A Survey on Nifty 50 Stocks Price Prediction by IRJET Journal

A Survey on Nifty 50 Stocks Price Prediction

Prof. Anoop Kushwaha1 , Neha Kandhare2 , Dnyaneshwari Nikat3 , Shreedhar Parge4 , Snehal Jagdale5

1Professor, Dept. of Computer Engineering, Alard College of Engineering and Management Pune,Maharashtra

2Student, Dept. of Computer Engineering, Alard College of Engineering and Management Pune,Maharashtra

3Student, Dept. of Computer Engineering, Alard College of Engineering and Management Pune,Maharashtra

4Student, Dept. of Computer Engineering, Alard College of Engineering and Management Pune,Maharashtra

5Student, Dept. of Computer Engineering, Alard College of Engineering and Management Pune,Maharashtra

Abstract - Stock price prediction is a critical area of research and application in financial markets. This project employs Facebook's Prophet, a robust time series forecasting tool, to predict future stocks prices. The aim is to build a predictive model capable of analyzing historical stocks data and forecasting future trends. The project begins with the collectionofhistoricalstocks price data fromreliable sources. After preprocessing and feature engineering, the data is structured into the required format for analysis. The Prophet model is then trained using this prepared dataset, learning patterns and trends in the stock’s prices. The study's implementation is executed through an interactive web applicationdevelopedusingStreamlit.Thisapplicationallows users to input stocks preferences, select date ranges, and receivereal-time stocks price predictions basedonthetrained Prophet model. Users can visualize historical and predicted stocks prices, enabling them to make informed decisions. The project contributes to the field of stocks market prediction by demonstrating the application of Prophet in a user-friendly and accessible manner for traders and investors seeking valuable insights into future stocks price trends.

Key Words: Nifty 50 Stocks Price Prediction, Data Science, Machine Learning, Streamlit, Stocks Market, Yahoo Finance, Prophet.

1. INTRODUCTION

Inlatertimes,stocksthat publishassumptionsaregetting more thought, potentially because of the truth that if the natureofthefeatureisreallyexpected,theexaminersmight be predominantly directed. The advantages gained by contributingandtradinginsidethestocksfeaturemassively dependontheconsistency.Ontheoffchancethatthere'sa structure that can dependably expect the heading of the enthusiastic stocks to be revealed, it will empower the clientsofthesystemtoframeinstructeddecisions.

Moreover,theexpectedexamplesofthepromotewillhelp the regulators of the grandstand go to healing lengths. Blueprint of the money related market's stream and the meaningofaccuratestocks'costassumptionsinadventure decision-making.[2]

TheincreasereportonstockscostestimateusingFacebook Prophet fills in as an exhaustive direct indicating the utilization, examination, and consequences of using the Prophetfordecidingstockscosts.Thereportcoversvarious parts,techniques,anddisclosuresregardingtheutilizationof Prophet in predicting stocks promote patterns. Show the endeavor, explaining the explanation, targets, and importance of using Facebook Prophet for stocks cost prediction.Showtotheendeavor,targets,andsignificanceof usingFacebookProphetforstockscostexpectation.[3]

Asper[7],clarificationofthemethodologyembracedinside theexpand,countingdataassortment,preprocessing,exhibit decision,andassessment.Incasethere'sastructurethatcan dependablyexpecttheheadingofthefierystocksexhibitto engagetheclientsofthesystemtomaketaughtdecisions, morethantheexpectedexamplesoftheexhibitwillhelpthe regulators of the promote going to medicinal lengths. Present the test of anticipating stocks costs in monetary businesssectors,underscoringitsintricacyandimportance inchoices.Lookatthewhimofstockgrandstanddesigns,the closenessofnoiseinmonetarydata,andthelimitationsof traditionaldecidingstrategies.

Asindicated by[8],address the expectation fora reliable, exact,andflexibleperceptiveshowequippedwithcatching vivacious promote conduct. Discuss the deficiencies of routinedecidingmodelsinwatchingoutfortheperplexing ideaofstocksexhibitinformation.Addressissuesconnected withmanagingconsistency,events,andunexpectedchanges in patterns. Direct backslide might be a way to deal with displaying the connection between a scalar response (or subordinatevariable)andatleastoneillustrativefactor(or free factor). An instance of one useful variable is called fundamentalstraightrelapse.

1.1IntroductiontoMachineLearning

AI (ML) is a field of logical request zeroed in on the improvementofcalculationsandfactualmodelsusedbyPC frameworks to execute explicit errands without express programmingdirections.TheessentialobjectiveofAIisto empower frameworks to learn and further develop their

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 11 Issue: 03 | Mar 2024 www.irjet.net p-ISSN: 2395-0072

presentationinviewofinformationinputs.Theaccentison improving machine independence and effectiveness in errands by utilizing the inborn examples and experiences inside broad information archives. Various investigations have dug into enabling machines to learn independently, without the requirement for unequivocal programming. Mathematicians and software engineers utilize different waystodealwiththistest,especiallywhilemanaginghuge datasets.[1][14][7]

2. METHODOLGY

DataCollection:UtilizetheYahooFinanceAPItocollect historicalstockspricedataforthedesiredstocks(s).Extract relevantfeaturessuchasdate,openingprice,closingprice, volume,etc.

Model Training: Use Facebook Prophet, an open-source forecastingtool,totrainapredictivemodelonthehistorical stockspricedata.Prophetiscapableofhandlingseasonality, trends,andholidaysinthedata,makingitsuitableforstocks priceprediction.

Prediction:Generatefuturepredictionsofstockspricesusing thetrainedProphetmodel.Forecastthestockspricesfora specifiedperiodintothefuture.

Visualization: Use Streamlit, a Python library for building interactive web applications, to create a user-friendly interface. Visualize the historical stocks price data, model predictions, and performance metrics using interactive charts,graphs,andtables.Allowuserstocustomizeinputs suchasthestockssymbol,predictionhorizon,etc.

Deployment: Deploy the Streamlit application on a web server or cloud platform for accessibility. Ensure proper scalability, security, and performance of the deployed application.

3. LITERATURE SURVEY

1. Stock Market Prediction Using Machine Learning. Publishedin:2018FirstInternationalConferenceonSecure Cyber Computing and Communication (ICSCCC) Ishita Parmar1,NavanshuAgarwal2,SheirshSaxena3,RidamArora4 , Shikhin Gupta5 , Himanshu Dhiman6 , Lokesh Chouhan7.: In Securities exchange Forecast, the point is to foresee the futureworthofthemonetarysuppliesofanorganization.The newpatterninfinancialexchangeexpectationadvancements istheutilizationofAIwhichmakesforecastsinlightofthe upsidesofcurrentsecuritiesexchangerecordsviapreparing on their past qualities. AI itself utilizes various models to makeexpectationmorestraightforwardandreal.Thepaper centersaroundtheutilizationofRelapseandLSTMbasedAI toforeseestockqualities.Factorsconsideredareopen,close, low,highandvolume.[14]

2. 2020TheAuthors.PublishedbyElsevierB.V. InternationalWorkshoponStatisticalMethodsandArtificial Intelligence (IWSMAI 2020) April 6-9, 2020, Warsaw, Poland. Stock Market Prediction Using LSTM Recurrent Neural Network.: It has never been not difficult to put resources into a bunch of resources, the strangely of monetary market doesn't permit basic models to foresee future resource values with higher precision. AI, which comprise of causing PCs to perform errands that typically requiring human knowledge is as of now the prevailing pattern in logical examination. This article expects to fabricate a model utilizing Repetitive Brain Organizations (RNN) and particularly Lengthy Transient Memory model (LSTM) to foresee future securities exchange values. The fundamentalgoalofthispaperistofindinwhichaccuracyan AIcalculationcanforeseeandhowmuchtheagescanwork onourmodel.[4]

3. Anusha Garlapati, Doredla Radha Krishna, Kavya Garlapati, Nandigama mani srikara yaswanth, Udayagiri Rahul, Gayathri Narayanan, “Stocks Price Prediction Using Facebook Prophet and Arima Models”, April 2021.: In this paper, they discussed stocks market trends and analyzed different patterns of data, and done analysis for future forecasting of stocks prices. For this commentary and foresightmodelslikeARIMAandFACEBOOKPROPHETare used.Toconstructthesemodels,dataisdeducedonStocks pricepredictionsfrom2012-2020.Thisanalysishasfurther prospectiveforinvestigationinthefuture.MAPEisusedasa parameter that shows that the models are adequate in forecastingretailvaluation.Thisempiricalinquirydesignated that the ARIMA (2,1,2) model is best for predicting Stocks prices.FACEBOOKPROPHEThereisutilizedtoexhibitfuture forecastingofstocksprices.[8]

4 SumedhKaninde,ManishMahajan,AdityaJanghaleand Bharti Joshi, “Stocks Price Prediction using Facebook Prophet,” ITM Web of Conferences 44, 03060 (2022). : Frameworkisintendedforexpectationsrepresentingthings tocomecostsofstocksfornext5yearsutilizingFacebook Prophet that can be utilized for better speculations. This makes it simple to figure out which stocks to decide for speculation in view of the expectations giving the most noteworthy level of profits in a given time of time.The forecast precision can be expanded by utilizing a few different highlights of Facebook Prophet and furthermore make the application intelligent and simple to utilize. In future, the stocks market forecast framework can be additionally expanded by using a bigger dataset than one being utilized as of now. This will assist with rising the precisionofexpectationmodels.[9]

5 YashSaxenaMs.IndervatiMs.GarimaRathiStudentat DeptofComputerAsst.Prof.atDeptofComputerAsst.Prof. at Dept of Computer Science and Engineering Science and Engineering Science and Engineering Galgotias University, Galgotias University, Galgotias University, Greater Noida,

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Volume: 11 Issue: 03 | Mar 2024 www.irjet.net p-ISSN: 2395-0072

India.GreaterNoida,India.GreaterNoida,India,“StocksPrice Prediction Using Facebook Prophet”, June 2022.: In this paper,theyhaveexaminedstocksmarketdriftsandbroke down different information designs, and performed examinationtoanticipatestockscostsfromhereonout.For thisexaminationandpremonitionFACEBOOKPROPHETis utilized. The FACEBOOK PROPHET here is used to show future stocks costs. To build these models, information is taken from stocks cost gauges from 2012-2020. This examinationhasthepotentialforadditionalexamination.The FACEBOOKPROPHEThereisutilizedtoshowfuturestocks prices.[10]

6 HumNathBhandari1,BinodRimal2,NawaRajPokhrel3 , RamchandraRimal4,KeshabR.Dahal5,RajendraK.C.Khatri6 Predicting stock market index using LSTM, 15 September 2022.: The quick progression in man-made consciousness and AI procedures, accessibility of enormous scope information, and expanded computational abilities of the machine makes the way for foster modern strategies in anticipating stock cost. Meanwhile, simple admittance to speculation open doors has made the financial exchange moremindbogglingandunpredictablethananyothertime. Theworldissearchingforanexactandsolidprescientmodel whichcancatchthemarket'sprofoundlyunpredictableand nonlinear conduct in an all-encompassing structure. This studyutilizesalongmomentarymemory(LSTM),aspecific brainnetworkdesign,toforeseethefollowingdayshutting costoftheS&P500file.Anevenblendofnineindicatorsis painstakinglybuiltundertheumbrellaofthecentralmarket information, macroeconomic information, and specialized pointers to catch the way of behaving of the securities exchangefromamoreextensiveperspective.Singlelayerand multi-facet LSTM models are created utilizing the picked input factors, and their exhibitions are analyzed utilizing standard evaluation measurements Root Mean Square Mistake(RMSE),MeanOutrightRateBlunder(MAPE),and ConnectionCoefficient(R).Theexploratoryoutcomesshow that the single layer LSTM model gives a prevalent fit and highexpectationexactnesscontrastedwithmulti-facetLSTM models.[5]

7. ResearchonStockPriceTimeSeriesPredictionBasedon Deep Learning and Autoregressive Integrated Moving Average.2022DaiyouXiaoandJinxiaSu.:Notquitethesame asconventionalcalculationsandmodel,AIisanefficientand exhaustive utilization of PC calculations and measurable models,andithasbeengenerallyutilizedinmanyfields.In thefieldofmoney,AIisessentiallyusedtoconcentrateonthe future pattern of capital market cost. In this paper, to anticipatethetime-seriesinformationofstock,weapplied the conventional models and AI models for estimating the directandnon-straightissue,separately.Tostartwith,stock examplesthathappenedfromyear2010to2019attheNew York Stock Trade are gathered. Then, the ARIMA (autoregressiveincorporatedmovingnormalmodel)model andLSTM(longtransientmemory)brainnetworkmodelare

appliedtoprepareandforeseestockcostandstockcostsub relationship.Atlast,weassesstheproposedmodelbyafew markers,andtheexaminationresultsshowthat:Stockcost andstockvaluerelationshiparepreciselyanticipatedbythe ARIMAmodelandLSTMmodel;contrastedandARIMA,the LSTMmodelexecutionbetterinexpectation;andtheoutfit modelofARIMALSTMfundamentallybeatsotherbenchmark strategies.[6]

8 EmpiricalAnalysisforStockPricePredictionUsingNARX ModelwithExogenousTechnicalIndicators AliH.Dhafer1 , Fauzias Mat Nor 2, Gamal Alkawsi2 , Abdulaleem Z. AlOthmani3, Nuradli Ridzwan Shah4, Huda M. Alshanbari5 , KhairilFaizalBinKhairi6 ,andYahiaBaashar7 Published25 March2022.:Stockvalueexpectationisoneofthesignificant difficulties for financial backers who take part in the securitiesexchanges.Consequently,variousstrategieshave been investigated by professionals and academicians to foresee stock cost development. Man-made brainpower models are one of the strategies that pulled in numerous specialistsinthefieldofmonetaryexpectationinthefinancial exchange.Thisstudyexplorestheexpectationofthedayto daystock costs forBusiness Worldwide Trader Financiers (CIMB) involving specialized markers in a NARX brain networkmodel.Theprocedureutilizesfarreachingboundary trailsforvariousblendsofinformationfactorsanddifferent brainnetworkplans.Thereviewlookstoresearchtheideal counterfeit brain organizations (ANN) boundaries and settingsthatimprovetheexhibitionoftheNARXmodel.In thismanner,broadboundarytrailswerereadupfordifferent mixesofinfofactorsandNARXbrainnetworkarrangements expectationforCIMBstockinMalaysia.Theproposedmodel isadditionallyupgradedbypreprocessingandenhancingthe NARX model's feedback and result boundaries. The expectation execution is surveyed in light of the mean squared mistake (MSE), R-squared, and hit rate. The presentation of the proposed model is contrasted and differentmodels,anditisshownthattheusageofspecialized markers with the NARX brain network works on the precisionofone-strideaheadexpectationforCIMBstockin Malaysia.[13]

9. Stock Price Prediction Using the ARIMA Model [18 March 2022]. Ayodele A. Adebiyi1, Aderemi O. Adewumi2 , CharlesK.Ayo3.:Stockvalueforecastisasignificantsubject in money and financial aspects which has prodded the premiumofspecialiststhroughouttheyearstofosterbetter prescientmodels.Theautoregressivecoordinatedmoving normal(ARIMA)modelshavebeeninvestigatedinwriting for time series expectation. This paper presents broad courseof buildingstock costprescientmodel utilizingthe ARIMAmodel.DistributedstockinformationgotfromNew YorkStockTrade(NYSE)andNigeriaStockTrade(NSE)are utilizedwithstockcostprescientmodelcreated.Resultsgot uncoveredthattheARIMAmodelhasareasofstrengthfora fortransientexpectationandcancontendwellwithexisting proceduresforstockcostprediction.[14]

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 11 Issue: 03 | Mar 2024 www.irjet.net p-ISSN: 2395-0072

10 Mangesh V. Dole1 , Vishal N. Sonawane2, Sharique S. Farooqui3,EhteshamN.Ansari4,Prof.AlgatY.S.5,“StocksPrice Prediction using Facebook Prophet”, March-April 2023.: System is designed for predictions of the future prices of stockssfornext5yearsusingFacebookProphetthatcanbe usedforbetterinvestments.Thismakesiteasytodetermine which stocks to choose for investment based on the predictions giving the highest percentage of returns in a given period of time. The prediction accuracy can be increased by using several other features of Facebook Prophetandalsomaketheapplicationinteractiveandeasyto use. In future, the stocks market prediction system can be furtherincreasedbyutilizingalargerdatasetthanonebeing used presently. This will help to rise the accuracy of predictionmodels.[11]

4. CONCLUSION

Stocksadvancelongingmaybeanintricatezonetoacesince it consolidates learning and down to soil utilizations of various contraptions, outlines, and markers, as well as investigatingthebasicsofanorganizationsolikewisewell. Bethatsinceitmight,theharderitistogeniusthisidea,the morevaluabletheideasthatcomeaboutare.Notone,not two, there are various inclinations associated with stocks marketwant,andtheyareunendinglyandonthegrounds thatitisplannedtoassistyouwithcausingbenefitsonyour speculationsintheeventthatyougetthingsright.Thestocks advertise gauge has extra focal concentrations for novice vendorsastheyarethesortofsellerswhoaremoreskewed to making bungles and going toward authentic difficulties insidethenotificationcontrastedwithexperiencedshippers. Youwillbeassessedandforeseethestockspitchbygetting anamounttocomprehensionoftheequivalent.Forthis,you will show off stocks through different inducements and advance your methods for contributing. Stocks Caused SignificantDamageWantistheerrandofconcludingfuture stocks costs in light of genuine information and unmistakable show markers. It consolidates using honest models and AI computations to examine cash related information and make wants almost in the long run of a stock.

ACKNOWLEDGEMENT

WeextendourheartfeltgratitudetoProf.AnoopKushwaha, our esteemed project guide, for her invaluable insight, awareness,guidance,encourage,andconstructivecriticism throughouttheproject’sduration.

Our sincere thanks are also due to Prof. Sayli M. Haldavanekar, Head of Department, for her support and permission to present our project, titled "Nifty 50 Stocks Price Prediction" at our department premises. This presentation is part of the requirements for the partial fulfillment of the Bachelor's degree in Computer Engineering.

Weacknowledgeandappreciatethesupportreceivedfrom allourfacultymemberswhoplayedadirectorindirectrole inourproject.Weexpressdeeprespectandgratitudetoour parents,familymembers,andfriendsfortheirconstantlove and encouragement throughout our academic journey. Lastly, we extend our thanks to our friends for their unwaveringcooperationandsupport.

REFERENCES

[1]MeharVijh,DeekshaChandola,VinayAnandTikkiwal,Arun Kumar."Stocks Closing Price Prediction using Machine Learning Techniques" in International Conference on Computational Intelligence and Data Science (ICCIDS 2019),167(4):599-606,January 2020,DOI:10.1016/j.procs.2020.03.326

[2] HuZ,Zhao Y, KhushiM."ASurveyofForexandStocks Price Prediction Using Deep Learning" in Applied System Innovation.4(1):9,February 2021,DOI: https://doi.org/10.3390/asi4010009

[3]Huang,Nakamori&Wang(2005)HuangW,NakamoriY, Wang S-Y. Forecasting stocks market movement direction with support vector machine. Computers & Operations Research. 2005;32(10):2513–2522.Sri Rajini, S. An examination of the essential aspects, in the execution of enterprise resource planning in manufacturing and production industries (2017) International Journal of Mechanical and Production Engineering Research and Development,7(6),pp.395-402.C

[4] 2020 The Authors. Published by Elsevier B.V.International Workshop on Statistical Methods and Artificial Intelligence (IWSMAI 2020) April 6-9, 2020, Warsaw,Poland

[5]HumNathBhandari1,BinodRimal2,NawaRajPokhrel3 , Ramchandra Rimal 4, Keshab R. Dahal5, Rajendra K.C. Khatri6 Predicting stock market index using LSTM. 15 September2022

[6]ResearchonStockPriceTimeSeriesPredictionBasedon Deep Learning and Autoregressive Integrated Moving Average 2022DaiyouXiaoandJinxiaSu

[7] Stock Market Prediction Using Machine Learning. Publishedin:2018FirstInternationalConferenceonSecure Cyber Computing and Communication (ICSCCC) Ishita Parmar1,NavanshuAgarwal2,SheirshSaxena3,RidamArora4 , ShikhinGupta5 ,HimanshuDhiman6 ,LokeshChouhan7

[8] Anusha Garlapati, Doredla Radha Krishna, Kavya Garlapati, Nandigama mani srikara yaswanth, Udayagiri Rahul, Gayathri Narayanan, “Stocks Price Prediction Using FacebookProphetandArimaModels”,April2021.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 11 Issue: 03 | Mar 2024 www.irjet.net p-ISSN: 2395-0072

[9]SumedhKaninde,ManishMahajan,AdityaJanghaleand Bharti Joshi, “Stocks Price Prediction using Facebook Prophet,”ITMWebofConferences44,03060(2022).

[10]YashSaxenaMs.IndervatiMs.GarimaRathiStudentat DeptofComputerAsst.Prof.atDeptofComputerAsst.Prof. at Dept of Computer Science and Engineering Science and Engineering Science and Engineering Galgotias University, Galgotias University, Galgotias University, Greater Noida, India.GreaterNoida,India.GreaterNoida,India,“StocksPrice PredictionUsingFacebookProphet”,June2022.

[11] Mangesh V. Dole1 , Vishal N. Sonawane2, ShariqueS. Farooqui3,EhteshamN.Ansari4,Prof.AlgatY.S.5,“Stocks PricePredictionusingFacebookProphet”,March-April2023.

[12]Goutam Dutta, Pankaj Jha, Arnab Kumar Laha and Neeraj Mohan Artificial Neural Network Models for Forecasting Stocks Price Index in the Bombay Stocks ExchangeJournalofEmergingMarketFinance,Vol.5,No.3, 283-295.

[13] Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators Ali H. Dhafer1,FauziasMatNor2,GamalAlkawsi2,AbdulaleemZ.AlOthmani3, Nuradli Ridzwan Shah4, Huda M. Alshanbari5 , KhairilFaizalBinKhairi6 ,andYahiaBaashar7 Published25 March2022

[14] Stock Price Prediction Using the ARIMA Model [18 Mrach 2022]. Ayodele A. Adebiyi1, Aderemi O. Adewumi2 , CharlesK.Ayo3 .