Stock Price Prediction And Forecasting Using Stacked LSTM- Deep Learning

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A Machine Learning Model for Stock Market Prediction. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange
References: Jason Browniee Machine Learning Mastery Blogs

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I asked my teacher where should I start if I want to apply machine learning for stocks and he sent me this video.
You are AMAZING. Thanks a lot sir!!

preetkamal

First time I understood this LSTM. You have explained the LSTM very easily and in a very crisp manner.

dubaimetrodude

Let me congratulations first, I saw other videos that explain the same method using LSTM. However, you were the only one that made it predicting 30 days in the future, amazing! if you can make more videos like this maybe improving this method, explain it with all the details and logic behind it no matter if the video last 2 or 3 hours I'll see it. Finally an idea that you can make with this method is doing it with some other features using volume as an example and close price, instead of one feature, amazing.

aandresriera

When you compute the mean squared error you used "train_predict" thier values 200, 201 (not scaling) with "y_train" thier values are scaling ( betwen -1 and 1) so you get a big value of mean_squared error

youssefkhachaf

Hi, this is a good tutorial about using LSTM in Python. BUT: it is the wrong application. When you zoom into the curve, e.g. day 1150 to day 1258, you can see that the "prediction" works pretty much like a low pass filter. That means: there is a lot of delay. Also, when you compare the performance using RSME of your example to a simple 1-step delay ( predicted cost for the next day = actual cost from actual day), the 1-step delay gives better results.

michaelg.

Very much impressed by the way you presented it. So simple and informative which no one shares as secret that is predicting nxt 30 days. Only few members share it in youtube. Hats off to you. Thanks and all the best for your future videos. May god bless you.

loganathansiva

SIr, you have done data leakage twice.
1) You should use scaling after splitting the data not before
2) You are fitting the model using test data as validation data which should not be done

Correct me if I'm wrong

arnabdas

Krish, the best part in your teaching is... somehow, you match the audience frequency!

shalinianunay

Easily the best video about the LSTM in YouTube. Incredible explanation skill. Thank you for this priceless source Sir!

reverseengineer

If I may, add an edit in the code...the mean squared error you compared are of scaler transformed and non scalar transformed data..eg: y_train values are scalered transformed while train_predict data is already inversed scalar. Hence you got RMSE value of 100+. Compare root mean squared error before inverse transforming the predicted data. RMSE of 0 to 0.5 is quite acceptable.

prasannarajbanshi

"Wow!!" is the word for explanation skill.

aniketsharma

Dear Sir, If you are should not use for real world project then please mention the step also what kind of step should be consider in real world case study solution interms of stock prise prediction?
Please provide your valuable feedback?

vipindube

scaling using a min-max scaler before the train and test split is a kind of data leakage for the test set. Isn't it?

mridulpandey

At this step:
model=Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(100, 1)))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
I am getting a following error:
NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported

SDM

This is good from an academic point of view and learning about LSTMs and application, but I think that a lot more goes into building a robust profitable automated trading system, specially given the current situation(volatile market), anyways an informative video, keep up the good work!

rahulbhatia

Aren’t you supposed to apply the Min Max scaler after the train split? I believe what you did might result in a data leak.

yaswanthbangaru

one problem is you are taking the price for MinMax scaling between 0 and 1. That would create a problem as you can't enter a "maximum price". Maybe use % change instead.

subodh.r

You can also use yahoo to fetch the stocks data. It doesn't have api call restrictions

radioactive

may god give you more strength to make such valuable video man!! so grateful for you.

KomalVerma-oonx

After data is split please check the train size and test size, total size is 1258 including zero but the train and test combined is 1256, two data points are missing, also there is a lag in prediction

Vamshikrishnadomala

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