From f1acfabffe583cf61326197f13645e3e63182f88 Mon Sep 17 00:00:00 2001
From: user_client2024 <nitin.jiloha@solytics-partners.com>
Date: Mon, 7 Oct 2024 10:10:05 +0000
Subject: [PATCH] System save at 07/10/2024 15:40 by user_client2024

---
 .ipynb_checkpoints/main-checkpoint.py | 112 ++++++++++++++++++++++++++
 1 file changed, 112 insertions(+)
 create mode 100644 .ipynb_checkpoints/main-checkpoint.py

diff --git a/.ipynb_checkpoints/main-checkpoint.py b/.ipynb_checkpoints/main-checkpoint.py
new file mode 100644
index 0000000..04fdca1
--- /dev/null
+++ b/.ipynb_checkpoints/main-checkpoint.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+from datetime import datetime, timedelta
+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,
+
+        -- Subtract seconds from the end_time using date_add() with negative integer interval
+        date_add('second', -{time_window_s}, date_time) AS start_time,
+
+        -- 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
+"""
+
+
+from tms_data_interface import SQLQueryInterface
+
+class Scenario:
+    seq = SQLQueryInterface(schema="trade_schema")
+    def logic(self, **kwargs):
+        validation_window = kwargs.get('validation_window')
+        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",
+                                                    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',
+        ]
+        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'
+        final_scenario_df.dropna(inplace=True)
+        # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']
+        return final_scenario_df
+