QBotAI

QBotAI is a trading robot designed to automate trading in financial markets. It uses a reinforcement learning technique called Q-learning to optimize its trading strategies.

Q-learning allows the robot to learn from experience through trial and error. The robot constantly interacts with the trading environment, makes trade transactions and receives a reward or penalty depending on the result of the transaction. Over time, QBotAI learns to choose the optimal actions to maximize the reward received.

For example, the classic Atari game Breakout was used to train AI to play the game using Q-learning. At first, the agent acted randomly, but with practice, he learned to optimally move the racket to hit the ball and prevent it from falling. Thus, AI was able to reach and even surpass human levels in this game thanks to reinforcement learning.

This multi-currency expert uses 28 currency pairs. For forecasts, the advisor uses recurrent neural networks of the LSTM type, models trained in the Python language environment, and imported into the advisor using a new function in mql5 for loading ONNX models

In addition to the main model UseModel1_QLearning, the advisor has an auxiliary model UseModel2 which is trained in the usual standard way, and if you use two models simultaneously (these are the default settings), then the result of a decision to sell or buy will be made as the sum of these two models at once, which is much more accurate makes advisor predictions than using models separately.

The profitability of currency pairs can be viewed in the strategy tester after the test in the "Journal" tab, and unprofitable pairs can be disabled as needed in the advisor settings, or you can select the most profitable ones using the Active option. Before the test, set Active = True for all pairs and after the test, disable unprofitable pairs Active = False, and you can start trading.

By default, the most profitable 9 pairs out of 28 are selected, but you can choose more currency pairs.

The advisor was trained until 2023 year, it is advisable to run the back test from 2023