#!/usr/bin/env python3
"""
📊 Analyze Historical JSON Performance Data
Detaillierte Analyse der 166 historischen Trades
"""

import json
from collections import defaultdict
from datetime import datetime

# Load JSON data
with open('trade_performance_v16_XAUUSD_202511.json', 'r') as f:
    data = json.load(f)

# Handle both list and dict formats
if isinstance(data, list):
    trades = data
elif isinstance(data, dict):
    trades = data.get('closed_trades', data.get('trades', []))
else:
    trades = []

print("=" * 80)
print("📊 DETAILLIERTE PERFORMANCE ANALYSE")
print("=" * 80)
print(f"\nTotal Closed Trades: {len(trades)}")

# ==========================================
# CONFIDENCE ANALYSIS
# ==========================================

print("\n" + "=" * 80)
print("🎯 PERFORMANCE BY CONFIDENCE LEVEL")
print("=" * 80)

confidence_bins = {
    '60-70': [],
    '70-75': [],
    '75-80': [],
    '80-85': [],
    '85+': []
}

for trade in trades:
    conf = trade.get('confidence', 0)
    profit = trade.get('profit', 0)

    if conf < 70:
        confidence_bins['60-70'].append(profit)
    elif conf < 75:
        confidence_bins['70-75'].append(profit)
    elif conf < 80:
        confidence_bins['75-80'].append(profit)
    elif conf < 85:
        confidence_bins['80-85'].append(profit)
    else:
        confidence_bins['85+'].append(profit)

print("\nRange    | Trades | Wins | Win% | Total Profit | Avg Profit | Recommendation")
print("-" * 80)

for conf_range, profits in confidence_bins.items():
    if not profits:
        continue

    total = len(profits)
    wins = sum(1 for p in profits if p > 0)
    win_rate = (wins / total * 100) if total > 0 else 0
    total_profit = sum(profits)
    avg_profit = total_profit / total if total > 0 else 0

    # Recommendation
    if win_rate >= 40 and avg_profit > 5:
        rec = "✅ EXCELLENT"
    elif win_rate >= 35 and avg_profit > 2:
        rec = "✅ Good"
    elif win_rate >= 30:
        rec = "⚠️ Marginal"
    else:
        rec = "❌ Avoid"

    print(f"{conf_range:8} | {total:6} | {wins:4} | {win_rate:4.1f} | ${total_profit:11.2f} | ${avg_profit:9.2f} | {rec}")

# ==========================================
# SESSION + CONFIDENCE COMBINED
# ==========================================

print("\n" + "=" * 80)
print("📍 SESSION × CONFIDENCE MATRIX")
print("=" * 80)

session_conf = defaultdict(lambda: defaultdict(list))

for trade in trades:
    session = trade.get('session', 'unknown')
    conf = trade.get('confidence', 0)
    profit = trade.get('profit', 0)

    if conf >= 75:
        conf_level = 'High (75+)'
    elif conf >= 70:
        conf_level = 'Med (70-75)'
    else:
        conf_level = 'Low (<70)'

    session_conf[session][conf_level].append(profit)

for session in ['ny', 'asian', 'london', 'overlap']:
    if session not in session_conf:
        continue

    print(f"\n{session.upper()} Session:")
    print("  Confidence  | Trades | Wins | Win% | Total Profit | Avg | Verdict")
    print("  " + "-" * 70)

    for conf_level in ['High (75+)', 'Med (70-75)', 'Low (<70)']:
        profits = session_conf[session].get(conf_level, [])
        if not profits:
            continue

        total = len(profits)
        wins = sum(1 for p in profits if p > 0)
        win_rate = (wins / total * 100) if total > 0 else 0
        total_profit = sum(profits)
        avg_profit = total_profit / total if total > 0 else 0

        # Verdict
        if win_rate >= 40 and total_profit > 100:
            verdict = "✅ BEST"
        elif win_rate >= 35 and total_profit > 0:
            verdict = "✅ Good"
        elif total_profit > 0:
            verdict = "⚠️ OK"
        else:
            verdict = "❌ Bad"

        print(f"  {conf_level:11} | {total:6} | {wins:4} | {win_rate:4.1f} | ${total_profit:11.2f} | ${avg_profit:4.1f} | {verdict}")

# ==========================================
# TIME-BASED ANALYSIS
# ==========================================

print("\n" + "=" * 80)
print("⏰ PERFORMANCE BY HOUR (UTC)")
print("=" * 80)

hourly = defaultdict(list)

for trade in trades:
    entry_time = trade.get('entry_time', '')
    if not entry_time:
        continue

    # Parse hour
    try:
        dt = datetime.fromisoformat(entry_time.replace('Z', '+00:00'))
        hour = dt.hour
        profit = trade.get('profit', 0)
        hourly[hour].append(profit)
    except:
        continue

# Sort by total profit
hourly_stats = []
for hour, profits in hourly.items():
    total = len(profits)
    wins = sum(1 for p in profits if p > 0)
    win_rate = (wins / total * 100) if total > 0 else 0
    total_profit = sum(profits)
    avg_profit = total_profit / total if total > 0 else 0

    hourly_stats.append({
        'hour': hour,
        'trades': total,
        'wins': wins,
        'win_rate': win_rate,
        'total_profit': total_profit,
        'avg_profit': avg_profit
    })

hourly_stats.sort(key=lambda x: x['total_profit'], reverse=True)

print("\nTop 10 Best Hours:")
print("Hour | Trades | Wins | Win% | Total Profit | Avg Profit")
print("-" * 60)

for stat in hourly_stats[:10]:
    hour = stat['hour']
    session_marker = ""
    if 13 <= hour < 21:
        session_marker = " (NY)"
    elif 7 <= hour < 15:
        session_marker = " (London/Asian)"

    print(f"{hour:2d}{session_marker:15} | {stat['trades']:6} | {stat['wins']:4} | "
          f"{stat['win_rate']:4.1f} | ${stat['total_profit']:11.2f} | ${stat['avg_profit']:9.2f}")

print("\nWorst 5 Hours:")
print("Hour | Trades | Wins | Win% | Total Profit | Avg Profit")
print("-" * 60)

for stat in hourly_stats[-5:]:
    hour = stat['hour']
    session_marker = ""
    if 13 <= hour < 21:
        session_marker = " (NY)"
    elif 7 <= hour < 15:
        session_marker = " (London/Asian)"

    print(f"{hour:2d}{session_marker:15} | {stat['trades']:6} | {stat['wins']:4} | "
          f"{stat['win_rate']:4.1f} | ${stat['total_profit']:11.2f} | ${stat['avg_profit']:9.2f}")

# ==========================================
# HOLD TIME ANALYSIS
# ==========================================

print("\n" + "=" * 80)
print("⏱️ PERFORMANCE BY HOLD TIME")
print("=" * 80)

hold_times = {
    '< 1h': [],
    '1-2h': [],
    '2-4h': [],
    '4-8h': [],
    '8h+': []
}

for trade in trades:
    hold_time = trade.get('hold_time', 0)
    profit = trade.get('profit', 0)

    if hold_time < 1:
        hold_times['< 1h'].append(profit)
    elif hold_time < 2:
        hold_times['1-2h'].append(profit)
    elif hold_time < 4:
        hold_times['2-4h'].append(profit)
    elif hold_time < 8:
        hold_times['4-8h'].append(profit)
    else:
        hold_times['8h+'].append(profit)

print("\nRange  | Trades | Wins | Win% | Total Profit | Avg Profit")
print("-" * 60)

for time_range, profits in hold_times.items():
    if not profits:
        continue

    total = len(profits)
    wins = sum(1 for p in profits if p > 0)
    win_rate = (wins / total * 100) if total > 0 else 0
    total_profit = sum(profits)
    avg_profit = total_profit / total if total > 0 else 0

    print(f"{time_range:6} | {total:6} | {wins:4} | {win_rate:4.1f} | ${total_profit:11.2f} | ${avg_profit:9.2f}")

# ==========================================
# RECOMMENDATIONS
# ==========================================

print("\n" + "=" * 80)
print("💡 KEY RECOMMENDATIONS")
print("=" * 80)

# Find best session + confidence combo
best_combos = []

for session in ['ny', 'asian', 'london', 'overlap']:
    for conf_level in ['High (75+)', 'Med (70-75)', 'Low (<70)']:
        profits = session_conf.get(session, {}).get(conf_level, [])
        if not profits or len(profits) < 5:  # Min 5 trades for significance
            continue

        total_profit = sum(profits)
        wins = sum(1 for p in profits if p > 0)
        win_rate = (wins / len(profits) * 100)

        if total_profit > 0:
            best_combos.append({
                'session': session,
                'conf': conf_level,
                'trades': len(profits),
                'win_rate': win_rate,
                'profit': total_profit,
                'avg': total_profit / len(profits)
            })

best_combos.sort(key=lambda x: x['profit'], reverse=True)

print("\n✅ TOP 5 PROFITABLE COMBINATIONS:")
for i, combo in enumerate(best_combos[:5], 1):
    print(f"\n{i}. {combo['session'].upper()} + {combo['conf']}")
    print(f"   Trades: {combo['trades']}, Win Rate: {combo['win_rate']:.1f}%")
    print(f"   Profit: ${combo['profit']:.2f} (Avg: ${combo['avg']:.2f})")

print("\n\n❌ WORST 3 COMBINATIONS TO AVOID:")
for i, combo in enumerate(best_combos[-3:], 1):
    print(f"\n{i}. {combo['session'].upper()} + {combo['conf']}")
    print(f"   Trades: {combo['trades']}, Win Rate: {combo['win_rate']:.1f}%")
    print(f"   Loss: ${combo['profit']:.2f} (Avg: ${combo['avg']:.2f})")

print("\n" + "=" * 80)
print("✅ Analysis Complete!")
print("=" * 80)