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  1. Neural networks made easy (Part 61): Optimism issue in offline reinforcement learning

    by , 06-15-2024 at 11:58 AM
    Recently, offline reinforcement learning methods have become widespread, which promises many prospects in solving problems of varying complexity. However, one of the main problems that researchers face is the optimism that can arise while learning. The agent optimizes its strategy based on the data from the training set and gains confidence in its actions. But the training set is quite often not able to cover the entire variety of possible states and transitions of the environment. In a stochastic
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  2. Neural networks made easy (Part 60): Online Decision Transformer (ODT)

    by , 05-17-2024 at 11:58 AM
    The last two articles were devoted to the Decision Transformer method, which models action sequences in the context of an autoregressive model of desired rewards. As you might remember, according to the results of practical tests of two articles, the beginning of the testing period saw a fairly good increase in the profitability of the trained model results. Further on, the performance of the model decreases and a number of unprofitable transactions are observed, which leads to losses. The amount
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  3. Trader-friendly stop loss and take profit

    by , 04-17-2024 at 05:23 AM
    Stop loss and take profit are stop orders that close a position when the price reaches their value. Stop loss allows traders to limit losses, while take profit enables thjem to save their gains. The main advantage of using stop loss and take profit is the ability to control financial risks and use money management.
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  4. Deep Learning Forecast and ordering with Python and MetaTrader5 python package and ONNX model file

    by , 02-28-2024 at 04:53 PM
    Deep learning is a subfield of machine learning that focuses on artificial neural networks, inspired by the structure and function of the human brain. It involves training models to perform tasks without explicit programming but by learning patterns and representations from data. Deep learning has gained significant attention due to its ability to automatically learn hierarchical features and representations, making it effective in various domains such as image and speech recognition, natural language
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  5. Data label for time series mining (Part 6):Apply and Test in EA Using ONNX

    by , 02-10-2024 at 04:39 PM
    We discussed in the previous article how to use socket (websocket) to communicate between EA and python server to solve the backtesting problem, and also discussed why we adopted this technique. In this article, we will discuss how to use onnx, which is natively supported by mql5, to perform inference with our model, but this method has some limitations. If your model uses operators that are not supported by onnx, it may end in failure, so this method is not suitable for all models (of course, you
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