How to fetch pre-open market data

Programming NSE Python API
1

is this available in present API ?
also how to fetch pre-open market data?

2

Yes.

You want a function on getting Pre Open data?

3

Yes to get pre open gainers, losers.

4 
key = "NIFTY"
key = "BANKNIFTY"
key = "SME"
key = "FO"
key = "OTHERS"
key = "ALL"

def nse_preopen(key="NIFTY"):
    payload = nsefetch("https://www.nseindia.com/api/market-data-pre-open?key="+key+"")    
    return payload

print(nse_preopen("NIFTY"))

Now just manipulate the output, If you do something interesting, Share back. I will add this function or similar function when I update.

5 
def nse_preopen_movers(key="FO"):
    positions = nsefetch("https://www.nseindia.com/api/market-data-pre-open?key="+key+"")    
    idf = pd.DataFrame(positions['data'])
    df  = pd.json_normalize(idf['metadata'])[['symbol', 'pChange', 'lastPrice', 'totalTurnover']].dropna()
    adv_dec = np.array([[positions['advances'],positions['declines'],positions['unchanged']]])
    return df.loc[df['pChange'] > 1.5], df.loc[df['pChange'] < -1.5],adv_dec

preOpen_gainer, preOpen_loser, adv_dec = nse_preopen_movers()

I’m filtering pre-open gainers and losers with a 1.5% range, with advances and declines.

1 Like
6

Lovely.

I added the functions to the main library with certain changes. Update the main library.
Two functions added.

Command:
nse_preopen(key,type)

Variations:

key = "NIFTY" (default)
key = "BANKNIFTY"
key = "SME"
key = "FO"
key = "OTHERS"
key = "ALL"

type = "raw"
type = "pandas" (default)

Usage:

payload=nse_preopen("FO","raw")
print(payload) # It will print raw JSON
print(payload["advances"])
print(payload["declines"])
print(payload["unchanged"])

payload=nse_preopen("FO","pandas") # It will post Pandas DataFrame
1 Like
7

Another one is yours. Also added your name there in code. See in Github.

Usage:

nse_preopen_movers(key="FO",filter=1.5)
  • key is same like just pasted above.
  • filter is where you can choose how much gap up or down.

So,

gainers,movers=nse_preopen_movers("NIFTY")
print(gainers)
print(movers)

PS: I recoded this way because I desperately wanted to avoid adding a new library like numpy. Adding library can cause error in all past installations.

1 Like