Hey man great video, only thing I'd recommend is moving your camera overlay to the top right corner though, as it often blocks the current line of code that you are writing/discussing.
The stock market is still a fantastic tool for building wealth, however, so it's wise to consider investing even if you don't have much money to spare.
Money is a tool that can help you to achieve your goals. It can provide comfort and stability for your family, make it easier to plan for the future, and allow you to save towards important milestones. But to achieve these things, you need to know how to make your money work for you by investing with the right signal.
does anyone know this error ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 5, 1), found shape=(None, 4, 1)
Instead of reshaping with reshape: x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)), there is a shortcut syntax: x_train = x_train[..., None], where None stands for the new axis added as the last axis. Maybe you want it to be understandable for begginers though, which I totally understand :).
Very nice video, learned a lot from it. Only one advice, you could every now and then print the data we are working with so that we see exactly how the data should look. I am guessing that for someone that advanced as you, visualizing the data in your mind is pretty simple, I for example, print it pretty often, just to make sure that I am on the right track. Apart from that, really awesome video, I'm glad the algorithm recommended your channel.
I am so happy that I am one of the oldest channel members you taught me a lot about sockets and also your project ideas are amazing cant wait to see much more I think you are in the road of becoming one of the biggest channels
I used the code from this lesson yesterday 5/12/2022 to predict today's close on DIA. The prediction last night was 322.19 - Today's 5/13/2022 real-time close was 322.24!
Very cool! I did the same, but tried it with linear regression. I really recommend changing the Programm a bit, so it tells you If the price will Go Up or down, NOT the exact Number. This is way more accurate and even though it's Not as informative, it probably is more usefull. I did the same with my linear regression attempt and that improved the average accuracy by quite a few percent.
love these very specific python videos! there are too many tutorial videos but not enough videos on specialized programming topics. I have been meaning to get started on algorithmic trading but had no idea where to start. It is as if you read my mind. Thank you!
Here is a tip for everyone that is new to programming: Learn clean coding as you learn how to code. You're gonna thank me later. Cause I'll garantee if you don't know how to clean code and you just copy pasting stuff and adding lines of code your code is gonna be horribly hard to read and understand. Especially algorithms with heavy maths involved.
How to predict for next 90 or 100 days if we want to ? For other situations in which we need to predict data for a whole day for every 15 mins intervals
I’d love to see a more technical video. I like to understand what is happening that actually takes me to the results I get, so I can then understand how to make it better. Nice video, man, congrats for your work
Even though the models aren't perfect, it's amazing how close they are! They could definitely be handy in assisting day traders with determining how long to hold a position for :) Thanks so much dude!!
Just an example of NN overfitting. Predicted curve should be shifted to the left (to the past) from the actual curve - then it will mean the prediction. Actualy NN just repeating curve from the past to the future with some bias and curve approximation, and this is the opposite of the meaning of prediction. Whatever if you just need to smooth any curve and shift it to the right, then there are many simple ways, not a neural network.
I absolutely love your videos and they have really helped me get started with coding as a beginner. Would love it if you could put your video in another corner so code is visible at all times. Thankss
man, you're amazing. just amazing, no words i calculated the r2_score for FB dataset prediction it's 0.86 you're predicting stock market with that much accuracy. i might just trade on that 😁 love your content man, keep up the good work❤👍
I don’t know if you’re going to see this comment, but I must say it: The way you teach the content in details explaining why and what that section does is amazing! Very good content, I’ll see all your videos now that I understand more about machine learning stuff than 3 months ago when I found your channel. Thank you so much! A hello from Brazil!
You need to train it differently...find historic data where a very large move to the upside or downside has occurred, and then take the 120 candles before that, and label them either "bullish" or "bearish"...then show it the current chart and ask it what will happen next
I have this error : ValueError: Input 0 of layer "sequential_1" is incompatible with the layer: expected shape=(None, 60, 1), found shape=(None, 59, 1)
This is more simple code than i use, if you try to use epochs up to 1000 and batch size 64, maybe the accuracy will be same as my model, mine is always above 99% predicting the next day closing
Hai sir. Thanks for casting your videos. Learnt how to make predictions on stock prices watching your video. I was in search of videos like this. Luckily i got it. Very well narated and explained. Hats off to you sir. May god bless you. .
Great video. Can you move the code up (hit the return 4-7 times) bc your camera is blocking part of the code to the right. If you press return, it will shift the code up so we can see entire line of code. Looking forward to seeing more. Do a video on Q learning.
Hey, thank you so much for the video, very smooth and understandable. I agree the fellow commentators, please make a separate video to describe the mathematics behind the model. Thanks!
Really like your videos, I've watch many at this point. Only thing I would comment is that as much as its nice to see you at the beginning/end, I think it would help to turn off your video while actually writing code. I was frustrated a few times trying to keep up when you were coding along the bottom of the window and the right extents were hidden by your pip which is kind of unnecessary. Anyway that said, enjoy the content. Keep it up.
I would like to see a detailed video where you explain the tensorflow implementation of the LSTM, because I'm not really sure how they work when you combine them with Dense layers.
A scatter plot and/or calculating the correlation of the predicted price change vs actual price change would give much more insight in wether or not the model has some predictability
When running (22.00) i get this error. How to fix? Traceback (most recent call last): File "C:\Users\soubi\PycharmProjects\tensorflow\Code\linear regression.py", line 19, in data = web.DataReader(company, 'yahoo', start, end) File "C:\Users\soubi\PycharmProjects\tensorflow\venv\lib\site-packages\pandas\util\_decorators.py", line 211, in wrapper return func(*args, **kwargs) File "C:\Users\soubi\PycharmProjects\tensorflow\venv\lib\site-packages\pandas_datareader\data.py", line 379, in DataReader ).read() File "C:\Users\soubi\PycharmProjects\tensorflow\venv\lib\site-packages\pandas_datareader\base.py", line 253, in read df = self._read_one_data(self.url, params=self._get_params(self.symbols)) File "C:\Users\soubi\PycharmProjects\tensorflow\venv\lib\site-packages\pandas_datareader\yahoo\daily.py", line 153, in _read_one_data data = j["context"]["dispatcher"]["stores"]["HistoricalPriceStore"] TypeError: string indices must be integers Also, I can't do from tensorflow.keras.models Import sequential or tensorflow.keras.layers... So instead i've done: from tensorflow import keras from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, Dropout, LSTM Is this okay?
Isn't TimeSeriesSplit Cross-Validation similar to your type of split? It kind of does the same thing. For example: "Train on first 3 days, predict on 4th, then train on first 4 days, predict on 5th" and so on, thank you!
Hi, thank you for the quality content! I just wanted to let you know, that I would be also interested in the math behind the whole AI thing. Have a nice day and be safe :)
mate i am having error ValueError: Input 0 of layer "sequential_1" is incompatible with the layer: expected shape=(None, 60, 1), found shape=(None, 59, 1) THIS ISSUE IN LAST3RD LINE prediction=model.predict(real_data)
Very well explained! I am trying to figure out if I can predict the price of certain currency and then calculate the "value at risk". My doubt is, I have to train this model every day to get a better result? I plan to use it on production to compare it with the actual state of art of "value at risk", using the covariance or the historical method.
Great stuff. Could you possibly do a video on how to deploy these into production where newer data is automatically fed in, so we do not have to keep training the models again and again?
You do the best programming tutorials. Easy to understand and learn. I have a question. Can you make a tutorial about how to make a Discord Bot written in Python. If you don't know what Discord is, it is one very popular chat platform.
Great video, very clear. Please make a video on roadmap to learning python from scratch, specifically for stock analysis, chart analysis, getting trade signals using charts and statistical analysis of stocks. I mean create a roadmap on the course tailored cut for only stock trading. Regards Farid
Great job man. But i also have some confusion about some lines: 1. The for loop of x_train and y_train append values. 2. x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) For the two points above, hope you can more explain to me. thank you so much.
Please do make more theoretical videos. It really helps to learn and understand the fundamentals of the underlying. We're gonna learn them fast and more accurate. Please do and let us join the channel. I would like to be a paid subscriber.
Good videos , notes of possible ways to improve .. A. Lower keyboard sound relative to your voice ..maybe a noise cancel lapel microphone ? Maybe AI noise cancelling B. Some commentary on the commands used and what library they are from to connect the resources used and what goes on in each command . Thanks
Thanks! Love your videos. Wonder if there is a good way to detect pattern (instead of predict future prices) such as inverse head-and-shoulder and cup-and-handle?
I am trying to reproduce your model and I get this errors: 'Traceback (most recent call last): line 21, in data = web.DataReader(company, 'yahoo', start, end) line 210, in wrapper return func(*args, **kwargs) line 370, in DataReader return YahooDailyReader( line 253, in read df = self._read_one_data(self.url, params=self._get_params(self.symbols)) line 153, in _read_one_data data = j["context"]["dispatcher"]["stores"]["HistoricalPriceStore"] TypeError: string indices must be integers Does anybody now what is wrong? I tried to fix it in many different ways but nothing worked.
@@atasozuvesiirler Hey, if you're trying to get a project like this to work, I recommend trying to understant the basics and then building up the code with ChatGPT for example. By doing so I got it to work. Just so you can have some reference, I used yfinance to fetch the data. Good luck!
Could I say i need to change the last value from (model_inputs+1) to let us say '+5' to see price prediction in 5 days? real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs+1), 0]]
I love this video but I have a couple errors. 1) When I run the code, I don't get any lines just a blank graph. 2) If I keep going and finish out the code, I get this error. line 77, in plt.plot(actual_prices, color="black", label=f"Actual {company} Price") 3) plt.legend() line 83, in asarray return array(a, dtype, copy=False, order=order) TypeError: float() argument must be a string or a number, not 'builtin_function_or_method'
I am interested in why are you choosing that topology of the neural network. Why 3 LSTM layer each with 50 neurons. It is clear why the last layer is of one neuron. But the previous ones are a mystery.
There is no sense in predictions, because you predict test values using test values, so we can see that lag on graphics. So there is no prediction, only delta to test_data. So, to be correct, you should use your predictions to predict another day. Then you see, that there is nothing common in your predictions and real data.
I am facing NotImplementedError while executing this statement ----> 3 model.add(LSTM(units=50, return_sequences=True, input_shape = (x_train.shape[1],1))) can anyone help?
Everything worked great until the "Predict Next Day" section, which resulted in the following error: > ValueError: Error when checking input: expected lstm_input to have shape (60, 1) but got array with shape (59, 1) Still struggling to find the fix.
January 15 was Friday, you recorded on Monday which was 18th, and there was no trading that day. The day after, 19th, the price was 261.10, which is far far from the prediction. Most ML for trading today is not working really, and DSP gives better results since its more transparent.
Traceback (most recent call last): data = web.DataReader(company, 'yahoo', start, end) TypeError: string indices must be integers Why I am getting this error and how to overcome? Pls, help!
Thank you so much for the video, this is my first time using python, I copy the code step by step and lots of errors when it run maybe I do not have correct settings for those imports. Luckily it works well on Colab. I've learnt some logic on how it works thanks
It's not about which programming language you use, or which statistical model is applied. If you ever read the famous paper on the random walk theory, you will know it's impossible to predict stock prices.
Hi ! When i tried to run the last part of the code in spyder, i had this error (not a warning like the video): ValueError: Error when checking input: expected lstm_4_input to have shape (60, 1) but got array with shape (59, 1) How can i solve this?
Hey man great video, only thing I'd recommend is moving your camera overlay to the top right corner though, as it often blocks the current line of code that you are writing/discussing.
I agree!!!
you're underrated man, you deserve a lot more
Patience is the key ^^ Thank you
Totally agree
True
Yeah
Your content is amazing! I can't find one video that sucks or that's useless. Keep it up dude, your channel will hit big numbers 👌🏽
Thanks brother :)
That's for sure i believe..
@@NeuralNine which yahoo api is that ? Please tell me the package name and the author name.
@@c0dakw0lfgaang48 there are several yahoo finance apis in python that you can use, but i recommend using yahoo_fin (pip install yahoo_fin)
@@martinwestin4539 thanks mate
The stock market is still a fantastic tool for building wealth, however, so it's wise to consider investing even if you don't have much money to spare.
Money is a tool that can help you to achieve your goals. It can provide comfort and stability for your family, make it easier to plan for the future, and allow you to save towards important milestones. But to achieve these things, you need to know how to make your money work for you by investing with the right signal.
@@greenquake11931 Hello, what signal do you invest with ? I'm new here.
@@mayacho4910 'BRIDGET MARY TUROW"
@@greenquake11931 I'll like to connect with her. I want to invest.
@@mayacho4910 look with her name online for her page.
Line 60:
model_inputs=total_dataset[len(total_dataset)-len(test_data)-prediction_days:].values
Took me a while but the purpose of this line is to get from 60 days before test and include the test set
does anyone know this error
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 5, 1), found shape=(None, 4, 1)
@@Kotavedavyas I am on the same boat... did you find how to fix it?
same... help
Anyone able to resolve this error? "expected shape=(None, 5, 1), found shape=(None, 4, 1)". Please help with a workaround
thank you for this amazing content. I now went bankrupt
LOL (sorry if it wasn`t a joke)
hahaha
lmao
RIP... i invested using this .. im kinda profitable
😂😂
Instead of reshaping with reshape: x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)), there is a shortcut syntax: x_train = x_train[..., None], where None stands for the new axis added as the last axis. Maybe you want it to be understandable for begginers though, which I totally understand :).
Thanks for the nice tip.
Hello, can you please help me
Very nice video, learned a lot from it. Only one advice, you could every now and then print the data we are working with so that we see exactly how the data should look. I am guessing that for someone that advanced as you, visualizing the data in your mind is pretty simple, I for example, print it pretty often, just to make sure that I am on the right track. Apart from that, really awesome video, I'm glad the algorithm recommended your channel.
I am so happy that I am one of the oldest channel members you taught me a lot about sockets and also your project ideas are amazing cant wait to see much more I think you are in the road of becoming one of the biggest channels
Those muscles.... damn nerds are jacked now... thanks brother
21 skinny guys liked your comment
lol
yeah, theory behind would be great to know how to apply the knowledge in different projects and situations. great work man
I used the code from this lesson yesterday 5/12/2022 to predict today's close on DIA. The prediction last night was 322.19 - Today's 5/13/2022 real-time close was 322.24!
So?
Very cool! I did the same, but tried it with linear regression. I really recommend changing the Programm a bit, so it tells you If the price will Go Up or down, NOT the exact Number. This is way more accurate and even though it's Not as informative, it probably is more usefull.
I did the same with my linear regression attempt and that improved the average accuracy by quite a few percent.
hey, kai is this possible to share your code with me?
Hey ,what did you change to make that edit? Thanks
@@ShakTMT Well, the easiest solution would be to compare the current price and the predicted price.
love these very specific python videos! there are too many tutorial videos but not enough videos on specialized programming topics. I have been meaning to get started on algorithmic trading but had no idea where to start. It is as if you read my mind. Thank you!
Here is a tip for everyone that is new to programming: Learn clean coding as you learn how to code. You're gonna thank me later. Cause I'll garantee if you don't know how to clean code and you just copy pasting stuff and adding lines of code your code is gonna be horribly hard to read and understand. Especially algorithms with heavy maths involved.
Great video! And i would love to see some theorical mathematics about what's behind the functions you were talking about
Look for Denuit, Hainaut & Truffin « Effective Statistical Learning for Actuaries Vol. 3 - Neural Networks & Extensions » 😉
How to predict for next 90 or 100 days if we want to ?
For other situations in which we need to predict data for a whole day for every 15 mins intervals
I’d love to see a more technical video. I like to understand what is happening that actually takes me to the results I get, so I can then understand how to make it better. Nice video, man, congrats for your work
you should check out coding trains neural network playlist it goes into a lot of depth into how neural networks function
MIT posts all of their lectures in algorithmic models on RUclips, more technical than you probably even want hahah
Man believe me you would surely get a lots of subscribers in a very few time
You are siriously awesome dude
Even though the models aren't perfect, it's amazing how close they are! They could definitely be handy in assisting day traders with determining how long to hold a position for :) Thanks so much dude!!
FB Stock the next day from his prediction was 261.10 (1/19/21). The model seems to be pretty good at predicting momentum at least.
Great stuff! Don't stop posting, man. This is very cool.
These videos recorded while coding are the best! please keep producing them
Just an example of NN overfitting. Predicted curve should be shifted to the left (to the past) from the actual curve - then it will mean the prediction. Actualy NN just repeating curve from the past to the future with some bias and curve approximation, and this is the opposite of the meaning of prediction. Whatever if you just need to smooth any curve and shift it to the right, then there are many simple ways, not a neural network.
I absolutely love your videos and they have really helped me get started with coding as a beginner. Would love it if you could put your video in another corner so code is visible at all times. Thankss
man, you're amazing.
just amazing, no words
i calculated the r2_score for FB dataset prediction it's 0.86
you're predicting stock market with that much accuracy.
i might just trade on that 😁
love your content man, keep up the good work❤👍
could you tell me the code for it
thank u man u are the only youtuber who do this things
I don’t know if you’re going to see this comment, but I must say it:
The way you teach the content in details explaining why and what that section does is amazing! Very good content, I’ll see all your videos now that I understand more about machine learning stuff than 3 months ago when I found your channel.
Thank you so much! A hello from Brazil!
I’m a CS student and I gotta say, you really have fantastic helpful content
You need to train it differently...find historic data where a very large move to the upside or downside has occurred, and then take the 120 candles before that, and label them either "bullish" or "bearish"...then show it the current chart and ask it what will happen next
and it still wouldn't change anything. This approach is no better than human guessing
I have this error :
ValueError: Input 0 of layer "sequential_1" is incompatible with the layer: expected shape=(None, 60, 1), found shape=(None, 59, 1)
@Juanpablete12 Have you found a fix to this? I'm running into the same problem.
this is actually my 6 months engineers degree condensed in 30 minutes
You got an engineering degree in 6 months ?
Lol
Bs
This is more simple code than i use, if you try to use epochs up to 1000 and batch size 64, maybe the accuracy will be same as my model, mine is always above 99% predicting the next day closing
Is your test data in your train dataset?
Yes please, theoretical videos would be of great help!
Hai sir. Thanks for casting your videos. Learnt how to make predictions on stock prices watching your video. I was in search of videos like this. Luckily i got it. Very well narated and explained. Hats off to you sir. May god bless you. .
We need more and more videos of this kind. Just loved it.
Great video. Can you move the code up (hit the return 4-7 times) bc your camera is blocking part of the code to the right. If you press return, it will shift the code up so we can see entire line of code. Looking forward to seeing more. Do a video on Q learning.
Hey, thank you so much for the video, very smooth and understandable. I agree the fellow commentators, please make a separate video to describe the mathematics behind the model. Thanks!
Learning python at the same time predicting stock success. Fantastic!
Great content as always. Keep going !!!
Thanks for sharing, good work! For whom is watching this video, again, the previous data does not correctly predict the future!
I immediately hit the subscribe button. Nice content, keep up the good work.
Wow man that’s good! Can’t wait to hear more about optimizers and loss functions :) Thanks
Really like your videos, I've watch many at this point. Only thing I would comment is that as much as its nice to see you at the beginning/end, I think it would help to turn off your video while actually writing code. I was frustrated a few times trying to keep up when you were coding along the bottom of the window and the right extents were hidden by your pip which is kind of unnecessary. Anyway that said, enjoy the content. Keep it up.
I would like to see a detailed video where you explain the tensorflow implementation of the LSTM, because I'm not really sure how they work when you combine them with Dense layers.
Can you make a video where you explain the mathematics?😄
Three blue one brown does an excellent job of this!
A scatter plot and/or calculating the correlation of the predicted price change vs actual price change would give much more insight in wether or not the model has some predictability
When running (22.00) i get this error. How to fix?
Traceback (most recent call last):
File "C:\Users\soubi\PycharmProjects\tensorflow\Code\linear regression.py", line 19, in
data = web.DataReader(company, 'yahoo', start, end)
File "C:\Users\soubi\PycharmProjects\tensorflow\venv\lib\site-packages\pandas\util\_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "C:\Users\soubi\PycharmProjects\tensorflow\venv\lib\site-packages\pandas_datareader\data.py", line 379, in DataReader
).read()
File "C:\Users\soubi\PycharmProjects\tensorflow\venv\lib\site-packages\pandas_datareader\base.py", line 253, in read
df = self._read_one_data(self.url, params=self._get_params(self.symbols))
File "C:\Users\soubi\PycharmProjects\tensorflow\venv\lib\site-packages\pandas_datareader\yahoo\daily.py", line 153, in _read_one_data
data = j["context"]["dispatcher"]["stores"]["HistoricalPriceStore"]
TypeError: string indices must be integers
Also, I can't do from tensorflow.keras.models Import sequential or tensorflow.keras.layers...
So instead i've done:
from tensorflow import keras
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
Is this okay?
same
did you guys find the solution.Im stuck too
@@melihaksoy1129 Nope. Not yet
same
tensorflow.python.keras.models import Sequential worked for me,as for the string indices must be integers, im struggling to figure that out now
Isn't TimeSeriesSplit Cross-Validation similar to your type of split? It kind of does the same thing. For example: "Train on first 3 days, predict on 4th, then train on first 4 days, predict on 5th" and so on, thank you!
I am interested in the theory and maths! Also, top quality video.
Thank you very much. I was looking for predictions made like this.. May god bless u.
Thank you so much! This is exactly what i had been looking for! 🙏🏼
I would love to see some theory about the opimizer. Really interesting but hard to understand on the Mathematical point of view
Thank you, @NeuralNine! The people who gave you dislikes need a neural network to predict better taste in tutorials.
Hi, thank you for the quality content! I just wanted to let you know, that I would be also interested in the math behind the whole AI thing. Have a nice day and be safe :)
I’m looking for someone to adapt this code with my parameter and formulas
Awesome work dude🔥
thanks :)
Line 87 should be
real_data = [model_inputs[len(model_inputs) + 1 - prediction_days: len(model_inputs) + 1, 0]]
mate i am having error
ValueError: Input 0 of layer "sequential_1" is incompatible with the layer: expected shape=(None, 60, 1), found shape=(None, 59, 1)
THIS ISSUE IN LAST3RD LINE
prediction=model.predict(real_data)
Very well explained! I am trying to figure out if I can predict the price of certain currency and then calculate the "value at risk". My doubt is, I have to train this model every day to get a better result? I plan to use it on production to compare it with the actual state of art of "value at risk", using the covariance or the historical method.
Great stuff. Could you possibly do a video on how to deploy these into production where newer data is automatically fed in, so we do not have to keep training the models again and again?
if that was possible then the stock market would be pointless cause everyone would use it
Man you do be grinding!
always ^^
You do the best programming tutorials. Easy to understand and learn. I have a question. Can you make a tutorial about how to make a Discord Bot written in Python. If you don't know what Discord is, it is one very popular chat platform.
can any one help?
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 60, 1), found shape=(None, 59, 1)
Same error here, not sure either
did you figure it out?
Great video, very clear.
Please make a video on roadmap to learning python from scratch, specifically for stock analysis, chart analysis, getting trade signals using charts and statistical analysis of stocks. I mean create a roadmap on the course tailored cut for only stock trading.
Regards
Farid
I wrote the whole code and installed Pandas, Anaconda, Tensorflow, etc. but I can't see anything because it gives so many errors
Great job man. But i also have some confusion about some lines:
1. The for loop of x_train and y_train append values.
2. x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
For the two points above, hope you can more explain to me.
thank you so much.
thanks for the video man it's great!
and yeah it would be super cool if you made a video explaining the math!
Please do make more theoretical videos. It really helps to learn and understand the fundamentals of the underlying. We're gonna learn them fast and more accurate. Please do and let us join the channel. I would like to be a paid subscriber.
THANK YOU for making this.
Good videos , notes of possible ways to improve ..
A. Lower keyboard sound relative to your voice ..maybe a noise cancel lapel microphone ? Maybe AI noise cancelling
B. Some commentary on the commands used and what library they are from to connect the resources used and what goes on in each command . Thanks
Thanks! Love your videos. Wonder if there is a good way to detect pattern (instead of predict future prices) such as inverse head-and-shoulder and cup-and-handle?
I am trying to reproduce your model and I get this errors:
'Traceback (most recent call last):
line 21, in
data = web.DataReader(company, 'yahoo', start, end)
line 210, in wrapper
return func(*args, **kwargs)
line 370, in DataReader
return YahooDailyReader(
line 253, in read
df = self._read_one_data(self.url, params=self._get_params(self.symbols))
line 153, in _read_one_data
data = j["context"]["dispatcher"]["stores"]["HistoricalPriceStore"]
TypeError: string indices must be integers
Does anybody now what is wrong? I tried to fix it in many different ways but nothing worked.
Same for me. I don't know what to do. Can some one help us?
Same for me
Same for me..
@@atasozuvesiirler Hey, if you're trying to get a project like this to work, I recommend trying to understant the basics and then building up the code with ChatGPT for example. By doing so I got it to work. Just so you can have some reference, I used yfinance to fetch the data. Good luck!
@@bielvv9148 thanks I already have the solution in same way too. 👍
Could I say i need to change the last value from (model_inputs+1) to let us say '+5' to see price prediction in 5 days?
real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs+1), 0]]
Hey were you able to figure this out I’m trying to do it now
dude thank u !! u are really great !!! Thank god i found your video !!! This helped me in the project thanks.
Super nice content!
A video about optimizers would be super welcomed !!
Wow! As a trader that's amazing thing to learn. Thanks for the video
Great videos. Love this type of content. Please make more
Amazing channel! Thanks for the tutorial dude!
Optimizer and loss model could be a brain-breaking topic, but I'm curious. Very informative and interesting contests sir! GoodJob!
I love this video but I have a couple errors.
1) When I run the code, I don't get any lines just a blank graph.
2) If I keep going and finish out the code, I get this error.
line 77, in
plt.plot(actual_prices, color="black", label=f"Actual {company} Price")
3) plt.legend()
line 83, in asarray
return array(a, dtype, copy=False, order=order)
TypeError: float() argument must be a string or a number, not 'builtin_function_or_method'
wow! nice! i can learn phyton! awesome content!
I am interested in why are you choosing that topology of the neural network. Why 3 LSTM layer each with 50 neurons. It is clear why the last layer is of one neuron. But the previous ones are a mystery.
There is no sense in predictions, because you predict test values using test values, so we can see that lag on graphics. So there is no prediction, only delta to test_data.
So, to be correct, you should use your predictions to predict another day. Then you see, that there is nothing common in your predictions and real data.
This is one of the best stock price prediction programming I have ever seen in my entire life, would love to see the math theory behind it as well
This was great! Thank you!
I would appreciate it if you explain the theory behind this project. Anyway, excellent video
Thanks for your efforts and collection of Good informations 👍👍
The yahoo api is not working, is there an alternate solution
Is there a way to predict not only next day’s price, but also next few days’ prices?
💯% true n actual content u r on extremely right way
I am facing NotImplementedError while executing this statement ----> 3 model.add(LSTM(units=50, return_sequences=True, input_shape = (x_train.shape[1],1))) can anyone help?
Everything worked great until the "Predict Next Day" section, which resulted in the following error:
> ValueError: Error when checking input: expected lstm_input to have shape (60, 1) but got array with shape (59, 1)
Still struggling to find the fix.
did you ever figure it out?
@@MrZach4454 remove one +1
for create x_train you can use sliding_window_view in numpy library
January 15 was Friday, you recorded on Monday which was 18th, and there was no trading that day. The day after, 19th, the price was 261.10, which is far far from the prediction.
Most ML for trading today is not working really, and DSP gives better results since its more transparent.
Can someone tell me what he wrote in .value in line number 60??
I can’t wait to fully comprehend this
Traceback (most recent call last):
data = web.DataReader(company, 'yahoo', start, end)
TypeError: string indices must be integers
Why I am getting this error and how to overcome?
Pls, help!
I have the same error and I don't know what to do. Have you solved it?
@@bielvv9148 you can use yfinance instead
Thank you so much for the video, this is my first time using python, I copy the code step by step and lots of errors when it run maybe I do not have correct settings for those imports. Luckily it works well on Colab. I've learnt some logic on how it works thanks
It's not about which programming language you use, or which statistical model is applied. If you ever read the famous paper on the random walk theory, you will know it's impossible to predict stock prices.
ICDST ai predict really beats all forcasting tools.
Hi !
When i tried to run the last part of the code in spyder, i had this error (not a warning like the video):
ValueError: Error when checking input: expected lstm_4_input to have shape (60, 1) but got array with shape (59, 1)
How can i solve this?
I ran into the same problem, did you get a solution?
@@parthbansal2775 Look at your x_train = np.reshape lines.. those errors are regarding the reshaping usually
Look at your x_train = np.reshape lines.. those errors are regarding the reshaping usually
@@kyleyoung4974 Thank you very much I fixed that
@@parthbansal2775 Can you share how did you fixed pls? Im with the same issue, thanks!!
Awesome content! Keep up going