Why is my explained variance a negative value for regression models?

This is the code I'm using to compare performance metrics of different regression models on my timeseries data (basically I'm trying to predict certain values based off the month & day of the year)

import sklearn.metrics as metrics
def regression_results(y_true, y_pred):
    predictions=y_pred
    test_labels=y_true
    errors = abs(predictions - test_labels)
    # Print out the mean absolute error (mae)
    print('Mean Absolute Error:', round(np.mean(errors), 2))
    # Calculate mean absolute percentage error (MAPE)
    mape = 100 * (errors / test_labels)
    # Calculate and display accuracy
    accuracy = 100 - np.mean(mape)
    print('Accuracy:', round(accuracy, 2), '%.')
    # Regression metrics
    explained_variance=metrics.explained_variance_score(y_true, y_pred)
    mean_absolute_error=metrics.mean_absolute_error(y_true, y_pred) 
    mse=metrics.mean_squared_error(y_true, y_pred) 
    mean_squared_log_error=metrics.mean_squared_log_error(y_true, y_pred)
    median_absolute_error=metrics.median_absolute_error(y_true, y_pred)
    r2=metrics.r2_score(y_true, y_pred)
    print('explained_variance: ', round(explained_variance,4))    
    print('mean_squared_log_error: ', round(mean_squared_log_error,4))
    print('r2: ', round(r2,4))
    print('MAE: ', round(mean_absolute_error,4))
    print('MSE: ', round(mse,4))
    print('RMSE: ', round(np.sqrt(mse),4))

These are the results I'm getting for randomforestregressor model (and all other regression models display similar results, including the negative explained variance value).

Mean Absolute Error: 0.02
Accuracy: 98.41 %.
explained_variance:  -0.4901
mean_squared_log_error:  0.0001
r2:  -0.5035
MAE:  0.0163
MSE:  0.0004
RMSE:  0.0205

Does this mean my data is bad?