250 lines
12 KiB
Python
250 lines
12 KiB
Python
import os
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import traceback
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from config_manager import get_config_manager
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from database_manager import DatabaseManager
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from logger_manager import get_logger
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class MoneyflowAnalyzer:
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"""
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市场资金流分析器，分析各类资金流向指标对行业在随后1-N天表现的影响
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"""
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def __init__(self):
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self.db_manager = DatabaseManager()
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self.config = get_config_manager()
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self.logger = get_logger()
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def main_flow_analyze(self, days_forward=10, use_consistent_samples=False, max_days=10):
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"""
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分析各类资金流向指标对行业在随后1-N天表现的影响
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参数:
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days_forward (int): 要分析的未来天数，默认为10天
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"""
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self.logger.info(f"开始分析资金流数据，未来{days_forward}天的表现...")
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self.logger.info(f"使用一致样本集: {use_consistent_samples}")
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# 读取资金流数据
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try:
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df = self.db_manager.load_df_from_db('moneyflow_ind_dc')
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self.logger.info(f"成功从数据库加载资金流数据，共计{len(df)}条记录")
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except Exception as e:
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self.logger.error(f"从数据库读取数据失败: {e}")
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self.logger.debug(f"完整的错误追踪信息:\n{traceback.format_exc()}")
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return False
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# 检查是否成功获取数据
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if df.empty:
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self.logger.info("获取的资金流数据为空，无法进行分析")
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return False
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# 数据预处理 - 计算主力资金
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df['main_force_amount'] = df['buy_elg_amount'] + df['buy_lg_amount']
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# 将日期格式转换为datetime
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df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d', errors='coerce')
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df = df[~df['trade_date'].isna()]
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# 按日期排序
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df = df.sort_values('trade_date')
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# 获取所有交易日期
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all_dates = df['trade_date'].unique()
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# 确定样本范围
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if use_consistent_samples:
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# 如果max_days大于days_forward，则预测天数使用max_days
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if max_days > days_forward:
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days_forward = max_days
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self.logger.info(f"max_days大于days_forward，使用max_days进行分析")
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# 使用固定的最大预测天数来确保所有分析使用相同的样本集
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sample_limit = len(all_dates) - max_days
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self.logger.info(f"使用统一样本集进行分析，样本量: {sample_limit}")
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else:
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# 使用当前分析所需的预测天数
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sample_limit = len(all_dates) - days_forward
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self.logger.info(f"使用动态样本集进行分析，样本量: {sample_limit}")
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# 定义要分析的资金流指标
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# 格式: (指标名, 排序方向, 关联性)
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# 关联性: 正相关=1, 负相关=-1 (用于确定是取最高还是最低)
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flow_indicators = [
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('main_force_amount', 1, '主力净额')
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]
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# 确保结果目录存在
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os.makedirs('result', exist_ok=True)
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# 为每个指标进行分析
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for indicator, correlation, indicator_name in flow_indicators:
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self.logger.info(f"分析 {indicator_name} 与未来指数关系...")
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# 创建结果数据结构
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results = []
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# 遍历每个交易日期（固定样本集大小）
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for i in range(sample_limit):
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current_date = all_dates[i]
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# 获取当前日期的数据
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current_day_data = df[df['trade_date'] == current_date]
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# 找出该指标排名靠前的行业
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if correlation > 0:
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# 正相关，找最高值
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top_sectors = current_day_data.sort_values(indicator, ascending=False).head(1)['name'].tolist()
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else:
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# 负相关，找最低值
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top_sectors = current_day_data.sort_values(indicator, ascending=True).head(1)['name'].tolist()
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# 分析每个行业在随后1-N天的表现
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for sector in top_sectors:
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# 获取该行业当天的指数变化和指标值
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sector_current = current_day_data[current_day_data['name'] == sector]
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if sector_current.empty:
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continue
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current_pct_change = sector_current['pct_change'].values[0]
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current_indicator_value = sector_current[indicator].values[0]
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# 分析随后1-N天的表现
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future_changes = []
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for day_offset in range(1, days_forward + 1):
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if i + day_offset < len(all_dates):
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future_date = all_dates[i + day_offset]
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future_data = df[(df['trade_date'] == future_date) & (df['name'] == sector)]
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if not future_data.empty:
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future_changes.append(future_data['pct_change'].values[0])
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else:
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future_changes.append(None)
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else:
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future_changes.append(None)
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# 如果至少有一个未来日期有数据
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if any(x is not None for x in future_changes):
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result_entry = {
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'date': current_date.strftime('%Y%m%d'), # 将日期格式化为YYYYMMDD字符串
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'sector': sector,
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f'{indicator}': current_indicator_value,
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'current_pct_change': current_pct_change,
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}
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# 添加1-N天的变化
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for day in range(1, days_forward + 1):
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result_entry[f'day{day}_change'] = future_changes[day - 1]
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# 计算平均变化
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result_entry[f'avg_{days_forward}day_change'] = np.nanmean(
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[x for x in future_changes if x is not None])
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results.append(result_entry)
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# 转换为DataFrame
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results_df = pd.DataFrame(results)
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if results_df.empty:
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self.logger.info(f"没有足够的数据来分析{indicator_name}与后续表现的关系")
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continue
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# 保存结果
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os.makedirs('result', exist_ok=True)
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output_file = f'result/{indicator}_performance_{days_forward}days.xlsx'
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results_df.to_excel(output_file, index=False)
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self.logger.info(f"{indicator_name}表现分析已保存至{output_file}")
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# 分析整体表现
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avg_performance = {}
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success_rates = {}
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for day in range(1, days_forward + 1):
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# 计算每一天的变化均值
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avg_performance[f'day{day}'] = results_df[f'day{day}_change'].mean()
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# 计算每一天的胜率（正收益的概率）
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success_rates[f'day{day}'] = (results_df[f'day{day}_change'] > 0).mean() * 100
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avg_performance[f'avg_{days_forward}day'] = results_df[f'avg_{days_forward}day_change'].mean()
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# 输出每天的表现数据
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self.logger.info(f"{indicator_name}极值行业在未来{days_forward}天的表现分析：")
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self.logger.info("日期\t\t平均收益率\t\t胜率")
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for day in range(1, days_forward + 1):
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self.logger.info(f"T+{day}\t\t{avg_performance[f'day{day}']:.5f}%\t\t{success_rates[f'day{day}']:.2f}%")
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self.logger.info(f"{days_forward}天平均: {avg_performance[f'avg_{days_forward}day']:.5f}%")
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# 绘制折线图显示未来N天的平均表现和胜率
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plt.figure(figsize=(14, 8))
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# 设置中文字体
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try:
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'WenQuanYi Micro Hei']
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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days_labels = ['当天'] + [f'第{i}天' for i in range(1, days_forward + 1)]
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direction = "最高" if correlation > 0 else "最低"
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title = f'{indicator_name}{direction}后的平均表现 ({days_forward}天)'
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xlabel = '时间'
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ylabel = '指数变化率 (%)'
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except:
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# 如果没有中文字体，使用英文
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days_labels = ['Current'] + [f'Day+{i}' for i in range(1, days_forward + 1)]
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direction = "Highest" if correlation > 0 else "Lowest"
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title = f'Average Performance After {indicator_name} {direction} ({days_forward} Days)'
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xlabel = 'Time'
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ylabel = 'Index Change Rate (%)'
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# 创建双y轴图表
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fig, ax1 = plt.subplots(figsize=(14, 8))
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# 设置收益率曲线 (左y轴)
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values = [results_df['current_pct_change'].mean()] + [avg_performance[f'day{i}'] for i in
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range(1, days_forward + 1)]
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ax1.plot(days_labels, values, marker='o', linewidth=2, color='blue', label='平均收益率')
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ax1.axhline(y=0, color='r', linestyle='--')
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ax1.set_xlabel(xlabel)
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ax1.set_ylabel('收益率 (%)', color='blue')
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ax1.tick_params(axis='y', labelcolor='blue')
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# 设置胜率曲线 (右y轴)
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ax2 = ax1.twinx()
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win_rates = [50] # 当天默认为50%
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for i in range(1, days_forward + 1):
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win_rates.append(success_rates[f'day{i}'])
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ax2.plot(days_labels, win_rates, marker='s', linewidth=2, color='green', label='胜率')
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ax2.set_ylabel('胜率 (%)', color='green')
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ax2.tick_params(axis='y', labelcolor='green')
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ax2.axhline(y=50, color='green', linestyle='--', alpha=0.5)
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# 添加图例
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(lines1 + lines2, labels1 + labels2, loc='best')
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plt.title(title, fontsize=14)
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plt.grid(True)
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plt.xticks(rotation=45) # 旋转x轴标签以避免重叠
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# 保存图表
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output_image = f'result/{indicator}_performance_{days_forward}days.png'
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plt.savefig(output_image, dpi=300, bbox_inches='tight')
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self.logger.info(f"{indicator_name}表现图表已保存至{output_image}")
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# 额外保存胜率图表
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plt.figure(figsize=(14, 8))
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plt.bar(range(1, days_forward + 1), [success_rates[f'day{i}'] for i in range(1, days_forward + 1)],
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color='green', alpha=0.7)
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plt.axhline(y=50, color='r', linestyle='--')
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plt.title(f'{indicator_name}{direction}后的未来胜率分布 ({days_forward}天)', fontsize=14)
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plt.xlabel('未来天数')
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plt.ylabel('胜率 (%)')
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plt.xticks(range(1, days_forward + 1), [f'T+{i}' for i in range(1, days_forward + 1)])
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plt.grid(True, axis='y')
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# 在柱状图上标注具体数值
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for i in range(1, days_forward + 1):
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plt.text(i, success_rates[f'day{i}'] + 1, f"{success_rates[f'day{i}']:.1f}%",
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ha='center', va='bottom', fontsize=9)
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win_rate_image = f'result/{indicator}_win_rate_{days_forward}days.png'
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plt.savefig(win_rate_image, dpi=300, bbox_inches='tight')
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self.logger.info(f"{indicator_name}胜率分布图已保存至{win_rate_image}")
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return True |