Stock Price Prediction Using Python & Machine Learning

[@rajeshdua558]

I have seen 1000+ videos, read 1000+ articles on the internet. The best of the videos I have ever seen on machine learning.

[@sahilsharma-gm1ed]

Rajesh Dua chutiye .

[@SebastianWaisgold]

True

[@AnonymUwo]

Just look for Andrew Ng's courses. I'm sure it will change your mind

[@inasissa8077]

Thank you for your response i will watch it after watching alot of video

[@yx2023]

For anyone criticising the effectiveness of this algorithm...you are missing the point. This is a very good FREE tutorial on applying LSTM & RNN to real world datasets using python, for learning purposes only. It's a standard and basic learning topic for Neural Networks. To criticise this would be same as saying Man City should win UCL every year based on their FIFA20 stats...

[@yassinraissouni9130]

Hahahahaahahahahhaahhaha

[@alexroseland9286]

I recreated this exact model and then checked to see how often the simple up/down movement of the price was correct (never mind the actual change amount), it couldn't reliably get better than 50% so it's basically the same thing as randomly guessing a slight up or down movement. So this was a cute demonstration of the concept but you'll definitely lose your ass if you tried to trade on anything like this haha. You need a vastly more complex model to start consistently getting higher than 50% and even then if there are large market corrections, they will definitely break your model. So Something like this might give you some minor assistance in day trading but you wont get rich one it. But anyways, we certainly appreciate the explanation on how general LSTM model functions

[@martinholmes3305]

I am glad to see someone tried it while using only past data. The conclusion you make is what I would concur with just by simple reasoning. There should be a need for another factor at least to hope to get some sort of accuracy. Unless there is a repeated pattern to be found somewhere due to a factor we are unaware of. Like patterns in roulette due to flaws making the results not quite random. Something that has surely been caught onto, and is probably now verified for at casinos.

[@ishanawasthi1975]

Actually this model will accurately tell you the range of values that the stock has high probability of trading in. Example if model prediction is 180 with rmse of 5 then there is 65% chance that it will close between 175 - 185 range, and there is 95% chance that it will close between 170 - 190 range given that stock returns are randomly distributed and it follows standard normal distribution. If someone knows the range maybe they can wait for the stock to hit either high or low before taking trade or they can deploy option strategies based on the information they got from the model.

[@themoneymaker03]

@@ishanawasthi1975 good point!!! 👍

[@Sachin-ww1ns]

How can use multiple data as a input to classify which stock to pick ? from high to low. let's say I input 10 datasets at once & it classifies top 3. (just for project purpose)

[@bloglifenguyen3028]

His model is literally overfitting with the absurd accuracy

[@snivesz32]

The flaw nobody seems to have realized is that it’s predicting 1 day in the future GIVEN the last 60 days of actual values. So each prediction on the graph is actually using the validation data from yesterday + the NN prediction for today. Anyone can now see why it tracks the chart so accurately. I could get the same chart with a random number generator. Caveat emptor.

[@diegotrazzi]

I see your point: if you seeded any number within 1 standard deviation form the previous day to predict today's price, then zooming out to a large timeframe you could get a very similar chart. You brought up a good point and seem to be knowledgeable on this topic, so what would you do to implement a better forecast model ? Suggestions are appreciated, thanks!

[@weimondo]

@@diegotrazzi It's not possible. Assuming a sufficiently efficient market, if it were possible to forecast asset prices then people would take advantage and trade on such information. Price actions would then disappear.

[@RobertvonSarbacher]

@@weimondo NOT TRUE that's a total fallacy - the whole insane and inane premise that you cannot predict the future to find for example - a cycle in the market that no one knows about it and make a profit off of it b/c everyone will eventually learn it and use it thus incorporating into the market therefore it is not longer a predictor - this whole premise is so absurb as to make even the term "laughing stock" blush - all you have to do is NOT tell someone else what you discovered and the prediction holds - take for example the Scandavian Finance professor in the 90s that discovered an 80% accurate super cycles that was so freakishly exact like to 1 or 2 day out of every 2 months ish in finding the total and absolute highs and lows of almost everything that occured every 2-3 months - but being a brainwashed bonehead - tells the whole world about it - it was then put onto every Media in the world like Nightly Business Report - the euro continents finance channel and all the finance channels in the USA - it stilll took 2 MONTHS to have it incorporated by rest of the world but in mean time predicted every major and sometimes even minor turns in the USA stockmarket (never mind every other stockmarket index on the planet ) - now despite the fact that that is MORE than enough to make a few billion dollars and retire - keep in mind had he not been a brainwashed bonehead and had he kept this info secret it would never have been known and never incorporated into the stockmarket at all.... is this not common sense - FEEL FREE TO NOT REPLY BACK GEEZZZZ

[@dweh]

@Diego Trazzi There is the question that everyone in trading market are looking for! Who knows?

[@drcesarvarela]

@@dweh I guess CITADEL LLC knows about it pretty well.

[@touriafransform6280]

40:03 It is meaningless and misleading to plot predictions (especially zoomed out) unless you do out-of-sample forecasting. Many people are just obsessed with plotting forecast curves and comparing them with actual curves. It has got to stop! You can get a better fit by predicting the next value to be the current value. Out-of-sample forecasting is when the model is forced to predict N steps successively while not being provided with the true values of the previous time steps. Over time, errors would accumulate and the prediction curve would sway away from the actual curve. Only if you are doing out-of-sample forecasting, it makes sense to plot.

[@leonidasat]

Hi! I agree with you. I'm new to this so I'd like to know if you see a way to deal with this problem so you can better predict the time series. Thanks!

[@Otvazhnii]

Why predicting several steps? Why not predicting simply if it goes up or down in the next several steps? Is it not easier for a model to predict that?

[@amandinelevecq6664]

@@Otvazhnii Maybe not easier to get the acurate direction, but a lot more useful 😄

[@LovingLego]

This is the best video of LSTM I saw. I don't understand why do some people dislike this video ?

[@ComputerSciencecompsci112358]

Thank you very much !

[@InsightsbyMahmoud]

Great ,but there is one small thing you missed which may introduce bias into your model. To avoid leakage from the test set into the training set, it is important to split the data before applying any preprocessing steps. This is because the preprocessing steps can introduce information from the test set into the training set, leading to overly optimistic performance estimates and poor generalization performance on unseen data. so you have to split the data before scaling

[@neto_x]

I noticed this too...

[@paultvshow]

Exactly what I thought. I have noticed many KZbin’s make some mistakes here and there too. I think they should be more professional to rely on.

[@srivastava_aru]

true, also instead of spliting train/test data manually, one could use train_test_split from scikitlearn i think

[@oy_oy]

The script here is in the right direction, naturally it needs two key columns: Add high/low/volume variables, since it represents people's sentiment and join a new dataset from other currencies. I would say low performance coins with the highest ones is a good combination.

[@mikethecoder341]

one change that can be done here is increasing epochs if your ram can handle , because that would increase accuracy. Great video!

[@SahilKhan-td6rv]

Bro tell me what is the best value for epoch and batch size

[@zalakborad617]

there is no best value found yet on planet earth, you have to try different combinations. @@SahilKhan-td6rv

[@MrBobtwang]

I wouldn't put much store in the model created to be honest for reasons others have raised about scaling but as a 'get you going' video into the perilous world of trading using ML then it's an honest attempt - well done.

[@tanmaysharma2742]

Hey you seem like an expert . Can you please guide me as I have been really wanting to get into trading and finance using machine learning. I would really appreciate it if you could give me resources that could get me industry ready to create complex algorithms that can help me out.

[@mariahhayes5089]

Absolutely amazing video, I have finally realised that as a beginner in the financial investment market, you can achieve close to nothing yourself because you still have a lot to learn. Trading with a professional broker is more profitable and my advice for beginners is to always take advantage of that.

[@raymondbarnes5264]

Instead of timing the market, you should try to diversify your portfolio in order to get a dollar-cost average when it’s time to retire. Keep in mind that you don't need a ton of money to invest. Investing in small amounts can build long-term wealth too!

[@greenquake11931]

I'm an amateur making terrible picks and I really need assistance.

[@stellamoore720]

Trading is Profitable with the help of a seasoned broker managing your trading account .

[@mariahhayes5089]

@@greenquake11931 'BRIDGET MARY TUROW"".

[@greenquake11931]

@@mariahhayes5089 How can i reach her?.

[@natih8585]

I think this model is good for practice but not for real use. 1st issue is the scaling, you should scale based on the training set, not the full dataset, 2nd issue is the test dataset, the prediction works if you know the previous 60 days of the data for all the testing set. It would be good to see how is the performance of predicting next days (1+ days) just using a single vector of previous 60 days.

[@arcsaber1127]

This video helped me a lot. I was struggling with the inputs to the Conv. Turns out it just required simple 3d inputs to fit and another 3d array input to predict

[@AtillaYurtseven]

Well don't get too excited guys. Because every model predicts a price very close to the closing price. If you zoom the chart, you can see that yellow prediction is next to red ones. So this is not going to help you to make money. It's almost similar to shifting price one day to the future. Stop trying to predict. YOU CANNOT FORECAST THE FUTURE PRICES!

[@merrymouse2008]

Wonderful tutorial! Thank you very much for making it. I am making a project to impress the interviewer at the potential working place. I already did a prediction model using ML and Python but it was a simple Linear regression model. Thank you for your work. My model works perfectly. The only thing I can't figure out is how to trained it connected to timestamp. But otherwise it works. My tiny laptop CPU almost melted counting 38 000 rows of data XD

[@Gamermiddleclass]

I am trying to make the same including the optimal time to buy and sell the stock . Give me tips if you have any .

[@aashishkandel6112]

There are so much remaining to learn, but this was some intresting stuff. I learned about LSTM(Long Short Term Memory), taking the data from web directly, preprocessing the data,training the data and also visualizing the data.

[@icutoo2699]

The video was great, saved me a lot of time trying to figure it out myself. Also learn how to use the python syntax. On a side note, back of the envelope calculations. dec 17,19 aapl = 70.1, dec 17, 09 appl = 6.85. (70.1-6.85)/6.85 = 9.233 or 923% return over 10 years, 923%/10yr = 9.23%/year, 9.23%/365 days/yr = 0.0253%/day. If we use the previous day closing as an estimate plus the average daily change then, 70.1*(1.000253) = 70.118, accounting for the 1 to 4 stock split the price on the 18th is estimated at 70.1177 x 4 = 280.47, act close 279.74, vs AI 263.66. Would have been interesting to see which one would be better est, previous Close +% or AI, with the standard error.

[@sabashioyaki6227]

The comment at the end is priceless, "...off by a little bit." Go back and look at the price action from the end of August 2019 until about mid Jan 2020 and you'll see why this prediction of a 6% drop in the closing price is surprising. It would be nice if there had been some explanation of why the prediction was so far off and how to improve the result.

[@ekinrota1638]

Just saw the video, thanks for the effort! One question though, isn't it leaking data when you do the scaling before splitting data into train/test sets?

[@robertweber475]

you have to scale train and test data with the same parameters.. otherwise you distort results

[@ekinrota1638]

@@robertweber475 yes but you can do that with scaling on train data first and using the same trained scaler on the test data

[@robertweber475]

@@ekinrota1638 yes, that would be possible

[@peterjamieson4082]

@Jeremy Tran Jeremy is correct. The presentation is wrong and needs to be edited.

[@AMA_RILDO]

I’m always enjoy and bless the people like you who teaching cool stuff

[@samha1513]

*Good for learning purposes only*

[@aryanchauhan8711]

Seriously I believe it is impossible to forecast prices so that results are profitable. Still this is a very nice educational video.

[@martinholmes3305]

Love the video, very intriguing. One really feels like repeating this program design with a pre-edited set of data to verify the accuracy of the hypothesis created by the viewing of it. It is like a psychological experiment, social experiment, statistical experiment, computer science lesson all rolled into one. Hello World of Python.

[@krizh289]

Would be fairly easy to verify the accuracy, you should do it

[@npomfret]

It would be interesting to see how this compares with a basic strategy, like just predicting todays close will be the same as yesterdays close. I imagine the LSTM doesn't perform as well

[@PoulJulle-wb9iu]

Why do you imagine that?

[@PrabhuCS]

Ah! Beautifully expalined. May you reach 10 Million subscribers. Your classes are gold :)

[@nelsondesantijunior1527]

Thank you for the video. Could you demonstrate how to use lstm with two input data, for example close price and volume?

[@cyberspider78910]

One very promising application of this approach is path finding by robot vehicle where in it has to use earlier data to predict next step !!! and check using sensors that whether the step it is going to take has any obstacle or not and correct/update its trajocatory....see...its totally a different field of application but it will definitely work - just keep you mind open, fellas !! He is good at what he does..

[@javierhugo5412]

Reading about people grabbing multi-figures monthly as income in investments even in this crazy days in the market,any pointers on how to make substantial progress in earnings?would be appreciated

[@sandraken5250]

You have to have an idea on what you wanna invest in carefully before going in

[@georgewilson2434]

You make it seem unreal to make up to that as a passive income annually,when it’s clearly possible😁😁Albertmathe has really made me rich through his strategies💰💯

[@georgewilson2434]

Access him through the tele gram page

[@georgewilson2434]

Albertmathe is the name

[@albertpitts417]

This man right here,I trade with him he’s inventive and the P.O.T are no jokes, I won’t look awestricken u made mention of him

[@ItisAbuTDMM]

Really enjoyed learning the LSTM application for stock price prediction. I noticed that the prediction has overfitting and this is probably because you have not included Dropout in your model. Thanks

[@kyucho1018]

Data Science 101 : Never scale your data set with a test or validation set. You can't scale the entire dataset which introduces significant leakage to your model.

[@daspittin9954]

can you elaborate on what you mean, please?

[@ianmoore957]

​@@daspittin9954 Looking through the code, I see that he did a fit_transform on the entire dataset, then used that same scaler model to transform the test set back; he should have only used the training set when he fit the original scaler model; not both training and test, its akin to someone writing an exam, and giving them hints as to what the answers are (when estimating the predictions), but not providing the actual answers

[@surengrigorian7888]

How would one correct this error, in terms of the code?

[@ianmoore957]

@@surengrigorian7888 This is the general approach, which you'll have to implement after the step where he implemented the lag structure > scaler.fit(X_train) # scaler model fit to training set only > X_train_scaled= scaler.transform(X_train) > # some code .... > X_test_scaled = scaler.transform(X_test)

[@lucast2212]

I see how this is an unsound practice, but scaling the input data should only make the training a bit more simple. In the end you should get the same result, up to a factor for the weights in the first layer. Plus the difference in scaling is rather low, so it is really not a big deal here.

[@wfm_3.1416]

Nice job. However, there is a small error in your equation for RMSE. You need to take the mean of the squared residuals rather than the square of the mean of the residuals.

[@evandieterich8129]

w michaux can I send you some code to take a look at and maybe give me some pointers on what i can do better. my code runs im just not getting the results i intended and do not know where or why im stuck

[@amidatongassassin]

If I'm not mistaken it is rmse = np.sqrt( np.mean( (predictions - y_test)**2 ))

[@ashokwadel2502]

@@amidatongassassin nope getting the same error its giving me nan output

[@arupbiswas3487]

model.add(Dense(25)) model.add(Dense(1)) This has resulted into RMSE of 0.38 , adding one more neural trace model.add(Dense(25)) may be the reason

[@jeyasuriyam5185]

why we take range as 60. Can you please explain

[@kshitijsharma24]

How to fetch prices of Indian stocks

[@jjpmip]

Amazing video. It went thru everything from start to finish and explained it very clear and was easy to understand what was exactly going on and how things worked. Thanks for such a great video.

[@christospapageorgiou9560]

Very good job! If you combine this with technical indicator values it will give you very good predictions. And probably split it to different timeframes. Also, I noticed that if you retrain the model until you get better results and then try to predict the prices you got accuracy near to 99%. So, with a combination of technical indicators and by retraining the neural network until it is more accurate you are getting great results! Excellent job! Great Video.

[@ComputerSciencecompsci112358]

Thank you Christos !

[@shogun9409]

Can i get a video on how to combine the technical indicators to this pls? I am thinking of doing this as my final year project

[@orenka1991]

I am kinda new to machine learning and I never made any projects, but the fact that the result matched almost perfectly made me a bit suspicious about the method you use here. My first observation would be that the usage of scale would bound all result between the training dataset maximum and minimum, so there is no way that you could get a higher price as a prediction than the highest number found in the training dataset. My second obesrvation is about the actual training dataset. According to my knowledge testing dataset should not be included in the training data. I guess this is the reason it got the prediction so perfectly. I would suggest creating a dataset by actual % returns day by day or week by week. Maybe setting up some technical indicators. Volatility data, VIX, SP500 prices or anything should help predicting better the price in my opinion. However as commented by others already, pricemovements are not easy to predict and entire businesses are built to figure it out and still fail to beat the market. Thank you for the video! It was helpful to understand better how ML works in practice :)

[@ComputerSciencecompsci112358]

Excellent comment thank you. There is indeed "data leakage" in this model.

[@lindajoebert9406]

Excellent Cryptonaire course! I now have a core understanding of trading & investing in Cryptocurrencies. Thank you Mr Paulson Dmitriev. He explain it all so in depth, even for the advanced users. So no matter your experience there’s always something you can learn!

[@jaybarton457]

What's his contact details ?

[@sergiysergiy8875]

As you say, there was leakage in the model. That's why it showed such good results.

[@kolavithonduraski5031]

i dont understand anything... but the voice is calming down... and it sounds interesting 😆👍 Thanks for the video. Maybe i will understand it in 2 Years or so 🤓

[@delectomorfo]

Any luck?

[@kolavithonduraski5031]

i started to research python+tensorflow, but i sadly didnt have time to stick to it. (i am just a hobby programmer)... BUT i appreciate you asking❗😁

[@surendra1990]

in trading 0.1% matters. so these type of models are not going to help at all. unless they are constantly adjusted n number of times for the sample size taken to train the model, hardly any practical use. but an excellent way to begin with. THE EFFROTS ARE HIGHLY APRRECIATED. A BIG THANK YOU.

[@mrcc9589]

Foremost.. great tutorial! I really enjoyed the practical example for the purpose of learning how to use LSTM in python. Regarding the results of the neural network: It is obvious that the predictions are chasing the trend, similarly how a simple moving average would do. But a good starting point for exploring the possibilities of a RNN.

[@omkarmasal2148]

What if I want to predict the for the date that is not in my train dataset i.e I would use my whole dataset as training dataset and would create a module 2 predict the future price?

[@complexobjects]

Going to vouch for what many have commented here. Highly misleading thumbnail. Newcomers should pay close attention to what data is being used to predict and why this is not the way you would forecast multiple timesteps into the future.

[@ComputerSciencecompsci112358]

The thumbnail is an image from the program in this video.

[@Abdolahy]

That was a great debut for me in this field, thank you so much for sharing this kind of stuff with us. :)

[@KeshikaDeSoysa]

Amazing video. I learnt a lot. Thanks for sharing your knowledge.

[@mohamednedal]

Thanks for the great tutorial! A couple of questions please. To establish a Multivariate Multi-Step LSTM Models - Multiple Input Multi-Step Output: 1. How can I modify this code to take, for instance, 3 inputs to forecast a different single output that depends on those 3 inputs? 2. How can I forecast multiple timesteps in the future without knowing the new inputs (because they're in the future)?

[@anujsrivastava5316]

Same doubt, anyone please share any resources about it?

[@mvst4u493]

@@anujsrivastava5316 If you find solution, Kindly mention

[@folashadeolaitan6222]

Hi guys, Did you find a way to do the multivariate?

[@LasonyaCondren]

you are the big pro in trading! thank you for sharing your experience😄

[@Ronybech]

Using the previous value "yesterday" in the forecast, it is likely that the forecast will be more accurate. You need to predict for a more impressive period to assess the strength of the algorithm.

[@finaldaylight3804]

but what if i just want tomorrows prediction, shouldn't i be satisfied with that?

[@nilskohring]

It's crucial to square before taking the average when calculating the RMSE. Otherwise, any unbiased model should get a really low value with just enough data...

[@ComputerSciencecompsci112358]

I made a mistake on the rmse calculation but have the correct code for it in the description.

[@dreamtreater]

Knowing nothing about price movements, you just show a neural net 60 days of price 1 time, and then ask it: "oh oracle, what will price be the next day". It really needs more work than that. This is exactly what I was afraid of when deep learning was made more available. Just fire a DL at everything you don't grasp. Waste some electricity while you're at it.

[@malepatirahul7339]

I think I have seen every tutorial on KZbin for predicting the extrapolated price of stock and i found it in your video Thanks a lot for your time

[@shaw7598]

If you just prediction every day's close price as previous day's close price, you get a better model than this

[@saravanvel466]

Ur brilliant u will sure succeed in share market like warrent Buffett

[@RoboticusMusic]

What do you mean?

[@shaw7598]

@@RoboticusMusic This RNN yields an error of ~16 dollars. If you always predict tomorrow's closing price as yesterday's closing price from 2001 to 2020. You get 0.71 dollar mean absolute error. abs(df.close.diff(1)).mean() == 0.7417740650899958

[@RoboticusMusic]

@@shaw7598 If yesterday's close is $100 and today's close is $101, you're saying predicting that tomorrows close is $100 is accurate on average of how many ticks?

[@shaw7598]

@@RoboticusMusic Nobody is talking about tick data

[@anshumansingh2146]

Very well planned video. This predictor can be helpful for getting trends and can be awesome. Thank You

[@bangladesh6027]

I can now say I've watched someone piss into the wind for 49 minutes

[@devfromthefuture506]

Why?

[@nczioox1116]

It can be done. Look up billionaire mathematician Jim Simons

[@ClearVista]

@@nczioox1116 I see this as being more useful for Options

[@Zwiesel66]

I like the metaphor 👍

[@kacper3958p6p]

​@@nczioox1116 Yes - but the video shows the common missconception about stock price prediction with LSTMs. This model just adds random noise of small amplitude to previous values. It cannot predict anything, its structure is just unsuitable to do so.

[@WillsJazzLoft]

Okay here's the deal,.... The Yahoo finance API has likely changed since this video was first published. For all of those introductory lines of code importing all those different packages you only need a single line now as follows: import yfinance as yf

[@KennTollens]

11:07 Lieutenant commander Data, nice rough estimated guess lol ! Thanks for the video, very useful.

[@Bokgat]

But the shape of the predicted graph was uncannily accurate no? Surely something there? Did you watch the entire thing? Just asking and thanks for your post here. Cheers

[@karthikb5]

Sir, I believe the model overfits. If you can take 60% train and 20% test and unseen 20% for Cross validation!

[@MrEmzeezy]

Good job, but I think you only wrote simple moving average using RNN. can you expand the dates and see what prices it predicts? THANK YOU for your tutorial!

[@omkarmasal2148]

What if I want to predict the for the date that is not in my train dataset i.e I would use my whole dataset as training dataset and would create a module 2 predict the future price?

[@mwredfern]

Love the video. And love the comments! So much learned here. Thanks for the post! 👍🏽

[@atyabtosif584]

Really nice tutorial, however if anyone could tell me why did we keep the range from 60 specifically for the xtrain and ytrain timeseries datasets, it would be a huge help

[@adithyakondiparthy4797]

0.3

[@WillsJazzLoft]

When you undertake this project in Python notebook as the presenter has done Gemini AI will complete the code for you by suggesting the lines of code. To accept what is suggested, just press the tab key on your keyboard. Through running the code can and getting an explanation from the Gemini AI regarding the errors, I determined that the API had changed.

[@insoucyant]

Great flow and explanations. Please keep uploading such wonderful videos. :)

[@ComputerSciencecompsci112358]

Thank you, I will

[@pritigoel8662]

@@ComputerSciencecompsci112358 I found an error predictions=model.predict(x_test) predictions=scaler.inverse_transform(predictions) Output: Valueerror: Found array with dim 3. Estimator expected

[@flydr2]

Wow .. that's impressive... Very useful tutorial. Many thanks

[@gusinthecloud]

awesome, thank you very much for this video!! A big Hug from Argentina!!

[@manuelpatricioacuna59]

Hola. Acabo de ver este video. Has intentado usar el código para invertir? De ser así, q tal salió todo?

[@ivanpize]

Thank you a lot for this sharing of knowledge. A question: ........ did you predict two years (2018-2020) at once? ...... OR ........ you have predicted day by day (or week) every day during two years ???????

[@Gatolailo]

45:43 how you predict 5 o 10 consecutive days... not just 1?

[@loganathansiva7063]

Thank you for your valuable sharing predictions on stock prices in a simple manner and lucid way of expressions.

[@MrArindamd]

First of all , thanks for a wonderful session. One question about scaling though. Shouldn't the MinMaxScaler be used to fit_transform the training data and then use the "fitted" scaler to the test data ?

[@raghavverma120]

Yeah! There was information leak in this demonstration

[@R-Waze]

More videos like this, with different type of ML technique for stock prediction or Sell prediction in industry (like distribution), with more variety of technique, but with the same type of slow explanation. Super video, you definitely win my sub with this one. Thanks from the silent majority! 🤔

[@bobjazz2000]

What you want to predict is whether tomorrow’s price is higher or lower than today’s price.

[@goodyonsen77]

This isn't for coin prediction. It's for "stocks" (AAPL in particular). Stocks historically attract investors "based on their past price performances", if not on their specific ratios. These past price data is vulnerable to very little or no manipulation due to strict regulations by the governments. So there's no point in predicting coin prices when there's NO REGULATION and zillions of tons of manipulation going on thru the so-called pump channels, etc. Stay away from coins unless you're part of a community that do those pumps because it's merely a gambling.

[@arioncheng3024]

df = web.DataReader('AAPL',data_source='yahoo',start='2012-01-01',end='2019-12-17'), this command seemed not work any more!

[@oscura15]

do you know what else i can write here?

[@sibusisoharvey2652]

@@oscura15 there's similar question above yours with a different answer you can check that out, but i used data_source = 'stooq' and it works fine

[@sibusisoharvey2652]

remember to import yfinance as yf

[@ChillbabaKaGyan]

Your video was very much helpful for me to learn and understand ML with stock market price prediction. Keep making such useful videos sir. Thank you once again for your time and efforts.

[@cudanakiju0]

It would be super useful if you could provide the raw code as well

[@srishtygoyal3675]

++

[@boleideng5707]

Cool video. But LSTM is not working so well here. Baseline test: if you simply predict the next day's stock price to be THE SAME as this day, you get an RMSE of 2.25 or so.

[@robertjulesyoung9994]

I think the markets are moving solely based on the psychology of the investors. therefore, technical analysis is the only valid approach. for example, it does not really matter how good a company is doing, if investors are paying attention, understanding their future. AI is making things a lot easier, by looking into investors' behavior. thanks for the great insight.

[@patchiang13]

Even the price is close, but the most important is the trading strategy. What is the accuracy of the strategy?

[@tomasemilio]

I have 2 issues with this: 1. can't use testing data for the scaler. 2. I would predict a sequence instead of tomorrow's price. these plots are a bit deceiving because they are so close to each other but it is bc you are always using yesterday's close. It would be interesting to actually predict a sequence into the future.

[@sifisojuniorhadebe476]

Yea that's true, I used a similar model projecting EURUSD but the spread between actual and projected averaged 100pips which is a nono

[@laurahaydee6007]

Thank you so much for this tutorial, it was very helpful and I learned a great deal :) I have a question, if I want to predict more days should I change the number of neurons? Could you clarify this, please?

[@rohanshah8129]

You will need to use for loop on the last cell he made for predicting next day value. The for loop will help to execute same lines of code for how many days you want to predict in future.

[@Baranoeda]

I don't know if the author does not see this himself, or? The reason that the predicted graph follows the real one so close is that it only predicts 1 day into the future each time. If it would actually create the prediced yellow line ONLY based on the data up to mid 2018, that would be completely revolutionary. But of course, it is not. Its only predicting next day all the time. If I predicted next days close completely random +/- 1% it would look approximately the same.

[@arsenyturin]

40:30 save your time and just watch this part: “It looks like our model is pretty decent”. It’s not. The end.

[@aliciabasilio3520]

Thanks! but thats underestood since he is trying to do some money in youtube insted of getting rich!

[@RogerCarelli]

Why being so harsh?

[@arsenyturin]

@@RogerCarelli Because the author is not telling you the full truth. The model is pretty much as useless as a random guess. You can't use it neither for day trading nor for swing not for any kind of trading or investing. It's useless because it makes an assumption that the next day's return will be the same as the previous. So, it made a prediction for tomorrow. What happens tomorrow? Whatever it was right or wrong, it will take the real return and use it to predict the next day, making the same assumption. And that's for an entire year. In the end, predicted values walking around real ones, showing correct return approximately 50/50. But if you zoom out, show graphs for the entire year, they're pretty close. That creates an illusion that the model works. That's why so harsh.

[@GustavoBeneduzi]

@@arsenyturin I think you got confused , he is not showing how to create a trading bot, and not showing a forward feed model. This is a very well made example on building simple models, using simple time series.

[@mrityunjayrai63]

let's say you need data for the next 30 days, just change the Y to 30, so instead of predicting for 1 day the model will predict for 30 days. This is an alternative and effective solution I guess..

[@AkhzarFarhan]

You should probably use sentiment analysis for prediction rather than past closing prices because stock prices much depend upon the sentiment of people rather than its old price.

[@tlayta8823]

Hello great Video really helpful for understanding the concepts of LSTM models and stock price prediction 😊! I just have one question how can we add a dropout function in order to reduce overfitting and improve the performance of the model ?

[@rohanshah8129]

Add it after your Lstm layer in similar way we do for ANN.

[@Smarttradingchannel]

Great video thanks for the effort even the predictive price is so far from the real price which makes the model really inusable in the real life

[@MrJuliogracietti]

Hello, great job. How could I make a 5 or 10 day prediction from today's date?

[@meirbiton1741]

I also have no idea, have you figured it out?

[@gustavoreyes6442]

This has helped me a lot!! Thank you so much for the video

[@nikhilrao6701]

Can I have your GIT credentials and an amazing project. Loved the notes for each line and that voice is damn calming. It calms me down when i get an error.

[@Donald_Putin48]

So, as a piece of general information to everyone, the process is alright but the model is not so powerful. Try to tweak this model and you would get a very good result.

[@stefano8936]

I haven't spot the trick, but that result looks a bit too much accurate, according to my experience

[@vornamenachname906]

yep but they dont care. they fell for "invest 250€ in amazon for second income" too. you cant help these people

[@tigerbear3038]

Can you explain what you are doing? It feels like you are just telling us what to type and not explaining what the code is actually doing.

[@ajumadryn]

I'd see a year backtest ;)

[@gosavirealty]

Did you backtest?

[@madisonfieldsolutions2158]

Great video. Very easy to follow. Thanks

[@ComputerSciencecompsci112358]

Thanks for watching!

[@holaahora7527]

Great video! Can someone explain to me why the input layer has 50 neurons and we pass 60 close prices each time, please? I thought that the number of neurons had to match with the number of inputs that we were passing. Thanks in advance :)

[@vincenthughes5795]

it's irrelevant, you've got input shape

[@martinholmes3305]

Scaling helps compare different factor variables? For example, to compare volumes with prices?

[@chriswong8636]

Most newbies fail simply because they don't understand how the market works in general or in particular how the market relates to stock or currency pair they entering. If a retail trades doesn't grasp what the market makers are doing and when they are doing it, the greatest strategy in the world will fall. For new traders the markets are like entering an F1 race before you've passed your drivers license test. I am a beginner I never believe I made $30,000 in just a week from trading and with the market. an expert financial analysis and he made me-learn to read and understand the language of price action. He guides me with the exact time frame to trade and now I just received my first withdrawals of $30k in my bank account today I'm very happy, my advice is for you to contact him he will guide you perfectly well, and thank me later, I guess this is a good way to show my heartfelt appreciation for literally breaking the chain of my financial debit when I needed it most, you can contact him on his email ([e-mail])

[@jianzuo4389]

Hello, Thanks a lot for this interesting instruction about stock price prediction. I am working on the prediction related to battery performance deterioration, so I have a few questions on the prediction part of the prediction algorithm. To my knowledge, the prediction part you proposed in the video can only make one step ahead prediction, right? Though the first algorithm you create a NumPy array contains test close stock price (many rows), but in your pred function, you iterate the prediction only based on the x_test, without the predicted close stock values. Is it possible to overcome this to help make a real prediction that can predict the stock price for a longer period, like 10 days, one month? Thank you very much.

[@rohanshah8129]

This video is simply to demonstrate the implementation of LSTM. There are many factors to consider while making real predictions. Imagine if this was effecient way then wont all the LSTM algorithm makers be making huge profit 🤍

[@adelabou1986]

My understanding is that you are predicting the price of a day based on the actual price of all the previous days. For example, the last day you are predicting, 2019-12-17 is predicted based on the actual closing price of all previous days. In other words, 2019-12-17 is predicted based on actual close price of 2019-12-16, 2019-12-15,....... and so on. This means that the model is off by $10 in the last day even though it used actual price of all previous days, which i think is a big deal. Thanks for the effort though.

[@andrefonzar6222]

Thanks for the video! I also got a question, how can you extend the prediction for the next 60 days for example ? thanks

[@hjluna7760]

same question

[@EnochAppiah]

Try using incremental (online) learning. Create a loop to predict daily whiles updating your data with the predicted values incrementally.

[@nialloconnor9686]

With 3/1 risk reward you only need 50% on the model to make 10% gains on every ten trades. Training the model on only the closing prices is the wrong approach. You need open low high close and volume to correctly see the flow of money. And you only need the model to predict a day or two ahead. Thats it :-)

[@moddinski]

this is nice as a thought experiment, but you'll loose your money if you use this for real. There is no edge or competitive advantage to be found by using this model.

[@JunSim-t2o]

Hello, I am plotting the graph of train, val, predictions. But my graph shows that the val, prediction are on the left tail(starting point of the graph) not the right tail. why is it? from the video, the data is in ascending order but my data is descending order.

[@waelzayat2830]

That's pretty cool! thank you man. I just didn't know why you imported the pandas

[@ComputerSciencecompsci112358]

Thanks for watching !

[@pritigoel8662]

Hey bro do you have the source code? If yes, then can you please give it to me?

[@creatorstudio5945]

Excellent tutorial!!!! btw how much % accuracy did this model can achieve?