user_client2024/61
1
0
Fork 0
generated from user_client2024/58
Code Issues Pull Requests Packages Projects Releases Wiki Activity

System save at 14/10/2024 12:07 by user_client2024

Browse Source
This commit is contained in:
user_client2024 2024-10-14 06:37:23 +00:00
parent 539f6bd9bb
commit c65df70254
3 changed files with 546 additions and 234 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

193
.ipynb_checkpoints/main-checkpoint.ipynb
View File

@ -2,9 +2,11 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from datetime import datetime, timedelta\n",
@ -12,71 +14,83 @@
"from tms_data_interface import SQLQueryInterface\n",
"\n",
"query = \"\"\"\n",
"WITH time_windows AS (\n",
" SELECT\n",
" -- End time is the current trade time\n",
" date_time AS end_time,\n",
"SELECT \n",
" n.TRADER_ID,\n",
" n.trade_time_window,\n",
" n.net_volume,\n",
" n.order_count, -- Include number of orders\n",
" COALESCE(t.total_trade_volume, 0) AS total_trade_volume,\n",
" CASE \n",
" WHEN COALESCE(t.total_trade_volume, 0) > 0 THEN n.net_volume / t.total_trade_volume\n",
" ELSE 0 -- or another value to indicate no trades\n",
" END AS order_trade_ratio,\n",
" CASE \n",
" WHEN net_volume_all.total_net_volume_all > 0 THEN \n",
" (n.net_volume / net_volume_all.total_net_volume_all) * 100 \n",
" ELSE 0 \n",
" END AS volume_percentage -- Calculate volume percentage\n",
"FROM (\n",
" -- Step 2: Subquery for net_order_volume\n",
" SELECT \n",
" o.TRADER_ID,\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 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",
" -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
" date_add('second', -{time_window_s}, date_time) AS start_time,\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",
" -- Trade details\n",
" trade_price,\n",
" trade_volume,\n",
" trader_id,\n",
"\n",
" -- Calculate minimum price within the time window\n",
" MIN(trade_price) OVER (\n",
" ORDER BY date_time \n",
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) AS min_price,\n",
"\n",
" -- Calculate maximum price within the time window\n",
" MAX(trade_price) OVER (\n",
" ORDER BY date_time \n",
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) AS max_price,\n",
"\n",
" -- Calculate total trade volume within the time window\n",
" SUM(trade_volume) OVER ( \n",
" ORDER BY date_time \n",
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) AS total_volume,\n",
"\n",
" -- Calculate participant's trade volume within the time window\n",
" SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
" PARTITION BY trader_id \n",
" ORDER BY date_time \n",
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) AS participant_volume\n",
" FROM\n",
" {trade_data_1b}\n",
")\n",
"SELECT\n",
" -- Select the time window details\n",
" start_time,\n",
" end_time,\n",
"\n",
" -- Select the participant (trader) ID\n",
" trader_id AS \"Participant\",\n",
"\n",
" -- Select the calculated min and max prices\n",
" min_price,\n",
" max_price,\n",
"\n",
" -- Calculate the price change percentage\n",
" (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
"\n",
" -- Calculate the participant's volume as a percentage of total volume\n",
" (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
"\n",
" -- Participant volume\n",
" participant_volume,\n",
"\n",
" -- Select the total volume within the window\n",
" total_volume AS \"Total Volume\"\n",
"FROM\n",
" time_windows\n",
"ORDER BY n.trade_time_window\n",
"limit 1000\n",
"\"\"\"\n",
"\n",
"\n",
@ -86,26 +100,24 @@
" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
" def logic(self, **kwargs):\n",
" validation_window = kwargs.get('validation_window')\n",
" spoofing_side = kwargs.get('buy')\n",
" time_window_s = int(validation_window/1000)\n",
" query_start_time = datetime.now()\n",
" print(\"Query start time :\",query_start_time)\n",
" row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
" order_10m = 'order_10m',\n",
" time_window_s = time_window_s)\n",
" )\n",
" cols = [\n",
" 'START_DATE_TIME',\n",
" 'END_DATE_TIME',\n",
" 'Focal_id',\n",
" 'MIN_PRICE',\n",
" 'MAX_PRICE',\n",
" 'PRICE_CHANGE_PCT',\n",
" 'PARTICIPANT_VOLUME_PCT',\n",
" 'PARTICIPANT_VOLUME',\n",
" 'TOTAL_VOLUME',\n",
" 'TRADER_ID',\n",
" 'trade_time_window',\n",
" 'net_volume',\n",
" 'order_count',\n",
" 'total_trade_volume',\n",
" 'order_trade_ratio',\n",
" 'volume_percentage'\n",
" ]\n",
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
" final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\\\n",
" final_scenario_df['TOTAL_VOLUME'] * 100\n",
" final_scenario_df['Segment'] = 'Default'\n",
" final_scenario_df['SAR_FLAG'] = 'N'\n",
" final_scenario_df['Risk'] = 'Low Risk'\n",
@ -113,6 +125,35 @@
" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
" return final_scenario_df\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5c4307f-bc35-47e2-b680-fd1df2168d6c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Query start time : 2024-10-14 06:23:07.242919\n"
]
}
],
"source": [
"scenario = Scenario()\n",
"scenario.logic(validation_window=300000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36b1b24a-aeca-4d22-a2b3-6e04aca31695",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

416
main.ipynb
View File

@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 16,
"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
"metadata": {
"tags": []
@ -14,72 +14,83 @@
"from tms_data_interface import SQLQueryInterface\n",
"\n",
"query = \"\"\"\n",
"WITH time_windows AS (\n",
" SELECT\n",
" -- End time is the current trade time\n",
" date_time AS end_time,\n",
"SELECT \n",
" n.TRADER_ID,\n",
" n.trade_time_window,\n",
" n.net_volume,\n",
" n.order_count, -- Include number of orders\n",
" COALESCE(t.total_trade_volume, 0) AS total_trade_volume,\n",
" CASE \n",
" WHEN COALESCE(t.total_trade_volume, 0) > 0 THEN n.net_volume / t.total_trade_volume\n",
" ELSE 0 -- or another value to indicate no trades\n",
" END AS order_trade_ratio,\n",
" CASE \n",
" WHEN net_volume_all.total_net_volume_all > 0 THEN \n",
" (n.net_volume / net_volume_all.total_net_volume_all) * 100 \n",
" ELSE 0 \n",
" END AS volume_percentage -- Calculate volume percentage\n",
"FROM (\n",
" -- Step 2: Subquery for net_order_volume\n",
" SELECT \n",
" o.TRADER_ID,\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 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",
" -- Subtract seconds from the end_time using date_add() with negative integer interval\n",
" date_add('second', -{time_window_s}, date_time) AS start_time,\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",
" -- Trade details\n",
" trade_price,\n",
" trade_volume,\n",
" trader_id,\n",
"\n",
" -- Calculate minimum price within the time window\n",
" MIN(trade_price) OVER (\n",
" ORDER BY date_time \n",
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) AS min_price,\n",
"\n",
" -- Calculate maximum price within the time window\n",
" MAX(trade_price) OVER (\n",
" ORDER BY date_time \n",
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) AS max_price,\n",
"\n",
" -- Calculate total trade volume within the time window\n",
" SUM(trade_volume) OVER ( \n",
" ORDER BY date_time \n",
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) AS total_volume,\n",
"\n",
" -- Calculate participant's trade volume within the time window\n",
" SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (\n",
" PARTITION BY trader_id \n",
" ORDER BY date_time \n",
" RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW\n",
" ) AS participant_volume\n",
" FROM\n",
" {trade_data_1b}\n",
")\n",
"SELECT\n",
" -- Select the time window details\n",
" start_time,\n",
" end_time,\n",
"\n",
" -- Select the participant (trader) ID\n",
" trader_id AS \"Participant\",\n",
"\n",
" -- Select the calculated min and max prices\n",
" min_price,\n",
" max_price,\n",
"\n",
" -- Calculate the price change percentage\n",
" (max_price - min_price) / NULLIF(min_price, 0) * 100 AS \"Price Change (%)\",\n",
"\n",
" -- Calculate the participant's volume as a percentage of total volume\n",
" (participant_volume / NULLIF(total_volume, 0)) * 100 AS \"Volume (%)\",\n",
"\n",
" -- Participant volume\n",
" participant_volume,\n",
"\n",
" -- Select the total volume within the window\n",
" total_volume AS \"Total Volume\"\n",
"FROM\n",
" time_windows\n",
" limit 1000\n",
"ORDER BY n.trade_time_window\n",
"limit 1000\n",
"\"\"\"\n",
"\n",
"\n",
@ -89,26 +100,24 @@
" seq = SQLQueryInterface(schema=\"trade_schema\")\n",
" def logic(self, **kwargs):\n",
" validation_window = kwargs.get('validation_window')\n",
" spoofing_side = kwargs.get('buy')\n",
" time_window_s = int(validation_window/1000)\n",
" query_start_time = datetime.now()\n",
" print(\"Query start time :\",query_start_time)\n",
" row_list = self.seq.execute_raw(query.format(trade_data_1b=\"trade_10m_v3\",\n",
" order_10m = 'order_10m',\n",
" time_window_s = time_window_s)\n",
" )\n",
" cols = [\n",
" 'START_DATE_TIME',\n",
" 'END_DATE_TIME',\n",
" 'Focal_id',\n",
" 'MIN_PRICE',\n",
" 'MAX_PRICE',\n",
" 'PRICE_CHANGE_PCT',\n",
" 'PARTICIPANT_VOLUME_PCT',\n",
" 'PARTICIPANT_VOLUME',\n",
" 'TOTAL_VOLUME',\n",
" 'TRADER_ID',\n",
" 'trade_time_window',\n",
" 'net_volume',\n",
" 'order_count',\n",
" 'total_trade_volume',\n",
" 'order_trade_ratio',\n",
" 'volume_percentage'\n",
" ]\n",
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\n",
" final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\\\n",
" final_scenario_df['TOTAL_VOLUME'] * 100\n",
" final_scenario_df['Segment'] = 'Default'\n",
" final_scenario_df['SAR_FLAG'] = 'N'\n",
" final_scenario_df['Risk'] = 'Low Risk'\n",
@ -119,14 +128,261 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"id": "b5c4307f-bc35-47e2-b680-fd1df2168d6c",
"metadata": {},
"outputs": [],
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Query start time : 2024-10-14 06:23:07.242919\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TRADER_ID</th>\n",
" <th>trade_time_window</th>\n",
" <th>net_volume</th>\n",
" <th>order_count</th>\n",
" <th>total_trade_volume</th>\n",
" <th>order_trade_ratio</th>\n",
" <th>volume_percentage</th>\n",
" <th>Segment</th>\n",
" <th>SAR_FLAG</th>\n",
" <th>Risk</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3097728207</td>\n",
" <td>2024-01-01 00:03:00</td>\n",
" <td>-92.0</td>\n",
" <td>1</td>\n",
" <td>92</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3228645322</td>\n",
" <td>2024-01-01 00:06:00</td>\n",
" <td>-689.0</td>\n",
" <td>1</td>\n",
" <td>689</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2701872727</td>\n",
" <td>2024-01-01 00:09:00</td>\n",
" <td>-42.0</td>\n",
" <td>1</td>\n",
" <td>42</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1659056655</td>\n",
" <td>2024-01-01 00:11:00</td>\n",
" <td>-167.0</td>\n",
" <td>1</td>\n",
" <td>167</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1661288887</td>\n",
" <td>2024-01-01 00:13:00</td>\n",
" <td>-756.0</td>\n",
" <td>1</td>\n",
" <td>756</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>3382197985</td>\n",
" <td>2024-01-01 02:30:00</td>\n",
" <td>-159.0</td>\n",
" <td>1</td>\n",
" <td>159</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>1129008990</td>\n",
" <td>2024-01-01 02:30:00</td>\n",
" <td>-582.0</td>\n",
" <td>1</td>\n",
" <td>582</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>2944122893</td>\n",
" <td>2024-01-01 02:30:00</td>\n",
" <td>-65.0</td>\n",
" <td>1</td>\n",
" <td>65</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>2910876405</td>\n",
" <td>2024-01-01 02:30:00</td>\n",
" <td>-117.0</td>\n",
" <td>1</td>\n",
" <td>117</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>1816942226</td>\n",
" <td>2024-01-01 02:30:00</td>\n",
" <td>-732.0</td>\n",
" <td>1</td>\n",
" <td>732</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>Default</td>\n",
" <td>N</td>\n",
" <td>Low Risk</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" TRADER_ID trade_time_window net_volume order_count \\\n",
"0 3097728207 2024-01-01 00:03:00 -92.0 1 \n",
"1 3228645322 2024-01-01 00:06:00 -689.0 1 \n",
"2 2701872727 2024-01-01 00:09:00 -42.0 1 \n",
"3 1659056655 2024-01-01 00:11:00 -167.0 1 \n",
"4 1661288887 2024-01-01 00:13:00 -756.0 1 \n",
".. ... ... ... ... \n",
"995 3382197985 2024-01-01 02:30:00 -159.0 1 \n",
"996 1129008990 2024-01-01 02:30:00 -582.0 1 \n",
"997 2944122893 2024-01-01 02:30:00 -65.0 1 \n",
"998 2910876405 2024-01-01 02:30:00 -117.0 1 \n",
"999 1816942226 2024-01-01 02:30:00 -732.0 1 \n",
"\n",
" total_trade_volume order_trade_ratio volume_percentage Segment \\\n",
"0 92 -1.0 0.0 Default \n",
"1 689 -1.0 0.0 Default \n",
"2 42 -1.0 0.0 Default \n",
"3 167 -1.0 0.0 Default \n",
"4 756 -1.0 0.0 Default \n",
".. ... ... ... ... \n",
"995 159 -1.0 0.0 Default \n",
"996 582 -1.0 0.0 Default \n",
"997 65 -1.0 0.0 Default \n",
"998 117 -1.0 0.0 Default \n",
"999 732 -1.0 0.0 Default \n",
"\n",
" SAR_FLAG Risk \n",
"0 N Low Risk \n",
"1 N Low Risk \n",
"2 N Low Risk \n",
"3 N Low Risk \n",
"4 N Low Risk \n",
".. ... ... \n",
"995 N Low Risk \n",
"996 N Low Risk \n",
"997 N Low Risk \n",
"998 N Low Risk \n",
"999 N Low Risk \n",
"\n",
"[1000 rows x 10 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scenario = Scenario()\n",
"scenario.logic()"
"scenario.logic(validation_window=300000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36b1b24a-aeca-4d22-a2b3-6e04aca31695",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

171
main.py
View File

@ -1,7 +1,7 @@
#!/usr/bin/env python
# coding: utf-8
# In[2]:
# In[16]:
from datetime import datetime, timedelta
@ -9,72 +9,83 @@ import pandas as pd
from tms_data_interface import SQLQueryInterface
query = """
WITH time_windows AS (
SELECT
-- End time is the current trade time
date_time AS end_time,
SELECT
n.TRADER_ID,
n.trade_time_window,
n.net_volume,
n.order_count, -- Include number of orders
COALESCE(t.total_trade_volume, 0) AS total_trade_volume,
CASE
WHEN COALESCE(t.total_trade_volume, 0) > 0 THEN n.net_volume / t.total_trade_volume
ELSE 0 -- or another value to indicate no trades
END AS order_trade_ratio,
CASE
WHEN net_volume_all.total_net_volume_all > 0 THEN
(n.net_volume / net_volume_all.total_net_volume_all) * 100
ELSE 0
END AS volume_percentage -- Calculate volume percentage
FROM (
-- Step 2: Subquery for net_order_volume
SELECT
o.TRADER_ID,
t.DATE_TIME AS trade_time_window,
SUM(CASE
WHEN o.ORDER_STATUS = 'New' THEN o.ORDER_VOLUME
WHEN o.ORDER_STATUS = 'Cancelled' THEN -o.ORDER_VOLUME
WHEN o.ORDER_STATUS = 'Fulfilled' THEN -o.ORDER_VOLUME
ELSE 0 END
) AS net_volume,
COUNT(o.ORDER_ID) AS order_count -- Count the number of orders
FROM {order_10m} o
JOIN {trade_data_1b} t
ON o.TRADER_ID = t.TRADER_ID
WHERE o.SIDE = 'buy'
AND o.DATE_TIME BETWEEN t.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t.DATE_TIME
GROUP BY o.TRADER_ID, t.DATE_TIME
) AS n
LEFT JOIN (
-- Step 6: Subquery for total_trade_volume (opposite side trades after spoofing)
SELECT
t.TRADER_ID,
t.DATE_TIME,
SUM(t.TRADE_VOLUME) AS total_trade_volume
FROM (
-- Step 5: Subquery for relevant_trades
SELECT t1.*
FROM {trade_data_1b} t1
WHERE t1.TRADE_SIDE = 'buy'
AND EXISTS (
SELECT 1
FROM {trade_data_1b} t2
WHERE t2.TRADER_ID = t1.TRADER_ID
AND t2.DATE_TIME BETWEEN t1.DATE_TIME - INTERVAL '{time_window_s}' SECOND AND t1.DATE_TIME
)
) AS t
GROUP BY t.DATE_TIME, t.TRADER_ID
) AS t
ON n.TRADER_ID = t.TRADER_ID AND n.trade_time_window = t.DATE_TIME
-- Subtract seconds from the end_time using date_add() with negative integer interval
date_add('second', -{time_window_s}, date_time) AS start_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
-- Trade details
trade_price,
trade_volume,
trader_id,
-- Calculate minimum price within the time window
MIN(trade_price) OVER (
ORDER BY date_time
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
) AS min_price,
-- Calculate maximum price within the time window
MAX(trade_price) OVER (
ORDER BY date_time
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
) AS max_price,
-- Calculate total trade volume within the time window
SUM(trade_volume) OVER (
ORDER BY date_time
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
) AS total_volume,
-- Calculate participant's trade volume within the time window
SUM(CASE WHEN trader_id = trader_id THEN trade_volume ELSE 0 END) OVER (
PARTITION BY trader_id
ORDER BY date_time
RANGE BETWEEN INTERVAL '{time_window_s}' SECOND PRECEDING AND CURRENT ROW
) AS participant_volume
FROM
{trade_data_1b}
)
SELECT
-- Select the time window details
start_time,
end_time,
-- Select the participant (trader) ID
trader_id AS "Participant",
-- Select the calculated min and max prices
min_price,
max_price,
-- Calculate the price change percentage
(max_price - min_price) / NULLIF(min_price, 0) * 100 AS "Price Change (%)",
-- Calculate the participant's volume as a percentage of total volume
(participant_volume / NULLIF(total_volume, 0)) * 100 AS "Volume (%)",
-- Participant volume
participant_volume,
-- Select the total volume within the window
total_volume AS "Total Volume"
FROM
time_windows
limit 1000
ORDER BY n.trade_time_window
limit 1000
"""
@ -84,26 +95,24 @@ class Scenario:
seq = SQLQueryInterface(schema="trade_schema")
def logic(self, **kwargs):
validation_window = kwargs.get('validation_window')
spoofing_side = kwargs.get('buy')
time_window_s = int(validation_window/1000)
query_start_time = datetime.now()
print("Query start time :",query_start_time)
row_list = self.seq.execute_raw(query.format(trade_data_1b="trade_10m_v3",
order_10m = 'order_10m',
time_window_s = time_window_s)
)
cols = [
'START_DATE_TIME',
'END_DATE_TIME',
'Focal_id',
'MIN_PRICE',
'MAX_PRICE',
'PRICE_CHANGE_PCT',
'PARTICIPANT_VOLUME_PCT',
'PARTICIPANT_VOLUME',
'TOTAL_VOLUME',
'TRADER_ID',
'trade_time_window',
'net_volume',
'order_count',
'total_trade_volume',
'order_trade_ratio',
'volume_percentage'
]
final_scenario_df = pd.DataFrame(row_list, columns = cols)
final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\
final_scenario_df['TOTAL_VOLUME'] * 100
final_scenario_df['Segment'] = 'Default'
final_scenario_df['SAR_FLAG'] = 'N'
final_scenario_df['Risk'] = 'Low Risk'
@ -112,9 +121,15 @@ class Scenario:
return final_scenario_df
# In[ ]:
# In[17]:
scenario = Scenario()
scenario.logic()
scenario.logic(validation_window=300000)
# In[ ]: