Nifty Analysis

vagar11

Well-Known Member
#31
Coorelation in opening bar and daily closing

opening range time, number of times same direction , total days

Code:
NF.txt 1 709 1185 0.0
NF.txt 2 709 1185 0.0
NF.txt 3 735 1185 0.0
NF.txt 4 734 1185 0.0
NF.txt 5 726 1185 0.0
NF.txt 6 732 1185 0.0
NF.txt 7 733 1185 0.0
NF.txt 8 726 1185 0.0
NF.txt 9 738 1185 0.0
NF.txt 10 746 1185 0.0
NF.txt 11 755 1185 0.0
NF.txt 12 752 1185 0.0
NF.txt 13 745 1185 0.0
NF.txt 14 756 1185 0.0
So, if we simply buy if we get a 5 min green bar and vice versa.

Pts and opening bar time.

NF.txt 3885.15 1
NF.txt 3966.8 2
NF.txt 5824.95 3
NF.txt 5335.5 4
NF.txt 1927.1 5
NF.txt 2784.5 6
NF.txt 3600.45 7
NF.txt 3177.3 8
NF.txt 3621.9 9
NF.txt 4003.0 10
NF.txt 4106.65 11
NF.txt 4613.35 12
NF.txt 3440.2 13
NF.txt 4920.15 14
NF.txt 3425.9 15
 

vagar11

Well-Known Member
#32
Quandl Data tested 5 years 2012-2017. Inital 3 posts of this thread are wrong as data source was yahoo.

Nifty gap up by points, close-open sum, absolute sum (close-open), Number of trading days satisfied that criteria
on 50, NIFTY_50 284.70 4704.30 93
on 60, NIFTY_50 -457.50 3140.80 59
on 80, NIFTY_50 -327.85 1400.55 27
on 100, NIFTY_50 -223.25 704.75 14

Nifty gap down, open - close sum, absolute sum , number of trading days
Gap down 50, selling NIFTY_50 -535.25 3639.55 53
On 40, NIFTY_50 -44.80 4946.20 75
on 60, NIFTY_50 -508.40 2810.90 40
on 80 NIFTY_50 -606.10 1304.70 14

Range is good on gap down, long straddle might be worth it.

Funny thing is Nifty went from 4000 to 8000. But, the sum of close-open for last 5 years is negative. No wonder new comers lose money, as they mostly are on the long trade. Markets goes up because of gaps.

NIFTY_50 -5372.40 53960.60 1232
 

TradeOptions

Well-Known Member
#33
Yes, the thread should not be deleted. Important Stats.

Infact, I am also thinking of posting some stats, but not just related to Nifty, but related to ALL Futures and Options Contracts of NSE. If vagar bhai, do not mind, I will post all that here in this thread itself.

Best Regards
Dear Friends, I have been working on preparing a gift for the traders who like to do Relative Strength Studies, as a part of their trading game plan. I have used the method to NORMALIZE the data, from any specific start point date. This way, we can see the relative performance of any stocks and sectors, starting from that point of time. I have taken the example of 9 Nov 2016 – Trump Election Results Announcement Date, to clarify this point. The benefit of this technique is that, since all the data is normalized, therefor an apple to apple kind of direct comparison is possible, between any counters, by placing them on the same chart.

I have also done this analysis for fixed time duration of past One Week, Month and Quarter as well. So that we can see the relative performance over these different time frames.

This sheet will help in answering the question, which particular Stocks and Sectors are Strongest or Weakest, over the past One Week, Month and Quarter, as well as starting from any specific date, like some Major Gap Event etc. It will also help in spotting the Major Turning Points on the charts. Gives the overall view of different sectors in an instant, without any need of manual calculations. Also shows if most of the stocks within the sector are participating in the movement or not. It also highlights the outliers, as they stand apart in such RS Charts. Right now, I have prepared just the normal sectors, but going forward, we can even prepare Custom Weighted Sectors as well, which will give more weightage to big size stocks within a sector.

Here are the snaps for the RS Chart and Conditional Color Formatted Normalized Data –




I sincerely request all those traders who like to do Relative Strength Analysis, to please check out this excel file once. Your kind suggestions will help me to make this sheet better, so that it becomes useful for our trading community. A lot of improvements could be made in this sheet, going forward.

Please note that these excel sheets would be uploaded on google drive, from where they can be FREELY DOWNLOADED by ANYONE, so that they can upgrade and modify it according to their own personal trading requirements. No Strings Attached. Free gift for all traders :)

Please DOWNLOAD the excel file on you PC first and then go through it, as the online version does not show the formatting and charts properly.

All kind of ideas, positive / negative critics etc. is Most Welcome.

Thanks and best regards :)
Download Link - https://drive.google.com/file/d/0B7NTL0LY2MbIM0V0aWMwMzR1Qms/view
 
Last edited:

VJAY

Well-Known Member
#34
Dear Friends, I have been working on preparing a gift for the traders who like to do Relative Strength Studies, as a part of their trading game plan. I have used the method to NORMALIZE the data, from any specific start point date. This way, we can see the relative performance of any stocks and sectors, starting from that point of time. I have taken the example of 9 Nov 2016 – Trump Election Results Announcement Date, to clarify this point. The benefit of this technique is that, since all the data is normalized, therefor an apple to apple kind of direct comparison is possible, between any counters, by placing them on the same chart.

I have also done this analysis for fixed time duration of past One Week, Month and Quarter as well. So that we can see the relative performance over these different time frames.

This sheet will help in answering the question, which particular Stocks and Sectors are Strongest or Weakest, over the past One Week, Month and Quarter, as well as starting from any specific date, like some Major Gap Event etc. It will also help in spotting the Major Turning Points on the charts. Gives the overall view of different sectors in an instant, without any need of manual calculations. Also shows if most of the stocks within the sector are participating in the movement or not. It also highlights the outliers, as they stand apart in such RS Charts. Right now, I have prepared just the normal sectors, but going forward, we can even prepare Custom Weighted Sectors as well, which will give more weightage to big size stocks within a sector.

Here are the snaps for the RS Chart and Conditional Color Formatted Normalized Data –




I sincerely request all those traders who like to do Relative Strength Analysis, to please check out this excel file once. Your kind suggestions will help me to make this sheet better, so that it becomes useful for our trading community. A lot of improvements could be made in this sheet, going forward.

Please note that these excel sheets would be uploaded on google drive, from where they can be FREELY DOWNLOADED by ANYONE, so that they can upgrade and modify it according to their own personal trading requirements. No Strings Attached. Free gift for all traders :)

Please DOWNLOAD the excel file on you PC first and then go through it, as the online version does not show the formatting and charts properly.

All kind of ideas, positive / negative critics etc. is Most Welcome.

Thanks and best regards :)
Download Link - https://drive.google.com/file/d/0B7NTL0LY2MbIM0V0aWMwMzR1Qms/view
Dear tradeoption,
Thanks for your study and sharing hardworked excell file fre with us...I know how much trouble you take to make this file....hats off to you:thumb::clapping:
 

TracerBullet

Well-Known Member
#35
Quandl Data tested 5 years 2012-2017. Inital 3 posts of this thread are wrong as data source was yahoo.

Nifty gap up by points, close-open sum, absolute sum (close-open), Number of trading days satisfied that criteria
on 50, NIFTY_50 284.70 4704.30 93
on 60, NIFTY_50 -457.50 3140.80 59
on 80, NIFTY_50 -327.85 1400.55 27
on 100, NIFTY_50 -223.25 704.75 14

Nifty gap down, open - close sum, absolute sum , number of trading days
Gap down 50, selling NIFTY_50 -535.25 3639.55 53
On 40, NIFTY_50 -44.80 4946.20 75
on 60, NIFTY_50 -508.40 2810.90 40
on 80 NIFTY_50 -606.10 1304.70 14

Range is good on gap down, long straddle might be worth it.

Funny thing is Nifty went from 4000 to 8000. But, the sum of close-open for last 5 years is negative. No wonder new comers lose money, as they mostly are on the long trade. Markets goes up because of gaps.

NIFTY_50 -5372.40 53960.60 1232
good stuff, read this for same observation for US markets

Last 2 para
One last example will drive home the point: The stock of Apple, Inc. (Nasdaq: AAPL) more than doubled over these two years. With overnight gaps backed out, the stock price fluctuated between the initial price and a 25% loss. In all of these examples, a significant rally and price appreciation could have been entirely attributed to the overnight gaps. Take them out, and you have… nothing.

So what does this mean? In markets, we are paid for assuming the correct risks at the correct time, and managing them appropriately. Many times, understanding what those correct risks are requires some counter-intuitive thinking, and things that might seem to be prudent risk management (e.g., very tight stops) often amount to virtually certain losses over a large sample size. Daytraders often see overnight gaps as insurmountable risks to be avoided, but they do not understand that the majority of the opportunities in equities come in those overnight gaps. Risk is the unavoidable companion of opportunity. There can be no enduring success in the market until we fully embrace this truth.
 

TradeOptions

Well-Known Member
#37
Dear tradeoption,
Thanks for your study and sharing hardworked excell file fre with us...I know how much trouble you take to make this file....hats off to you:thumb::clapping:
Thanks for your kind words VJAY bhai. If you prefer any kind of modifications in it, please let me know. I would try my best to implement them.

Best Regards
 

TracerBullet

Well-Known Member
#38
Dear Friends, I have been working on preparing a gift for the traders who like to do Relative Strength Studies, as a part of their trading game plan. I have used the method to NORMALIZE the data, from any specific start point date. This way, we can see the relative performance of any stocks and sectors, starting from that point of time. I have taken the example of 9 Nov 2016 – Trump Election Results Announcement Date, to clarify this point. The benefit of this technique is that, since all the data is normalized, therefor an apple to apple kind of direct comparison is possible, between any counters, by placing them on the same chart.

I have also done this analysis for fixed time duration of past One Week, Month and Quarter as well. So that we can see the relative performance over these different time frames.

This sheet will help in answering the question, which particular Stocks and Sectors are Strongest or Weakest, over the past One Week, Month and Quarter, as well as starting from any specific date, like some Major Gap Event etc. It will also help in spotting the Major Turning Points on the charts. Gives the overall view of different sectors in an instant, without any need of manual calculations. Also shows if most of the stocks within the sector are participating in the movement or not. It also highlights the outliers, as they stand apart in such RS Charts. Right now, I have prepared just the normal sectors, but going forward, we can even prepare Custom Weighted Sectors as well, which will give more weightage to big size stocks within a sector.

Here are the snaps for the RS Chart and Conditional Color Formatted Normalized Data –




I sincerely request all those traders who like to do Relative Strength Analysis, to please check out this excel file once. Your kind suggestions will help me to make this sheet better, so that it becomes useful for our trading community. A lot of improvements could be made in this sheet, going forward.

Please note that these excel sheets would be uploaded on google drive, from where they can be FREELY DOWNLOADED by ANYONE, so that they can upgrade and modify it according to their own personal trading requirements. No Strings Attached. Free gift for all traders :)

Please DOWNLOAD the excel file on you PC first and then go through it, as the online version does not show the formatting and charts properly.

All kind of ideas, positive / negative critics etc. is Most Welcome.

Thanks and best regards :)
Download Link - https://drive.google.com/file/d/0B7NTL0LY2MbIM0V0aWMwMzR1Qms/view
If you want some resources for ideas on how to use relative strength(or some different ways of looking at it) , there are some free webinars here. Select industry sponsored > Adam Grimes. And some articles here
There are many many screens in above links for Relative Strength and Sectors comparison.

Anyway, what i am starting with is below
1) Calculation, We can use a reference starting point in Index as you do. So pick a turning point in Index and compare normalized returns from there. So for ex, say Nifty is is UT. If Nifty is making a PB, we can see which stocks are holding better. They may lead next rally
2) Another more generic way to do it is to use weighted average of multiple returns. So you can try to combine Daily/Weekly/Monthly returns and get a front-weighted or back-weighted score depening on how you want to use it.
Ex use, Buy strongest candidates but on weakness. Always using the strongest /weakest stock might not work well due to mean reversion. So wait for temporary weakness. Then after entry monitor whether it resumes leadership.
3) Along with ranks, we can also look at changes in ranks. So say look at what moved in rankings in last week.
4) You can also look at sector spreads, that show sector relative performance vs index in chart
 

TradeOptions

Well-Known Member
#39
If you want some resources for ideas on how to use relative strength(or some different ways of looking at it) , there are some free webinars here. Select industry sponsored > Adam Grimes. And some articles here
There are many many screens in above links for Relative Strength and Sectors comparison.

Anyway, what i am starting with is below
1) Calculation, We can use a reference starting point in Index as you do. So pick a turning point in Index and compare normalized returns from there. So for ex, say Nifty is is UT. If Nifty is making a PB, we can see which stocks are holding better. They may lead next rally
2) Another more generic way to do it is to use weighted average of multiple returns. So you can try to combine Daily/Weekly/Monthly returns and get a front-weighted or back-weighted score depening on how you want to use it.
Ex use, Buy strongest candidates but on weakness. Always using the strongest /weakest stock might not work well due to mean reversion. So wait for temporary weakness. Then after entry monitor whether it resumes leadership.
3) Along with ranks, we can also look at changes in ranks. So say look at what moved in rankings in last week.
4) You can also look at sector spreads, that show sector relative performance vs index in chart
Thank you so much brother for sharing those links and for those ideas. I think, all these 4 points can be implemented in these excel sheets going ahead. Tonight I am trying to complete the MonthlySheet on a priority basis and I am trying to create all the individual sector charts first. Then one by one, I will try my best, to implement the ideas that you mentioned. All of them look good to me. :thumb:

With my Best Regards
 

vagar11

Well-Known Member
#40
Buying on 2 day high break and exit/SL at previous day low.

Code:
Data tested from 1/1/2012 - 30/1/2016
% Return, Profit/Loss Ratio %

('ACC', -16.700785671921945, 35)
('ADANIPORTS', -5.563520011682657, 40)
('AMBUJACEM', -35.072593879027245, 33)
('APOLLOHOSP', 21.650374389951292, 37)
('ASHOKLEY', 104.10004758608025, 47)
('ASIANPAINT', 4.12642317740334, 48)
('AUROPHARMA', 116.21225162163239, 46)
('AXISBANK', 79.94661755224112, 40)
('BAJAJFINSV', -40.59567204651194, 36)
('BAJAJHLDNG', -131.02659335197114, 23)
('BANKBARODA', -41.53500859130621, 37)
('BANKINDIA', -25.531035319499306, 30)
('BHARATFORG', 68.6784910558742, 51)
('BHEL', 94.02795994094802, 43)
('BPCL', 11.557937474458825, 40)
('BHARTIARTL', -58.75922959157792, 32)
('INFRATEL', -70.79169766552135, 27)
('BOSCHLTD', 24.266584185247346, 33)
('BRITANNIA', 26.408220082032297, 38)
('CAIRN', -88.53735632581086, 31)
('CANBK', 21.32997038089434, 35)
('CIPLA', 21.395932472862256, 43)
('COALINDIA', -0.6447121562919849, 39)
('COLPAL', 29.942158100874167, 42)
('CONCOR', -117.69400868792857, 29)
('CUMMINSIND', -69.91850521947978, 32)
('DABUR', -58.31942115857245, 34)
('DIVISLAB', -35.516969937653, 37)
('DRREDDY', 78.12583638899582, 49)
('EICHERMOT', 175.36539577963046, 49)
('EXIDEIND', -42.95110561251986, 38)
('FEDERALBNK', -161.31240836232828, 30)
('GAIL', -34.65265537296137, 26)
('GSKCONS', -37.057057417876464, 30)
('GLAXO', -7.866260131901625, 29)
('GLENMARK', 46.583293086695974, 42)
('GODREJCP', -19.126452300661953, 38)
('GRASIM', -7.707633235894425, 31)
('HCLTECH', 22.221115901187847, 39)
('HDFCBANK', -63.001842436138, 33)
('HEROMOTOCO', 28.270717829826847, 38)
('HINDALCO', 5.687148250170292, 37)
('HINDPETRO', 73.42333438866224, 38)
('HINDUNILVR', 64.7178921856651, 40)
('HDFC', 1.5562294141219384, 40)
('ITC', -2.0848965789564153, 42)
('ICICIBANK', -3.6802625153416826, 43)
('IDEA', 1.280579423883566, 36)
('IBULHSGFIN', 20.657004442885587, 38)
('IOC', 13.390698176734787, 40)
('INDUSINDBK', -10.710291825113504, 40)
('INFY', 21.927043607850525, 41)
('JSWSTEEL', 21.390298860411264, 41)
('KOTAKBANK', -4.884340276610036, 36)
('LICHSGFIN', 49.80330569498796, 43)
('LT', 58.43200636151961, 41)
('LUPIN', 46.07772550171193, 44)
('MRF', 87.5212259016133, 43)
('MARICO', -119.23988877707899, 32)
('MARUTI', 74.68107025340669, 45)
('MOTHERSUMI', -33.911351045367994, 40)
('NMDC', -30.4852839499125, 38)
('NTPC', 38.03712057288538, 40)
('ONGC', -8.488422085493806, 37)
('OIL', -6.810641135772302, 36)
('OFSS', -33.61451478062895, 33)
('PETRONET', -24.63577880215177, 38)
('PFC', 30.04620751450365, 41)
('POWERGRID', -47.235115075403236, 34)
('PNB', 51.18648897612909, 42)
('RELCAPITAL', 99.21307158220982, 44)
('RCOM', 113.40404838873518, 40)
('RELIANCE', 46.82317459352932, 44)
('RELINFRA', 105.54275914054962, 41)
('RECLTD', 43.99978630852385, 43)
('SRTRANSFIN', 41.66888085994388, 39)
('SIEMENS', 98.11805246684305, 40)
('SBIN', 64.6824880784779, 41)
('SAIL', 42.015351085888085, 39)
('SUNPHARMA', 51.77209837538577, 44)
('SUNDARMFIN', -45.93861861978214, 31)
('TATACHEM', -46.27100989427679, 34)
('TCS', 66.69669149424533, 51)
('TATAGLOBAL', -16.736344544963796, 40)
('TATAMOTORS', 17.987477035584746, 44)
('TATAPOWER', -43.77688331417132, 33)
('TATASTEEL', 105.15541516216481, 42)
('TECHM', 48.69453337683248, 36)
('TITAN', -53.587463742338265, 32)
('UPL', 33.600917974069844, 42)
('ULTRACEMCO', -25.193068068307078, 35)
('UBL', -6.362486295841878, 33)
('VEDL', -20.747281483584178, 14)
('WIPRO', -30.54465248102755, 38)
('YESBANK', 149.6150152807351, 45)

Weekly data performance.

('ACC', 11.850603712829647, 33)
('ADANIPORTS', -75.0275926094681, 30)
('AMBUJACEM', -23.69431355496324, 26)
('APOLLOHOSP', -13.076447800876142, 39)
('ASHOKLEY', 93.76015570372218, 37)
('ASIANPAINT', 60.59299202891497, 43)
('AUROPHARMA', 31.98599763610305, 40)
('AXISBANK', -26.338911672900995, 43)
('BAJAJFINSV', 91.75515939624414, 35)
('BAJAJHLDNG', -2.5958768682895053, 35)
('BANKBARODA', 23.85634985610536, 35)
('BANKINDIA', 16.08000621566663, 32)
('BHARATFORG', 67.86997532516966, 41)
('BHEL', 7.084865743591372, 34)
('BPCL', -43.918051988499236, 42)
('BHARTIARTL', 16.04594963846455, 35)
('INFRATEL', -47.83341392339433, 23)
('BOSCHLTD', 79.69391983995637, 51)
('BRITANNIA', 111.83883497134075, 59)
('CAIRN', -71.11293325551276, 26)
('CANBK', 19.489013856601304, 36)
('CIPLA', 6.25764011486452, 34)
('COALINDIA', -31.29364929727828, 34)
('COLPAL', -7.377683602097356, 47)
('CONCOR', -20.804768460928447, 41)
('CUMMINSIND', 63.10216966215804, 37)
('DABUR', -29.886641384216865, 35)
('DIVISLAB', 35.398224737514276, 38)
('DRREDDY', 36.41878884003568, 45)
('EICHERMOT', 88.94352286342867, 40)
('EXIDEIND', -17.549484019802943, 30)
('FEDERALBNK', -82.59769167081728, 40)
('GAIL', -19.891947626774737, 36)
('GSKCONS', 8.440417314815145, 40)
('GLAXO', 4.922484492100125, 45)
('GLENMARK', 17.916669793134094, 39)
('GODREJCP', -36.11708391713326, 21)
('GRASIM', 46.87734449675967, 44)
('HCLTECH', 34.874958247746825, 32)
('HDFCBANK', 58.72637693209208, 51)
('HEROMOTOCO', 0.8453831643404879, 40)
('HINDALCO', 7.1218068136020145, 40)
('HINDPETRO', 70.73629275241017, 41)
('HINDUNILVR', 23.673116251088217, 36)
('HDFC', 2.7495833909020337, 38)
('ITC', 13.705638997042762, 34)
('ICICIBANK', -50.481363119475574, 35)
('IDEA', 3.44474711692535, 37)
('IBULHSGFIN', 61.009006514200586, 48)
('IOC', -20.018586110297964, 22)
('INDUSINDBK', 85.53792946160321, 52)
('INFY', -30.186939542233624, 46)
('JSWSTEEL', 38.363208270375985, 30)
('KOTAKBANK', 8.172502448429002, 44)
('LICHSGFIN', 72.02944492698832, 50)
('LT', 33.655300506093, 40)
('LUPIN', 71.55783024990389, 46)
('MRF', 111.98809591513731, 34)
('MARICO', -72.40771820164555, 31)
('MARUTI', 64.87248371060697, 42)
('MOTHERSUMI', -10.13248833180685, 35)
('NMDC', -36.34817572392671, 21)
('NTPC', -24.603540128606912, 30)
('ONGC', -11.396343142477596, 20)
('OIL', -61.45824778788895, 23)
('OFSS', -4.393185377250979, 37)
('PETRONET', -3.846563083083314, 27)
('PFC', 49.815636308871234, 37)
('POWERGRID', 8.575582007133246, 37)
('PNB', 62.876308408595854, 42)
('RELCAPITAL', 102.4601068686286, 42)
('RCOM', 24.656564154304114, 34)
 ('RELIANCE', 8.732935025755362, 35)
('RELINFRA', 119.39855125972714, 56)
('RECLTD', 38.96124839765626, 32)
('SRTRANSFIN', 20.01426901830627, 39)
('SIEMENS', 11.505669981798082, 44)
('SBIN', 79.71254321272191, 44)
('SAIL', 14.523541377388577, 45)
('SUNPHARMA', 54.099426840114646, 54)
('SUNDARMFIN', 21.74587306965113, 29)
('TATACHEM', 10.783542454173928, 35)
('TCS', 39.56922532234461, 47)
('TATAGLOBAL', 0.37208488057939215, 34)
('TATAMOTORS', 20.8488436109941, 30)
('TATAPOWER', 7.053592384282457, 32)
('TATASTEEL', -12.20679601176514, 27)
('TECHM', 41.88087403977825, 38)
('TITAN', 8.980071527866157, 35)
('UPL', 44.56049891233866, 36)
('ULTRACEMCO', 59.83691180867265, 47)
('UBL', 3.081822207734658, 32)
('VEDL', 4.711318241726679, 33)
('WIPRO', 0.22099874729609437, 34)
('YESBANK', -1.0573521515975441, 42)


Monthly data performance

('ACC', -28.34762534851428, 10)
('ADANIPORTS', 58.03988060817569, 75)
('AMBUJACEM', 10.958089576983951, 28)
('APOLLOHOSP', 9.82172812458397, 36)
('ASHOKLEY', 10.264237788120854, 33)
('ASIANPAINT', 28.088576233686823, 40)
('AUROPHARMA', 191.95014827750987, 50)
('AXISBANK', -53.97400972134232, 22)
('BAJAJFINSV', 68.13717760587998, 55)
('BAJAJHLDNG', 86.83059912205633, 50)
('BANKBARODA', -87.5642576565183, 16)
('BANKINDIA', 10.915589954062252, 33)
('BHARATFORG', 80.0887670079451, 44)
('BHEL', -68.65387845572428, 18)
('BPCL', -5.445368008944523, 54)
('BHARTIARTL', -31.36751001241727, 20)
('INFRATEL', 130.4365515308094, 50)
('BOSCHLTD', 86.23291825035949, 33)
('BRITANNIA', 196.14565853993597, 57)
('CAIRN', -56.021232419210754, 0)
('CANBK', 3.9320477213931166, 22)
('CIPLA', 3.004626730087992, 16)
('COALINDIA', -49.67287726547711, 7)
('COLPAL', 15.620638951398211, 54)
('CONCOR', 43.139772130599695, 57)
('CUMMINSIND', 21.09262060055219, 50)
('DABUR', 33.237042547566105, 45)
('DIVISLAB', 19.140297587492746, 37)
('DRREDDY', -12.170884046210759, 45)
('EICHERMOT', 317.2700195875267, 100)
('EXIDEIND', -31.23210500776704, 18)
('FEDERALBNK', -53.07436374988083, 44)
('GAIL', -21.9928989372655, 40)
('GSKCONS', 25.36928017343547, 37)
('GLAXO', -2.7106515152794577, 36)
('GLENMARK', 35.33024294577332, 50)
('GODREJCP', 52.86947181998761, 66)
('GRASIM', -0.7783572112199593, 33)
('HCLTECH', 30.284798498273588, 75)
('HDFCBANK', 35.823399535628994, 37)
('HEROMOTOCO', 10.334985253915036, 44)
('HINDALCO', -42.07094894428381, 22)
('HINDPETRO', 81.58711912820216, 50)
('HINDUNILVR', 36.025798852482026, 37)
('HDFC', 6.010356245935284, 45)
('ITC', -33.490289219557845, 35)
('ICICIBANK', -105.62558247543186, 10)
('IDEA', 36.133407422512626, 50)
('IBULHSGFIN', 107.14565316766827, 60)
('IOC', 24.821269606866093, 33)
('INDUSINDBK', 43.368778227335326, 33)
('INFY', -33.19741115745245, 25)
('JSWSTEEL', 13.400465419787746, 33)
('KOTAKBANK', -17.462882911796267, 40)
('LICHSGFIN', 53.40646498382996, 44)
('LT', 21.25750919828326, 33)
('LUPIN', 37.52245304474261, 30)
('MRF', 118.54875382001124, 62)
('MARICO', 27.170345612895197, 25)
('MARUTI', 62.08760375986075, 44)
('MOTHERSUMI', 15.338243532995506, 28)
('NMDC', -17.73666945834306, 33)
('NTPC', -24.652595978536024, 14)
('ONGC', 0.9627433357123492, 22)
('OIL', -73.72593431367096, 27)
('OFSS', 42.099531348039505, 57)
('PETRONET', 26.376243620264763, 33)
('PFC', -47.88330965883611, 30)
('POWERGRID', 19.991946403449738, 50)
('PNB', -103.70026875973755, 28)
('RELCAPITAL', 35.62134433289063, 22)
('RCOM', -31.210940106337265, 22)
('RELIANCE', 1.3943199566595046, 42)
('RELINFRA', 38.855113534365245, 33)
('RECLTD', 27.876286867928002, 50)
('SRTRANSFIN', -13.244585251295007, 45)
('SIEMENS', -8.538895433044772, 22)
('SBIN', -119.32599051401685, 18)
('SAIL', 10.854626134670855, 25)
('SUNPHARMA', 36.01222467803903, 33)
('SUNDARMFIN', 28.113371000631343, 37)
('TATACHEM', 12.748339252170881, 33)
('TCS', 4.034590799599942, 33)
('TATAGLOBAL', -32.71772871765573, 12)
('TATAMOTORS', 2.88163941538712, 40)
('TATAPOWER', -65.07540164181495, 0)
('TATASTEEL', 13.49236983674745, 28)
('TECHM', 87.45386192898634, 44)
('TITAN', -11.113841309256463, 50)
('UPL', 49.81178888953271, 50)
('ULTRACEMCO', 39.70442719708676, 44)
('UBL', 37.05885522914488, 33)
('VEDL', -12.71428571428571, 0)
('WIPRO', 7.5623218453924235, 30)
('YESBANK', 61.3013538682986, 30)

Yearly data performance
('ACC', -16.700785671921945, 35)
('ADANIPORTS', -5.563520011682657, 40)
('AMBUJACEM', -35.072593879027245, 33)
('APOLLOHOSP', 21.650374389951292, 37)
('ASHOKLEY', 104.10004758608025, 47)
('ASIANPAINT', 4.12642317740334, 48)
('AUROPHARMA', 116.21225162163239, 46)
('AXISBANK', 79.94661755224112, 40)
('BAJAJFINSV', -40.59567204651194, 36)
('BAJAJHLDNG', -131.02659335197114, 23)
('BANKBARODA', -41.53500859130621, 37)
('BANKINDIA', -25.531035319499306, 30)
('BHARATFORG', 68.6784910558742, 51)
('BHEL', 94.02795994094802, 43)
('BPCL', 11.557937474458825, 40)
('BHARTIARTL', -58.75922959157792, 32)
('INFRATEL', -70.79169766552135, 27)
('BOSCHLTD', 24.266584185247346, 33)
('BRITANNIA', 26.408220082032297, 38)
('CAIRN', -88.53735632581086, 31)
('CANBK', 21.32997038089434, 35)
('CIPLA', 21.395932472862256, 43)
('COALINDIA', -0.6447121562919849, 39)
('COLPAL', 29.942158100874167, 42)
('CONCOR', -117.69400868792857, 29)
('CUMMINSIND', -69.91850521947978, 32)
('DABUR', -58.31942115857245, 34)
('DIVISLAB', -35.516969937653, 37)
('DRREDDY', 78.12583638899582, 49)
('EICHERMOT', 175.36539577963046, 49)
('EXIDEIND', -42.95110561251986, 38)
('FEDERALBNK', -161.31240836232828, 30)
('GAIL', -34.65265537296137, 26)
('GSKCONS', -37.057057417876464, 30)
('GLAXO', -7.866260131901625, 29)
('GLENMARK', 46.583293086695974, 42)
('GODREJCP', -19.126452300661953, 38)
('GRASIM', -7.707633235894425, 31)
('HCLTECH', 22.221115901187847, 39)
('HDFCBANK', -63.001842436138, 33)
('HEROMOTOCO', 28.270717829826847, 38)
('HINDALCO', 5.687148250170292, 37)
('HINDPETRO', 73.42333438866224, 38)
('HINDUNILVR', 64.7178921856651, 40)
('HDFC', 1.5562294141219384, 40)
('ITC', -2.0848965789564153, 42)
('ICICIBANK', -3.6802625153416826, 43)
('IDEA', 1.280579423883566, 36)
('IBULHSGFIN', 20.657004442885587, 38)
('IOC', 13.390698176734787, 40)
('INDUSINDBK', -10.710291825113504, 40)
('INFY', 21.927043607850525, 41)
('JSWSTEEL', 21.390298860411264, 41)
('KOTAKBANK', -4.884340276610036, 36)
('LICHSGFIN', 49.80330569498796, 43)
('LT', 58.43200636151961, 41)
('LUPIN', 46.07772550171193, 44)
('MRF', 87.5212259016133, 43)
('MARICO', -119.23988877707899, 32)
('MARUTI', 74.68107025340669, 45)
('MOTHERSUMI', -33.911351045367994, 40)
('NMDC', -30.4852839499125, 38)
('NTPC', 38.03712057288538, 40)
('ONGC', -8.488422085493806, 37)
('OIL', -6.810641135772302, 36)
('OFSS', -33.61451478062895, 33)
('PETRONET', -24.63577880215177, 38)
('PFC', 30.04620751450365, 41)
('POWERGRID', -47.235115075403236, 34)
('PNB', 51.18648897612909, 42)
('RELCAPITAL', 99.21307158220982, 44)
('RCOM', 113.40404838873518, 40)
('RELIANCE', 46.82317459352932, 44)
('RELINFRA', 105.54275914054962, 41)
('RECLTD', 43.99978630852385, 43)
('SRTRANSFIN', 41.66888085994388, 39)
('SIEMENS', 98.11805246684305, 40)
('SBIN', 64.6824880784779, 41)
('SAIL', 42.015351085888085, 39)
('SUNPHARMA', 51.77209837538577, 44)
('SUNDARMFIN', -45.93861861978214, 31)
('TATACHEM', -46.27100989427679, 34)
('TCS', 66.69669149424533, 51)
('TATAGLOBAL', -16.736344544963796, 40)
('TATAMOTORS', 17.987477035584746, 44)
('TATAPOWER', -43.77688331417132, 33)
('TATASTEEL', 105.15541516216481, 42)
('TECHM', 48.69453337683248, 36)
('TITAN', -53.587463742338265, 32)
('UPL', 33.600917974069844, 42)
('ULTRACEMCO', -25.193068068307078, 35)
('UBL', -6.362486295841878, 33)
('VEDL', -20.747281483584178, 14)
('WIPRO', -30.54465248102755, 38)
('YESBANK', 149.6150152807351, 45)
 
Last edited:

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