#!/usr/bin/env python3
"""
Simple dashboard server for Quantitative Trading Platform.
Provides mock metrics for PnL, drawdown, and win rate.
"""

from flask import Flask, render_template, jsonify, request
import random
from datetime import datetime, timedelta
from data.connectors import create_moomoo_client, Interval

app = Flask(__name__)

# Initialize MoomooClient in mock mode (using our LLM scenario generator)
client = create_moomoo_client(mock_mode=True)

@app.route('/')
def dashboard():
    """Serve the dashboard HTML page."""
    return render_template('index.html')

@app.route('/api/market-data/<symbol>')
def market_data(symbol):
    """
    API endpoint returning market data for a symbol.
    Fetches 30 days of daily data by default for the dashboard.
    """
    try:
        # Fetch OHLCV data from our connector (which uses the simulator)
        end_date = datetime.now()
        start_date = end_date - timedelta(days=30)
        
        # Determine interval from query param, default to 1d
        interval_str = request.args.get('interval', '1d')
        interval = Interval(interval_str)
        
        ohlcv_data = client.get_ohlcv(
            symbol=symbol.upper(),
            interval=interval,
            start_date=start_date,
            end_date=end_date,
            limit=100
        )
        
        # Transform for frontend
        daily_pnl = []
        cumulative = 0
        
        # Simple simulation of PnL based on close price changes
        # In a real app, this would come from the Strategy/Portfolio engine
        initial_balance = 10000.0
        position_size = 10  # shares
        
        prev_close = ohlcv_data[0].close if ohlcv_data else 0
        
        for point in ohlcv_data:
            change = point.close - prev_close
            pnl = change * position_size
            cumulative += pnl
            
            daily_pnl.append({
                'date': point.timestamp.strftime('%Y-%m-%d'),
                'pnl': round(pnl, 2),
                'cumulative': round(cumulative, 2),
                'close': point.close
            })
            prev_close = point.close

        # Calculate summary metrics
        total_pnl = cumulative
        peak = max([d['cumulative'] for d in daily_pnl]) if daily_pnl else 0
        trough = min([d['cumulative'] for d in daily_pnl]) if daily_pnl else 0
        max_drawdown = min(0, trough - peak)
        
        profitable_days = sum(1 for d in daily_pnl if d['pnl'] > 0)
        win_rate = (profitable_days / len(daily_pnl) * 100) if daily_pnl else 0
        
        return jsonify({
            'symbol': symbol.upper(),
            'metrics': {
                'total_pnl': round(total_pnl, 2),
                'max_drawdown': round(max_drawdown, 2),
                'win_rate': round(win_rate, 1),
                'portfolio_value': round(initial_balance + total_pnl, 2),
                'peak': round(peak, 2),
                'trough': round(trough, 2)
            },
            'daily_pnl': daily_pnl
        })
        
    except Exception as e:
        return jsonify({'error': str(e)}), 500

@app.route('/health')
def health():
    """Health check endpoint."""
    return jsonify({'status': 'healthy', 'timestamp': datetime.now().isoformat()})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)