TCPDashboard/components/charts/__init__.py

"""
Modular Chart System for Crypto Trading Bot Dashboard

This package provides a flexible, strategy-driven chart system that supports:
- Technical indicator overlays (SMA, EMA, Bollinger Bands)
- Subplot management (RSI, MACD)
- Strategy-specific configurations
- Future bot signal integration

Main Components:
- ChartBuilder: Main orchestrator for chart creation
- Layer System: Modular rendering components
- Configuration System: Strategy-driven chart configs
"""

import plotly.graph_objects as go
from typing import List
from .builder import ChartBuilder
from .utils import (
    validate_market_data,
    prepare_chart_data,
    get_indicator_colors
)
from .config import (
    get_available_indicators,
    calculate_indicators,
    get_overlay_indicators,
    get_subplot_indicators,
    get_indicator_display_config
)
from .data_integration import (
    MarketDataIntegrator,
    DataIntegrationConfig,
    get_market_data_integrator,
    fetch_indicator_data,
    check_symbol_data_quality
)
from .error_handling import (
    ChartErrorHandler,
    ChartError,
    ErrorSeverity,
    InsufficientDataError,
    DataValidationError,
    IndicatorCalculationError,
    DataConnectionError,
    check_data_sufficiency,
    get_error_message,
    create_error_annotation
)

# Layer imports with error handling
from .layers.base import (
    LayerConfig, 
    BaseLayer,
    CandlestickLayer, 
    VolumeLayer, 
    LayerManager
)

from .layers.indicators import (
    IndicatorLayerConfig,
    BaseIndicatorLayer,
    SMALayer,
    EMALayer, 
    BollingerBandsLayer
)

from .layers.subplots import (
    SubplotLayerConfig,
    BaseSubplotLayer,
    RSILayer,
    MACDLayer
)

# Version information
__version__ = "0.1.0"
__package_name__ = "charts"

# Public API exports
__all__ = [
    # Core components
    "ChartBuilder",
    "validate_market_data",
    "prepare_chart_data", 
    "get_indicator_colors",
    
    # Chart creation functions
    "create_candlestick_chart",
    "create_strategy_chart",
    "create_empty_chart",
    "create_error_chart",
    
    # Data integration
    "MarketDataIntegrator",
    "DataIntegrationConfig",
    "get_market_data_integrator",
    "fetch_indicator_data",
    "check_symbol_data_quality",
    
    # Error handling
    "ChartErrorHandler",
    "ChartError",
    "ErrorSeverity",
    "InsufficientDataError",
    "DataValidationError",
    "IndicatorCalculationError",
    "DataConnectionError",
    "check_data_sufficiency",
    "get_error_message",
    "create_error_annotation",
    
    # Utility functions
    "get_supported_symbols",
    "get_supported_timeframes", 
    "get_market_statistics",
    "check_data_availability",
    "create_data_status_indicator",
    
    # Base layers
    "LayerConfig",
    "BaseLayer",
    "CandlestickLayer",
    "VolumeLayer",
    "LayerManager",
    
    # Indicator layers
    "IndicatorLayerConfig",
    "BaseIndicatorLayer",
    "SMALayer",
    "EMALayer",
    "BollingerBandsLayer",
    
    # Subplot layers
    "SubplotLayerConfig",
    "BaseSubplotLayer",
    "RSILayer",
    "MACDLayer",
    
    # Convenience functions
    "create_basic_chart",
    "create_indicator_chart",
    "create_chart_with_indicators"
]

# Initialize logger
from utils.logger import get_logger
logger = get_logger("charts")

def create_candlestick_chart(symbol: str, timeframe: str, days_back: int = 7, **kwargs) -> go.Figure:
    """
    Create a candlestick chart with enhanced data integration.
    
    Args:
        symbol: Trading pair (e.g., 'BTC-USDT')
        timeframe: Timeframe (e.g., '1h', '1d')
        days_back: Number of days to look back
        **kwargs: Additional chart parameters
        
    Returns:
        Plotly figure with candlestick chart
    """
    builder = ChartBuilder()
    
    # Check data quality first
    data_quality = builder.check_data_quality(symbol, timeframe)
    if not data_quality['available']:
        logger.warning(f"Data not available for {symbol} {timeframe}: {data_quality['message']}")
        return builder._create_error_chart(f"No data available: {data_quality['message']}")
    
    if not data_quality['sufficient_for_indicators']:
        logger.warning(f"Insufficient data for indicators: {symbol} {timeframe}")
    
    # Use enhanced data fetching
    try:
        candles = builder.fetch_market_data_enhanced(symbol, timeframe, days_back)
        if not candles:
            return builder._create_error_chart(f"No market data found for {symbol} {timeframe}")
        
        # Prepare data for charting
        df = prepare_chart_data(candles)
        if df.empty:
            return builder._create_error_chart("Failed to prepare chart data")
        
        # Create chart with data quality info
        fig = builder._create_candlestick_with_volume(df, symbol, timeframe)
        
        # Add data quality annotation if data is stale
        if not data_quality['is_recent']:
            age_hours = data_quality['data_age_minutes'] / 60
            fig.add_annotation(
                text=f"⚠️ Data is {age_hours:.1f}h old",
                xref="paper", yref="paper",
                x=0.02, y=0.98,
                showarrow=False,
                bgcolor="rgba(255,193,7,0.8)",
                bordercolor="orange",
                borderwidth=1
            )
        
        logger.debug(f"Created enhanced candlestick chart for {symbol} {timeframe} with {len(candles)} candles")
        return fig
        
    except Exception as e:
        logger.error(f"Error creating enhanced candlestick chart: {e}")
        return builder._create_error_chart(f"Chart creation failed: {str(e)}")

def create_strategy_chart(symbol: str, timeframe: str, strategy_name: str, **kwargs):
    """
    Convenience function to create a strategy-specific chart.
    
    Args:
        symbol: Trading pair
        timeframe: Timeframe  
        strategy_name: Name of the strategy configuration
        **kwargs: Additional parameters
        
    Returns:
        Plotly Figure object with strategy indicators
    """
    builder = ChartBuilder()
    return builder.create_strategy_chart(symbol, timeframe, strategy_name, **kwargs)

def get_supported_symbols():
    """Get list of symbols that have data in the database."""
    builder = ChartBuilder()
    candles = builder.fetch_market_data("BTC-USDT", "1m", days_back=1)  # Test query
    if candles:
        from database.operations import get_database_operations
        from utils.logger import get_logger
        logger = get_logger("default_logger")
        
        try:
            db = get_database_operations(logger)
            with db.market_data.get_session() as session:
                from sqlalchemy import text
                result = session.execute(text("SELECT DISTINCT symbol FROM market_data ORDER BY symbol"))
                return [row[0] for row in result]
        except Exception:
            pass
    
    return ['BTC-USDT', 'ETH-USDT']  # Fallback

def get_supported_timeframes():
    """Get list of timeframes that have data in the database.""" 
    builder = ChartBuilder()
    candles = builder.fetch_market_data("BTC-USDT", "1m", days_back=1)  # Test query
    if candles:
        from database.operations import get_database_operations
        from utils.logger import get_logger
        logger = get_logger("default_logger")
        
        try:
            db = get_database_operations(logger)
            with db.market_data.get_session() as session:
                from sqlalchemy import text
                result = session.execute(text("SELECT DISTINCT timeframe FROM market_data ORDER BY timeframe"))
                return [row[0] for row in result]
        except Exception:
            pass
    
    return ['5s', '1m', '15m', '1h']  # Fallback

def get_market_statistics(symbol: str, timeframe: str = "1h", days_back: int = 1):
    """Calculate market statistics from recent data over a specified period."""
    builder = ChartBuilder()
    candles = builder.fetch_market_data(symbol, timeframe, days_back=days_back)
    
    if not candles:
        return {'Price': 'N/A', f'Change ({days_back}d)': 'N/A', f'Volume ({days_back}d)': 'N/A', f'High ({days_back}d)': 'N/A', f'Low ({days_back}d)': 'N/A'}
    
    import pandas as pd
    df = pd.DataFrame(candles)
    latest = df.iloc[-1]
    current_price = float(latest['close'])
    
    # Calculate change over the period
    if len(df) > 1:
        price_period_ago = float(df.iloc[0]['open'])
        change_percent = ((current_price - price_period_ago) / price_period_ago) * 100
    else:
        change_percent = 0
    
    from .utils import format_price, format_volume
    
    # Determine label for period (e.g., "24h", "7d", "1h")
    if days_back == 1/24:
        period_label = "1h"
    elif days_back == 4/24:
        period_label = "4h"
    elif days_back == 6/24:
        period_label = "6h"
    elif days_back == 12/24:
        period_label = "12h"
    elif days_back < 1: # For other fractional days, show as hours
        period_label = f"{int(days_back * 24)}h"
    elif days_back == 1:
        period_label = "24h" # Keep 24h for 1 day for clarity
    else:
        period_label = f"{days_back}d"

    return {
        'Price': format_price(current_price, decimals=2),
        f'Change ({period_label})': f"{'+' if change_percent >= 0 else ''}{change_percent:.2f}%",
        f'Volume ({period_label})': format_volume(df['volume'].sum()),
        f'High ({period_label})': format_price(df['high'].max(), decimals=2),
        f'Low ({period_label})': format_price(df['low'].min(), decimals=2)
    }

def check_data_availability(symbol: str, timeframe: str):
    """Check data availability for a symbol and timeframe."""
    from datetime import datetime, timezone, timedelta
    from database.operations import get_database_operations
    from utils.logger import get_logger
    
    try:
        logger = get_logger("charts_data_check")
        db = get_database_operations(logger)
        latest_candle = db.market_data.get_latest_candle(symbol, timeframe)
        
        if latest_candle:
            latest_time = latest_candle['timestamp']
            time_diff = datetime.now(timezone.utc) - latest_time.replace(tzinfo=timezone.utc)
            
            return {
                'has_data': True,
                'latest_timestamp': latest_time,
                'time_since_last': time_diff,
                'is_recent': time_diff < timedelta(hours=1),
                'message': f"Latest data: {latest_time.strftime('%Y-%m-%d %H:%M:%S UTC')}"
            }
        else:
            return {
                'has_data': False,
                'latest_timestamp': None,
                'time_since_last': None,
                'is_recent': False,
                'message': f"No data available for {symbol} {timeframe}"
            }
    except Exception as e:
        return {
            'has_data': False,
            'latest_timestamp': None, 
            'time_since_last': None,
            'is_recent': False,
            'message': f"Error checking data: {str(e)}"
        }

def create_data_status_indicator(symbol: str, timeframe: str):
    """Create a data status indicator for the dashboard."""
    status = check_data_availability(symbol, timeframe)
    
    if status['has_data']:
        if status['is_recent']:
            icon, color, status_text = "🟢", "#27ae60", "Real-time Data"
        else:
            icon, color, status_text = "🟡", "#f39c12", "Delayed Data"
    else:
        icon, color, status_text = "🔴", "#e74c3c", "No Data"
    
    return f'<span style="color: {color}; font-weight: bold;">{icon} {status_text}</span><br><small>{status["message"]}</small>'

def create_error_chart(error_message: str):
    """Create an error chart with error message."""
    builder = ChartBuilder()
    return builder._create_error_chart(error_message)

def create_basic_chart(symbol: str, data: list, 
                      indicators: list = None,
                      error_handling: bool = True) -> 'go.Figure':
    """
    Create a basic chart with error handling.
    
    Args:
        symbol: Trading symbol
        data: OHLCV data as list of dictionaries
        indicators: List of indicator configurations
        error_handling: Whether to use comprehensive error handling
        
    Returns:
        Plotly figure with chart or error display
    """
    try:
        from plotly import graph_objects as go
        
        # Initialize chart builder
        builder = ChartBuilder()
        
        if error_handling:
            # Use error-aware chart creation
            error_handler = ChartErrorHandler()
            is_valid = error_handler.validate_data_sufficiency(data, indicators=indicators or [])
            
            if not is_valid:
                # Create error chart
                fig = go.Figure()
                error_msg = error_handler.get_user_friendly_message()
                fig.add_annotation(create_error_annotation(error_msg, position='center'))
                fig.update_layout(
                    title=f"Chart Error - {symbol}",
                    xaxis={'visible': False},
                    yaxis={'visible': False},
                    template='plotly_white',
                    height=400
                )
                return fig
        
        # Create chart normally
        return builder.create_candlestick_chart(data, symbol=symbol, indicators=indicators or [])
        
    except Exception as e:
        # Fallback error chart
        from plotly import graph_objects as go
        fig = go.Figure()
        fig.add_annotation(create_error_annotation(
            f"Chart creation failed: {str(e)}", 
            position='center'
        ))
        fig.update_layout(
            title=f"Chart Error - {symbol}",
            template='plotly_white',
            height=400
        )
        return fig

def create_indicator_chart(symbol: str, data: list, 
                         indicator_type: str, **params) -> 'go.Figure':
    """
    Create a chart focused on a specific indicator.
    
    Args:
        symbol: Trading symbol
        data: OHLCV data
        indicator_type: Type of indicator ('sma', 'ema', 'bollinger_bands', 'rsi', 'macd')
        **params: Indicator parameters
        
    Returns:
        Plotly figure with indicator chart
    """
    try:
        # Map indicator types to configurations
        indicator_map = {
            'sma': {'type': 'sma', 'parameters': {'period': params.get('period', 20)}},
            'ema': {'type': 'ema', 'parameters': {'period': params.get('period', 20)}},
            'bollinger_bands': {
                'type': 'bollinger_bands', 
                'parameters': {
                    'period': params.get('period', 20),
                    'std_dev': params.get('std_dev', 2)
                }
            },
            'rsi': {'type': 'rsi', 'parameters': {'period': params.get('period', 14)}},
            'macd': {
                'type': 'macd',
                'parameters': {
                    'fast_period': params.get('fast_period', 12),
                    'slow_period': params.get('slow_period', 26),
                    'signal_period': params.get('signal_period', 9)
                }
            }
        }
        
        if indicator_type not in indicator_map:
            raise ValueError(f"Unknown indicator type: {indicator_type}")
        
        indicator_config = indicator_map[indicator_type]
        return create_basic_chart(symbol, data, indicators=[indicator_config])
        
    except Exception as e:
        return create_basic_chart(symbol, data, indicators=[])  # Fallback to basic chart 

def create_chart_with_indicators(symbol: str, timeframe: str, 
                               overlay_indicators: List[str] = None,
                               subplot_indicators: List[str] = None,
                               days_back: int = 7, **kwargs) -> go.Figure:
    """
    Create a chart with dynamically selected indicators.
    
    Args:
        symbol: Trading pair (e.g., 'BTC-USDT')
        timeframe: Timeframe (e.g., '1h', '1d')
        overlay_indicators: List of overlay indicator names
        subplot_indicators: List of subplot indicator names
        days_back: Number of days to look back
        **kwargs: Additional chart parameters
        
    Returns:
        Plotly figure with selected indicators
    """
    builder = ChartBuilder()
    return builder.create_chart_with_indicators(
        symbol, timeframe, overlay_indicators, subplot_indicators, days_back, **kwargs
    ) 

def initialize_indicator_manager():
    # Implementation of initialize_indicator_manager function
    pass