e5dd5b5593
## 核心功能 ### 1. 成交量序列分析 (volume_price_sequence.py) - 按累计成交量排序的价格趋势分析 - 三合一综合图表：价格序列+成交量分布+时间序列 - 关键价格水平自动标注 ### 2. 成交量分布深度分析 (volume_distribution_analysis.py) - 7种专业可视化图表 - 统计特征分析和分布拟合 - 交易模式识别和业务洞察 ### 3. 大额订单分析工具集 (large_orders/) - 买1/卖1量大单分析 (阈值99) - 买卖挂单合计分析 (阈值200) - 当前成交量分析 (阈值150) - 信号抑制优化算法 (38%抑制率) ## 技术特性 - 信号抑制算法：有效减少重复信号干扰 - 多维度分析：支持多种信号类型 - 专业可视化：四宫格综合分析图 - 业务洞察：基于数据的交易建议 ## 分析结果 - 卖1量大单：短期下跌，长期大幅上涨反转 - 买挂合计：各时间窗口小幅正收益 - 信号抑制：短期收益从-0.0778提升至+0.1347 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
333 lines
14 KiB
Python
333 lines
14 KiB
Python
import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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import os
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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 analyze_large_orders_extended():
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"""分析买1量和卖1量大于99的数据点后不同长度的成交价走势"""
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print("正在读取数据文件...")
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# 读取数据（从上级目录的data文件夹）
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df = pd.read_parquet('../data/au2512_20251013.parquet')
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print(f"数据总行数: {len(df)}")
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print(f"数据列名: {df.columns.tolist()}")
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# 查找买1量和卖1量列的实际名称
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buy1_vol_col = None
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sell1_vol_col = None
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for col in df.columns:
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if '买1量' in str(col) or 'buy1' in str(col).lower():
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buy1_vol_col = col
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if '卖1量' in str(col) or 'sell1' in str(col).lower():
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sell1_vol_col = col
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if buy1_vol_col is None:
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print("未找到买1量列")
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return
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if sell1_vol_col is None:
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print("未找到卖1量列")
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return
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print(f"使用买1量列: {buy1_vol_col}")
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print(f"使用卖1量列: {sell1_vol_col}")
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# 获取成交价列名
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price_col = None
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for col in df.columns:
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if '成交价' in str(col) or 'price' in str(col).lower():
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price_col = col
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break
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if price_col is None:
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print("未找到成交价列")
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return
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print(f"使用成交价列: {price_col}")
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# 筛选大额买1和卖1订单
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large_buy1_mask = df[buy1_vol_col] > 99
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large_sell1_mask = df[sell1_vol_col] > 99
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large_buy1_indices = df[large_buy1_mask].index.tolist()
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large_sell1_indices = df[large_sell1_mask].index.tolist()
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print(f"找到买1量大于99的数据点数量: {len(large_buy1_indices)}")
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print(f"找到卖1量大于99的数据点数量: {len(large_sell1_indices)}")
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# 提取价格序列的函数
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def extract_price_sequences(indices, order_type, max_points):
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sequences = []
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sequence_info = []
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for idx in indices:
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remaining_points = len(df) - idx - 1
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take_points = min(max_points, remaining_points)
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if take_points > 0:
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base_price = df.loc[idx, price_col]
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future_prices = df.loc[idx + 1: idx + take_points, price_col].values
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price_changes = future_prices - base_price
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sequences.append(price_changes)
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volume_col = buy1_vol_col if order_type == 'buy' else sell1_vol_col
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sequence_info.append({
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'start_index': idx,
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'volume': df.loc[idx, volume_col],
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'base_price': base_price,
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'sequence_length': take_points
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})
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return sequences, sequence_info
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# 分析不同时间长度的数据
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analysis_lengths = [100, 200, 500] # 100, 200, 500个数据点
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for length in analysis_lengths:
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print(f"\n{'='*60}")
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print(f"分析后{length}个数据点的价格走势")
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print(f"{'='*60}")
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# 提取买1和卖1的价格序列
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buy1_sequences, buy1_info = extract_price_sequences(large_buy1_indices, 'buy', length)
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sell1_sequences, sell1_info = extract_price_sequences(large_sell1_indices, 'sell', length)
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print(f"成功提取 {len(buy1_sequences)} 个买1价格序列 (最大长度: {length})")
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print(f"成功提取 {len(sell1_sequences)} 个卖1价格序列 (最大长度: {length})")
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# 创建综合对比图表
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fig, axes = plt.subplots(2, 2, figsize=(20, 16))
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fig.suptitle(f'大额订单对比分析：买1量>99 vs 卖1量>99 (后{length}个数据点)',
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fontsize=16, fontweight='bold')
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# 1. 买1量价格变化图
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ax1 = axes[0, 0]
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if buy1_sequences:
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colors_buy = plt.cm.Blues(np.linspace(0.3, 0.9, len(buy1_sequences)))
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for i, (sequence, info) in enumerate(zip(buy1_sequences, buy1_info)):
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x_axis = range(len(sequence))
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ax1.plot(x_axis, sequence, color=colors_buy[i], alpha=0.6, linewidth=0.8)
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ax1.axhline(y=0, color='red', linestyle='--', alpha=0.7, linewidth=1.5)
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ax1.set_xlabel('数据点序号')
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ax1.set_ylabel('相对价格变化')
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ax1.set_title(f'买1量>99的价格变化走势 (后{length}点)\n共{len(buy1_sequences)}个序列')
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ax1.grid(True, alpha=0.3)
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ax1.text(0.02, 0.98, f'序列数: {len(buy1_sequences)}', transform=ax1.transAxes,
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verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.8))
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# 2. 卖1量价格变化图
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ax2 = axes[0, 1]
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if sell1_sequences:
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colors_sell = plt.cm.Reds(np.linspace(0.3, 0.9, len(sell1_sequences)))
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for i, (sequence, info) in enumerate(zip(sell1_sequences, sell1_info)):
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x_axis = range(len(sequence))
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ax2.plot(x_axis, sequence, color=colors_sell[i], alpha=0.6, linewidth=0.8)
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ax2.axhline(y=0, color='red', linestyle='--', alpha=0.7, linewidth=1.5)
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ax2.set_xlabel('数据点序号')
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ax2.set_ylabel('相对价格变化')
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ax2.set_title(f'卖1量>99的价格变化走势 (后{length}点)\n共{len(sell1_sequences)}个序列')
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ax2.grid(True, alpha=0.3)
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ax2.text(0.02, 0.98, f'序列数: {len(sell1_sequences)}', transform=ax2.transAxes,
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verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightcoral', alpha=0.8))
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# 3. 平均变化对比图
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ax3 = axes[1, 0]
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def calculate_avg_changes(sequences):
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if not sequences:
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return []
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max_len = max(len(seq) for seq in sequences)
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avg_changes = []
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for i in range(max_len):
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point_changes = [seq[i] for seq in sequences if len(seq) > i]
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if point_changes:
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avg_changes.append(np.mean(point_changes))
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return avg_changes
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buy1_avg_changes = calculate_avg_changes(buy1_sequences)
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sell1_avg_changes = calculate_avg_changes(sell1_sequences)
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if buy1_avg_changes:
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ax3.plot(range(len(buy1_avg_changes)), buy1_avg_changes,
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color='blue', linewidth=2.5, label=f'买1量>99 (n={len(buy1_sequences)})')
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if sell1_avg_changes:
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ax3.plot(range(len(sell1_avg_changes)), sell1_avg_changes,
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color='red', linewidth=2.5, label=f'卖1量>99 (n={len(sell1_sequences)})')
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ax3.axhline(y=0, color='black', linestyle='--', alpha=0.7, linewidth=1.5)
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ax3.set_xlabel('数据点序号')
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ax3.set_ylabel('平均相对价格变化')
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ax3.set_title(f'平均价格变化对比 (后{length}点)')
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ax3.legend(fontsize=12)
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ax3.grid(True, alpha=0.3)
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# 4. 统计信息文本框
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ax4 = axes[1, 1]
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ax4.axis('off')
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# 计算统计信息
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def calculate_stats(sequences, name):
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if not sequences:
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return {}
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final_changes = [seq[-1] for seq in sequences if len(seq) > 0]
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if final_changes:
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return {
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'name': name,
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'count': len(sequences),
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'avg_final_change': np.mean(final_changes),
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'std_final_change': np.std(final_changes),
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'max_rise': np.max(final_changes),
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'max_fall': np.min(final_changes)
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}
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return {}
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buy1_stats = calculate_stats(buy1_sequences, '买1量>99')
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sell1_stats = calculate_stats(sell1_sequences, '卖1量>99')
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# 显示统计信息
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stats_text = f"=== 统计信息对比 (后{length}点) ===\n\n"
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if buy1_stats:
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stats_text += f"【买1量>99】\n"
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stats_text += f"序列数量: {buy1_stats['count']}\n"
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stats_text += f"平均最终变化: {buy1_stats['avg_final_change']:.4f}\n"
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stats_text += f"变化标准差: {buy1_stats['std_final_change']:.4f}\n"
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stats_text += f"最大上涨: {buy1_stats['max_rise']:.4f}\n"
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stats_text += f"最大下跌: {buy1_stats['max_fall']:.4f}\n\n"
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if sell1_stats:
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stats_text += f"【卖1量>99】\n"
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stats_text += f"序列数量: {sell1_stats['count']}\n"
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stats_text += f"平均最终变化: {sell1_stats['avg_final_change']:.4f}\n"
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stats_text += f"变化标准差: {sell1_stats['std_final_change']:.4f}\n"
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stats_text += f"最大上涨: {sell1_stats['max_rise']:.4f}\n"
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stats_text += f"最大下跌: {sell1_stats['max_fall']:.4f}\n\n"
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# 添加关键时间点对比
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if buy1_avg_changes and sell1_avg_changes:
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stats_text += f"=== 关键时间点对比 (后{length}点) ===\n"
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# 根据数据长度选择关键点
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if length >= 500:
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points_to_check = [49, 199, 499] # 第50、200、500点
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point_names = ['第50点', '第200点', '第500点']
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elif length >= 200:
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points_to_check = [49, 199] # 第50、200点
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point_names = ['第50点', '第200点']
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else:
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points_to_check = [49] # 第50点
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point_names = ['第50点']
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for i, point in enumerate(points_to_check):
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if point < len(buy1_avg_changes) and point < len(sell1_avg_changes):
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stats_text += f"{point_names[i]}: "
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stats_text += f"买1={buy1_avg_changes[point]:.4f}, "
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stats_text += f"卖1={sell1_avg_changes[point]:.4f}, "
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stats_text += f"差值={buy1_avg_changes[point] - sell1_avg_changes[point]:.4f}\n"
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ax4.text(0.05, 0.95, stats_text, transform=ax4.transAxes, fontsize=11,
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verticalalignment='top',
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bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8))
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plt.tight_layout()
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# 保存综合图表
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output_file = f'large_orders_comprehensive_analysis_{length}points.png'
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plt.savefig(output_file, dpi=300, bbox_inches='tight')
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print(f"\n{length}点综合分析图表已保存为: {output_file}")
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print(f"完整路径: {os.path.abspath(output_file)}")
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# 保存单独的买1和卖1图表
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# 买1量单独图表
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fig_buy, ax_buy = plt.subplots(figsize=(15, 10))
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if buy1_sequences:
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colors_buy = plt.cm.Blues(np.linspace(0.3, 0.9, len(buy1_sequences)))
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for i, (sequence, info) in enumerate(zip(buy1_sequences, buy1_info)):
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x_axis = range(len(sequence))
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ax_buy.plot(x_axis, sequence, color=colors_buy[i], alpha=0.6, linewidth=1)
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ax_buy.axhline(y=0, color='red', linestyle='--', alpha=0.7, linewidth=1.5)
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ax_buy.set_xlabel(f'数据点序号 (相对于大额买1订单, 后{length}点)', fontsize=12)
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ax_buy.set_ylabel('相对价格变化 (相对于基准点)', fontsize=12)
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ax_buy.set_title(f'买1量>99的数据点后{length}个相对价格变化走势\n所有序列从基准点(0)开始\n共{len(buy1_sequences)}个序列',
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fontsize=14, fontweight='bold')
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ax_buy.grid(True, alpha=0.3)
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plt.tight_layout()
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buy_output_file = f'large_buy1_relative_price_changes_{length}points.png'
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plt.savefig(buy_output_file, dpi=300, bbox_inches='tight')
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print(f"买1量{length}点分析图表已保存为: {buy_output_file}")
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plt.close()
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# 卖1量单独图表
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fig_sell, ax_sell = plt.subplots(figsize=(15, 10))
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if sell1_sequences:
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colors_sell = plt.cm.Reds(np.linspace(0.3, 0.9, len(sell1_sequences)))
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for i, (sequence, info) in enumerate(zip(sell1_sequences, sell1_info)):
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x_axis = range(len(sequence))
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ax_sell.plot(x_axis, sequence, color=colors_sell[i], alpha=0.6, linewidth=1)
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ax_sell.axhline(y=0, color='red', linestyle='--', alpha=0.7, linewidth=1.5)
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ax_sell.set_xlabel(f'数据点序号 (相对于大额卖1订单, 后{length}点)', fontsize=12)
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ax_sell.set_ylabel('相对价格变化 (相对于基准点)', fontsize=12)
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ax_sell.set_title(f'卖1量>99的数据点后{length}个相对价格变化走势\n所有序列从基准点(0)开始\n共{len(sell1_sequences)}个序列',
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fontsize=14, fontweight='bold')
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ax_sell.grid(True, alpha=0.3)
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plt.tight_layout()
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sell_output_file = f'large_sell1_relative_price_changes_{length}points.png'
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plt.savefig(sell_output_file, dpi=300, bbox_inches='tight')
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print(f"卖1量{length}点分析图表已保存为: {sell_output_file}")
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plt.close()
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# 显示统计信息
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print(f"\n{'='*50}")
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print(f"详细统计信息 (后{length}点):")
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print(f"{'='*50}")
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if buy1_stats:
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print(f"\n【买1量>99】")
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print(f"序列数量: {buy1_stats['count']}")
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print(f"平均最终变化: {buy1_stats['avg_final_change']:.4f}")
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print(f"变化标准差: {buy1_stats['std_final_change']:.4f}")
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print(f"最大上涨: {buy1_stats['max_rise']:.4f}")
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print(f"最大下跌: {buy1_stats['max_fall']:.4f}")
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if sell1_stats:
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print(f"\n【卖1量>99】")
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print(f"序列数量: {sell1_stats['count']}")
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print(f"平均最终变化: {sell1_stats['avg_final_change']:.4f}")
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print(f"变化标准差: {sell1_stats['std_final_change']:.4f}")
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print(f"最大上涨: {sell1_stats['max_rise']:.4f}")
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print(f"最大下跌: {sell1_stats['max_fall']:.4f}")
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# 关键时间点分析
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if buy1_avg_changes and sell1_avg_changes:
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print(f"\n关键时间点对比:")
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if length >= 500:
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key_points = [(49, '第50点'), (199, '第200点'), (499, '第500点')]
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elif length >= 200:
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key_points = [(49, '第50点'), (199, '第200点')]
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else:
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key_points = [(49, '第50点')]
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for point, name in key_points:
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if point < len(buy1_avg_changes) and point < len(sell1_avg_changes):
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diff = buy1_avg_changes[point] - sell1_avg_changes[point]
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print(f"{name}: 买1={buy1_avg_changes[point]:.4f}, 卖1={sell1_avg_changes[point]:.4f}, 差值={diff:.4f}")
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plt.close('all') # 关闭所有图形以释放内存
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print(f"\n{'='*60}")
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print("所有分析完成！")
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print(f"{'='*60}")
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if __name__ == "__main__":
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analyze_large_orders_extended() |