generated from user_client2024/58
438 lines
15 KiB
Plaintext
438 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 21,
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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_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 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 1000\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=\"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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"\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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" '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": 22,
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"id": "b5c4307f-bc35-47e2-b680-fd1df2168d6c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Query start time : 2024-10-14 07:40:43.846637\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>focal_ID</th>\n",
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" <th>trade_time_window</th>\n",
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" <th>net_volume</th>\n",
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" <th>order_count</th>\n",
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" <th>total_trade_volume</th>\n",
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" <th>order_trade_ratio</th>\n",
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" <th>volume_percentage</th>\n",
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" <th>Segment</th>\n",
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" <th>SAR_FLAG</th>\n",
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" <th>Risk</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>3097728207</td>\n",
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" <td>2024-01-01 00:03:00</td>\n",
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" <td>-92.0</td>\n",
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" <td>1</td>\n",
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" <td>92</td>\n",
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" <td>-1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>Default</td>\n",
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" <td>N</td>\n",
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" <td>Low Risk</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>3228645322</td>\n",
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" <td>2024-01-01 00:06:00</td>\n",
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" <td>-1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>Default</td>\n",
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" <td>N</td>\n",
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" <td>Low Risk</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2701872727</td>\n",
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" <td>2024-01-01 00:09:00</td>\n",
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" <td>-42.0</td>\n",
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" <td>1</td>\n",
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" <td>42</td>\n",
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" <td>-1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>Default</td>\n",
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" <td>N</td>\n",
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" <td>Low Risk</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1659056655</td>\n",
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" <td>2024-01-01 00:11:00</td>\n",
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" <td>-167.0</td>\n",
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" <td>1</td>\n",
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" <td>167</td>\n",
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" <td>-1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>Default</td>\n",
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" <td>N</td>\n",
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" <td>Low Risk</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1661288887</td>\n",
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" <td>2024-01-01 00:13:00</td>\n",
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" <td>-756.0</td>\n",
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" <td>1</td>\n",
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" <td>756</td>\n",
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" <td>-1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>Default</td>\n",
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" <td>N</td>\n",
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" <td>Low Risk</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>95</th>\n",
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" <td>1945772682</td>\n",
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" <td>2024-01-01 00:43:00</td>\n",
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" <td>-854.0</td>\n",
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" <td>1</td>\n",
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" <td>854</td>\n",
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" <td>-1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>Default</td>\n",
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" <td>N</td>\n",
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" <td>Low Risk</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>96</th>\n",
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" <td>2137478041</td>\n",
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" <td>2024-01-01 00:43:00</td>\n",
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" <td>-926.0</td>\n",
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" <td>1</td>\n",
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" <td>926</td>\n",
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" <td>-1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>Default</td>\n",
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" <td>N</td>\n",
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" <td>Low Risk</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>97</th>\n",
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" <td>7138329164</td>\n",
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" <td>2024-01-01 00:43:00</td>\n",
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" <td>-433.0</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>Default</td>\n",
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" <td>N</td>\n",
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" <td>Low Risk</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>98</th>\n",
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" <td>1867007441</td>\n",
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" <td>2024-01-01 00:43:00</td>\n",
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" <td>-626.0</td>\n",
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" <td>1</td>\n",
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" <td>-1.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>Default</td>\n",
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||
" <td>N</td>\n",
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" <td>Low Risk</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>99</th>\n",
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||
" <td>2347906349</td>\n",
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||
" <td>2024-01-01 00:43:00</td>\n",
|
||
" <td>-69.0</td>\n",
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||
" <td>1</td>\n",
|
||
" <td>69</td>\n",
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||
" <td>-1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>Default</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Low Risk</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>100 rows × 10 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" focal_ID trade_time_window net_volume order_count \\\n",
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"0 3097728207 2024-01-01 00:03:00 -92.0 1 \n",
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"1 3228645322 2024-01-01 00:06:00 -689.0 1 \n",
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||
"2 2701872727 2024-01-01 00:09:00 -42.0 1 \n",
|
||
"3 1659056655 2024-01-01 00:11:00 -167.0 1 \n",
|
||
"4 1661288887 2024-01-01 00:13:00 -756.0 1 \n",
|
||
".. ... ... ... ... \n",
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||
"95 1945772682 2024-01-01 00:43:00 -854.0 1 \n",
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||
"96 2137478041 2024-01-01 00:43:00 -926.0 1 \n",
|
||
"97 7138329164 2024-01-01 00:43:00 -433.0 1 \n",
|
||
"98 1867007441 2024-01-01 00:43:00 -626.0 1 \n",
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"99 2347906349 2024-01-01 00:43:00 -69.0 1 \n",
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"\n",
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" total_trade_volume order_trade_ratio volume_percentage Segment \\\n",
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"0 92 -1.0 0.0 Default \n",
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"1 689 -1.0 0.0 Default \n",
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"2 42 -1.0 0.0 Default \n",
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||
"3 167 -1.0 0.0 Default \n",
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||
"4 756 -1.0 0.0 Default \n",
|
||
".. ... ... ... ... \n",
|
||
"95 854 -1.0 0.0 Default \n",
|
||
"96 926 -1.0 0.0 Default \n",
|
||
"97 433 -1.0 0.0 Default \n",
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||
"98 626 -1.0 0.0 Default \n",
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"99 69 -1.0 0.0 Default \n",
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"\n",
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" SAR_FLAG Risk \n",
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"0 N Low Risk \n",
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"1 N Low Risk \n",
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"2 N Low Risk \n",
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"3 N Low Risk \n",
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"4 N Low Risk \n",
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".. ... ... \n",
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"95 N Low Risk \n",
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"96 N Low Risk \n",
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"97 N Low Risk \n",
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"98 N Low Risk \n",
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"99 N Low Risk \n",
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"\n",
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"[100 rows x 10 columns]"
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||
]
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||
},
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||
"execution_count": 22,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
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],
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"source": [
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"scenario = Scenario()\n",
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"scenario.logic(validation_window=300000)"
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]
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||
},
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||
{
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||
"cell_type": "code",
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"id": "36b1b24a-aeca-4d22-a2b3-6e04aca31695",
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"metadata": {},
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|
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"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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"file_extension": ".py",
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