"""Plotly visualization utilities for financial data.

Provides professional, interactive charts for portfolio analysis including:
- Portfolio allocation pie charts
- Risk metrics gauge charts
- Performance time series
- Correlation heatmaps
- Efficient frontier visualizations
"""

import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
import os
import logging
from typing import Dict, List, Any, Optional
from decimal import Decimal

logger = logging.getLogger(__name__)


def get_plotly_template() -> str:
    """Get Plotly template that supports light/dark mode.

    Returns:
        Template name for Plotly charts that auto-adapts to theme
    """
    return "plotly_dark"


def get_chart_theme() -> Dict[str, Any]:
    """Detect theme and return appropriate colours for charts.

    Returns:
        Dictionary with template and colour settings for light/dark mode
    """
    return {
        'template': 'plotly_dark',
        'font_color': '#f2f5fa',
        'grid_color': 'rgba(128, 128, 128, 0.2)',
        'plot_bgcolor': 'rgba(0,0,0,0)',
        'paper_bgcolor': 'rgba(0,0,0,0)',
    }


def get_optimised_chart_config() -> Dict[str, Any]:
    """Get optimised configuration for chart rendering performance.

    Returns:
        Configuration dict with optimised settings for reduced file size and better UX
    """
    return {
        'displayModeBar': True,
        'displaylogo': False,
        'modeBarButtonsToRemove': [
            'pan2d', 'select2d', 'lasso2d', 'resetScale2d',
            'zoomIn2d', 'zoomOut2d', 'autoScale2d'
        ],
        'responsive': True,
        'toImageButtonOptions': {
            'format': 'png',
            'filename': 'portfolio_chart',
            'height': 800,
            'width': 1200,
            'scale': 2
        }
    }


def safe_create_chart(chart_function, *args, **kwargs):
    """Error boundary wrapper for chart creation functions.

    Args:
        chart_function: The chart creation function to wrap
        *args: Positional arguments for the chart function
        **kwargs: Keyword arguments for the chart function

    Returns:
        Plotly figure or None if chart creation fails
    """
    import logging
    logger = logging.getLogger(__name__)

    try:
        return chart_function(*args, **kwargs)
    except Exception as e:
        logger.error(f"Chart creation failed in {chart_function.__name__}: {e}")
        return None


def create_portfolio_allocation_chart(holdings: List[Dict[str, Any]]) -> go.Figure:
    """Create interactive portfolio allocation pie chart.

    Args:
        holdings: List of portfolio holdings with ticker, market_value, weight

    Returns:
        Plotly figure with interactive pie chart
    """
    tickers = [h.get('ticker', 'Unknown') for h in holdings]
    values = [float(h.get('market_value', 0)) for h in holdings]
    weights = [float(h.get('weight', 0)) * 100 for h in holdings]

    fig = go.Figure(go.Pie(
        labels=tickers,
        values=values,
        customdata=list(zip(weights)),
        hovertemplate=(
            "<b>%{label}</b><br>" +
            "Value: $%{value:,.2f}<br>" +
            "Weight: %{customdata[0]:.2f}%<br>" +
            "<extra></extra>"
        ),
        textposition='inside',
        textinfo='percent+label',
        marker=dict(
            line=dict(width=2),
            colors=px.colors.qualitative.Set3
        )
    ))

    theme = get_chart_theme()

    fig.update_layout(
        title={
            'text': 'Portfolio Allocation',
            'x': 0.5,
            'xanchor': 'center',
            'font': {'size': 20, 'color': theme.get('font_color', '#f2f5fa')}
        },
        showlegend=True,
        uniformtext_minsize=12,
        uniformtext_mode='hide',
        autosize=True,
        template=theme['template'],
        plot_bgcolor=theme['plot_bgcolor'],
        paper_bgcolor=theme['paper_bgcolor'],
        font=dict(
            color=theme.get('font_color', '#f2f5fa'),
            family='Inter, sans-serif'
        )
    )

    return fig


def create_risk_metrics_dashboard(
    sharpe: float,
    var: float,
    cvar: float,
    volatility: float
) -> go.Figure:
    """Create comprehensive risk metrics dashboard with gauges.

    Args:
        sharpe: Sharpe ratio
        var: Value at Risk (percentage, negative)
        cvar: Conditional VaR (percentage, negative)
        volatility: Annual volatility (decimal)

    Returns:
        Plotly figure with 4 gauge charts
    """
    # Convert to float in case values are strings from serialization
    sharpe = float(sharpe) if sharpe else 0
    var = float(var) if var else 0
    cvar = float(cvar) if cvar else 0
    volatility = float(volatility) if volatility else 0

    fig = make_subplots(
        rows=2, cols=2,
        specs=[
            [{'type': 'indicator'}, {'type': 'indicator'}],
            [{'type': 'indicator'}, {'type': 'indicator'}]
        ],
        subplot_titles=("Sharpe Ratio", "Volatility", "VaR (95%)", "CVaR (95%)")
    )

    # Sharpe Ratio (top-left)
    sharpe_color = "#28a745" if sharpe > 1 else "#ffc107" if sharpe > 0.5 else "#dc3545"
    fig.add_trace(go.Indicator(
        mode="gauge+number+delta",
        value=sharpe,
        delta={'reference': 1.0, 'increasing': {'color': "#28a745"}, 'decreasing': {'color': "#dc3545"}},
        gauge={
            'axis': {'range': [0, 4], 'tickwidth': 1},
            'bar': {'color': sharpe_color},
            'steps': [
                {'range': [0, 1], 'color': '#fee5e5'},
                {'range': [1, 2], 'color': '#fff4e5'},
                {'range': [2, 4], 'color': '#e5f5e5'}
            ],
            'threshold': {
                'line': {'color': "red", 'width': 3},
                'thickness': 0.75,
                'value': 3.0
            }
        }
    ), row=1, col=1)

    # Volatility (top-right)
    vol_pct = volatility * 100
    vol_color = "#dc3545" if vol_pct > 30 else "#ffc107" if vol_pct > 20 else "#28a745"
    fig.add_trace(go.Indicator(
        mode="gauge+number",
        value=vol_pct,
        number={'suffix': "%", 'font': {'size': 40}},
        gauge={
            'axis': {'range': [0, 50], 'ticksuffix': "%"},
            'bar': {'color': vol_color},
            'steps': [
                {'range': [0, 15], 'color': '#e5f5e5'},
                {'range': [15, 25], 'color': '#fff4e5'},
                {'range': [25, 50], 'color': '#fee5e5'}
            ]
        }
    ), row=1, col=2)

    # VaR (bottom-left)
    var_abs = abs(var)
    fig.add_trace(go.Indicator(
        mode="gauge+number",
        value=var_abs,
        number={'suffix': "%", 'font': {'size': 40}},
        gauge={
            'axis': {'range': [0, 30], 'ticksuffix': "%"},
            'bar': {'color': "#dc3545"},
            'steps': [
                {'range': [0, 10], 'color': '#e5f5e5'},
                {'range': [10, 20], 'color': '#fff4e5'},
                {'range': [20, 30], 'color': '#fee5e5'}
            ],
            'threshold': {
                'line': {'color': "darkred", 'width': 4},
                'thickness': 0.75,
                'value': 15
            }
        }
    ), row=2, col=1)

    # CVaR (bottom-right)
    cvar_abs = abs(cvar)
    fig.add_trace(go.Indicator(
        mode="gauge+number",
        value=cvar_abs,
        number={'suffix': "%", 'font': {'size': 40}},
        gauge={
            'axis': {'range': [0, 30], 'ticksuffix': "%"},
            'bar': {'color': "#ff6b6b"},
            'steps': [
                {'range': [0, 10], 'color': '#e5f5e5'},
                {'range': [10, 20], 'color': '#fff4e5'},
                {'range': [20, 30], 'color': '#fee5e5'}
            ],
            'threshold': {
                'line': {'color': "darkred", 'width': 4},
                'thickness': 0.75,
                'value': 18
            }
        }
    ), row=2, col=2)

    theme = get_chart_theme()

    fig.update_layout(
        autosize=True,
        height=600,
        template=theme['template'],
        plot_bgcolor=theme['plot_bgcolor'],
        paper_bgcolor=theme['paper_bgcolor'],
        font=dict(
            color=theme.get('font_color', '#f2f5fa'),
            family='Inter, sans-serif'
        ),
        margin=dict(t=80, b=50, l=50, r=50)
    )

    return fig


def create_performance_chart(
    holdings: List[Dict[str, Any]],
    historical_data: Dict[str, Dict[str, Any]],
    benchmark_data: Optional[Dict[str, Any]] = None
) -> Optional[go.Figure]:
    """Create performance time series chart with optional benchmark comparison.

    Args:
        holdings: Portfolio holdings
        historical_data: Historical price data by ticker
        benchmark_data: Optional benchmark data (e.g., S&P 500)

    Returns:
        Plotly figure with time series or None if insufficient data
    """
    if not historical_data:
        return None

    fig = go.Figure()

    for holding in holdings:
        ticker = holding.get('ticker')
        if ticker not in historical_data:
            continue

        hist = historical_data[ticker]
        dates = hist.get('dates', [])
        prices = hist.get('close_prices', [])

        if not dates or not prices:
            continue

        # Convert prices to float in case they're strings
        prices = [float(p) for p in prices]

        # Calculate percentage returns (normalise to 100)
        if prices[0] > 0:
            normalized = [(p / prices[0]) * 100 for p in prices]
        else:
            normalized = prices

        fig.add_trace(go.Scatter(
            x=dates,
            y=normalized,
            name=ticker,
            mode='lines',
            line=dict(width=2),
            hovertemplate=(
                f"<b>{ticker}</b><br>" +
                "%{x|%B %d, %Y}<br>" +
                "Price: %{y:.2f}<br>" +
                "<extra></extra>"
            )
        ))

    # Add benchmark if provided
    if benchmark_data:
        benchmark_dates = benchmark_data.get('dates', [])
        benchmark_prices = benchmark_data.get('close_prices', [])

        if benchmark_dates and benchmark_prices:
            # Convert benchmark prices to float in case they're strings
            benchmark_prices = [float(p) for p in benchmark_prices]

            if benchmark_prices[0] > 0:
                normalized_benchmark = [(p / benchmark_prices[0]) * 100 for p in benchmark_prices]

                fig.add_trace(go.Scatter(
                    x=benchmark_dates,
                    y=normalized_benchmark,
                    name="S&P 500 (Benchmark)",
                    mode='lines',
                    line=dict(width=3, dash='dash', color='#FFA500'),
                    hovertemplate=(
                        "<b>S&P 500</b><br>" +
                        "%{x|%B %d, %Y}<br>" +
                        "Value: %{y:.2f}<br>" +
                        "<extra></extra>"
                    )
                ))

    theme = get_chart_theme()

    fig.update_layout(
        title={
            'text': 'Historical Performance (Normalised to 100)',
            'x': 0.5,
            'xanchor': 'center',
            'font': {'size': 20, 'color': theme.get('font_color', '#f2f5fa')}
        },
        xaxis=dict(
            title='Date',
            title_font=dict(color=theme.get('font_color', '#f2f5fa')),
            showgrid=True,
            gridcolor=theme['grid_color'],
            rangeslider=dict(visible=True, bgcolor=theme['plot_bgcolor']),
            tickfont=dict(color=theme.get('font_color', '#f2f5fa'))
        ),
        yaxis=dict(
            title='Normalised Value',
            title_font=dict(color=theme.get('font_color', '#f2f5fa')),
            showgrid=True,
            gridcolor=theme['grid_color'],
            tickformat='.1f',
            tickfont=dict(color=theme.get('font_color', '#f2f5fa'))
        ),
        hovermode='x unified',
        autosize=True,
        showlegend=True,
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        template=theme['template'],
        plot_bgcolor=theme['plot_bgcolor'],
        paper_bgcolor=theme['paper_bgcolor'],
        font=dict(
            color=theme.get('font_color', '#f2f5fa'),
            family='Inter, sans-serif'
        )
    )

    return fig


def create_correlation_heatmap(
    historical_data: Dict[str, Dict[str, Any]]
) -> Optional[go.Figure]:
    """Create correlation heatmap for assets.

    Args:
        historical_data: Historical price data by ticker

    Returns:
        Plotly figure with heatmap or None if insufficient data
    """
    if not historical_data or len(historical_data) < 2:
        return None

    # Build returns dataframe - ENSURE ALL ASSETS ARE INCLUDED
    returns_data = {}
    min_length = float('inf')

    # First pass: calculate all returns and find minimum length
    for ticker, hist in historical_data.items():
        prices = hist.get('close_prices', [])
        if len(prices) > 1:
            returns = pd.Series(prices).pct_change().dropna()
            returns_data[ticker] = returns
            min_length = min(min_length, len(returns))

    if len(returns_data) < 2:
        logger.warning("Insufficient returns data for correlation matrix")
        return None

    # Second pass: align all series to same length
    aligned_returns = {}
    for ticker, returns in returns_data.items():
        aligned_returns[ticker] = returns.iloc[-min_length:]

    # Create DataFrame and calculate correlation
    df = pd.DataFrame(aligned_returns)

    # VERIFY: Check that df contains all expected tickers
    logger.info(f"Correlation matrix includes {len(df.columns)} assets: {list(df.columns)}")

    corr_matrix = df.corr()

    fig = go.Figure(data=go.Heatmap(
        z=corr_matrix.values,
        x=corr_matrix.columns,
        y=corr_matrix.index,
        colorscale='RdBu',
        zmid=0,
        zmin=-1,
        zmax=1,
        text=np.round(corr_matrix.values, 2),
        texttemplate='%{text}',
        textfont={"size": 10},
        colorbar=dict(
            title="Correlation",
            tickmode='linear',
            tick0=-1,
            dtick=0.5
        ),
        hovertemplate=(
            "%{y} vs %{x}<br>" +
            "Correlation: %{z:.3f}<br>" +
            "<extra></extra>"
        )
    ))

    theme = get_chart_theme()

    fig.update_layout(
        title={
            'text': 'Asset Correlation Matrix',
            'x': 0.5,
            'xanchor': 'center',
            'font': {'size': 20, 'color': theme.get('font_color', '#f2f5fa')}
        },
        xaxis=dict(
            showgrid=False,
            side='bottom',
            tickangle=-45,
            tickfont=dict(color=theme.get('font_color', '#f2f5fa'))
        ),
        yaxis=dict(
            showgrid=False,
            autorange='reversed',
            tickfont=dict(color=theme.get('font_color', '#f2f5fa'))
        ),
        autosize=True,
        width=700,
        height=700,
        template=theme['template'],
        plot_bgcolor=theme['plot_bgcolor'],
        paper_bgcolor=theme['paper_bgcolor'],
        font=dict(
            color=theme.get('font_color', '#f2f5fa'),
            family='Inter, sans-serif'
        )
    )

    return fig


def create_optimization_comparison(
    optimization_results: Dict[str, Any]
) -> Optional[go.Figure]:
    """Create bar chart comparing optimization methods.

    Args:
        optimization_results: Results from portfolio optimizer

    Returns:
        Plotly figure with comparison chart or None if no data
    """
    if not optimization_results:
        return None

    methods = []
    sharpe_ratios = []
    volatilities = []

    for method, result in optimization_results.items():
        if isinstance(result, dict):
            sharpe = result.get('sharpe_ratio', 0)
            vol = result.get('volatility', 0)

            # Convert to float in case values are strings
            sharpe = float(sharpe) if sharpe else 0
            vol = float(vol) if vol else 0

            methods.append(method.replace('_', ' ').title())
            sharpe_ratios.append(sharpe)
            volatilities.append(vol * 100 if vol < 1 else vol)  # Convert to percentage

    if not methods:
        return None

    fig = make_subplots(
        rows=1, cols=2,
        subplot_titles=('Sharpe Ratio Comparison', 'Volatility Comparison'),
        specs=[[{'type': 'bar'}, {'type': 'bar'}]]
    )

    # Sharpe ratios
    fig.add_trace(
        go.Bar(
            x=methods,
            y=sharpe_ratios,
            name='Sharpe Ratio',
            marker_color='#288cfa',
            text=[f'{s:.3f}' for s in sharpe_ratios],
            textposition='outside'
        ),
        row=1, col=1
    )

    # Volatilities
    fig.add_trace(
        go.Bar(
            x=methods,
            y=volatilities,
            name='Volatility (%)',
            marker_color='#dc3545',
            text=[f'{v:.2f}%' for v in volatilities],
            textposition='outside'
        ),
        row=1, col=2
    )

    theme = get_chart_theme()

    fig.update_layout(
        title={
            'text': 'Portfolio Optimisation Methods Comparison',
            'x': 0.5,
            'xanchor': 'center',
            'font': {'size': 20, 'color': theme.get('font_color', '#f2f5fa')}
        },
        showlegend=False,
        autosize=True,
        height=400,
        template=theme['template'],
        plot_bgcolor=theme['plot_bgcolor'],
        paper_bgcolor=theme['paper_bgcolor'],
        font=dict(
            color=theme.get('font_color', '#f2f5fa'),
            family='Inter, sans-serif'
        )
    )

    fig.update_xaxes(
        tickangle=-45,
        tickfont=dict(color=theme.get('font_color', '#f2f5fa')),
        row=1, col=1
    )
    fig.update_xaxes(
        tickangle=-45,
        tickfont=dict(color=theme.get('font_color', '#f2f5fa')),
        row=1, col=2
    )
    fig.update_yaxes(
        title_text="Sharpe Ratio",
        title_font=dict(color=theme.get('font_color', '#f2f5fa')),
        tickfont=dict(color=theme.get('font_color', '#f2f5fa')),
        row=1, col=1
    )
    fig.update_yaxes(
        title_text="Volatility (%)",
        title_font=dict(color=theme.get('font_color', '#f2f5fa')),
        tickfont=dict(color=theme.get('font_color', '#f2f5fa')),
        row=1, col=2
    )

    return fig


def apply_financial_theme(fig: go.Figure) -> go.Figure:
    """Apply professional financial styling to any Plotly figure.

    Args:
        fig: Plotly figure to style

    Returns:
        Styled figure
    """
    theme = get_chart_theme()

    fig.update_layout(
        font=dict(
            family="Inter, Arial, sans-serif",
            size=12,
            color=theme.get('font_color', '#f2f5fa')
        ),
        title_font=dict(
            family="Inter, Arial, sans-serif",
            size=18,
            color=theme.get('font_color', '#f2f5fa')
        ),
        template=theme['template'],
        plot_bgcolor=theme['plot_bgcolor'],
        paper_bgcolor=theme['paper_bgcolor'],
        xaxis=dict(
            showgrid=True,
            gridwidth=1,
            gridcolor=theme['grid_color'],
            zeroline=False,
            tickfont=dict(color=theme.get('font_color', '#f2f5fa'))
        ),
        yaxis=dict(
            showgrid=True,
            gridwidth=1,
            gridcolor=theme['grid_color'],
            zeroline=False,
            tickfont=dict(color=theme.get('font_color', '#f2f5fa'))
        ),
        legend=dict(
            borderwidth=1
        ),
        hoverlabel=dict(
            font_size=12,
            font_family="Inter, Arial, sans-serif"
        )
    )

    return fig