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120 lines
3.5 KiB
120 lines
3.5 KiB
"""Common plotting utilities for experiments."""
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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import pandas as pd
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def setup_plot_style():
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"""Set up default plotting style."""
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plt.style.use('seaborn-v0_8-whitegrid')
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sns.set_palette("husl")
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plt.rcParams['figure.figsize'] = (12, 6)
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plt.rcParams['font.size'] = 10
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def plot_ic_series(ic_by_date: pd.Series, title: str = "IC Over Time",
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figsize: tuple = (14, 4)) -> plt.Figure:
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"""Plot IC time series with rolling mean.
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Args:
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ic_by_date: Series with datetime index and IC values
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title: Plot title
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figsize: Figure size
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Returns:
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Matplotlib figure
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"""
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fig, ax = plt.subplots(figsize=figsize)
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# Plot raw IC
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ax.plot(ic_by_date.index, ic_by_date.values, alpha=0.5, color='gray', label='Daily IC')
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# Plot rolling mean
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rolling = ic_by_date.rolling(20, min_periods=5).mean()
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ax.plot(rolling.index, rolling.values, color='blue', linewidth=2, label='20-day MA')
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# Add mean line
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mean_ic = ic_by_date.mean()
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ax.axhline(y=mean_ic, color='red', linestyle='--',
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label=f'Mean IC: {mean_ic:.4f}')
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ax.axhline(y=0, color='black', linestyle='-', alpha=0.3)
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ax.set_title(title)
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ax.set_xlabel('Date')
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ax.set_ylabel('Information Coefficient')
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ax.legend(loc='upper right')
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plt.tight_layout()
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return fig
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def plot_cumulative_returns(returns: pd.Series, title: str = "Cumulative Returns",
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figsize: tuple = (12, 6)) -> plt.Figure:
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"""Plot cumulative returns.
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Args:
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returns: Series with datetime index and daily returns
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title: Plot title
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figsize: Figure size
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Returns:
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Matplotlib figure
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"""
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fig, ax = plt.subplots(figsize=figsize)
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cumulative = (1 + returns).cumprod()
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ax.plot(cumulative.index, cumulative.values, linewidth=1.5)
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ax.set_title(title)
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ax.set_xlabel('Date')
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ax.set_ylabel('Cumulative Return')
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ax.set_yscale('log')
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# Add final return annotation
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final_return = cumulative.iloc[-1] - 1
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ax.annotate(f'{final_return:.2%}',
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xy=(cumulative.index[-1], cumulative.iloc[-1]),
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xytext=(10, 0), textcoords='offset points',
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fontsize=10, color='green' if final_return > 0 else 'red')
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plt.tight_layout()
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return fig
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def plot_factor_distribution(factor: pd.Series, title: str = "Factor Distribution",
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figsize: tuple = (10, 6)) -> plt.Figure:
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"""Plot factor distribution with statistics.
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Args:
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factor: Series of factor values
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title: Plot title
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figsize: Figure size
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Returns:
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Matplotlib figure
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"""
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fig, axes = plt.subplots(1, 2, figsize=figsize)
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# Histogram
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axes[0].hist(factor.dropna(), bins=100, alpha=0.7, edgecolor='black')
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axes[0].set_title(f'{title} - Distribution')
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axes[0].set_xlabel('Value')
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axes[0].set_ylabel('Frequency')
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# Q-Q plot
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from scipy import stats
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stats.probplot(factor.dropna(), dist="norm", plot=axes[1])
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axes[1].set_title(f'{title} - Q-Q Plot')
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# Add statistics text
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stats_text = f"Mean: {factor.mean():.4f}\nStd: {factor.std():.4f}\n"
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stats_text += f"Skew: {factor.skew():.4f}\nKurt: {factor.kurtosis():.4f}"
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axes[0].text(0.95, 0.95, stats_text, transform=axes[0].transAxes,
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verticalalignment='top', horizontalalignment='right',
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bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
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plt.tight_layout()
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return fig
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