CODE:-
from datetime import date
from datetime import timedelta
from nsepy import get_history
import pandas as pd
end1 = date.today()
start1 = end1 - timedelta(days=25)
exp_date1 = date(2022,8,25)
exp_date2 = date(2022,9,29)
# stock = ['HDFCLIFE']
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']
target_stocks = []
# oi_change = []
for stock in stock:
stock_jan = get_history(symbol=stock,
start=start1,
end=end1,
futures=True,
expiry_date=exp_date1)
stock_feb = get_history(symbol=stock,
start=start1,
end=end1,
futures=True,
expiry_date=exp_date2)
delivery_per_age = get_history(symbol=stock,
start=start1,
end=end1)
symbol_s = get_history(symbol=stock,
start=start1,
end=end1)
oi_combined = pd.concat([stock_jan['Change in OI'] + stock_feb['Change in OI']])
total_oi = pd.concat([stock_jan['Open Interest']+stock_feb['Open Interest']])
delivery_vol = pd.concat([delivery_per_age['Deliverable Volume']])
# delivery_per = pd.concat([delivery_per_age['%Deliverble']*100])
na_me = pd.concat([symbol_s['Symbol']])
close = pd.concat([delivery_per_age['Close']])
df = pd.DataFrame(na_me)
df['TOTAL_OPN_INT'] = total_oi
df['OI_COMBINED'] = oi_combined
df['%_CHANGE'] = ((df['OI_COMBINED'] / df['TOTAL_OPN_INT']) * 100).__round__(2)
df['AVG_OI_COMBINED'] = df['OI_COMBINED'].rolling(5).mean()
# df['DELIVERY_VOL'] = delivery_vol
# df['AVG_DELIVERY_VOL'] = df['DELIVERY_VOL'].rolling(5).mean()
# df['DELIVERY_PER'] = delivery_per
# df['AVG_DELIVERY_%'] = df['DELIVERY_PER'].rolling(5).mean()
df['_CLOSE_PRICE_'] = close
pd.set_option('display.max_columns',8)
pd.set_option('display.width',200)
# print(df)
cond = ((df.loc[df.index[-5:-1], '%_CHANGE'].agg(min) > 0) |(df.loc[df.index[-6:-1], '%_CHANGE'].agg(min) > 0)) & (df.loc[df.index[-1], '%_CHANGE'] < 0)
if(cond):
target_stocks.append(df)
print(target_stocks)
OUTPUT:-
From above code I am getting the output for the day 11-aug-2022 which is displayed below.
[ Symbol TOTAL_OPN_INT OI_COMBINED %_CHANGE AVG_OI_COMBINED _CLOSE_PRICE_
Date
2022-07-18 EICHERMOT 489650 61250 12.51 NaN 3036.50
2022-07-19 EICHERMOT 547400 57750 10.55 NaN 3077.70
2022-07-20 EICHERMOT 556150 8750 1.57 NaN 3045.00
2022-07-21 EICHERMOT 572250 16100 2.81 NaN 3081.20
2022-07-22 EICHERMOT 728000 155750 21.39 59920.0 3147.60
2022-07-25 EICHERMOT 1358700 630700 46.42 173810.0 3086.70
2022-07-26 EICHERMOT 1789900 431200 24.09 248500.0 3023.30
2022-07-27 EICHERMOT 2226700 436800 19.62 334110.0 3057.40
2022-07-28 EICHERMOT 2843750 617050 21.70 454300.0 3054.00
2022-07-29 EICHERMOT 2878400 34650 1.20 430080.0 3093.45
2022-08-01 EICHERMOT 3047100 168700 5.54 337680.0 3088.40
2022-08-02 EICHERMOT 3491250 444150 12.72 340270.0 3120.95
2022-08-03 EICHERMOT 3871700 380450 9.83 329000.0 3138.20
2022-08-04 EICHERMOT 3943100 71400 1.81 219870.0 3145.80
2022-08-05 EICHERMOT 4058950 115850 2.85 236110.0 3089.60
2022-08-08 EICHERMOT 4060000 1050 0.03 202580.0 3116.75
2022-08-10 EICHERMOT 4165000 105000 2.52 134750.0 3154.55
2022-08-11 EICHERMOT 3880450 -284550 -7.33 1750.0 3176.45, Symbol TOTAL_OPN_INT OI_COMBINED %_CHANGE AVG_OI_COMBINED _CLOSE_PRICE_
Date
2022-07-18 COALINDIA 7631400 1965600 25.76 NaN 195.60
2022-07-19 COALINDIA 8400000 768600 9.15 NaN 198.25
2022-07-20 COALINDIA 9361800 961800 10.27 NaN 197.85
2022-07-21 COALINDIA 10042200 680400 6.78 NaN 198.60
2022-07-22 COALINDIA 11020800 978600 8.88 1071000.0 197.10
2022-07-25 COALINDIA 18131400 7110600 39.22 2100000.0 200.90
2022-07-26 COALINDIA 25368000 7236600 28.53 3393600.0 202.30
2022-07-27 COALINDIA 29454600 4086600 13.87 4018560.0 203.45
2022-07-28 COALINDIA 31941000 2486400 7.78 4379760.0 202.85
2022-07-29 COALINDIA 33121200 1180200 3.56 4420080.0 211.25
2022-08-01 COALINDIA 32928000 -193200 -0.59 2959320.0 212.75
2022-08-02 COALINDIA 33398400 470400 1.41 1606080.0 215.25
2022-08-03 COALINDIA 32646600 -751800 -2.30 638400.0 212.10
2022-08-04 COALINDIA 33734400 1087800 3.22 358680.0 207.15
2022-08-05 COALINDIA 33780600 46200 0.14 131880.0 208.45
2022-08-08 COALINDIA 37044000 3263400 8.81 823200.0 215.40
2022-08-10 COALINDIA 38186400 1142400 2.99 957600.0 219.85
2022-08-11 COALINDIA 35653800 -2532600 -7.10 601440.0 218.60, Symbol TOTAL_OPN_INT OI_COMBINED %_CHANGE AVG_OI_COMBINED _CLOSE_PRICE_
Date
2022-07-18 SHREECEM 30675 2850 9.29 NaN 20055.70
2022-07-19 SHREECEM 34800 4125 11.85 NaN 20068.20
2022-07-20 SHREECEM 38250 3450 9.02 NaN 20208.20
2022-07-21 SHREECEM 41800 3550 8.49 NaN 20442.95
2022-07-22 SHREECEM 58250 16450 28.24 6085.0 20780.00
2022-07-25 SHREECEM 118700 60450 50.93 17605.0 20679.05
2022-07-26 SHREECEM 194375 75675 38.93 31915.0 20652.35
2022-07-27 SHREECEM 241500 47125 19.51 40650.0 21023.50
2022-07-28 SHREECEM 285400 43900 15.38 48720.0 20415.45
2022-07-29 SHREECEM 294975 9575 3.25 47345.0 20498.00
2022-08-01 SHREECEM 295275 300 0.10 35315.0 20947.00
2022-08-02 SHREECEM 297050 1775 0.60 20535.0 21110.95
2022-08-03 SHREECEM 303500 6450 2.13 12400.0 20956.45
2022-08-04 SHREECEM 319375 15875 4.97 6795.0 20687.90
2022-08-05 SHREECEM 322725 3350 1.04 5550.0 21237.40
2022-08-08 SHREECEM 327450 4725 1.44 6435.0 21195.60
2022-08-10 SHREECEM 333275 5825 1.75 7245.0 21104.90
2022-08-11 SHREECEM 332225 -1050 -0.32 5745.0 21192.95]
Now when I run the code I am getting this output. How to load this output into the excel as .csv file with the name as ’11-08-2022.csv’. And suppose if I run the code on 12-08-2022 then another .csv file should add in the same folder where first .csv file has saved and now the file name should be 12-08-2022……in this way when I run the code each time there should be one .csv file created with the name as todays date.
thank you.
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Answer
first import libraries
import pandas as pd import datetime
to load data from csv file
df = pd.read_csv('file_path')
example
df = pd.read_csv('/content/sample_data/california_housing_test.csv')
to save data to csv file
df.to_csv('file_name')
file will be saved to your current directory
to save some other folder
df.to_csv('absolute_path/file_name.csv')
example
df2.to_csv('/content/drive/MyDrive/some_folder/modified_df.csv')
in your case, you want something like this
file_name = f'{datetime.datetime.now().day}-{datetime.datetime.now().month}-{datetime.datetime.now().year}.csv'
target_stocks.to_csv(f'file_path/{file_name}')