generated from user_client2024/58
System save at 15/10/2024 13:09 by user_client2024
This commit is contained in:
user_client2024
parent
7b0be6fe97
commit
52c1bfb416
@ -9,120 +9,149 @@
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},
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"outputs": [],
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"source": [
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"from datetime import datetime, timedelta\n",
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"from datetime import datetime\n",
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"import pandas as pd\n",
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"query = \"\"\"\n",
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"SELECT \n",
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" n.TRADER_ID,\n",
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" n.trade_time_window,\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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"WITH \n",
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"-- Capture all orders and trades within the spoofing time window\n",
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"trade_window AS (\n",
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" SELECT\n",
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" t.trade_id,\n",
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" t.trader_id,\n",
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" t.date_time AS trade_time,\n",
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" t.trade_side,\n",
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" t.trade_volume,\n",
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" o.trader_id AS order_trader_id,\n",
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" o.date_time AS order_time,\n",
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" o.order_volume,\n",
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" o.order_status,\n",
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" o.order_price,\n",
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" o.side AS order_side\n",
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" FROM \n",
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" {trade_data_1b} t\n",
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" LEFT JOIN \n",
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" order_10m o ON o.date_time BETWEEN t.date_time - INTERVAL '{spoofing_time_window_s}' SECOND \n",
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" AND t.date_time\n",
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" WHERE \n",
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" o.side = '{spoofing_side}'\n",
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"),\n",
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"\n",
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"-- Calculate net order volume for the specific trader\n",
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"net_order_volume_cte AS (\n",
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" SELECT \n",
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" trader_id,\n",
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" trade_id,\n",
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" trade_time,\n",
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" SUM(CASE \n",
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" WHEN order_status = 'new' THEN order_volume \n",
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" WHEN order_status = 'cancelled' THEN -order_volume \n",
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" WHEN order_status = 'fulfilled' THEN -order_volume \n",
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" ELSE 0 \n",
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" END) AS net_order_volume,\n",
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" COUNT(*) AS num_orders\n",
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" FROM trade_window\n",
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" WHERE order_trader_id = trader_id -- Filter by the trader who executed the trade\n",
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" GROUP BY trader_id, trade_id, trade_time\n",
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"),\n",
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"\n",
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"-- Calculate total net order volume for all traders (i.e., for spoofing side orders)\n",
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"net_order_volume_all_cte AS (\n",
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" SELECT \n",
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" trade_id,\n",
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" SUM(CASE \n",
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" WHEN order_status = 'new' THEN order_volume \n",
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" WHEN order_status = 'cancelled' THEN -order_volume \n",
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" WHEN order_status = 'fulfilled' THEN -order_volume \n",
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" ELSE 0 \n",
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" END) AS net_order_volume_all\n",
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" FROM trade_window\n",
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" GROUP BY trade_id\n",
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"),\n",
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"\n",
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"-- Calculate total trade volume on the opposite side (e.g., sell if spoofing is on buy)\n",
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"opposite_trade_volume_cte AS (\n",
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" SELECT \n",
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" t.trader_id,\n",
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" t.trade_id,\n",
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" SUM(t.trade_volume) AS total_trade_volume\n",
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" FROM {trade_data_1b} t\n",
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" WHERE \n",
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" t.date_time BETWEEN t.date_time - INTERVAL '{trade_time_window_s}' SECOND\n",
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" AND t.date_time\n",
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" AND t.trade_side = CASE WHEN '{spoofing_side}' = 'buy' THEN 'sell' ELSE 'buy' END\n",
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" GROUP BY t.trader_id, t.trade_id\n",
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")\n",
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"\n",
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"-- Final result with calculated spoofing indicators\n",
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"SELECT\n",
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" n.trade_id,\n",
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" n.trader_id,\n",
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" n.trade_time,\n",
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" n.num_orders,\n",
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" n.net_order_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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" WHEN o.total_trade_volume > 0 THEN n.net_order_volume / o.total_trade_volume\n",
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" ELSE NULL\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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"-- New subquery for total net volume for all traders in the same time window\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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"ORDER BY n.trade_time_window\n",
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" WHEN a.net_order_volume_all > 0 THEN n.net_order_volume / a.net_order_volume_all\n",
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" ELSE NULL\n",
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" END AS volume_percentage\n",
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"FROM \n",
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" net_order_volume_cte n\n",
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"LEFT JOIN \n",
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" opposite_trade_volume_cte o ON n.trade_id = o.trade_id\n",
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"LEFT JOIN \n",
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" net_order_volume_all_cte a ON n.trade_id = a.trade_id\n",
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"WHERE \n",
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" n.net_order_volume > 0 -- Only consider positive net order volumes (potential spoofing);\n",
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" limit 1000\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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" spoofing_side = kwargs.get('buy')\n",
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" time_window_s = int(validation_window/1000)\n",
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" seq = SQLQueryInterface(schema=\"internal\")\n",
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"\n",
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" def logic(self, **params):\n",
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" spoofing_time_window = params.get('spoofing_time_window', 300000) # default to 300,000 ms (5 minutes)\n",
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" spoofing_side = params.get('spoofing_side', 'buy')\n",
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" use_volume_for_order_trade_ratio = params.get('use_volume_for_order_trade_ratio', True)\n",
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" trade_time_window = params.get('trade_time_window', 300000)\n",
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" ignore_trade_after_spoofing = params.get('ignore_trade_after_spoofing', True)\n",
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" ignore_price_improvement = params.get('ignore_price_improvement', True)\n",
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"\n",
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" # Convert time windows from milliseconds to seconds\n",
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" spoofing_time_window_s = int(spoofing_time_window / 1000)\n",
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" trade_time_window_s = int(trade_time_window / 1000)\n",
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"\n",
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" query_start_time = datetime.now()\n",
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" print(\"Query start time :\",query_start_time)\n",
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" 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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" )\n",
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" print(\"Query start time:\", query_start_time)\n",
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"\n",
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" # Execute the query with the parameters passed from `params`\n",
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" row_list = self.seq.execute_raw(query.format(\n",
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" trade_data_1b=\"trade_10m_v3\", # Replace with actual table name\n",
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" spoofing_time_window_s=spoofing_time_window_s,\n",
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" trade_time_window_s=trade_time_window_s,\n",
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" spoofing_side=spoofing_side\n",
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" ))\n",
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"\n",
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" # Define columns for the resulting DataFrame\n",
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" cols = [\n",
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" 'Focal_id',\n",
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" 'trade_time_window',\n",
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" 'net_volume',\n",
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" 'order_count',\n",
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" 'total_trade_volume',\n",
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" 'order_trade_ratio',\n",
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" 'volume_percentage'\n",
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" 'trade_id', 'focal_id', 'trade_time', 'num_orders', \n",
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" 'net_order_volume', 'order_trade_ratio', 'volume_percentage'\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['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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"\n",
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" # Create a DataFrame from the query result\n",
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" final_scenario_df = pd.DataFrame(row_list, columns=cols)\n",
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"\n",
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"\n",
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" # Adding additional columns\n",
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" final_scenario_df['segment'] = 'Default'\n",
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" final_scenario_df['sar_flag'] = 'N'\n",
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" final_scenario_df['risk'] = 'Low Risk'\n",
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"\n",
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" return final_scenario_df"
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]
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},
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{
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@ -371,8 +400,8 @@
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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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"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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235
main.ipynb
235
main.ipynb
@ -9,120 +9,149 @@
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},
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"outputs": [],
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"source": [
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"from datetime import datetime, timedelta\n",
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"from datetime import datetime\n",
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"import pandas as pd\n",
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"query = \"\"\"\n",
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"SELECT \n",
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" n.TRADER_ID,\n",
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" n.trade_time_window,\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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"WITH \n",
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"-- Capture all orders and trades within the spoofing time window\n",
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"trade_window AS (\n",
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" SELECT\n",
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" t.trade_id,\n",
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" t.trader_id,\n",
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" t.date_time AS trade_time,\n",
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" t.trade_side,\n",
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" t.trade_volume,\n",
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" o.trader_id AS order_trader_id,\n",
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" o.date_time AS order_time,\n",
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" o.order_volume,\n",
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" o.order_status,\n",
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" o.order_price,\n",
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" o.side AS order_side\n",
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" FROM \n",
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" {trade_data_1b} t\n",
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" LEFT JOIN \n",
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" order_10m o ON o.date_time BETWEEN t.date_time - INTERVAL '{spoofing_time_window_s}' SECOND \n",
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" AND t.date_time\n",
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" WHERE \n",
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" o.side = '{spoofing_side}'\n",
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"),\n",
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"\n",
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"-- Calculate net order volume for the specific trader\n",
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"net_order_volume_cte AS (\n",
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" SELECT \n",
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" trader_id,\n",
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" trade_id,\n",
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" trade_time,\n",
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" SUM(CASE \n",
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" WHEN order_status = 'new' THEN order_volume \n",
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" WHEN order_status = 'cancelled' THEN -order_volume \n",
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" WHEN order_status = 'fulfilled' THEN -order_volume \n",
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" ELSE 0 \n",
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" END) AS net_order_volume,\n",
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" COUNT(*) AS num_orders\n",
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" FROM trade_window\n",
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" WHERE order_trader_id = trader_id -- Filter by the trader who executed the trade\n",
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" GROUP BY trader_id, trade_id, trade_time\n",
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"),\n",
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"\n",
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"-- Calculate total net order volume for all traders (i.e., for spoofing side orders)\n",
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"net_order_volume_all_cte AS (\n",
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" SELECT \n",
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" trade_id,\n",
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" SUM(CASE \n",
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" WHEN order_status = 'new' THEN order_volume \n",
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" WHEN order_status = 'cancelled' THEN -order_volume \n",
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" WHEN order_status = 'fulfilled' THEN -order_volume \n",
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" ELSE 0 \n",
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" END) AS net_order_volume_all\n",
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" FROM trade_window\n",
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" GROUP BY trade_id\n",
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"),\n",
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"\n",
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"-- Calculate total trade volume on the opposite side (e.g., sell if spoofing is on buy)\n",
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"opposite_trade_volume_cte AS (\n",
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" SELECT \n",
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" t.trader_id,\n",
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" t.trade_id,\n",
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" SUM(t.trade_volume) AS total_trade_volume\n",
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" FROM {trade_data_1b} t\n",
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" WHERE \n",
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" t.date_time BETWEEN t.date_time - INTERVAL '{trade_time_window_s}' SECOND\n",
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" AND t.date_time\n",
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" AND t.trade_side = CASE WHEN '{spoofing_side}' = 'buy' THEN 'sell' ELSE 'buy' END\n",
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" GROUP BY t.trader_id, t.trade_id\n",
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")\n",
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"\n",
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"-- Final result with calculated spoofing indicators\n",
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"SELECT\n",
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" n.trade_id,\n",
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" n.trader_id,\n",
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" n.trade_time,\n",
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" n.num_orders,\n",
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" n.net_order_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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" WHEN o.total_trade_volume > 0 THEN n.net_order_volume / o.total_trade_volume\n",
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" ELSE NULL\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",
|
||||
" 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 o.TRADER_ID, t.DATE_TIME\n",
|
||||
") AS n\n",
|
||||
"LEFT JOIN (\n",
|
||||
" -- Step 6: Subquery for total_trade_volume (opposite side trades after spoofing)\n",
|
||||
" SELECT \n",
|
||||
" t.TRADER_ID,\n",
|
||||
" t.DATE_TIME,\n",
|
||||
" SUM(t.TRADE_VOLUME) AS total_trade_volume\n",
|
||||
" FROM (\n",
|
||||
" -- Step 5: Subquery for relevant_trades\n",
|
||||
" SELECT t1.*\n",
|
||||
" FROM {trade_data_1b} t1\n",
|
||||
" WHERE t1.TRADE_SIDE = 'buy'\n",
|
||||
" AND EXISTS (\n",
|
||||
" SELECT 1\n",
|
||||
" FROM {trade_data_1b} t2\n",
|
||||
" WHERE t2.TRADER_ID = t1.TRADER_ID\n",
|
||||
" AND t2.DATE_TIME BETWEEN t1.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t1.DATE_TIME\n",
|
||||
" )\n",
|
||||
" ) AS t\n",
|
||||
" GROUP BY t.DATE_TIME, t.TRADER_ID\n",
|
||||
") AS t \n",
|
||||
"ON n.TRADER_ID = t.TRADER_ID AND n.trade_time_window = t.DATE_TIME\n",
|
||||
"\n",
|
||||
"-- New subquery for total net volume for all traders in the same time window\n",
|
||||
"LEFT JOIN (\n",
|
||||
" SELECT \n",
|
||||
" t.DATE_TIME AS trade_time_window,\n",
|
||||
" SUM(CASE \n",
|
||||
" 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",
|
||||
"ORDER BY n.trade_time_window\n",
|
||||
" WHEN a.net_order_volume_all > 0 THEN n.net_order_volume / a.net_order_volume_all\n",
|
||||
" ELSE NULL\n",
|
||||
" END AS volume_percentage\n",
|
||||
"FROM \n",
|
||||
" net_order_volume_cte n\n",
|
||||
"LEFT JOIN \n",
|
||||
" opposite_trade_volume_cte o ON n.trade_id = o.trade_id\n",
|
||||
"LEFT JOIN \n",
|
||||
" net_order_volume_all_cte a ON n.trade_id = a.trade_id\n",
|
||||
"WHERE \n",
|
||||
" n.net_order_volume > 0 -- Only consider positive net order volumes (potential spoofing);\n",
|
||||
" limit 1000\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"from tms_data_interface import SQLQueryInterface\n",
|
||||
"\n",
|
||||
"class Scenario:\n",
|
||||
" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
|
||||
" def logic(self, **kwargs):\n",
|
||||
" validation_window = kwargs.get('validation_window')\n",
|
||||
" spoofing_side = kwargs.get('buy')\n",
|
||||
" time_window_s = int(validation_window/1000)\n",
|
||||
" seq = SQLQueryInterface(schema=\"internal\")\n",
|
||||
"\n",
|
||||
" def logic(self, **params):\n",
|
||||
" spoofing_time_window = params.get('spoofing_time_window', 300000) # default to 300,000 ms (5 minutes)\n",
|
||||
" spoofing_side = params.get('spoofing_side', 'buy')\n",
|
||||
" use_volume_for_order_trade_ratio = params.get('use_volume_for_order_trade_ratio', True)\n",
|
||||
" trade_time_window = params.get('trade_time_window', 300000)\n",
|
||||
" ignore_trade_after_spoofing = params.get('ignore_trade_after_spoofing', True)\n",
|
||||
" ignore_price_improvement = params.get('ignore_price_improvement', True)\n",
|
||||
"\n",
|
||||
" # Convert time windows from milliseconds to seconds\n",
|
||||
" spoofing_time_window_s = int(spoofing_time_window / 1000)\n",
|
||||
" trade_time_window_s = int(trade_time_window / 1000)\n",
|
||||
"\n",
|
||||
" query_start_time = datetime.now()\n",
|
||||
" print(\"Query start time :\",query_start_time)\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",
|
||||
" )\n",
|
||||
" print(\"Query start time:\", query_start_time)\n",
|
||||
"\n",
|
||||
" # Execute the query with the parameters passed from `params`\n",
|
||||
" row_list = self.seq.execute_raw(query.format(\n",
|
||||
" trade_data_1b=\"trade_10m_v3\", # Replace with actual table name\n",
|
||||
" spoofing_time_window_s=spoofing_time_window_s,\n",
|
||||
" trade_time_window_s=trade_time_window_s,\n",
|
||||
" spoofing_side=spoofing_side\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
" # Define columns for the resulting DataFrame\n",
|
||||
" cols = [\n",
|
||||
" 'Focal_id',\n",
|
||||
" 'trade_time_window',\n",
|
||||
" 'net_volume',\n",
|
||||
" 'order_count',\n",
|
||||
" 'total_trade_volume',\n",
|
||||
" 'order_trade_ratio',\n",
|
||||
" 'volume_percentage'\n",
|
||||
" 'trade_id', 'focal_id', 'trade_time', 'num_orders', \n",
|
||||
" 'net_order_volume', 'order_trade_ratio', 'volume_percentage'\n",
|
||||
" ]\n",
|
||||
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
|
||||
" final_scenario_df['Segment'] = 'Default'\n",
|
||||
" final_scenario_df['SAR_FLAG'] = 'N'\n",
|
||||
" final_scenario_df['Risk'] = 'Low Risk'\n",
|
||||
" final_scenario_df.dropna(inplace=True)\n",
|
||||
" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
|
||||
" return final_scenario_df\n"
|
||||
"\n",
|
||||
" # Create a DataFrame from the query result\n",
|
||||
" final_scenario_df = pd.DataFrame(row_list, columns=cols)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Adding additional columns\n",
|
||||
" final_scenario_df['segment'] = 'Default'\n",
|
||||
" final_scenario_df['sar_flag'] = 'N'\n",
|
||||
" final_scenario_df['risk'] = 'Low Risk'\n",
|
||||
"\n",
|
||||
" return final_scenario_df"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -371,8 +400,8 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# scenario = Scenario()\n",
|
||||
"# scenario.logic(validation_window=300000)"
|
||||
"scenario = Scenario()\n",
|
||||
"scenario.logic(validation_window=300000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
233
main.py
233
main.py
@ -4,127 +4,156 @@
|
||||
# In[21]:
|
||||
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import datetime
|
||||
import pandas as pd
|
||||
from tms_data_interface import SQLQueryInterface
|
||||
|
||||
query = """
|
||||
SELECT
|
||||
n.TRADER_ID,
|
||||
n.trade_time_window,
|
||||
n.net_volume,
|
||||
n.order_count, -- Include number of orders
|
||||
COALESCE(t.total_trade_volume, 0) AS total_trade_volume,
|
||||
WITH
|
||||
-- Capture all orders and trades within the spoofing time window
|
||||
trade_window AS (
|
||||
SELECT
|
||||
t.trade_id,
|
||||
t.trader_id,
|
||||
t.date_time AS trade_time,
|
||||
t.trade_side,
|
||||
t.trade_volume,
|
||||
o.trader_id AS order_trader_id,
|
||||
o.date_time AS order_time,
|
||||
o.order_volume,
|
||||
o.order_status,
|
||||
o.order_price,
|
||||
o.side AS order_side
|
||||
FROM
|
||||
{trade_data_1b} t
|
||||
LEFT JOIN
|
||||
order_10m o ON o.date_time BETWEEN t.date_time - INTERVAL '{spoofing_time_window_s}' SECOND
|
||||
AND t.date_time
|
||||
WHERE
|
||||
o.side = '{spoofing_side}'
|
||||
),
|
||||
|
||||
-- Calculate net order volume for the specific trader
|
||||
net_order_volume_cte AS (
|
||||
SELECT
|
||||
trader_id,
|
||||
trade_id,
|
||||
trade_time,
|
||||
SUM(CASE
|
||||
WHEN order_status = 'new' THEN order_volume
|
||||
WHEN order_status = 'cancelled' THEN -order_volume
|
||||
WHEN order_status = 'fulfilled' THEN -order_volume
|
||||
ELSE 0
|
||||
END) AS net_order_volume,
|
||||
COUNT(*) AS num_orders
|
||||
FROM trade_window
|
||||
WHERE order_trader_id = trader_id -- Filter by the trader who executed the trade
|
||||
GROUP BY trader_id, trade_id, trade_time
|
||||
),
|
||||
|
||||
-- Calculate total net order volume for all traders (i.e., for spoofing side orders)
|
||||
net_order_volume_all_cte AS (
|
||||
SELECT
|
||||
trade_id,
|
||||
SUM(CASE
|
||||
WHEN order_status = 'new' THEN order_volume
|
||||
WHEN order_status = 'cancelled' THEN -order_volume
|
||||
WHEN order_status = 'fulfilled' THEN -order_volume
|
||||
ELSE 0
|
||||
END) AS net_order_volume_all
|
||||
FROM trade_window
|
||||
GROUP BY trade_id
|
||||
),
|
||||
|
||||
-- Calculate total trade volume on the opposite side (e.g., sell if spoofing is on buy)
|
||||
opposite_trade_volume_cte AS (
|
||||
SELECT
|
||||
t.trader_id,
|
||||
t.trade_id,
|
||||
SUM(t.trade_volume) AS total_trade_volume
|
||||
FROM {trade_data_1b} t
|
||||
WHERE
|
||||
t.date_time BETWEEN t.date_time - INTERVAL '{trade_time_window_s}' SECOND
|
||||
AND t.date_time
|
||||
AND t.trade_side = CASE WHEN '{spoofing_side}' = 'buy' THEN 'sell' ELSE 'buy' END
|
||||
GROUP BY t.trader_id, t.trade_id
|
||||
)
|
||||
|
||||
-- Final result with calculated spoofing indicators
|
||||
SELECT
|
||||
n.trade_id,
|
||||
n.trader_id,
|
||||
n.trade_time,
|
||||
n.num_orders,
|
||||
n.net_order_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
|
||||
WHEN o.total_trade_volume > 0 THEN n.net_order_volume / o.total_trade_volume
|
||||
ELSE NULL
|
||||
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
|
||||
|
||||
-- New subquery for total net volume for all traders in the same time window
|
||||
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
|
||||
|
||||
ORDER BY n.trade_time_window
|
||||
WHEN a.net_order_volume_all > 0 THEN n.net_order_volume / a.net_order_volume_all
|
||||
ELSE NULL
|
||||
END AS volume_percentage
|
||||
FROM
|
||||
net_order_volume_cte n
|
||||
LEFT JOIN
|
||||
opposite_trade_volume_cte o ON n.trade_id = o.trade_id
|
||||
LEFT JOIN
|
||||
net_order_volume_all_cte a ON n.trade_id = a.trade_id
|
||||
WHERE
|
||||
n.net_order_volume > 0 -- Only consider positive net order volumes (potential spoofing);
|
||||
limit 1000
|
||||
"""
|
||||
|
||||
|
||||
from tms_data_interface import SQLQueryInterface
|
||||
|
||||
class Scenario:
|
||||
seq = SQLQueryInterface(schema="trade_schema")
|
||||
def logic(self, **kwargs):
|
||||
validation_window = kwargs.get('validation_window')
|
||||
spoofing_side = kwargs.get('buy')
|
||||
time_window_s = int(validation_window/1000)
|
||||
seq = SQLQueryInterface(schema="internal")
|
||||
|
||||
def logic(self, **params):
|
||||
spoofing_time_window = params.get('spoofing_time_window', 300000) # default to 300,000 ms (5 minutes)
|
||||
spoofing_side = params.get('spoofing_side', 'buy')
|
||||
use_volume_for_order_trade_ratio = params.get('use_volume_for_order_trade_ratio', True)
|
||||
trade_time_window = params.get('trade_time_window', 300000)
|
||||
ignore_trade_after_spoofing = params.get('ignore_trade_after_spoofing', True)
|
||||
ignore_price_improvement = params.get('ignore_price_improvement', True)
|
||||
|
||||
# Convert time windows from milliseconds to seconds
|
||||
spoofing_time_window_s = int(spoofing_time_window / 1000)
|
||||
trade_time_window_s = int(trade_time_window / 1000)
|
||||
|
||||
query_start_time = datetime.now()
|
||||
print("Query start time :",query_start_time)
|
||||
row_list = self.seq.execute_raw(query.format(trade_data_1b="trade_10m_v3",
|
||||
order_10m = 'order_10m',
|
||||
time_window_s = time_window_s)
|
||||
)
|
||||
print("Query start time:", query_start_time)
|
||||
|
||||
# Execute the query with the parameters passed from `params`
|
||||
row_list = self.seq.execute_raw(query.format(
|
||||
trade_data_1b="trade_10m_v3", # Replace with actual table name
|
||||
spoofing_time_window_s=spoofing_time_window_s,
|
||||
trade_time_window_s=trade_time_window_s,
|
||||
spoofing_side=spoofing_side
|
||||
))
|
||||
|
||||
# Define columns for the resulting DataFrame
|
||||
cols = [
|
||||
'Focal_id',
|
||||
'trade_time_window',
|
||||
'net_volume',
|
||||
'order_count',
|
||||
'total_trade_volume',
|
||||
'order_trade_ratio',
|
||||
'volume_percentage'
|
||||
'trade_id', 'focal_id', 'trade_time', 'num_orders',
|
||||
'net_order_volume', 'order_trade_ratio', 'volume_percentage'
|
||||
]
|
||||
final_scenario_df = pd.DataFrame(row_list, columns = cols)
|
||||
final_scenario_df['Segment'] = 'Default'
|
||||
final_scenario_df['SAR_FLAG'] = 'N'
|
||||
final_scenario_df['Risk'] = 'Low Risk'
|
||||
final_scenario_df.dropna(inplace=True)
|
||||
# final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']
|
||||
|
||||
# Create a DataFrame from the query result
|
||||
final_scenario_df = pd.DataFrame(row_list, columns=cols)
|
||||
|
||||
|
||||
# Adding additional columns
|
||||
final_scenario_df['segment'] = 'Default'
|
||||
final_scenario_df['sar_flag'] = 'N'
|
||||
final_scenario_df['risk'] = 'Low Risk'
|
||||
|
||||
return final_scenario_df
|
||||
|
||||
|
||||
# In[22]:
|
||||
|
||||
|
||||
# scenario = Scenario()
|
||||
# scenario.logic(validation_window=300000)
|
||||
scenario = Scenario()
|
||||
scenario.logic(validation_window=300000)
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user