#!/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