spread分组标修改方法

本文介绍如何在数据表格中实现自定义分组显示功能,通过编程技巧为每组添加个性化标签,提升用户体验。

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private static readonly string[] HEADER = { "编号", "姓名", "类型"};


private void fpdMember_Grouped(object sender, EventArgs e)

        {
            FarPoint.Win.Spread.FpSpread ss = (FarPoint.Win.Spread.FpSpread)sender;
            FarPoint.Win.Spread.Model.GroupDataModel gm;


            if (ss.ActiveSheet.Models.Data.GetType() == typeof(FarPoint.Win.Spread.Model.GroupDataModel))
            {
                
                gm = (FarPoint.Win.Spread.Model.GroupDataModel)ss.Sheets[0].Models.Data;
                string txt = string.Empty;
                object obj = null;
                FarPoint.Win.Spread.Model.Group g;
                for (int i = 0; i < gm.RowCount; i++)
                {
                    g = gm.GetGroup(i);
                    obj = gm.TargetModel.GetValue(getRow(g), g.Column);
                    txt = obj == null ? string.Empty : obj.ToString();
                    g.Text = string.Format("{0}:{1}", HEADER[g.Column], txt);
                }
            }
        }


        private int getRow(FarPoint.Win.Spread.Model.Group group)
         {
             if (group.Rows[0] is FarPoint.Win.Spread.Model.Group)
             {
                 return getRow(group.Rows[0] as FarPoint.Win.Spread.Model.Group);
             }
             return (int)group.Rows[0];
         }
# 安装必要库 !pip install yfinance pandas matplotlib mplfinance import yfinance as yf import pandas as pd # 下载WTI原油期货数据(代码CL=F) crude_oil = yf.download('CL=F', start='2020-01-01', end='2023-12-31') # 数据清洗示例 data = crude_oil[['Open', 'High', 'Low', 'Close', 'Volume']].dropna() print(data.head()) import yfinance as yf import pandas as pd # 下载WTI原油期货数据(代码CL=F) crude_oil = yf.download('CL=F', start='2020-01-01', end='2023-12-31') # 数据清洗示例 data = crude_oil[['Open', 'High', 'Low', 'Close', 'Volume']].dropna() print(data.head()) import matplotlib.pyplot as plt # 计算短期(20日)和长期(60日)均线 data['MA20'] = data['Close'].rolling(window=20).mean() data['MA60'] = data['Close'].rolling(window=60).mean() # 可视化 plt.figure(figsize=(12,6)) plt.plot(data['Close'], label='Price') plt.plot(data['MA20'], label='20-day MA') plt.plot(data['MA60'], label='60-day MA') plt.title('Crude Oil Price Trend Analysis') plt.legend() plt.show() # 计算日收益率和30日波动率 data['Returns'] = data['Close'].pct_change() data['Volatility'] = data['Returns'].rolling(window=30).std() * (252**0.5) # 年化波动率 brent = yf.download('BZ=F', start='2020-01-01', end='2023-12-31') spread = crude_oil['Close'] - brent['Close'] spread.plot(title='WTI-Brent Spread') # 按月份统计历史表现 monthly_returns = data.groupby(data.index.month)['Returns'].mean() monthly_returns.plot(kind='bar', title='Seasonality Pattern') 帮我完善这个代码并给出完整版
04-03
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