CODE:-
from datetime import date from datetime import timedelta from nsepy import get_history import pandas as pd import datetime end1 = date.today() start1 = end1 - timedelta(days=180) stock = ['RELIANCE']#,'HDFCBANK','INFY','ICICIBANK','HDFC'] ,'TCS','KOTAKBANK','LT','SBIN','HINDUNILVR','AXISBANK', # 'ITC','BAJFINANCE','BHARTIARTL','ASIANPAINT','HCLTECH','MARUTI','TITAN','BAJAJFINSV','TATAMOTORS', # 'TECHM','SUNPHARMA','TATASTEEL','M&M','WIPRO','ULTRACEMCO','POWERGRID','HINDALCO','NTPC','NESTLEIND', # 'GRASIM','ONGC','JSWSTEEL','HDFCLIFE','INDUSINDBK','SBILIFE','DRREDDY','ADANIPORTS','DIVISLAB','CIPLA', # 'BAJAJ-AUTO','TATACONSUM','UPL','BRITANNIA','BPCL','EICHERMOT','HEROMOTOCO','COALINDIA','SHREECEM','IOC'] for stock in stock: vol = get_history(symbol=stock, start=start1, end=end1) d_vol = pd.concat([vol['Deliverable Volume']]) symbol_s = pd.concat([vol['Symbol']]) close = pd.concat([vol['Close']]) df = pd.DataFrame(symbol_s) df['D_vol'] = d_vol pd.set_option('display.max_columns',10) pd.set_option('display.max_rows',200) print(df) PRODUCT:-
Symbol D_vol Date 2022-02-17 RELIANCE 2872619 2022-02-18 RELIANCE 1216161 2022-02-21 RELIANCE 2888548 2022-02-22 RELIANCE 3920789 2022-02-23 RELIANCE 1928054 2022-02-24 RELIANCE 7266877 2022-02-25 RELIANCE 3670287 2022-02-28 RELIANCE 5321030 2022-03-02 RELIANCE 6277829 2022-03-03 RELIANCE 2479449 2022-03-04 RELIANCE 2754095 2022-03-07 RELIANCE 4962748 2022-03-08 RELIANCE 5602260 2022-03-09 RELIANCE 4647153 2022-03-10 RELIANCE 4102149 2022-03-11 RELIANCE 3028604 2022-03-14 RELIANCE 1800381 2022-03-15 RELIANCE 3924279 2022-03-16 RELIANCE 2824434 2022-03-17 RELIANCE 5961590 2022-03-21 RELIANCE 3019258 2022-03-22 RELIANCE 5753732 2022-03-23 RELIANCE 4321837 2022-03-24 RELIANCE 3208900 2022-03-25 RELIANCE 3821279 2022-03-28 RELIANCE 2149565 2022-03-29 RELIANCE 2280912 2022-03-30 RELIANCE 4259734 2022-03-31 RELIANCE 3893403 2022-04-01 RELIANCE 1845831 2022-04-04 RELIANCE 1859891 2022-04-05 RELIANCE 3962890 2022-04-06 RELIANCE 3640461 2022-04-07 RELIANCE 4936384 2022-04-08 RELIANCE 4458242 2022-04-11 RELIANCE 2103573 2022-04-12 RELIANCE 4019726 2022-04-13 RELIANCE 3009160 2022-04-18 RELIANCE 3079230 2022-04-19 RELIANCE 6735937 2022-04-20 RELIANCE 5767347 2022-04-21 RELIANCE 5151153 2022-04-22 RELIANCE 3113096 2022-04-25 RELIANCE 4210856 2022-04-26 RELIANCE 2382379 2022-04-27 RELIANCE 4014016 2022-04-28 RELIANCE 6937716 2022-04-29 RELIANCE 4308908 2022-05-02 RELIANCE 1939501 2022-05-04 RELIANCE 4758487 2022-05-05 RELIANCE 4135949 2022-05-06 RELIANCE 4117945 2022-05-09 RELIANCE 3496083 2022-05-10 RELIANCE 4214002 2022-05-11 RELIANCE 3471644 2022-05-12 RELIANCE 5344949 2022-05-13 RELIANCE 4652716 2022-05-16 RELIANCE 3089139 2022-05-17 RELIANCE 4085578 2022-05-18 RELIANCE 3948750 2022-05-19 RELIANCE 3713450 2022-05-20 RELIANCE 5198311 2022-05-23 RELIANCE 3078691 2022-05-24 RELIANCE 3094755 2022-05-25 RELIANCE 2740033 2022-05-26 RELIANCE 3568836 2022-05-27 RELIANCE 3519149 2022-05-30 RELIANCE 2716871 2022-05-31 RELIANCE 19734107 2022-06-01 RELIANCE 2072536 2022-06-02 RELIANCE 4788206 2022-06-03 RELIANCE 5136072 2022-06-06 RELIANCE 1680712 2022-06-07 RELIANCE 2712556 2022-06-08 RELIANCE 3012536 2022-06-09 RELIANCE 4301647 2022-06-10 RELIANCE 4608963 2022-06-13 RELIANCE 4242241 2022-06-14 RELIANCE 3299932 2022-06-15 RELIANCE 1888736 2022-06-16 RELIANCE 3664860 2022-06-17 RELIANCE 5410573 2022-06-20 RELIANCE 2410448 2022-06-21 RELIANCE 1906058 2022-06-22 RELIANCE 2129069 2022-06-23 RELIANCE 2980195 2022-06-24 RELIANCE 3440303 2022-06-27 RELIANCE 4127406 2022-06-28 RELIANCE 2590698 2022-06-29 RELIANCE 3617967 2022-06-30 RELIANCE 5419202 2022-07-01 RELIANCE 16124097 2022-07-04 RELIANCE 8925205 2022-07-05 RELIANCE 3241427 2022-07-06 RELIANCE 5356881 2022-07-07 RELIANCE 6400200 2022-07-08 RELIANCE 5266144 2022-07-11 RELIANCE 2790542 2022-07-12 RELIANCE 2091493 2022-07-13 RELIANCE 3820557 2022-07-14 RELIANCE 3162562 2022-07-15 RELIANCE 2013822 2022-07-18 RELIANCE 3641338 2022-07-19 RELIANCE 3280412 2022-07-20 RELIANCE 5571093 2022-07-21 RELIANCE 4032523 2022-07-22 RELIANCE 2374481 2022-07-25 RELIANCE 5024203 2022-07-26 RELIANCE 2368186 2022-07-27 RELIANCE 2097378 2022-07-28 RELIANCE 2973888 2022-07-29 RELIANCE 3783646 2022-08-01 RELIANCE 3695549 2022-08-02 RELIANCE 3420282 2022-08-03 RELIANCE 3725408 2022-08-04 RELIANCE 3215051 2022-08-05 RELIANCE 4106312 2022-08-08 RELIANCE 2615697 2022-08-10 RELIANCE 2941793 2022-08-11 RELIANCE 2296573 2022-08-12 RELIANCE 2683554 PROBLEM:- See in above data frame there are around 180 rows. I have a list of 50 stocks but here I have displayed only one stock and its product and 49 comment out. Now from the above data frame column D_vol contains around 180 rows. My criteria is that the last row of column D_vol should contain highest number than previous 90 rows or more than 90 rows. And if this condition satisfy any stock then it should reflect on output table. ex:- if suppose RELIANCE contain 10000 as D_vol on 12-aug-2022 and before 12-aug it was contain less than 10000 for 90 days or more than 90 then that stock should reflect on my output table. thank you.
