"""
Sakata Chart Plotter v2.0
K 線圖表繪製 + 型態標註

改良: 使用英文標籤避免中文字體問題
"""

import pandas as pd
import numpy as np
import mplfinance as mpf
import matplotlib.pyplot as plt
from matplotlib.patches import FancyBboxPatch
import matplotlib.font_manager as fm
import os
from datetime import datetime


def calculate_support_resistance(df: pd.DataFrame, window: int = 20) -> dict:
    """Calculate support and resistance levels."""
    highs = df['High'].values
    lows = df['Low'].values
    closes = df['Close'].values
    
    resistance_levels = []
    support_levels = []
    
    for i in range(window, len(df) - window):
        if highs[i] == max(highs[i-window:i+window+1]):
            resistance_levels.append(highs[i])
        if lows[i] == min(lows[i-window:i+window+1]):
            support_levels.append(lows[i])
    
    def cluster_levels(levels, threshold=0.02):
        if not levels:
            return []
        levels = sorted(levels)
        clusters = [[levels[0]]]
        for level in levels[1:]:
            if (level - clusters[-1][-1]) / clusters[-1][-1] < threshold:
                clusters[-1].append(level)
            else:
                clusters.append([level])
        return [np.mean(c) for c in clusters]
    
    resistance = cluster_levels(resistance_levels)[-3:] if resistance_levels else []
    support = cluster_levels(support_levels)[:3] if support_levels else []
    
    return {
        'resistance': resistance,
        'support': support,
        'current_price': closes[-1]
    }


def calculate_rsi(df: pd.DataFrame, period: int = 14) -> pd.Series:
    """Calculate RSI indicator."""
    delta = df['Close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi


def create_sakata_chart(
    df: pd.DataFrame,
    detected_patterns: list,
    signals: list,
    ticker: str,
    output_dir: str = './output'
) -> str:
    """
    Create a candlestick chart with Sakata pattern annotations.
    Uses English labels for better font compatibility.
    """
    # Ensure output directory exists
    os.makedirs(output_dir, exist_ok=True)
    
    # Calculate technical indicators
    df = df.copy()
    df['MA20'] = df['Close'].rolling(window=20).mean()
    df['MA50'] = df['Close'].rolling(window=50).mean()
    df['RSI'] = calculate_rsi(df)
    
    # Calculate support/resistance
    sr_levels = calculate_support_resistance(df)
    
    # Prepare annotation markers
    buy_signals = []
    sell_signals = []
    
    # 去重: 每天每型態只標記一次
    seen_dates_buy = set()
    seen_dates_sell = set()
    
    for pattern in detected_patterns[-30:]:
        date = pattern['date']
        if date in df.index:
            date_key = str(date)[:10]
            if pattern['direction'] == 'bullish':
                if date_key not in seen_dates_buy:
                    buy_signals.append({
                        'date': date,
                        'price': df.loc[date, 'Low'] * 0.98,
                        'name': pattern['english']  # 使用英文名
                    })
                    seen_dates_buy.add(date_key)
            else:
                if date_key not in seen_dates_sell:
                    sell_signals.append({
                        'date': date,
                        'price': df.loc[date, 'High'] * 1.02,
                        'name': pattern['english']  # 使用英文名
                    })
                    seen_dates_sell.add(date_key)
    
    # Create marker arrays
    buy_markers = np.nan * np.ones(len(df))
    sell_markers = np.nan * np.ones(len(df))
    
    for sig in buy_signals:
        if sig['date'] in df.index:
            idx = df.index.get_loc(sig['date'])
            buy_markers[idx] = df.iloc[idx]['Low'] * 0.97
    
    for sig in sell_signals:
        if sig['date'] in df.index:
            idx = df.index.get_loc(sig['date'])
            sell_markers[idx] = df.iloc[idx]['High'] * 1.03
    
    # Define custom style
    mc = mpf.make_marketcolors(
        up='#26a69a',
        down='#ef5350',
        edge='inherit',
        wick='inherit',
        volume='in',
        ohlc='i'
    )
    
    s = mpf.make_mpf_style(
        marketcolors=mc,
        gridstyle='-',
        gridcolor='#e0e0e0',
        y_on_right=True,
        rc={
            'font.size': 10,
            'axes.labelsize': 12,
            'axes.titlesize': 14
        }
    )
    
    # Create additional plots
    apds = []
    
    # Buy/Sell markers
    if not np.all(np.isnan(buy_markers)):
        apds.append(mpf.make_addplot(
            buy_markers, type='scatter', markersize=100,
            marker='^', color='#26a69a', panel=0
        ))
    
    if not np.all(np.isnan(sell_markers)):
        apds.append(mpf.make_addplot(
            sell_markers, type='scatter', markersize=100,
            marker='v', color='#ef5350', panel=0
        ))
    
    # Moving averages
    apds.append(mpf.make_addplot(df['MA20'], color='#2196F3', width=1.5, panel=0))
    apds.append(mpf.make_addplot(df['MA50'], color='#FF9800', width=1.5, panel=0))
    
    # RSI in separate panel
    apds.append(mpf.make_addplot(df['RSI'], color='#9C27B0', width=1, panel=2, ylabel='RSI'))
    
    # RSI overbought/oversold lines
    rsi_70 = np.full(len(df), 70)
    rsi_30 = np.full(len(df), 30)
    apds.append(mpf.make_addplot(rsi_70, color='#ef5350', width=0.5, linestyle='--', panel=2))
    apds.append(mpf.make_addplot(rsi_30, color='#26a69a', width=0.5, linestyle='--', panel=2))
    
    # Generate main chart (使用英文標題避免字體問題)
    fig, axes = mpf.plot(
        df,
        type='candle',
        style=s,
        title=f'\n{ticker} - Sakata Pattern Analysis',
        ylabel='Price',
        ylabel_lower='Volume',
        volume=True,
        addplot=apds if apds else None,
        figsize=(18, 12),
        returnfig=True,
        panel_ratios=(5, 1.5, 1.5)
    )
    
    ax = axes[0]
    
    # Draw support lines
    for level in sr_levels['support']:
        ax.axhline(y=level, color='#26a69a', linestyle='--', linewidth=1.5, alpha=0.7)
        ax.text(df.index[2], level, f'  Support ${level:.2f}', 
                fontsize=9, color='#26a69a', va='bottom')
    
    # Draw resistance lines
    for level in sr_levels['resistance']:
        ax.axhline(y=level, color='#ef5350', linestyle='--', linewidth=1.5, alpha=0.7)
        ax.text(df.index[2], level, f'  Resistance ${level:.2f}', 
                fontsize=9, color='#ef5350', va='top')
    
    # Add signal summary box (使用英文)
    bullish_count = len(set(p['date'] for p in detected_patterns if p['direction'] == 'bullish'))
    bearish_count = len(set(p['date'] for p in detected_patterns if p['direction'] == 'bearish'))
    
    summary_text = (
        f"Signal Summary\n"
        f"{'='*14}\n"
        f"Bullish: {bullish_count}\n"
        f"Bearish: {bearish_count}\n"
        f"{'='*14}\n"
        f"Close: ${sr_levels['current_price']:.2f}"
    )
    ax.text(
        0.02, 0.98, summary_text,
        transform=ax.transAxes, fontsize=10,
        verticalalignment='top',
        bbox=dict(boxstyle='round,pad=0.5', facecolor='white', alpha=0.9, edgecolor='#ccc'),
        family='monospace'
    )
    
    # Add recent patterns annotation (使用英文)
    if detected_patterns:
        # 去重
        seen = set()
        unique_recent = []
        for p in detected_patterns[:10]:
            key = (str(p['date'])[:10], p['english'])
            if key not in seen:
                seen.add(key)
                unique_recent.append(p)
        
        pattern_text = "Recent Patterns\n" + "="*16 + "\n"
        for p in unique_recent[:6]:
            date_str = p['date'].strftime('%m/%d') if hasattr(p['date'], 'strftime') else str(p['date'])[:10]
            signal_emoji = '+' if p['direction'] == 'bullish' else '-'
            # 截斷過長的型態名稱
            name = p['english'][:18] if len(p['english']) > 18 else p['english']
            pattern_text += f"{date_str}: {name} [{signal_emoji}]\n"
        
        ax.text(
            0.98, 0.98, pattern_text,
            transform=ax.transAxes, fontsize=9,
            verticalalignment='top', horizontalalignment='right',
            bbox=dict(boxstyle='round,pad=0.5', facecolor='white', alpha=0.9, edgecolor='#ccc'),
            family='monospace'
        )
    
    # Add legend
    from matplotlib.lines import Line2D
    legend_elements = [
        Line2D([0], [0], color='#2196F3', linewidth=2, label='MA20'),
        Line2D([0], [0], color='#FF9800', linewidth=2, label='MA50'),
        Line2D([0], [0], color='#26a69a', linestyle='--', linewidth=1.5, label='Support'),
        Line2D([0], [0], color='#ef5350', linestyle='--', linewidth=1.5, label='Resistance'),
        Line2D([0], [0], marker='^', color='#26a69a', linestyle='None', markersize=10, label='Bullish Signal'),
        Line2D([0], [0], marker='v', color='#ef5350', linestyle='None', markersize=10, label='Bearish Signal'),
    ]
    ax.legend(handles=legend_elements, loc='upper center', ncol=6, fontsize=8,
              bbox_to_anchor=(0.5, 1.02), framealpha=0.9)
    
    # Save chart
    output_path = os.path.join(output_dir, f'{ticker}_sakata.png')
    fig.savefig(output_path, dpi=150, bbox_inches='tight', facecolor='white')
    plt.close(fig)
    
    return output_path


def create_pattern_detail_chart(
    df: pd.DataFrame,
    pattern: dict,
    ticker: str,
    output_dir: str = './output'
) -> str:
    """
    Create a detailed chart for a specific pattern occurrence.
    """
    # Focus on 20 days around the pattern
    pattern_date = pattern['date']
    
    if pattern_date in df.index:
        idx = df.index.get_loc(pattern_date)
        start_idx = max(0, idx - 10)
        end_idx = min(len(df), idx + 10)
        focused_df = df.iloc[start_idx:end_idx].copy()
    else:
        focused_df = df.tail(20).copy()
    
    # Create marker for the pattern
    marker = np.nan * np.ones(len(focused_df))
    if pattern_date in focused_df.index:
        local_idx = focused_df.index.get_loc(pattern_date)
        marker[local_idx] = focused_df.iloc[local_idx]['Low'] * 0.97
    
    mc = mpf.make_marketcolors(up='#26a69a', down='#ef5350', edge='inherit', wick='inherit')
    s = mpf.make_mpf_style(marketcolors=mc, gridstyle='-', gridcolor='#e0e0e0')
    
    apds = []
    if not np.all(np.isnan(marker)):
        apds.append(mpf.make_addplot(marker, type='scatter', markersize=200, marker='*', color='gold'))
    
    fig, axes = mpf.plot(
        focused_df,
        type='candle',
        style=s,
        title=f"\n{ticker} - {pattern['english']}",
        figsize=(10, 6),
        returnfig=True,
        addplot=apds if apds else None
    )
    
    output_path = os.path.join(output_dir, f"{ticker}_{pattern['pattern']}_detail.png")
    fig.savefig(output_path, dpi=150, bbox_inches='tight', facecolor='white')
    plt.close(fig)
    
    return output_path