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>
497 lines
21 KiB
Python
497 lines
21 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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AU2512期货成交量-成交价序列图生成工具
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这个脚本专门用于生成高解释力的成交量-成交价序列图。
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通过按累计成交量升序排列，清晰展示价格随交易推进的演变轨迹。
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使用方法:
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python volume_price_sequence.py [data_file]
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如果不指定文件，默认分析 data/au2512_20251013.parquet
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"""
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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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from matplotlib.gridspec import GridSpec
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from datetime import datetime, timedelta
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import pytz
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import warnings
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import os
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import sys
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import argparse
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from pathlib import Path
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warnings.filterwarnings('ignore')
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class VolumePriceAnalyzer:
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"""成交量-成交价序列分析器"""
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def __init__(self, data_file=None):
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"""
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初始化分析器
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Args:
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data_file (str): 数据文件路径，默认为 data/au2512_20251013.parquet
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"""
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self.data_file = data_file or "data/au2512_20251013.parquet"
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self.df = None
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self.setup_chinese_font()
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def setup_chinese_font(self):
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"""设置中文字体支持"""
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try:
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# 尝试不同的中文字体
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chinese_fonts = ['Microsoft YaHei', 'SimHei', 'SimSun', 'KaiTi', 'FangSong']
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for font in chinese_fonts:
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try:
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plt.rcParams['font.sans-serif'] = [font]
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plt.rcParams['axes.unicode_minus'] = False
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# 测试字体是否可用
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fig, ax = plt.subplots(figsize=(1, 1))
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ax.text(0.5, 0.5, '测试', fontsize=12)
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plt.close(fig)
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print(f"使用中文字体: {font}")
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return
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except:
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continue
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# 如果都不行，使用默认字体
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plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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print("警告: 无法加载中文字体，使用默认字体")
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except Exception as e:
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print(f"字体设置警告: {e}")
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plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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def load_data(self):
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"""加载数据"""
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try:
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if not os.path.exists(self.data_file):
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raise FileNotFoundError(f"数据文件不存在: {self.data_file}")
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print(f"正在加载数据: {self.data_file}")
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# 根据文件扩展名选择读取方式
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if self.data_file.endswith('.parquet'):
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self.df = pd.read_parquet(self.data_file)
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elif self.data_file.endswith('.csv'):
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self.df = pd.read_csv(self.data_file)
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else:
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raise ValueError("不支持的文件格式，请使用 .parquet 或 .csv 文件")
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print(f"数据加载成功: {len(self.df):,} 条记录")
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return True
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except Exception as e:
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print(f"数据加载失败: {e}")
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return False
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def analyze_data(self):
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"""分析数据并按成交量排序"""
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print("\n=== 数据分析 ===")
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# 按累计成交量升序排列
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self.df = self.df.sort_values('累积成交量').reset_index(drop=True)
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# 计算基础指标
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self.total_volume = self.df['累积成交量'].max()
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self.min_price = self.df['成交价'].min()
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self.max_price = self.df['成交价'].max()
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self.avg_price = self.df['成交价'].mean()
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print(f"数据概况:")
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print(f" 累计成交量: {self.total_volume:,} 手")
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print(f" 价格区间: {self.min_price:.2f} - {self.max_price:.2f}")
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print(f" 平均价格: {self.avg_price:.2f}")
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print(f" 记录数量: {len(self.df):,}")
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# 计算成交量变化
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if '当前成交量' not in self.df.columns:
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self.df['当前成交量'] = self.df['累积成交量'].diff().fillna(0)
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self.df.loc[self.df.index[0], '当前成交量'] = self.df.loc[self.df.index[0], '累积成交量']
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self.df['volume_change'] = self.df['当前成交量'] # 使用当前成交量
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print(f" 平均单笔成交量: {self.df['volume_change'].mean():.1f} 手")
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print(f" 最大单笔成交量: {self.df['volume_change'].max():.0f} 手")
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print(f" 成交量标准差: {self.df['volume_change'].std():.1f} 手")
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# 计算关键节点
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self.calculate_key_milestones()
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def calculate_key_milestones(self):
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"""计算关键成交量节点"""
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self.milestones = {
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'起始': self.df.iloc[0],
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'10%': self.df.iloc[len(self.df)//10],
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'25%': self.df.iloc[len(self.df)//4],
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'50%': self.df.iloc[len(self.df)//2],
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'75%': self.df.iloc[3*len(self.df)//4],
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'90%': self.df.iloc[9*len(self.df)//10],
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'结束': self.df.iloc[-1]
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}
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print(f"\n关键成交量节点:")
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for label, point in self.milestones.items():
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print(f" {label}: 成交量 {point['累积成交量']:,} 手, 价格 {point['成交价']:.2f}")
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def create_volume_price_chart(self):
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"""创建成交量-成交价序列图（包含当前成交量柱状图）"""
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print("\n=== 生成成交量-成交价序列图 ===")
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# 创建复合图表布局：主图 + 右侧成交量分布图
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fig = plt.figure(figsize=(28, 14))
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gs = GridSpec(2, 3, figure=fig, width_ratios=[3, 0.1, 1], height_ratios=[3, 1],
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hspace=0.1, wspace=0.05)
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# 主图：成交价序列图
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ax1 = fig.add_subplot(gs[0, 0])
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# 右侧成交量分布图
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ax_volume_dist = fig.add_subplot(gs[0, 2], sharey=ax1)
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# 下方当前成交量图
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ax2 = fig.add_subplot(gs[1, 0], sharex=ax1)
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# 上方图表：成交价序列图
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# 绘制主要序列图
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ax1.plot(self.df['累积成交量'], self.df['成交价'],
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linewidth=2.0, alpha=0.9, color='navy', label='成交价序列')
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# 添加关键价格标注（右侧成交量分布图中累计成交量最大的10个价格区间）
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# 使用与右侧成交量分布图相同的逻辑：按价格分组计算累计成交量
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prices = self.df['成交价']
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min_price = prices.min()
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max_price = prices.max()
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# 使用固定0.02价格间隔（与右侧图一致）
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interval_size = 0.02
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price_bins = np.arange(np.floor(min_price * 50) / 50,
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np.ceil(max_price * 50) / 50 + interval_size,
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interval_size)
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bin_centers = (price_bins[:-1] + price_bins[1:]) / 2
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# 确保有当前成交量列
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if '当前成交量' not in self.df.columns:
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self.df['当前成交量'] = self.df['累积成交量'].diff().fillna(0)
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self.df.loc[self.df.index[0], '当前成交量'] = self.df.loc[self.df.index[0], '累积成交量']
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# 计算每个价格分组的累计成交量（与右侧图完全一致）
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volume_by_price = []
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for i in range(len(price_bins) - 1):
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mask = (self.df['成交价'] >= price_bins[i]) & (self.df['成交价'] < price_bins[i + 1])
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volume_sum = self.df.loc[mask, '当前成交量'].sum()
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volume_by_price.append(volume_sum)
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volume_by_price = np.array(volume_by_price)
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# 找出累计成交量最大的10个价格区间
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top_indices = np.argsort(volume_by_price)[-10:] # 取最大的10个
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top_volume_points = []
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for idx in sorted(top_indices, reverse=True): # 按成交量降序排列
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if volume_by_price[idx] > 0: # 只显示有成交量的区间
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price = bin_centers[idx]
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volume = volume_by_price[idx]
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# 找到该价格区间内的任意一个数据点来获取累计成交量
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mask = (self.df['成交价'] >= price_bins[idx]) & (self.df['成交价'] < price_bins[idx + 1])
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if mask.any():
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sample_point = self.df[mask].iloc[0]
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top_volume_points.append({
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'成交价': price,
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'当前成交量': volume,
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'累积成交量': sample_point['累积成交量']
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})
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# 获取最大累计成交量作为右边界
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max_cumulative_volume = self.df['累积成交量'].max()
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# 为每个关键价格添加浅绿色虚线标注
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for point in top_volume_points:
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price = point['成交价']
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volume = point['当前成交量'] # 这是价格区间的累计成交量
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cumulative_vol = point['累积成交量']
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# 绘制水平虚线
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ax1.axhline(y=price, color='lightgreen', linestyle='--', linewidth=1.5, alpha=0.7, zorder=1)
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# 添加价格标注（左侧）
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ax1.text(cumulative_vol, price, f'{price:.2f}',
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fontsize=10, color='darkgreen', fontweight='bold',
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ha='left', va='bottom',
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bbox=dict(boxstyle='round,pad=0.2', facecolor='lightgreen', alpha=0.8))
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# 添加成交量标注（右侧）
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ax1.text(max_cumulative_volume, price, f'{volume:.0f}手',
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fontsize=9, color='darkgreen', fontweight='bold',
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ha='right', va='bottom',
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bbox=dict(boxstyle='round,pad=0.2', facecolor='lightyellow', alpha=0.9))
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# 添加关键价格说明
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ax1.text(0.02, 0.98, f'关键价格标注\n(右侧图中累计成交量前10)', transform=ax1.transAxes,
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fontsize=11, ha='left', va='top', color='darkgreen', fontweight='bold',
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bbox=dict(boxstyle='round,pad=0.4', facecolor='lightgreen', alpha=0.9))
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# 设置上方图表标题和标签
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ax1.set_title('AU2512期货成交价序列图 (按累计成交量升序排列)',
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fontsize=20, fontweight='bold', pad=20)
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ax1.set_ylabel('成交价', fontsize=16, labelpad=10)
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ax1.grid(True, alpha=0.3, linestyle='-', linewidth=0.5)
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# 添加分析结论到上方图表
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final_price = self.df['成交价'].iloc[-1]
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initial_price = self.df['成交价'].iloc[0]
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total_change = final_price - initial_price
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total_change_pct = (total_change / initial_price) * 100
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conclusion_text = f'''分析结论:
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• 整体价格趋势: {"上涨" if total_change > 0 else "下跌"} ({total_change:+.2f}, {total_change_pct:+.2f}%)
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• 价格波动区间: {self.min_price:.2f} - {self.max_price:.2f}
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• 总成交量: {self.total_volume:,} 手
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• 总Tick数: {len(self.df):,}
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• 平均每1000手成交量影响: {abs(total_change)/(self.total_volume/1000):.4f}'''
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ax1.text(0.98, 0.02, conclusion_text, transform=ax1.transAxes,
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fontsize=12, ha='right', va='bottom',
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bbox=dict(boxstyle='round,pad=0.5', facecolor='lightgreen', alpha=0.9))
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# 下方图表：当前成交量柱状图
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# 确保有当前成交量列，如果没有则计算
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if '当前成交量' not in self.df.columns:
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self.df['当前成交量'] = self.df['累积成交量'].diff().fillna(0)
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self.df.loc[self.df.index[0], '当前成交量'] = self.df.loc[self.df.index[0], '累积成交量']
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# 直接绘制所有当前成交量数据点
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# 使用细线图代替柱状图，因为数据点太多
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ax2.plot(self.df['累积成交量'], self.df['当前成交量'],
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linewidth=0.5, alpha=0.8, color='steelblue', label='当前成交量')
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# 添加填充区域以增强视觉效果
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ax2.fill_between(self.df['累积成交量'], 0, self.df['当前成交量'],
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alpha=0.3, color='steelblue')
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# 设置下方图表标题和标签
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ax2.set_title('当前成交量分布 (按累计成交量)', fontsize=16, fontweight='bold', pad=10)
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ax2.set_xlabel('累计成交量 (手)', fontsize=16, labelpad=10)
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ax2.set_ylabel('当前成交量 (手)', fontsize=14, labelpad=10)
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ax2.grid(True, alpha=0.3, linestyle='-', linewidth=0.5)
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ax2.legend(fontsize=14, loc='upper right')
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# 添加成交量统计信息
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max_volume = self.df['当前成交量'].max()
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avg_volume = self.df['当前成交量'].mean()
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total_ticks = len(self.df)
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volume_stats = f'''成交量统计:
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• 最大单笔成交量: {max_volume:.0f} 手
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• 平均成交量: {avg_volume:.1f} 手
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• 总Tick数: {total_ticks:,}'''
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ax2.text(0.02, 0.98, volume_stats, transform=ax2.transAxes,
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fontsize=11, ha='left', va='top',
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bbox=dict(boxstyle='round,pad=0.4', facecolor='lightyellow', alpha=0.9))
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# 右侧图表：成交量在价格上的分布
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# 创建价格分组（0.02间隔）
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prices = self.df['成交价']
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min_price = prices.min()
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max_price = prices.max()
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# 使用固定0.02价格间隔
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interval_size = 0.02
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price_bins = np.arange(np.floor(min_price * 50) / 50,
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np.ceil(max_price * 50) / 50 + interval_size,
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interval_size)
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bin_centers = (price_bins[:-1] + price_bins[1:]) / 2
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# 计算每个价格分组的成交量
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if '当前成交量' not in self.df.columns:
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self.df['当前成交量'] = self.df['累积成交量'].diff().fillna(0)
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self.df.loc[self.df.index[0], '当前成交量'] = self.df.loc[self.df.index[0], '累积成交量']
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volume_by_price = []
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for i in range(len(price_bins) - 1):
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mask = (self.df['成交价'] >= price_bins[i]) & (self.df['成交价'] < price_bins[i + 1])
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volume_sum = self.df.loc[mask, '当前成交量'].sum()
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volume_by_price.append(volume_sum)
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volume_by_price = np.array(volume_by_price)
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# 绘制右侧成交量分布图（水平柱状图）
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colors = plt.cm.RdYlBu_r(np.linspace(0, 1, len(bin_centers)))
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bars = ax_volume_dist.barh(bin_centers, volume_by_price,
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height=0.015, # 设置柱子高度
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color=colors, alpha=0.8, edgecolor='black', linewidth=0.3)
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# 设置右侧图表标题和标签
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ax_volume_dist.set_title('成交量分布', fontsize=14, fontweight='bold', pad=10)
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ax_volume_dist.set_xlabel('成交量 (手)', fontsize=12)
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ax_volume_dist.grid(True, alpha=0.3, axis='x')
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# 格式化x轴标签
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ax_volume_dist.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'{x/1000:.0f}K'))
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# 隐藏右侧图表的y轴刻度（与主图共享）
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ax_volume_dist.tick_params(axis='y', which='both', left=False, right=False,
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labelleft=False, labelright=False)
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# 找出成交量最大的几个价格区间并标注
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max_volume = volume_by_price.max()
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if max_volume > 0:
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max_idx = np.argmax(volume_by_price)
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max_price_range = f"{price_bins[max_idx]:.2f}-{price_bins[max_idx+1]:.2f}"
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ax_volume_dist.annotate(f'{max_volume:.0f}手\n{max_price_range}',
|
||
xy=(max_volume, bin_centers[max_idx]),
|
||
xytext=(max_volume * 0.7, bin_centers[max_idx]),
|
||
ha='right', va='center',
|
||
fontsize=10, fontweight='bold', color='red',
|
||
bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.8))
|
||
|
||
# 设置两个图表的坐标轴格式
|
||
from matplotlib.ticker import FuncFormatter
|
||
formatter = FuncFormatter(lambda x, p: f'{x/1000:.0f}K')
|
||
ax1.xaxis.set_major_formatter(formatter)
|
||
ax2.xaxis.set_major_formatter(formatter)
|
||
|
||
# 设置y轴格式
|
||
ax2.yaxis.set_major_formatter(FuncFormatter(lambda x, p: f'{x/1000:.1f}K' if x >= 1000 else f'{x:.0f}'))
|
||
|
||
# 调整布局
|
||
plt.tight_layout()
|
||
plt.subplots_adjust(wspace=0.05) # 调整子图间距
|
||
|
||
# 保存图表
|
||
chart_file = 'au2512_volume_price_sequence.png'
|
||
plt.savefig(chart_file, dpi=300, bbox_inches='tight',
|
||
facecolor='white', edgecolor='none')
|
||
plt.close()
|
||
|
||
print(f"成交量-成交价序列图已保存: {chart_file}")
|
||
return chart_file
|
||
|
||
def print_detailed_analysis(self):
|
||
"""打印详细分析结果"""
|
||
print("\n" + "="*60)
|
||
print("AU2512期货成交量-成交价序列分析报告")
|
||
print("="*60)
|
||
|
||
# 基础信息
|
||
print(f"\n【基础数据】")
|
||
print(f"数据文件: {self.data_file}")
|
||
print(f"记录数量: {len(self.df):,} 条")
|
||
print(f"累计成交量: {self.total_volume:,} 手")
|
||
|
||
# 价格分析
|
||
print(f"\n【价格分析】")
|
||
print(f"起始价格: {self.df['成交价'].iloc[0]:.2f}")
|
||
print(f"结束价格: {self.df['成交价'].iloc[-1]:.2f}")
|
||
print(f"最高价格: {self.max_price:.2f}")
|
||
print(f"最低价格: {self.min_price:.2f}")
|
||
print(f"平均价格: {self.avg_price:.2f}")
|
||
print(f"价格波动: {(self.max_price - self.min_price):.2f}")
|
||
|
||
# 成交量分析
|
||
print(f"\n【成交量分析】")
|
||
print(f"平均单笔成交量: {self.df['volume_change'].mean():.1f} 手")
|
||
print(f"最大单笔成交量: {self.df['volume_change'].max():.0f} 手")
|
||
print(f"成交量标准差: {self.df['volume_change'].std():.1f} 手")
|
||
|
||
# 价格变化分析
|
||
final_price = self.df['成交价'].iloc[-1]
|
||
initial_price = self.df['成交价'].iloc[0]
|
||
total_change = final_price - initial_price
|
||
total_change_pct = (total_change / initial_price) * 100
|
||
|
||
print(f"\n【价格变化分析】")
|
||
print(f"总体变化: {total_change:+.2f} ({total_change_pct:+.2f}%)")
|
||
print(f"最大涨幅: {self.max_price - initial_price:+.2f}")
|
||
print(f"最大跌幅: {self.min_price - initial_price:+.2f}")
|
||
|
||
# 关键节点分析
|
||
print(f"\n【关键节点分析】")
|
||
for label, point in self.milestones.items():
|
||
if label != '起始':
|
||
prev_point = self.milestones.get('起始', self.df.iloc[0])
|
||
if label in ['10%', '25%', '50%', '75%', '90%', '结束']:
|
||
price_change = point['成交价'] - prev_point['成交价']
|
||
price_change_pct = (price_change / prev_point['成交价']) * 100
|
||
volume_added = point['累积成交量'] - prev_point['累积成交量']
|
||
|
||
print(f" {label:>6}: 价格 {point['成交价']:7.2f} ({price_change_pct:+6.2f}%) | "
|
||
f"成交量 +{volume_added:7,} 手")
|
||
|
||
print(f"\n【数据质量】")
|
||
missing_values = self.df.isnull().sum().sum()
|
||
duplicates = self.df.duplicated().sum()
|
||
print(f"缺失值: {missing_values} 个")
|
||
print(f"重复记录: {duplicates} 行")
|
||
print(f"数据完整性: {'优秀' if missing_values == 0 else '需要关注'}")
|
||
|
||
print(f"\n【业务洞察】")
|
||
print(f"1. 价格随成交量变化的完整轨迹清晰可见")
|
||
print(f"2. 整体趋势: {'上涨' if total_change > 0 else '下跌'} ({total_change_pct:+.2f}%)")
|
||
print(f"3. 交易过程中价格呈现 {'逐步上涨' if total_change > 0 else '逐步下跌'} 趋势")
|
||
print(f"4. 关键成交量节点对价格的影响显著")
|
||
|
||
print(f"\n【生成的图表】")
|
||
print(f" au2512_volume_price_sequence.png - 成交量-成交价序列图（含当前成交量柱状图）")
|
||
|
||
print(f"\n" + "="*60)
|
||
print("分析完成！")
|
||
print("="*60)
|
||
|
||
def run_analysis(self):
|
||
"""运行完整分析"""
|
||
print("开始AU2512期货成交量-成交价序列分析...")
|
||
|
||
# 加载数据
|
||
if not self.load_data():
|
||
return False
|
||
|
||
# 分析数据
|
||
self.analyze_data()
|
||
|
||
# 创建图表
|
||
self.create_volume_price_chart()
|
||
|
||
# 打印详细分析
|
||
self.print_detailed_analysis()
|
||
|
||
return True
|
||
|
||
def main():
|
||
"""主函数"""
|
||
parser = argparse.ArgumentParser(description='AU2512期货成交量-成交价序列分析工具')
|
||
parser.add_argument('data_file', nargs='?',
|
||
default='data/au2512_20251013.parquet',
|
||
help='数据文件路径 (默认: data/au2512_20251013.parquet)')
|
||
parser.add_argument('--output-dir', '-o', default='.',
|
||
help='输出目录 (默认: 当前目录)')
|
||
|
||
args = parser.parse_args()
|
||
|
||
# 检查数据文件是否存在
|
||
if not os.path.exists(args.data_file):
|
||
print(f"错误: 数据文件不存在 - {args.data_file}")
|
||
print("\n使用方法:")
|
||
print(" python volume_price_sequence.py [数据文件路径]")
|
||
print(f" python volume_price_sequence.py # 使用默认文件: {args.data_file}")
|
||
sys.exit(1)
|
||
|
||
# 创建分析器并运行
|
||
analyzer = VolumePriceAnalyzer(args.data_file)
|
||
success = analyzer.run_analysis()
|
||
|
||
if success:
|
||
print(f"\n分析完成！图表已保存到: {args.output_dir}")
|
||
else:
|
||
print(f"\n分析失败！")
|
||
sys.exit(1)
|
||
|
||
if __name__ == "__main__":
|
||
main() |