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 ...
In the previous article, we have laid the foundation for easy usage of the results obtained from optimization to build a ready-made EA with multiple instances of trading strategies working together. Now we do not have to manually enter the parameters of all used instances in the code or in the EA inputs. We only need to save the initialization string in a certain format to a file, or insert it as text into the source code so that the EA can use it. So far, the initialization ...
In this article, we will develop a risk manager class to control risks in algorithmic trading. The purpose of this article is to adapt the principles of controlled risk to algorithmic trading and to implement them in a separate class, so that everyone can verify the effectiveness of the risk standardization approach in intraday trading and investing in financial markets. The materials presented here will use and supplement the information summarized in the previous article "Risk manager for manual trading". ...
In the previous article, we talked a bit about machine learning, performed data augmentation, developed features for the future model and selected the best of them. Now it is time to move on and create a working machine learning model that will learn from our features and trade (hopefully successfully). To evaluate the model, we will write a custom Python tester that will help us evaluate the performance of the model and the beauty of the test graphs. For more beautiful test graphs and greater ...
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