In our previous discussion on Markov Chains, linked here, we demonstrated how to use a transition matrix to understand the probabilistic behavior of the market. Our transition matrix summarized a lot of information for us. It not only guided us on when to buy and sell, it also informed us whether our market had strong trends or was mostly mean reverting. In today's discussion, we shall change our definition of the system state from the moving averages we used in our first discussion to the ...
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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