#!/usr/bin/env python3
"""
Run market analysis and display results
"""
import sys
import json
import logging
from pathlib import Path

# Add parent directory to path
sys.path.insert(0, str(Path(__file__).parent.parent))

# Load .env file
from dotenv import load_dotenv
load_dotenv(Path(__file__).parent.parent / '.env')

from analysis.engine import MarketAnalysisEngine


# Setup logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)


def print_section(title: str, content: dict, indent: int = 0):
    """Pretty print a section"""
    indent_str = "  " * indent
    print(f"\n{indent_str}{'=' * 60}")
    print(f"{indent_str}{title}")
    print(f"{indent_str}{'=' * 60}")

    for key, value in content.items():
        if isinstance(value, dict):
            print(f"{indent_str}{key}:")
            for k, v in value.items():
                print(f"{indent_str}  {k}: {v}")
        elif isinstance(value, list):
            print(f"{indent_str}{key}: {', '.join(map(str, value)) if value else '[]'}")
        else:
            print(f"{indent_str}{key}: {value}")


def main():
    print("🚀 Starting Market Analysis Engine...")

    engine = MarketAnalysisEngine()

    # Check data availability
    print("\n📊 Checking data availability...")
    status = engine.check_data_availability()
    print(json.dumps(status, indent=2, ensure_ascii=False))

    # Perform analysis
    print("\n🔍 Performing market analysis...")
    analysis = engine.analyze_current_market(timeframe='5m')

    if 'error' in analysis:
        print(f"❌ Error: {analysis['error']}")
        return

    # Display results
    print_section("📈 MARKET OVERVIEW", {
        'Symbol': analysis['symbol'],
        'Timeframe': analysis['timeframe'],
        'Current Price': f"${analysis['current_price']:,.2f}",
        'Timestamp': analysis['timestamp'],
    })

    print_section("📊 TREND ANALYSIS", analysis['trend_analysis'])

    print_section("💰 SUPPORT & RESISTANCE", {
        'Nearest Support': analysis['support_resistance'].get('nearest_support'),
        'Nearest Resistance': analysis['support_resistance'].get('nearest_resistance'),
        'All Support': analysis['support_resistance'].get('support', []),
        'All Resistance': analysis['support_resistance'].get('resistance', []),
    })

    print_section("⚡ MOMENTUM", analysis['momentum'])

    print_section("📉 KEY INDICATORS", {
        'RSI': analysis['indicators']['rsi'],
        'MACD': analysis['indicators']['macd'],
        'MACD Histogram': analysis['indicators']['macd_hist'],
        'ADX': analysis['indicators']['adx'],
        'ATR': analysis['indicators']['atr'],
        'BB Width': analysis['indicators']['bb_width'],
        'Volume Ratio': analysis['indicators']['volume_ratio'],
    })

    # Order flow if available
    if 'orderflow' in analysis:
        print_section("💸 ORDER FLOW", {
            'Imbalance': f"{analysis['orderflow']['imbalance']['imbalance_pct']}%",
            'Status': analysis['orderflow']['imbalance']['status'],
            'Pressure': analysis['orderflow']['imbalance']['pressure'],
            'Strength': analysis['orderflow']['strength']['strength'],
            'Large Bids': analysis['orderflow']['large_orders']['large_bids_count'],
            'Large Asks': analysis['orderflow']['large_orders']['large_asks_count'],
        })

        print_section("🏦 LIQUIDITY", {
            'Spread': f"{analysis['orderflow']['liquidity']['spread_pct']:.4f}%",
            'Best Bid': f"${analysis['orderflow']['liquidity']['best_bid']:,.2f}",
            'Best Ask': f"${analysis['orderflow']['liquidity']['best_ask']:,.2f}",
        })

    # Breakout detection
    if analysis['breakout'].get('has_breakout'):
        print_section("🚨 BREAKOUT DETECTED", analysis['breakout'])
    elif 'approaching' in analysis['breakout']:
        print_section("⚠️ APPROACHING KEY LEVEL", analysis['breakout'])

    # Multi-timeframe
    print("\n\n🕐 MULTI-TIMEFRAME ANALYSIS")
    print("=" * 60)
    mtf = engine.get_multi_timeframe_analysis()
    for tf, data in mtf.items():
        print(f"\n{tf:>4}: {data['trend']:^6} | Strength: {data['strength']:^8} | RSI: {data['rsi']:>5.1f} | ADX: {data['adx']:>5.1f}")

    # LLM Context
    print("\n\n🤖 LLM CONTEXT (Simplified)")
    print("=" * 60)
    llm_context = engine.get_llm_context(format='simplified')
    print(json.dumps(llm_context, indent=2, ensure_ascii=False))

    print("\n✅ Analysis complete!")


if __name__ == "__main__":
    main()