From e6de322fa232b6fa47ddc276c8c999f0f470852a Mon Sep 17 00:00:00 2001 From: bingyi Date: Sat, 15 Nov 2025 17:25:43 +0800 Subject: [PATCH] =?UTF-8?q?=E8=B5=9A=E9=92=B1=E6=9C=BA=E5=99=A8=E6=8C=87?= =?UTF-8?q?=E6=A0=87=E5=88=86=E6=9E=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- finance/money-machine-A.py | 304 ++++++++++++++++++++++++++++++++++++ gogogo/result_visualizer.py | 8 +- gogogo/strategy_executor.py | 166 ++++++++++++++++++-- 3 files changed, 460 insertions(+), 18 deletions(-) create mode 100644 finance/money-machine-A.py diff --git a/finance/money-machine-A.py b/finance/money-machine-A.py new file mode 100644 index 0000000..39bdd8f --- /dev/null +++ b/finance/money-machine-A.py @@ -0,0 +1,304 @@ +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +from datetime import datetime + +try: + from WindPy import w + WIND_AVAILABLE = True + # 启动Wind + w.start() + if not w.isconnected(): + WIND_AVAILABLE = False + print("❌ 请先启动Wind金融终端") + else: + print("✅ Wind连接成功") +except ImportError: + WIND_AVAILABLE = False + print("❌ 未安装WindPy库") + +# 设置中文字体 +plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans'] +plt.rcParams['axes.unicode_minus'] = False + +def get_ten_year_financial_data(symbol): + """ + 使用ED-10Y参数获取十年财务数据 + """ + if not WIND_AVAILABLE: + print("Wind不可用") + return None + + # 转换为Wind代码格式 + if symbol.startswith('6'): + wind_code = f"{symbol}.SH" + else: + wind_code = f"{symbol}.SZ" + + try: + end_date = datetime.now().strftime('%Y-%m-%d') + + print(f"📊 获取 {wind_code} 十年财务数据...") + print(f"查询参数: ED-10Y 到 {end_date}") + + # 获取资本支出数据 - 使用您提供的参数格式 + print("获取资本支出数据...") + capex_data = w.wsd(wind_code, "cash_pay_acq_const_fiolta", "ED-10Y", end_date, "unit=1;rptType=1;Period=Y;Days=Alldays") + + # 获取净利润数据 - 使用相同参数格式 + print("获取净利润数据...") + profit_data = w.wsd(wind_code, "net_profit_is", "ED-10Y", end_date, "unit=1;rptType=1;Period=Y;Days=Alldays") + + print(f"资本支出数据点: {len(capex_data.Data[0]) if capex_data.Data else 0}") + print(f"净利润数据点: {len(profit_data.Data[0]) if profit_data.Data else 0}") + + # 检查数据错误代码 + print(f"资本支出错误代码: {capex_data.ErrorCode}") + print(f"净利润错误代码: {profit_data.ErrorCode}") + + # 创建包含所有数据的DataFrame + data_list = [] + + if capex_data.Data and profit_data.Data: + # 确定数据长度 + data_length = min(len(capex_data.Times), len(capex_data.Data[0]), len(profit_data.Data[0])) + print(f"有效数据长度: {data_length}") + + for i in range(data_length): + date = capex_data.Times[i] + capex_value = capex_data.Data[0][i] + profit_value = profit_data.Data[0][i] + + # 计算比率（如果数据有效）- 改为百分比 + ratio = None + is_capex_valid = capex_value is not None and not np.isnan(capex_value) + is_profit_valid = profit_value is not None and not np.isnan(profit_value) and profit_value != 0 + + if is_capex_valid and is_profit_valid: + ratio = (abs(capex_value) / profit_value) * 100 # 乘以100转换为百分比 + + data_list.append({ + 'year': date.year, + 'report_date': date, + 'capital_expenditure': capex_value, + 'net_profit': profit_value, + 'capex_to_profit_ratio_pct': ratio, # 改为百分比 + 'is_capex_valid': is_capex_valid, + 'is_profit_valid': is_profit_valid, + 'is_ratio_valid': ratio is not None + }) + else: + print("❌ 数据获取失败") + return None + + df = pd.DataFrame(data_list) + + print(f"✅ 成功获取 {len(df)} 年数据") + return df, capex_data, profit_data + + except Exception as e: + print(f"❌ 数据获取失败: {e}") + import traceback + traceback.print_exc() + return None, None, None + +def print_ten_year_analysis(df, symbol): + """打印十年数据分析""" + print(f"\n{'='*80}") + print(f"📊 {symbol} - 十年财务数据分析报告") + print(f"{'='*80}") + + print(f"\n📈 数据完整性统计:") + total_years = len(df) + valid_capex = df['is_capex_valid'].sum() + valid_profit = df['is_profit_valid'].sum() + valid_ratio = df['is_ratio_valid'].sum() + + print(f"总年份数: {total_years}") + print(f"有效资本支出年份: {valid_capex} ({valid_capex/total_years*100:.1f}%)") + print(f"有效净利润年份: {valid_profit} ({valid_profit/total_years*100:.1f}%)") + print(f"有效比率年份: {valid_ratio} ({valid_ratio/total_years*100:.1f}%)") + + print(f"\n🔍 详细年度数据:") + for i, row in df.iterrows(): + year = row['year'] + capex_str = f"{row['capital_expenditure']/1e8:8.2f}亿" if row['is_capex_valid'] else " NaN" + profit_str = f"{row['net_profit']/1e8:8.2f}亿" if row['is_profit_valid'] else " NaN" + 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 + + capex_status = "✅" if row['is_capex_valid'] else "❌" + profit_status = "✅" if row['is_profit_valid'] else "❌" + ratio_status = "✅" if row['is_ratio_valid'] else "❌" + + print(f" {year}年: 资本支出={capex_str} {capex_status}, " + f"净利润={profit_str} {profit_status}, 比率={ratio_str} {ratio_status}") + +def analyze_valid_ten_year_data(df, symbol): + """分析十年有效数据""" + valid_df = df[df['is_ratio_valid']].copy() + + if valid_df.empty: + print(f"\n❌ {symbol} 无有效比率数据") + return None + + print(f"\n📊 {symbol} - 有效数据分析 (共{len(valid_df)}年):") + print("-" * 60) + + for i, row in valid_df.iterrows(): + year = row['year'] + capex = row['capital_expenditure'] / 1e8 + profit = row['net_profit'] / 1e8 + ratio = row['capex_to_profit_ratio_pct'] # 已经是百分比 + + 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 + + # 统计信息 + capex_mean = valid_df['capital_expenditure'].mean() / 1e8 + profit_mean = valid_df['net_profit'].mean() / 1e8 + ratio_mean = valid_df['capex_to_profit_ratio_pct'].mean() + ratio_std = valid_df['capex_to_profit_ratio_pct'].std() + ratio_min = valid_df['capex_to_profit_ratio_pct'].min() + ratio_max = valid_df['capex_to_profit_ratio_pct'].max() + + print(f"\n💡 统计摘要:") + print(f" 平均资本支出: {capex_mean:.2f} 亿元") + print(f" 平均净利润: {profit_mean:.2f} 亿元") + print(f" 平均比率: {ratio_mean:.2f}%") # 改为百分比显示 + print(f" 比率标准差: {ratio_std:.2f}%") # 改为百分比显示 + print(f" 比率范围: {ratio_min:.2f}% - {ratio_max:.2f}%") # 改为百分比显示 + + return valid_df + +def plot_ten_year_analysis(valid_df, symbol): + """绘制十年分析图表""" + if valid_df is None or valid_df.empty: + print("❌ 无有效数据可绘制") + return + + # 提取数据 + years = valid_df['year'].tolist() + capex = valid_df['capital_expenditure'] / 1e8 # 转换为亿元 + profit = valid_df['net_profit'] / 1e8 # 转换为亿元 + ratios = valid_df['capex_to_profit_ratio_pct'] # 已经是百分比 + + # 创建图表 + fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 15)) + + x = np.arange(len(years)) + + # 1. 资本支出和净利润对比 + bars1 = ax1.bar(x - 0.2, capex, 0.4, label='资本支出(亿元)', alpha=0.8, color='#FF6B6B') + bars2 = ax1.bar(x + 0.2, profit, 0.4, label='净利润(亿元)', alpha=0.8, color='#4ECDC4') + ax1.set_title(f'{symbol} - 十年资本支出 vs 净利润', fontsize=14, fontweight='bold') + ax1.set_xticks(x) + ax1.set_xticklabels(years, rotation=45) + ax1.legend() + ax1.grid(True, alpha=0.3) + + # 添加数值标签 + for bar in bars1: + height = bar.get_height() + ax1.text(bar.get_x() + bar.get_width()/2., height, f'{height:.1f}', + ha='center', va='bottom', fontsize=8) + + for bar in bars2: + height = bar.get_height() + ax1.text(bar.get_x() + bar.get_width()/2., height, f'{height:.0f}', + ha='center', va='bottom', fontsize=8) + + # 2. 比率趋势图 - 改为百分比 + ax2.plot(x, ratios, 'o-', linewidth=3, markersize=8, color='#45B7D1', + markerfacecolor='white', markeredgewidth=2) + ax2.axhline(y=ratios.mean(), color='red', linestyle='--', alpha=0.7, + label=f'平均比率: {ratios.mean():.2f}%') # 改为百分比 + ax2.set_title(f'{symbol} - 十年资本支出/净利润比率趋势', fontsize=14, fontweight='bold') + ax2.set_xlabel('年份') + ax2.set_ylabel('比率 (%)') # 改为百分比 + ax2.set_xticks(x) + ax2.set_xticklabels(years, rotation=45) + ax2.legend() + ax2.grid(True, alpha=0.3) + + # 添加比率数值标签 - 改为百分比 + for i, (xi, ratio) in enumerate(zip(x, ratios)): + ax2.annotate(f'{ratio:.2f}%', (xi, ratio), textcoords="offset points", + xytext=(0,10), ha='center', fontsize=9, fontweight='bold') + + # 3. 比率柱状图 - 改为百分比 + colors = ['#FF9999' if ratio > ratios.mean() else '#99FF99' for ratio in ratios] + bars3 = ax3.bar(x, ratios, color=colors, alpha=0.8, edgecolor='black', linewidth=0.5) + ax3.axhline(y=ratios.mean(), color='red', linestyle='--', alpha=0.7, + label=f'平均比率: {ratios.mean():.2f}%') # 改为百分比 + ax3.set_title(f'{symbol} - 十年资本支出/净利润比率', fontsize=14, fontweight='bold') + ax3.set_xlabel('年份') + ax3.set_ylabel('比率 (%)') # 改为百分比 + ax3.set_xticks(x) + ax3.set_xticklabels(years, rotation=45) + ax3.legend() + ax3.grid(True, alpha=0.3) + + # 添加比率数值标签 - 改为百分比 + for i, (bar, ratio) in enumerate(zip(bars3, ratios)): + ax3.text(bar.get_x() + bar.get_width()/2., ratio, f'{ratio:.2f}%', + ha='center', va='bottom', fontsize=9, fontweight='bold') + + plt.tight_layout() + plt.show() + +def print_financial_insights(valid_df, symbol): + """打印财务洞察""" + if valid_df is None or valid_df.empty: + return + + avg_ratio = valid_df['capex_to_profit_ratio_pct'].mean() # 已经是百分比 + + print(f"\n💡 {symbol} 财务洞察:") + print("=" * 50) + + if avg_ratio < 10: # 调整为百分比阈值 + insight = "公司资本支出相对净利润非常保守" + 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" + elif avg_ratio < 30: + insight = "公司资本支出相对净利润较为适中" + explanation = "公司在维持现有业务的同时进行适度投资" + elif avg_ratio < 50: + insight = "公司资本支出相对净利润较高" + explanation = "公司可能处于扩张期或进行重大投资项目" + else: + insight = "公司资本支出相对净利润非常高" + 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" + + print(f"平均比率: {avg_ratio:.2f}%") # 改为百分比显示 + print(f"主要特征: {insight}") + print(f"含义: {explanation}") + +# 使用示例 +if __name__ == "__main__": + if WIND_AVAILABLE: + # 测试股票 + symbol = "688981" # 贵州茅台 + + print(f"🔍 开始分析 {symbol}...") + + # 获取十年数据 + df, capex_data, profit_data = get_ten_year_financial_data(symbol) + + if df is not None: + # 打印十年数据分析 + print_ten_year_analysis(df, symbol) + + # 分析有效数据 + valid_df = analyze_valid_ten_year_data(df, symbol) + + # 绘制图表 + if valid_df is not None: + plot_ten_year_analysis(valid_df, symbol) + + # 打印财务洞察 + print_financial_insights(valid_df, symbol) + + else: + print("❌ 无法获取数据") + + else: + print("❌ Wind不可用") \ No newline at end of file diff --git a/gogogo/result_visualizer.py b/gogogo/result_visualizer.py index f9b223b..6220fa8 100644 --- a/gogogo/result_visualizer.py +++ b/gogogo/result_visualizer.py @@ -2,7 +2,7 @@ import vectorbt as vbt import pandas as pd import matplotlib.pyplot as plt -from strategy_executor import generate_strategy, create_portfolio, create_real_stock_portfolio +from strategy_executor import generate_strategy, create_portfolio, create_real_stock_portfolio, debug_signal_consistency, debug_signal_logic # 设置中文显示 plt.rcParams['font.sans-serif'] = ['SimHei'] @@ -372,12 +372,18 @@ def main(): # 生成策略 strategy_data = generate_strategy() + # 调试信号生成逻辑 + debug_signal_logic(strategy_data) + # 创建基于比率的投资组合（用于分析） ratio_portfolio = create_portfolio(strategy_data) # 创建基于真实股票的投资组合（用于真实收益计算） stock_portfolio = create_real_stock_portfolio(strategy_data) + # 调试信号一致性 + debug_signal_consistency(strategy_data, stock_portfolio) + # 打印基于比率的统计 print_statistics(strategy_data, ratio_portfolio) diff --git a/gogogo/strategy_executor.py b/gogogo/strategy_executor.py index e31ea12..7901373 100644 --- a/gogogo/strategy_executor.py +++ b/gogogo/strategy_executor.py @@ -7,7 +7,7 @@ from data_fetcher import get_processed_data def calculate_ratio_signals(ratio_series, window=20, num_std=2): """ 基于价格比率计算配对交易信号 - 优化信号逻辑，避免过于频繁的交易 + 修正信号逻辑：比率低时做多价差，比率高时做空价差 """ # 计算布林带 ratio_ma = ratio_series.rolling(window=window).mean() @@ -26,15 +26,22 @@ def calculate_ratio_signals(ratio_series, window=20, num_std=2): ratio_val = ratio_series.iloc[i] ma_val = ratio_ma.iloc[i] + upper_val = upper_band.iloc[i] + lower_val = lower_band.iloc[i] # 当前无仓位时的开仓条件 if current_signal == 0: - if ratio_val < lower_band.iloc[i]: # 比率突破下轨，做多价差 - signals.iloc[i] = 1 + # 比率突破下轨：SMIC相对低估，做多价差（买入SMIC，卖空HHIC） + if ratio_val < lower_val: + signals.iloc[i] = 1 # 做多价差 current_signal = 1 - elif ratio_val > upper_band.iloc[i]: # 比率突破上轨，做空价差 - signals.iloc[i] = -1 + print(f"开仓做多价差: {ratio_series.index[i]} - 比率{ratio_val:.4f} < 下轨{lower_val:.4f}") + + # 比率突破上轨：SMIC相对高估，做空价差（卖空SMIC，买入HHIC） + elif ratio_val > upper_val: + signals.iloc[i] = -1 # 做空价差 current_signal = -1 + print(f"开仓做空价差: {ratio_series.index[i]} - 比率{ratio_val:.4f} > 上轨{upper_val:.4f}") # 当前有仓位时的平仓条件 elif current_signal != 0: @@ -43,6 +50,7 @@ def calculate_ratio_signals(ratio_series, window=20, num_std=2): (current_signal == -1 and ratio_val <= ma_val): signals.iloc[i] = 0 current_signal = 0 + print(f"平仓: {ratio_series.index[i]} - 比率{ratio_val:.4f} 回归均线{ma_val:.4f}") return signals, ratio_ma, upper_band, lower_band @@ -50,10 +58,10 @@ def calculate_ratio_signals(ratio_series, window=20, num_std=2): def generate_ratio_size(signals, price_ratio, position_size=0.5): """ 生成比率交易的size数据 - 返回一个与price_ratio相同形状的Series，包含交易数量 + 修复数据类型问题 """ # 创建与price_ratio相同形状的size Series，初始为0 - size_series = pd.Series(0, index=signals.index, name='size') + size_series = pd.Series(0.0, index=signals.index, name='size') # 改为float类型 current_position = 0 for i in range(len(signals)): @@ -63,25 +71,24 @@ def generate_ratio_size(signals, price_ratio, position_size=0.5): signal = signals.iloc[i] if signal == 1 and current_position != 1: # 做多价差 -> 买入比率 - # 买入相当于做多中芯/做空华虹 - size_series.iloc[i] = position_size # 正数表示买入比率 + size_series.iloc[i] = float(position_size) # 明确转换为float current_position = 1 elif signal == -1 and current_position != -1: # 做空价差 -> 卖空比率 - # 卖空相当于做空中芯/做多华虹 - size_series.iloc[i] = -position_size # 负数表示卖空比率 + size_series.iloc[i] = -float(position_size) # 明确转换为float current_position = -1 elif signal == 0 and current_position != 0: # 平仓 - size_series.iloc[i] = 0 # 平仓 + size_series.iloc[i] = 0.0 # 明确转换为float current_position = 0 return size_series + def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, position_ratio=0.5): """ 生成真实股票交易的size数据 - 确保每次配对交易都是等市值对冲，避免使用杠杆 + 添加详细的调试信息来跟踪信号执行 """ # 创建空的size Series smic_size = pd.Series(0.0, index=signals.index, name='SMIC') @@ -91,29 +98,43 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p smic_position = 0.0 # 当前中芯持仓数量 hhic_position = 0.0 # 当前华虹持仓数量 + print(f"\n=== 交易执行调试信息 ===") + print(f"信号总数: {len(signals)}") + print(f"非零信号数量: {(signals != 0).sum()}") + print(f"做多信号: {(signals == 1).sum()}, 做空信号: {(signals == -1).sum()}, 平仓信号: {(signals == 0).sum()}") + for i in range(len(signals)): if i < 20: # 跳过布林带计算期 continue signal = signals.iloc[i] + date = signals.index[i] smic_price = close_smic.iloc[i] hhic_price = close_hhic.iloc[i] + # 只有信号变化时才执行交易 + if signal != 0 or (signal == 0 and current_position != 0): + print(f"\n日期: {date}, 信号: {signal}, 当前仓位: {current_position}") + # 平仓条件：信号为0且当前有仓位 if signal == 0 and current_position != 0: + print(f" 执行平仓: SMIC持仓{smic_position:.2f}, HHIC持仓{hhic_position:.2f}") + # 平掉所有仓位 if smic_position != 0: smic_size.iloc[i] = -smic_position smic_position = 0.0 + print(f" 平仓SMIC: {-smic_size.iloc[i]:.2f}股") if hhic_position != 0: hhic_size.iloc[i] = -hhic_position hhic_position = 0.0 + print(f" 平仓HHIC: {-hhic_size.iloc[i]:.2f}股") current_position = 0 continue # 开仓条件：只有当前无仓位时才开新仓 - if current_position == 0: + if current_position == 0 and signal != 0: if signal == 1: # 做多价差：买入中芯，卖空华虹 # 计算每只股票的仓位价值（等市值对冲） position_value = initial_cash * position_ratio @@ -129,7 +150,9 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p hhic_position = hhic_shares current_position = 1 - print(f"开仓做多价差: {signals.index[i]} - 买入SMIC {smic_shares:.2f}股 @{smic_price:.2f}, 卖空HHIC {abs(hhic_shares):.2f}股 @{hhic_price:.2f}") + print(f" 执行做多价差开仓:") + print(f" 买入SMIC: {smic_shares:.2f}股 @{smic_price:.2f}") + print(f" 卖空HHIC: {abs(hhic_shares):.2f}股 @{hhic_price:.2f}") elif signal == -1: # 做空价差：卖空中芯，买入华虹 # 计算每只股票的仓位价值（等市值对冲） @@ -146,16 +169,28 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p hhic_position = hhic_shares current_position = -1 - print(f"开仓做空价差: {signals.index[i]} - 卖空SMIC {abs(smic_shares):.2f}股 @{smic_price:.2f}, 买入HHIC {hhic_shares:.2f}股 @{hhic_price:.2f}") + print(f" 执行做空价差开仓:") + print(f" 卖空SMIC: {abs(smic_shares):.2f}股 @{smic_price:.2f}") + print(f" 买入HHIC: {hhic_shares:.2f}股 @{hhic_price:.2f}") # 最后检查是否有未平仓的仓位，如果有则在最后一天平仓 if current_position != 0: last_index = len(signals) - 1 + last_date = signals.index[last_index] + print(f"\n最终平仓: {last_date}") if smic_position != 0: smic_size.iloc[last_index] = -smic_position + print(f" 平仓SMIC: {-smic_size.iloc[last_index]:.2f}股") if hhic_position != 0: hhic_size.iloc[last_index] = -hhic_position - print(f"最终平仓: {signals.index[last_index]} - 平掉所有剩余仓位") + print(f" 平仓HHIC: {-hhic_size.iloc[last_index]:.2f}股") + + # 统计实际执行的交易 + smic_trades = (smic_size != 0).sum() + hhic_trades = (hhic_size != 0).sum() + print(f"\n实际执行交易统计:") + print(f" SMIC交易次数: {smic_trades}") + print(f" HHIC交易次数: {hhic_trades}") # 创建size DataFrame size_df = pd.DataFrame({ @@ -165,6 +200,103 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p return size_df +def debug_signal_logic(strategy_data): + """调试信号生成逻辑""" + print("\n" + "="*60) + print("=== 信号生成逻辑调试 ===") + print("="*60) + + signals = strategy_data['signals'] + price_ratio = strategy_data['price_ratio'] + ratio_ma = strategy_data['ratio_ma'] + upper_band = strategy_data['upper_band'] + lower_band = strategy_data['lower_band'] + + # 检查关键日期的信号逻辑 + key_dates = [ + '2024-12-20', '2024-12-23', '2025-02-18', '2025-02-19', + '2025-04-22', '2025-04-23', '2025-05-16', '2025-05-19' + ] + + for date_str in key_dates: + date = pd.Timestamp(date_str) + if date in signals.index: + signal = signals.loc[date] + ratio_val = price_ratio.loc[date] + ma_val = ratio_ma.loc[date] + upper_val = upper_band.loc[date] + lower_val = lower_band.loc[date] + + print(f"\n{date}:") + print(f" 比率: {ratio_val:.4f}, 均线: {ma_val:.4f}") + print(f" 上轨: {upper_val:.4f}, 下轨: {lower_val:.4f}") + print(f" 信号: {signal} ({'平仓' if signal == 0 else '做多' if signal == 1 else '做空'})") + + # 检查信号逻辑 + if signal == 1: + print(f" 逻辑: 比率{ratio_val:.4f} < 下轨{lower_val:.4f} = {ratio_val < lower_val}") + elif signal == -1: + print(f" 逻辑: 比率{ratio_val:.4f} > 上轨{upper_val:.4f} = {ratio_val > upper_val}") + elif signal == 0: + print(f" 逻辑: 平仓信号") + +def debug_signal_consistency(strategy_data, stock_portfolio): + """调试信号和交易的一致性""" + print("\n" + "="*60) + print("=== 信号与交易一致性调试 ===") + print("="*60) + + signals = strategy_data['signals'] + price_ratio = strategy_data['price_ratio'] + ratio_ma = strategy_data['ratio_ma'] + upper_band = strategy_data['upper_band'] + lower_band = strategy_data['lower_band'] + + # 获取订单记录 + orders_df = stock_portfolio.orders.records_readable + + print(f"\n信号数据:") + print(f"信号时间范围: {signals.index[0]} 到 {signals.index[-1]}") + print(f"信号总数: {len(signals)}") + + # 找出所有信号变化点 + signal_changes = signals[signals != signals.shift(1)] + print(f"\n信号变化点数量: {len(signal_changes)}") + + # 显示前10个信号变化点 + print(f"\n前10个信号变化点:") + for i, (date, signal) in enumerate(signal_changes.head(10).items()): + ratio_val = price_ratio.loc[date] + ma_val = ratio_ma.loc[date] + upper_val = upper_band.loc[date] + lower_val = lower_band.loc[date] + + signal_desc = "平仓" if signal == 0 else ("做多" if signal == 1 else "做空") + print(f" {date}: {signal_desc} (比率: {ratio_val:.4f}, 均线: {ma_val:.4f})") + + # 检查订单时间与信号时间的对应关系 + if 'Timestamp' in orders_df.columns: + order_dates = pd.to_datetime(orders_df['Timestamp']).unique() + print(f"\n订单执行日期: {len(order_dates)}个") + print("订单日期:", sorted(order_dates)) + + # 检查哪些信号日期有对应的订单 + signal_dates_with_orders = [] + signal_dates_without_orders = [] + + for date in signal_changes.index: + if date in order_dates: + signal_dates_with_orders.append(date) + else: + signal_dates_without_orders.append(date) + + print(f"\n有对应订单的信号日期: {len(signal_dates_with_orders)}") + print(f"无对应订单的信号日期: {len(signal_dates_without_orders)}") + + if signal_dates_without_orders: + print("缺失订单的信号日期:", signal_dates_without_orders[:5]) + + def generate_strategy(): """生成配对交易策略""" # 获取数据