The strategy consists of a form of Deep Q-Learning (DQN), which is a reinforcement learning technique used to make decisions based on interaction with an environment. In this case, the environment is the returns of two cryptoassets, and the decisions are whether to take a long (buy) or short (sell) position in these assets.
Basic Steps of the Strategy:
Initialization:
A main neural network is created to predict Q-values, which indicate the quality of actions in a given state.
A target neural network is created that is periodically updated to stabilize learning.
Exploration vs. Exploitation:
At first, random actions are chosen to explore the environment.
As time passes, more actions based on the neural network are chosen to exploit the acquired knowledge.
Interaction with the Environment:
The agent (your model) takes an action in the environment and receives a reward based on the action taken and the new state reached.
This information (state, action, reward, new state) is stored in an experience memory.
Training:
Regularly, random samples are taken from the experience memory and used to train the main neural network.
The target neural network is periodically updated to reflect the weights of the main network.
Policy Update:
The agent's policy (the trading strategy) is constantly adjusted based on the learning of the neural network.
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