1. Correlation Analysis: Calculate the correlation between the Ichimoku cloud components and the next day candlestick price to understand the strength and direction of the relationship.
2. Visualization: Create scatter plots to visualize the relationship between each Ichimoku cloud component and the next day candlestick price.

1. Correlation Analysis: Calculate the correlation between each column (from column 1 to column 50) and the next day candlestick price to understand the strength and direction of the relationship.
2. Visualization: Create scatter plots to visualize the relationship between each column and the next day candlestick price.

1. Data Preparation: Prepare the data by selecting the relevant columns (from column 1 to column 50) as features and the next day candlestick price as the target variable.
2. Train-Test Split: Split the data into training and testing sets to evaluate the model's performance.
3. Model Training: Train a regression model (e.g., Linear Regression) on the training data.
4. Model Evaluation: Evaluate the model's performance on the testing data using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
5. Prediction: Use the trained model to make predictions on the testing data.

Regression Analysis Summary

• Features: Columns 1 to 50
• Target: Next day candlestick price
• Train-Test Split: 80% training, 20% testing

• Mean Absolute Error (MAE): 1.36e-11
• Mean Squared Error (MSE): 3.98e-22
• R-squared (R²): 1.0

• The feature importance plot shows the contribution of each feature to the model's predictions. Features with higher importance values have a greater impact on the prediction of the next day candlestick price.

• The scatter plot of actual vs predicted values shows that the model's predictions are very close to the actual values, indicating a good fit.

Detailed Explanation of Feature Importance

1. Coefficient Values: The coefficients (or weights) assigned to each feature by the linear regression model indicate the strength and direction of the relationship between the feature and the target variable. A positive coefficient means that as the feature value increases, the target variable also increases, while a negative coefficient means that as the feature value increases, the target variable decreases.
2. Magnitude of Coefficients: The magnitude (absolute value) of the coefficients indicates the importance of the feature. Features with larger absolute coefficient values have a greater impact on the prediction of the target variable. For example, if a feature has a coefficient of 2.5, it has a stronger influence on the target variable compared to a feature with a coefficient of 0.5.
3. Feature Importance Plot: The bar plot of feature importance provides a visual representation of the coefficients. Features are sorted by their importance, with the most important features at the top. This helps in identifying which features are most influential in predicting the next day candlestick price.
4. Interpreting the Plot: In the feature importance plot, each bar represents a feature, and the length of the bar represents the magnitude of its coefficient. Features with longer bars are more important. The direction of the bar (positive or negative) indicates whether the feature has a positive or negative relationship with the target variable.