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Predict The Stock Market With Machine Learning And Python

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Hi everyone! You can find the code for this tutorial here - githubcom/dataquestio/project-walkthroughs/tree/master/sp_500
Comment from : @vikasparuchuri

can you do a demo of stock predictions with a LSTM RNN?
Comment from : @jtluns9

sir i dont know how but my precision_score is coming out to be 1 which is not possible
Comment from : @prathamrana8916

Thank you so much
Comment from : @trojeancedric

I'm sorry but this video is bullshit If this guy could predict stock market he would be rich and not making youtube videos
Comment from : @MatkoFaka

5:27
Comment from : @HackDiary1

I take it all you folks are now multi millionaires by now ? That would be the true test of the effectiveness of this method
Comment from : @ddoc1964

Have you tried deep learning such as LSTM algorithm? It's great for time-series data
Comment from : @fantom065boo8

why not try using an input proba of 80 in stead of 60 ?brhow would it change the result ?
Comment from : @PGDave-v8i

so why aren't you billionaire?
Comment from : @istaruscanada6572

I ALREADY KNOW THIS VIDEO IS BULLSHIT , B/C THIS DOES b*NOT*/b WORK IF IT DID , A LOT OF PEOPLE WOULD BE RETIRED MILLIONAIRES SAVED 35 MINS OF MY LIFE, I HOPE I SAVED SOME TIME FOR SOMEONE WHO WAS SMART ENOUGH TO READ THIS
Comment from : @freddurst4420

How much accuracy
Comment from : @krishjaswal2975

BroHOW MUCH (crores) you earned so far in this technique ???????
Comment from : @MrTally0000

Thx and god bless , regards from Hong Kong 😃
Comment from : @wuyanchu

Great video, would you consider doing a follow up on some of the stuff you mentioned that would further enhance it?
Comment from : @rosscortb

haha good joke
Comment from : @DEEPAKCHAUDHARI-d9g

Before you start investing, it's crucial to understand the basics of investing, different asset classes (stocks, bonds, real estate, etc), and the associated risks are you investing for retirement, buying a home, or building an emergency fund? Your goals will help shape your investment strategy
Comment from : @micheal_mills

Very nice video and a great explanation You didn’t mention finally how to get stock price predictions for tomorrow
Comment from : @sundsrik2154

Is he doing classification? (I wonder because most people do Regression) Thank you for your reply
Comment from : @rathanonsriwong2462

Is is not clear to me why you decided to re-train your model as you are backtesting Wouldn't it be more correct (and more fair) to re-use the same trained model as you back test? Otherwise you won't even know what model to use at the end Also I would calculate the precision score across the training data to have insights for under/over fitting
Comment from : @soliveirajr

36109 Olson Extension
Comment from : @rileycacamacdonald9690

Lewis Amy Hall Jennifer Hernandez Mark
Comment from : @РодионЧаускин

Jones Melissa Johnson Barbara Hall Angela
Comment from : @pauleedavidson9251

Great tutorial dude!
Comment from : @saulbenavides4037

This is completely useless
Comment from : @jakob4371

Lewis Carol Lee Donald Walker George
Comment from : @IsaiahUla-r6w

Impressive video, and very well narrated !!!brI have certainly learnt more about PY and MLbrI am curious as to taking the model and learning, knowing how to tweak it to improve its successful trade ratio (with the conditioned factors already in place)brThank you for taking the time to do this video
Comment from : @garyandrewranford

Why does his face look AI generated?
Comment from : @Yog3shPatel

Thanks for tutrial Not sure it's working but I Lost 50k - lol
Comment from : @michaeltse321

I love Dataquest learning method Thanks for the video
Comment from : @roxy_badass

Could you make another video like this but with Forex pairs like EURUSD, GBPUSD & AUDUSD please? 👍
Comment from : @realisedd

Thanks a lot for the content 🙏
Comment from : @ujjwalchetan4907

Hello there thank you for this excellent education just I have one problem my yfinance does not work and gives some errors please introduce another library that I can catch the data
Comment from : @FinanceCollege2024

Please make videos again
Comment from : @viewpoint8976

Can you fit this model for all stocks or just this one?
Comment from : @SakshiDwivedi-p8e

This is very nice way to get started using data science with the markets This gives a nice framework to get started And attempt to expand the predictors (on RSI based or Change in Open Interest , some correlation with the major stocks composing that index) Thank you for sharing
Comment from : @tsrinivas2406

This was an excellent presentation
Comment from : @KR-good

I hate this sort of useless BS clogging my feed
Comment from : @Charles-m7j

I am getting a zero division error while calculating precision score so please help
Comment from : @trynagethitbytruckkun7835

I did pip install yfinance in cmd I run jupyiter, and import yfinance as yf it does not work why?
Comment from : @sungkim1589

Shouldn't you normalize the data first? It's an excellent tutorial btw
Comment from : @EGspider

how to make its front end
Comment from : @harshkorani6642

very complex video, things could have been broken down like the rolling part onwards
Comment from : @ridj41

Hi can we use this for Indian stock markets?❤
Comment from : @nanjundarao9568

can this be put on resume?
Comment from : @sakshipanchal9711

I'm getting errors in [22]
Comment from : @maheshbodduluri2286

Do a part 2 please!!!
Comment from : @r8AlgoTrading

prediction accuracy is 57?if you flip a coin everyday to buy or sell , accuracy is 50 percent, WITH NO ML
Comment from : @TahaOraee

This guy talks like a robo
Comment from : @tanjirahmed114

The confusion I have on this video is if you call 'fit' multiple times on the different periods of data does not sklearn create a new random forest model from scratch? Shouldn't you build all the data together in one training set with the test chunks left out and put into a singular data set?
Comment from : @uptonster

Disclaimer from a professional trader: Do not waste your money and time on this
Comment from : @cassiojp

Hi I 'm a complete noob here When Vik shares his screen at 1:31, how can I get to that page? I tried a new python file in Jupyter Lab but it looks different I downloaded all the packages already jupyter lab, pandas, yfinance, scikit-learn
Comment from : @chessjess510

This will be my ticket to get out of the rat race
Comment from : @JEREMYMSTONE

😂😂 so why is this bob and vegene guy not rich yet?
Comment from : @maalikserebryakov

bob and vegene
Comment from : @maalikserebryakov

Poo in the Loo Saar
Comment from : @maalikserebryakov

Would be great to see an updated or enhanced version that incorporated a LLM to show how easy data manipulation can be…
Comment from : @majorkuntz

Vik, I echo the compliments on the excellent video I was able to use my own bespoke weekly market timing signals aligned with weekly S&P closes to finally get a grounded statistical "opinion" on the predictability of forward returns - as only my second Python exercise! Thanks!
Comment from : @circus14

Machine learning is artificial learning from a geat many individual experiences And like the wize man said "Experience is a lantern that we carry on our back and which only ever illuminates the path traveled" The experience of the Stock Market was done with a dominant economic model Who knows what other models creative minds will come up in the future
Comment from : @The0ldg0at

My man is doing noble work Kudos!
Comment from : @khushaalb2688

Explaining is on top Thank you!
Comment from : @Ivan-ou5nq

Y o u C a n n o t P r e d I c t S t o c h a s t I c s G o d d a m n I t
Comment from : @ShanyGolan

no your name is vikas
Comment from : @studenthub-q9g

mind displays an error the module yfinance not found
Comment from : @DismusOtieno

CAN U JUST BECOME THE RICHEST MAN
Comment from : @figh761

I request you to create a video considering Fundamental Analysis news integration prediction model as its happening behind the scenes to change the values Its just a request if possible
Comment from : @Ghjjj-r5d

The features used for the random forest cannot be the high, close, low , open values directly without any transformation because what the model is essentially doing is creating a overfit of non linear decisions to certain prices ranges It is basically memorizing that when the close was above X value and open below Y value predict 1 or 0 You need to normalize the predictors in some way so that the model can use them independently of how high the value the stock is and truly create generalizable rules Ratios are good since they use percentage instead of using absolute values and allow the model to use information of multiple candles as well
Comment from : @henriquesousa4789

AttributeError: 'RandomForestClassifier' object has no attribute 'predictors' brbrbrI am getting this error while doing backtesting what is its solution
Comment from : @puneetkumar2638

Hi! Maybe you can compare your algorithm with the real optimal decision in every season, so you could "asign points" to this algorithm and compare with others!
Comment from : @RodrigoMoreno-f9h

Is here using Long Short-Term Memory (LSTM) Recurrent Neural Networks
Comment from : @krishnathisari6758

Alert !!! just won a new suscriber
Comment from : @mojimoji2537

I'm hoping you can do a follow up video to this Would be great to see how you would incorporate macro data into your model, such as news or interest rates
Comment from : @anujsaraswat2257

Great video Thank you for the insights Going to be tuning into more of your work
Comment from : @logannon

Unable to use yfinance package Not found error Has Yahoo disabled or am I missing something Please help looking forward using te learnings here
Comment from : @lindsayk3599

HellobrI would like to learn data science from scratch, do you have any setup or interested to teach from scratch, i am definetly your First studentbrPlease let me knowbrThanks
Comment from : @MuhammadKhan-wp9zn

Wow, the concept of predicting the stock market using machine learning and Python is such a fascinating topic! The blend of finance and technology is always an area ripe for innovative approaches It's impressive how machine learning can analyze vast amounts of data to find patterns that might not be obvious at first glance Python, with its extensive libraries and community support, is an excellent choice for such complex computations It's exciting to think about how these tools can provide insights into market trends and possibly even predict future movements The intersection of machine learning and finance is definitely a space to watch! 📈💡🤖
Comment from : @AVOWIRENEWS

How has this worked in the past year? What would you change to make it better based on another years experience?
Comment from : @danielpaquette1597

What is missing is comparing it to another strategy What about Buy & Hold? What about "if the price increases yesterday, it will increase today"?
Comment from : @Donvito293