-align: top;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe thead th {\n",
|
||||||
|
" text-align: right;\n",
|
||||||
|
" }\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>TRADER_ID</th>\n",
|
||||||
|
" <th>trade_time_window</th>\n",
|
||||||
|
" <th>net_volume</th>\n",
|
||||||
|
" <th>order_count</th>\n",
|
||||||
|
" <th>total_trade_volume</th>\n",
|
||||||
|
" <th>order_trade_ratio</th>\n",
|
||||||
|
" <th>volume_percentage</th>\n",
|
||||||
|
" <th>Segment</th>\n",
|
||||||
|
" <th>SAR_FLAG</th>\n",
|
||||||
|
" <th>Risk</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>3097728207</td>\n",
|
||||||
|
" <td>2024-01-01 00:03:00</td>\n",
|
||||||
|
" <td>-92.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>92</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>3228645322</td>\n",
|
||||||
|
" <td>2024-01-01 00:06:00</td>\n",
|
||||||
|
" <td>-689.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>689</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>2701872727</td>\n",
|
||||||
|
" <td>2024-01-01 00:09:00</td>\n",
|
||||||
|
" <td>-42.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>42</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>1659056655</td>\n",
|
||||||
|
" <td>2024-01-01 00:11:00</td>\n",
|
||||||
|
" <td>-167.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>167</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>1661288887</td>\n",
|
||||||
|
" <td>2024-01-01 00:13:00</td>\n",
|
||||||
|
" <td>-756.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>756</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>...</th>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>995</th>\n",
|
||||||
|
" <td>3382197985</td>\n",
|
||||||
|
" <td>2024-01-01 02:30:00</td>\n",
|
||||||
|
" <td>-159.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>159</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>996</th>\n",
|
||||||
|
" <td>1129008990</td>\n",
|
||||||
|
" <td>2024-01-01 02:30:00</td>\n",
|
||||||
|
" <td>-582.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>582</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>997</th>\n",
|
||||||
|
" <td>2944122893</td>\n",
|
||||||
|
" <td>2024-01-01 02:30:00</td>\n",
|
||||||
|
" <td>-65.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>65</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>998</th>\n",
|
||||||
|
" <td>2910876405</td>\n",
|
||||||
|
" <td>2024-01-01 02:30:00</td>\n",
|
||||||
|
" <td>-117.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>117</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>999</th>\n",
|
||||||
|
" <td>1816942226</td>\n",
|
||||||
|
" <td>2024-01-01 02:30:00</td>\n",
|
||||||
|
" <td>-732.0</td>\n",
|
||||||
|
" <td>1</td>\n",
|
||||||
|
" <td>732</td>\n",
|
||||||
|
" <td>-1.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>Default</td>\n",
|
||||||
|
" <td>N</td>\n",
|
||||||
|
" <td>Low Risk</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"<p>1000 rows × 10 columns</p>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" TRADER_ID trade_time_window net_volume order_count \\\n",
|
||||||
|
"0 3097728207 2024-01-01 00:03:00 -92.0 1 \n",
|
||||||
|
"1 3228645322 2024-01-01 00:06:00 -689.0 1 \n",
|
||||||
|
"2 2701872727 2024-01-01 00:09:00 -42.0 1 \n",
|
||||||
|
"3 1659056655 2024-01-01 00:11:00 -167.0 1 \n",
|
||||||
|
"4 1661288887 2024-01-01 00:13:00 -756.0 1 \n",
|
||||||
|
".. ... ... ... ... \n",
|
||||||
|
"995 3382197985 2024-01-01 02:30:00 -159.0 1 \n",
|
||||||
|
"996 1129008990 2024-01-01 02:30:00 -582.0 1 \n",
|
||||||
|
"997 2944122893 2024-01-01 02:30:00 -65.0 1 \n",
|
||||||
|
"998 2910876405 2024-01-01 02:30:00 -117.0 1 \n",
|
||||||
|
"999 1816942226 2024-01-01 02:30:00 -732.0 1 \n",
|
||||||
|
"\n",
|
||||||
|
" total_trade_volume order_trade_ratio volume_percentage Segment \\\n",
|
||||||
|
"0 92 -1.0 0.0 Default \n",
|
||||||
|
"1 689 -1.0 0.0 Default \n",
|
||||||
|
"2 42 -1.0 0.0 Default \n",
|
||||||
|
"3 167 -1.0 0.0 Default \n",
|
||||||
|
"4 756 -1.0 0.0 Default \n",
|
||||||
|
".. ... ... ... ... \n",
|
||||||
|
"995 159 -1.0 0.0 Default \n",
|
||||||
|
"996 582 -1.0 0.0 Default \n",
|
||||||
|
"997 65 -1.0 0.0 Default \n",
|
||||||
|
"998 117 -1.0 0.0 Default \n",
|
||||||
|
"999 732 -1.0 0.0 Default \n",
|
||||||
|
"\n",
|
||||||
|
" SAR_FLAG Risk \n",
|
||||||
|
"0 N Low Risk \n",
|
||||||
|
"1 N Low Risk \n",
|
||||||
|
"2 N Low Risk \n",
|
||||||
|
"3 N Low Risk \n",
|
||||||
|
"4 N Low Risk \n",
|
||||||
|
".. ... ... \n",
|
||||||
|
"995 N Low Risk \n",
|
||||||
|
"996 N Low Risk \n",
|
||||||
|
"997 N Low Risk \n",
|
||||||
|
"998 N Low Risk \n",
|
||||||
|
"999 N Low Risk \n",
|
||||||
|
"\n",
|
||||||
|
"[1000 rows x 10 columns]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 17,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"scenario = Scenario()\n",
|
"scenario = Scenario()\n",
|
||||||
"scenario.logic()"
|
"scenario.logic(validation_window=300000)"
|
||||||
]
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "36b1b24a-aeca-4d22-a2b3-6e04aca31695",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
|
|||||||
171
main.py
171
main.py
@ -1,7 +1,7 @@
|
|||||||
#!/usr/bin/env python
|
#!/usr/bin/env python
|
||||||
# coding: utf-8
|
# coding: utf-8
|
||||||
|
|
||||||
# In[2]:
|
# In[16]:
|
||||||
|
|
||||||
|
|
||||||
from datetime import datetime, timedelta
|
from datetime import datetime, timedelta
|
||||||
@ -9,72 +9,83 @@ import pandas as pd
|
|||||||
from tms_data_interface import SQLQueryInterface
|
from tms_data_interface import SQLQueryInterface
|
||||||
|
|
||||||
query = """
|
query = """
|
||||||
WITH time_windows AS (
|
SELECT
|
||||||
SELECT
|
n.TRADER_ID,
|
||||||
-- End time is the current trade time
|
n.trade_time_window,
|
||||||
date_time AS end_time,
|
n.net_volume,
|
||||||
|
n.order_count, -- Include number of orders
|
||||||
|
COALESCE(t.total_trade_volume, 0) AS total_trade_volume,
|
||||||
|
CASE
|
||||||
|
WHEN COALESCE(t.total_trade_volume, 0) > 0 THEN n.net_volume / t.total_trade_volume
|
||||||
|
ELSE 0 -- or another value to indicate no trades
|
||||||
|
END AS order_trade_ratio,
|
||||||
|
CASE
|
||||||
|
WHEN net_volume_all.total_net_volume_all > 0 THEN
|
||||||
|
(n.net_volume / net_volume_all.total_net_volume_all) * 100
|
||||||
|
ELSE 0
|
||||||
|
END AS volume_percentage -- Calculate volume percentage
|
||||||
|
FROM (
|
||||||
|
-- Step 2: Subquery for net_order_volume
|
||||||
|
SELECT
|
||||||
|
o.TRADER_ID,
|
||||||
|
t.DATE_TIME AS trade_time_window,
|
||||||
|
SUM(CASE
|
||||||
|
WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME
|
||||||
|
WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME
|
||||||
|
WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME
|
||||||
|
ELSE 0 END
|
||||||
|
) AS net_volume,
|
||||||
|
COUNT(o.ORDER_ID) AS order_count -- Count the number of orders
|
||||||
|
FROM {order_10m} o
|
||||||
|
JOIN {trade_data_1b} t
|
||||||
|
ON o.TRADER_ID = t.TRADER_ID
|
||||||
|
WHERE o.SIDE = 'buy'
|
||||||
|
AND o.DATE_TIME BETWEEN t.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t.DATE_TIME
|
||||||
|
GROUP BY o.TRADER_ID, t.DATE_TIME
|
||||||
|
) AS n
|
||||||
|
LEFT JOIN (
|
||||||
|
-- Step 6: Subquery for total_trade_volume (opposite side trades after spoofing)
|
||||||
|
SELECT
|
||||||
|
t.TRADER_ID,
|
||||||
|
t.DATE_TIME,
|
||||||
|
SUM(t.TRADE_VOLUME) AS total_trade_volume
|
||||||
|
FROM (
|
||||||
|
-- Step 5: Subquery for relevant_trades
|
||||||
|
SELECT t1.*
|
||||||
|
FROM {trade_data_1b} t1
|
||||||
|
WHERE t1.TRADE_SIDE = 'buy'
|
||||||
|
AND EXISTS (
|
||||||
|
SELECT 1
|
||||||
|
FROM {trade_data_1b} t2
|
||||||
|
WHERE t2.TRADER_ID = t1.TRADER_ID
|
||||||
|
AND t2.DATE_TIME BETWEEN t1.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t1.DATE_TIME
|
||||||
|
)
|
||||||
|
) AS t
|
||||||
|
GROUP BY t.DATE_TIME, t.TRADER_ID
|
||||||
|
) AS t
|
||||||
|
ON n.TRADER_ID = t.TRADER_ID AND n.trade_time_window = t.DATE_TIME
|
||||||
|
|
||||||
-- Subtract seconds from the end_time using date_add() with negative integer interval
|
-- New subquery for total net volume for all traders in the same time window
|
||||||
date_add('second', -{time_window_s}, date_time) AS start_time,
|
LEFT JOIN (
|
||||||
|
SELECT
|
||||||
|
t.DATE_TIME AS trade_time_window,
|
||||||
|
SUM(CASE
|
||||||
|
WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME
|
||||||
|
WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME
|
||||||
|
WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME
|
||||||
|
ELSE 0 END
|
||||||
|
) AS total_net_volume_all
|
||||||
|
FROM {order_10m} o
|
||||||
|
JOIN {trade_data_1b} t
|
||||||
|
ON o.TRADER_ID = t.TRADER_ID
|
||||||
|
WHERE o.SIDE = 'buy'
|
||||||
|
AND o.DATE_TIME BETWEEN t.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t.DATE_TIME
|
||||||
|
GROUP BY t.DATE_TIME
|
||||||
|
) AS net_volume_all
|
||||||
|
ON n.trade_time_window = net_volume_all.trade_time_window
|
||||||
|
|
||||||
-- Trade details
|
ORDER BY n.trade_time_window
|
||||||
trade_price,
|
limit 1000
|
||||||
trade_volume,
|
|
||||||
trader_id,
|
|
||||||
|
|
||||||
-- Calculate minimum price within the time window
|
|
||||||
MIN(trade_price) OVER (
|
|
||||||
ORDER BY date_time
|
|
||||||
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
|
|
||||||
) AS min_price,
|
|
||||||
|
|
||||||
-- Calculate maximum price within the time window
|
|
||||||
MAX(trade_price) OVER (
|
|
||||||
ORDER BY date_time
|
|
||||||
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
|
|
||||||
) AS max_price,
|
|
||||||
|
|
||||||
-- Calculate total trade volume within the time window
|
|
||||||
SUM(trade_volume) OVER (
|
|
||||||
ORDER BY date_time
|
|
||||||
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
|
|
||||||
) AS total_volume,
|
|
||||||
|
|
||||||
-- Calculate participant's trade volume within the time window
|
|
||||||
SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (
|
|
||||||
PARTITION BY trader_id
|
|
||||||
ORDER BY date_time
|
|
||||||
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
|
|
||||||
) AS participant_volume
|
|
||||||
FROM
|
|
||||||
{trade_data_1b}
|
|
||||||
)
|
|
||||||
SELECT
|
|
||||||
-- Select the time window details
|
|
||||||
start_time,
|
|
||||||
end_time,
|
|
||||||
|
|
||||||
-- Select the participant (trader) ID
|
|
||||||
trader_id AS "Participant",
|
|
||||||
|
|
||||||
-- Select the calculated min and max prices
|
|
||||||
min_price,
|
|
||||||
max_price,
|
|
||||||
|
|
||||||
-- Calculate the price change percentage
|
|
||||||
(max_price - min_price) / NULLIF(min_price, 0) * 100 AS "Price Change (%)",
|
|
||||||
|
|
||||||
-- Calculate the participant's volume as a percentage of total volume
|
|
||||||
(participant_volume / NULLIF(total_volume, 0)) * 100 AS "Volume (%)",
|
|
||||||
|
|
||||||
-- Participant volume
|
|
||||||
participant_volume,
|
|
||||||
|
|
||||||
-- Select the total volume within the window
|
|
||||||
total_volume AS "Total Volume"
|
|
||||||
FROM
|
|
||||||
time_windows
|
|
||||||
limit 1000
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
|
|
||||||
@ -84,26 +95,24 @@ class Scenario:
|
|||||||
seq = SQLQueryInterface(schema="trade_schema")
|
seq = SQLQueryInterface(schema="trade_schema")
|
||||||
def logic(self, **kwargs):
|
def logic(self, **kwargs):
|
||||||
validation_window = kwargs.get('validation_window')
|
validation_window = kwargs.get('validation_window')
|
||||||
|
spoofing_side = kwargs.get('buy')
|
||||||
time_window_s = int(validation_window/1000)
|
time_window_s = int(validation_window/1000)
|
||||||
query_start_time = datetime.now()
|
query_start_time = datetime.now()
|
||||||
print("Query start time :",query_start_time)
|
print("Query start time :",query_start_time)
|
||||||
row_list = self.seq.execute_raw(query.format(trade_data_1b="trade_10m_v3",
|
row_list = self.seq.execute_raw(query.format(trade_data_1b="trade_10m_v3",
|
||||||
|
order_10m = 'order_10m',
|
||||||
time_window_s = time_window_s)
|
time_window_s = time_window_s)
|
||||||
)
|
)
|
||||||
cols = [
|
cols = [
|
||||||
'START_DATE_TIME',
|
'TRADER_ID',
|
||||||
'END_DATE_TIME',
|
'trade_time_window',
|
||||||
'Focal_id',
|
'net_volume',
|
||||||
'MIN_PRICE',
|
'order_count',
|
||||||
'MAX_PRICE',
|
'total_trade_volume',
|
||||||
'PRICE_CHANGE_PCT',
|
'order_trade_ratio',
|
||||||
'PARTICIPANT_VOLUME_PCT',
|
'volume_percentage'
|
||||||
'PARTICIPANT_VOLUME',
|
|
||||||
'TOTAL_VOLUME',
|
|
||||||
]
|
]
|
||||||
final_scenario_df = pd.DataFrame(row_list, columns = cols)
|
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
|
|
||||||
final_scenario_df['Segment'] = 'Default'
|
final_scenario_df['Segment'] = 'Default'
|
||||||
final_scenario_df['SAR_FLAG'] = 'N'
|
final_scenario_df['SAR_FLAG'] = 'N'
|
||||||
final_scenario_df['Risk'] = 'Low Risk'
|
final_scenario_df['Risk'] = 'Low Risk'
|
||||||
@ -112,9 +121,15 @@ class Scenario:
|
|||||||
return final_scenario_df
|
return final_scenario_df
|
||||||
|
|
||||||
|
|
||||||
# In[ ]:
|
# In[17]:
|
||||||
|
|
||||||
|
|
||||||
scenario = Scenario()
|
scenario = Scenario()
|
||||||
scenario.logic()
|
scenario.logic(validation_window=300000)
|
||||||
|
|
||||||
|
|
||||||
|
# In[ ]:
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user