import json
import os
from pathlib import Path
import pandas as pd

# 定义结果目录
data_dir = Path('./result')

# 指定要分析的文件
target_file = data_dir / 'backtest-result-2025-10-14_13-45-04.json'

# 输出文件
export_file = data_dir / 'backtest_trades_with_adjustments.csv'

print(f"分析文件: {target_file}")
print(f"文件大小: {target_file.stat().st_size / (1024 * 1024):.2f} MB")

# 读取文件
try:
    with open(target_file, 'r', encoding='utf-8') as f:
        data = json.load(f)
    print("文件加载成功")
except Exception as e:
    print(f"文件加载失败: {e}")
    exit(1)

# 检查数据结构
if 'strategy' not in data:
    print("数据中没有 'strategy' 字段")
    exit(1)

strategy_name = 'FreqaiPrimer'
if strategy_name not in data['strategy']:
    print(f"策略 '{strategy_name}' 不在数据中")
    print(f"可用策略: {list(data['strategy'].keys())}")
    exit(1)

strategy_data = data['strategy'][strategy_name]
if 'trades' not in strategy_data:
    print("策略数据中没有 'trades' 字段")
    exit(1)

trades = strategy_data['trades']
print(f"总交易数: {len(trades)}")

# 分析交易中的加仓情况
results = []
trades_with_adjustments = 0

for i, trade in enumerate(trades):
    # 每1000笔交易显示进度
    if (i + 1) % 1000 == 0:
        print(f"已分析 {i + 1} 笔交易")
        
    pair = trade.get('pair', 'Unknown')
    open_date = trade.get('open_date', 'Unknown')
    open_rate = trade.get('open_rate', 0)
    profit_ratio = trade.get('profit_ratio', 0)
    
    # 检查是否有orders字段
    if 'orders' in trade:
        orders = trade['orders']
        # 筛选入场订单
        entry_orders = [order for order in orders if order.get('ft_is_entry', False)]
        
        # 按时间戳排序
        entry_orders.sort(key=lambda x: x.get('order_filled_timestamp', 0))
        
        # 统计加仓情况
        if len(entry_orders) > 1:
            trades_with_adjustments += 1
            base_price = entry_orders[0].get('safe_price', open_rate)
            
            for j in range(1, len(entry_orders)):
                current_order = entry_orders[j]
                current_price = current_order.get('safe_price', 0)
                
                if base_price > 0 and current_price > 0:
                    # 计算降幅百分比
                    price_drop_percent = ((base_price - current_price) / base_price) * 100
                    
                    # 保存结果
                    results.append({
                        'trade_index': i,
                        'pair': pair,
                        'open_date': open_date,
                        'adjustment_number': j,
                        'base_price': base_price,
                        'current_price': current_price,
                        'price_drop_percent': price_drop_percent,
                        'open_rate': open_rate,
                        'profit_ratio': profit_ratio
                    })

# 输出统计结果
print(f"\n分析完成！")
print(f"总交易数: {len(trades)}")
print(f"包含加仓的交易数: {trades_with_adjustments}")
print(f"总共分析到的加仓次数: {len(results)}")

# 如果有结果，保存到CSV文件
if results:
    df = pd.DataFrame(results)
    df.to_csv(export_file, index=False)
    print(f"\n加仓分析结果已保存到: {export_file}")
    
    # 显示统计信息
    print("\n加仓价格降幅统计:")
    print(f"平均降幅: {df['price_drop_percent'].mean():.2f}%")
    print(f"最大降幅: {df['price_drop_percent'].max():.2f}%")
    print(f"最小降幅: {df['price_drop_percent'].min():.2f}%")
    
    # 显示前10条记录
    print("\n前10条加仓记录:")
    print(df.head(10).to_string(index=False))
else:
    print("\n未发现任何加仓记录")
    # 创建一个空的CSV文件，包含所有字段
    if not export_file.exists():
        empty_df = pd.DataFrame(columns=[
            'trade_index', 'pair', 'open_date', 'adjustment_number',
            'base_price', 'current_price', 'price_drop_percent',
            'open_rate', 'profit_ratio'
        ])
        empty_df.to_csv(export_file, index=False)
        print(f"已创建空的结果文件: {export_file}")