generated from user_client2024/58
214 lines
7.5 KiB
Plaintext
214 lines
7.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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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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"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_10m_v3} 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_10m_v3} 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 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 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 100\n",
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"\"\"\"\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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"\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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"\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_10m_v3=\"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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" '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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"\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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"cell_type": "code",
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"execution_count": null,
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"id": "b5c4307f-bc35-47e2-b680-fd1df2168d6c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Query start time: 2024-10-15 09:25:10.320161\n"
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]
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}
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],
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"source": [
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"scenario = Scenario()\n",
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"scenario.logic(spoofing_time_window = 300000,\n",
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" spoofing_side = 'buy',\n",
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" use_volume_for_order_trade_ratio = True,\n",
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" trade_time_window = 300000,\n",
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" ignore_trade_after_spoofing = True,\n",
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" ignore_price_improvement = True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "36b1b24a-aeca-4d22-a2b3-6e04aca31695",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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"nbformat": 4,
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"nbformat_minor": 5
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