赚钱机器指标分析
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304
finance/money-machine-A.py
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304
finance/money-machine-A.py
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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from datetime import datetime
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try:
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from WindPy import w
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WIND_AVAILABLE = True
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# 启动Wind
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w.start()
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if not w.isconnected():
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WIND_AVAILABLE = False
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print("❌ 请先启动Wind金融终端")
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else:
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print("✅ Wind连接成功")
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except ImportError:
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WIND_AVAILABLE = False
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print("❌ 未安装WindPy库")
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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def get_ten_year_financial_data(symbol):
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"""
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使用ED-10Y参数获取十年财务数据
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"""
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if not WIND_AVAILABLE:
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print("Wind不可用")
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return None
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# 转换为Wind代码格式
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if symbol.startswith('6'):
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wind_code = f"{symbol}.SH"
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else:
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wind_code = f"{symbol}.SZ"
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try:
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end_date = datetime.now().strftime('%Y-%m-%d')
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print(f"📊 获取 {wind_code} 十年财务数据...")
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print(f"查询参数: ED-10Y 到 {end_date}")
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# 获取资本支出数据 - 使用您提供的参数格式
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print("获取资本支出数据...")
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capex_data = w.wsd(wind_code, "cash_pay_acq_const_fiolta", "ED-10Y", end_date, "unit=1;rptType=1;Period=Y;Days=Alldays")
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# 获取净利润数据 - 使用相同参数格式
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print("获取净利润数据...")
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profit_data = w.wsd(wind_code, "net_profit_is", "ED-10Y", end_date, "unit=1;rptType=1;Period=Y;Days=Alldays")
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print(f"资本支出数据点: {len(capex_data.Data[0]) if capex_data.Data else 0}")
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print(f"净利润数据点: {len(profit_data.Data[0]) if profit_data.Data else 0}")
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# 检查数据错误代码
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print(f"资本支出错误代码: {capex_data.ErrorCode}")
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print(f"净利润错误代码: {profit_data.ErrorCode}")
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# 创建包含所有数据的DataFrame
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data_list = []
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if capex_data.Data and profit_data.Data:
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# 确定数据长度
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data_length = min(len(capex_data.Times), len(capex_data.Data[0]), len(profit_data.Data[0]))
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print(f"有效数据长度: {data_length}")
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for i in range(data_length):
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date = capex_data.Times[i]
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capex_value = capex_data.Data[0][i]
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profit_value = profit_data.Data[0][i]
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# 计算比率（如果数据有效）- 改为百分比
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ratio = None
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is_capex_valid = capex_value is not None and not np.isnan(capex_value)
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is_profit_valid = profit_value is not None and not np.isnan(profit_value) and profit_value != 0
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if is_capex_valid and is_profit_valid:
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ratio = (abs(capex_value) / profit_value) * 100 # 乘以100转换为百分比
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data_list.append({
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'year': date.year,
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'report_date': date,
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'capital_expenditure': capex_value,
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'net_profit': profit_value,
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'capex_to_profit_ratio_pct': ratio, # 改为百分比
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'is_capex_valid': is_capex_valid,
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'is_profit_valid': is_profit_valid,
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'is_ratio_valid': ratio is not None
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})
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else:
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print("❌ 数据获取失败")
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return None
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df = pd.DataFrame(data_list)
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print(f"✅ 成功获取 {len(df)} 年数据")
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return df, capex_data, profit_data
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except Exception as e:
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print(f"❌ 数据获取失败: {e}")
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import traceback
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traceback.print_exc()
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return None, None, None
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def print_ten_year_analysis(df, symbol):
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"""打印十年数据分析"""
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print(f"\n{'='*80}")
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print(f"📊 {symbol} - 十年财务数据分析报告")
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print(f"{'='*80}")
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print(f"\n📈 数据完整性统计:")
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total_years = len(df)
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valid_capex = df['is_capex_valid'].sum()
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valid_profit = df['is_profit_valid'].sum()
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valid_ratio = df['is_ratio_valid'].sum()
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print(f"总年份数: {total_years}")
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print(f"有效资本支出年份: {valid_capex} ({valid_capex/total_years*100:.1f}%)")
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print(f"有效净利润年份: {valid_profit} ({valid_profit/total_years*100:.1f}%)")
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print(f"有效比率年份: {valid_ratio} ({valid_ratio/total_years*100:.1f}%)")
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print(f"\n🔍 详细年度数据:")
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for i, row in df.iterrows():
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year = row['year']
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capex_str = f"{row['capital_expenditure']/1e8:8.2f}亿" if row['is_capex_valid'] else " NaN"
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profit_str = f"{row['net_profit']/1e8:8.2f}亿" if row['is_profit_valid'] else " NaN"
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ratio_str = f"{row['capex_to_profit_ratio_pct']:6.2f}%" if row['is_ratio_valid'] else " NaN" # 改为百分比显示
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capex_status = "✅" if row['is_capex_valid'] else "❌"
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profit_status = "✅" if row['is_profit_valid'] else "❌"
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ratio_status = "✅" if row['is_ratio_valid'] else "❌"
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print(f" {year}年: 资本支出={capex_str} {capex_status}, "
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f"净利润={profit_str} {profit_status}, 比率={ratio_str} {ratio_status}")
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def analyze_valid_ten_year_data(df, symbol):
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"""分析十年有效数据"""
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valid_df = df[df['is_ratio_valid']].copy()
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if valid_df.empty:
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print(f"\n❌ {symbol} 无有效比率数据")
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return None
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print(f"\n📊 {symbol} - 有效数据分析 (共{len(valid_df)}年):")
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print("-" * 60)
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for i, row in valid_df.iterrows():
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year = row['year']
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capex = row['capital_expenditure'] / 1e8
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profit = row['net_profit'] / 1e8
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ratio = row['capex_to_profit_ratio_pct'] # 已经是百分比
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print(f" {year}年: 资本支出 {capex:6.2f}亿 / 净利润 {profit:6.2f}亿 = 比率 {ratio:.2f}%") # 改为百分比显示
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# 统计信息
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capex_mean = valid_df['capital_expenditure'].mean() / 1e8
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profit_mean = valid_df['net_profit'].mean() / 1e8
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ratio_mean = valid_df['capex_to_profit_ratio_pct'].mean()
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ratio_std = valid_df['capex_to_profit_ratio_pct'].std()
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ratio_min = valid_df['capex_to_profit_ratio_pct'].min()
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ratio_max = valid_df['capex_to_profit_ratio_pct'].max()
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print(f"\n💡 统计摘要:")
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print(f" 平均资本支出: {capex_mean:.2f} 亿元")
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print(f" 平均净利润: {profit_mean:.2f} 亿元")
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print(f" 平均比率: {ratio_mean:.2f}%") # 改为百分比显示
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print(f" 比率标准差: {ratio_std:.2f}%") # 改为百分比显示
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print(f" 比率范围: {ratio_min:.2f}% - {ratio_max:.2f}%") # 改为百分比显示
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return valid_df
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def plot_ten_year_analysis(valid_df, symbol):
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"""绘制十年分析图表"""
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if valid_df is None or valid_df.empty:
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print("❌ 无有效数据可绘制")
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return
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# 提取数据
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years = valid_df['year'].tolist()
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capex = valid_df['capital_expenditure'] / 1e8 # 转换为亿元
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profit = valid_df['net_profit'] / 1e8 # 转换为亿元
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ratios = valid_df['capex_to_profit_ratio_pct'] # 已经是百分比
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# 创建图表
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 15))
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x = np.arange(len(years))
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# 1. 资本支出和净利润对比
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bars1 = ax1.bar(x - 0.2, capex, 0.4, label='资本支出(亿元)', alpha=0.8, color='#FF6B6B')
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bars2 = ax1.bar(x + 0.2, profit, 0.4, label='净利润(亿元)', alpha=0.8, color='#4ECDC4')
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ax1.set_title(f'{symbol} - 十年资本支出 vs 净利润', fontsize=14, fontweight='bold')
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ax1.set_xticks(x)
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ax1.set_xticklabels(years, rotation=45)
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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# 添加数值标签
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for bar in bars1:
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height = bar.get_height()
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ax1.text(bar.get_x() + bar.get_width()/2., height, f'{height:.1f}',
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ha='center', va='bottom', fontsize=8)
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for bar in bars2:
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height = bar.get_height()
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ax1.text(bar.get_x() + bar.get_width()/2., height, f'{height:.0f}',
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ha='center', va='bottom', fontsize=8)
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# 2. 比率趋势图 - 改为百分比
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ax2.plot(x, ratios, 'o-', linewidth=3, markersize=8, color='#45B7D1',
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markerfacecolor='white', markeredgewidth=2)
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ax2.axhline(y=ratios.mean(), color='red', linestyle='--', alpha=0.7,
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label=f'平均比率: {ratios.mean():.2f}%') # 改为百分比
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ax2.set_title(f'{symbol} - 十年资本支出/净利润比率趋势', fontsize=14, fontweight='bold')
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ax2.set_xlabel('年份')
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ax2.set_ylabel('比率 (%)') # 改为百分比
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ax2.set_xticks(x)
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ax2.set_xticklabels(years, rotation=45)
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ax2.legend()
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ax2.grid(True, alpha=0.3)
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# 添加比率数值标签 - 改为百分比
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for i, (xi, ratio) in enumerate(zip(x, ratios)):
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ax2.annotate(f'{ratio:.2f}%', (xi, ratio), textcoords="offset points",
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xytext=(0,10), ha='center', fontsize=9, fontweight='bold')
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# 3. 比率柱状图 - 改为百分比
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colors = ['#FF9999' if ratio > ratios.mean() else '#99FF99' for ratio in ratios]
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bars3 = ax3.bar(x, ratios, color=colors, alpha=0.8, edgecolor='black', linewidth=0.5)
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ax3.axhline(y=ratios.mean(), color='red', linestyle='--', alpha=0.7,
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label=f'平均比率: {ratios.mean():.2f}%') # 改为百分比
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ax3.set_title(f'{symbol} - 十年资本支出/净利润比率', fontsize=14, fontweight='bold')
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ax3.set_xlabel('年份')
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ax3.set_ylabel('比率 (%)') # 改为百分比
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ax3.set_xticks(x)
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ax3.set_xticklabels(years, rotation=45)
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ax3.legend()
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ax3.grid(True, alpha=0.3)
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# 添加比率数值标签 - 改为百分比
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for i, (bar, ratio) in enumerate(zip(bars3, ratios)):
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ax3.text(bar.get_x() + bar.get_width()/2., ratio, f'{ratio:.2f}%',
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ha='center', va='bottom', fontsize=9, fontweight='bold')
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plt.tight_layout()
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plt.show()
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def print_financial_insights(valid_df, symbol):
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"""打印财务洞察"""
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if valid_df is None or valid_df.empty:
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return
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avg_ratio = valid_df['capex_to_profit_ratio_pct'].mean() # 已经是百分比
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print(f"\n💡 {symbol} 财务洞察:")
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print("=" * 50)
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if avg_ratio < 10: # 调整为百分比阈值
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insight = "公司资本支出相对净利润非常保守"
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explanation = "这表明公司可能处于成熟期，不需要大量资本投入来维持运营"
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elif avg_ratio < 30:
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insight = "公司资本支出相对净利润较为适中"
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explanation = "公司在维持现有业务的同时进行适度投资"
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elif avg_ratio < 50:
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insight = "公司资本支出相对净利润较高"
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explanation = "公司可能处于扩张期或进行重大投资项目"
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else:
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insight = "公司资本支出相对净利润非常高"
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explanation = "这可能表明公司正在进行大规模扩张或面临高资本需求"
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print(f"平均比率: {avg_ratio:.2f}%") # 改为百分比显示
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print(f"主要特征: {insight}")
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print(f"含义: {explanation}")
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# 使用示例
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if __name__ == "__main__":
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if WIND_AVAILABLE:
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# 测试股票
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symbol = "688981" # 贵州茅台
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print(f"🔍 开始分析 {symbol}...")
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# 获取十年数据
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df, capex_data, profit_data = get_ten_year_financial_data(symbol)
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if df is not None:
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# 打印十年数据分析
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print_ten_year_analysis(df, symbol)
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# 分析有效数据
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valid_df = analyze_valid_ten_year_data(df, symbol)
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# 绘制图表
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if valid_df is not None:
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plot_ten_year_analysis(valid_df, symbol)
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# 打印财务洞察
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print_financial_insights(valid_df, symbol)
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else:
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print("❌ 无法获取数据")
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else:
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print("❌ Wind不可用")
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