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| import pandas as pd | |
| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from seaborn import FacetGrid | |
| import plotly.express as px | |
| def get_top_best_behaviour_markets(markets_data: pd.DataFrame): | |
| """Function to paint the top markets with the lowest metric of distribution gap""" | |
| sorted_data = markets_data.sort_values(by="dist_gap_perc", ascending=False) | |
| top_best_markets = sorted_data[["title", "sample_datetime", "dist_gap_perc"]].head( | |
| 5 | |
| ) | |
| return gr.DataFrame(top_best_markets) | |
| def get_distribution_plot(markets_data: pd.DataFrame): | |
| """Function to paint the density plot of the metric distribution gap percentage""" | |
| # A kernel density estimate (KDE) plot is a method for visualizing the distribution of | |
| # observations in a dataset, analogous to a histogram. KDE represents the data using a | |
| # continuous probability density curve in one or more dimensions. | |
| sns.set_theme(palette="viridis") | |
| plt.figure(figsize=(10, 5)) | |
| plot = sns.kdeplot(markets_data, x="dist_gap_perc", fill=True) | |
| # TODO Add title and labels | |
| # Display the plot using gr.Plot | |
| return gr.Plot(value=plot.get_figure()) | |
| def get_kde_with_trades(markets_data: pd.DataFrame): | |
| """Function to paint the density plot of the metric in terms of the number of trades""" | |
| plot = sns.kdeplot(markets_data, x="dist_gap_perc", y="total_trades", fill=True) | |
| plt.ylabel("Total number of trades per market") | |
| return gr.Plot(value=plot.get_figure()) | |
| def get_regplot_with_mean_trade_size(markets_data: pd.DataFrame): | |
| """Function to Plot data and a linear regression model fit between the metric and the mean trade size""" | |
| regplot = sns.regplot(markets_data, x="dist_gap_perc", y="mean_trade_size") | |
| plt.ylabel("Mean trade size in USD") | |
| return gr.Plot(value=regplot.get_figure()) | |
| def get_correlation_map(markets_data: pd.DataFrame): | |
| """Function to paint the correlation between different variables""" | |
| columns_of_interest = [ | |
| "total_trades", | |
| "dist_gap_perc", | |
| "liquidityMeasure", | |
| "mean_trade_size", | |
| ] | |
| data = markets_data[columns_of_interest] | |
| # Compute the correlation matrix | |
| correlation_matrix = data.corr() | |
| # Create the heatmap | |
| heatmap = sns.heatmap( | |
| correlation_matrix, | |
| annot=True, # Show the correlation values | |
| cmap="coolwarm", # Color scheme | |
| vmin=-1, | |
| vmax=1, # Set the range of values | |
| center=0, # Center the colormap at 0 | |
| square=True, # Make each cell square-shaped | |
| linewidths=0.5, # Add lines between cells | |
| cbar_kws={"shrink": 0.8}, | |
| ) # Adjust the size of the colorbar | |
| # Set the title | |
| plt.title("Correlation Heatmap") | |
| # Rotate the y-axis labels for better readability | |
| plt.yticks(rotation=0) | |
| # Show the plot | |
| plt.tight_layout() | |
| return gr.Plot(value=heatmap.get_figure()) | |