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In this article, we will develop a script from scratch to visualize deals during retrospective analysis of trading decisions. Simply put, if we trade manually and analyze our past market entries in order to improve our trading performance, we want to spend as little time as possible on manual history analysis and the purely technical work associated with it: opening charts, searching for deals in history, manually saving print screens in the terminal, independently drawing Stop Loss and Take

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Reinforcement Learning (RL) allows trading systems to learn from their environment or market data and thus improve their ability to trade over time. RL enables adaptation to changing market conditions, making it suitable for certain dynamic financial markets and securities. Financial markets are unpredictable, as often they feature a high degree of uncertainty. RL excels at making decisions under uncertainty by continuously adjusting its actions based on received feedback (rewards), thus

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William Howe Foote (1874-1965)
Summer 1913

Bo Larsson (Swedish, b. 1945)
Västerbron 1983

Louis Ritman (1889-1963)
A Day in July 1918