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How to Download NSE Historical Data - EOD & Intraday | 1,5,15, 30, 60 min Options and Futures Data
Apr 15, 2025
Want NSE historical data for backtesting, analysis, or algo trading? In this video, I’ll walk you through a powerful tool — the NSE Historical Data Download Utility — to fetch both End-of-Day (EOD) and Intraday data straight from the official NSE website. Includes 1 min / 5 min / 15 min / 30 min / 60mins data.
Read Article and Download Python Code from our community site (link):
https://fabtrader.in/download-intraday-and-eod-historical-data-for-nse-stocks-indices-futures-options-using-python/
Read also: Only Python Utility to extract NSE Data!
https://fabtrader.in/a-python-utility-to-fetch-live-data-from-nse-india-website/
💬 This video comes by popular demand from our community—especially those new to algo trading and just starting out with Python. If you’ve been struggling to find clean, reliable, and free Indian stock market data—this is for you!
🔍 What you’ll learn:
Where to find the NSE data download utility
How to download EOD and Intraday data including Indices, Futures and Options data
Why this is a go-to resource for any Indian market trader or data enthusiast
💬 Have questions? Drop a comment below! If you found this useful, Like 👍, Share 📢, and Subscribe 🔔 for more Python and trading-related content! 🚀
I would love to hear from you! 😊
Email : [email protected]
Website: https://fabtrader.in/
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0:00
Hello everyone, welcome back to Fat
0:01
Trader uh where we talk all things
0:03
trading, investing, and building wealth.
0:04
Today's video is something that's been
0:06
in popular demand from many of you in
0:07
the community, especially those of you
0:09
who just starting in algo trading or
0:10
learning Python. Um, one of the first
0:12
questions that I get asked is how do I
0:14
download NC historical data
0:15
automatically, both EOD as well as
0:17
intraday. Uh, by intraday, I mean the 1
0:19
minute, 5 minute, 15, 30, 60 minutes uh
0:21
type of data uh for stocks, futures and
0:24
option symbols. For most uh experienced
0:26
algo traders, they get this data
0:27
directly from their broker APIs.
0:28
However, for people who are starting new
0:30
and don't have a broker API setup, they
0:32
need this type of uh utility. Well, uh
0:34
the good news is per your request, I've
0:35
created a simple Python utility uh that
0:37
can download the NSE historical data. So
0:39
whether you're back testing strategies
0:40
or just want to analyze price action,
0:42
this tool gives you clean, reliable
0:43
market data straight from the official
0:45
NSE website, and it's uh completely free
0:47
as well. In this video, I'll walk you
0:49
through the utility, its code, and I'll
0:50
show you also how to use it step by
0:52
step. You can copy this code for free
0:53
and use it as you please. So stick
0:55
around and smash that like button if you
0:57
haven't done so already.
0:59
If this is your first time here,
1:00
welcome. My name is Vive and I'm a
1:02
financially independent algo trader.
1:03
This channel is all about building a
1:04
community of algo traders. We discuss
1:06
everything about a trading using Python,
1:07
building and practicing trading
1:08
strategies, market updates, much more.
1:10
Please do visit our community website
1:12
fabtrader.in. Also, do check out my
1:13
other YouTube channel Fabe where I talk
1:15
about my own financial independence
1:16
journey and share tools, methods, and
1:18
strategies that help me achieve my
1:20
financial freedom. Thank you.
1:26
A few days back I'd uh written this blog
1:28
article. I also did a video on this
1:29
where I had created an NS utility uh in
1:32
Python which using which I think you can
1:33
do a bunch of activities like for
1:35
example tracking live price you can
1:36
download B copies uh you can extract
1:38
corporate auctions corporate
1:40
announcements um you know a whole lot of
1:42
things uh that you can basically fetch
1:43
directly from the NSE website and this
1:45
is that video uh if you haven't seen
1:46
this video uh or read this article I'll
1:48
provide the link in the description so
1:50
take a look at it. Um the video and the
1:52
blog article was received quite well. Uh
1:54
a lot of people told me that they are
1:56
now starting to use this on daily basis
1:58
and they found it very useful. And as
2:00
part of that video comments uh and
2:01
people who pinged me told me that you
2:03
know if uh I can do something on the
2:05
historic data as well because most
2:07
people are looking to download NSE
2:08
historic data both the EOD data as well
2:10
as the intraday data which is the 1
2:12
minute 5 minute 15-minut type data. Um
2:15
not just for the stock data they also
2:16
wanted uh you know stock options uh and
2:19
then uh futures data as well. So they
2:21
had asked me if I can create a similar
2:23
utility uh where I can download uh this
2:24
data from the nse. Uh it'll be very
2:26
useful to them. So based on popular
2:28
demand, I've created a a simple tool
2:30
that will do just that. So the details
2:32
that I'm going to be talking through the
2:33
in this video, all that information is
2:34
available in this blog article and I'll
2:36
post the link uh in the description so
2:38
you can read through this. Uh and then
2:40
what this tool uh would do is basically
2:42
uh just as you guys asked uh it'll get
2:44
historical data not just for the the uh
2:47
the underlying and the the dys but also
2:49
for the futures and options data for
2:51
those stocks and uh uh indices. The the
2:53
article gives you a very detailed
2:55
step-by-step process on how to install
2:56
this and how to use this. And this is
2:58
the entire code. Uh you can just simply
3:00
copy this and then uh you know run it as
3:02
you wish. You don't really need anything
3:03
apart from just pandas and uh python
3:05
installed on your machine. Before I walk
3:07
you through the Python code and the the
3:09
actual Python implementation, uh let
3:10
let's just start from the fundamentals,
3:12
right? What exactly are we trying to do
3:13
here and how are we getting this data.
3:15
So I'm sure you are aware of this
3:16
website which is charting.nse.com which
3:18
is the official nse website. Uh and then
3:20
this basically has charts for all the
3:22
instruments uh that that are in
3:23
question. Uh all that the Python program
3:25
that we trying to do is trying to
3:27
extract data from this chart and that's
3:29
where you're getting your EOD data and
3:31
all the intraday data. Right? At at a
3:32
very high level this is exactly what
3:34
that Python uh code is doing. Um for
3:37
people who are slightly interested in uh
3:38
you know technically understanding how
3:39
this is done I know a couple of
3:40
subscribers had asked me that you
3:42
provide code which is all good but try
3:44
and do uh you know try and explain how
3:45
this code works also so that we can
3:47
learn a thing or two. So for people who
3:48
are slightly more interested technically
3:50
on how this works um what we're trying
3:52
to do is that if any website if you go
3:54
uh it basically works uh as a series of
3:56
requests and response right um so if you
3:59
press F12 from the from any web screen
4:00
that you're currently in uh you would
4:02
you would see something like this and
4:03
then when you go into network uh this is
4:05
where you see how this particular client
4:07
which is your browser is talking to the
4:09
the server which in this case is the NSE
4:11
server right how how these two uh items
4:14
basically talk to each other uh can be
4:15
seen from this window So at a high level
4:18
how this works is uh you know the the
4:20
the browser or the client basically
4:21
sends a web request to the server in a
4:23
specific format that the server can
4:24
understand and then then the server gets
4:26
that request it basically responds back
4:28
with the actual data. So in this case if
4:30
you really see these are the server
4:32
requests that are going out to the
4:33
server from this particular web browser
4:35
and it is sending the data in this
4:36
particular format where it is telling uh
4:38
the server as to which specific uh you
4:40
know um uh script code or the the script
4:42
code in this case nifty uh 26,000 as as
4:44
everybody knows this is a script code
4:45
for nifty and then what time frame the
4:47
data is is is required in this case the
4:50
interval is 1 minute as you can see here
4:52
the time interval is one so it basically
4:53
sends this information in a JSON format
4:56
that the server can understand and then
4:57
server basically uh serves that page
4:59
with the with the data that is required.
5:00
Right? So in in the Python program, what
5:02
we're trying to do is just emulate as if
5:04
a real user is trying to, you know,
5:06
request NSE for data. Right? In this
5:08
case, there's no real web browser or
5:09
anything involved. But we're using
5:11
Python program to basically emulate as
5:13
if a user is trying to get this uh
5:14
request done through a web browser. And
5:16
that's exactly how we are trying to send
5:17
that request and we get that data back
5:19
from the server and we we trap that data
5:21
and that's how we get our historical
5:23
data. If you haven't joined our Telegram
5:25
group, please do so. I share market
5:27
insights, algo trading tips and new
5:28
video notifications and this way you can
5:29
stay up to date with our community news
5:31
and
5:32
events. Now with that background now
5:34
let's look at the Python implementation.
5:36
U maybe if you look at the code it'll
5:37
it'll make a lot more sense. Now so this
5:39
is the code uh right now from a intraday
5:42
perspective these are all the timelines
5:43
uh the time frames that it's you know
5:45
kind of supports which is 1 minute 3
5:46
minute 5 minute 10 minutes 15 30 minutes
5:48
and 1 hour which is 60 minutes the daily
5:50
1 week and 1 month. So this is all the
5:52
data that you can basically extract for
5:54
any stock any indices uh including you
5:56
know futures or option symbols even
5:58
stock options including right uh so
6:00
that's what this does um the way it is
6:02
implemented is we basically have created
6:03
a class where the the the key
6:05
functionality uh is basically already
6:06
coded in this is the class uh for people
6:09
who are not familiar with class class is
6:10
one of the most powerful data structures
6:11
within Python uh if you're familiar with
6:13
object-oriented programming um I'm sure
6:15
you've heard of class um if I want to
6:18
give you a very cr example on what this
6:20
really means assume that you you're
6:21
building plastic dolls or silicone
6:23
dolls, right? Um to to build a doll like
6:25
that, what you first do is you create a
6:27
mold first, right? The mold is basically
6:29
the class year and then once the mold is
6:31
ready, you can create as many dolls as
6:33
you want using that mold and and and
6:35
these dolls are basically the objects
6:37
that you're creating based out of the
6:38
the class that you're creating, right?
6:39
So the the class becomes a mold and your
6:41
dolls becomes the object. In this case,
6:43
we we're creating a a class called the
6:44
NC master data. And uh like I mentioned
6:47
what this is trying to do is that it's
6:49
it's basically trying to access these
6:51
URLs and then emulating as if a user is
6:54
sending the request to the NCS web
6:55
servers and then the server responds
6:57
with the actual data that we want which
6:59
is this data and then once the data
7:01
comes through uh we we're basically
7:02
extracting the data that we want convert
7:04
that into a pandas data frame and uh we
7:06
we download it and use it the way we
7:07
want it. Hope that kind of makes sense
7:09
and I hope I'm not confusing you. Uh but
7:11
but let's just move on to how this code
7:13
really works. So at the start uh I told
7:15
you about the nse master data being the
7:16
class and then you create an instant
7:18
called nse. This is this is where this
7:19
this becomes the mold and nse becomes
7:21
the doll that you're creating. So the
7:22
nse is the the object or the instance
7:24
that you're creating based on the mold
7:25
which is the class. Right? So in this
7:27
case nse comes to life here as an
7:28
object. Uh first thing is before we even
7:30
download anything we'll have to download
7:32
the entire nse symbol master which is
7:33
both including the nse as well as the
7:35
nfo. This step is very important. You'll
7:37
have to run it every time. uh this all
7:39
it does is basically downloads the
7:40
entire symbol master for NSE and NFO and
7:42
creates a cross reference file. Um and
7:44
then for downloading any data of course
7:46
you need the the start date and end
7:47
date. In this case I'm just taking just
7:49
for testing purposes uh 6 days. So uh
7:51
end date is today and my start date is 6
7:53
days prior to the the end date which is
7:55
basically the 6 day window is what I'm
7:56
considering. So feel free to change this
7:58
uh number to whatever uh time frame that
8:00
you want and accordingly the data will
8:01
be downloaded. Um before we download the
8:04
historic data, there is one additional u
8:06
utility that I've created here which can
8:07
be of a bit of use. Um not all of us
8:10
know the entire uh you know the symbol
8:11
list and its namesh by heart. So this is
8:14
basically a simple search facility. So
8:16
you can you can give even the entire
8:18
full uh name of this uh the symbol or
8:20
partially whatever you know and then you
8:22
can uh tell which exchange that you're
8:24
basically looking at and then when you
8:25
try to run this it'll give you the
8:26
entire list of the symbol code from nse.
8:29
In this case, I've given a nifty bank
8:31
and uh there is only one uh symbol there
8:33
within nse which is this nifty bank
8:34
which is again very clear very familiar
8:36
2609 is the bank nifty's uh symbol. So
8:39
it basically searches and gives you the
8:40
the details along with the script code.
8:42
Similarly you can do a search on nfo uh
8:44
the fo symbols as well. So in this case
8:46
I've given the partial bank nifty 25th
8:48
April but I want to extract all symbols
8:50
that kind of roughly match uh this wild
8:52
card. So when I run this, this gives me
8:54
the entire list of all the you know the
8:56
futures as well as the the option
8:58
symbols for bank nifty. So using this
9:00
you can basically zero in copy and then
9:01
run uh historic download on any specific
9:04
uh code that you
9:06
want. Now that you understand how the
9:08
the symbol search utility works uh let's
9:10
get to the the meat of the video which
9:11
is how to actually download the historic
9:13
data. Right? Uh so I've given sample
9:15
snippet for every type here and then I'
9:18
I've just printed the first two rows so
9:19
that you know you can you get to see the
9:21
actual output also. I've run this
9:22
already. Uh so let's quickly take a look
9:24
at you know what are the things that we
9:25
can do here. In this case I'm basically
9:26
downloading the EOD data for Nifty. Uh
9:29
here I've given interval as 1 day. Uh
9:31
that's why you getting the the EOD data
9:33
for Nifty which is here. Right. So you
9:34
typically get the open high low close
9:36
and volume for Nifty50 in this case. U
9:39
moving on. Um this is index data but
9:42
this is for a lower time frame time
9:43
frame which is 1 minute data. So I'm
9:44
basically having Bank Nifty data for 1
9:46
minute data uh which is again the
9:48
underlying data index for it. So this is
9:50
how it basically prints for every minute
9:52
it gives you the um the data for 1
9:55
minute and then similarly you can do it
9:57
for uh in this case I'm doing it for the
9:59
TCS which is again the the underlying
10:00
stock alone for 10 minutes so this is
10:02
where the TCS 10 minutes data is printed
10:05
um you can again extend this to um you
10:07
know index futures in this case nifty 50
10:10
futures is what I'm taking the April
10:11
futures so that also is downloaded I've
10:13
given a 1 hour data here so that is
10:14
downloaded um and then if you want the
10:17
stock futures again you can give the
10:18
stock name and then the the future
10:20
symbol that you want and in this case
10:22
Reliance data is printed. I've asked for
10:24
1 hour of data and 1 hour data is what
10:25
we get here.
10:28
Um similarly if you go for index options
10:31
uh given bank nifty index options uh you
10:33
know the 50,000 PE and that's downloaded
10:35
for 5 minutes and then uh if you also
10:38
want the stock options that is also
10:39
available in this case the TCS stock
10:40
options were 3,000 CE and that is also
10:42
available here. So I' I've given a
10:44
flavor of each of the functionalities or
10:46
the symbol types that you can download
10:48
so that you know you you can run it and
10:49
then uh you know try it out for
10:51
yourself. There are a couple of items
10:53
that I would like to remind you. Uh
10:54
number one is uh nse the data is not
10:58
very clean. I mean in the sense that you
10:59
know for example if uh sometimes what
11:01
happens is instead of 9:15 uh it gives
11:03
you dates timestamps as 94 59 seconds
11:06
right? Uh I I've done my best to clean
11:09
up all of that to to ensure that you
11:10
know you get a proper time stamp just
11:12
the way you usually get it on uh you
11:13
know trading view. So I've done most of
11:15
the cleanup uh you know using uh I've
11:17
tampered with the code a little bit and
11:18
then cleaned up all of that issues for
11:19
you. Um but I would still like to warn
11:22
you that for some of the instruments
11:23
especially you know stock options where
11:25
there's not lot of you know volume and
11:26
all that you sometimes might get some
11:28
weird time stamps here. Uh so take a a
11:30
closer look at it and then fix it you
11:32
know accordingly. Uh the second aspect
11:34
is uh for majority of the the stock
11:36
options you know when you run it it'll
11:37
give you an empty one. So if there is
11:39
not enough activity for a specific
11:40
expiry or a strike you know you'll get
11:42
an empty uh data frame. So please make
11:44
sure so in case there is no data coming
11:46
from some of your options instruments.
11:48
So don't blame me. That's how NYSE is
11:49
right because u I've seen some of the
11:52
comments saying that your code is
11:53
and it's not working and all
11:54
that. So uh so I try to do my best here
11:57
uh to give you a cleaner code but
11:59
typically the data is something that's
12:00
coming from NC and I can't do much about
12:02
it. So uh so don't blame me,
12:04
right? Hope you like this tool. Um a lot
12:07
of effort has gone into doing this. So I
12:09
mean I would really appreciate if you
12:10
can like and subscribe uh because almost
12:12
85% of the viewers don't subscribe to
12:14
the channel. So if you can really do
12:15
that, that'll encourage me to do more
12:16
and more uh of these type of tools that
12:18
will be useful to you. So do try this
12:20
out and let me know how this goes and if
12:22
there are any issues uh just put those
12:23
uh you know in the comment section. I'll
12:25
definitely get to it. Until then um
12:27
thanks for watching and I'll see you on
12:28
another video. Bye-bye. If you genuinely
12:31
found this video useful, please consider
12:32
subscribing and liking the video. And I
12:34
will see you soon in another video. And
12:35
until then, take care and happy trading.