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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},
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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\n",
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"import pandas as pd\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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"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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"SELECT \n",
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"WITH \n",
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" n.TRADER_ID,\n",
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"-- Capture all orders and trades within the spoofing time window\n",
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" n.trade_time_window,\n",
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"trade_window AS (\n",
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" n.net_volume,\n",
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" SELECT\n",
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" n.order_count, -- Include number of orders\n",
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" t.trade_id,\n",
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" COALESCE(t.total_trade_volume, 0) AS total_trade_volume,\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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" 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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" WHEN o.total_trade_volume > 0 THEN n.net_order_volume / o.total_trade_volume\n",
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" ELSE 0 -- or another value to indicate no trades\n",
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" ELSE NULL\n",
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" END AS order_trade_ratio,\n",
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" END AS order_trade_ratio,\n",
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" CASE \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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" WHEN a.net_order_volume_all > 0 THEN n.net_order_volume / a.net_order_volume_all\n",
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" (n.net_volume / net_volume_all.total_net_volume_all) * 100 \n",
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" ELSE NULL\n",
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" ELSE 0 \n",
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" END AS volume_percentage\n",
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" END AS volume_percentage -- Calculate volume percentage\n",
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"FROM \n",
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"FROM (\n",
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" net_order_volume_cte n\n",
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" -- Step 2: Subquery for net_order_volume\n",
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"LEFT JOIN \n",
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" SELECT \n",
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" opposite_trade_volume_cte o ON n.trade_id = o.trade_id\n",
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" o.TRADER_ID,\n",
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"LEFT JOIN \n",
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" t.DATE_TIME AS trade_time_window,\n",
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" net_order_volume_all_cte a ON n.trade_id = a.trade_id\n",
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" SUM(CASE \n",
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"WHERE \n",
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" WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME\n",
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" n.net_order_volume > 0 -- Only consider positive net order volumes (potential spoofing);\n",
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" WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME\n",
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" limit 1000\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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"\"\"\"\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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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"class Scenario:\n",
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"class Scenario:\n",
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" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
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" seq = SQLQueryInterface(schema=\"internal\")\n",
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" def logic(self, **kwargs):\n",
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"\n",
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" validation_window = kwargs.get('validation_window')\n",
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" def logic(self, **params):\n",
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" spoofing_side = kwargs.get('buy')\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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" time_window_s = int(validation_window/1000)\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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" 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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"\n",
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" order_10m = 'order_10m',\n",
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" # Execute the query with the parameters passed from `params`\n",
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" time_window_s = time_window_s)\n",
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" row_list = self.seq.execute_raw(query.format(\n",
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" )\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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" cols = [\n",
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" 'Focal_id',\n",
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" 'trade_id', 'focal_id', 'trade_time', 'num_orders', \n",
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" 'trade_time_window',\n",
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" 'net_order_volume', 'order_trade_ratio', 'volume_percentage'\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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" ]\n",
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" ]\n",
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" final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
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"\n",
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" final_scenario_df['Segment'] = 'Default'\n",
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" # Create a DataFrame from the query result\n",
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" final_scenario_df['SAR_FLAG'] = 'N'\n",
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" final_scenario_df = pd.DataFrame(row_list, columns=cols)\n",
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" final_scenario_df['Risk'] = 'Low Risk'\n",
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"\n",
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" final_scenario_df.dropna(inplace=True)\n",
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"\n",
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" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
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" # Adding additional columns\n",
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" return final_scenario_df\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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},
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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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],
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],
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"source": [
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"source": [
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"# scenario = Scenario()\n",
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"scenario = Scenario()\n",
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"# scenario.logic(validation_window=300000)"
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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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},
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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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},
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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\n",
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"import pandas as pd\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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"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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"SELECT \n",
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"WITH \n",
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" n.TRADER_ID,\n",
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"-- Capture all orders and trades within the spoofing time window\n",
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" n.trade_time_window,\n",
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"trade_window AS (\n",
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" n.net_volume,\n",
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" SELECT\n",
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" n.order_count, -- Include number of orders\n",
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" t.trade_id,\n",
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" COALESCE(t.total_trade_volume, 0) AS total_trade_volume,\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",
|
||||||
|
"-- Calculate total trade volume on the opposite side (e.g., sell if spoofing is on buy)\n",
|
||||||
|
"opposite_trade_volume_cte AS (\n",
|
||||||
|
" SELECT \n",
|
||||||
|
" t.trader_id,\n",
|
||||||
|
" t.trade_id,\n",
|
||||||
|
" SUM(t.trade_volume) AS total_trade_volume\n",
|
||||||
|
" FROM {trade_data_1b} t\n",
|
||||||
|
" WHERE \n",
|
||||||
|
" t.date_time BETWEEN t.date_time - INTERVAL '{trade_time_window_s}' SECOND\n",
|
||||||
|
" AND t.date_time\n",
|
||||||
|
" AND t.trade_side = CASE WHEN '{spoofing_side}' = 'buy' THEN 'sell' ELSE 'buy' END\n",
|
||||||
|
" GROUP BY t.trader_id, t.trade_id\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"-- Final result with calculated spoofing indicators\n",
|
||||||
|
"SELECT\n",
|
||||||
|
" n.trade_id,\n",
|
||||||
|
" n.trader_id,\n",
|
||||||
|
" n.trade_time,\n",
|
||||||
|
" n.num_orders,\n",
|
||||||
|
" n.net_order_volume,\n",
|
||||||
" CASE \n",
|
" CASE \n",
|
||||||
" WHEN COALESCE(t.total_trade_volume, 0) > 0 THEN n.net_volume / t.total_trade_volume\n",
|
" WHEN o.total_trade_volume > 0 THEN n.net_order_volume / o.total_trade_volume\n",
|
||||||
" ELSE 0 -- or another value to indicate no trades\n",
|
" ELSE NULL\n",
|
||||||
" END AS order_trade_ratio,\n",
|
" END AS order_trade_ratio,\n",
|
||||||
" CASE \n",
|
" CASE \n",
|
||||||
" WHEN net_volume_all.total_net_volume_all > 0 THEN \n",
|
" WHEN a.net_order_volume_all > 0 THEN n.net_order_volume / a.net_order_volume_all\n",
|
||||||
" (n.net_volume / net_volume_all.total_net_volume_all) * 100 \n",
|
" ELSE NULL\n",
|
||||||
" ELSE 0 \n",
|
" END AS volume_percentage\n",
|
||||||
" END AS volume_percentage -- Calculate volume percentage\n",
|
"FROM \n",
|
||||||
"FROM (\n",
|
" net_order_volume_cte n\n",
|
||||||
" -- Step 2: Subquery for net_order_volume\n",
|
"LEFT JOIN \n",
|
||||||
" SELECT \n",
|
" opposite_trade_volume_cte o ON n.trade_id = o.trade_id\n",
|
||||||
" o.TRADER_ID,\n",
|
"LEFT JOIN \n",
|
||||||
" t.DATE_TIME AS trade_time_window,\n",
|
" net_order_volume_all_cte a ON n.trade_id = a.trade_id\n",
|
||||||
" SUM(CASE \n",
|
"WHERE \n",
|
||||||
" WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME\n",
|
" n.net_order_volume > 0 -- Only consider positive net order volumes (potential spoofing);\n",
|
||||||
" WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME\n",
|
" limit 1000\n",
|
||||||
" WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME\n",
|
|
||||||
" ELSE 0 END\n",
|
|
||||||
" ) AS net_volume,\n",
|
|
||||||
" COUNT(o.ORDER_ID) AS order_count -- Count the number of orders\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 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",
|
|
||||||
"\"\"\"\n",
|
"\"\"\"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
|
||||||
"from tms_data_interface import SQLQueryInterface\n",
|
|
||||||
"\n",
|
|
||||||
"class Scenario:\n",
|
"class Scenario:\n",
|
||||||
" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
|
" seq = SQLQueryInterface(schema=\"internal\")\n",
|
||||||
" def logic(self, **kwargs):\n",
|
"\n",
|
||||||
" validation_window = kwargs.get('validation_window')\n",
|
" def logic(self, **params):\n",
|
||||||
" spoofing_side = kwargs.get('buy')\n",
|
" spoofing_time_window = params.get('spoofing_time_window', 300000) # default to 300,000 ms (5 minutes)\n",
|
||||||
" time_window_s = int(validation_window/1000)\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",
|
" 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",
|
"\n",
|
||||||
" order_10m = 'order_10m',\n",
|
" # Execute the query with the parameters passed from `params`\n",
|
||||||
" time_window_s = time_window_s)\n",
|
" row_list = self.seq.execute_raw(query.format(\n",
|
||||||
" )\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",
|
" cols = [\n",
|
||||||
" 'Focal_id',\n",
|
" 'trade_id', 'focal_id', 'trade_time', 'num_orders', \n",
|
||||||
" 'trade_time_window',\n",
|
" 'net_order_volume', 'order_trade_ratio', 'volume_percentage'\n",
|
||||||
" 'net_volume',\n",
|
|
||||||
" 'order_count',\n",
|
|
||||||
" 'total_trade_volume',\n",
|
|
||||||
" 'order_trade_ratio',\n",
|
|
||||||
" 'volume_percentage'\n",
|
|
||||||
" ]\n",
|
" ]\n",
|
||||||
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
|
"\n",
|
||||||
" final_scenario_df['Segment'] = 'Default'\n",
|
" # Create a DataFrame from the query result\n",
|
||||||
" final_scenario_df['SAR_FLAG'] = 'N'\n",
|
" final_scenario_df = pd.DataFrame(row_list, columns=cols)\n",
|
||||||
" final_scenario_df['Risk'] = 'Low Risk'\n",
|
"\n",
|
||||||
" final_scenario_df.dropna(inplace=True)\n",
|
"\n",
|
||||||
" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
|
" # Adding additional columns\n",
|
||||||
" return final_scenario_df\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": [
|
"source": [
|
||||||
"# scenario = Scenario()\n",
|
"scenario = Scenario()\n",
|
||||||
"# scenario.logic(validation_window=300000)"
|
"scenario.logic(validation_window=300000)"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
|
|||||||
233
main.py
233
main.py
@ -4,127 +4,156 @@
|
|||||||
# In[21]:
|
# In[21]:
|
||||||
|
|
||||||
|
|
||||||
from datetime import datetime, timedelta
|
from datetime import datetime
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
from tms_data_interface import SQLQueryInterface
|
from tms_data_interface import SQLQueryInterface
|
||||||
|
|
||||||
query = """
|
query = """
|
||||||
SELECT
|
WITH
|
||||||
n.TRADER_ID,
|
-- Capture all orders and trades within the spoofing time window
|
||||||
n.trade_time_window,
|
trade_window AS (
|
||||||
n.net_volume,
|
SELECT
|
||||||
n.order_count, -- Include number of orders
|
t.trade_id,
|
||||||
COALESCE(t.total_trade_volume, 0) AS total_trade_volume,
|
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
|
CASE
|
||||||
WHEN COALESCE(t.total_trade_volume, 0) > 0 THEN n.net_volume / t.total_trade_volume
|
WHEN o.total_trade_volume > 0 THEN n.net_order_volume / o.total_trade_volume
|
||||||
ELSE 0 -- or another value to indicate no trades
|
ELSE NULL
|
||||||
END AS order_trade_ratio,
|
END AS order_trade_ratio,
|
||||||
CASE
|
CASE
|
||||||
WHEN net_volume_all.total_net_volume_all > 0 THEN
|
WHEN a.net_order_volume_all > 0 THEN n.net_order_volume / a.net_order_volume_all
|
||||||
(n.net_volume / net_volume_all.total_net_volume_all) * 100
|
ELSE NULL
|
||||||
ELSE 0
|
END AS volume_percentage
|
||||||
END AS volume_percentage -- Calculate volume percentage
|
FROM
|
||||||
FROM (
|
net_order_volume_cte n
|
||||||
-- Step 2: Subquery for net_order_volume
|
LEFT JOIN
|
||||||
SELECT
|
opposite_trade_volume_cte o ON n.trade_id = o.trade_id
|
||||||
o.TRADER_ID,
|
LEFT JOIN
|
||||||
t.DATE_TIME AS trade_time_window,
|
net_order_volume_all_cte a ON n.trade_id = a.trade_id
|
||||||
SUM(CASE
|
WHERE
|
||||||
WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME
|
n.net_order_volume > 0 -- Only consider positive net order volumes (potential spoofing);
|
||||||
WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME
|
limit 1000
|
||||||
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
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
|
|
||||||
from tms_data_interface import SQLQueryInterface
|
|
||||||
|
|
||||||
class Scenario:
|
class Scenario:
|
||||||
seq = SQLQueryInterface(schema="trade_schema")
|
seq = SQLQueryInterface(schema="internal")
|
||||||
def logic(self, **kwargs):
|
|
||||||
validation_window = kwargs.get('validation_window')
|
def logic(self, **params):
|
||||||
spoofing_side = kwargs.get('buy')
|
spoofing_time_window = params.get('spoofing_time_window', 300000) # default to 300,000 ms (5 minutes)
|
||||||
time_window_s = int(validation_window/1000)
|
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()
|
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",
|
|
||||||
order_10m = 'order_10m',
|
# Execute the query with the parameters passed from `params`
|
||||||
time_window_s = time_window_s)
|
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 = [
|
cols = [
|
||||||
'Focal_id',
|
'trade_id', 'focal_id', 'trade_time', 'num_orders',
|
||||||
'trade_time_window',
|
'net_order_volume', 'order_trade_ratio', 'volume_percentage'
|
||||||
'net_volume',
|
|
||||||
'order_count',
|
|
||||||
'total_trade_volume',
|
|
||||||
'order_trade_ratio',
|
|
||||||
'volume_percentage'
|
|
||||||
]
|
]
|
||||||
final_scenario_df = pd.DataFrame(row_list, columns = cols)
|
|
||||||
final_scenario_df['Segment'] = 'Default'
|
# Create a DataFrame from the query result
|
||||||
final_scenario_df['SAR_FLAG'] = 'N'
|
final_scenario_df = pd.DataFrame(row_list, columns=cols)
|
||||||
final_scenario_df['Risk'] = 'Low Risk'
|
|
||||||
final_scenario_df.dropna(inplace=True)
|
|
||||||
# final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']
|
# Adding additional columns
|
||||||
|
final_scenario_df['segment'] = 'Default'
|
||||||
|
final_scenario_df['sar_flag'] = 'N'
|
||||||
|
final_scenario_df['risk'] = 'Low Risk'
|
||||||
|
|
||||||
return final_scenario_df
|
return final_scenario_df
|
||||||
|
|
||||||
|
|
||||||
# In[22]:
|
# In[22]:
|
||||||
|
|
||||||
|
|
||||||
# scenario = Scenario()
|
scenario = Scenario()
|
||||||
# scenario.logic(validation_window=300000)
|
scenario.logic(validation_window=300000)
|
||||||
|
|
||||||
|
|
||||||
# In[ ]:
|
# In[ ]:
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user