Algorithmic Trading and Price Prediction using Python Neural Network Models

Welcome to our video on Algorithmic Trading and Price Prediction using Neural Network Models in Python. In this tutorial, we will be demonstrating how to apply neural network models to algorithmic trading and price prediction.

We will start with an introduction to algorithmic trading and explain its importance in today's financial markets. Then, we will discuss how neural network models can be used to make predictions on stock prices and other financial data.

Throughout the video, we will be using Python to build and train our neural network models. We will be working with real-world financial data, and we will show you how to preprocess and clean the data before feeding it into our models.

If you're new to the topic of neural networks, don't worry! We will provide a clear and straightforward explanation of how neural networks work, and how they can be applied to solve complex prediction problems in the financial industry.

So, if you're a data scientist looking to expand your skillset, or a trader looking to improve your trading strategies, this video is for you! Make sure to watch the previous video in this playlist for additional background information.

Thank you for watching, and good luck with your algorithmic trading and price prediction journey!

#tradingbots #pythoncoding #forexanalysis #python #algorithmictrading #machinelearning #technicalindicators #trading

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To download the code:

previous video to check for more info

00:00 Neural Networks Trading Signal Introduction
04:00 Neural Networks Trading Strategy In Python Code
12:13 Trend Predictions Results Using Neural Networks

[Nino]

This is insanely creative, this man coded an engulfing candlestick pattern into an ml model 😂😂 props to you

[sauce]

You have a great channel, thank you for including the download, its rare and its harder as a hands on learner. Have a great day sir

[bayestraat]

Very insightful perspective of using OHLC data. If anyone is wondering what's a good start to learn ML, I'd recommend the book ML For Finance by Jannes Klaas. Just try to study the book, research and do some code practice through one chapter each week and you'll be an expert in no time.

[diegorc]

Hi code trading I'm data scientist too. My suggestion is to optimize your threshold of your model prediction. In this case you have 3 labels so the threshold are 0.33, 0.66, 0.99

[JohnQuezadaHuayamave]

Excelente video, es muy bueno lo eh revisado varias veces uniendo esta información con otros videos queda excelente la estrategia.

[Jamil.francis]

Will watch it this evening. Thank you again

[ahmedegy]

Thank you for this video...as far as parameters, you may want to use keras tuner or similar optimization package for hyperparameter tuning, i.e, num of layers, nodes, activation function, learning rate, etc..also I would recommend that you focus more on features and feature engineering cause this is what actually makes the difference in any NN model

[kukikuki]

I think the problem may be that you use prices as inputs. The network learn static prices. Try to use percentage changes between prices.

[chandrimad]

Extremely beneficial. Thanks for sharing. I am learning a lot from your videos.

[dukeubong]

Very interesting work here, thanks for sharing this.

[TheAnonymus]

Hi, as I have seen in different researches, combining the LSTM model with CNN makes the accuracy better and outperforms other models as well. May you plan creating video on CNN as well?

[walidwardak]

thanks for providing good content and thanks for the code your videos make difference thanks again

[wingsoftechnology]

Thanks alot...It was a great knowledge sharing

[wayne]

Solid work! Nice to see the results change as the complexity of the model increased.

What's the most complex model that you've tried? Anything with multiple CNN layers?

[Master_of_Chess_Shorts]

Have you tried simplifying the problem? Removing the notrend target would probably help. We only want to act when we notice an uptrend or a downtrend. I find trying to classify 3 outcomes is much more complicated than one or two and makes it difficult to do better than naive or random.

[ShimoriUta]

You may be having a thing called Gradient Descente due to ReLU. Try switching to ELU.

[souradeepdas]

You are doing this little wrong. To predict correctly you need to use only tick value for every second for min 5 years. Then change the hidden_layer_sizes use 10 layers=(100, 100, 100, 200, 200, 200, 400, 400, 400, 800). I am using gpu mining rig 3070 ti x8 running 10 days continue, to predict. The accuracy is 70% in uptrend and 72% in downtrend. You can also implement moving average to make it little more accurate. But then you will need gpu. ^2= 64 3070 ti for 10 days with my case.

[ionjauregui]

Great channel and content, thumbs up. I will follow your contents from now on

[rverm]

ive been checking this book out. Machine Learning with Python for Everyone by addison wesley. dont know if will help . from what i gather in the preface it is a very hands on and practical book.

[leamon]

Awesome! Thanks for sharing. Would you consider making a video that uses convolutional neural network which converts all the technical analysis charts to images to predict stock market?