#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 检查买1量大于99的挂单后是否出现低于当前价格的数据 详细分析买1量大单出现后的价格行为，验证是否真的全部上涨 """ import pandas as pd import numpy as np import os def check_buy1_price_decline(): """检查买1量大单后的价格下跌情况""" # 加载数据 data_file = 'data/au2512_20251013.parquet' if not os.path.exists(data_file): print(f"数据文件不存在: {data_file}") return print("正在加载数据...") df = pd.read_parquet(data_file) print(f"数据加载成功: {len(df):,} 条记录") # 确保按数列号排序 if '数列号' in df.columns: df = df.sort_values('数列号').reset_index(drop=True) # 找到买1量列 buy1_col = None for col in df.columns: if '买1量' in col: buy1_col = col break if buy1_col is None: print("未找到买1量列") return print(f"使用买1量列: {buy1_col}") # 筛选买1量大于99的数据 threshold = 99 large_buy1_mask = df[buy1_col] > threshold large_buy1_orders = df[large_buy1_mask].copy().reset_index(drop=True) print(f"\n买1量 > {threshold} 的记录数: {len(large_buy1_orders):,}") print(f"占总记录比例: {len(large_buy1_orders)/len(df)*100:.2f}%") if len(large_buy1_orders) == 0: print("未找到符合条件的记录") return # 详细分析每个大单后的价格行为 print(f"\n" + "="*80) print("买1量大单后价格行为详细分析") print("="*80) decline_100_count = 0 decline_200_count = 0 detailed_results = [] for idx, row in large_buy1_orders.iterrows(): current_seq_num = row['数列号'] if '数列号' in row else idx current_price = row['成交价'] buy1_volume = row[buy1_col] # 获取后续100笔数据 future_mask_100 = df['数列号'] >= current_seq_num + 1 future_data_100 = df[future_mask_100].head(100) # 获取后续200笔数据 future_mask_200 = df['数列号'] >= current_seq_num + 1 future_data_200 = df[future_mask_200].head(200) # 分析100笔内的价格行为 if len(future_data_100) > 0: min_price_100 = future_data_100['成交价'].min() max_price_100 = future_data_100['成交价'].max() price_decline_100 = current_price - min_price_100 # 价格下跌幅度 price_rise_100 = max_price_100 - current_price # 价格上涨幅度 # 检查是否出现低于当前价格的情况 has_decline_100 = min_price_100 < current_price if has_decline_100: decline_100_count += 1 decline_info_100 = f"是 (最低: {min_price_100:.2f}, 下跌: {price_decline_100:.2f})" else: decline_info_100 = f"否 (最低: {min_price_100:.2f})" # 分析200笔内的价格行为 if len(future_data_200) > 0: min_price_200 = future_data_200['成交价'].min() max_price_200 = future_data_200['成交价'].max() price_decline_200 = current_price - min_price_200 # 价格下跌幅度 price_rise_200 = max_price_200 - current_price # 价格上涨幅度 # 检查是否出现低于当前价格的情况 has_decline_200 = min_price_200 < current_price if has_decline_200: decline_200_count += 1 decline_info_200 = f"是 (最低: {min_price_200:.2f}, 下跌: {price_decline_200:.2f})" else: decline_info_200 = f"否 (最低: {min_price_200:.2f})" # 记录详细结果 detailed_results.append({ '序号': idx + 1, '数列号': current_seq_num, '当前价格': current_price, '买1量': buy1_volume, '100笔样本数': len(future_data_100), '100笔最低价': min_price_100 if len(future_data_100) > 0 else None, '100笔最高价': max_price_100 if len(future_data_100) > 0 else None, '100笔是否下跌': has_decline_100 if len(future_data_100) > 0 else None, '100笔下跌幅度': price_decline_100 if len(future_data_100) > 0 else None, '100笔上涨幅度': price_rise_100 if len(future_data_100) > 0 else None, '200笔样本数': len(future_data_200), '200笔最低价': min_price_200 if len(future_data_200) > 0 else None, '200笔最高价': max_price_200 if len(future_data_200) > 0 else None, '200笔是否下跌': has_decline_200 if len(future_data_200) > 0 else None, '200笔下跌幅度': price_decline_200 if len(future_data_200) > 0 else None, '200笔上涨幅度': price_rise_200 if len(future_data_200) > 0 else None, }) # 保存详细结果 results_df = pd.DataFrame(detailed_results) results_df.to_csv('large_orders/buy1_detailed_price_analysis.csv', index=False, encoding='utf-8-sig') # 打印汇总统计 print(f"\n【汇总统计】") print(f"总样本数: {len(large_buy1_orders)}") print(f"100笔内出现价格下跌的样本数: {decline_100_count} ({decline_100_count/len(large_buy1_orders)*100:.1f}%)") print(f"200笔内出现价格下跌的样本数: {decline_200_count} ({decline_200_count/len(large_buy1_orders)*100:.1f}%)") # 打印详细分析 print(f"\n【详细分析 - 前15个样本】") print(f"{'序号':>4} {'数列号':>8} {'当前价格':>10} {'买1量':>8} {'100笔下跌?':>12} {'200笔下跌?':>12}") print("-" * 80) for i, result in enumerate(detailed_results[:15]): decline_100_status = "是" if result['100笔是否下跌'] else "否" decline_200_status = "是" if result['200笔是否下跌'] else "否" print(f"{result['序号']:>4} {result['数列号']:>8.0f} {result['当前价格']:>10.2f} " f"{result['买1量']:>8.0f} {decline_100_status:>12} {decline_200_status:>12}") # 分析下跌样本的详细信息 decline_samples_100 = [r for r in detailed_results if r['100笔是否下跌']] decline_samples_200 = [r for r in detailed_results if r['200笔是否下跌']] if decline_samples_100: print(f"\n【100笔内出现价格下跌的样本详情】") for result in decline_samples_100: print(f"序号{result['序号']}: 当前价格={result['当前价格']:.2f}, " f"最低价格={result['100笔最低价']:.2f}, " f"下跌幅度={result['100笔下跌幅度']:.2f}元, " f"买1量={result['买1量']:.0f}手") if decline_samples_200: print(f"\n【200笔内出现价格下跌的样本详情】") for result in decline_samples_200: print(f"序号{result['序号']}: 当前价格={result['当前价格']:.2f}, " f"最低价格={result['200笔最低价']:.2f}, " f"下跌幅度={result['200笔下跌幅度']:.2f}元, " f"买1量={result['买1量']:.0f}手") # 价格波动统计 print(f"\n【价格波动统计】") if len(detailed_results) > 0: all_declines_100 = [r['100笔下跌幅度'] for r in detailed_results if r['100笔下跌幅度'] is not None] all_rises_100 = [r['100笔上涨幅度'] for r in detailed_results if r['100笔上涨幅度'] is not None] if all_declines_100: avg_decline_100 = np.mean(all_declines_100) max_decline_100 = np.max(all_declines_100) print(f"100笔内平均下跌幅度: {avg_decline_100:.3f}元") print(f"100笔内最大下跌幅度: {max_decline_100:.3f}元") if all_rises_100: avg_rise_100 = np.mean(all_rises_100) max_rise_100 = np.max(all_rises_100) print(f"100笔内平均上涨幅度: {avg_rise_100:.3f}元") print(f"100笔内最大上涨幅度: {max_rise_100:.3f}元") print(f"\n分析完成！详细结果已保存到: large_orders/buy1_detailed_price_analysis.csv") return decline_100_count, decline_200_count if __name__ == "__main__": check_buy1_price_decline()