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Visualization API

PortfolioVisualization

Static methods for visualizing portfolio performance.

Examples:

>>> PortfolioVisualization.plot_performance(data)
>>> PortfolioVisualization.plot_portfolio_return(data, weights)
>>> PortfolioVisualization.plot_allocation_pie(weights, tickers)
Source code in portfolio_analysis/visualization/plots.py 
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class PortfolioVisualization:
    """
    Static methods for visualizing portfolio performance.

    Examples
    --------
    >>> PortfolioVisualization.plot_performance(data)
    >>> PortfolioVisualization.plot_portfolio_return(data, weights)
    >>> PortfolioVisualization.plot_allocation_pie(weights, tickers)
    """

    @staticmethod
    def plot_performance(
        data: pd.DataFrame, figsize: tuple = (10, 6), show: bool = True
    ) -> plt.Figure:
        """
        Plot cumulative returns for all assets.

        Parameters
        ----------
        data : pd.DataFrame
            Price data with datetime index
        figsize : tuple, default (10, 6)
            Figure size
        show : bool, default True
            Whether to display the plot. Set to False for automated/server contexts.

        Returns
        -------
        plt.Figure
            The matplotlib figure object
        """
        fig, ax = plt.subplots(figsize=figsize)
        cumulative_returns = (1 + data.pct_change()).cumprod()
        cumulative_returns.plot(ax=ax)
        ax.set_title("Cumulative Returns by Asset")
        ax.set_xlabel("Date")
        ax.set_ylabel("Cumulative Returns")
        ax.grid(True, alpha=0.3)
        ax.legend()
        fig.tight_layout()

        if show:
            plt.show()

        return fig

    @staticmethod
    def plot_portfolio_return(
        data: pd.DataFrame,
        weights: list[float],
        figsize: tuple = (10, 6),
        show: bool = True,
    ) -> plt.Figure:
        """
        Plot cumulative portfolio return.

        Parameters
        ----------
        data : pd.DataFrame
            Price data with datetime index
        weights : list of float
            Portfolio weights
        figsize : tuple, default (10, 6)
            Figure size
        show : bool, default True
            Whether to display the plot. Set to False for automated/server contexts.

        Returns
        -------
        plt.Figure
            The matplotlib figure object
        """
        returns = data.pct_change().dropna()
        weighted_returns = returns.dot(weights)
        cumulative_portfolio_returns = (1 + weighted_returns).cumprod()

        fig, ax = plt.subplots(figsize=figsize)
        ax.plot(cumulative_portfolio_returns, linewidth=2, color="blue")
        ax.set_title("Portfolio Cumulative Return")
        ax.set_xlabel("Date")
        ax.set_ylabel("Growth of $1")
        ax.grid(True, alpha=0.3)
        fig.tight_layout()

        if show:
            plt.show()

        return fig

    @staticmethod
    def plot_allocation_pie(
        weights: list[float],
        tickers: list[str],
        figsize: tuple = (8, 8),
        show: bool = True,
    ) -> plt.Figure:
        """
        Plot portfolio allocation as a pie chart.

        Parameters
        ----------
        weights : list of float
            Portfolio weights
        tickers : list of str
            Ticker symbols
        figsize : tuple, default (8, 8)
            Figure size
        show : bool, default True
            Whether to display the plot. Set to False for automated/server contexts.

        Returns
        -------
        plt.Figure
            The matplotlib figure object
        """
        # Filter out zero weights
        non_zero = [(t, w) for t, w in zip(tickers, weights) if w > 0.001]
        labels, sizes = zip(*non_zero) if non_zero else ([], [])

        fig, ax = plt.subplots(figsize=figsize)
        ax.pie(
            sizes,
            labels=labels,
            autopct="%1.1f%%",
            startangle=90,
            colors=plt.cm.Paired.colors,
        )
        ax.set_title("Portfolio Allocation")
        ax.axis("equal")
        fig.tight_layout()

        if show:
            plt.show()

        return fig

    @staticmethod
    def plot_drawdown(
        data: pd.DataFrame,
        weights: list[float],
        figsize: tuple = (12, 6),
        show: bool = True,
    ) -> plt.Figure:
        """
        Plot portfolio drawdown over time.

        Parameters
        ----------
        data : pd.DataFrame
            Price data with datetime index
        weights : list of float
            Portfolio weights
        figsize : tuple, default (12, 6)
            Figure size
        show : bool, default True
            Whether to display the plot. Set to False for automated/server contexts.

        Returns
        -------
        plt.Figure
            The matplotlib figure object
        """
        returns = data.pct_change().dropna()
        weighted_returns = returns.dot(weights)
        cumulative = (1 + weighted_returns).cumprod()
        peak = cumulative.expanding(min_periods=1).max()
        drawdown = (cumulative / peak) - 1

        fig, ax = plt.subplots(figsize=figsize)
        ax.fill_between(
            drawdown.index, drawdown.values * 100, 0, alpha=0.5, color="red"
        )
        ax.plot(drawdown.index, drawdown.values * 100, color="darkred", linewidth=1)
        ax.set_title("Portfolio Drawdown")
        ax.set_xlabel("Date")
        ax.set_ylabel("Drawdown (%)")
        ax.grid(True, alpha=0.3)
        fig.tight_layout()

        if show:
            plt.show()

        return fig

    @staticmethod
    def plot_rolling_volatility(
        data: pd.DataFrame,
        weights: list[float],
        window: int = 21,
        figsize: tuple = (12, 6),
        show: bool = True,
    ) -> plt.Figure:
        """
        Plot rolling portfolio volatility.

        Parameters
        ----------
        data : pd.DataFrame
            Price data with datetime index
        weights : list of float
            Portfolio weights
        window : int, default 21
            Rolling window in trading days
        figsize : tuple, default (12, 6)
            Figure size
        show : bool, default True
            Whether to display the plot. Set to False for automated/server contexts.

        Returns
        -------
        plt.Figure
            The matplotlib figure object
        """
        returns = data.pct_change().dropna()
        weighted_returns = returns.dot(weights)
        rolling_vol = weighted_returns.rolling(window=window).std() * np.sqrt(
            TRADING_DAYS_PER_YEAR
        )

        fig, ax = plt.subplots(figsize=figsize)
        ax.plot(
            rolling_vol.index, rolling_vol.values * 100, linewidth=1.5, color="blue"
        )
        ax.set_title(f"{window}-Day Rolling Volatility (Annualized)")
        ax.set_xlabel("Date")
        ax.set_ylabel("Volatility (%)")
        ax.grid(True, alpha=0.3)
        fig.tight_layout()

        if show:
            plt.show()

        return fig

    @staticmethod
    def plot_returns_distribution(
        data: pd.DataFrame,
        weights: list[float],
        bins: int = 50,
        figsize: tuple = (10, 6),
        show: bool = True,
    ) -> plt.Figure:
        """
        Plot histogram of portfolio returns.

        Parameters
        ----------
        data : pd.DataFrame
            Price data with datetime index
        weights : list of float
            Portfolio weights
        bins : int, default 50
            Number of histogram bins
        figsize : tuple, default (10, 6)
            Figure size
        show : bool, default True
            Whether to display the plot. Set to False for automated/server contexts.

        Returns
        -------
        plt.Figure
            The matplotlib figure object
        """
        returns = data.pct_change().dropna()
        weighted_returns = returns.dot(weights)

        fig, ax = plt.subplots(figsize=figsize)
        ax.hist(weighted_returns * 100, bins=bins, edgecolor="black", alpha=0.7)
        ax.axvline(x=0, color="red", linestyle="--", linewidth=1)
        ax.axvline(
            x=weighted_returns.mean() * 100,
            color="green",
            linestyle="--",
            linewidth=2,
            label=f"Mean: {weighted_returns.mean()*100:.2f}%",
        )
        ax.set_title("Daily Returns Distribution")
        ax.set_xlabel("Daily Return (%)")
        ax.set_ylabel("Frequency")
        ax.legend()
        ax.grid(True, alpha=0.3)
        fig.tight_layout()

        if show:
            plt.show()

        return fig

plot_performance(data, figsize=(10, 6), show=True) staticmethod

Plot cumulative returns for all assets.

Parameters:

Name Type Description Default
data DataFrame

Price data with datetime index

 required
figsize tuple

Figure size

 (10, 6)
show bool

Whether to display the plot. Set to False for automated/server contexts.

 True

Returns:

Type Description
Figure

The matplotlib figure object
Source code in portfolio_analysis/visualization/plots.py 
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@staticmethod
def plot_performance(
    data: pd.DataFrame, figsize: tuple = (10, 6), show: bool = True
) -> plt.Figure:
    """
    Plot cumulative returns for all assets.

    Parameters
    ----------
    data : pd.DataFrame
        Price data with datetime index
    figsize : tuple, default (10, 6)
        Figure size
    show : bool, default True
        Whether to display the plot. Set to False for automated/server contexts.

    Returns
    -------
    plt.Figure
        The matplotlib figure object
    """
    fig, ax = plt.subplots(figsize=figsize)
    cumulative_returns = (1 + data.pct_change()).cumprod()
    cumulative_returns.plot(ax=ax)
    ax.set_title("Cumulative Returns by Asset")
    ax.set_xlabel("Date")
    ax.set_ylabel("Cumulative Returns")
    ax.grid(True, alpha=0.3)
    ax.legend()
    fig.tight_layout()

    if show:
        plt.show()

    return fig

plot_portfolio_return(data, weights, figsize=(10, 6), show=True) staticmethod

Plot cumulative portfolio return.

Parameters:

Name Type Description Default
data DataFrame

Price data with datetime index

 required
weights list of float

Portfolio weights

 required
figsize tuple

Figure size

 (10, 6)
show bool

Whether to display the plot. Set to False for automated/server contexts.

 True

Returns:

Type Description
Figure

The matplotlib figure object
Source code in portfolio_analysis/visualization/plots.py 
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@staticmethod
def plot_portfolio_return(
    data: pd.DataFrame,
    weights: list[float],
    figsize: tuple = (10, 6),
    show: bool = True,
) -> plt.Figure:
    """
    Plot cumulative portfolio return.

    Parameters
    ----------
    data : pd.DataFrame
        Price data with datetime index
    weights : list of float
        Portfolio weights
    figsize : tuple, default (10, 6)
        Figure size
    show : bool, default True
        Whether to display the plot. Set to False for automated/server contexts.

    Returns
    -------
    plt.Figure
        The matplotlib figure object
    """
    returns = data.pct_change().dropna()
    weighted_returns = returns.dot(weights)
    cumulative_portfolio_returns = (1 + weighted_returns).cumprod()

    fig, ax = plt.subplots(figsize=figsize)
    ax.plot(cumulative_portfolio_returns, linewidth=2, color="blue")
    ax.set_title("Portfolio Cumulative Return")
    ax.set_xlabel("Date")
    ax.set_ylabel("Growth of $1")
    ax.grid(True, alpha=0.3)
    fig.tight_layout()

    if show:
        plt.show()

    return fig

plot_allocation_pie(weights, tickers, figsize=(8, 8), show=True) staticmethod

Plot portfolio allocation as a pie chart.

Parameters:

Name Type Description Default
weights list of float

Portfolio weights

 required
tickers list of str

Ticker symbols

 required
figsize tuple

Figure size

 (8, 8)
show bool

Whether to display the plot. Set to False for automated/server contexts.

 True

Returns:

Type Description
Figure

The matplotlib figure object
Source code in portfolio_analysis/visualization/plots.py 
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@staticmethod
def plot_allocation_pie(
    weights: list[float],
    tickers: list[str],
    figsize: tuple = (8, 8),
    show: bool = True,
) -> plt.Figure:
    """
    Plot portfolio allocation as a pie chart.

    Parameters
    ----------
    weights : list of float
        Portfolio weights
    tickers : list of str
        Ticker symbols
    figsize : tuple, default (8, 8)
        Figure size
    show : bool, default True
        Whether to display the plot. Set to False for automated/server contexts.

    Returns
    -------
    plt.Figure
        The matplotlib figure object
    """
    # Filter out zero weights
    non_zero = [(t, w) for t, w in zip(tickers, weights) if w > 0.001]
    labels, sizes = zip(*non_zero) if non_zero else ([], [])

    fig, ax = plt.subplots(figsize=figsize)
    ax.pie(
        sizes,
        labels=labels,
        autopct="%1.1f%%",
        startangle=90,
        colors=plt.cm.Paired.colors,
    )
    ax.set_title("Portfolio Allocation")
    ax.axis("equal")
    fig.tight_layout()

    if show:
        plt.show()

    return fig

plot_drawdown(data, weights, figsize=(12, 6), show=True) staticmethod

Plot portfolio drawdown over time.

Parameters:

Name Type Description Default
data DataFrame

Price data with datetime index

 required
weights list of float

Portfolio weights

 required
figsize tuple

Figure size

 (12, 6)
show bool

Whether to display the plot. Set to False for automated/server contexts.

 True

Returns:

Type Description
Figure

The matplotlib figure object
Source code in portfolio_analysis/visualization/plots.py 
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@staticmethod
def plot_drawdown(
    data: pd.DataFrame,
    weights: list[float],
    figsize: tuple = (12, 6),
    show: bool = True,
) -> plt.Figure:
    """
    Plot portfolio drawdown over time.

    Parameters
    ----------
    data : pd.DataFrame
        Price data with datetime index
    weights : list of float
        Portfolio weights
    figsize : tuple, default (12, 6)
        Figure size
    show : bool, default True
        Whether to display the plot. Set to False for automated/server contexts.

    Returns
    -------
    plt.Figure
        The matplotlib figure object
    """
    returns = data.pct_change().dropna()
    weighted_returns = returns.dot(weights)
    cumulative = (1 + weighted_returns).cumprod()
    peak = cumulative.expanding(min_periods=1).max()
    drawdown = (cumulative / peak) - 1

    fig, ax = plt.subplots(figsize=figsize)
    ax.fill_between(
        drawdown.index, drawdown.values * 100, 0, alpha=0.5, color="red"
    )
    ax.plot(drawdown.index, drawdown.values * 100, color="darkred", linewidth=1)
    ax.set_title("Portfolio Drawdown")
    ax.set_xlabel("Date")
    ax.set_ylabel("Drawdown (%)")
    ax.grid(True, alpha=0.3)
    fig.tight_layout()

    if show:
        plt.show()

    return fig

plot_rolling_volatility(data, weights, window=21, figsize=(12, 6), show=True) staticmethod

Plot rolling portfolio volatility.

Parameters:

Name Type Description Default
data DataFrame

Price data with datetime index

 required
weights list of float

Portfolio weights

 required
window int

Rolling window in trading days

 21
figsize tuple

Figure size

 (12, 6)
show bool

Whether to display the plot. Set to False for automated/server contexts.

 True

Returns:

Type Description
Figure

The matplotlib figure object
Source code in portfolio_analysis/visualization/plots.py 
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@staticmethod
def plot_rolling_volatility(
    data: pd.DataFrame,
    weights: list[float],
    window: int = 21,
    figsize: tuple = (12, 6),
    show: bool = True,
) -> plt.Figure:
    """
    Plot rolling portfolio volatility.

    Parameters
    ----------
    data : pd.DataFrame
        Price data with datetime index
    weights : list of float
        Portfolio weights
    window : int, default 21
        Rolling window in trading days
    figsize : tuple, default (12, 6)
        Figure size
    show : bool, default True
        Whether to display the plot. Set to False for automated/server contexts.

    Returns
    -------
    plt.Figure
        The matplotlib figure object
    """
    returns = data.pct_change().dropna()
    weighted_returns = returns.dot(weights)
    rolling_vol = weighted_returns.rolling(window=window).std() * np.sqrt(
        TRADING_DAYS_PER_YEAR
    )

    fig, ax = plt.subplots(figsize=figsize)
    ax.plot(
        rolling_vol.index, rolling_vol.values * 100, linewidth=1.5, color="blue"
    )
    ax.set_title(f"{window}-Day Rolling Volatility (Annualized)")
    ax.set_xlabel("Date")
    ax.set_ylabel("Volatility (%)")
    ax.grid(True, alpha=0.3)
    fig.tight_layout()

    if show:
        plt.show()

    return fig

plot_returns_distribution(data, weights, bins=50, figsize=(10, 6), show=True) staticmethod

Plot histogram of portfolio returns.

Parameters:

Name Type Description Default
data DataFrame

Price data with datetime index

 required
weights list of float

Portfolio weights

 required
bins int

Number of histogram bins

 50
figsize tuple

Figure size

 (10, 6)
show bool

Whether to display the plot. Set to False for automated/server contexts.

 True

Returns:

Type Description
Figure

The matplotlib figure object
Source code in portfolio_analysis/visualization/plots.py 
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@staticmethod
def plot_returns_distribution(
    data: pd.DataFrame,
    weights: list[float],
    bins: int = 50,
    figsize: tuple = (10, 6),
    show: bool = True,
) -> plt.Figure:
    """
    Plot histogram of portfolio returns.

    Parameters
    ----------
    data : pd.DataFrame
        Price data with datetime index
    weights : list of float
        Portfolio weights
    bins : int, default 50
        Number of histogram bins
    figsize : tuple, default (10, 6)
        Figure size
    show : bool, default True
        Whether to display the plot. Set to False for automated/server contexts.

    Returns
    -------
    plt.Figure
        The matplotlib figure object
    """
    returns = data.pct_change().dropna()
    weighted_returns = returns.dot(weights)

    fig, ax = plt.subplots(figsize=figsize)
    ax.hist(weighted_returns * 100, bins=bins, edgecolor="black", alpha=0.7)
    ax.axvline(x=0, color="red", linestyle="--", linewidth=1)
    ax.axvline(
        x=weighted_returns.mean() * 100,
        color="green",
        linestyle="--",
        linewidth=2,
        label=f"Mean: {weighted_returns.mean()*100:.2f}%",
    )
    ax.set_title("Daily Returns Distribution")
    ax.set_xlabel("Daily Return (%)")
    ax.set_ylabel("Frequency")
    ax.legend()
    ax.grid(True, alpha=0.3)
    fig.tight_layout()

    if show:
        plt.show()

    return fig