From 64181e87ae5d9600b2c1e1bd09ccacac0653dcd1 Mon Sep 17 00:00:00 2001
From: user_client2024 <nitin.jiloha@solytics-partners.com>
Date: Fri, 11 Oct 2024 08:24:59 +0000
Subject: [PATCH] System save at 11/10/2024 13:54 by user_client2024

---
 .ipynb_checkpoints/main-checkpoint.py | 104 ++++++++++++++++++++++++++
 1 file changed, 104 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..d54d540
--- /dev/null
+++ b/.ipynb_checkpoints/main-checkpoint.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+from datetime import datetime
+import pandas as pd
+from tms_data_interface import SQLQueryInterface
+
+# SQL query to aggregate trade data and compute metrics
+query = """
+WITH trade_data AS (
+    SELECT 
+        trader_id,
+        date_time,
+        trade_price,
+        trade_volume,
+        -- Create a time window for each trade
+        date_time - INTERVAL '1 second' * {time_window_s} AS window_start,
+        date_time AS window_end
+    FROM {trade_data_1b}
+),
+
+aggregated_trades AS (
+    SELECT 
+        td.trader_id,
+        td.window_start,
+        td.window_end,
+        SUM(CASE WHEN trade_side = 'buy' THEN trade_volume ELSE 0 END) 
+            OVER (PARTITION BY td.trader_id ORDER BY td.date_time 
+                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS buy_volume,
+        SUM(CASE WHEN trade_side = 'sell' THEN trade_volume ELSE 0 END) 
+            OVER (PARTITION BY td.trader_id ORDER BY td.date_time 
+                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS sell_volume,
+        SUM(trade_volume) OVER (ORDER BY td.date_time 
+                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS total_volume,
+        MAX(trade_price) OVER (ORDER BY td.date_time 
+                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS highest_price,
+        MIN(trade_price) OVER (ORDER BY td.date_time 
+                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS lowest_price,
+        COUNT(*) OVER (PARTITION BY td.trader_id ORDER BY td.date_time 
+                  RANGE BETWEEN {time_window_s} PRECEDING AND CURRENT ROW) AS number_of_trades
+    FROM trade_data td
+)
+
+SELECT 
+    window_start AS start_time,
+    window_end AS end_time,
+    trader_id AS "Participant",
+    lowest_price AS min_price,
+    highest_price AS max_price,
+    (highest_price - lowest_price) / NULLIF(lowest_price, 0) * 100 AS "Price Change (%)",
+    buy_volume AS participant_volume,
+    total_volume,
+    (buy_volume / NULLIF(total_volume, 0)) * 100 AS "Volume (%)"
+FROM aggregated_trades
+WHERE buy_volume > 0 OR sell_volume > 0
+"""
+
+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_data_2b",
+            time_window_s=time_window_s
+        ))
+
+        cols = [
+            'START_DATE_TIME',
+            'END_DATE_TIME',
+            'FOCAL ID',
+            'MIN_PRICE',
+            'MAX_PRICE',
+            'PRICE_CHANGE (%)',
+            'PARTICIPANT_VOLUME',
+            'TOTAL_VOLUME',
+            '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
+
+        # Adding additional columns
+        final_scenario_df['Segment'] = 'Default'
+        final_scenario_df['SAR_FLAG'] = 'N'
+        final_scenario_df['Risk'] = 'Low Risk'
+
+        return final_scenario_df
+
+
+# In[ ]:
+
+
+
+