📊Machine Learning Techniques

Sol-More utilizes machine learning techniques to enhance its price prediction models, enabling it to analyze historical data, identify patterns, and predict future price movements with high accuracy. These techniques allow the bot to adapt to changing market conditions and improve its trading strategies over time.

Training and Objective Functions

Machine learning models require training on historical data to make accurate predictions. The training process involves feeding the model with a large dataset of past prices and other relevant features, allowing it to learn the underlying patterns and relationships. Sol-More uses supervised learning techniques, where the model is trained on labeled data (i.e., input features with known output prices).

Linear Regression: Linear regression is a fundamental machine learning technique used for predicting continuous values. In the context of price prediction, it models the relationship between input features (e.g., historical prices, volume) and the target variable (future price). The model aims to find the optimal weights θ\thetaθ that minimize the prediction error. The hypothesis function for linear regression is:

Neural Networks: Neural networks can capture complex nonlinear relationships in the data, making them suitable for more sophisticated price prediction tasks. A neural network consists of multiple layers of neurons, each applying a nonlinear activation function to a weighted sum of inputs. The output of a layer is given by:

The network is trained using back propagation and gradient descent, adjusting the weights and biases to minimize a loss function, typically mean squared error for regression tasks.