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Stock Price Prediction using Transformer Deep Learning Architecture

Project description

TrendMaster: Stock Price Prediction using Transformer Deep Learning Architecture

TrendMaster leverages advanced Transformer deep learning architecture to provide highly accurate stock price predictions, enabling informed investment decisions.

Utilizing a wealth of data and sophisticated algorithms, TrendMaster stands out as a top-tier tool for financial forecasting.

Result

Installation

To get started with TrendMaster, run the following installation command:

pip install TrendMaster

Usage

Here's how to integrate TrendMaster into your Python projects:

from trendmaster import TrendMaster
#Initialize the TrendMaster object
tm = TrendMaster()
#Load your data
data = tm.load_data('path_to_your_data.csv')
#Train the model
tm.train(data, transformer_params={'num_layers': 3, 'dropout': 0.1})
#Perform inference
predictions = tm.infer('path_to_trained_model.pth')
print(predictions)

Star History

Star History Chart

Our Transformer-based prediction model is trained on a large dataset of historical stock prices, giving it the ability to identify patterns and trends that would be impossible for a human to discern. The model's predictions are also highly accurate, with a mean average error of just a few percentage points.

Transformer-Future200

In addition to stock price prediction, TrendMaster also offers a range of other features, such as real-time data visualization and a user-friendly interface. With TrendMaster, you'll have all the information you need to make smart investment decisions.

Screenshot from 2021-07-15 18-26-49

So why wait? Try TrendMaster today and see the difference for yourself!

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0.1

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