import numpy as np import dash from dash import dcc, html import plotly.graph_objs as go import threading def display_actual_vs_predicted(y_test, test_preds, timestamps, n_plot=200): import plotly.offline as pyo n_plot = min(n_plot, len(y_test)) plot_indices = timestamps[:n_plot] actual = y_test[:n_plot] predicted = test_preds[:n_plot] trace_actual = go.Scatter(x=plot_indices, y=actual, mode='lines', name='Actual') trace_predicted = go.Scatter(x=plot_indices, y=predicted, mode='lines', name='Predicted') data = [trace_actual, trace_predicted] layout = go.Layout( title='Actual vs. Predicted BTC Close Prices (Test Set)', xaxis={'title': 'Timestamp'}, yaxis={'title': 'BTC Close Price'}, legend={'x': 0, 'y': 1}, margin={'l': 40, 'b': 40, 't': 40, 'r': 10}, hovermode='closest' ) fig = go.Figure(data=data, layout=layout) pyo.plot(fig) def plot_target_distribution(y_train, y_test): import plotly.offline as pyo trace_train = go.Histogram( x=y_train, nbinsx=100, opacity=0.5, name='Train', marker=dict(color='blue') ) trace_test = go.Histogram( x=y_test, nbinsx=100, opacity=0.5, name='Test', marker=dict(color='orange') ) data = [trace_train, trace_test] layout = go.Layout( title='Distribution of Target Variable (Close Price)', xaxis=dict(title='BTC Close Price'), yaxis=dict(title='Frequency'), barmode='overlay' ) fig = go.Figure(data=data, layout=layout) pyo.plot(fig) def plot_predicted_vs_actual_log_returns(y_test, test_preds, timestamps=None, n_plot=200): import plotly.offline as pyo import plotly.graph_objs as go n_plot = min(n_plot, len(y_test)) actual = y_test[:n_plot] predicted = test_preds[:n_plot] if timestamps is not None: x_axis = timestamps[:n_plot] x_label = 'Timestamp' else: x_axis = list(range(n_plot)) x_label = 'Index' # Line plot: Actual vs Predicted over time trace_actual = go.Scatter(x=x_axis, y=actual, mode='lines', name='Actual') trace_predicted = go.Scatter(x=x_axis, y=predicted, mode='lines', name='Predicted') data_line = [trace_actual, trace_predicted] layout_line = go.Layout( title='Actual vs. Predicted Log Returns (Test Set)', xaxis={'title': x_label}, yaxis={'title': 'Log Return'}, legend={'x': 0, 'y': 1}, margin={'l': 40, 'b': 40, 't': 40, 'r': 10}, hovermode='closest' ) fig_line = go.Figure(data=data_line, layout=layout_line) pyo.plot(fig_line, filename='log_return_line_plot.html') # Scatter plot: Predicted vs Actual trace_scatter = go.Scatter( x=actual, y=predicted, mode='markers', name='Predicted vs Actual', opacity=0.5 ) # Diagonal reference line min_val = min(np.min(actual), np.min(predicted)) max_val = max(np.max(actual), np.max(predicted)) trace_diag = go.Scatter( x=[min_val, max_val], y=[min_val, max_val], mode='lines', name='Ideal', line=dict(dash='dash', color='red') ) data_scatter = [trace_scatter, trace_diag] layout_scatter = go.Layout( title='Predicted vs Actual Log Returns (Scatter)', xaxis={'title': 'Actual Log Return'}, yaxis={'title': 'Predicted Log Return'}, showlegend=True, margin={'l': 40, 'b': 40, 't': 40, 'r': 10}, hovermode='closest' ) fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter) pyo.plot(fig_scatter, filename='log_return_scatter_plot.html') def plot_predicted_vs_actual_prices(actual_prices, predicted_prices, timestamps=None, n_plot=200): import plotly.offline as pyo import plotly.graph_objs as go n_plot = min(n_plot, len(actual_prices)) actual = actual_prices[:n_plot] predicted = predicted_prices[:n_plot] if timestamps is not None: x_axis = timestamps[:n_plot] x_label = 'Timestamp' else: x_axis = list(range(n_plot)) x_label = 'Index' # Line plot: Actual vs Predicted over time trace_actual = go.Scatter(x=x_axis, y=actual, mode='lines', name='Actual Price') trace_predicted = go.Scatter(x=x_axis, y=predicted, mode='lines', name='Predicted Price') data_line = [trace_actual, trace_predicted] layout_line = go.Layout( title='Actual vs. Predicted BTC Prices (Test Set)', xaxis={'title': x_label}, yaxis={'title': 'BTC Price'}, legend={'x': 0, 'y': 1}, margin={'l': 40, 'b': 40, 't': 40, 'r': 10}, hovermode='closest' ) fig_line = go.Figure(data=data_line, layout=layout_line) pyo.plot(fig_line, filename='price_line_plot.html') # Scatter plot: Predicted vs Actual trace_scatter = go.Scatter( x=actual, y=predicted, mode='markers', name='Predicted vs Actual', opacity=0.5 ) # Diagonal reference line min_val = min(np.min(actual), np.min(predicted)) max_val = max(np.max(actual), np.max(predicted)) trace_diag = go.Scatter( x=[min_val, max_val], y=[min_val, max_val], mode='lines', name='Ideal', line=dict(dash='dash', color='red') ) data_scatter = [trace_scatter, trace_diag] layout_scatter = go.Layout( title='Predicted vs Actual Prices (Scatter)', xaxis={'title': 'Actual Price'}, yaxis={'title': 'Predicted Price'}, showlegend=True, margin={'l': 40, 'b': 40, 't': 40, 'r': 10}, hovermode='closest' ) fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter) pyo.plot(fig_scatter, filename='price_scatter_plot.html') def plot_prediction_error_distribution(predicted_prices, actual_prices, nbins=100): """ Plots the distribution of signed prediction errors between predicted and actual prices, coloring negative errors (under-prediction) and positive errors (over-prediction) differently. """ import plotly.offline as pyo import plotly.graph_objs as go errors = np.array(predicted_prices) - np.array(actual_prices) # Separate negative and positive errors neg_errors = errors[errors < 0] pos_errors = errors[errors >= 0] trace_neg = go.Histogram( x=neg_errors, nbinsx=nbins, opacity=0.75, marker=dict(color='blue'), name='Negative Error (Under-prediction)' ) trace_pos = go.Histogram( x=pos_errors, nbinsx=nbins, opacity=0.75, marker=dict(color='orange'), name='Positive Error (Over-prediction)' ) layout = go.Layout( title='Distribution of Prediction Errors (Signed)', xaxis=dict(title='Prediction Error (Predicted - Actual)'), yaxis=dict(title='Frequency'), barmode='overlay', bargap=0.05 ) fig = go.Figure(data=[trace_neg, trace_pos], layout=layout) pyo.plot(fig, filename='prediction_error_distribution.html')