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PissarN9

Fully automated Expert developed to trade with EURUSD. Experts use unique artificial intelligence technology for market analysis to find the best entry points. EA contains self-adaptive market algorithms with reinforcement learning elements. Reinforcement machine learning differs from supervised learning in a way that it does not need labelled input/output pairs to be present, and it does not need sub-optimal actions to be explicitly corrected. Instead it focuses on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

Experts showed stable results on EURUSD in the 2010-2020 period. No dangerous methods of money management used, no martingale, no grid or hedge. Suitable for any broker conditions.


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Minus Shift
Hatsawat Khantayapiratkul
Fully automated Expert developed to trade with EURUSD. Experts use unique artificial intelligence technology for market analysis to find the best entry points. EA contains self-adaptive market algorithms with reinforcement learning elements. Reinforcement machine learning differs from supervised learning in a way that it does not need labelled input/output pairs to be present, and it does not need sub-optimal actions to be explicitly corrected. Instead it focuses on finding a balance between exp
EMA Hedging
Hatsawat Khantayapiratkul
Fully automated Expert developed to trade with EURUSD. Experts use unique artificial intelligence technology for market analysis to find the best entry points. EA contains self-adaptive market algorithms with reinforcement learning elements. Reinforcement machine learning differs from supervised learning in a way that it does not need labelled input/output pairs to be present, and it does not need sub-optimal actions to be explicitly corrected. Instead it focuses on finding a balance between exp
Base EMA
Hatsawat Khantayapiratkul
Fully automated Expert developed to trade with EURUSD. Experts use unique artificial intelligence technology for market analysis to find the best entry points. EA contains self-adaptive market algorithms with reinforcement learning elements. Reinforcement machine learning differs from supervised learning in a way that it does not need labelled input/output pairs to be present, and it does not need sub-optimal actions to be explicitly corrected. Instead it focuses on finding a balance between exp
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