From 1f394cace3a3b35f49336689168f27dd12dee97c Mon Sep 17 00:00:00 2001
From: Charles <larrylandor@gmail.com>
Date: Tue, 26 Aug 2025 16:19:21 +0800
Subject: [PATCH 1/7] =?UTF-8?q?feat:=20=E6=B7=BB=E5=8A=A0Kronos=20Web=20UI?=
 =?UTF-8?q?=E5=AE=8C=E6=95=B4=E5=8A=9F=E8=83=BD?=
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- 集成Kronos模型（mini/small/base）
- 支持CPU/CUDA/MPS设备选择
- 时间窗口滑条选择器（400+120固定窗口）
- 预测质量参数控制（Temperature, Top-P, Sample Count）
- 预测vs实际数据对比分析
- 完整的Flask后端和现代化前端界面
- 支持CSV和Feather格式数据文件
- 完整的启动脚本和文档
---
 .gitignore                 |   76 +++
 webui/README.md            |  135 ++++
 webui/app.py               |  603 ++++++++++++++++++
 webui/requirements.txt     |    7 +
 webui/run.py               |   89 +++
 webui/start.sh             |   40 ++
 webui/templates/index.html | 1238 ++++++++++++++++++++++++++++++++++++
 7 files changed, 2188 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 webui/README.md
 create mode 100644 webui/app.py
 create mode 100644 webui/requirements.txt
 create mode 100644 webui/run.py
 create mode 100755 webui/start.sh
 create mode 100644 webui/templates/index.html

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..8266c2d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,76 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# PyCharm
+.idea/
+
+# VS Code
+.vscode/
+
+# macOS
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Windows
+Thumbs.db
+ehthumbs.db
+Desktop.ini
+
+# Linux
+*~
+
+# Data files (large files)
+*.feather
+*.csv
+*.parquet
+*.h5
+*.hdf5
+
+# Model files (large files)
+*.pth
+*.pt
+*.ckpt
+*.bin
+
+# Logs
+*.log
+logs/
+
+# Environment
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Temporary files
+*.tmp
+*.temp
+temp/
+tmp/
diff --git a/webui/README.md b/webui/README.md
new file mode 100644
index 0000000..46dfcb3
--- /dev/null
+++ b/webui/README.md
@@ -0,0 +1,135 @@
+# Kronos Web UI
+
+Kronos金融预测模型的Web用户界面，提供直观的图形化操作界面。
+
+## ✨ 功能特性
+
+- **多格式数据支持**: 支持CSV、Feather等格式的金融数据
+- **智能时间窗口**: 固定400+120数据点的时间窗口滑条选择
+- **真实模型预测**: 集成真实的Kronos模型，支持多种模型大小
+- **预测质量控制**: 可调节温度、核采样、样本数量等参数
+- **多设备支持**: 支持CPU、CUDA、MPS等计算设备
+- **对比分析**: 预测结果与实际数据的详细对比
+- **K线图显示**: 专业的金融K线图表展示
+
+## 🚀 快速开始
+
+### 方法1: 使用Python脚本启动
+```bash
+cd webui
+python run.py
+```
+
+### 方法2: 使用Shell脚本启动
+```bash
+cd webui
+chmod +x start.sh
+./start.sh
+```
+
+### 方法3: 直接启动Flask应用
+```bash
+cd webui
+python app.py
+```
+
+启动成功后，访问 http://localhost:7070
+
+## 📋 使用步骤
+
+1. **加载数据**: 选择data目录中的金融数据文件
+2. **加载模型**: 选择Kronos模型和计算设备
+3. **设置参数**: 调整预测质量参数
+4. **选择时间窗口**: 使用滑条选择400+120数据点的时间范围
+5. **开始预测**: 点击预测按钮生成结果
+6. **查看结果**: 在图表和表格中查看预测结果
+
+## 🔧 预测质量参数
+
+### 温度 (T)
+- **范围**: 0.1 - 2.0
+- **作用**: 控制预测的随机性
+- **建议**: 1.2-1.5 获得更好的预测质量
+
+### 核采样 (top_p)
+- **范围**: 0.1 - 1.0
+- **作用**: 控制预测的多样性
+- **建议**: 0.95-1.0 考虑更多可能性
+
+### 样本数量
+- **范围**: 1 - 5
+- **作用**: 生成多个预测样本
+- **建议**: 2-3 个样本提高质量
+
+## 📊 支持的数据格式
+
+### 必需列
+- `open`: 开盘价
+- `high`: 最高价
+- `low`: 最低价
+- `close`: 收盘价
+
+### 可选列
+- `volume`: 成交量
+- `amount`: 成交额（不用于预测）
+- `timestamps`/`timestamp`/`date`: 时间戳
+
+## 🤖 模型支持
+
+- **Kronos-mini**: 4.1M参数，轻量级快速预测
+- **Kronos-small**: 24.7M参数，平衡性能和速度
+- **Kronos-base**: 102.3M参数，高质量预测
+
+## 🖥️ GPU加速支持
+
+- **CPU**: 通用计算，兼容性最好
+- **CUDA**: NVIDIA GPU加速，性能最佳
+- **MPS**: Apple Silicon GPU加速，Mac用户推荐
+
+## ⚠️ 注意事项
+
+- `amount`列不会被用于预测，仅用于显示
+- 时间窗口固定为400+120=520个数据点
+- 确保数据文件包含足够的历史数据
+- 首次加载模型可能需要下载，请耐心等待
+
+## 🔍 对比分析
+
+系统会自动提供预测结果与实际数据的对比分析，包括：
+- 价格差异统计
+- 误差分析
+- 预测质量评估
+
+## 🛠️ 技术架构
+
+- **后端**: Flask + Python
+- **前端**: HTML + CSS + JavaScript
+- **图表**: Plotly.js
+- **数据处理**: Pandas + NumPy
+- **模型**: Hugging Face Transformers
+
+## 📝 故障排除
+
+### 常见问题
+1. **端口占用**: 修改app.py中的端口号
+2. **依赖缺失**: 运行 `pip install -r requirements.txt`
+3. **模型加载失败**: 检查网络连接和模型ID
+4. **数据格式错误**: 确保数据列名和格式正确
+
+### 日志查看
+启动时会在控制台显示详细的运行信息，包括模型状态和错误信息。
+
+## 📄 许可证
+
+本项目遵循原Kronos项目的许可证条款。
+
+## 🤝 贡献
+
+欢迎提交Issue和Pull Request来改进这个Web UI！
+
+## 📞 支持
+
+如有问题，请查看：
+1. 项目文档
+2. GitHub Issues
+3. 控制台错误信息
diff --git a/webui/app.py b/webui/app.py
new file mode 100644
index 0000000..49a5d58
--- /dev/null
+++ b/webui/app.py
@@ -0,0 +1,603 @@
+import os
+import pandas as pd
+import numpy as np
+import json
+import plotly.graph_objects as go
+import plotly.utils
+from flask import Flask, render_template, request, jsonify
+from flask_cors import CORS
+import sys
+import warnings
+warnings.filterwarnings('ignore')
+
+# 添加项目根目录到路径
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+try:
+    from model import Kronos, KronosTokenizer, KronosPredictor
+    MODEL_AVAILABLE = True
+except ImportError:
+    MODEL_AVAILABLE = False
+    print("警告: Kronos模型无法导入，将使用模拟数据进行演示")
+
+app = Flask(__name__)
+CORS(app)
+
+# 全局变量存储模型
+tokenizer = None
+model = None
+predictor = None
+
+# 可用的模型配置
+AVAILABLE_MODELS = {
+    'kronos-mini': {
+        'name': 'Kronos-mini',
+        'model_id': 'NeoQuasar/Kronos-mini',
+        'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-2k',
+        'context_length': 2048,
+        'params': '4.1M',
+        'description': '轻量级模型，适合快速预测'
+    },
+    'kronos-small': {
+        'name': 'Kronos-small',
+        'model_id': 'NeoQuasar/Kronos-small',
+        'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
+        'context_length': 512,
+        'params': '24.7M',
+        'description': '小型模型，平衡性能和速度'
+    },
+    'kronos-base': {
+        'name': 'Kronos-base',
+        'model_id': 'NeoQuasar/Kronos-base',
+        'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
+        'context_length': 512,
+        'params': '102.3M',
+        'description': '基础模型，提供更好的预测质量'
+    }
+}
+
+def load_data_files():
+    """扫描data目录并返回可用的数据文件"""
+    data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data')
+    data_files = []
+    
+    if os.path.exists(data_dir):
+        for file in os.listdir(data_dir):
+            if file.endswith(('.csv', '.feather')):
+                file_path = os.path.join(data_dir, file)
+                file_size = os.path.getsize(file_path)
+                data_files.append({
+                    'name': file,
+                    'path': file_path,
+                    'size': f"{file_size / 1024:.1f} KB" if file_size < 1024*1024 else f"{file_size / (1024*1024):.1f} MB"
+                })
+    
+    return data_files
+
+def load_data_file(file_path):
+    """加载数据文件"""
+    try:
+        if file_path.endswith('.csv'):
+            df = pd.read_csv(file_path)
+        elif file_path.endswith('.feather'):
+            df = pd.read_feather(file_path)
+        else:
+            return None, "不支持的文件格式"
+        
+        # 检查必要的列
+        required_cols = ['open', 'high', 'low', 'close']
+        if not all(col in df.columns for col in required_cols):
+            return None, f"缺少必要的列: {required_cols}"
+        
+        # 处理时间戳列
+        if 'timestamps' in df.columns:
+            df['timestamps'] = pd.to_datetime(df['timestamps'])
+        elif 'timestamp' in df.columns:
+            df['timestamps'] = pd.to_datetime(df['timestamp'])
+        elif 'date' in df.columns:
+            # 如果列名是'date'，将其重命名为'timestamps'
+            df['timestamps'] = pd.to_datetime(df['date'])
+        else:
+            # 如果没有时间戳列，创建一个
+            df['timestamps'] = pd.date_range(start='2024-01-01', periods=len(df), freq='1H')
+        
+        # 确保数值列是数值类型
+        for col in ['open', 'high', 'low', 'close']:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+        
+        # 处理volume列（可选）
+        if 'volume' in df.columns:
+            df['volume'] = pd.to_numeric(df['volume'], errors='coerce')
+        
+        # 处理amount列（可选，但不用于预测）
+        if 'amount' in df.columns:
+            df['amount'] = pd.to_numeric(df['amount'], errors='coerce')
+        
+        # 删除包含NaN的行
+        df = df.dropna()
+        
+        return df, None
+        
+    except Exception as e:
+        return None, f"加载文件失败: {str(e)}"
+
+def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, historical_start_idx=0):
+    """创建预测图表"""
+    # 使用指定的历史数据起始位置，而不是总是从df的开头开始
+    if historical_start_idx + lookback + pred_len <= len(df):
+        # 显示指定位置开始的lookback个历史点 + pred_len个预测点
+        historical_df = df.iloc[historical_start_idx:historical_start_idx+lookback]
+        prediction_range = range(historical_start_idx+lookback, historical_start_idx+lookback+pred_len)
+    else:
+        # 如果数据不够，调整到可用的最大范围
+        available_lookback = min(lookback, len(df) - historical_start_idx)
+        available_pred_len = min(pred_len, max(0, len(df) - historical_start_idx - available_lookback))
+        historical_df = df.iloc[historical_start_idx:historical_start_idx+available_lookback]
+        prediction_range = range(historical_start_idx+available_lookback, historical_start_idx+available_lookback+available_pred_len)
+    
+    # 创建图表
+    fig = go.Figure()
+    
+    # 添加历史数据（K线图）
+    fig.add_trace(go.Candlestick(
+        x=historical_df['timestamps'] if 'timestamps' in historical_df.columns else historical_df.index,
+        open=historical_df['open'],
+        high=historical_df['high'],
+        low=historical_df['low'],
+        close=historical_df['close'],
+        name='历史数据 (400个数据点)',
+        increasing_line_color='#26A69A',
+        decreasing_line_color='#EF5350'
+    ))
+    
+    # 添加预测数据（K线图）
+    if pred_df is not None and len(pred_df) > 0:
+        # 计算预测数据的时间戳 - 确保与历史数据连续
+        if 'timestamps' in df.columns and len(historical_df) > 0:
+            # 从历史数据的最后一个时间点开始，按相同的时间间隔创建预测时间戳
+            last_timestamp = historical_df['timestamps'].iloc[-1]
+            time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1)
+            
+            pred_timestamps = pd.date_range(
+                start=last_timestamp + time_diff,
+                periods=len(pred_df),
+                freq=time_diff
+            )
+        else:
+            # 如果没有时间戳，使用索引
+            pred_timestamps = range(len(historical_df), len(historical_df) + len(pred_df))
+        
+        fig.add_trace(go.Candlestick(
+            x=pred_timestamps,
+            open=pred_df['open'],
+            high=pred_df['high'],
+            low=pred_df['low'],
+            close=pred_df['close'],
+            name='预测数据 (120个数据点)',
+            increasing_line_color='#66BB6A',
+            decreasing_line_color='#FF7043'
+        ))
+    
+    # 添加实际数据用于对比（如果存在）
+    if actual_df is not None and len(actual_df) > 0:
+        # 实际数据应该与预测数据在同一个时间段
+        if 'timestamps' in df.columns:
+            # 实际数据应该使用与预测数据相同的时间戳，确保时间对齐
+            if 'pred_timestamps' in locals():
+                actual_timestamps = pred_timestamps
+            else:
+                # 如果没有预测时间戳，从历史数据最后一个时间点开始计算
+                if len(historical_df) > 0:
+                    last_timestamp = historical_df['timestamps'].iloc[-1]
+                    time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1)
+                    actual_timestamps = pd.date_range(
+                        start=last_timestamp + time_diff,
+                        periods=len(actual_df),
+                        freq=time_diff
+                    )
+                else:
+                    actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df))
+        else:
+            actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df))
+        
+        fig.add_trace(go.Candlestick(
+            x=actual_timestamps,
+            open=actual_df['open'],
+            high=actual_df['high'],
+            low=actual_df['low'],
+            close=actual_df['close'],
+            name='实际数据 (120个数据点)',
+            increasing_line_color='#FF9800',
+            decreasing_line_color='#F44336'
+        ))
+    
+    # 更新布局
+    fig.update_layout(
+        title='Kronos 金融预测结果 - 400个历史点 + 120个预测点 vs 120个实际点',
+        xaxis_title='时间',
+        yaxis_title='价格',
+        template='plotly_white',
+        height=600,
+        showlegend=True
+    )
+    
+    # 确保x轴时间连续
+    if 'timestamps' in historical_df.columns:
+        # 获取所有时间戳并排序
+        all_timestamps = []
+        if len(historical_df) > 0:
+            all_timestamps.extend(historical_df['timestamps'])
+        if 'pred_timestamps' in locals():
+            all_timestamps.extend(pred_timestamps)
+        if 'actual_timestamps' in locals():
+            all_timestamps.extend(actual_timestamps)
+        
+        if all_timestamps:
+            all_timestamps = sorted(all_timestamps)
+            fig.update_xaxes(
+                range=[all_timestamps[0], all_timestamps[-1]],
+                rangeslider_visible=False,
+                type='date'
+            )
+    
+    return json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
+
+@app.route('/')
+def index():
+    """主页"""
+    return render_template('index.html')
+
+@app.route('/api/data-files')
+def get_data_files():
+    """获取可用的数据文件列表"""
+    data_files = load_data_files()
+    return jsonify(data_files)
+
+@app.route('/api/load-data', methods=['POST'])
+def load_data():
+    """加载数据文件"""
+    try:
+        data = request.get_json()
+        file_path = data.get('file_path')
+        
+        if not file_path:
+            return jsonify({'error': '文件路径不能为空'}), 400
+        
+        df, error = load_data_file(file_path)
+        if error:
+            return jsonify({'error': error}), 400
+        
+        # 检测数据的时间频率
+        def detect_timeframe(df):
+            if len(df) < 2:
+                return "未知"
+            
+            time_diffs = []
+            for i in range(1, min(10, len(df))):  # 检查前10个时间差
+                diff = df['timestamps'].iloc[i] - df['timestamps'].iloc[i-1]
+                time_diffs.append(diff)
+            
+            if not time_diffs:
+                return "未知"
+            
+            # 计算平均时间差
+            avg_diff = sum(time_diffs, pd.Timedelta(0)) / len(time_diffs)
+            
+            # 转换为可读格式
+            if avg_diff < pd.Timedelta(minutes=1):
+                return f"{avg_diff.total_seconds():.0f}秒"
+            elif avg_diff < pd.Timedelta(hours=1):
+                return f"{avg_diff.total_seconds() / 60:.0f}分钟"
+            elif avg_diff < pd.Timedelta(days=1):
+                return f"{avg_diff.total_seconds() / 3600:.0f}小时"
+            else:
+                return f"{avg_diff.days}天"
+        
+        # 返回数据信息
+        data_info = {
+            'rows': len(df),
+            'columns': list(df.columns),
+            'start_date': df['timestamps'].min().isoformat() if 'timestamps' in df.columns else 'N/A',
+            'end_date': df['timestamps'].max().isoformat() if 'timestamps' in df.columns else 'N/A',
+            'price_range': {
+                'min': float(df[['open', 'high', 'low', 'close']].min().min()),
+                'max': float(df[['open', 'high', 'low', 'close']].max().max())
+            },
+            'prediction_columns': ['open', 'high', 'low', 'close'] + (['volume'] if 'volume' in df.columns else []),
+            'timeframe': detect_timeframe(df)
+        }
+        
+        return jsonify({
+            'success': True,
+            'data_info': data_info,
+            'message': f'成功加载数据，共 {len(df)} 行'
+        })
+        
+    except Exception as e:
+        return jsonify({'error': f'加载数据失败: {str(e)}'}), 500
+
+@app.route('/api/predict', methods=['POST'])
+def predict():
+    """进行预测"""
+    try:
+        data = request.get_json()
+        file_path = data.get('file_path')
+        lookback = int(data.get('lookback', 400))
+        pred_len = int(data.get('pred_len', 120))
+        
+        # 获取预测质量参数
+        temperature = float(data.get('temperature', 1.0))
+        top_p = float(data.get('top_p', 0.9))
+        sample_count = int(data.get('sample_count', 1))
+        
+        if not file_path:
+            return jsonify({'error': '文件路径不能为空'}), 400
+        
+        # 加载数据
+        df, error = load_data_file(file_path)
+        if error:
+            return jsonify({'error': error}), 400
+        
+        if len(df) < lookback:
+            return jsonify({'error': f'数据长度不足，需要至少 {lookback} 行数据'}), 400
+        
+        # 进行预测
+        if MODEL_AVAILABLE and predictor is not None:
+            try:
+                # 使用真实的Kronos模型
+                # 只使用必要的列：OHLCV，不包含amount
+                required_cols = ['open', 'high', 'low', 'close']
+                if 'volume' in df.columns:
+                    required_cols.append('volume')
+                
+                # 处理时间段选择
+                start_date = data.get('start_date')
+                
+                if start_date:
+                    # 自定义时间段 - 修复逻辑：使用选择的窗口内的数据
+                    start_dt = pd.to_datetime(start_date)
+                    
+                    # 找到开始时间之后的数据
+                    mask = df['timestamps'] >= start_dt
+                    time_range_df = df[mask]
+                    
+                    # 确保有足够的数据：lookback + pred_len
+                    if len(time_range_df) < lookback + pred_len:
+                        return jsonify({'error': f'从开始时间 {start_dt.strftime("%Y-%m-%d %H:%M")} 开始的数据不足，需要至少 {lookback + pred_len} 个数据点，当前只有 {len(time_range_df)} 个'}), 400
+                    
+                    # 使用选择的窗口内的前lookback个数据点进行预测
+                    x_df = time_range_df.iloc[:lookback][required_cols]
+                    x_timestamp = time_range_df.iloc[:lookback]['timestamps']
+                    
+                    # 使用选择的窗口内的后pred_len个数据点作为实际值
+                    y_timestamp = time_range_df.iloc[lookback:lookback+pred_len]['timestamps']
+                    
+                    # 计算实际的时间段长度
+                    start_timestamp = time_range_df['timestamps'].iloc[0]
+                    end_timestamp = time_range_df['timestamps'].iloc[lookback+pred_len-1]
+                    time_span = end_timestamp - start_timestamp
+                    
+                    prediction_type = f"Kronos模型预测 (选择的窗口内：前{lookback}个数据点预测，后{pred_len}个数据点对比，时间跨度: {time_span})"
+                else:
+                    # 使用最新数据
+                    x_df = df.iloc[:lookback][required_cols]
+                    x_timestamp = df.iloc[:lookback]['timestamps']
+                    y_timestamp = df.iloc[lookback:lookback+pred_len]['timestamps']
+                    prediction_type = "Kronos模型预测 (最新数据)"
+                
+                # 确保时间戳是Series格式，不是DatetimeIndex，避免Kronos模型的.dt属性错误
+                if isinstance(x_timestamp, pd.DatetimeIndex):
+                    x_timestamp = pd.Series(x_timestamp, name='timestamps')
+                if isinstance(y_timestamp, pd.DatetimeIndex):
+                    y_timestamp = pd.Series(y_timestamp, name='timestamps')
+                
+                pred_df = predictor.predict(
+                    df=x_df,
+                    x_timestamp=x_timestamp,
+                    y_timestamp=y_timestamp,
+                    pred_len=pred_len,
+                    T=temperature,
+                    top_p=top_p,
+                    sample_count=sample_count
+                )
+                
+            except Exception as e:
+                return jsonify({'error': f'Kronos模型预测失败: {str(e)}'}), 500
+        else:
+            return jsonify({'error': 'Kronos模型未加载，请先加载模型'}), 400
+        
+        # 准备实际数据用于对比（如果存在）
+        actual_data = []
+        actual_df = None
+        
+        if start_date:  # 自定义时间段
+            # 修复逻辑：使用选择的窗口内的数据
+            # 预测使用的是选择的窗口内的前400个数据点
+            # 实际数据应该是选择的窗口内的后120个数据点
+            start_dt = pd.to_datetime(start_date)
+            
+            # 找到从start_date开始的数据
+            mask = df['timestamps'] >= start_dt
+            time_range_df = df[mask]
+            
+            if len(time_range_df) >= lookback + pred_len:
+                # 获取选择的窗口内的后120个数据点作为实际值
+                actual_df = time_range_df.iloc[lookback:lookback+pred_len]
+                
+                for i, (_, row) in enumerate(actual_df.iterrows()):
+                    actual_data.append({
+                        'timestamp': row['timestamps'].isoformat(),
+                        'open': float(row['open']),
+                        'high': float(row['high']),
+                        'low': float(row['low']),
+                        'close': float(row['close']),
+                        'volume': float(row['volume']) if 'volume' in row else 0,
+                        'amount': float(row['amount']) if 'amount' in row else 0
+                    })
+        else:  # 最新数据
+            # 预测使用的是前400个数据点
+            # 实际数据应该是400个数据点之后的120个数据点
+            if len(df) >= lookback + pred_len:
+                actual_df = df.iloc[lookback:lookback+pred_len]
+                for i, (_, row) in enumerate(actual_df.iterrows()):
+                    actual_data.append({
+                        'timestamp': row['timestamps'].isoformat(),
+                        'open': float(row['open']),
+                        'high': float(row['high']),
+                        'low': float(row['low']),
+                        'close': float(row['close']),
+                        'volume': float(row['volume']) if 'volume' in row else 0,
+                        'amount': float(row['amount']) if 'amount' in row else 0
+                    })
+        
+        # 创建图表 - 传递历史数据的起始位置
+        if start_date:
+            # 自定义时间段：找到历史数据在原始df中的起始位置
+            start_dt = pd.to_datetime(start_date)
+            mask = df['timestamps'] >= start_dt
+            historical_start_idx = df[mask].index[0] if len(df[mask]) > 0 else 0
+        else:
+            # 最新数据：从开头开始
+            historical_start_idx = 0
+        
+        chart_json = create_prediction_chart(df, pred_df, lookback, pred_len, actual_df, historical_start_idx)
+        
+        # 准备预测结果数据 - 修复时间戳计算逻辑
+        if 'timestamps' in df.columns:
+            if start_date:
+                # 自定义时间段：使用选择的窗口数据计算时间戳
+                start_dt = pd.to_datetime(start_date)
+                mask = df['timestamps'] >= start_dt
+                time_range_df = df[mask]
+                
+                if len(time_range_df) >= lookback:
+                    # 从选择的窗口的最后一个时间点开始计算预测时间戳
+                    last_timestamp = time_range_df['timestamps'].iloc[lookback-1]
+                    time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0]
+                    future_timestamps = pd.date_range(
+                        start=last_timestamp + time_diff,
+                        periods=pred_len,
+                        freq=time_diff
+                    )
+                else:
+                    future_timestamps = []
+            else:
+                # 最新数据：从整个数据文件的最后时间点开始计算
+                last_timestamp = df['timestamps'].iloc[-1]
+                time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0]
+                future_timestamps = pd.date_range(
+                    start=last_timestamp + time_diff,
+                    periods=pred_len,
+                    freq=time_diff
+                )
+        else:
+            future_timestamps = range(len(df), len(df) + pred_len)
+        
+        prediction_results = []
+        for i, (_, row) in enumerate(pred_df.iterrows()):
+            prediction_results.append({
+                'timestamp': future_timestamps[i].isoformat() if i < len(future_timestamps) else f"T{i}",
+                'open': float(row['open']),
+                'high': float(row['high']),
+                'low': float(row['low']),
+                'close': float(row['close']),
+                'volume': float(row['volume']) if 'volume' in row else 0,
+                'amount': float(row['amount']) if 'amount' in row else 0
+            })
+        
+        return jsonify({
+            'success': True,
+            'prediction_type': prediction_type,
+            'chart': chart_json,
+            'prediction_results': prediction_results,
+            'actual_data': actual_data,
+            'has_comparison': len(actual_data) > 0,
+            'message': f'预测完成，生成了 {pred_len} 个预测点' + (f'，包含 {len(actual_data)} 个实际数据点用于对比' if len(actual_data) > 0 else '')
+        })
+        
+    except Exception as e:
+        return jsonify({'error': f'预测失败: {str(e)}'}), 500
+
+@app.route('/api/load-model', methods=['POST'])
+def load_model():
+    """加载Kronos模型"""
+    global tokenizer, model, predictor
+    
+    try:
+        if not MODEL_AVAILABLE:
+            return jsonify({'error': 'Kronos模型库不可用'}), 400
+        
+        data = request.get_json()
+        model_key = data.get('model_key', 'kronos-small')
+        device = data.get('device', 'cpu')
+        
+        if model_key not in AVAILABLE_MODELS:
+            return jsonify({'error': f'不支持的模型: {model_key}'}), 400
+        
+        model_config = AVAILABLE_MODELS[model_key]
+        
+        # 加载tokenizer和模型
+        tokenizer = KronosTokenizer.from_pretrained(model_config['tokenizer_id'])
+        model = Kronos.from_pretrained(model_config['model_id'])
+        
+        # 创建predictor
+        predictor = KronosPredictor(model, tokenizer, device=device, max_context=model_config['context_length'])
+        
+        return jsonify({
+            'success': True,
+            'message': f'模型加载成功: {model_config["name"]} ({model_config["params"]}) on {device}',
+            'model_info': {
+                'name': model_config['name'],
+                'params': model_config['params'],
+                'context_length': model_config['context_length'],
+                'description': model_config['description']
+            }
+        })
+        
+    except Exception as e:
+        return jsonify({'error': f'模型加载失败: {str(e)}'}), 500
+
+@app.route('/api/available-models')
+def get_available_models():
+    """获取可用的模型列表"""
+    return jsonify({
+        'models': AVAILABLE_MODELS,
+        'model_available': MODEL_AVAILABLE
+    })
+
+@app.route('/api/model-status')
+def get_model_status():
+    """获取模型状态"""
+    if MODEL_AVAILABLE:
+        if predictor is not None:
+            return jsonify({
+                'available': True,
+                'loaded': True,
+                'message': 'Kronos模型已加载并可用',
+                'current_model': {
+                    'name': predictor.model.__class__.__name__,
+                    'device': str(next(predictor.model.parameters()).device)
+                }
+            })
+        else:
+            return jsonify({
+                'available': True,
+                'loaded': False,
+                'message': 'Kronos模型可用但未加载'
+            })
+    else:
+        return jsonify({
+            'available': False,
+            'loaded': False,
+            'message': 'Kronos模型库不可用，请安装相关依赖'
+        })
+
+if __name__ == '__main__':
+    print("启动Kronos Web UI...")
+    print(f"模型可用性: {MODEL_AVAILABLE}")
+    if MODEL_AVAILABLE:
+        print("提示: 可以通过 /api/load-model 接口加载Kronos模型")
+    else:
+        print("提示: 将使用模拟数据进行演示")
+    
+    app.run(debug=True, host='0.0.0.0', port=7070)
diff --git a/webui/requirements.txt b/webui/requirements.txt
new file mode 100644
index 0000000..cfce804
--- /dev/null
+++ b/webui/requirements.txt
@@ -0,0 +1,7 @@
+flask==2.3.3
+flask-cors==4.0.0
+pandas==2.2.2
+numpy==1.24.3
+plotly==5.17.0
+torch>=2.1.0
+huggingface_hub==0.33.1
diff --git a/webui/run.py b/webui/run.py
new file mode 100644
index 0000000..6638ddb
--- /dev/null
+++ b/webui/run.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+"""
+Kronos Web UI 启动脚本
+"""
+
+import os
+import sys
+import subprocess
+import webbrowser
+import time
+
+def check_dependencies():
+    """检查依赖是否安装"""
+    try:
+        import flask
+        import flask_cors
+        import pandas
+        import numpy
+        import plotly
+        print("✅ 所有依赖已安装")
+        return True
+    except ImportError as e:
+        print(f"❌ 缺少依赖: {e}")
+        print("请运行: pip install -r requirements.txt")
+        return False
+
+def install_dependencies():
+    """安装依赖"""
+    print("正在安装依赖...")
+    try:
+        subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"])
+        print("✅ 依赖安装完成")
+        return True
+    except subprocess.CalledProcessError:
+        print("❌ 依赖安装失败")
+        return False
+
+def main():
+    """主函数"""
+    print("🚀 启动 Kronos Web UI...")
+    print("=" * 50)
+    
+    # 检查依赖
+    if not check_dependencies():
+        print("\n是否自动安装依赖? (y/n): ", end="")
+        if input().lower() == 'y':
+            if not install_dependencies():
+                return
+        else:
+            print("请手动安装依赖后重试")
+            return
+    
+    # 检查模型可用性
+    try:
+        sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        from model import Kronos, KronosTokenizer, KronosPredictor
+        print("✅ Kronos模型库可用")
+        model_available = True
+    except ImportError:
+        print("⚠️  Kronos模型库不可用，将使用模拟预测")
+        model_available = False
+    
+    # 启动Flask应用
+    print("\n🌐 启动Web服务器...")
+    
+    # 设置环境变量
+    os.environ['FLASK_APP'] = 'app.py'
+    os.environ['FLASK_ENV'] = 'development'
+    
+    # 启动服务器
+    try:
+        from app import app
+        print("✅ Web服务器启动成功!")
+        print(f"🌐 访问地址: http://localhost:7070")
+        print("💡 提示: 按 Ctrl+C 停止服务器")
+        
+        # 自动打开浏览器
+        time.sleep(2)
+        webbrowser.open('http://localhost:7070')
+        
+        # 启动Flask应用
+        app.run(debug=True, host='0.0.0.0', port=7070)
+        
+    except Exception as e:
+        print(f"❌ 启动失败: {e}")
+        print("请检查端口7070是否被占用")
+
+if __name__ == "__main__":
+    main()
diff --git a/webui/start.sh b/webui/start.sh
new file mode 100755
index 0000000..2174dc8
--- /dev/null
+++ b/webui/start.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+
+# Kronos Web UI 启动脚本
+
+echo "🚀 启动 Kronos Web UI..."
+echo "================================"
+
+# 检查Python是否安装
+if ! command -v python3 &> /dev/null; then
+    echo "❌ Python3 未安装，请先安装Python3"
+    exit 1
+fi
+
+# 检查是否在正确的目录
+if [ ! -f "app.py" ]; then
+    echo "❌ 请在webui目录下运行此脚本"
+    exit 1
+fi
+
+# 检查依赖
+echo "📦 检查依赖..."
+if ! python3 -c "import flask, flask_cors, pandas, numpy, plotly" &> /dev/null; then
+    echo "⚠️  缺少依赖，正在安装..."
+    pip3 install -r requirements.txt
+    if [ $? -ne 0 ]; then
+        echo "❌ 依赖安装失败"
+        exit 1
+    fi
+    echo "✅ 依赖安装完成"
+else
+    echo "✅ 所有依赖已安装"
+fi
+
+# 启动应用
+echo "🌐 启动Web服务器..."
+echo "访问地址: http://localhost:7070"
+echo "按 Ctrl+C 停止服务器"
+echo ""
+
+python3 app.py
diff --git a/webui/templates/index.html b/webui/templates/index.html
new file mode 100644
index 0000000..bc321b5
--- /dev/null
+++ b/webui/templates/index.html
@@ -0,0 +1,1238 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Kronos 金融预测 Web UI</title>
+    <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            min-height: 100vh;
+            color: #333;
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 20px;
+        }
+
+        .header {
+            text-align: center;
+            margin-bottom: 30px;
+            color: white;
+        }
+
+        .header h1 {
+            font-size: 2.5rem;
+            margin-bottom: 10px;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+        }
+
+        .header p {
+            font-size: 1.1rem;
+            opacity: 0.9;
+        }
+
+        .main-content {
+            display: grid;
+            grid-template-columns: 1fr 2fr;
+            gap: 30px;
+            margin-bottom: 30px;
+        }
+
+        .control-panel {
+            background: white;
+            border-radius: 15px;
+            padding: 25px;
+            box-shadow: 0 10px 30px rgba(0,0,0,0.2);
+            height: fit-content;
+        }
+
+        .control-panel h2 {
+            color: #4a5568;
+            margin-bottom: 20px;
+            font-size: 1.5rem;
+            border-bottom: 2px solid #e2e8f0;
+            padding-bottom: 10px;
+        }
+
+        .form-group {
+            margin-bottom: 20px;
+        }
+
+        .form-group label {
+            display: block;
+            margin-bottom: 8px;
+            font-weight: 600;
+            color: #4a5568;
+        }
+
+        .form-group select,
+        .form-group input {
+            width: 100%;
+            padding: 12px;
+            border: 2px solid #e2e8f0;
+            border-radius: 8px;
+            font-size: 14px;
+            transition: border-color 0.3s ease;
+        }
+
+        .form-group select:focus,
+        .form-group input:focus {
+            outline: none;
+            border-color: #667eea;
+        }
+
+        /* 预测质量参数样式 */
+        .form-group input[type="range"] {
+            width: 70%;
+            margin-right: 10px;
+        }
+
+        .form-group input[type="number"] {
+            width: 100%;
+        }
+
+        .form-group span {
+            display: inline-block;
+            min-width: 40px;
+            font-weight: 600;
+            color: #667eea;
+        }
+
+        .form-text {
+            font-size: 12px;
+            color: #718096;
+            margin-top: 5px;
+        }
+
+        .btn {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            border: none;
+            padding: 12px 24px;
+            border-radius: 8px;
+            font-size: 16px;
+            font-weight: 600;
+            cursor: pointer;
+            transition: transform 0.2s ease, box-shadow 0.2s ease;
+            width: 100%;
+            margin-bottom: 10px;
+        }
+
+        .btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        .btn:disabled {
+            opacity: 0.6;
+            cursor: not-allowed;
+            transform: none;
+        }
+
+        .btn-secondary {
+            background: linear-gradient(135deg, #718096 0%, #4a5568 100%);
+        }
+
+        .btn-success {
+            background: linear-gradient(135deg, #48bb78 0%, #38a169 100%);
+        }
+
+        .btn-warning {
+            background: linear-gradient(135deg, #ed8936 0%, #dd6b20 100%);
+        }
+
+        .status {
+            padding: 15px;
+            border-radius: 8px;
+            margin-bottom: 20px;
+            font-weight: 500;
+        }
+
+        .status.success {
+            background: #c6f6d5;
+            color: #22543d;
+            border: 1px solid #9ae6b4;
+        }
+
+        .status.error {
+            background: #fed7d7;
+            color: #742a2a;
+            border: 1px solid #feb2b2;
+        }
+
+        .status.info {
+            background: #bee3f8;
+            color: #2a4365;
+            border: 1px solid #90cdf4;
+        }
+
+        .status.warning {
+            background: #fef5e7;
+            color: #744210;
+            border: 1px solid #fbd38d;
+        }
+
+        .chart-container {
+            background: white;
+            border-radius: 15px;
+            padding: 25px;
+            box-shadow: 0 10px 30px rgba(0,0,0,0.2);
+        }
+
+        .chart-container h2 {
+            color: #4a5568;
+            margin-bottom: 20px;
+            font-size: 1.5rem;
+            border-bottom: 2px solid #e2e8f0;
+            padding-bottom: 10px;
+        }
+
+        #chart {
+            width: 100%;
+            height: 600px;
+        }
+
+        .data-info {
+            background: #f7fafc;
+            border: 1px solid #e2e8f0;
+            border-radius: 8px;
+            padding: 15px;
+            margin-bottom: 20px;
+        }
+
+        .data-info h3 {
+            color: #4a5568;
+            margin-bottom: 10px;
+            font-size: 1.1rem;
+        }
+
+        .data-info p {
+            margin-bottom: 5px;
+            color: #4a5568;
+        }
+
+        .data-info strong {
+            color: #2d3748;
+        }
+
+        /* 时间窗口选择器样式 */
+        .time-window-container {
+            background: #f7fafc;
+            border: 1px solid #e2e8f0;
+            border-radius: 8px;
+            padding: 20px;
+            margin-bottom: 20px;
+        }
+
+        .time-window-container h3 {
+            color: #4a5568;
+            margin-bottom: 15px;
+            font-size: 1.1rem;
+        }
+
+        .time-window-info {
+            display: flex;
+            justify-content: space-between;
+            margin-bottom: 15px;
+            font-size: 12px;
+            color: #666;
+        }
+
+        .time-window-slider {
+            position: relative;
+            margin-bottom: 10px;
+        }
+
+        .slider-track {
+            position: relative;
+            height: 6px;
+            background: #e2e8f0;
+            border-radius: 3px;
+            cursor: pointer;
+        }
+
+        .slider-handle {
+            position: absolute;
+            top: -7px;
+            width: 20px;
+            height: 20px;
+            background: #667eea;
+            border-radius: 50%;
+            cursor: grab;
+            border: 2px solid white;
+            box-shadow: 0 2px 4px rgba(0,0,0,0.2);
+            z-index: 10;
+        }
+
+        .slider-handle:hover {
+            background: #5a67d8;
+            transform: scale(1.1);
+        }
+
+        .slider-handle:active {
+            cursor: grabbing;
+        }
+
+        .slider-selection {
+            position: absolute;
+            height: 6px;
+            background: #48bb78;
+            border-radius: 3px;
+            top: 0;
+        }
+
+        .slider-labels {
+            display: flex;
+            justify-content: space-between;
+            font-size: 11px;
+            color: #999;
+            margin-top: 5px;
+        }
+
+        /* 对比分析样式 */
+        .comparison-section {
+            background: #f7fafc;
+            border: 1px solid #e2e8f0;
+            border-radius: 8px;
+            padding: 20px;
+            margin-top: 20px;
+        }
+
+        .comparison-section h3 {
+            color: #4a5568;
+            margin-bottom: 15px;
+            font-size: 1.1rem;
+        }
+
+        .comparison-info {
+            background: white;
+            border: 1px solid #e2e8f0;
+            border-radius: 6px;
+            padding: 15px;
+            margin-bottom: 15px;
+        }
+
+        .comparison-table {
+            width: 100%;
+            border-collapse: collapse;
+            margin-top: 15px;
+        }
+
+        .comparison-table th,
+        .comparison-table td {
+            border: 1px solid #e2e8f0;
+            padding: 8px;
+            text-align: center;
+            font-size: 12px;
+        }
+
+        .comparison-table th {
+            background: #f7fafc;
+            font-weight: 600;
+        }
+
+        .error-stats {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+            gap: 15px;
+            margin-top: 15px;
+        }
+
+        .error-stat {
+            background: white;
+            border: 1px solid #e2e8f0;
+            border-radius: 6px;
+            padding: 15px;
+            text-align: center;
+        }
+
+        .error-stat h4 {
+            color: #4a5568;
+            margin-bottom: 5px;
+            font-size: 0.9rem;
+        }
+
+        .error-stat .value {
+            font-size: 1.5rem;
+            font-weight: 600;
+            color: #667eea;
+        }
+
+        .error-stat .unit {
+            font-size: 0.8rem;
+            color: #718096;
+        }
+
+        .loading {
+            display: none;
+            text-align: center;
+            padding: 20px;
+        }
+
+        .loading.show {
+            display: block;
+        }
+
+        .spinner {
+            border: 4px solid #f3f3f3;
+            border-top: 4px solid #667eea;
+            border-radius: 50%;
+            width: 40px;
+            height: 40px;
+            animation: spin 1s linear infinite;
+            margin: 0 auto 10px;
+        }
+
+        @keyframes spin {
+            0% { transform: rotate(0deg); }
+            100% { transform: rotate(360deg); }
+        }
+
+        .model-info {
+            background: #e6fffa;
+            border: 1px solid #81e6d9;
+            border-radius: 8px;
+            padding: 15px;
+            margin-bottom: 20px;
+        }
+
+        .model-info h3 {
+            color: #234e52;
+            margin-bottom: 10px;
+            font-size: 1.1rem;
+        }
+
+        .model-info p {
+            margin-bottom: 5px;
+            color: #234e52;
+        }
+
+        .model-info strong {
+            color: #0f2027;
+        }
+
+        @media (max-width: 768px) {
+            .main-content {
+                grid-template-columns: 1fr;
+            }
+            
+            .container {
+                padding: 10px;
+            }
+            
+            .header h1 {
+                font-size: 2rem;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🚀 Kronos 金融预测 Web UI</h1>
+            <p>基于AI的金融K线数据预测分析平台</p>
+        </div>
+
+        <div class="main-content">
+            <div class="control-panel">
+                <h2>🎯 控制面板</h2>
+
+                <!-- 模型选择 -->
+                <div class="form-group">
+                    <label for="model-select">选择模型:</label>
+                    <select id="model-select">
+                        <option value="">请先加载可用模型</option>
+                    </select>
+                    <small class="form-text">选择要使用的Kronos模型</small>
+                </div>
+
+                <!-- 设备选择 -->
+                <div class="form-group">
+                    <label for="device-select">选择设备:</label>
+                    <select id="device-select">
+                        <option value="cpu">CPU</option>
+                        <option value="cuda">CUDA (NVIDIA GPU)</option>
+                        <option value="mps">MPS (Apple Silicon)</option>
+                    </select>
+                    <small class="form-text">选择模型运行的设备</small>
+                </div>
+
+                <!-- 模型状态 -->
+                <div id="model-status" class="status info" style="display: none;">
+                    模型状态信息
+                </div>
+
+                <!-- 加载模型按钮 -->
+                <button id="load-model-btn" class="btn btn-secondary">
+                    🔄 加载模型
+                </button>
+
+                <hr style="margin: 20px 0; border: 1px solid #e2e8f0;">
+
+                <!-- 数据文件选择 -->
+                <div class="form-group">
+                    <label for="data-file-select">选择数据文件:</label>
+                    <select id="data-file-select">
+                        <option value="">请先加载数据文件列表</option>
+                    </select>
+                    <small class="form-text">从data目录选择K线数据文件</small>
+                </div>
+
+                <button id="load-data-btn" class="btn btn-secondary">
+                    📁 加载数据
+                </button>
+
+                <!-- 数据信息显示 -->
+                <div id="data-info" class="data-info" style="display: none;">
+                    <h3>📊 数据信息</h3>
+                    <p><strong>行数:</strong> <span id="data-rows">-</span></p>
+                    <p><strong>列数:</strong> <span id="data-cols">-</span></p>
+                    <p><strong>时间范围:</strong> <span id="data-time-range">-</span></p>
+                    <p><strong>价格范围:</strong> <span id="data-price-range">-</span></p>
+                    <p><strong>时间频率:</strong> <span id="data-timeframe">-</span></p>
+                    <p><strong>预测列:</strong> <span id="data-prediction-cols">-</span></p>
+                </div>
+
+                <hr style="margin: 20px 0; border: 1px solid #e2e8f0;">
+
+                <!-- 时间窗口选择器 -->
+                <div class="time-window-container">
+                    <h3>⏰ 时间窗口选择</h3>
+                    <div class="time-window-info">
+                        <span id="window-start">开始: --</span>
+                        <span id="window-end">结束: --</span>
+                        <span id="window-size">窗口大小: 400+120=520个数据点</span>
+                    </div>
+                    
+                    <div class="time-window-slider">
+                        <div class="slider-track">
+                            <div class="slider-handle start-handle" id="start-handle"></div>
+                            <div class="slider-selection" id="slider-selection"></div>
+                            <div class="slider-handle end-handle" id="end-handle"></div>
+                        </div>
+                        <div class="slider-labels">
+                            <span id="min-label">最早</span>
+                            <span id="max-label">最新</span>
+                        </div>
+                    </div>
+                    
+                    <small class="form-text">拖动滑条选择520个数据点的时间窗口位置，绿色区域表示固定的400+120数据点范围</small>
+                </div>
+
+                <!-- 预测参数 -->
+                <div class="form-group">
+                    <label for="lookback">回看窗口大小:</label>
+                    <input type="number" id="lookback" value="400" readonly>
+                    <small class="form-text">固定为400个数据点</small>
+                </div>
+
+                <div class="form-group">
+                    <label for="pred-len">预测长度:</label>
+                    <input type="number" id="pred-len" value="120" readonly>
+                    <small class="form-text">固定为120个数据点</small>
+                </div>
+
+                <!-- 预测质量参数 -->
+                <div class="form-group">
+                    <label for="temperature">预测温度 (T):</label>
+                    <input type="range" id="temperature" value="1.0" min="0.1" max="2.0" step="0.1">
+                    <span id="temperature-value">1.0</span>
+                    <small class="form-text">控制预测的随机性，值越高预测越多样化，值越低预测越保守</small>
+                </div>
+
+                <div class="form-group">
+                    <label for="top-p">核采样参数 (top_p):</label>
+                    <input type="range" id="top-p" value="0.9" min="0.1" max="1.0" step="0.1">
+                    <span id="top-p-value">0.9</span>
+                    <small class="form-text">控制预测的多样性，值越高考虑的概率分布越广</small>
+                </div>
+
+                <div class="form-group">
+                    <label for="sample-count">样本数量:</label>
+                    <input type="number" id="sample-count" value="1" min="1" max="5" step="1">
+                    <small class="form-text">生成多个预测样本以提高质量（建议1-3个）</small>
+                </div>
+
+                <button id="predict-btn" class="btn btn-success" disabled>
+                    🔮 开始预测
+                </button>
+
+                <!-- 加载状态 -->
+                <div id="loading" class="loading">
+                    <div class="spinner"></div>
+                    <p>正在处理，请稍候...</p>
+                </div>
+            </div>
+
+            <div class="chart-container">
+                <h2>📈 预测结果图表</h2>
+                <div id="chart"></div>
+                
+                <!-- 对比分析 -->
+                <div id="comparison-section" class="comparison-section" style="display: none;">
+                    <h3>📊 预测 vs 实际数据对比</h3>
+                    <div id="comparison-info" class="comparison-info">
+                        <p><strong>预测类型:</strong> <span id="prediction-type">-</span></p>
+                        <p><strong>对比数据:</strong> <span id="comparison-data">-</span></p>
+                    </div>
+                    
+                    <div class="error-stats">
+                        <div class="error-stat">
+                            <h4>平均绝对误差</h4>
+                            <div class="value" id="mae">-</div>
+                            <div class="unit">价格单位</div>
+                        </div>
+                        <div class="error-stat">
+                            <h4>均方根误差</h4>
+                            <div class="value" id="rmse">-</div>
+                            <div class="unit">价格单位</div>
+                        </div>
+                        <div class="error-stat">
+                            <h4>平均绝对百分比误差</h4>
+                            <div class="value" id="mape">-</div>
+                            <div class="unit">%</div>
+                        </div>
+                    </div>
+                    
+                    <div class="error-details">
+                        <h4>详细对比数据:</h4>
+                        <div style="max-height: 300px; overflow-y: auto;">
+                            <table class="comparison-table">
+                                <thead>
+                                    <tr>
+                                        <th>时间</th>
+                                        <th>实际开盘</th>
+                                        <th>预测开盘</th>
+                                        <th>实际最高</th>
+                                        <th>预测最高</th>
+                                        <th>实际最低</th>
+                                        <th>预测最低</th>
+                                        <th>实际收盘</th>
+                                        <th>预测收盘</th>
+                                    </tr>
+                                </thead>
+                                <tbody id="comparison-tbody">
+                                </tbody>
+                            </table>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // 全局变量
+        let currentDataFile = null;
+        let currentDataInfo = null;
+        let availableModels = [];
+        let modelLoaded = false;
+
+        // 页面加载完成后初始化
+        document.addEventListener('DOMContentLoaded', function() {
+            initializeApp();
+        });
+
+        // 初始化应用
+        async function initializeApp() {
+            console.log('🚀 初始化 Kronos Web UI...');
+            
+            // 加载可用模型
+            await loadAvailableModels();
+            
+            // 加载数据文件列表
+            await loadDataFiles();
+            
+            // 设置事件监听器
+            setupEventListeners();
+            
+            // 初始化时间滑块
+            initializeTimeSlider();
+            
+            console.log('✅ 应用初始化完成');
+        }
+
+        // 加载可用模型
+        async function loadAvailableModels() {
+            try {
+                const response = await axios.get('/api/available-models');
+                if (response.data.model_available) {
+                    availableModels = response.data.models;
+                    populateModelSelect();
+                    console.log('✅ 可用模型加载成功:', availableModels);
+                } else {
+                    console.warn('⚠️ Kronos模型库不可用');
+                    showStatus('warning', 'Kronos模型库不可用，将使用模拟预测');
+                }
+            } catch (error) {
+                console.error('❌ 加载可用模型失败:', error);
+                showStatus('error', '加载可用模型失败');
+            }
+        }
+
+        // 填充模型选择下拉框
+        function populateModelSelect() {
+            const modelSelect = document.getElementById('model-select');
+            modelSelect.innerHTML = '<option value="">请选择模型</option>';
+            
+            Object.entries(availableModels).forEach(([key, model]) => {
+                const option = document.createElement('option');
+                option.value = key;
+                option.textContent = `${model.name} (${model.params}) - ${model.description}`;
+                modelSelect.appendChild(option);
+            });
+        }
+
+        // 加载模型
+        async function loadModel() {
+            const modelKey = document.getElementById('model-select').value;
+            const device = document.getElementById('device-select').value;
+            
+            if (!modelKey) {
+                showStatus('error', '请选择要加载的模型');
+                return;
+            }
+            
+            try {
+                showLoading(true);
+                document.getElementById('load-model-btn').disabled = true;
+                
+                const response = await axios.post('/api/load-model', {
+                    model_key: modelKey,
+                    device: device
+                });
+                
+                if (response.data.success) {
+                    modelLoaded = true;
+                    showStatus('success', response.data.message);
+                    updateModelStatus();
+                    document.getElementById('predict-btn').disabled = false;
+                    console.log('✅ 模型加载成功:', response.data.model_info);
+                } else {
+                    showStatus('error', response.data.error);
+                }
+            } catch (error) {
+                console.error('❌ 模型加载失败:', error);
+                showStatus('error', `模型加载失败: ${error.response?.data?.error || error.message}`);
+            } finally {
+                showLoading(false);
+                document.getElementById('load-model-btn').disabled = false;
+            }
+        }
+
+        // 更新模型状态
+        async function updateModelStatus() {
+            try {
+                const response = await axios.get('/api/model-status');
+                const status = response.data;
+                
+                if (status.loaded) {
+                    showStatus('success', `模型已加载: ${status.current_model.name} on ${status.current_model.device}`);
+                } else if (status.available) {
+                    showStatus('info', '模型可用但未加载');
+                } else {
+                    showStatus('warning', '模型库不可用');
+                }
+            } catch (error) {
+                console.error('❌ 获取模型状态失败:', error);
+            }
+        }
+
+        // 加载数据文件列表
+        async function loadDataFiles() {
+            try {
+                const response = await axios.get('/api/data-files');
+                const dataFiles = response.data;
+                
+                const dataFileSelect = document.getElementById('data-file-select');
+                dataFileSelect.innerHTML = '<option value="">请选择数据文件</option>';
+                
+                dataFiles.forEach(file => {
+                    const option = document.createElement('option');
+                    option.value = file.path;
+                    option.textContent = `${file.name} (${file.size})`;
+                    dataFileSelect.appendChild(option);
+                });
+                
+                console.log('✅ 数据文件列表加载成功:', dataFiles);
+            } catch (error) {
+                console.error('❌ 加载数据文件列表失败:', error);
+                showStatus('error', '加载数据文件列表失败');
+            }
+        }
+
+        // 加载数据文件
+        async function loadData() {
+            const filePath = document.getElementById('data-file-select').value;
+            
+            if (!filePath) {
+                showStatus('error', '请选择要加载的数据文件');
+                return;
+            }
+            
+            try {
+                showLoading(true);
+                document.getElementById('load-data-btn').disabled = true;
+                
+                const response = await axios.post('/api/load-data', {
+                    file_path: filePath
+                });
+                
+                if (response.data.success) {
+                    currentDataFile = filePath;
+                    currentDataInfo = response.data.data_info;
+                    showDataInfo(response.data.data_info);
+                    showStatus('success', response.data.message);
+                    
+                    // 更新预测按钮状态
+                    if (modelLoaded) {
+                        document.getElementById('predict-btn').disabled = false;
+                    }
+                    
+                    console.log('✅ 数据加载成功:', response.data.data_info);
+                } else {
+                    showStatus('error', response.data.error);
+                }
+            } catch (error) {
+                console.error('❌ 数据加载失败:', error);
+                showStatus('error', `数据加载失败: ${error.response?.data?.error || error.message}`);
+            } finally {
+                showLoading(false);
+                document.getElementById('load-data-btn').disabled = false;
+            }
+        }
+
+        // 显示数据信息
+        function showDataInfo(dataInfo) {
+            document.getElementById('data-info').style.display = 'block';
+            document.getElementById('data-rows').textContent = dataInfo.rows;
+            document.getElementById('data-cols').textContent = dataInfo.columns.length;
+            document.getElementById('data-time-range').textContent = `${dataInfo.start_date} 至 ${dataInfo.end_date}`;
+            document.getElementById('data-price-range').textContent = `${dataInfo.price_range.min.toFixed(4)} - ${dataInfo.price_range.max.toFixed(4)}`;
+            document.getElementById('data-timeframe').textContent = dataInfo.timeframe;
+            document.getElementById('data-prediction-cols').textContent = dataInfo.prediction_columns.join(', ');
+            
+            // 初始化时间窗口滑条
+            initializeTimeWindowSlider(dataInfo);
+        }
+
+        // 时间窗口滑条相关变量
+        let sliderData = null;
+        let isDragging = false;
+        let currentHandle = null;
+
+        // 初始化时间窗口滑条
+        function initializeTimeSlider() {
+            // 设置滑条事件监听器
+            setupSliderEventListeners();
+        }
+
+        // 设置滑条事件监听器
+        function setupSliderEventListeners() {
+            const startHandle = document.getElementById('start-handle');
+            const endHandle = document.getElementById('end-handle');
+            const track = document.querySelector('.slider-track');
+            
+            // 开始拖拽
+            startHandle.addEventListener('mousedown', (e) => {
+                isDragging = true;
+                currentHandle = 'start';
+                e.preventDefault();
+            });
+            
+            endHandle.addEventListener('mousedown', (e) => {
+                isDragging = true;
+                currentHandle = 'end';
+                e.preventDefault();
+            });
+            
+            // 拖拽中
+            document.addEventListener('mousemove', (e) => {
+                if (!isDragging) return;
+                
+                const rect = track.getBoundingClientRect();
+                const x = e.clientX - rect.left;
+                const percentage = Math.max(0, Math.min(1, x / rect.width));
+                
+                if (currentHandle === 'start') {
+                    updateStartHandle(percentage);
+                } else if (currentHandle === 'end') {
+                    updateEndHandle(percentage);
+                }
+                
+                updateSliderFromHandles();
+            });
+            
+            // 结束拖拽
+            document.addEventListener('mouseup', () => {
+                isDragging = false;
+                currentHandle = null;
+            });
+            
+            // 点击轨道直接设置位置
+            track.addEventListener('click', (e) => {
+                const rect = track.getBoundingClientRect();
+                const x = e.clientX - rect.left;
+                const percentage = Math.max(0, Math.min(1, x / rect.width));
+                
+                // 判断点击位置更接近哪个手柄
+                const startHandle = document.getElementById('start-handle');
+                const endHandle = document.getElementById('end-handle');
+                const startRect = startHandle.getBoundingClientRect();
+                const endRect = endHandle.getBoundingClientRect();
+                
+                if (Math.abs(x - (startRect.left - rect.left)) < Math.abs(x - (endRect.left - rect.left))) {
+                    updateStartHandle(percentage);
+                } else {
+                    updateEndHandle(percentage);
+                }
+                
+                updateSliderFromHandles();
+            });
+        }
+
+        // 更新开始手柄位置
+        function updateStartHandle(percentage) {
+            const startHandle = document.getElementById('start-handle');
+            const selection = document.getElementById('slider-selection');
+            
+            // 固定窗口大小为520个数据点
+            const windowSize = 520;
+            const totalRows = sliderData ? sliderData.totalRows : 1000;
+            const windowPercentage = windowSize / totalRows;
+            
+            // 确保开始手柄不会导致窗口超出数据范围
+            if (percentage + windowPercentage > 1) {
+                percentage = 1 - windowPercentage;
+            }
+            
+            startHandle.style.left = (percentage * 100) + '%';
+            selection.style.left = (percentage * 100) + '%';
+            selection.style.width = (windowPercentage * 100) + '%';
+            
+            // 自动调整结束手柄位置，保持固定窗口大小
+            const endHandle = document.getElementById('end-handle');
+            endHandle.style.left = ((percentage + windowPercentage) * 100) + '%';
+        }
+
+        // 更新结束手柄位置
+        function updateEndHandle(percentage) {
+            const endHandle = document.getElementById('end-handle');
+            const selection = document.getElementById('slider-selection');
+            
+            // 固定窗口大小为520个数据点
+            const windowSize = 520;
+            const totalRows = sliderData ? sliderData.totalRows : 1000;
+            const windowPercentage = windowSize / totalRows;
+            
+            // 确保结束手柄不会导致窗口超出数据范围
+            if (percentage - windowPercentage < 0) {
+                percentage = windowPercentage;
+            }
+            
+            endHandle.style.left = (percentage * 100) + '%';
+            selection.style.left = ((percentage - windowPercentage) * 100) + '%';
+            selection.style.width = (windowPercentage * 100) + '%';
+            
+            // 自动调整开始手柄位置，保持固定窗口大小
+            const startHandle = document.getElementById('start-handle');
+            startHandle.style.left = ((percentage - windowPercentage) * 100) + '%';
+        }
+
+        // 根据手柄位置更新滑条显示
+        function updateSliderFromHandles() {
+            const startHandle = document.getElementById('start-handle');
+            const endHandle = document.getElementById('end-handle');
+            
+            const startPercentage = parseFloat(startHandle.style.left) / 100;
+            const endPercentage = parseFloat(endHandle.style.left) / 100;
+            
+            if (!sliderData) return;
+            
+            // 计算选中的时间范围
+            const totalTime = sliderData.endDate.getTime() - sliderData.startDate.getTime();
+            const startTime = sliderData.startDate.getTime() + (totalTime * startPercentage);
+            const endTime = sliderData.startDate.getTime() + (totalTime * endPercentage);
+            
+            const startDate = new Date(startTime);
+            const endDate = new Date(endTime);
+            
+            // 更新显示信息
+            document.getElementById('window-start').textContent = `开始: ${startDate.toLocaleDateString()}`;
+            document.getElementById('window-end').textContent = `结束: ${endDate.toLocaleDateString()}`;
+            
+            // 显示固定的窗口大小
+            document.getElementById('window-size').textContent = `窗口大小: 400 + 120 = 520 个数据点 (固定)`;
+            
+            // 输入框值保持固定
+            document.getElementById('lookback').value = 400;
+            document.getElementById('pred-len').value = 120;
+        }
+
+        // 根据输入框更新滑条
+        function updateSliderFromInputs() {
+            if (!sliderData) return;
+            
+            // 固定窗口大小：400 + 120 = 520个数据点
+            const lookback = 400;
+            const predLen = 120;
+            const windowSize = lookback + predLen; // 固定为520
+            
+            // 计算滑条位置
+            const totalRows = sliderData.totalRows;
+            
+            if (windowSize > totalRows) {
+                // 如果窗口大小超过总数据量，显示错误
+                showStatus('error', `数据量不足，需要至少${windowSize}个数据点，当前只有${totalRows}个`);
+                return;
+            }
+            
+            // 计算滑条位置（默认选择数据的前半部分）
+            const startPercentage = 0.1; // 从10%开始
+            const endPercentage = startPercentage + (windowSize / totalRows);
+            
+            // 更新手柄位置
+            updateStartHandle(startPercentage);
+            updateEndHandle(endPercentage);
+            
+            // 更新显示信息
+            updateSliderFromHandles();
+        }
+
+        // 初始化时间窗口滑条
+        function initializeTimeWindowSlider(dataInfo) {
+            sliderData = {
+                startDate: new Date(dataInfo.start_date),
+                endDate: new Date(dataInfo.end_date),
+                totalRows: dataInfo.rows,
+                timeframe: dataInfo.timeframe
+            };
+            
+            // 设置滑条标签
+            document.getElementById('min-label').textContent = dataInfo.start_date.split('T')[0];
+            document.getElementById('max-label').textContent = dataInfo.end_date.split('T')[0];
+            
+            // 初始化滑条位置
+            updateSliderFromInputs();
+        }
+
+        // 开始预测
+        async function startPrediction() {
+            if (!currentDataFile) {
+                showStatus('error', '请先加载数据文件');
+                return;
+            }
+            
+            if (!modelLoaded) {
+                showStatus('error', '请先加载模型');
+                return;
+            }
+            
+            try {
+                showLoading(true);
+                document.getElementById('predict-btn').disabled = true;
+                
+                const lookback = parseInt(document.getElementById('lookback').value);
+                const predLen = parseInt(document.getElementById('pred-len').value);
+                
+                // 从时间窗口滑条获取选择的时间范围
+                const startHandle = document.getElementById('start-handle');
+                const startPercentage = parseFloat(startHandle.style.left) / 100;
+                
+                if (!sliderData) {
+                    showStatus('error', '时间窗口滑条未初始化');
+                    return;
+                }
+                
+                // 计算选择的时间范围
+                const totalTime = sliderData.endDate.getTime() - sliderData.startDate.getTime();
+                const startTime = sliderData.startDate.getTime() + (totalTime * startPercentage);
+                const startDate = new Date(startTime);
+                
+                // 获取预测质量参数
+                const temperature = parseFloat(document.getElementById('temperature').value);
+                const topP = parseFloat(document.getElementById('top-p').value);
+                const sampleCount = parseInt(document.getElementById('sample-count').value);
+                
+                let predictionParams = {
+                    file_path: currentDataFile,
+                    lookback: lookback,
+                    pred_len: predLen,
+                    start_date: startDate.toISOString().slice(0, 16), // 格式化为 YYYY-MM-DDTHH:MM
+                    temperature: temperature,
+                    top_p: topP,
+                    sample_count: sampleCount
+                };
+                
+                console.log('🚀 开始预测，参数:', predictionParams);
+                
+                const response = await axios.post('/api/predict', predictionParams);
+                
+                if (response.data.success) {
+                    // 显示预测结果
+                    displayPredictionResult(response.data);
+                    showStatus('success', response.data.message);
+                } else {
+                    showStatus('error', response.data.error);
+                }
+            } catch (error) {
+                console.error('❌ 预测失败:', error);
+                showStatus('error', `预测失败: ${error.response?.data?.error || error.message}`);
+            } finally {
+                showLoading(false);
+                document.getElementById('predict-btn').disabled = false;
+            }
+        }
+
+        // 显示预测结果
+        function displayPredictionResult(result) {
+            // 显示图表
+            const chartData = JSON.parse(result.chart);
+            Plotly.newPlot('chart', chartData.data, chartData.layout);
+            
+            // 显示对比分析（如果有实际数据）
+            if (result.has_comparison) {
+                displayComparisonAnalysis(result);
+            } else {
+                document.getElementById('comparison-section').style.display = 'none';
+            }
+        }
+
+        // 显示对比分析
+        function displayComparisonAnalysis(result) {
+            document.getElementById('comparison-section').style.display = 'block';
+            
+            // 更新对比信息
+            document.getElementById('prediction-type').textContent = result.prediction_type;
+            document.getElementById('comparison-data').textContent = `${result.actual_data.length} 个实际数据点`;
+            
+            // 计算误差统计
+            const errorStats = getPredictionQuality(result.prediction_results, result.actual_data);
+            
+            // 显示误差统计
+            document.getElementById('mae').textContent = errorStats.mae.toFixed(4);
+            document.getElementById('rmse').textContent = errorStats.rmse.toFixed(4);
+            document.getElementById('mape').textContent = errorStats.mape.toFixed(2);
+            
+            // 填充对比表格
+            fillComparisonTable(result.prediction_results, result.actual_data);
+        }
+
+        // 计算预测质量指标
+        function getPredictionQuality(predictions, actuals) {
+            if (!predictions || !actuals || predictions.length === 0 || actuals.length === 0) {
+                return { mae: 0, rmse: 0, mape: 0 };
+            }
+            
+            const minLen = Math.min(predictions.length, actuals.length);
+            let mae = 0, rmse = 0, mape = 0;
+            
+            for (let i = 0; i < minLen; i++) {
+                const pred = predictions[i];
+                const act = actuals[i];
+                
+                // 使用收盘价计算误差
+                const error = Math.abs(pred.close - act.close);
+                const percentError = (error / act.close) * 100;
+                
+                mae += error;
+                rmse += error * error;
+                mape += percentError;
+            }
+            
+            mae /= minLen;
+            rmse = Math.sqrt(rmse / minLen);
+            mape /= minLen;
+            
+            return { mae, rmse, mape };
+        }
+
+        // 填充对比表格
+        function fillComparisonTable(predictions, actuals) {
+            const tbody = document.getElementById('comparison-tbody');
+            tbody.innerHTML = '';
+            
+            const minLen = Math.min(predictions.length, actuals.length);
+            
+            for (let i = 0; i < minLen; i++) {
+                const pred = predictions[i];
+                const act = actuals[i];
+                
+                const row = document.createElement('tr');
+                row.innerHTML = `
+                    <td>${new Date(pred.timestamp).toLocaleString()}</td>
+                    <td>${act.open.toFixed(4)}</td>
+                    <td>${pred.open.toFixed(4)}</td>
+                    <td>${act.high.toFixed(4)}</td>
+                    <td>${pred.high.toFixed(4)}</td>
+                    <td>${act.low.toFixed(4)}</td>
+                    <td>${pred.low.toFixed(4)}</td>
+                    <td>${act.close.toFixed(4)}</td>
+                    <td>${pred.close.toFixed(4)}</td>
+                `;
+                tbody.appendChild(row);
+            }
+        }
+
+        // 设置事件监听器
+        function setupEventListeners() {
+            // 加载模型按钮
+            document.getElementById('load-model-btn').addEventListener('click', loadModel);
+            
+            // 加载数据按钮
+            document.getElementById('load-data-btn').addEventListener('click', loadData);
+            
+            // 预测按钮
+            document.getElementById('predict-btn').addEventListener('click', startPrediction);
+            
+            // 预测质量参数滑块
+            document.getElementById('temperature').addEventListener('input', function() {
+                document.getElementById('temperature-value').textContent = this.value;
+            });
+            
+            document.getElementById('top-p').addEventListener('input', function() {
+                document.getElementById('top-p-value').textContent = this.value;
+            });
+            
+            // 回看窗口大小变化时更新滑条
+            document.getElementById('lookback').addEventListener('input', updateSliderFromInputs);
+            document.getElementById('pred-len').addEventListener('input', updateSliderFromInputs);
+        }
+
+        // 显示状态信息
+        function showStatus(type, message) {
+            const statusDiv = document.getElementById('model-status');
+            statusDiv.className = `status ${type}`;
+            statusDiv.textContent = message;
+            statusDiv.style.display = 'block';
+            
+            // 自动隐藏
+            setTimeout(() => {
+                statusDiv.style.display = 'none';
+            }, 5000);
+        }
+
+        // 显示/隐藏加载状态
+        function showLoading(show) {
+            const loadingDiv = document.getElementById('loading');
+            if (show) {
+                loadingDiv.classList.add('show');
+            } else {
+                loadingDiv.classList.remove('show');
+            }
+        }
+    </script>
+</body>
+</html>

From 609235c0778461a5d0eb002ce290451e2b150c6c Mon Sep 17 00:00:00 2001
From: Charles <larrylandor@gmail.com>
Date: Tue, 26 Aug 2025 16:35:34 +0800
Subject: [PATCH 2/7] =?UTF-8?q?feat:=20=E6=B7=BB=E5=8A=A0=E9=A2=84?=
 =?UTF-8?q?=E6=B5=8B=E7=BB=93=E6=9E=9C=E4=BF=9D=E5=AD=98=E5=8A=9F=E8=83=BD?=
 =?UTF-8?q?=EF=BC=8C=E7=94=A8=E4=BA=8E=E5=88=86=E6=9E=90=E9=A2=84=E6=B5=8B?=
 =?UTF-8?q?=E8=B4=A8=E9=87=8F=E9=97=AE=E9=A2=98?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 webui/app.py | 105 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)

diff --git a/webui/app.py b/webui/app.py
index 49a5d58..313c157 100644
--- a/webui/app.py
+++ b/webui/app.py
@@ -8,6 +8,7 @@ from flask import Flask, render_template, request, jsonify
 from flask_cors import CORS
 import sys
 import warnings
+import datetime
 warnings.filterwarnings('ignore')
 
 # 添加项目根目录到路径
@@ -121,6 +122,90 @@ def load_data_file(file_path):
     except Exception as e:
         return None, f"加载文件失败: {str(e)}"
 
+def save_prediction_results(file_path, prediction_type, prediction_results, actual_data, input_data, prediction_params):
+    """保存预测结果到文件"""
+    try:
+        # 创建预测结果目录
+        results_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'prediction_results')
+        os.makedirs(results_dir, exist_ok=True)
+        
+        # 生成文件名
+        timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+        filename = f'prediction_{timestamp}.json'
+        filepath = os.path.join(results_dir, filename)
+        
+        # 准备保存的数据
+        save_data = {
+            'timestamp': datetime.datetime.now().isoformat(),
+            'file_path': file_path,
+            'prediction_type': prediction_type,
+            'prediction_params': prediction_params,
+            'input_data_summary': {
+                'rows': len(input_data),
+                'columns': list(input_data.columns),
+                'price_range': {
+                    'open': {'min': float(input_data['open'].min()), 'max': float(input_data['open'].max())},
+                    'high': {'min': float(input_data['high'].min()), 'max': float(input_data['high'].max())},
+                    'low': {'min': float(input_data['low'].min()), 'max': float(input_data['low'].max())},
+                    'close': {'min': float(input_data['close'].min()), 'max': float(input_data['close'].max())}
+                },
+                'last_values': {
+                    'open': float(input_data['open'].iloc[-1]),
+                    'high': float(input_data['high'].iloc[-1]),
+                    'low': float(input_data['low'].iloc[-1]),
+                    'close': float(input_data['close'].iloc[-1])
+                }
+            },
+            'prediction_results': prediction_results,
+            'actual_data': actual_data,
+            'analysis': {}
+        }
+        
+        # 如果有实际数据，进行对比分析
+        if actual_data and len(actual_data) > 0:
+            # 计算连续性分析
+            if len(prediction_results) > 0 and len(actual_data) > 0:
+                last_pred = prediction_results[0]  # 第一个预测点
+                first_actual = actual_data[0]      # 第一个实际点
+                
+                save_data['analysis']['continuity'] = {
+                    'last_prediction': {
+                        'open': last_pred['open'],
+                        'high': last_pred['high'],
+                        'low': last_pred['low'],
+                        'close': last_pred['close']
+                    },
+                    'first_actual': {
+                        'open': first_actual['open'],
+                        'high': first_actual['high'],
+                        'low': first_actual['low'],
+                        'close': first_actual['close']
+                    },
+                    'gaps': {
+                        'open_gap': abs(last_pred['open'] - first_actual['open']),
+                        'high_gap': abs(last_pred['high'] - first_actual['high']),
+                        'low_gap': abs(last_pred['low'] - first_actual['low']),
+                        'close_gap': abs(last_pred['close'] - first_actual['close'])
+                    },
+                    'gap_percentages': {
+                        'open_gap_pct': (abs(last_pred['open'] - first_actual['open']) / first_actual['open']) * 100,
+                        'high_gap_pct': (abs(last_pred['high'] - first_actual['high']) / first_actual['high']) * 100,
+                        'low_gap_pct': (abs(last_pred['low'] - first_actual['low']) / first_actual['low']) * 100,
+                        'close_gap_pct': (abs(last_pred['close'] - first_actual['close']) / first_actual['close']) * 100
+                    }
+                }
+        
+        # 保存到文件
+        with open(filepath, 'w', encoding='utf-8') as f:
+            json.dump(save_data, f, indent=2, ensure_ascii=False)
+        
+        print(f"预测结果已保存到: {filepath}")
+        return filepath
+        
+    except Exception as e:
+        print(f"保存预测结果失败: {e}")
+        return None
+
 def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, historical_start_idx=0):
     """创建预测图表"""
     # 使用指定的历史数据起始位置，而不是总是从df的开头开始
@@ -505,6 +590,26 @@ def predict():
                 'amount': float(row['amount']) if 'amount' in row else 0
             })
         
+        # 保存预测结果到文件
+        try:
+            save_prediction_results(
+                file_path=file_path,
+                prediction_type=prediction_type,
+                prediction_results=prediction_results,
+                actual_data=actual_data,
+                input_data=x_df,
+                prediction_params={
+                    'lookback': lookback,
+                    'pred_len': pred_len,
+                    'temperature': temperature,
+                    'top_p': top_p,
+                    'sample_count': sample_count,
+                    'start_date': start_date if start_date else 'latest'
+                }
+            )
+        except Exception as e:
+            print(f"保存预测结果失败: {e}")
+        
         return jsonify({
             'success': True,
             'prediction_type': prediction_type,

From ca2502757f613280ce531ade1ec5e829aca4f42e Mon Sep 17 00:00:00 2001
From: Charles <larrylandor@gmail.com>
Date: Tue, 26 Aug 2025 16:49:56 +0800
Subject: [PATCH 3/7] =?UTF-8?q?refactor:=20=E5=B0=86prediction=5Fresults?=
 =?UTF-8?q?=E7=9B=AE=E5=BD=95=E7=A7=BB=E5=8A=A8=E5=88=B0webui=E7=9B=AE?=
 =?UTF-8?q?=E5=BD=95=E4=B8=8B=EF=BC=8C=E4=BC=98=E5=8C=96=E9=A1=B9=E7=9B=AE?=
 =?UTF-8?q?=E7=BB=93=E6=9E=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 webui/app.py                                  |    2 +-
 .../prediction_20250826_163800.json           | 2239 +++++++++++++++++
 .../prediction_20250826_164030.json           | 2239 +++++++++++++++++
 .../prediction_20250826_164422.json           | 2239 +++++++++++++++++
 4 files changed, 6718 insertions(+), 1 deletion(-)
 create mode 100644 webui/prediction_results/prediction_20250826_163800.json
 create mode 100644 webui/prediction_results/prediction_20250826_164030.json
 create mode 100644 webui/prediction_results/prediction_20250826_164422.json

diff --git a/webui/app.py b/webui/app.py
index 313c157..38de059 100644
--- a/webui/app.py
+++ b/webui/app.py
@@ -126,7 +126,7 @@ def save_prediction_results(file_path, prediction_type, prediction_results, actu
     """保存预测结果到文件"""
     try:
         # 创建预测结果目录
-        results_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'prediction_results')
+        results_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prediction_results')
         os.makedirs(results_dir, exist_ok=True)
         
         # 生成文件名
diff --git a/webui/prediction_results/prediction_20250826_163800.json b/webui/prediction_results/prediction_20250826_163800.json
new file mode 100644
index 0000000..5c3f7ba
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_163800.json
@@ -0,0 +1,2239 @@
+{
+  "timestamp": "2025-08-26T16:38:00.302387",
+  "file_path": "/Users/charles/Kronos/data/BTC_USDT_USDT-5m-futures.feather",
+  "prediction_type": "Kronos模型预测 (选择的窗口内：前400个数据点预测，后120个数据点对比，时间跨度: 1 days 19:15:00)",
+  "prediction_params": {
+    "lookback": 400,
+    "pred_len": 120,
+    "temperature": 1.0,
+    "top_p": 0.9,
+    "sample_count": 1,
+    "start_date": "2025-08-02T13:24"
+  },
+  "input_data_summary": {
+    "rows": 400,
+    "columns": [
+      "open",
+      "high",
+      "low",
+      "close",
+      "volume"
+    ],
+    "price_range": {
+      "open": {
+        "min": 112046.9,
+        "max": 114231.95
+      },
+      "high": {
+        "min": 112200.0,
+        "max": 114260.31
+      },
+      "low": {
+        "min": 111920.0,
+        "max": 114085.61
+      },
+      "close": {
+        "min": 112046.9,
+        "max": 114231.95
+      }
+    },
+    "last_values": {
+      "open": 113769.56,
+      "high": 113852.6,
+      "low": 113731.29,
+      "close": 113818.87
+    }
+  },
+  "prediction_results": [
+    {
+      "timestamp": "2025-08-03T22:45:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.25
+    },
+    {
+      "timestamp": "2025-08-03T22:50:00",
+      "open": 113381.140625,
+      "high": 113437.6171875,
+      "low": 113326.859375,
+      "close": 113379.7890625,
+      "volume": 36.73231506347656,
+      "amount": 4109972.25
+    },
+    {
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diff --git a/webui/prediction_results/prediction_20250826_164030.json b/webui/prediction_results/prediction_20250826_164030.json
new file mode 100644
index 0000000..1f54336
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_164030.json
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diff --git a/webui/prediction_results/prediction_20250826_164422.json b/webui/prediction_results/prediction_20250826_164422.json
new file mode 100644
index 0000000..cc55ba7
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_164422.json
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+      "close": 341.2,
+      "volume": 23.351,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T10:45:00",
+      "open": 341.1,
+      "high": 341.8,
+      "low": 340.5,
+      "close": 341.8,
+      "volume": 253.881,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T10:50:00",
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+      "high": 342.6,
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+      "close": 341.6,
+      "volume": 265.534,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T10:55:00",
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+      "volume": 260.872,
+      "amount": 0
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+    {
+      "timestamp": "2024-09-30T11:00:00",
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+      "high": 342.8,
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+      "close": 342.2,
+      "volume": 199.335,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T11:05:00",
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+      "close": 342.7,
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+      "volume": 90.602,
+      "amount": 0
+    },
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+      "volume": 98.124,
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+    },
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+      "volume": 298.909,
+      "amount": 0
+    },
+    {
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+      "close": 344.0,
+      "volume": 94.481,
+      "amount": 0
+    },
+    {
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+      "volume": 61.813,
+      "amount": 0
+    },
+    {
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+      "close": 343.6,
+      "volume": 55.894,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T11:45:00",
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+      "close": 342.5,
+      "volume": 224.42,
+      "amount": 0
+    },
+    {
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+      "amount": 0
+    },
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+      "close": 343.9,
+      "volume": 601.379,
+      "amount": 0
+    },
+    {
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+      "close": 344.1,
+      "volume": 71.782,
+      "amount": 0
+    },
+    {
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+      "low": 343.7,
+      "close": 344.4,
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+      "amount": 0
+    },
+    {
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+      "high": 344.5,
+      "low": 343.7,
+      "close": 344.4,
+      "volume": 70.129,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T12:15:00",
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+      "high": 344.5,
+      "low": 343.7,
+      "close": 343.8,
+      "volume": 40.088,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T12:20:00",
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+      "low": 343.7,
+      "close": 344.0,
+      "volume": 32.27,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T12:25:00",
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+      "close": 343.9,
+      "volume": 74.202,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T12:30:00",
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+      "high": 344.4,
+      "low": 343.5,
+      "close": 343.9,
+      "volume": 127.508,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T12:35:00",
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+      "close": 344.1,
+      "volume": 173.166,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T12:40:00",
+      "open": 344.1,
+      "high": 344.1,
+      "low": 343.3,
+      "close": 343.3,
+      "volume": 74.342,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T12:45:00",
+      "open": 343.4,
+      "high": 344.4,
+      "low": 343.4,
+      "close": 344.2,
+      "volume": 78.02,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T12:50:00",
+      "open": 344.5,
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+      "low": 344.4,
+      "close": 345.2,
+      "volume": 187.004,
+      "amount": 0
+    },
+    {
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+      "low": 344.8,
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+      "volume": 142.948,
+      "amount": 0
+    },
+    {
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+      "amount": 0
+    },
+    {
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+      "open": 345.6,
+      "high": 345.7,
+      "low": 344.8,
+      "close": 345.4,
+      "volume": 97.587,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T13:10:00",
+      "open": 345.4,
+      "high": 345.8,
+      "low": 345.2,
+      "close": 345.7,
+      "volume": 74.658,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T13:15:00",
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+      "high": 346.0,
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+      "close": 345.5,
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+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T13:20:00",
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+      "close": 345.5,
+      "volume": 77.217,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T13:25:00",
+      "open": 345.5,
+      "high": 345.6,
+      "low": 344.6,
+      "close": 344.8,
+      "volume": 65.549,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T13:30:00",
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+      "low": 344.5,
+      "close": 345.6,
+      "volume": 38.706,
+      "amount": 0
+    },
+    {
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+      "open": 345.6,
+      "high": 346.0,
+      "low": 345.0,
+      "close": 345.0,
+      "volume": 44.248,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T13:40:00",
+      "open": 345.1,
+      "high": 345.1,
+      "low": 344.2,
+      "close": 345.0,
+      "volume": 228.234,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T13:45:00",
+      "open": 345.0,
+      "high": 345.5,
+      "low": 344.8,
+      "close": 344.9,
+      "volume": 43.917,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T13:50:00",
+      "open": 344.9,
+      "high": 345.1,
+      "low": 344.2,
+      "close": 344.3,
+      "volume": 90.842,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T13:55:00",
+      "open": 344.2,
+      "high": 344.4,
+      "low": 343.6,
+      "close": 343.6,
+      "volume": 35.045,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:00:00",
+      "open": 343.6,
+      "high": 344.3,
+      "low": 342.2,
+      "close": 342.5,
+      "volume": 170.414,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:05:00",
+      "open": 342.4,
+      "high": 342.8,
+      "low": 341.3,
+      "close": 342.2,
+      "volume": 475.667,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:10:00",
+      "open": 342.2,
+      "high": 342.9,
+      "low": 341.8,
+      "close": 342.6,
+      "volume": 49.75,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:15:00",
+      "open": 342.6,
+      "high": 342.8,
+      "low": 341.5,
+      "close": 341.5,
+      "volume": 205.455,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:20:00",
+      "open": 341.6,
+      "high": 342.3,
+      "low": 341.4,
+      "close": 341.6,
+      "volume": 114.635,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:25:00",
+      "open": 341.6,
+      "high": 342.8,
+      "low": 341.5,
+      "close": 342.7,
+      "volume": 233.185,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:30:00",
+      "open": 342.7,
+      "high": 343.0,
+      "low": 342.3,
+      "close": 342.9,
+      "volume": 53.325,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:35:00",
+      "open": 343.0,
+      "high": 343.7,
+      "low": 342.9,
+      "close": 343.6,
+      "volume": 81.241,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:40:00",
+      "open": 343.6,
+      "high": 343.8,
+      "low": 343.0,
+      "close": 343.4,
+      "volume": 56.364,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:45:00",
+      "open": 343.3,
+      "high": 343.7,
+      "low": 342.5,
+      "close": 342.9,
+      "volume": 55.659,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:50:00",
+      "open": 342.8,
+      "high": 343.3,
+      "low": 342.6,
+      "close": 342.7,
+      "volume": 61.589,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T14:55:00",
+      "open": 342.6,
+      "high": 343.1,
+      "low": 342.4,
+      "close": 342.5,
+      "volume": 104.965,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T15:00:00",
+      "open": 342.4,
+      "high": 343.4,
+      "low": 342.3,
+      "close": 343.4,
+      "volume": 87.071,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T15:05:00",
+      "open": 343.3,
+      "high": 343.4,
+      "low": 341.8,
+      "close": 341.9,
+      "volume": 64.655,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T15:10:00",
+      "open": 341.9,
+      "high": 342.7,
+      "low": 341.5,
+      "close": 342.5,
+      "volume": 113.05,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T15:15:00",
+      "open": 342.5,
+      "high": 343.5,
+      "low": 342.5,
+      "close": 343.3,
+      "volume": 197.467,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T15:20:00",
+      "open": 343.4,
+      "high": 344.0,
+      "low": 343.2,
+      "close": 343.6,
+      "volume": 87.985,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T15:25:00",
+      "open": 343.5,
+      "high": 343.9,
+      "low": 343.3,
+      "close": 343.5,
+      "volume": 38.255,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T15:30:00",
+      "open": 343.6,
+      "high": 343.9,
+      "low": 343.1,
+      "close": 343.2,
+      "volume": 63.602,
+      "amount": 0
+    },
+    {
+      "timestamp": "2024-09-30T15:35:00",
+      "open": 343.2,
+      "high": 343.2,
+      "low": 342.2,
+      "close": 342.4,
+      "volume": 62.896,
+      "amount": 0
+    }
+  ],
+  "analysis": {
+    "continuity": {
+      "last_prediction": {
+        "open": 349.88104248046875,
+        "high": 350.3778076171875,
+        "low": 350.4383850097656,
+        "close": 351.1005859375
+      },
+      "first_actual": {
+        "open": 347.1,
+        "high": 347.1,
+        "low": 346.5,
+        "close": 346.5
+      },
+      "gaps": {
+        "open_gap": 2.7810424804687273,
+        "high_gap": 3.2778076171874773,
+        "low_gap": 3.938385009765625,
+        "close_gap": 4.6005859375
+      },
+      "gap_percentages": {
+        "open_gap_pct": 0.801222264612137,
+        "high_gap_pct": 0.9443410017826208,
+        "low_gap_pct": 1.1366190504374099,
+        "close_gap_pct": 1.3277304292929293
+      }
+    }
+  }
+}
\ No newline at end of file

From b4b270954bb87b48ec8fbd7549e3dc39ca5d8629 Mon Sep 17 00:00:00 2001
From: Charles <larrylandor@gmail.com>
Date: Tue, 26 Aug 2025 17:02:24 +0800
Subject: [PATCH 4/7] =?UTF-8?q?fix:=20=E8=A7=A3=E5=86=B3Kronos=E6=A8=A1?=
 =?UTF-8?q?=E5=9E=8B=E9=A2=84=E6=B5=8B=E8=BF=9E=E7=BB=AD=E6=80=A7=E9=97=AE?=
 =?UTF-8?q?=E9=A2=98=EF=BC=8C=E9=80=9A=E8=BF=87=E4=BB=B7=E6=A0=BC=E8=BD=AC?=
 =?UTF-8?q?=E6=8D=A2=E4=BF=9D=E6=8C=81=E6=95=B0=E6=8D=AE=E8=BF=9E=E7=BB=AD?=
 =?UTF-8?q?=E6=80=A7?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 webui/app.py | 51 +++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 49 insertions(+), 2 deletions(-)

diff --git a/webui/app.py b/webui/app.py
index 38de059..b478cd8 100644
--- a/webui/app.py
+++ b/webui/app.py
@@ -122,6 +122,31 @@ def load_data_file(file_path):
     except Exception as e:
         return None, f"加载文件失败: {str(e)}"
 
+def convert_price_to_returns(df, price_cols=['open', 'high', 'low', 'close']):
+    """将绝对价格转换为相对变化率，解决Kronos模型的连续性问题"""
+    returns_df = df.copy()
+    
+    # 计算相对变化率 (pct_change)
+    for col in price_cols:
+        if col in returns_df.columns:
+            returns_df[col] = returns_df[col].pct_change()
+    
+    # 第一行会是NaN，用0填充
+    returns_df = returns_df.fillna(0)
+    
+    return returns_df
+
+def convert_returns_to_price(returns_df, initial_prices, price_cols=['open', 'high', 'low', 'close']):
+    """将相对变化率转换回绝对价格"""
+    price_df = returns_df.copy()
+    
+    for col in price_cols:
+        if col in price_df.columns:
+            # 从初始价格开始，逐步计算绝对价格
+            price_df[col] = (1 + returns_df[col]) * initial_prices[col]
+    
+    return price_df
+
 def save_prediction_results(file_path, prediction_type, prediction_results, actual_data, input_data, prediction_params):
     """保存预测结果到文件"""
     try:
@@ -476,8 +501,21 @@ def predict():
                 if isinstance(y_timestamp, pd.DatetimeIndex):
                     y_timestamp = pd.Series(y_timestamp, name='timestamps')
                 
-                pred_df = predictor.predict(
-                    df=x_df,
+                # 解决Kronos模型连续性问题：将绝对价格转换为相对变化率
+                original_x_df = x_df.copy()
+                initial_prices = {
+                    'open': x_df['open'].iloc[0],
+                    'high': x_df['high'].iloc[0],
+                    'low': x_df['low'].iloc[0],
+                    'close': x_df['close'].iloc[0]
+                }
+                
+                # 转换为相对变化率
+                x_df_returns = convert_price_to_returns(x_df, ['open', 'high', 'low', 'close'])
+                
+                # 使用转换后的数据进行预测
+                pred_df_returns = predictor.predict(
+                    df=x_df_returns,
                     x_timestamp=x_timestamp,
                     y_timestamp=y_timestamp,
                     pred_len=pred_len,
@@ -486,6 +524,15 @@ def predict():
                     sample_count=sample_count
                 )
                 
+                # 将预测结果转换回绝对价格
+                pred_df = convert_returns_to_price(pred_df_returns, initial_prices, ['open', 'high', 'low', 'close'])
+                
+                # 保持volume列不变（如果存在）
+                if 'volume' in pred_df.columns:
+                    pred_df['volume'] = pred_df_returns['volume']
+                if 'amount' in pred_df.columns:
+                    pred_df['amount'] = pred_df_returns['amount']
+                
             except Exception as e:
                 return jsonify({'error': f'Kronos模型预测失败: {str(e)}'}), 500
         else:

From 35d35f740f85707d9a16a699aafbf72356383f65 Mon Sep 17 00:00:00 2001
From: Charles <larrylandor@gmail.com>
Date: Tue, 26 Aug 2025 17:13:18 +0800
Subject: [PATCH 5/7] =?UTF-8?q?feat:=20=E6=B7=BB=E5=8A=A0=E8=AF=A6?=
 =?UTF-8?q?=E7=BB=86=E7=9A=84=E8=B0=83=E8=AF=95=E4=BF=A1=E6=81=AF=EF=BC=8C?=
 =?UTF-8?q?=E6=98=BE=E7=A4=BA=E4=BB=B7=E6=A0=BC=E8=BD=AC=E6=8D=A2=E7=9A=84?=
 =?UTF-8?q?=E6=AF=8F=E4=B8=80=E6=AD=A5=E6=93=8D=E4=BD=9C?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 webui/app.py | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/webui/app.py b/webui/app.py
index b478cd8..5f36971 100644
--- a/webui/app.py
+++ b/webui/app.py
@@ -124,27 +124,44 @@ def load_data_file(file_path):
 
 def convert_price_to_returns(df, price_cols=['open', 'high', 'low', 'close']):
     """将绝对价格转换为相对变化率，解决Kronos模型的连续性问题"""
+    print(f"[DEBUG] convert_price_to_returns: 开始转换，输入数据形状: {df.shape}")
+    print(f"[DEBUG] convert_price_to_returns: 输入数据前3行:")
+    print(df[price_cols].head(3))
+    
     returns_df = df.copy()
     
     # 计算相对变化率 (pct_change)
     for col in price_cols:
         if col in returns_df.columns:
+            print(f"[DEBUG] convert_price_to_returns: 转换列 {col}")
             returns_df[col] = returns_df[col].pct_change()
     
     # 第一行会是NaN，用0填充
     returns_df = returns_df.fillna(0)
     
+    print(f"[DEBUG] convert_price_to_returns: 转换完成，输出数据前3行:")
+    print(returns_df[price_cols].head(3))
+    
     return returns_df
 
 def convert_returns_to_price(returns_df, initial_prices, price_cols=['open', 'high', 'low', 'close']):
     """将相对变化率转换回绝对价格"""
+    print(f"[DEBUG] convert_returns_to_price: 开始转换，输入数据形状: {returns_df.shape}")
+    print(f"[DEBUG] convert_returns_to_price: 初始价格: {initial_prices}")
+    print(f"[DEBUG] convert_returns_to_price: 输入相对变化率前3行:")
+    print(returns_df[price_cols].head(3))
+    
     price_df = returns_df.copy()
     
     for col in price_cols:
         if col in price_df.columns:
+            print(f"[DEBUG] convert_returns_to_price: 转换列 {col}")
             # 从初始价格开始，逐步计算绝对价格
             price_df[col] = (1 + returns_df[col]) * initial_prices[col]
     
+    print(f"[DEBUG] convert_returns_to_price: 转换完成，输出绝对价格前3行:")
+    print(price_df[price_cols].head(3))
+    
     return price_df
 
 def save_prediction_results(file_path, prediction_type, prediction_results, actual_data, input_data, prediction_params):
@@ -495,12 +512,16 @@ def predict():
                     y_timestamp = df.iloc[lookback:lookback+pred_len]['timestamps']
                     prediction_type = "Kronos模型预测 (最新数据)"
                 
+                print(f"[DEBUG] 步骤1: 检查并转换时间戳格式...")
                 # 确保时间戳是Series格式，不是DatetimeIndex，避免Kronos模型的.dt属性错误
                 if isinstance(x_timestamp, pd.DatetimeIndex):
+                    print(f"[DEBUG] 将x_timestamp从DatetimeIndex转换为Series")
                     x_timestamp = pd.Series(x_timestamp, name='timestamps')
                 if isinstance(y_timestamp, pd.DatetimeIndex):
+                    print(f"[DEBUG] 将y_timestamp从DatetimeIndex转换为Series")
                     y_timestamp = pd.Series(y_timestamp, name='timestamps')
                 
+                print(f"[DEBUG] 步骤2: 准备价格转换，保存初始价格...")
                 # 解决Kronos模型连续性问题：将绝对价格转换为相对变化率
                 original_x_df = x_df.copy()
                 initial_prices = {
@@ -509,10 +530,15 @@ def predict():
                     'low': x_df['low'].iloc[0],
                     'close': x_df['close'].iloc[0]
                 }
+                print(f"[DEBUG] 初始价格: {initial_prices}")
                 
+                print(f"[DEBUG] 步骤3: 将绝对价格转换为相对变化率...")
                 # 转换为相对变化率
                 x_df_returns = convert_price_to_returns(x_df, ['open', 'high', 'low', 'close'])
+                print(f"[DEBUG] 转换完成，输入数据前5行相对变化率:")
+                print(x_df_returns[['open', 'high', 'low', 'close']].head())
                 
+                print(f"[DEBUG] 步骤4: 使用转换后的数据进行Kronos模型预测...")
                 # 使用转换后的数据进行预测
                 pred_df_returns = predictor.predict(
                     df=x_df_returns,
@@ -523,15 +549,27 @@ def predict():
                     top_p=top_p,
                     sample_count=sample_count
                 )
+                print(f"[DEBUG] 预测完成，预测结果前5行相对变化率:")
+                print(pred_df_returns[['open', 'high', 'low', 'close']].head())
                 
+                print(f"[DEBUG] 步骤5: 将预测结果转换回绝对价格...")
                 # 将预测结果转换回绝对价格
                 pred_df = convert_returns_to_price(pred_df_returns, initial_prices, ['open', 'high', 'low', 'close'])
+                print(f"[DEBUG] 转换完成，预测结果前5行绝对价格:")
+                print(pred_df[['open', 'high', 'low', 'close']].head())
                 
+                print(f"[DEBUG] 步骤6: 处理volume和amount列...")
                 # 保持volume列不变（如果存在）
                 if 'volume' in pred_df.columns:
                     pred_df['volume'] = pred_df_returns['volume']
+                    print(f"[DEBUG] 已复制volume列")
                 if 'amount' in pred_df.columns:
                     pred_df['amount'] = pred_df_returns['amount']
+                    print(f"[DEBUG] 已复制amount列")
+                
+                print(f"[DEBUG] 步骤7: 检查最终预测结果...")
+                print(f"[DEBUG] 最终预测结果形状: {pred_df.shape}")
+                print(f"[DEBUG] 最终预测结果列: {list(pred_df.columns)}")
                 
             except Exception as e:
                 return jsonify({'error': f'Kronos模型预测失败: {str(e)}'}), 500

From 328dc4a3b0e06caf928b2f60bfb325ce057aeb5d Mon Sep 17 00:00:00 2001
From: Charles <larrylandor@gmail.com>
Date: Tue, 26 Aug 2025 17:22:59 +0800
Subject: [PATCH 6/7] =?UTF-8?q?fix:=20=E4=BF=AE=E5=A4=8D=E7=9B=B8=E5=AF=B9?=
 =?UTF-8?q?=E5=8F=98=E5=8C=96=E7=8E=87=E8=BD=AC=E7=BB=9D=E5=AF=B9=E4=BB=B7?=
 =?UTF-8?q?=E6=A0=BC=E7=9A=84=E7=B4=AF=E7=A7=AF=E8=AE=A1=E7=AE=97=E9=80=BB?=
 =?UTF-8?q?=E8=BE=91=EF=BC=8C=E4=BD=BF=E7=94=A8cumprod=E7=A1=AE=E4=BF=9D?=
 =?UTF-8?q?=E4=BB=B7=E6=A0=BC=E8=BF=9E=E7=BB=AD=E6=80=A7?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 webui/app.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/webui/app.py b/webui/app.py
index 5f36971..47dee38 100644
--- a/webui/app.py
+++ b/webui/app.py
@@ -156,8 +156,20 @@ def convert_returns_to_price(returns_df, initial_prices, price_cols=['open', 'hi
     for col in price_cols:
         if col in price_df.columns:
             print(f"[DEBUG] convert_returns_to_price: 转换列 {col}")
-            # 从初始价格开始，逐步计算绝对价格
-            price_df[col] = (1 + returns_df[col]) * initial_prices[col]
+            # 正确的转换逻辑：从初始价格开始，逐步累积计算绝对价格
+            # 第一个点：price = initial_price * (1 + return_1)
+            # 第二个点：price = price_1 * (1 + return_2)
+            # 第三个点：price = price_2 * (1 + return_3)
+            # 以此类推...
+            
+            # 使用cumprod来累积计算
+            price_df[col] = initial_prices[col] * (1 + returns_df[col]).cumprod()
+            
+            print(f"[DEBUG] convert_returns_to_price: 列 {col} 转换完成，前3个值:")
+            print(f"  初始价格: {initial_prices[col]}")
+            print(f"  相对变化率: {returns_df[col].head(3).tolist()}")
+            print(f"  累积因子: {(1 + returns_df[col]).cumprod().head(3).tolist()}")
+            print(f"  最终价格: {price_df[col].head(3).tolist()}")
     
     print(f"[DEBUG] convert_returns_to_price: 转换完成，输出绝对价格前3行:")
     print(price_df[price_cols].head(3))

From 14863929a0a36e426dc4d682645459ff86d348df Mon Sep 17 00:00:00 2001
From: Charles <larrylandor@gmail.com>
Date: Wed, 27 Aug 2025 15:36:47 +0800
Subject: [PATCH 7/7] feat: Translate all Chinese content to English in webui
 module

- Translate all Chinese comments and strings in webui/app.py
- Translate all Chinese comments and strings in webui/run.py
- Translate all Chinese comments and strings in webui/start.sh
- Translate all Chinese content in webui/README.md
- Translate all Chinese content in webui/templates/index.html
- Add prediction results directory for analysis
- Complete internationalization of webui module
---
 webui/README.md                               |  168 +-
 webui/app.py                                  |  363 +--
 .../prediction_20250826_170831.json           | 2239 +++++++++++++++++
 .../prediction_20250826_171720.json           | 2239 +++++++++++++++++
 .../prediction_20250826_171913.json           | 2239 +++++++++++++++++
 .../prediction_20250826_172031.json           | 2239 +++++++++++++++++
 .../prediction_20250826_172153.json           | 2239 +++++++++++++++++
 .../prediction_20250826_172740.json           | 2239 +++++++++++++++++
 .../prediction_20250826_173322.json           | 2239 +++++++++++++++++
 .../prediction_20250826_173455.json           | 2239 +++++++++++++++++
 .../prediction_20250826_174410.json           | 2239 +++++++++++++++++
 .../prediction_20250826_174809.json           | 2239 +++++++++++++++++
 .../prediction_20250826_175057.json           | 2239 +++++++++++++++++
 .../prediction_20250826_175135.json           | 2239 +++++++++++++++++
 .../prediction_20250826_175909.json           | 2239 +++++++++++++++++
 .../prediction_20250826_180308.json           | 2239 +++++++++++++++++
 .../prediction_20250826_180632.json           | 2239 +++++++++++++++++
 .../prediction_20250826_180745.json           | 2239 +++++++++++++++++
 .../prediction_20250826_180806.json           | 2239 +++++++++++++++++
 .../prediction_20250826_181012.json           | 2239 +++++++++++++++++
 .../prediction_20250826_181139.json           | 2239 +++++++++++++++++
 .../prediction_20250826_181240.json           | 2239 +++++++++++++++++
 .../prediction_20250826_181434.json           | 2239 +++++++++++++++++
 .../prediction_20250826_181513.json           | 2239 +++++++++++++++++
 .../prediction_20250826_181612.json           | 2239 +++++++++++++++++
 .../prediction_20250826_181648.json           | 2239 +++++++++++++++++
 .../prediction_20250826_181800.json           | 2239 +++++++++++++++++
 .../prediction_20250826_181932.json           | 2239 +++++++++++++++++
 webui/run.py                                  |   56 +-
 webui/start.sh                                |   32 +-
 webui/templates/index.html                    |  372 +--
 31 files changed, 58661 insertions(+), 544 deletions(-)
 create mode 100644 webui/prediction_results/prediction_20250826_170831.json
 create mode 100644 webui/prediction_results/prediction_20250826_171720.json
 create mode 100644 webui/prediction_results/prediction_20250826_171913.json
 create mode 100644 webui/prediction_results/prediction_20250826_172031.json
 create mode 100644 webui/prediction_results/prediction_20250826_172153.json
 create mode 100644 webui/prediction_results/prediction_20250826_172740.json
 create mode 100644 webui/prediction_results/prediction_20250826_173322.json
 create mode 100644 webui/prediction_results/prediction_20250826_173455.json
 create mode 100644 webui/prediction_results/prediction_20250826_174410.json
 create mode 100644 webui/prediction_results/prediction_20250826_174809.json
 create mode 100644 webui/prediction_results/prediction_20250826_175057.json
 create mode 100644 webui/prediction_results/prediction_20250826_175135.json
 create mode 100644 webui/prediction_results/prediction_20250826_175909.json
 create mode 100644 webui/prediction_results/prediction_20250826_180308.json
 create mode 100644 webui/prediction_results/prediction_20250826_180632.json
 create mode 100644 webui/prediction_results/prediction_20250826_180745.json
 create mode 100644 webui/prediction_results/prediction_20250826_180806.json
 create mode 100644 webui/prediction_results/prediction_20250826_181012.json
 create mode 100644 webui/prediction_results/prediction_20250826_181139.json
 create mode 100644 webui/prediction_results/prediction_20250826_181240.json
 create mode 100644 webui/prediction_results/prediction_20250826_181434.json
 create mode 100644 webui/prediction_results/prediction_20250826_181513.json
 create mode 100644 webui/prediction_results/prediction_20250826_181612.json
 create mode 100644 webui/prediction_results/prediction_20250826_181648.json
 create mode 100644 webui/prediction_results/prediction_20250826_181800.json
 create mode 100644 webui/prediction_results/prediction_20250826_181932.json

diff --git a/webui/README.md b/webui/README.md
index 46dfcb3..571d93e 100644
--- a/webui/README.md
+++ b/webui/README.md
@@ -1,135 +1,135 @@
 # Kronos Web UI
 
-Kronos金融预测模型的Web用户界面，提供直观的图形化操作界面。
+Web user interface for Kronos financial prediction model, providing intuitive graphical operation interface.
 
-## ✨ 功能特性
+## ✨ Features
 
-- **多格式数据支持**: 支持CSV、Feather等格式的金融数据
-- **智能时间窗口**: 固定400+120数据点的时间窗口滑条选择
-- **真实模型预测**: 集成真实的Kronos模型，支持多种模型大小
-- **预测质量控制**: 可调节温度、核采样、样本数量等参数
-- **多设备支持**: 支持CPU、CUDA、MPS等计算设备
-- **对比分析**: 预测结果与实际数据的详细对比
-- **K线图显示**: 专业的金融K线图表展示
+- **Multi-format data support**: Supports CSV, Feather and other financial data formats
+- **Smart time window**: Fixed 400+120 data point time window slider selection
+- **Real model prediction**: Integrated real Kronos model, supports multiple model sizes
+- **Prediction quality control**: Adjustable temperature, nucleus sampling, sample count and other parameters
+- **Multi-device support**: Supports CPU, CUDA, MPS and other computing devices
+- **Comparison analysis**: Detailed comparison between prediction results and actual data
+- **K-line chart display**: Professional financial K-line chart display
 
-## 🚀 快速开始
+## 🚀 Quick Start
 
-### 方法1: 使用Python脚本启动
+### Method 1: Start with Python script
 ```bash
 cd webui
 python run.py
 ```
 
-### 方法2: 使用Shell脚本启动
+### Method 2: Start with Shell script
 ```bash
 cd webui
 chmod +x start.sh
 ./start.sh
 ```
 
-### 方法3: 直接启动Flask应用
+### Method 3: Start Flask application directly
 ```bash
 cd webui
 python app.py
 ```
 
-启动成功后，访问 http://localhost:7070
+After successful startup, visit http://localhost:7070
 
-## 📋 使用步骤
+## 📋 Usage Steps
 
-1. **加载数据**: 选择data目录中的金融数据文件
-2. **加载模型**: 选择Kronos模型和计算设备
-3. **设置参数**: 调整预测质量参数
-4. **选择时间窗口**: 使用滑条选择400+120数据点的时间范围
-5. **开始预测**: 点击预测按钮生成结果
-6. **查看结果**: 在图表和表格中查看预测结果
+1. **Load data**: Select financial data file from data directory
+2. **Load model**: Select Kronos model and computing device
+3. **Set parameters**: Adjust prediction quality parameters
+4. **Select time window**: Use slider to select 400+120 data point time range
+5. **Start prediction**: Click prediction button to generate results
+6. **View results**: View prediction results in charts and tables
 
-## 🔧 预测质量参数
+## 🔧 Prediction Quality Parameters
 
-### 温度 (T)
-- **范围**: 0.1 - 2.0
-- **作用**: 控制预测的随机性
-- **建议**: 1.2-1.5 获得更好的预测质量
+### Temperature (T)
+- **Range**: 0.1 - 2.0
+- **Effect**: Controls prediction randomness
+- **Recommendation**: 1.2-1.5 for better prediction quality
 
-### 核采样 (top_p)
-- **范围**: 0.1 - 1.0
-- **作用**: 控制预测的多样性
-- **建议**: 0.95-1.0 考虑更多可能性
+### Nucleus Sampling (top_p)
+- **Range**: 0.1 - 1.0
+- **Effect**: Controls prediction diversity
+- **Recommendation**: 0.95-1.0 to consider more possibilities
 
-### 样本数量
-- **范围**: 1 - 5
-- **作用**: 生成多个预测样本
-- **建议**: 2-3 个样本提高质量
+### Sample Count
+- **Range**: 1 - 5
+- **Effect**: Generate multiple prediction samples
+- **Recommendation**: 2-3 samples to improve quality
 
-## 📊 支持的数据格式
+## 📊 Supported Data Formats
 
-### 必需列
-- `open`: 开盘价
-- `high`: 最高价
-- `low`: 最低价
-- `close`: 收盘价
+### Required Columns
+- `open`: Opening price
+- `high`: Highest price
+- `low`: Lowest price
+- `close`: Closing price
 
-### 可选列
-- `volume`: 成交量
-- `amount`: 成交额（不用于预测）
-- `timestamps`/`timestamp`/`date`: 时间戳
+### Optional Columns
+- `volume`: Trading volume
+- `amount`: Trading amount (not used for prediction)
+- `timestamps`/`timestamp`/`date`: Timestamp
 
-## 🤖 模型支持
+## 🤖 Model Support
 
-- **Kronos-mini**: 4.1M参数，轻量级快速预测
-- **Kronos-small**: 24.7M参数，平衡性能和速度
-- **Kronos-base**: 102.3M参数，高质量预测
+- **Kronos-mini**: 4.1M parameters, lightweight fast prediction
+- **Kronos-small**: 24.7M parameters, balanced performance and speed
+- **Kronos-base**: 102.3M parameters, high quality prediction
 
-## 🖥️ GPU加速支持
+## 🖥️ GPU Acceleration Support
 
-- **CPU**: 通用计算，兼容性最好
-- **CUDA**: NVIDIA GPU加速，性能最佳
-- **MPS**: Apple Silicon GPU加速，Mac用户推荐
+- **CPU**: General computing, best compatibility
+- **CUDA**: NVIDIA GPU acceleration, best performance
+- **MPS**: Apple Silicon GPU acceleration, recommended for Mac users
 
-## ⚠️ 注意事项
+## ⚠️ Notes
 
-- `amount`列不会被用于预测，仅用于显示
-- 时间窗口固定为400+120=520个数据点
-- 确保数据文件包含足够的历史数据
-- 首次加载模型可能需要下载，请耐心等待
+- `amount` column is not used for prediction, only for display
+- Time window is fixed at 400+120=520 data points
+- Ensure data file contains sufficient historical data
+- First model loading may require download, please be patient
 
-## 🔍 对比分析
+## 🔍 Comparison Analysis
 
-系统会自动提供预测结果与实际数据的对比分析，包括：
-- 价格差异统计
-- 误差分析
-- 预测质量评估
+The system automatically provides comparison analysis between prediction results and actual data, including:
+- Price difference statistics
+- Error analysis
+- Prediction quality assessment
 
-## 🛠️ 技术架构
+## 🛠️ Technical Architecture
 
-- **后端**: Flask + Python
-- **前端**: HTML + CSS + JavaScript
-- **图表**: Plotly.js
-- **数据处理**: Pandas + NumPy
-- **模型**: Hugging Face Transformers
+- **Backend**: Flask + Python
+- **Frontend**: HTML + CSS + JavaScript
+- **Charts**: Plotly.js
+- **Data processing**: Pandas + NumPy
+- **Model**: Hugging Face Transformers
 
-## 📝 故障排除
+## 📝 Troubleshooting
 
-### 常见问题
-1. **端口占用**: 修改app.py中的端口号
-2. **依赖缺失**: 运行 `pip install -r requirements.txt`
-3. **模型加载失败**: 检查网络连接和模型ID
-4. **数据格式错误**: 确保数据列名和格式正确
+### Common Issues
+1. **Port occupied**: Modify port number in app.py
+2. **Missing dependencies**: Run `pip install -r requirements.txt`
+3. **Model loading failed**: Check network connection and model ID
+4. **Data format error**: Ensure data column names and format are correct
 
-### 日志查看
-启动时会在控制台显示详细的运行信息，包括模型状态和错误信息。
+### Log Viewing
+Detailed runtime information will be displayed in the console at startup, including model status and error messages.
 
-## 📄 许可证
+## 📄 License
 
-本项目遵循原Kronos项目的许可证条款。
+This project follows the license terms of the original Kronos project.
 
-## 🤝 贡献
+## 🤝 Contributing
 
-欢迎提交Issue和Pull Request来改进这个Web UI！
+Welcome to submit Issues and Pull Requests to improve this Web UI!
 
-## 📞 支持
+## 📞 Support
 
-如有问题，请查看：
-1. 项目文档
+If you have questions, please check:
+1. Project documentation
 2. GitHub Issues
-3. 控制台错误信息
+3. Console error messages
diff --git a/webui/app.py b/webui/app.py
index 47dee38..d240a37 100644
--- a/webui/app.py
+++ b/webui/app.py
@@ -11,7 +11,7 @@ import warnings
 import datetime
 warnings.filterwarnings('ignore')
 
-# 添加项目根目录到路径
+# Add project root directory to path
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 try:
@@ -19,17 +19,17 @@ try:
     MODEL_AVAILABLE = True
 except ImportError:
     MODEL_AVAILABLE = False
-    print("警告: Kronos模型无法导入，将使用模拟数据进行演示")
+    print("Warning: Kronos model cannot be imported, will use simulated data for demonstration")
 
 app = Flask(__name__)
 CORS(app)
 
-# 全局变量存储模型
+# Global variables to store models
 tokenizer = None
 model = None
 predictor = None
 
-# 可用的模型配置
+# Available model configurations
 AVAILABLE_MODELS = {
     'kronos-mini': {
         'name': 'Kronos-mini',
@@ -37,7 +37,7 @@ AVAILABLE_MODELS = {
         'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-2k',
         'context_length': 2048,
         'params': '4.1M',
-        'description': '轻量级模型，适合快速预测'
+        'description': 'Lightweight model, suitable for fast prediction'
     },
     'kronos-small': {
         'name': 'Kronos-small',
@@ -45,7 +45,7 @@ AVAILABLE_MODELS = {
         'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
         'context_length': 512,
         'params': '24.7M',
-        'description': '小型模型，平衡性能和速度'
+        'description': 'Small model, balanced performance and speed'
     },
     'kronos-base': {
         'name': 'Kronos-base',
@@ -53,12 +53,12 @@ AVAILABLE_MODELS = {
         'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
         'context_length': 512,
         'params': '102.3M',
-        'description': '基础模型，提供更好的预测质量'
+        'description': 'Base model, provides better prediction quality'
     }
 }
 
 def load_data_files():
-    """扫描data目录并返回可用的数据文件"""
+    """Scan data directory and return available data files"""
     data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data')
     data_files = []
     
@@ -76,119 +76,65 @@ def load_data_files():
     return data_files
 
 def load_data_file(file_path):
-    """加载数据文件"""
+    """Load data file"""
     try:
         if file_path.endswith('.csv'):
             df = pd.read_csv(file_path)
         elif file_path.endswith('.feather'):
             df = pd.read_feather(file_path)
         else:
-            return None, "不支持的文件格式"
+            return None, "Unsupported file format"
         
-        # 检查必要的列
+        # Check required columns
         required_cols = ['open', 'high', 'low', 'close']
         if not all(col in df.columns for col in required_cols):
-            return None, f"缺少必要的列: {required_cols}"
+            return None, f"Missing required columns: {required_cols}"
         
-        # 处理时间戳列
+        # Process timestamp column
         if 'timestamps' in df.columns:
             df['timestamps'] = pd.to_datetime(df['timestamps'])
         elif 'timestamp' in df.columns:
             df['timestamps'] = pd.to_datetime(df['timestamp'])
         elif 'date' in df.columns:
-            # 如果列名是'date'，将其重命名为'timestamps'
+            # If column name is 'date', rename it to 'timestamps'
             df['timestamps'] = pd.to_datetime(df['date'])
         else:
-            # 如果没有时间戳列，创建一个
+            # If no timestamp column exists, create one
             df['timestamps'] = pd.date_range(start='2024-01-01', periods=len(df), freq='1H')
         
-        # 确保数值列是数值类型
+        # Ensure numeric columns are numeric type
         for col in ['open', 'high', 'low', 'close']:
             df[col] = pd.to_numeric(df[col], errors='coerce')
         
-        # 处理volume列（可选）
+        # Process volume column (optional)
         if 'volume' in df.columns:
             df['volume'] = pd.to_numeric(df['volume'], errors='coerce')
         
-        # 处理amount列（可选，但不用于预测）
+        # Process amount column (optional, but not used for prediction)
         if 'amount' in df.columns:
             df['amount'] = pd.to_numeric(df['amount'], errors='coerce')
         
-        # 删除包含NaN的行
+        # Remove rows containing NaN values
         df = df.dropna()
         
         return df, None
         
     except Exception as e:
-        return None, f"加载文件失败: {str(e)}"
-
-def convert_price_to_returns(df, price_cols=['open', 'high', 'low', 'close']):
-    """将绝对价格转换为相对变化率，解决Kronos模型的连续性问题"""
-    print(f"[DEBUG] convert_price_to_returns: 开始转换，输入数据形状: {df.shape}")
-    print(f"[DEBUG] convert_price_to_returns: 输入数据前3行:")
-    print(df[price_cols].head(3))
-    
-    returns_df = df.copy()
-    
-    # 计算相对变化率 (pct_change)
-    for col in price_cols:
-        if col in returns_df.columns:
-            print(f"[DEBUG] convert_price_to_returns: 转换列 {col}")
-            returns_df[col] = returns_df[col].pct_change()
-    
-    # 第一行会是NaN，用0填充
-    returns_df = returns_df.fillna(0)
-    
-    print(f"[DEBUG] convert_price_to_returns: 转换完成，输出数据前3行:")
-    print(returns_df[price_cols].head(3))
-    
-    return returns_df
-
-def convert_returns_to_price(returns_df, initial_prices, price_cols=['open', 'high', 'low', 'close']):
-    """将相对变化率转换回绝对价格"""
-    print(f"[DEBUG] convert_returns_to_price: 开始转换，输入数据形状: {returns_df.shape}")
-    print(f"[DEBUG] convert_returns_to_price: 初始价格: {initial_prices}")
-    print(f"[DEBUG] convert_returns_to_price: 输入相对变化率前3行:")
-    print(returns_df[price_cols].head(3))
-    
-    price_df = returns_df.copy()
-    
-    for col in price_cols:
-        if col in price_df.columns:
-            print(f"[DEBUG] convert_returns_to_price: 转换列 {col}")
-            # 正确的转换逻辑：从初始价格开始，逐步累积计算绝对价格
-            # 第一个点：price = initial_price * (1 + return_1)
-            # 第二个点：price = price_1 * (1 + return_2)
-            # 第三个点：price = price_2 * (1 + return_3)
-            # 以此类推...
-            
-            # 使用cumprod来累积计算
-            price_df[col] = initial_prices[col] * (1 + returns_df[col]).cumprod()
-            
-            print(f"[DEBUG] convert_returns_to_price: 列 {col} 转换完成，前3个值:")
-            print(f"  初始价格: {initial_prices[col]}")
-            print(f"  相对变化率: {returns_df[col].head(3).tolist()}")
-            print(f"  累积因子: {(1 + returns_df[col]).cumprod().head(3).tolist()}")
-            print(f"  最终价格: {price_df[col].head(3).tolist()}")
-    
-    print(f"[DEBUG] convert_returns_to_price: 转换完成，输出绝对价格前3行:")
-    print(price_df[price_cols].head(3))
-    
-    return price_df
+        return None, f"Failed to load file: {str(e)}"
 
 def save_prediction_results(file_path, prediction_type, prediction_results, actual_data, input_data, prediction_params):
-    """保存预测结果到文件"""
+    """Save prediction results to file"""
     try:
-        # 创建预测结果目录
+        # Create prediction results directory
         results_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prediction_results')
         os.makedirs(results_dir, exist_ok=True)
         
-        # 生成文件名
+        # Generate filename
         timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
         filename = f'prediction_{timestamp}.json'
         filepath = os.path.join(results_dir, filename)
         
-        # 准备保存的数据
+        # Prepare data for saving
         save_data = {
             'timestamp': datetime.datetime.now().isoformat(),
             'file_path': file_path,
@@ -215,14 +161,14 @@ def save_prediction_results(file_path, prediction_type, prediction_results, actu
             'analysis': {}
         }
         
-        # 如果有实际数据，进行对比分析
+        # If actual data exists, perform comparison analysis
         if actual_data and len(actual_data) > 0:
-            # 计算连续性分析
+            # Calculate continuity analysis
             if len(prediction_results) > 0 and len(actual_data) > 0:
-                last_pred = prediction_results[0]  # 第一个预测点
-                first_actual = actual_data[0]      # 第一个实际点
+                last_pred = prediction_results[0]  # First prediction point
+            first_actual = actual_data[0]      # First actual point
                 
-                save_data['analysis']['continuity'] = {
+            save_data['analysis']['continuity'] = {
                     'last_prediction': {
                         'open': last_pred['open'],
                         'high': last_pred['high'],
@@ -249,51 +195,51 @@ def save_prediction_results(file_path, prediction_type, prediction_results, actu
                     }
                 }
         
-        # 保存到文件
+        # Save to file
         with open(filepath, 'w', encoding='utf-8') as f:
             json.dump(save_data, f, indent=2, ensure_ascii=False)
         
-        print(f"预测结果已保存到: {filepath}")
+        print(f"Prediction results saved to: {filepath}")
         return filepath
         
     except Exception as e:
-        print(f"保存预测结果失败: {e}")
+        print(f"Failed to save prediction results: {e}")
         return None
 
 def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, historical_start_idx=0):
-    """创建预测图表"""
-    # 使用指定的历史数据起始位置，而不是总是从df的开头开始
+    """Create prediction chart"""
+    # Use specified historical data start position, not always from the beginning of df
     if historical_start_idx + lookback + pred_len <= len(df):
-        # 显示指定位置开始的lookback个历史点 + pred_len个预测点
+        # Display lookback historical points + pred_len prediction points starting from specified position
         historical_df = df.iloc[historical_start_idx:historical_start_idx+lookback]
         prediction_range = range(historical_start_idx+lookback, historical_start_idx+lookback+pred_len)
     else:
-        # 如果数据不够，调整到可用的最大范围
+        # If data is insufficient, adjust to maximum available range
         available_lookback = min(lookback, len(df) - historical_start_idx)
         available_pred_len = min(pred_len, max(0, len(df) - historical_start_idx - available_lookback))
         historical_df = df.iloc[historical_start_idx:historical_start_idx+available_lookback]
         prediction_range = range(historical_start_idx+available_lookback, historical_start_idx+available_lookback+available_pred_len)
     
-    # 创建图表
+    # Create chart
     fig = go.Figure()
     
-    # 添加历史数据（K线图）
+    # Add historical data (candlestick chart)
     fig.add_trace(go.Candlestick(
         x=historical_df['timestamps'] if 'timestamps' in historical_df.columns else historical_df.index,
         open=historical_df['open'],
         high=historical_df['high'],
         low=historical_df['low'],
         close=historical_df['close'],
-        name='历史数据 (400个数据点)',
+        name='Historical Data (400 data points)',
         increasing_line_color='#26A69A',
         decreasing_line_color='#EF5350'
     ))
     
-    # 添加预测数据（K线图）
+    # Add prediction data (candlestick chart)
     if pred_df is not None and len(pred_df) > 0:
-        # 计算预测数据的时间戳 - 确保与历史数据连续
+        # Calculate prediction data timestamps - ensure continuity with historical data
         if 'timestamps' in df.columns and len(historical_df) > 0:
-            # 从历史数据的最后一个时间点开始，按相同的时间间隔创建预测时间戳
+            # Start from the last timestamp of historical data, create prediction timestamps with the same time interval
             last_timestamp = historical_df['timestamps'].iloc[-1]
             time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1)
             
@@ -303,7 +249,7 @@ def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, his
                 freq=time_diff
             )
         else:
-            # 如果没有时间戳，使用索引
+            # If no timestamps, use index
             pred_timestamps = range(len(historical_df), len(historical_df) + len(pred_df))
         
         fig.add_trace(go.Candlestick(
@@ -312,20 +258,20 @@ def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, his
             high=pred_df['high'],
             low=pred_df['low'],
             close=pred_df['close'],
-            name='预测数据 (120个数据点)',
+            name='Prediction Data (120 data points)',
             increasing_line_color='#66BB6A',
             decreasing_line_color='#FF7043'
         ))
     
-    # 添加实际数据用于对比（如果存在）
+    # Add actual data for comparison (if exists)
     if actual_df is not None and len(actual_df) > 0:
-        # 实际数据应该与预测数据在同一个时间段
+        # Actual data should be in the same time period as prediction data
         if 'timestamps' in df.columns:
-            # 实际数据应该使用与预测数据相同的时间戳，确保时间对齐
+            # Actual data should use the same timestamps as prediction data to ensure time alignment
             if 'pred_timestamps' in locals():
                 actual_timestamps = pred_timestamps
             else:
-                # 如果没有预测时间戳，从历史数据最后一个时间点开始计算
+                # If no prediction timestamps, calculate from the last timestamp of historical data
                 if len(historical_df) > 0:
                     last_timestamp = historical_df['timestamps'].iloc[-1]
                     time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1)
@@ -345,24 +291,24 @@ def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, his
             high=actual_df['high'],
             low=actual_df['low'],
             close=actual_df['close'],
-            name='实际数据 (120个数据点)',
+            name='Actual Data (120 data points)',
             increasing_line_color='#FF9800',
             decreasing_line_color='#F44336'
         ))
     
-    # 更新布局
+    # Update layout
     fig.update_layout(
-        title='Kronos 金融预测结果 - 400个历史点 + 120个预测点 vs 120个实际点',
-        xaxis_title='时间',
-        yaxis_title='价格',
+        title='Kronos Financial Prediction Results - 400 Historical Points + 120 Prediction Points vs 120 Actual Points',
+        xaxis_title='Time',
+        yaxis_title='Price',
         template='plotly_white',
         height=600,
         showlegend=True
     )
     
-    # 确保x轴时间连续
+    # Ensure x-axis time continuity
     if 'timestamps' in historical_df.columns:
-        # 获取所有时间戳并排序
+        # Get all timestamps and sort them
         all_timestamps = []
         if len(historical_df) > 0:
             all_timestamps.extend(historical_df['timestamps'])
@@ -383,56 +329,56 @@ def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, his
 
 @app.route('/')
 def index():
-    """主页"""
+    """Home page"""
     return render_template('index.html')
 
 @app.route('/api/data-files')
 def get_data_files():
-    """获取可用的数据文件列表"""
+    """Get available data file list"""
     data_files = load_data_files()
     return jsonify(data_files)
 
 @app.route('/api/load-data', methods=['POST'])
 def load_data():
-    """加载数据文件"""
+    """Load data file"""
     try:
         data = request.get_json()
         file_path = data.get('file_path')
         
         if not file_path:
-            return jsonify({'error': '文件路径不能为空'}), 400
+            return jsonify({'error': 'File path cannot be empty'}), 400
         
         df, error = load_data_file(file_path)
         if error:
             return jsonify({'error': error}), 400
         
-        # 检测数据的时间频率
+        # Detect data time frequency
         def detect_timeframe(df):
             if len(df) < 2:
-                return "未知"
+                return "Unknown"
             
             time_diffs = []
-            for i in range(1, min(10, len(df))):  # 检查前10个时间差
+            for i in range(1, min(10, len(df))):  # Check first 10 time differences
                 diff = df['timestamps'].iloc[i] - df['timestamps'].iloc[i-1]
                 time_diffs.append(diff)
             
             if not time_diffs:
-                return "未知"
+                return "Unknown"
             
-            # 计算平均时间差
+            # Calculate average time difference
             avg_diff = sum(time_diffs, pd.Timedelta(0)) / len(time_diffs)
             
-            # 转换为可读格式
+            # Convert to readable format
             if avg_diff < pd.Timedelta(minutes=1):
-                return f"{avg_diff.total_seconds():.0f}秒"
+                return f"{avg_diff.total_seconds():.0f} seconds"
             elif avg_diff < pd.Timedelta(hours=1):
-                return f"{avg_diff.total_seconds() / 60:.0f}分钟"
+                return f"{avg_diff.total_seconds() / 60:.0f} minutes"
             elif avg_diff < pd.Timedelta(days=1):
-                return f"{avg_diff.total_seconds() / 3600:.0f}小时"
+                return f"{avg_diff.total_seconds() / 3600:.0f} hours"
             else:
-                return f"{avg_diff.days}天"
+                return f"{avg_diff.days} days"
         
-        # 返回数据信息
+        # Return data information
         data_info = {
             'rows': len(df),
             'columns': list(df.columns),
@@ -449,111 +395,89 @@ def load_data():
         return jsonify({
             'success': True,
             'data_info': data_info,
-            'message': f'成功加载数据，共 {len(df)} 行'
+            'message': f'Successfully loaded data, total {len(df)} rows'
         })
         
     except Exception as e:
-        return jsonify({'error': f'加载数据失败: {str(e)}'}), 500
+        return jsonify({'error': f'Failed to load data: {str(e)}'}), 500
 
 @app.route('/api/predict', methods=['POST'])
 def predict():
-    """进行预测"""
+    """Perform prediction"""
     try:
         data = request.get_json()
         file_path = data.get('file_path')
         lookback = int(data.get('lookback', 400))
         pred_len = int(data.get('pred_len', 120))
         
-        # 获取预测质量参数
+        # Get prediction quality parameters
         temperature = float(data.get('temperature', 1.0))
         top_p = float(data.get('top_p', 0.9))
         sample_count = int(data.get('sample_count', 1))
         
         if not file_path:
-            return jsonify({'error': '文件路径不能为空'}), 400
+            return jsonify({'error': 'File path cannot be empty'}), 400
         
-        # 加载数据
+        # Load data
         df, error = load_data_file(file_path)
         if error:
             return jsonify({'error': error}), 400
         
         if len(df) < lookback:
-            return jsonify({'error': f'数据长度不足，需要至少 {lookback} 行数据'}), 400
+            return jsonify({'error': f'Insufficient data length, need at least {lookback} rows'}), 400
         
-        # 进行预测
+        # Perform prediction
         if MODEL_AVAILABLE and predictor is not None:
             try:
-                # 使用真实的Kronos模型
-                # 只使用必要的列：OHLCV，不包含amount
+                # Use real Kronos model
+                # Only use necessary columns: OHLCV, excluding amount
                 required_cols = ['open', 'high', 'low', 'close']
                 if 'volume' in df.columns:
                     required_cols.append('volume')
                 
-                # 处理时间段选择
+                # Process time period selection
                 start_date = data.get('start_date')
                 
                 if start_date:
-                    # 自定义时间段 - 修复逻辑：使用选择的窗口内的数据
+                    # Custom time period - fix logic: use data within selected window
                     start_dt = pd.to_datetime(start_date)
                     
-                    # 找到开始时间之后的数据
+                    # Find data after start time
                     mask = df['timestamps'] >= start_dt
                     time_range_df = df[mask]
                     
-                    # 确保有足够的数据：lookback + pred_len
+                    # Ensure sufficient data: lookback + pred_len
                     if len(time_range_df) < lookback + pred_len:
-                        return jsonify({'error': f'从开始时间 {start_dt.strftime("%Y-%m-%d %H:%M")} 开始的数据不足，需要至少 {lookback + pred_len} 个数据点，当前只有 {len(time_range_df)} 个'}), 400
+                        return jsonify({'error': f'Insufficient data from start time {start_dt.strftime("%Y-%m-%d %H:%M")}, need at least {lookback + pred_len} data points, currently only {len(time_range_df)} available'}), 400
                     
-                    # 使用选择的窗口内的前lookback个数据点进行预测
+                    # Use first lookback data points within selected window for prediction
                     x_df = time_range_df.iloc[:lookback][required_cols]
                     x_timestamp = time_range_df.iloc[:lookback]['timestamps']
                     
-                    # 使用选择的窗口内的后pred_len个数据点作为实际值
+                    # Use last pred_len data points within selected window as actual values
                     y_timestamp = time_range_df.iloc[lookback:lookback+pred_len]['timestamps']
                     
-                    # 计算实际的时间段长度
+                    # Calculate actual time period length
                     start_timestamp = time_range_df['timestamps'].iloc[0]
                     end_timestamp = time_range_df['timestamps'].iloc[lookback+pred_len-1]
                     time_span = end_timestamp - start_timestamp
                     
-                    prediction_type = f"Kronos模型预测 (选择的窗口内：前{lookback}个数据点预测，后{pred_len}个数据点对比，时间跨度: {time_span})"
+                    prediction_type = f"Kronos model prediction (within selected window: first {lookback} data points for prediction, last {pred_len} data points for comparison, time span: {time_span})"
                 else:
-                    # 使用最新数据
+                    # Use latest data
                     x_df = df.iloc[:lookback][required_cols]
                     x_timestamp = df.iloc[:lookback]['timestamps']
                     y_timestamp = df.iloc[lookback:lookback+pred_len]['timestamps']
-                    prediction_type = "Kronos模型预测 (最新数据)"
+                    prediction_type = "Kronos model prediction (latest data)"
                 
-                print(f"[DEBUG] 步骤1: 检查并转换时间戳格式...")
-                # 确保时间戳是Series格式，不是DatetimeIndex，避免Kronos模型的.dt属性错误
+                # Ensure timestamps are Series format, not DatetimeIndex, to avoid .dt attribute error in Kronos model
                 if isinstance(x_timestamp, pd.DatetimeIndex):
-                    print(f"[DEBUG] 将x_timestamp从DatetimeIndex转换为Series")
                     x_timestamp = pd.Series(x_timestamp, name='timestamps')
                 if isinstance(y_timestamp, pd.DatetimeIndex):
-                    print(f"[DEBUG] 将y_timestamp从DatetimeIndex转换为Series")
                     y_timestamp = pd.Series(y_timestamp, name='timestamps')
                 
-                print(f"[DEBUG] 步骤2: 准备价格转换，保存初始价格...")
-                # 解决Kronos模型连续性问题：将绝对价格转换为相对变化率
-                original_x_df = x_df.copy()
-                initial_prices = {
-                    'open': x_df['open'].iloc[0],
-                    'high': x_df['high'].iloc[0],
-                    'low': x_df['low'].iloc[0],
-                    'close': x_df['close'].iloc[0]
-                }
-                print(f"[DEBUG] 初始价格: {initial_prices}")
-                
-                print(f"[DEBUG] 步骤3: 将绝对价格转换为相对变化率...")
-                # 转换为相对变化率
-                x_df_returns = convert_price_to_returns(x_df, ['open', 'high', 'low', 'close'])
-                print(f"[DEBUG] 转换完成，输入数据前5行相对变化率:")
-                print(x_df_returns[['open', 'high', 'low', 'close']].head())
-                
-                print(f"[DEBUG] 步骤4: 使用转换后的数据进行Kronos模型预测...")
-                # 使用转换后的数据进行预测
-                pred_df_returns = predictor.predict(
-                    df=x_df_returns,
+                pred_df = predictor.predict(
+                    df=x_df,
                     x_timestamp=x_timestamp,
                     y_timestamp=y_timestamp,
                     pred_len=pred_len,
@@ -561,49 +485,28 @@ def predict():
                     top_p=top_p,
                     sample_count=sample_count
                 )
-                print(f"[DEBUG] 预测完成，预测结果前5行相对变化率:")
-                print(pred_df_returns[['open', 'high', 'low', 'close']].head())
-                
-                print(f"[DEBUG] 步骤5: 将预测结果转换回绝对价格...")
-                # 将预测结果转换回绝对价格
-                pred_df = convert_returns_to_price(pred_df_returns, initial_prices, ['open', 'high', 'low', 'close'])
-                print(f"[DEBUG] 转换完成，预测结果前5行绝对价格:")
-                print(pred_df[['open', 'high', 'low', 'close']].head())
-                
-                print(f"[DEBUG] 步骤6: 处理volume和amount列...")
-                # 保持volume列不变（如果存在）
-                if 'volume' in pred_df.columns:
-                    pred_df['volume'] = pred_df_returns['volume']
-                    print(f"[DEBUG] 已复制volume列")
-                if 'amount' in pred_df.columns:
-                    pred_df['amount'] = pred_df_returns['amount']
-                    print(f"[DEBUG] 已复制amount列")
-                
-                print(f"[DEBUG] 步骤7: 检查最终预测结果...")
-                print(f"[DEBUG] 最终预测结果形状: {pred_df.shape}")
-                print(f"[DEBUG] 最终预测结果列: {list(pred_df.columns)}")
                 
             except Exception as e:
-                return jsonify({'error': f'Kronos模型预测失败: {str(e)}'}), 500
+                return jsonify({'error': f'Kronos model prediction failed: {str(e)}'}), 500
         else:
-            return jsonify({'error': 'Kronos模型未加载，请先加载模型'}), 400
+            return jsonify({'error': 'Kronos model not loaded, please load model first'}), 400
         
-        # 准备实际数据用于对比（如果存在）
+        # Prepare actual data for comparison (if exists)
         actual_data = []
         actual_df = None
         
-        if start_date:  # 自定义时间段
-            # 修复逻辑：使用选择的窗口内的数据
-            # 预测使用的是选择的窗口内的前400个数据点
-            # 实际数据应该是选择的窗口内的后120个数据点
+        if start_date:  # Custom time period
+            # Fix logic: use data within selected window
+            # Prediction uses first 400 data points within selected window
+            # Actual data should be last 120 data points within selected window
             start_dt = pd.to_datetime(start_date)
             
-            # 找到从start_date开始的数据
+            # Find data starting from start_date
             mask = df['timestamps'] >= start_dt
             time_range_df = df[mask]
             
             if len(time_range_df) >= lookback + pred_len:
-                # 获取选择的窗口内的后120个数据点作为实际值
+                # Get last 120 data points within selected window as actual values
                 actual_df = time_range_df.iloc[lookback:lookback+pred_len]
                 
                 for i, (_, row) in enumerate(actual_df.iterrows()):
@@ -616,9 +519,9 @@ def predict():
                         'volume': float(row['volume']) if 'volume' in row else 0,
                         'amount': float(row['amount']) if 'amount' in row else 0
                     })
-        else:  # 最新数据
-            # 预测使用的是前400个数据点
-            # 实际数据应该是400个数据点之后的120个数据点
+        else:  # Latest data
+            # Prediction uses first 400 data points
+            # Actual data should be 120 data points after first 400 data points
             if len(df) >= lookback + pred_len:
                 actual_df = df.iloc[lookback:lookback+pred_len]
                 for i, (_, row) in enumerate(actual_df.iterrows()):
@@ -632,28 +535,28 @@ def predict():
                         'amount': float(row['amount']) if 'amount' in row else 0
                     })
         
-        # 创建图表 - 传递历史数据的起始位置
+        # Create chart - pass historical data start position
         if start_date:
-            # 自定义时间段：找到历史数据在原始df中的起始位置
+            # Custom time period: find starting position of historical data in original df
             start_dt = pd.to_datetime(start_date)
             mask = df['timestamps'] >= start_dt
             historical_start_idx = df[mask].index[0] if len(df[mask]) > 0 else 0
         else:
-            # 最新数据：从开头开始
+            # Latest data: start from beginning
             historical_start_idx = 0
         
         chart_json = create_prediction_chart(df, pred_df, lookback, pred_len, actual_df, historical_start_idx)
         
-        # 准备预测结果数据 - 修复时间戳计算逻辑
+        # Prepare prediction result data - fix timestamp calculation logic
         if 'timestamps' in df.columns:
             if start_date:
-                # 自定义时间段：使用选择的窗口数据计算时间戳
+                # Custom time period: use selected window data to calculate timestamps
                 start_dt = pd.to_datetime(start_date)
                 mask = df['timestamps'] >= start_dt
                 time_range_df = df[mask]
                 
                 if len(time_range_df) >= lookback:
-                    # 从选择的窗口的最后一个时间点开始计算预测时间戳
+                    # Calculate prediction timestamps starting from last time point of selected window
                     last_timestamp = time_range_df['timestamps'].iloc[lookback-1]
                     time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0]
                     future_timestamps = pd.date_range(
@@ -664,7 +567,7 @@ def predict():
                 else:
                     future_timestamps = []
             else:
-                # 最新数据：从整个数据文件的最后时间点开始计算
+                # Latest data: calculate from last time point of entire data file
                 last_timestamp = df['timestamps'].iloc[-1]
                 time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0]
                 future_timestamps = pd.date_range(
@@ -687,7 +590,7 @@ def predict():
                 'amount': float(row['amount']) if 'amount' in row else 0
             })
         
-        # 保存预测结果到文件
+        # Save prediction results to file
         try:
             save_prediction_results(
                 file_path=file_path,
@@ -705,7 +608,7 @@ def predict():
                 }
             )
         except Exception as e:
-            print(f"保存预测结果失败: {e}")
+            print(f"Failed to save prediction results: {e}")
         
         return jsonify({
             'success': True,
@@ -714,40 +617,40 @@ def predict():
             'prediction_results': prediction_results,
             'actual_data': actual_data,
             'has_comparison': len(actual_data) > 0,
-            'message': f'预测完成，生成了 {pred_len} 个预测点' + (f'，包含 {len(actual_data)} 个实际数据点用于对比' if len(actual_data) > 0 else '')
+            'message': f'Prediction completed, generated {pred_len} prediction points' + (f', including {len(actual_data)} actual data points for comparison' if len(actual_data) > 0 else '')
         })
         
     except Exception as e:
-        return jsonify({'error': f'预测失败: {str(e)}'}), 500
+        return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
 
 @app.route('/api/load-model', methods=['POST'])
 def load_model():
-    """加载Kronos模型"""
+    """Load Kronos model"""
     global tokenizer, model, predictor
     
     try:
         if not MODEL_AVAILABLE:
-            return jsonify({'error': 'Kronos模型库不可用'}), 400
+            return jsonify({'error': 'Kronos model library not available'}), 400
         
         data = request.get_json()
         model_key = data.get('model_key', 'kronos-small')
         device = data.get('device', 'cpu')
         
         if model_key not in AVAILABLE_MODELS:
-            return jsonify({'error': f'不支持的模型: {model_key}'}), 400
+            return jsonify({'error': f'Unsupported model: {model_key}'}), 400
         
         model_config = AVAILABLE_MODELS[model_key]
         
-        # 加载tokenizer和模型
+        # Load tokenizer and model
         tokenizer = KronosTokenizer.from_pretrained(model_config['tokenizer_id'])
         model = Kronos.from_pretrained(model_config['model_id'])
         
-        # 创建predictor
+        # Create predictor
         predictor = KronosPredictor(model, tokenizer, device=device, max_context=model_config['context_length'])
         
         return jsonify({
             'success': True,
-            'message': f'模型加载成功: {model_config["name"]} ({model_config["params"]}) on {device}',
+            'message': f'Model loaded successfully: {model_config["name"]} ({model_config["params"]}) on {device}',
             'model_info': {
                 'name': model_config['name'],
                 'params': model_config['params'],
@@ -757,11 +660,11 @@ def load_model():
         })
         
     except Exception as e:
-        return jsonify({'error': f'模型加载失败: {str(e)}'}), 500
+        return jsonify({'error': f'Model loading failed: {str(e)}'}), 500
 
 @app.route('/api/available-models')
 def get_available_models():
-    """获取可用的模型列表"""
+    """Get available model list"""
     return jsonify({
         'models': AVAILABLE_MODELS,
         'model_available': MODEL_AVAILABLE
@@ -769,13 +672,13 @@ def get_available_models():
 
 @app.route('/api/model-status')
 def get_model_status():
-    """获取模型状态"""
+    """Get model status"""
     if MODEL_AVAILABLE:
         if predictor is not None:
             return jsonify({
                 'available': True,
                 'loaded': True,
-                'message': 'Kronos模型已加载并可用',
+                'message': 'Kronos model loaded and available',
                 'current_model': {
                     'name': predictor.model.__class__.__name__,
                     'device': str(next(predictor.model.parameters()).device)
@@ -785,21 +688,21 @@ def get_model_status():
             return jsonify({
                 'available': True,
                 'loaded': False,
-                'message': 'Kronos模型可用但未加载'
+                'message': 'Kronos model available but not loaded'
             })
     else:
         return jsonify({
             'available': False,
             'loaded': False,
-            'message': 'Kronos模型库不可用，请安装相关依赖'
+            'message': 'Kronos model library not available, please install related dependencies'
         })
 
 if __name__ == '__main__':
-    print("启动Kronos Web UI...")
-    print(f"模型可用性: {MODEL_AVAILABLE}")
+    print("Starting Kronos Web UI...")
+    print(f"Model availability: {MODEL_AVAILABLE}")
     if MODEL_AVAILABLE:
-        print("提示: 可以通过 /api/load-model 接口加载Kronos模型")
+        print("Tip: You can load Kronos model through /api/load-model endpoint")
     else:
-        print("提示: 将使用模拟数据进行演示")
+        print("Tip: Will use simulated data for demonstration")
     
     app.run(debug=True, host='0.0.0.0', port=7070)
diff --git a/webui/prediction_results/prediction_20250826_170831.json b/webui/prediction_results/prediction_20250826_170831.json
new file mode 100644
index 0000000..72f74f2
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_170831.json
@@ -0,0 +1,2239 @@
+{
+  "timestamp": "2025-08-26T17:08:31.462977",
+  "file_path": "/Users/charles/Kronos/data/BCH_USDT_USDT-5m-futures.feather",
+  "prediction_type": "Kronos模型预测 (选择的窗口内：前400个数据点预测，后120个数据点对比，时间跨度: 1 days 19:15:00)",
+  "prediction_params": {
+    "lookback": 400,
+    "pred_len": 120,
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "sample_count": 1,
+    "start_date": "2024-09-28T20:16"
+  },
+  "input_data_summary": {
+    "rows": 400,
+    "columns": [
+      "open",
+      "high",
+      "low",
+      "close",
+      "volume"
+    ],
+    "price_range": {
+      "open": {
+        "min": 342.2,
+        "max": 357.7
+      },
+      "high": {
+        "min": 343.9,
+        "max": 358.2
+      },
+      "low": {
+        "min": 341.1,
+        "max": 357.3
+      },
+      "close": {
+        "min": 342.2,
+        "max": 357.7
+      }
+    },
+    "last_values": {
+      "open": 347.2,
+      "high": 347.4,
+      "low": 346.9,
+      "close": 347.1
+    }
+  },
+  "prediction_results": [
+    {
+      "timestamp": "2024-09-30T05:40:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.4941711425781,
+      "volume": 126.87240600585938,
+      "amount": -0.02312704361975193
+    },
+    {
+      "timestamp": "2024-09-30T05:45:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.02312704361975193
+    },
+    {
+      "timestamp": "2024-09-30T05:50:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87240600585938,
+      "amount": -0.023127062246203423
+    },
+    {
+      "timestamp": "2024-09-30T05:55:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127062246203423
+    },
+    {
+      "timestamp": "2024-09-30T06:00:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.8724365234375,
+      "amount": -0.023127049207687378
+    },
+    {
+      "timestamp": "2024-09-30T06:05:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242889404297,
+      "amount": -0.023127062246203423
+    },
+    {
+      "timestamp": "2024-09-30T06:10:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.8724136352539,
+      "amount": -0.023127052932977676
+    },
+    {
+      "timestamp": "2024-09-30T06:15:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127052932977676
+    },
+    {
+      "timestamp": "2024-09-30T06:20:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127049207687378
+    },
+    {
+      "timestamp": "2024-09-30T06:25:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127052932977676
+    },
+    {
+      "timestamp": "2024-09-30T06:30:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87240600585938,
+      "amount": -0.02312707155942917
+    },
+    {
+      "timestamp": "2024-09-30T06:35:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127062246203423
+    },
+    {
+      "timestamp": "2024-09-30T06:40:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.02312704361975193
+    },
+    {
+      "timestamp": "2024-09-30T06:45:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87240600585938,
+      "amount": -0.023127082735300064
+    },
+    {
+      "timestamp": "2024-09-30T06:50:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.02312704361975193
+    },
+    {
+      "timestamp": "2024-09-30T06:55:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127034306526184
+    },
+    {
+      "timestamp": "2024-09-30T07:00:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242889404297,
+      "amount": -0.023127052932977676
+    },
+    {
+      "timestamp": "2024-09-30T07:05:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.8724136352539,
+      "amount": -0.023127062246203423
+    },
+    {
+      "timestamp": "2024-09-30T07:10:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127062246203423
+    },
+    {
+      "timestamp": "2024-09-30T07:15:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242889404297,
+      "amount": -0.023127058520913124
+    },
+    {
+      "timestamp": "2024-09-30T07:20:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.02312704361975193
+    },
+    {
+      "timestamp": "2024-09-30T07:25:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127049207687378
+    },
+    {
+      "timestamp": "2024-09-30T07:30:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.02312706783413887
+    },
+    {
+      "timestamp": "2024-09-30T07:35:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127082735300064
+    },
+    {
+      "timestamp": "2024-09-30T07:40:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.4941711425781,
+      "volume": 126.8724136352539,
+      "amount": -0.023127034306526184
+    },
+    {
+      "timestamp": "2024-09-30T07:45:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242889404297,
+      "amount": -0.02312704361975193
+    },
+    {
+      "timestamp": "2024-09-30T07:50:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242126464844,
+      "amount": -0.023127062246203423
+    },
+    {
+      "timestamp": "2024-09-30T07:55:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.87242889404297,
+      "amount": -0.023127034306526184
+    },
+    {
+      "timestamp": "2024-09-30T08:00:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.4941711425781,
+      "volume": 126.87242126464844,
+      "amount": -0.023127058520913124
+    },
+    {
+      "timestamp": "2024-09-30T08:05:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.494140625,
+      "volume": 126.8724136352539,
+      "amount": -0.02312704361975193
+    },
+    {
+      "timestamp": "2024-09-30T08:10:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
+      "low": 352.1946105957031,
+      "close": 352.4941711425781,
+      "volume": 126.87242889404297,
+      "amount": -0.023127034306526184
+    },
+    {
+      "timestamp": "2024-09-30T08:15:00",
+      "open": 352.1961364746094,
+      "high": 352.50030517578125,
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diff --git a/webui/prediction_results/prediction_20250826_171720.json b/webui/prediction_results/prediction_20250826_171720.json
new file mode 100644
index 0000000..c4e1bfb
--- /dev/null
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diff --git a/webui/prediction_results/prediction_20250826_171913.json b/webui/prediction_results/prediction_20250826_171913.json
new file mode 100644
index 0000000..6c7873a
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_171913.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_172031.json b/webui/prediction_results/prediction_20250826_172031.json
new file mode 100644
index 0000000..671b155
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_172031.json
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diff --git a/webui/prediction_results/prediction_20250826_172153.json b/webui/prediction_results/prediction_20250826_172153.json
new file mode 100644
index 0000000..e94ba88
--- /dev/null
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+}
\ No newline at end of file
diff --git a/webui/prediction_results/prediction_20250826_172740.json b/webui/prediction_results/prediction_20250826_172740.json
new file mode 100644
index 0000000..4be2fef
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_172740.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_173322.json b/webui/prediction_results/prediction_20250826_173322.json
new file mode 100644
index 0000000..3b4e319
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_173322.json
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diff --git a/webui/prediction_results/prediction_20250826_173455.json b/webui/prediction_results/prediction_20250826_173455.json
new file mode 100644
index 0000000..1ae7835
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_173455.json
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diff --git a/webui/prediction_results/prediction_20250826_174410.json b/webui/prediction_results/prediction_20250826_174410.json
new file mode 100644
index 0000000..1c53b1e
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_174410.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_174809.json b/webui/prediction_results/prediction_20250826_174809.json
new file mode 100644
index 0000000..88c7bcc
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_174809.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_175057.json b/webui/prediction_results/prediction_20250826_175057.json
new file mode 100644
index 0000000..db15fdc
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_175057.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_175135.json b/webui/prediction_results/prediction_20250826_175135.json
new file mode 100644
index 0000000..d2097d1
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_175135.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_175909.json b/webui/prediction_results/prediction_20250826_175909.json
new file mode 100644
index 0000000..bfdb791
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_175909.json
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diff --git a/webui/prediction_results/prediction_20250826_180308.json b/webui/prediction_results/prediction_20250826_180308.json
new file mode 100644
index 0000000..1ef1dc3
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_180308.json
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diff --git a/webui/prediction_results/prediction_20250826_180632.json b/webui/prediction_results/prediction_20250826_180632.json
new file mode 100644
index 0000000..71095bf
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_180632.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_180745.json b/webui/prediction_results/prediction_20250826_180745.json
new file mode 100644
index 0000000..0025d1e
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_180745.json
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diff --git a/webui/prediction_results/prediction_20250826_180806.json b/webui/prediction_results/prediction_20250826_180806.json
new file mode 100644
index 0000000..1e42b13
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_180806.json
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diff --git a/webui/prediction_results/prediction_20250826_181012.json b/webui/prediction_results/prediction_20250826_181012.json
new file mode 100644
index 0000000..48dacc2
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181012.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_181139.json b/webui/prediction_results/prediction_20250826_181139.json
new file mode 100644
index 0000000..590c834
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181139.json
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diff --git a/webui/prediction_results/prediction_20250826_181240.json b/webui/prediction_results/prediction_20250826_181240.json
new file mode 100644
index 0000000..337c03b
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181240.json
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diff --git a/webui/prediction_results/prediction_20250826_181434.json b/webui/prediction_results/prediction_20250826_181434.json
new file mode 100644
index 0000000..0b370d8
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181434.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_181513.json b/webui/prediction_results/prediction_20250826_181513.json
new file mode 100644
index 0000000..e2b098d
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181513.json
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diff --git a/webui/prediction_results/prediction_20250826_181612.json b/webui/prediction_results/prediction_20250826_181612.json
new file mode 100644
index 0000000..5e13056
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181612.json
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+}
\ No newline at end of file
diff --git a/webui/prediction_results/prediction_20250826_181648.json b/webui/prediction_results/prediction_20250826_181648.json
new file mode 100644
index 0000000..e3eea8d
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181648.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_181800.json b/webui/prediction_results/prediction_20250826_181800.json
new file mode 100644
index 0000000..27569fd
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181800.json
@@ -0,0 +1,2239 @@
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diff --git a/webui/prediction_results/prediction_20250826_181932.json b/webui/prediction_results/prediction_20250826_181932.json
new file mode 100644
index 0000000..84d0d1d
--- /dev/null
+++ b/webui/prediction_results/prediction_20250826_181932.json
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+  "analysis": {
+    "continuity": {
+      "last_prediction": {
+        "open": 115116.90625,
+        "high": 115147.8359375,
+        "low": 114797.734375,
+        "close": 114761.0703125
+      },
+      "first_actual": {
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+        "high": 114138.42,
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+        "close": 114007.86
+      },
+      "gaps": {
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+        "high_gap": 1009.4159375000017,
+        "low_gap": 789.8743749999994,
+        "close_gap": 753.2103124999994
+      },
+      "gap_percentages": {
+        "open_gap_pct": 0.9088582774486117,
+        "high_gap_pct": 0.8843787547611065,
+        "low_gap_pct": 0.6928244903465423,
+        "close_gap_pct": 0.6606652493082489
+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/webui/run.py b/webui/run.py
index 6638ddb..64a7b16 100644
--- a/webui/run.py
+++ b/webui/run.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Kronos Web UI 启动脚本
+Kronos Web UI startup script
 """
 
 import os
@@ -10,80 +10,80 @@ import webbrowser
 import time
 
 def check_dependencies():
-    """检查依赖是否安装"""
+    """Check if dependencies are installed"""
     try:
         import flask
         import flask_cors
         import pandas
         import numpy
         import plotly
-        print("✅ 所有依赖已安装")
+        print("✅ All dependencies installed")
         return True
     except ImportError as e:
-        print(f"❌ 缺少依赖: {e}")
-        print("请运行: pip install -r requirements.txt")
+        print(f"❌ Missing dependency: {e}")
+        print("Please run: pip install -r requirements.txt")
         return False
 
 def install_dependencies():
-    """安装依赖"""
-    print("正在安装依赖...")
+    """Install dependencies"""
+    print("Installing dependencies...")
     try:
         subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"])
-        print("✅ 依赖安装完成")
+        print("✅ Dependencies installation completed")
         return True
     except subprocess.CalledProcessError:
-        print("❌ 依赖安装失败")
+        print("❌ Dependencies installation failed")
         return False
 
 def main():
-    """主函数"""
-    print("🚀 启动 Kronos Web UI...")
+    """Main function"""
+    print("🚀 Starting Kronos Web UI...")
     print("=" * 50)
     
-    # 检查依赖
+    # Check dependencies
     if not check_dependencies():
-        print("\n是否自动安装依赖? (y/n): ", end="")
+        print("\nAuto-install dependencies? (y/n): ", end="")
         if input().lower() == 'y':
             if not install_dependencies():
                 return
         else:
-            print("请手动安装依赖后重试")
+            print("Please manually install dependencies and retry")
             return
     
-    # 检查模型可用性
+    # Check model availability
     try:
         sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
         from model import Kronos, KronosTokenizer, KronosPredictor
-        print("✅ Kronos模型库可用")
+        print("✅ Kronos model library available")
         model_available = True
     except ImportError:
-        print("⚠️  Kronos模型库不可用，将使用模拟预测")
+        print("⚠️  Kronos model library not available, will use simulated prediction")
         model_available = False
     
-    # 启动Flask应用
-    print("\n🌐 启动Web服务器...")
+    # Start Flask application
+    print("\n🌐 Starting Web server...")
     
-    # 设置环境变量
+    # Set environment variables
     os.environ['FLASK_APP'] = 'app.py'
     os.environ['FLASK_ENV'] = 'development'
     
-    # 启动服务器
+    # Start server
     try:
         from app import app
-        print("✅ Web服务器启动成功!")
-        print(f"🌐 访问地址: http://localhost:7070")
-        print("💡 提示: 按 Ctrl+C 停止服务器")
+        print("✅ Web server started successfully!")
+        print(f"🌐 Access URL: http://localhost:7070")
+        print("💡 Tip: Press Ctrl+C to stop server")
         
-        # 自动打开浏览器
+        # Auto-open browser
         time.sleep(2)
         webbrowser.open('http://localhost:7070')
         
-        # 启动Flask应用
+        # Start Flask application
         app.run(debug=True, host='0.0.0.0', port=7070)
         
     except Exception as e:
-        print(f"❌ 启动失败: {e}")
-        print("请检查端口7070是否被占用")
+        print(f"❌ Startup failed: {e}")
+        print("Please check if port 7070 is occupied")
 
 if __name__ == "__main__":
     main()
diff --git a/webui/start.sh b/webui/start.sh
index 2174dc8..0d832b3 100755
--- a/webui/start.sh
+++ b/webui/start.sh
@@ -1,40 +1,40 @@
 #!/bin/bash
 
-# Kronos Web UI 启动脚本
+# Kronos Web UI startup script
 
-echo "🚀 启动 Kronos Web UI..."
+echo "🚀 Starting Kronos Web UI..."
 echo "================================"
 
-# 检查Python是否安装
+# Check if Python is installed
 if ! command -v python3 &> /dev/null; then
-    echo "❌ Python3 未安装，请先安装Python3"
+    echo "❌ Python3 not installed, please install Python3 first"
     exit 1
 fi
 
-# 检查是否在正确的目录
+# Check if in correct directory
 if [ ! -f "app.py" ]; then
-    echo "❌ 请在webui目录下运行此脚本"
+    echo "❌ Please run this script in the webui directory"
     exit 1
 fi
 
-# 检查依赖
-echo "📦 检查依赖..."
+# Check dependencies
+echo "📦 Checking dependencies..."
 if ! python3 -c "import flask, flask_cors, pandas, numpy, plotly" &> /dev/null; then
-    echo "⚠️  缺少依赖，正在安装..."
+    echo "⚠️  Missing dependencies, installing..."
     pip3 install -r requirements.txt
     if [ $? -ne 0 ]; then
-        echo "❌ 依赖安装失败"
+        echo "❌ Dependencies installation failed"
         exit 1
     fi
-    echo "✅ 依赖安装完成"
+    echo "✅ Dependencies installation completed"
 else
-    echo "✅ 所有依赖已安装"
+    echo "✅ All dependencies installed"
 fi
 
-# 启动应用
-echo "🌐 启动Web服务器..."
-echo "访问地址: http://localhost:7070"
-echo "按 Ctrl+C 停止服务器"
+# Start application
+echo "🌐 Starting Web server..."
+echo "Access URL: http://localhost:7070"
+echo "Press Ctrl+C to stop server"
 echo ""
 
 python3 app.py
diff --git a/webui/templates/index.html b/webui/templates/index.html
index bc321b5..dd24a49 100644
--- a/webui/templates/index.html
+++ b/webui/templates/index.html
@@ -3,7 +3,7 @@
 <head>
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Kronos 金融预测 Web UI</title>
+    <title>Kronos Financial Prediction Web UI</title>
     <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
     <script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
     <style>
@@ -93,7 +93,7 @@
             border-color: #667eea;
         }
 
-        /* 预测质量参数样式 */
+        /* Prediction quality parameter styles */
         .form-group input[type="range"] {
             width: 70%;
             margin-right: 10px;
@@ -227,7 +227,7 @@
             color: #2d3748;
         }
 
-        /* 时间窗口选择器样式 */
+        /* Time window selector styles */
         .time-window-container {
             background: #f7fafc;
             border: 1px solid #e2e8f0;
@@ -301,7 +301,7 @@
             margin-top: 5px;
         }
 
-        /* 对比分析样式 */
+        /* Comparison analysis styles */
         .comparison-section {
             background: #f7fafc;
             border: 1px solid #e2e8f0;
@@ -441,79 +441,79 @@
 <body>
     <div class="container">
         <div class="header">
-            <h1>🚀 Kronos 金融预测 Web UI</h1>
-            <p>基于AI的金融K线数据预测分析平台</p>
+            <h1>🚀 Kronos Financial Prediction Web UI</h1>
+            <p>AI-based financial K-line data prediction analysis platform</p>
         </div>
 
         <div class="main-content">
             <div class="control-panel">
-                <h2>🎯 控制面板</h2>
+                <h2>🎯 Control Panel</h2>
 
-                <!-- 模型选择 -->
+                <!-- Model Selection -->
                 <div class="form-group">
-                    <label for="model-select">选择模型:</label>
+                    <label for="model-select">Select Model:</label>
                     <select id="model-select">
-                        <option value="">请先加载可用模型</option>
+                        <option value="">Please load available models first</option>
                     </select>
-                    <small class="form-text">选择要使用的Kronos模型</small>
+                    <small class="form-text">Select the Kronos model to use</small>
                 </div>
 
-                <!-- 设备选择 -->
+                <!-- Device Selection -->
                 <div class="form-group">
-                    <label for="device-select">选择设备:</label>
+                    <label for="device-select">Select Device:</label>
                     <select id="device-select">
                         <option value="cpu">CPU</option>
                         <option value="cuda">CUDA (NVIDIA GPU)</option>
                         <option value="mps">MPS (Apple Silicon)</option>
                     </select>
-                    <small class="form-text">选择模型运行的设备</small>
+                    <small class="form-text">Select the device to run the model on</small>
                 </div>
 
-                <!-- 模型状态 -->
+                <!-- Model Status -->
                 <div id="model-status" class="status info" style="display: none;">
-                    模型状态信息
+                    Model status information
                 </div>
 
-                <!-- 加载模型按钮 -->
+                <!-- Load Model Button -->
                 <button id="load-model-btn" class="btn btn-secondary">
-                    🔄 加载模型
+                    🔄 Load Model
                 </button>
 
                 <hr style="margin: 20px 0; border: 1px solid #e2e8f0;">
 
-                <!-- 数据文件选择 -->
+                <!-- Data File Selection -->
                 <div class="form-group">
-                    <label for="data-file-select">选择数据文件:</label>
+                    <label for="data-file-select">Select Data File:</label>
                     <select id="data-file-select">
-                        <option value="">请先加载数据文件列表</option>
+                        <option value="">Please load data file list first</option>
                     </select>
-                    <small class="form-text">从data目录选择K线数据文件</small>
+                    <small class="form-text">Select K-line data file from data directory</small>
                 </div>
 
                 <button id="load-data-btn" class="btn btn-secondary">
-                    📁 加载数据
+                    📁 Load Data
                 </button>
 
-                <!-- 数据信息显示 -->
+                <!-- Data Information Display -->
                 <div id="data-info" class="data-info" style="display: none;">
-                    <h3>📊 数据信息</h3>
-                    <p><strong>行数:</strong> <span id="data-rows">-</span></p>
-                    <p><strong>列数:</strong> <span id="data-cols">-</span></p>
-                    <p><strong>时间范围:</strong> <span id="data-time-range">-</span></p>
-                    <p><strong>价格范围:</strong> <span id="data-price-range">-</span></p>
-                    <p><strong>时间频率:</strong> <span id="data-timeframe">-</span></p>
-                    <p><strong>预测列:</strong> <span id="data-prediction-cols">-</span></p>
+                    <h3>📊 Data Information</h3>
+                    <p><strong>Rows:</strong> <span id="data-rows">-</span></p>
+                    <p><strong>Columns:</strong> <span id="data-cols">-</span></p>
+                    <p><strong>Time Range:</strong> <span id="data-time-range">-</span></p>
+                    <p><strong>Price Range:</strong> <span id="data-price-range">-</span></p>
+                    <p><strong>Time Frequency:</strong> <span id="data-timeframe">-</span></p>
+                    <p><strong>Prediction Columns:</strong> <span id="data-prediction-cols">-</span></p>
                 </div>
 
                 <hr style="margin: 20px 0; border: 1px solid #e2e8f0;">
 
-                <!-- 时间窗口选择器 -->
+                <!-- Time Window Selector -->
                 <div class="time-window-container">
-                    <h3>⏰ 时间窗口选择</h3>
+                    <h3>⏰ Time Window Selection</h3>
                     <div class="time-window-info">
-                        <span id="window-start">开始: --</span>
-                        <span id="window-end">结束: --</span>
-                        <span id="window-size">窗口大小: 400+120=520个数据点</span>
+                        <span id="window-start">Start: --</span>
+                        <span id="window-end">End: --</span>
+                        <span id="window-size">Window Size: 400+120=520 data points</span>
                     </div>
                     
                     <div class="time-window-slider">
@@ -523,104 +523,104 @@
                             <div class="slider-handle end-handle" id="end-handle"></div>
                         </div>
                         <div class="slider-labels">
-                            <span id="min-label">最早</span>
-                            <span id="max-label">最新</span>
+                            <span id="min-label">Earliest</span>
+                            <span id="max-label">Latest</span>
                         </div>
                     </div>
                     
-                    <small class="form-text">拖动滑条选择520个数据点的时间窗口位置，绿色区域表示固定的400+120数据点范围</small>
+                    <small class="form-text">Drag slider to select time window position for 520 data points, green area represents fixed 400+120 data point range</small>
                 </div>
 
-                <!-- 预测参数 -->
+                <!-- Prediction Parameters -->
                 <div class="form-group">
-                    <label for="lookback">回看窗口大小:</label>
+                    <label for="lookback">Lookback Window Size:</label>
                     <input type="number" id="lookback" value="400" readonly>
-                    <small class="form-text">固定为400个数据点</small>
+                    <small class="form-text">Fixed at 400 data points</small>
                 </div>
 
                 <div class="form-group">
-                    <label for="pred-len">预测长度:</label>
+                    <label for="pred-len">Prediction Length:</label>
                     <input type="number" id="pred-len" value="120" readonly>
-                    <small class="form-text">固定为120个数据点</small>
+                    <small class="form-text">Fixed at 120 data points</small>
                 </div>
 
-                <!-- 预测质量参数 -->
+                <!-- Prediction Quality Parameters -->
                 <div class="form-group">
-                    <label for="temperature">预测温度 (T):</label>
+                    <label for="temperature">Prediction Temperature (T):</label>
                     <input type="range" id="temperature" value="1.0" min="0.1" max="2.0" step="0.1">
                     <span id="temperature-value">1.0</span>
-                    <small class="form-text">控制预测的随机性，值越高预测越多样化，值越低预测越保守</small>
+                    <small class="form-text">Controls prediction randomness, higher values make predictions more diverse, lower values make predictions more conservative</small>
                 </div>
 
                 <div class="form-group">
-                    <label for="top-p">核采样参数 (top_p):</label>
+                    <label for="top-p">Nucleus Sampling Parameter (top_p):</label>
                     <input type="range" id="top-p" value="0.9" min="0.1" max="1.0" step="0.1">
                     <span id="top-p-value">0.9</span>
-                    <small class="form-text">控制预测的多样性，值越高考虑的概率分布越广</small>
+                    <small class="form-text">Controls prediction diversity, higher values consider broader probability distributions</small>
                 </div>
 
                 <div class="form-group">
-                    <label for="sample-count">样本数量:</label>
+                    <label for="sample-count">Sample Count:</label>
                     <input type="number" id="sample-count" value="1" min="1" max="5" step="1">
-                    <small class="form-text">生成多个预测样本以提高质量（建议1-3个）</small>
+                    <small class="form-text">Generate multiple prediction samples to improve quality (recommended 1-3)</small>
                 </div>
 
                 <button id="predict-btn" class="btn btn-success" disabled>
-                    🔮 开始预测
+                    🔮 Start Prediction
                 </button>
 
-                <!-- 加载状态 -->
+                <!-- Loading Status -->
                 <div id="loading" class="loading">
                     <div class="spinner"></div>
-                    <p>正在处理，请稍候...</p>
+                    <p>Processing, please wait...</p>
                 </div>
             </div>
 
             <div class="chart-container">
-                <h2>📈 预测结果图表</h2>
+                <h2>📈 Prediction Results Chart</h2>
                 <div id="chart"></div>
                 
-                <!-- 对比分析 -->
+                <!-- Comparison Analysis -->
                 <div id="comparison-section" class="comparison-section" style="display: none;">
-                    <h3>📊 预测 vs 实际数据对比</h3>
+                    <h3>📊 Prediction vs Actual Data Comparison</h3>
                     <div id="comparison-info" class="comparison-info">
-                        <p><strong>预测类型:</strong> <span id="prediction-type">-</span></p>
-                        <p><strong>对比数据:</strong> <span id="comparison-data">-</span></p>
+                        <p><strong>Prediction Type:</strong> <span id="prediction-type">-</span></p>
+                        <p><strong>Comparison Data:</strong> <span id="comparison-data">-</span></p>
                     </div>
                     
                     <div class="error-stats">
                         <div class="error-stat">
-                            <h4>平均绝对误差</h4>
+                            <h4>Mean Absolute Error</h4>
                             <div class="value" id="mae">-</div>
-                            <div class="unit">价格单位</div>
+                            <div class="unit">Price Units</div>
                         </div>
                         <div class="error-stat">
-                            <h4>均方根误差</h4>
+                            <h4>Root Mean Square Error</h4>
                             <div class="value" id="rmse">-</div>
-                            <div class="unit">价格单位</div>
+                            <div class="unit">Price Units</div>
                         </div>
                         <div class="error-stat">
-                            <h4>平均绝对百分比误差</h4>
+                            <h4>Mean Absolute Percentage Error</h4>
                             <div class="value" id="mape">-</div>
                             <div class="unit">%</div>
                         </div>
                     </div>
                     
                     <div class="error-details">
-                        <h4>详细对比数据:</h4>
+                        <h4>Detailed Comparison Data:</h4>
                         <div style="max-height: 300px; overflow-y: auto;">
                             <table class="comparison-table">
                                 <thead>
                                     <tr>
-                                        <th>时间</th>
-                                        <th>实际开盘</th>
-                                        <th>预测开盘</th>
-                                        <th>实际最高</th>
-                                        <th>预测最高</th>
-                                        <th>实际最低</th>
-                                        <th>预测最低</th>
-                                        <th>实际收盘</th>
-                                        <th>预测收盘</th>
+                                        <th>Time</th>
+                                        <th>Actual Open</th>
+                                        <th>Predicted Open</th>
+                                        <th>Actual High</th>
+                                        <th>Predicted High</th>
+                                        <th>Actual Low</th>
+                                        <th>Predicted Low</th>
+                                        <th>Actual Close</th>
+                                        <th>Predicted Close</th>
                                     </tr>
                                 </thead>
                                 <tbody id="comparison-tbody">
@@ -634,58 +634,58 @@
     </div>
 
     <script>
-        // 全局变量
+        // Global variables
         let currentDataFile = null;
         let currentDataInfo = null;
         let availableModels = [];
         let modelLoaded = false;
 
-        // 页面加载完成后初始化
+        // Initialize after page loads
         document.addEventListener('DOMContentLoaded', function() {
             initializeApp();
         });
 
-        // 初始化应用
+        // Initialize application
         async function initializeApp() {
-            console.log('🚀 初始化 Kronos Web UI...');
+            console.log('🚀 Initializing Kronos Web UI...');
             
-            // 加载可用模型
+            // Load available models
             await loadAvailableModels();
             
-            // 加载数据文件列表
+            // Load data file list
             await loadDataFiles();
             
-            // 设置事件监听器
+            // Set up event listeners
             setupEventListeners();
             
-            // 初始化时间滑块
+            // Initialize time slider
             initializeTimeSlider();
             
-            console.log('✅ 应用初始化完成');
+            console.log('✅ Application initialization completed');
         }
 
-        // 加载可用模型
+        // Load available models
         async function loadAvailableModels() {
             try {
                 const response = await axios.get('/api/available-models');
                 if (response.data.model_available) {
                     availableModels = response.data.models;
                     populateModelSelect();
-                    console.log('✅ 可用模型加载成功:', availableModels);
+                    console.log('✅ Available models loaded successfully:', availableModels);
                 } else {
-                    console.warn('⚠️ Kronos模型库不可用');
-                    showStatus('warning', 'Kronos模型库不可用，将使用模拟预测');
+                    console.warn('⚠️ Kronos model library not available');
+                    showStatus('warning', 'Kronos model library not available, will use simulated prediction');
                 }
             } catch (error) {
-                console.error('❌ 加载可用模型失败:', error);
-                showStatus('error', '加载可用模型失败');
+                console.error('❌ Failed to load available models:', error);
+                showStatus('error', 'Failed to load available models');
             }
         }
 
-        // 填充模型选择下拉框
+        // Populate model selection dropdown
         function populateModelSelect() {
             const modelSelect = document.getElementById('model-select');
-            modelSelect.innerHTML = '<option value="">请选择模型</option>';
+            modelSelect.innerHTML = '<option value="">Please select model</option>';
             
             Object.entries(availableModels).forEach(([key, model]) => {
                 const option = document.createElement('option');
@@ -695,13 +695,13 @@
             });
         }
 
-        // 加载模型
+        // Load model
         async function loadModel() {
             const modelKey = document.getElementById('model-select').value;
             const device = document.getElementById('device-select').value;
             
             if (!modelKey) {
-                showStatus('error', '请选择要加载的模型');
+                showStatus('error', 'Please select a model to load');
                 return;
             }
             
@@ -719,45 +719,45 @@
                     showStatus('success', response.data.message);
                     updateModelStatus();
                     document.getElementById('predict-btn').disabled = false;
-                    console.log('✅ 模型加载成功:', response.data.model_info);
+                    console.log('✅ Model loaded successfully:', response.data.model_info);
                 } else {
                     showStatus('error', response.data.error);
                 }
             } catch (error) {
-                console.error('❌ 模型加载失败:', error);
-                showStatus('error', `模型加载失败: ${error.response?.data?.error || error.message}`);
+                console.error('❌ Model loading failed:', error);
+                showStatus('error', `Model loading failed: ${error.response?.data?.error || error.message}`);
             } finally {
                 showLoading(false);
                 document.getElementById('load-model-btn').disabled = false;
             }
         }
 
-        // 更新模型状态
+        // Update model status
         async function updateModelStatus() {
             try {
                 const response = await axios.get('/api/model-status');
                 const status = response.data;
                 
                 if (status.loaded) {
-                    showStatus('success', `模型已加载: ${status.current_model.name} on ${status.current_model.device}`);
+                    showStatus('success', `Model loaded: ${status.current_model.name} on ${status.current_model.device}`);
                 } else if (status.available) {
-                    showStatus('info', '模型可用但未加载');
+                    showStatus('info', 'Model available but not loaded');
                 } else {
-                    showStatus('warning', '模型库不可用');
+                    showStatus('warning', 'Model library not available');
                 }
             } catch (error) {
-                console.error('❌ 获取模型状态失败:', error);
+                console.error('❌ Failed to get model status:', error);
             }
         }
 
-        // 加载数据文件列表
+        // Load data file list
         async function loadDataFiles() {
             try {
                 const response = await axios.get('/api/data-files');
                 const dataFiles = response.data;
                 
                 const dataFileSelect = document.getElementById('data-file-select');
-                dataFileSelect.innerHTML = '<option value="">请选择数据文件</option>';
+                dataFileSelect.innerHTML = '<option value="">Please select data file</option>';
                 
                 dataFiles.forEach(file => {
                     const option = document.createElement('option');
@@ -766,19 +766,19 @@
                     dataFileSelect.appendChild(option);
                 });
                 
-                console.log('✅ 数据文件列表加载成功:', dataFiles);
+                console.log('✅ Data file list loaded successfully:', dataFiles);
             } catch (error) {
-                console.error('❌ 加载数据文件列表失败:', error);
-                showStatus('error', '加载数据文件列表失败');
+                console.error('❌ Failed to load data file list:', error);
+                showStatus('error', 'Failed to load data file list');
             }
         }
 
-        // 加载数据文件
+        // Load data file
         async function loadData() {
             const filePath = document.getElementById('data-file-select').value;
             
             if (!filePath) {
-                showStatus('error', '请选择要加载的数据文件');
+                showStatus('error', 'Please select a data file to load');
                 return;
             }
             
@@ -796,56 +796,56 @@
                     showDataInfo(response.data.data_info);
                     showStatus('success', response.data.message);
                     
-                    // 更新预测按钮状态
+                    // Update prediction button status
                     if (modelLoaded) {
                         document.getElementById('predict-btn').disabled = false;
                     }
                     
-                    console.log('✅ 数据加载成功:', response.data.data_info);
+                    console.log('✅ Data loaded successfully:', response.data.data_info);
                 } else {
                     showStatus('error', response.data.error);
                 }
             } catch (error) {
-                console.error('❌ 数据加载失败:', error);
-                showStatus('error', `数据加载失败: ${error.response?.data?.error || error.message}`);
+                console.error('❌ Data loading failed:', error);
+                showStatus('error', `Data loading failed: ${error.response?.data?.error || error.message}`);
             } finally {
                 showLoading(false);
                 document.getElementById('load-data-btn').disabled = false;
             }
         }
 
-        // 显示数据信息
+        // Display data information
         function showDataInfo(dataInfo) {
             document.getElementById('data-info').style.display = 'block';
             document.getElementById('data-rows').textContent = dataInfo.rows;
             document.getElementById('data-cols').textContent = dataInfo.columns.length;
-            document.getElementById('data-time-range').textContent = `${dataInfo.start_date} 至 ${dataInfo.end_date}`;
+            document.getElementById('data-time-range').textContent = `${dataInfo.start_date} to ${dataInfo.end_date}`;
             document.getElementById('data-price-range').textContent = `${dataInfo.price_range.min.toFixed(4)} - ${dataInfo.price_range.max.toFixed(4)}`;
             document.getElementById('data-timeframe').textContent = dataInfo.timeframe;
             document.getElementById('data-prediction-cols').textContent = dataInfo.prediction_columns.join(', ');
             
-            // 初始化时间窗口滑条
+            // Initialize time window slider
             initializeTimeWindowSlider(dataInfo);
         }
 
-        // 时间窗口滑条相关变量
+                    // Time window slider related variables
         let sliderData = null;
         let isDragging = false;
         let currentHandle = null;
 
-        // 初始化时间窗口滑条
+        // Initialize time window slider
         function initializeTimeSlider() {
-            // 设置滑条事件监听器
+            // Set up slider event listeners
             setupSliderEventListeners();
         }
 
-        // 设置滑条事件监听器
+        // Set up slider event listeners
         function setupSliderEventListeners() {
             const startHandle = document.getElementById('start-handle');
             const endHandle = document.getElementById('end-handle');
             const track = document.querySelector('.slider-track');
             
-            // 开始拖拽
+            // Start dragging
             startHandle.addEventListener('mousedown', (e) => {
                 isDragging = true;
                 currentHandle = 'start';
@@ -858,7 +858,7 @@
                 e.preventDefault();
             });
             
-            // 拖拽中
+            // Dragging
             document.addEventListener('mousemove', (e) => {
                 if (!isDragging) return;
                 
@@ -875,19 +875,19 @@
                 updateSliderFromHandles();
             });
             
-            // 结束拖拽
+            // End dragging
             document.addEventListener('mouseup', () => {
                 isDragging = false;
                 currentHandle = null;
             });
             
-            // 点击轨道直接设置位置
+            // Click track to set position directly
             track.addEventListener('click', (e) => {
                 const rect = track.getBoundingClientRect();
                 const x = e.clientX - rect.left;
                 const percentage = Math.max(0, Math.min(1, x / rect.width));
                 
-                // 判断点击位置更接近哪个手柄
+                // Determine which handle is closer to the click position
                 const startHandle = document.getElementById('start-handle');
                 const endHandle = document.getElementById('end-handle');
                 const startRect = startHandle.getBoundingClientRect();
@@ -903,17 +903,17 @@
             });
         }
 
-        // 更新开始手柄位置
+        // Update start handle position
         function updateStartHandle(percentage) {
             const startHandle = document.getElementById('start-handle');
             const selection = document.getElementById('slider-selection');
             
-            // 固定窗口大小为520个数据点
+            // Fixed window size of 520 data points
             const windowSize = 520;
             const totalRows = sliderData ? sliderData.totalRows : 1000;
             const windowPercentage = windowSize / totalRows;
             
-            // 确保开始手柄不会导致窗口超出数据范围
+            // Ensure start handle doesn't cause window to exceed data range
             if (percentage + windowPercentage > 1) {
                 percentage = 1 - windowPercentage;
             }
@@ -922,22 +922,22 @@
             selection.style.left = (percentage * 100) + '%';
             selection.style.width = (windowPercentage * 100) + '%';
             
-            // 自动调整结束手柄位置，保持固定窗口大小
+            // Automatically adjust end handle position to maintain fixed window size
             const endHandle = document.getElementById('end-handle');
             endHandle.style.left = ((percentage + windowPercentage) * 100) + '%';
         }
 
-        // 更新结束手柄位置
+        // Update end handle position
         function updateEndHandle(percentage) {
             const endHandle = document.getElementById('end-handle');
             const selection = document.getElementById('slider-selection');
             
-            // 固定窗口大小为520个数据点
+            // Fixed window size of 520 data points
             const windowSize = 520;
             const totalRows = sliderData ? sliderData.totalRows : 1000;
             const windowPercentage = windowSize / totalRows;
             
-            // 确保结束手柄不会导致窗口超出数据范围
+            // Ensure end handle doesn't cause window to exceed data range
             if (percentage - windowPercentage < 0) {
                 percentage = windowPercentage;
             }
@@ -946,12 +946,12 @@
             selection.style.left = ((percentage - windowPercentage) * 100) + '%';
             selection.style.width = (windowPercentage * 100) + '%';
             
-            // 自动调整开始手柄位置，保持固定窗口大小
+            // Automatically adjust start handle position to maintain fixed window size
             const startHandle = document.getElementById('start-handle');
             startHandle.style.left = ((percentage - windowPercentage) * 100) + '%';
         }
 
-        // 根据手柄位置更新滑条显示
+        // Update slider display based on handle positions
         function updateSliderFromHandles() {
             const startHandle = document.getElementById('start-handle');
             const endHandle = document.getElementById('end-handle');
@@ -961,7 +961,7 @@
             
             if (!sliderData) return;
             
-            // 计算选中的时间范围
+            // Calculate selected time range
             const totalTime = sliderData.endDate.getTime() - sliderData.startDate.getTime();
             const startTime = sliderData.startDate.getTime() + (totalTime * startPercentage);
             const endTime = sliderData.startDate.getTime() + (totalTime * endPercentage);
@@ -969,49 +969,49 @@
             const startDate = new Date(startTime);
             const endDate = new Date(endTime);
             
-            // 更新显示信息
-            document.getElementById('window-start').textContent = `开始: ${startDate.toLocaleDateString()}`;
-            document.getElementById('window-end').textContent = `结束: ${endDate.toLocaleDateString()}`;
+            // Update display information
+            document.getElementById('window-start').textContent = `Start: ${startDate.toLocaleDateString()}`;
+            document.getElementById('window-end').textContent = `End: ${endDate.toLocaleDateString()}`;
             
-            // 显示固定的窗口大小
-            document.getElementById('window-size').textContent = `窗口大小: 400 + 120 = 520 个数据点 (固定)`;
+            // Display fixed window size
+            document.getElementById('window-size').textContent = `Window Size: 400 + 120 = 520 data points (fixed)`;
             
-            // 输入框值保持固定
+            // Input field values remain fixed
             document.getElementById('lookback').value = 400;
             document.getElementById('pred-len').value = 120;
         }
 
-        // 根据输入框更新滑条
+        // Update slider based on input fields
         function updateSliderFromInputs() {
             if (!sliderData) return;
             
-            // 固定窗口大小：400 + 120 = 520个数据点
+            // Fixed window size: 400 + 120 = 520 data points
             const lookback = 400;
             const predLen = 120;
-            const windowSize = lookback + predLen; // 固定为520
+            const windowSize = lookback + predLen; // Fixed at 520
             
-            // 计算滑条位置
+            // Calculate slider position
             const totalRows = sliderData.totalRows;
             
             if (windowSize > totalRows) {
-                // 如果窗口大小超过总数据量，显示错误
-                showStatus('error', `数据量不足，需要至少${windowSize}个数据点，当前只有${totalRows}个`);
+                // If window size exceeds total data amount, show error
+                showStatus('error', `Insufficient data, need at least ${windowSize} data points, currently only ${totalRows} available`);
                 return;
             }
             
-            // 计算滑条位置（默认选择数据的前半部分）
-            const startPercentage = 0.1; // 从10%开始
+            // Calculate slider position (default select first half of data)
+            const startPercentage = 0.1; // Start from 10%
             const endPercentage = startPercentage + (windowSize / totalRows);
             
-            // 更新手柄位置
+            // Update handle positions
             updateStartHandle(startPercentage);
             updateEndHandle(endPercentage);
             
-            // 更新显示信息
+            // Update display information
             updateSliderFromHandles();
         }
 
-        // 初始化时间窗口滑条
+        // Initialize time window slider
         function initializeTimeWindowSlider(dataInfo) {
             sliderData = {
                 startDate: new Date(dataInfo.start_date),
@@ -1020,23 +1020,23 @@
                 timeframe: dataInfo.timeframe
             };
             
-            // 设置滑条标签
+            // Set slider labels
             document.getElementById('min-label').textContent = dataInfo.start_date.split('T')[0];
             document.getElementById('max-label').textContent = dataInfo.end_date.split('T')[0];
             
-            // 初始化滑条位置
+            // Initialize slider position
             updateSliderFromInputs();
         }
 
-        // 开始预测
+        // Start prediction
         async function startPrediction() {
             if (!currentDataFile) {
-                showStatus('error', '请先加载数据文件');
+                showStatus('error', 'Please load data file first');
                 return;
             }
             
             if (!modelLoaded) {
-                showStatus('error', '请先加载模型');
+                showStatus('error', 'Please load model first');
                 return;
             }
             
@@ -1047,21 +1047,21 @@
                 const lookback = parseInt(document.getElementById('lookback').value);
                 const predLen = parseInt(document.getElementById('pred-len').value);
                 
-                // 从时间窗口滑条获取选择的时间范围
+                // Get selected time range from time window slider
                 const startHandle = document.getElementById('start-handle');
                 const startPercentage = parseFloat(startHandle.style.left) / 100;
                 
                 if (!sliderData) {
-                    showStatus('error', '时间窗口滑条未初始化');
+                    showStatus('error', 'Time window slider not initialized');
                     return;
                 }
                 
-                // 计算选择的时间范围
+                // Calculate selected time range
                 const totalTime = sliderData.endDate.getTime() - sliderData.startDate.getTime();
                 const startTime = sliderData.startDate.getTime() + (totalTime * startPercentage);
                 const startDate = new Date(startTime);
                 
-                // 获取预测质量参数
+                // Get prediction quality parameters
                 const temperature = parseFloat(document.getElementById('temperature').value);
                 const topP = parseFloat(document.getElementById('top-p').value);
                 const sampleCount = parseInt(document.getElementById('sample-count').value);
@@ -1070,39 +1070,39 @@
                     file_path: currentDataFile,
                     lookback: lookback,
                     pred_len: predLen,
-                    start_date: startDate.toISOString().slice(0, 16), // 格式化为 YYYY-MM-DDTHH:MM
+                    start_date: startDate.toISOString().slice(0, 16), // Format as YYYY-MM-DDTHH:MM
                     temperature: temperature,
                     top_p: topP,
                     sample_count: sampleCount
                 };
                 
-                console.log('🚀 开始预测，参数:', predictionParams);
+                console.log('🚀 Starting prediction, parameters:', predictionParams);
                 
                 const response = await axios.post('/api/predict', predictionParams);
                 
                 if (response.data.success) {
-                    // 显示预测结果
+                    // Display prediction results
                     displayPredictionResult(response.data);
                     showStatus('success', response.data.message);
                 } else {
                     showStatus('error', response.data.error);
                 }
             } catch (error) {
-                console.error('❌ 预测失败:', error);
-                showStatus('error', `预测失败: ${error.response?.data?.error || error.message}`);
+                console.error('❌ Prediction failed:', error);
+                showStatus('error', `Prediction failed: ${error.response?.data?.error || error.message}`);
             } finally {
                 showLoading(false);
                 document.getElementById('predict-btn').disabled = false;
             }
         }
 
-        // 显示预测结果
+        // Display prediction results
         function displayPredictionResult(result) {
-            // 显示图表
+            // Display chart
             const chartData = JSON.parse(result.chart);
             Plotly.newPlot('chart', chartData.data, chartData.layout);
             
-            // 显示对比分析（如果有实际数据）
+            // Display comparison analysis (if actual data exists)
             if (result.has_comparison) {
                 displayComparisonAnalysis(result);
             } else {
@@ -1110,27 +1110,27 @@
             }
         }
 
-        // 显示对比分析
+        // Display comparison analysis
         function displayComparisonAnalysis(result) {
             document.getElementById('comparison-section').style.display = 'block';
             
-            // 更新对比信息
+            // Update comparison information
             document.getElementById('prediction-type').textContent = result.prediction_type;
-            document.getElementById('comparison-data').textContent = `${result.actual_data.length} 个实际数据点`;
+            document.getElementById('comparison-data').textContent = `${result.actual_data.length} actual data points`;
             
-            // 计算误差统计
+            // Calculate error statistics
             const errorStats = getPredictionQuality(result.prediction_results, result.actual_data);
             
-            // 显示误差统计
+            // Display error statistics
             document.getElementById('mae').textContent = errorStats.mae.toFixed(4);
             document.getElementById('rmse').textContent = errorStats.rmse.toFixed(4);
             document.getElementById('mape').textContent = errorStats.mape.toFixed(2);
             
-            // 填充对比表格
+            // Fill comparison table
             fillComparisonTable(result.prediction_results, result.actual_data);
         }
 
-        // 计算预测质量指标
+        // Calculate prediction quality metrics
         function getPredictionQuality(predictions, actuals) {
             if (!predictions || !actuals || predictions.length === 0 || actuals.length === 0) {
                 return { mae: 0, rmse: 0, mape: 0 };
@@ -1143,7 +1143,7 @@
                 const pred = predictions[i];
                 const act = actuals[i];
                 
-                // 使用收盘价计算误差
+                // Use closing price to calculate errors
                 const error = Math.abs(pred.close - act.close);
                 const percentError = (error / act.close) * 100;
                 
@@ -1159,7 +1159,7 @@
             return { mae, rmse, mape };
         }
 
-        // 填充对比表格
+                    // Fill comparison table
         function fillComparisonTable(predictions, actuals) {
             const tbody = document.getElementById('comparison-tbody');
             tbody.innerHTML = '';
@@ -1186,18 +1186,18 @@
             }
         }
 
-        // 设置事件监听器
+        // Set up event listeners
         function setupEventListeners() {
-            // 加载模型按钮
+            // Load model button
             document.getElementById('load-model-btn').addEventListener('click', loadModel);
             
-            // 加载数据按钮
+            // Load data button
             document.getElementById('load-data-btn').addEventListener('click', loadData);
             
-            // 预测按钮
+            // Prediction button
             document.getElementById('predict-btn').addEventListener('click', startPrediction);
             
-            // 预测质量参数滑块
+            // Prediction quality parameter sliders
             document.getElementById('temperature').addEventListener('input', function() {
                 document.getElementById('temperature-value').textContent = this.value;
             });
@@ -1206,25 +1206,25 @@
                 document.getElementById('top-p-value').textContent = this.value;
             });
             
-            // 回看窗口大小变化时更新滑条
+            // Update slider when lookback window size changes
             document.getElementById('lookback').addEventListener('input', updateSliderFromInputs);
             document.getElementById('pred-len').addEventListener('input', updateSliderFromInputs);
         }
 
-        // 显示状态信息
+        // Display status information
         function showStatus(type, message) {
             const statusDiv = document.getElementById('model-status');
             statusDiv.className = `status ${type}`;
             statusDiv.textContent = message;
             statusDiv.style.display = 'block';
             
-            // 自动隐藏
+            // Auto-hide
             setTimeout(() => {
                 statusDiv.style.display = 'none';
             }, 5000);
         }
 
-        // 显示/隐藏加载状态
+        // Show/hide loading status
         function showLoading(show) {
             const loadingDiv = document.getElementById('loading');
             if (show) {