Recurrent Neural Networks | LSTM Price Movement Predictions For Trading Algorithms

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This video presents a simple way to introduce RNN (recurrent neural networks) and LSTM (long short term memory networks) for price movement predictions in trading Forex, Stock Market and Crypto. The algorithm is written in python language and can be downloaded so you can experiment on by changing the algorithm parameters. Deep Learning has been advertised as the ultimate prediction algorithm, in here we put it to the test in trading and price movement predictions. Technical indicators are added as well such as the relative strength indicator RSI and moving averages MA to extend the input data for the trading model.

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#forexanalysis #neuralnetworks #deeplearning #tradingbots #tradingbot #forex #stockmarket #stocktrading #stocktradingstrategies #algotrading #python

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For anyone wondering, this model means nothing as many previous videos have done, it’s simply a lag prediction, day 1 is 100: day 2 is 120 and model predicts 99 for day 2 for example, on day 3 the model will predict a number near day 2 ie 120, thus generating a lag graph.

Now how to actually predict it works ? Let it predict percent change difference not just price therefore isn’t as affected by previous day price and the lag effect is gone and you can actually see how many correct predictions happen.

yaas
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Great job dude!! I'm learning a lot watching your videos. You have an excellent way of teaching, using plain, clear and concise English. Thank you!!!

vulture
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Wow, you're awesome! There's lots of stock/crypto predictions on YouTube but hardly any for forex so this is great for learning it

DalazG
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Mate, you did an amasing job, you are better instructor than 99% of university professors, they only give you books to read and 0 coding practice. Additionally, I believe that the main reason of this video is to show how to program and how actually machine learning works, therefore, it depends on the person what indicatora use and what timeframes use. You did 90% of the job. Congratulations and thanks for sharing

Maximus.
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Be careful, you did a very common mistake: the scaling of data must be done after train/test splitting and not before, otherwise it can introduce data leakage.

jorgitozor
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Thank u so much 4 ur efforts
most of predictions or all them kind of shifting actual data by certain value
it should be like this> use real data up to now then predict what after that

sbkyoutu
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Amazing work! Although I have a few questions, sorry for the noob question, as I’m still learning CNN, just a normal pandas and numpy user. When using these models to predict, are there any cases of accidental data snooping? What are the best practices to avoid it? Also when the neural network predicts one of the next days closing price and moves to the next one, does it use the predicted closing price from previous day or the actual data’s adjusted close for computation? I’m sorry if it’s a stupid question, I’m just curious to understand this better. Thankyou!

foobarAlgorithm
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first thank you a lot for such a great video. if I am not wrong the results you shared at the end of the video are the scaled one. what do you think will be the best approach to rescale them and will scaling the data increase the predation gaps?

abdsh
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Excellent work. As I got motivated with your video, I also watched your python training in Udemi. Congrats!!

antonioaqueiroz
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Thank you, very much brother. I have been searching for 3 days for how to make strategies for NN classification. I have got a lot of ideas from your video. seriously thank you very much brother.

jadonsumit
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helllo, thanks a lot for your video, it was wonderful to watch. I have a question, in the end, your predicted prices are not scaled back to normal, it is possible for you to telle me how i can scale everything back to what it originally waws ? thank you !

arthurhottier
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Great job, I learned a lot, but I have a question, what should I do/ what the code should I use if I want to forecast next 4-5-10 days. as per as I understood from this useful video that you built a model to predict the next closing price and compare it with the real data you already have, but my question is, what if I want to forecast for n days??

outlinefrom
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along with this.. can you also include how market depth affect prediction. . and financial news of a company affects the price prediction at that time.

phaneendhraajaythota
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I can not wait until the next video.Thank you so much!

a.winath
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instead of predicting the next day price outcome, can we make the model to predict a trend? I'm not sure about the ideal timeframe but maybe between 2 weeks to 2 months ahead? So it's more like a medium term price predictor...

If it's possible I would be very interested to see the next part the series to dig deeper into this.
As always great video!

steeltormentors
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Nice video! How do we use this to predict the prices for tomorrow??

aaravguduru
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Super!!!!

I didn't understand one point. When u define data[TargetClass] and set 1 if data ... Greater than 0 or 0 for i in a range.

What it means to set it to 1 or 0?

dfcastro
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Thank you for this video, would you have any idea, genreally speaking how to approach this if one had multiple stocks to predict?

PoncyPenguin
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Thanks for the Video!
Im currenly learnig a few things and your videos helped me a lot.
I noticed that the model will give me different results with the same dataset and same parameters.
Is there any way to avoid this behavior?

tradercrypto_lad
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Excelente explicación, me gusto mucho como fuiste detallando cada parte, estoy revisando todo tus videos y los veo varias veces para estar seguro que todo comprendí y no perdiera algo.

JohnQuezadaHuayamave