Antique

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 being very helpful to traders when handling volatile market conditions.

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