in progress stage
1.The scope of your project: Decide on the specific tasks AI to perform, such as predicting price trends or identifying patterns in the chart.
2.Collect and preprocess data: Collect historical data for the assets to trade, such as price and volume data. Preprocess the data by cleaning it, handling missing values, and scaling it to a suitable range.
3.Choose a machine learning algorithm: There are several machine learning algorithms that could use to analyze the trading chart. Some popular options include linear regression, decision trees, and support vector machines.
4.Train the model: Use preprocessed data to train machine learning model. This typically involves splitting the data into training and testing sets, and using the training set to fit the model to the data.
5.Evaluate the model: Use the testing set to evaluate the performance of your model. This may involve calculating metrics such as accuracy or mean squared error.
6.Fine-tune the model: If the performance of model is not satisfactory, may need to fine-tune it by adjusting the hyperparameters or using a different algorithm.
7.Implement the model: Once satisfied with the performance of model, implement it in code for trading. This may involve setting up an API connection to the exchange, and integrating the model's predictions into your trading strategy.