In the previous articles, we have seen how powerful both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are and how they can be deployed to help beat the market by providing us with valuable trading signals. In this one we are going to attempt combining two of the most powerful techniques CNN and RNN and observe their predictive impact in the stock market. But before that let us briefly understand what CNN and RNN are all about. more...
We will analyze all of Deriv’s synthetic markets individually, starting with their best known synthetic market, the Boom 1000. The Boom 1000 is notorious for its volatile and unpredictable behavior. The market is characterized by slow, short and equally sized bear candles that are randomly followed by violent, skyscraper sized bull candles. The bull candles are especially challenging to mitigate because the ticks associated with the candle normally aren’t sent to the client terminal, meaning ...
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 ...
The article considers the theoretical application of quantization in the construction of tree models No complex mathematical equations are used. While writing the article, I discovered the absence of established unified terminology in the scientific works of different authors, so I will choose the terminology options that, in my opinion, best reflect the meaning. Besides, I will use the terms of my own in the matters left unattended by other researchers. This article will use terms and concepts ...
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