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  1. Adaptive indicators

    by , 12-12-2022 at 12:31 PM
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    Probably, every trader's dream is an indicator able to adapt to the current market situation, define flat and trend segments and consider relevant price changes. Conventional technical indicators use constant ratios when handling input signals. These ratios do not depend in any way on the characteristics of the input signal and its changes over time.

    Adaptive indicators are distinguished by the presence of feedback between the values of
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  2. Developing an Expert Advisor from scratch (Part 30): CHART TRADE as an indicator?

    by , 12-10-2022 at 01:35 AM
    In a previous article Development of a trading EA from scratch (Part 29), we have removed Chart Trade from the EA. Previously, we did the same to some other thinks, like Volume At Price and Times & Trade, to improve the EA performance and reliability. By removing Chart Trade from the EA, we are left with only the basic order system. Although this may seem insufficient to some users, the EA can actually do all the work. But there are people who like to enter and exit trades in the market, but they
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  3. Neural networks made easy (Part 30): Genetic algorithms

    by , 12-07-2022 at 01:36 AM
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    We continue to study model training algorithms. All the previously considered algorithms used an analytical method for determining the direction and strength of changes in model parameters during the learning process. While the methods to solve these problems turn to be inefficient. In such cases, we resort to evolutionary optimization methods.
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    1. Neural networks made easy
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  4. Neural networks made easy (Part 29): Advantage Actor-Critic algorithm

    by , 12-02-2022 at 01:12 AM
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    We continue to explore reinforcement learning methods. In previous articles we discussed methods for approximating the Q-learning Reward function and the policy gradient function learning. Each method has its own advantages and disadvantages. It would be great to use the maximum of their advantages when building and training models. When trying to find methods minimizing the shortcomings of the algorithms used, we often try to build certain conglomerates
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  5. Population optimization algorithms: Particle swarm (PSO)

    by , 11-21-2022 at 01:43 AM
    In this article, I will try to find out if the algorithm is actually good for solving complex problems. In the classical version of the algorithm and in many of its modifications, there are significant limitations associated with the fact that the optimized function must be smooth and continuous, which means it is completely unsuitable for discrete functions. However, in line with the series of articles, all the algorithms under consideration will be changed in such a way (if there are any restrictions)
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