Python : NSEPY for multiple Stock

Python : NSEPY for multiple Stock

import numpy as np
import pandas as pd
from datetime import datetime,date,time,timedelta
from nsepy import get_history
import pandas as pd
import numpy as np
import pandas_ta as ta


stocks = ['JSWSTEEL','RELIANCE','AXISBANK','HCLTECH','TECHM','HDFC','ICICIBANK']
# stocks = ['ADANIENT', 'ADANIPOWER', 'AMARAJABAT', 'ACC', 'RAMCOCEM', 'AMBUJACEM', 'APOLLOHOSP', 'ASIANPAINT', 'AUROPHARMA', 'AXISBANK', 'BAJAJ-AUTO', 'BAJAJFINSV', 'BAJFINANCE', 'ADANIPORTS', 'BALKRISIND', 'BHARTIARTL', 'BANDHANBNK', 'BATAINDIA', 'BEL', 'BERGEPAINT', 'BHARATFORG', 'BHEL', 'BOSCHLTD', 'BRITANNIA', 'CANBK', 'CIPLA', 'COALINDIA', 'COLPAL', 'CUMMINSIND', 'DABUR', 'BANKBARODA', 'DIVISLAB', 'DRREDDY', 'EICHERMOT', 'EXIDEIND', 'FEDERALBNK', 'GAIL', 'GLENMARK', 'GRASIM', 'HAVELLS', 'HCLTECH', 'HDFCLIFE', 'HEROMOTOCO', 'HINDALCO', 'HINDPETRO', 'HINDUNILVR', 'ICICIBANK', 'ICICIPRULI', 'INDIGO', 'INDUSINDBK', 'INFY', 'ITC', 'JUBLFOOD', 'JUSTDIAL', 'KOTAKBANK', 'LICHSGFIN', 'LUPIN', 'MANAPPURAM', 'MARICO', 'MARUTI', 'BIOCON', 'CADILAHC', 'MCDOWELL-N', 'MFSL', 'MGL', 'MINDTREE', 'MOTHERSUMI', 'MRF', 'MUTHOOTFIN', 'NATIONALUM', 'NCC', 'NIITTECH', 'NMDC', 'NTPC', 'PEL', 'PETRONET', 'PFC', 'PIDILITIND', 'RBLBANK', 'RECLTD', 'SAIL', 'SBIN', 'SIEMENS', 'SRF', 'SRTRANSFIN', 'SUNTV', 'TATAPOWER', 'TECHM', 'TITAN', 'TORNTPHARM', 'TORNTPOWER', 'TVSMOTOR', 'UJJIVAN', 'ULTRACEMCO', 'UPL', 'VOLTAS', 'ASHOKLEY', 'CONCOR', 'INFRATEL', 'BPCL', 'CHOLAFIN', 'DLF', 'EQUITAS', 'ESCORTS', 'IDEA', 'JSWSTEEL', 'LT', 'GODREJCP', 'GODREJPROP', 'SBILIFE', 'HDFC', 'HDFCBANK', 'IDFCFIRSTB', 'JINDALSTEL', 'M&M', 'M&MFIN', 'UBL', 'NAUKRI', 'NESTLEIND', 'ONGC', 'PAGEIND', 'POWERGRID', 'RELIANCE', 'SHREECEM', 'SUNPHARMA', 'TATAMOTORS', 'TATASTEEL', 'CENTURYTEX', 'VEDL', 'APOLLOTYRE', 'PNB', 'TATACHEM', 'IGL', 'IOC', 'TATACONSUM', 'TCS', 'WIPRO', 'ZEEL', 'L&TFH', 'IBULHSGFIN', 'GMRINFRA']
start = datetime.today() - timedelta(5)
end = datetime.today()
close_price = {}

for tickers in stocks:
    close_price = get_history(tickers,start,end)
    df = pd.DataFrame(close_price)
    print(df)

C:\Users\mahen\PycharmProjects\pythonProject\venv\Scripts\python.exe “C:/Users/mahen/PycharmProjects/pythonProject/venv/Multiple nsepy.py”

Output

Symbol Series … Deliverable Volume %Deliverble
Date …
2022-08-01 JSWSTEEL EQ … 893402 0.2924
2022-08-02 JSWSTEEL EQ … 794778 0.2603
2022-08-03 JSWSTEEL EQ … 861052 0.1937
2022-08-04 JSWSTEEL EQ … 757341 0.1609
2022-08-05 JSWSTEEL EQ … 307373 0.1252

[5 rows x 14 columns]
Symbol Series … Deliverable Volume %Deliverble
Date …
2022-08-01 RELIANCE EQ … 3695549 0.5100
2022-08-02 RELIANCE EQ … 3420282 0.5325
2022-08-03 RELIANCE EQ … 3725408 0.5664
2022-08-04 RELIANCE EQ … 3215051 0.4815
2022-08-05 RELIANCE EQ … 4106312 0.6382

[5 rows x 14 columns]
Symbol Series … Deliverable Volume %Deliverble
Date …
2022-08-01 AXISBANK EQ … 2872346 0.5229
2022-08-02 AXISBANK EQ … 4300751 0.5911
2022-08-03 AXISBANK EQ … 5169070 0.5465
2022-08-04 AXISBANK EQ … 3737135 0.5005
2022-08-05 AXISBANK EQ … 1915410 0.4179

[5 rows x 14 columns]
Symbol Series Prev Close … Trades Deliverable Volume %Deliverble
Date …
2022-08-01 HCLTECH EQ 948.40 … 156568 1553023 0.6238
2022-08-02 HCLTECH EQ 952.15 … 76310 1540425 0.6052
2022-08-03 HCLTECH EQ 951.05 … 115023 2271996 0.5515
2022-08-04 HCLTECH EQ 957.20 … 122258 1953495 0.5560
2022-08-05 HCLTECH EQ 958.10 … 101848 1551732 0.6048

[5 rows x 14 columns]
Symbol Series Prev Close … Trades Deliverable Volume %Deliverble
Date …
2022-08-01 TECHM EQ 1048.65 … 123718 1468717 0.5782
2022-08-02 TECHM EQ 1049.60 … 133736 1562238 0.5575
2022-08-03 TECHM EQ 1032.60 … 152235 1593359 0.4541
2022-08-04 TECHM EQ 1052.65 … 148438 2711408 0.5058
2022-08-05 TECHM EQ 1056.05 … 75910 1375134 0.5205

[5 rows x 14 columns]
Symbol Series Prev Close … Trades Deliverable Volume %Deliverble
Date …
2022-08-01 HDFC EQ 2377.80 … 116880 1263242 0.5499
2022-08-02 HDFC EQ 2383.75 … 118058 1574984 0.5565
2022-08-03 HDFC EQ 2353.30 … 123915 1667882 0.6090
2022-08-04 HDFC EQ 2368.40 … 99381 1111653 0.6149
2022-08-05 HDFC EQ 2361.75 … 89233 943916 0.5236

[5 rows x 14 columns]
Symbol Series … Deliverable Volume %Deliverble
Date …
2022-08-01 ICICIBANK EQ … 4645204 0.5825
2022-08-02 ICICIBANK EQ … 6906789 0.6219
2022-08-03 ICICIBANK EQ … 7604666 0.6867
2022-08-04 ICICIBANK EQ … 6856925 0.6170
2022-08-05 ICICIBANK EQ … 9933579 0.6187

[5 rows x 14 columns]