Qihang Zhang
b652322061
✨ feat(main_force_strategy.py): 新增主力资金流向策略分析功能 🔧 refactor(utils.py): 增加数据库操作支持和辅助函数
313 lines
13 KiB
Python
313 lines
13 KiB
Python
import os
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import time
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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 tqdm import tqdm
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from utils import load_config, get_trade_cal
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from utils import save_df_to_db, load_df_from_db, get_existing_trade_dates
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# 加载配置并初始化tushare
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config = load_config()
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import tushare as ts
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import seaborn as sns
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ts.set_token(config['tushare_token'])
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pro = ts.pro_api()
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def get_sector_moneyflow_data(start_date=None, end_date=None):
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"""
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获取指定时间段内的板块资金流向数据，使用数据库缓存
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参数:
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start_date (str): 开始日期，格式'YYYYMMDD'
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end_date (str): 结束日期，格式'YYYYMMDD'
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返回:
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pandas.DataFrame: 所有板块资金流向数据
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"""
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# 获取目标交易日历
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all_trade_dates = get_trade_cal(start_date, end_date)
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# 从数据库获取已有的交易日期
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existing_dates = get_existing_trade_dates('sector_fund_flow')
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# 筛选出需要新获取的日期
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new_dates = [date for date in all_trade_dates if date not in existing_dates]
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if not new_dates:
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print("所有数据已在数据库中，无需更新")
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return load_df_from_db('sector_fund_flow')
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print(f"需要获取 {len(new_dates)} 个新交易日的数据")
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# 获取新日期的数据
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all_new_data = []
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# 使用tqdm显示进度
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for trade_date in tqdm(new_dates):
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try:
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# 从tushare获取当日板块资金流向数据
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df = pro.moneyflow_ind_dc(trade_date=trade_date)
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# 如果有数据，添加到列表
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if not df.empty:
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# 计算主力资金 = 超大单买入 + 大单买入
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df['main_force_amount'] = df['buy_elg_amount'] + df['buy_lg_amount']
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all_new_data.append(df)
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else:
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print(f"日期 {trade_date} 无数据")
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except Exception as e:
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print(f"获取 {trade_date} 的数据时出错: {e}")
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# 如果有新数据，合并并保存到数据库
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if all_new_data:
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# 将所有新数据合并为一个DataFrame
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new_df = pd.concat(all_new_data, ignore_index=True)
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# 保存到数据库
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save_df_to_db(new_df, table_name='sector_fund_flow', if_exists='append')
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print(f"已将 {len(new_df)} 条新记录保存到数据库")
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else:
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print("未获取到任何新数据")
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return load_df_from_db('sector_fund_flow')
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def analyze_money_flow():
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"""
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分析各类资金流向指标对行业在随后1-10天表现的影响
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包括期望收益分析和特定交易策略验证
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"""
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# 读取资金流数据
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try:
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df = load_df_from_db('sector_fund_flow')
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print(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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print(f"从数据库读取数据失败:{e}")
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return
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# 将日期格式转换为datetime - 如果存储在数据库中的是字符串格式
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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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# 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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print(f"\n\n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（除了最后10天）
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for i in range(len(all_dates) - 10):
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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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# 确定排序方向和选择逻辑
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sort_ascending = correlation < 0 # 负相关时升序(最小值), 正相关时降序(最大值)
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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1-10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1-10天的表现
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future_changes = []
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for day_offset in range(1, 11):
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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1-10天的变化
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for day in range(1, 11):
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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['avg_10day_change'] = np.nanmean([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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print(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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output_file = f'result/{indicator}_performance.xlsx'
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results_df.to_excel(output_file, index=False)
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print(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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for day in range(1, 11):
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avg_performance[f'day{day}'] = results_df[f'day{day}_change'].mean()
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avg_performance['avg_10day'] = results_df['avg_10day_change'].mean()
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print(f"\n{indicator_name}极值行业的平均表现：")
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for day, perf in avg_performance.items():
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print(f"{day}: {perf:.4f}%")
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# 分析期望值（正指数变化的百分比）
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success_rates = {}
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for day in range(1, 11):
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success_rates[f'day{day}'] = (results_df[f'day{day}_change'] > 0).mean() * 100
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print(f"\n{indicator_name}极值后上涨的概率：")
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for day, rate in success_rates.items():
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print(f"{day}: {rate:.2f}%")
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# ------------------ 验证特定交易策略 ------------------
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print("\n交易策略验证:")
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# T+1买入，T+2卖出
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day1_to_day2_change = results_df['day2_change'] - results_df['day1_change']
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avg_change_1_to_2 = day1_to_day2_change.mean()
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win_rate_1_to_2 = (day1_to_day2_change > 0).mean() * 100
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print(f"策略A - T+1(第1日)买入，T+2(第2日)卖出的平均收益: {avg_change_1_to_2:.4f}%")
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print(f"策略A - T+1(第1日)买入，T+2(第2日)卖出的盈利概率: {win_rate_1_to_2:.2f}%")
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# T+4买入，T+8卖出
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day4_to_day8_change = results_df['day8_change'] + results_df['day7_change'] + results_df['day6_change'] + \
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results_df['day5_change'] - results_df['day4_change']
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avg_change_3_to_8 = day4_to_day8_change.mean()
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win_rate_3_to_8 = (day4_to_day8_change > 0).mean() * 100
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print(f"策略B - T+4(第4日)买入，T+8(第8日)卖出的平均收益: {avg_change_3_to_8:.4f}%")
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print(f"策略B - T+4(第4日)买入，T+8(第8日)卖出的盈利概率: {win_rate_3_to_8:.2f}%")
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# 分析策略组合效果
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# 模拟完整策略：T+1买入，T+2卖出，T+3买入，T+8卖出
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combined_change = day1_to_day2_change + day4_to_day8_change
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avg_combined_change = combined_change.mean()
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win_rate_combined = (combined_change > 0).mean() * 100
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print(f"组合策略 - 完整策略组合的平均总收益: {avg_combined_change:.4f}%")
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print(f"组合策略 - 完整策略至少盈利的概率: {win_rate_combined:.2f}%")
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# 绘制策略示意图
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plt.figure(figsize=(14, 8))
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try:
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'WenQuanYi Micro Hei']
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title = f'{indicator_name}极值后交易策略示意图'
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xlabel = '交易日'
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ylabel = '指数变化率 (%)'
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strategy_names = ['策略A: T+1买入,T+2卖出', '策略B: T+4买入,T+8卖出']
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except:
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title = f'Trading Strategy after {indicator_name} Extreme Value'
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xlabel = 'Trading Day'
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ylabel = 'Index Change Rate (%)'
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strategy_names = ['Strategy A: Buy T+1, Sell T+2', 'Strategy B: Buy T+3, Sell T+8']
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days = range(11) # 0-10天
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values = [results_df['current_pct_change'].mean()] + [avg_performance[f'day{i}'] for i in range(1, 11)]
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plt.plot(days, values, marker='o', color='blue', linewidth=2, label='平均表现')
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# 标记策略A: T+1买入，T+2卖出
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plt.plot([1, 2], [values[1], values[2]], color='green', linewidth=4, alpha=0.7, label=strategy_names[0])
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plt.scatter([1, 2], [values[1], values[2]], color='green', s=100)
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# 标记策略B: T+4买入，T+8卖出
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plt.plot([3, 8], [values[3], values[8]], color='red', linewidth=4, alpha=0.7, label=strategy_names[1])
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plt.scatter([3, 8], [values[3], values[8]], color='red', s=100)
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plt.axhline(y=0, color='gray', linestyle='--')
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plt.title(title, fontsize=14)
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plt.ylabel(ylabel)
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plt.xlabel(xlabel)
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plt.xticks(days, ['T'] + [f'T+{i}' for i in range(1, 11)])
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plt.grid(True)
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plt.legend()
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# 保存策略图表
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strategy_image = f'result/{indicator}_strategy.png'
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plt.savefig(strategy_image, dpi=300, bbox_inches='tight')
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print(f"交易策略示意图已保存至{strategy_image}")
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# 绘制折线图显示未来10天的平均表现
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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 = ['当天'] + [f'第{i}天' for i in range(1, 11)]
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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 (10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 = ['Current'] + [f'Day+{i}' for i in range(1, 11)]
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direction = "Highest" if correlation > 0 else "Lowest"
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title = f'Average Performance After {indicator_name} {direction} (10 Days)'
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xlabel = 'Time'
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ylabel = 'Index Change Rate (%)'
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values = [results_df['current_pct_change'].mean()] + [avg_performance[f'day{i}'] for i in range(1, 11)]
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plt.plot(days, values, marker='o', linewidth=2)
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plt.axhline(y=0, color='r', linestyle='--')
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plt.title(title, fontsize=14)
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plt.ylabel(ylabel)
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plt.xlabel(xlabel)
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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.png'
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plt.savefig(output_image, dpi=300, bbox_inches='tight') # 添加bbox_inches参数确保所有标签都显示
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print(f"{indicator_name}表现图表已保存至{output_image}")
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return True
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if __name__ == "__main__":
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# 指定日期范围
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start_date = '20230912'
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end_date = None
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# 获取板块资金流向数据
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get_sector_moneyflow_data(start_date, end_date)
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analyze_money_flow() |