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  1. Market Reactions and Trading Strategies in Response to Dividend Announcements: Evaluating the Efficient Market Hypothesis in Stock Trading

    by , 09-10-2024 at 03:45 PM
    This paper intends to analyze the impact of dividend announcement on the stock market returns that investors earn.
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  2. How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session

    by , 08-24-2024 at 12:07 PM
    Several fellow traders sent emails or commented about how to use this Multi-Currency EA on brokers with symbol names that have prefixes and/or suffixes, and also how to implement trading time zones or trading time sessions on this Multi-Currency EA.

    Therefore, on this occasion I will explain and create a function to be implemented in the EA in the previous article FXSAR_MTF_MCEA by adding an automatic detection function for broker symbols with prefixes and/or suffixes and
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  3. Neural networks made easy (Part 40): Using Go-Explore on large amounts of data

    by , 06-29-2024 at 07:22 AM
    In the previous article "Neural networks made easy (Part 39): Go-Explore, a different approach to exploration", we familiarized ourselves with the Go-Explore algorithm and its ability to explore the environment.

    In this article, we will take a closer look at possible optimization methods for the Go-Explore algorithm to improve its efficiency over longer training periods.
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  4. Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method

    by , 06-25-2024 at 06:27 AM
    The Decision Transformer and all its modifications, which we discussed in recent articles, belong to the methods of Behavior Cloning (BC). We train models to repeat actions from "expert" trajectories depending on the state of the environment and the target outcomes. Thus, we teach the model to imitate the behavior of an expert in the current state of the environment in order to achieve the target.
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  5. Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)

    by , 06-21-2024 at 06:27 AM
    PDT jointly learns an embedding space of future trajectory as well as a future prior conditioned only on past information.. By conditioning action prediction on the target future embedding, PDT is endowed with the ability to "reason over the future". This ability is naturally task-independent and can be generalized to different task specifications.

    To achieve efficient online fine-tuning in downstream tasks, you can easily adapt the framework to new conditions by associating each
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