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+#!/usr/bin/env python
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+# -*- coding: utf-8 -*-
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+"""
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+期货开仓记录分析工具
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+分析 records.csv 中的期货交易数据，提供多维度统计分析
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+"""
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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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+import seaborn as sns
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+from datetime import datetime, timedelta
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+import re
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+import os
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+import sys
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+
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+# 设置中文字体
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+plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
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+plt.rcParams['axes.unicode_minus'] = False
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+
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+# 期货配置字典（从MAPatternStrategy_v002.py复制）
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+FUTURES_CONFIG = {
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+ # 贵金属
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+ 'AU': {'has_night_session': True, 'margin_rate': {'long': 0.21, 'short': 0.21}, 'multiplier': 1000, 'trading_start_time': '21:00'},
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+ 'AG': {'has_night_session': True, 'margin_rate': {'long': 0.22, 'short': 0.22}, 'multiplier': 15, 'trading_start_time': '21:00'},
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+
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+ # 有色金属
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+ 'CU': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'AL': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'ZN': {'has_night_session': True, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'PB': {'has_night_session': True, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'NI': {'has_night_session': True, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 1, 'trading_start_time': '21:00'},
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+ 'SN': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 1, 'trading_start_time': '21:00'},
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+ 'SS': {'has_night_session': True, 'margin_rate': {'long': 0.07, 'short': 0.07}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+
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+ # 黑色系
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+ 'RB': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'HC': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'I': {'has_night_session': True, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 100, 'trading_start_time': '21:00'},
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+ 'JM': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 100, 'trading_start_time': '21:00'},
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+ 'J': {'has_night_session': True, 'margin_rate': {'long': 0.25, 'short': 0.25}, 'multiplier': 60, 'trading_start_time': '21:00'},
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+
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+ # 能源化工
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+ 'SP': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'FU': {'has_night_session': True, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'BU': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'RU': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'BR': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'SC': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 1000, 'trading_start_time': '21:00'},
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+ 'NR': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'LU': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'LC': {'has_night_session': False, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 1, 'trading_start_time': '09:00'},
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+
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+ # 化工
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+ 'FG': {'has_night_session': True, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 20, 'trading_start_time': '21:00'},
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+ 'TA': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'MA': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'SA': {'has_night_session': True, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 20, 'trading_start_time': '21:00'},
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+ 'L': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'V': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'EG': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'PP': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'EB': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'PG': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 20, 'trading_start_time': '21:00'},
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+ 'PX': {'has_night_session': True, 'margin_rate': {'long': 0.1, 'short': 0.1}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+
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+ # 农产品
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+ 'RM': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'OI': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'CF': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'SR': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'PF': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'C': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'CS': {'has_night_session': True, 'margin_rate': {'long': 0.11, 'short': 0.11}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'CY': {'has_night_session': True, 'margin_rate': {'long': 0.11, 'short': 0.11}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'A': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'B': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'M': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'Y': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+ 'P': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
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+
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+ # 无夜盘品种
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+ 'IF': {'has_night_session': False, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 300, 'trading_start_time': '09:30'},
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+ 'IH': {'has_night_session': False, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 300, 'trading_start_time': '09:30'},
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+ 'IC': {'has_night_session': False, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 200, 'trading_start_time': '09:30'},
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+ 'IM': {'has_night_session': False, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 200, 'trading_start_time': '09:30'},
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+ 'AP': {'has_night_session': False, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 10, 'trading_start_time': '09:00'},
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+ 'CJ': {'has_night_session': False, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 5, 'trading_start_time': '09:00'},
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+ 'PK': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 5, 'trading_start_time': '09:00'},
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+ 'JD': {'has_night_session': False, 'margin_rate': {'long': 0.11, 'short': 0.11}, 'multiplier': 10, 'trading_start_time': '09:00'},
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+ 'LH': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 16, 'trading_start_time': '09:00'},
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+ 'T': {'has_night_session': False, 'margin_rate': {'long': 0.03, 'short': 0.03}, 'multiplier': 1000000, 'trading_start_time': '09:30'},
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+ 'PS': {'has_night_session': False, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 3, 'trading_start_time': '09:00'},
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+ 'UR': {'has_night_session': False, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 20, 'trading_start_time': '09:00'},
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+ 'MO': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 100, 'trading_start_time': '21:00'},
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+ 'HO': {'has_night_session': False, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 100, 'trading_start_time': '09:30'},
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+ 'LG': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 90, 'trading_start_time': '21:00'},
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+ 'EC': {'has_night_session': False, 'margin_rate': {'long': 0.23, 'short': 0.23}, 'multiplier': 50, 'trading_start_time': '09:00'},
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+ 'OP': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 40, 'trading_start_time': '09:00'},
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+ 'BC': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '21:00'},
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+ 'SH': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 30, 'trading_start_time': '21:00'},
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+ 'TS': {'has_night_session': False, 'margin_rate': {'long': 0.015, 'short': 0.015}, 'multiplier': 2000000, 'trading_start_time': '09:30'},
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+ 'AD': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '09:00'},
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+ 'PL': {'has_night_session': False, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 20, 'trading_start_time': '09:00'},
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+ 'SI': {'has_night_session': False, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '09:00'},
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+ 'SM': {'has_night_session': False, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '09:00'},
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+ 'AO': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 20, 'trading_start_time': '21:00'},
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+ 'TL': {'has_night_session': False, 'margin_rate': {'long': 0.045, 'short': 0.045}, 'multiplier': 1000000, 'trading_start_time': '09:00'},
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+ 'SF': {'has_night_session': False, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 5, 'trading_start_time': '09:00'},
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+ 'PR': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 15, 'trading_start_time': '21:00'},
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+ 'TF': {'has_night_session': False, 'margin_rate': {'long': 0.022, 'short': 0.022}, 'multiplier': 1000000, 'trading_start_time': '09:00'},
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+ 'BZ': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 30, 'trading_start_time': '09:00'},
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+}
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+
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+
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+def extract_symbol_code(target_str):
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+ """
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+ 从标的字段提取品种代码
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+ 例如: '原油2002(SC2002.XINE)' -> 'SC'
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+ """
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+ match = re.search(r'\(([A-Z]+)\d+\.', target_str)
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+ if match:
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+ return match.group(1)
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+ return None
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+
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+
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+def calculate_time_segment(order_time_str, trading_start_time_str):
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+ """
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+ 计算开仓时间相对于交易开始时间的时间段
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+ 返回: '<30分钟', '30-60分钟', '>1小时'
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+ """
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+ try:
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+ # 解析时间字符串
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+ order_time = datetime.strptime(order_time_str, '%H:%M:%S')
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+ start_time = datetime.strptime(trading_start_time_str, '%H:%M')
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+
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+ # 如果是夜盘品种（21:00开盘），需要特殊处理跨日情况
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+ if trading_start_time_str == '21:00':
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+ # 如果委托时间在21:00之前，说明是第二天的交易时间
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+ if order_time.hour < 21 and order_time.hour >= 0:
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+ # 加24小时处理跨日
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+ order_time = order_time + timedelta(days=1)
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+
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+ # 计算时间差（分钟）
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+ time_diff = (order_time - start_time).total_seconds() / 60
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+
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+ # 处理负数情况（可能是跨日）
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+ if time_diff < 0:
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+ time_diff += 24 * 60
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+
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+ # 分类
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+ if time_diff < 30:
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+ return '<30分钟'
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+ elif time_diff < 60:
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+ return '30-60分钟'
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+ else:
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+ return '>1小时'
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+ except Exception as e:
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+ print(f"时间计算错误: {order_time_str}, {trading_start_time_str}, {e}")
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+ return '未知'
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+
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+
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+def calculate_session_type(order_time_str, has_night_session):
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+ """
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+ 计算交易时段类型：夜盘、上午、下午
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+ """
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+ try:
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+ order_time = datetime.strptime(order_time_str, '%H:%M:%S')
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+ hour = order_time.hour
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+
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+ if has_night_session and (hour >= 21 or hour < 3):
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+ return '夜盘'
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+ elif 9 <= hour < 12:
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+ return '上午'
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+ elif 12 <= hour < 16:
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+ return '下午'
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+ else:
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+ return '其他'
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+ except:
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+ return '未知'
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+
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+
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+def load_and_preprocess_data(csv_path):
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+ """
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+ 加载并预处理数据
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+ """
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+ print("正在加载数据...")
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+ df = pd.read_csv(csv_path)
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+
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+ print(f"原始数据行数: {len(df)}")
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+ print(f"数据列: {df.columns.tolist()}")
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+
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+ # 提取品种代码
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+ df['品种代码'] = df['标的'].apply(extract_symbol_code)
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+
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+ # 获取品种配置信息
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+ df['trading_start_time'] = df['品种代码'].apply(
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+ lambda x: FUTURES_CONFIG.get(x, {}).get('trading_start_time', None)
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+ )
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+ df['has_night_session'] = df['品种代码'].apply(
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+ lambda x: FUTURES_CONFIG.get(x, {}).get('has_night_session', False)
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+ )
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+
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+ # 计算开盘后时间段
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+ df['开盘后时间段'] = df.apply(
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+ lambda row: calculate_time_segment(row['委托时间'], row['trading_start_time'])
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+ if pd.notna(row['trading_start_time']) else '未知',
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+ axis=1
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+ )
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+
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+ # 计算交易时段
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+ df['交易时段'] = df.apply(
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+ lambda row: calculate_session_type(row['委托时间'], row['has_night_session']),
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+ axis=1
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+ )
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+
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+ # 计算保证金收益率
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+ df['保证金收益率'] = (df['交易盈亏'] / df['保证金']) * 100
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+
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+ # 计算穿越均线数量
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+ df['穿越均线数量'] = df['crossed_ma_lines'].apply(
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+ lambda x: len(x.split(';')) if pd.notna(x) else 0
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+ )
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+
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+ # 判断是否盈利
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+ df['是否盈利'] = df['交易盈亏'] > 0
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+
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+ # 成交额分组
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+ df['成交额分组'] = pd.cut(df['成交额'],
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+ bins=[0, 100000, 200000, 500000, float('inf')],
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+ labels=['<10万', '10-20万', '20-50万', '>50万'])
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+
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+ print(f"预处理后数据行数: {len(df)}")
|
|
|
+ print(f"品种代码提取成功率: {df['品种代码'].notna().sum() / len(df) * 100:.2f}%")
|
|
|
+
|
|
|
+ return df
|
|
|
+
|
|
|
+
|
|
|
+def calculate_statistics(group_df):
|
|
|
+ """
|
|
|
+ 计算统计指标
|
|
|
+ """
|
|
|
+ total_count = len(group_df)
|
|
|
+ win_count = (group_df['交易盈亏'] > 0).sum()
|
|
|
+ win_rate = win_count / total_count if total_count > 0 else 0
|
|
|
+
|
|
|
+ avg_profit_loss = group_df['交易盈亏'].mean()
|
|
|
+
|
|
|
+ # 计算盈亏比
|
|
|
+ profit_trades = group_df[group_df['交易盈亏'] > 0]['交易盈亏']
|
|
|
+ loss_trades = group_df[group_df['交易盈亏'] <= 0]['交易盈亏']
|
|
|
+
|
|
|
+ avg_profit = profit_trades.mean() if len(profit_trades) > 0 else 0
|
|
|
+ avg_loss = abs(loss_trades.mean()) if len(loss_trades) > 0 else 0
|
|
|
+ profit_loss_ratio = avg_profit / avg_loss if avg_loss > 0 else np.inf
|
|
|
+
|
|
|
+ avg_margin_return = group_df['保证金收益率'].mean()
|
|
|
+
|
|
|
+ return pd.Series({
|
|
|
+ '出现次数': total_count,
|
|
|
+ '胜率': win_rate,
|
|
|
+ '平均盈亏': avg_profit_loss,
|
|
|
+ '盈亏比': profit_loss_ratio,
|
|
|
+ '平均保证金收益率': avg_margin_return
|
|
|
+ })
|
|
|
+
|
|
|
+
|
|
|
+def analyze_ma_lines(df):
|
|
|
+ """
|
|
|
+ 分析crossed_ma_lines维度
|
|
|
+ """
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("均线组合分析")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ ma_stats = df.groupby('crossed_ma_lines').apply(calculate_statistics).round(4)
|
|
|
+ ma_stats = ma_stats.sort_values('出现次数', ascending=False)
|
|
|
+
|
|
|
+ print(ma_stats.to_string())
|
|
|
+
|
|
|
+ return ma_stats
|
|
|
+
|
|
|
+
|
|
|
+def analyze_time_segment(df):
|
|
|
+ """
|
|
|
+ 分析开盘后时间段维度
|
|
|
+ """
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("开盘后时间段分析")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ time_stats = df.groupby('开盘后时间段').apply(calculate_statistics).round(4)
|
|
|
+
|
|
|
+ # 按指定顺序排列
|
|
|
+ order = ['<30分钟', '30-60分钟', '>1小时', '未知']
|
|
|
+ time_stats = time_stats.reindex([o for o in order if o in time_stats.index])
|
|
|
+
|
|
|
+ print(time_stats.to_string())
|
|
|
+
|
|
|
+ return time_stats
|
|
|
+
|
|
|
+
|
|
|
+def analyze_cross_dimension(df):
|
|
|
+ """
|
|
|
+ 交叉分析：均线组合 × 开盘后时间段
|
|
|
+ """
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("交叉分析：均线组合 × 开盘后时间段")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ # 样本量分布
|
|
|
+ cross_count = pd.crosstab(df['crossed_ma_lines'], df['开盘后时间段'])
|
|
|
+ print("\n样本量分布:")
|
|
|
+ print(cross_count.to_string())
|
|
|
+
|
|
|
+ # 胜率对比
|
|
|
+ cross_winrate = pd.crosstab(
|
|
|
+ df['crossed_ma_lines'],
|
|
|
+ df['开盘后时间段'],
|
|
|
+ values=df['是否盈利'],
|
|
|
+ aggfunc='mean'
|
|
|
+ ).round(4)
|
|
|
+ print("\n胜率对比:")
|
|
|
+ print(cross_winrate.to_string())
|
|
|
+
|
|
|
+ # 平均盈亏
|
|
|
+ cross_profit = pd.crosstab(
|
|
|
+ df['crossed_ma_lines'],
|
|
|
+ df['开盘后时间段'],
|
|
|
+ values=df['交易盈亏'],
|
|
|
+ aggfunc='mean'
|
|
|
+ ).round(2)
|
|
|
+ print("\n平均盈亏:")
|
|
|
+ print(cross_profit.to_string())
|
|
|
+
|
|
|
+ # 平均保证金收益率
|
|
|
+ cross_return = pd.crosstab(
|
|
|
+ df['crossed_ma_lines'],
|
|
|
+ df['开盘后时间段'],
|
|
|
+ values=df['保证金收益率'],
|
|
|
+ aggfunc='mean'
|
|
|
+ ).round(4)
|
|
|
+ print("\n平均保证金收益率(%):")
|
|
|
+ print(cross_return.to_string())
|
|
|
+
|
|
|
+ return cross_count, cross_winrate, cross_profit, cross_return
|
|
|
+
|
|
|
+
|
|
|
+def analyze_trade_type_and_variety(df):
|
|
|
+ """
|
|
|
+ 分析交易类型和品种维度
|
|
|
+ """
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("交易类型分析")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ trade_type_stats = df.groupby('交易类型').apply(calculate_statistics).round(4)
|
|
|
+ print(trade_type_stats.to_string())
|
|
|
+
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("品种类型分析")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ variety_stats = df.groupby('品种').apply(calculate_statistics).round(4)
|
|
|
+ print(variety_stats.to_string())
|
|
|
+
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("具体品种代码分析（前20名）")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ symbol_stats = df.groupby('品种代码').apply(calculate_statistics).round(4)
|
|
|
+ symbol_stats = symbol_stats.sort_values('出现次数', ascending=False).head(20)
|
|
|
+ print(symbol_stats.to_string())
|
|
|
+
|
|
|
+ return trade_type_stats, variety_stats, symbol_stats
|
|
|
+
|
|
|
+
|
|
|
+def analyze_additional_dimensions(df):
|
|
|
+ """
|
|
|
+ 其他维度分析
|
|
|
+ """
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("成交额分组分析")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ amount_stats = df.groupby('成交额分组').apply(calculate_statistics).round(4)
|
|
|
+ print(amount_stats.to_string())
|
|
|
+
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("交易时段分析")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ session_stats = df.groupby('交易时段').apply(calculate_statistics).round(4)
|
|
|
+ print(session_stats.to_string())
|
|
|
+
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("穿越均线数量分析")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ ma_count_stats = df.groupby('穿越均线数量').apply(calculate_statistics).round(4)
|
|
|
+ print(ma_count_stats.to_string())
|
|
|
+
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("多空对比（按均线组合）- 前10个组合")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ # 获取出现次数最多的前10个均线组合
|
|
|
+ top_ma_lines = df['crossed_ma_lines'].value_counts().head(10).index
|
|
|
+ df_top = df[df['crossed_ma_lines'].isin(top_ma_lines)]
|
|
|
+
|
|
|
+ long_short_stats = df_top.groupby(['crossed_ma_lines', '交易类型']).apply(
|
|
|
+ calculate_statistics
|
|
|
+ ).round(4)
|
|
|
+ print(long_short_stats.to_string())
|
|
|
+
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("品种特性分析（有夜盘 vs 无夜盘）")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ night_session_stats = df.groupby('has_night_session').apply(calculate_statistics).round(4)
|
|
|
+ night_session_stats.index = ['无夜盘', '有夜盘']
|
|
|
+ print(night_session_stats.to_string())
|
|
|
+
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("组合策略分析：最佳组合（样本量>=10）")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ # 三维组合分析
|
|
|
+ combo_stats = df.groupby(['crossed_ma_lines', '开盘后时间段', '交易类型']).apply(
|
|
|
+ calculate_statistics
|
|
|
+ ).round(4)
|
|
|
+
|
|
|
+ # 筛选样本量>=10的组合
|
|
|
+ combo_stats = combo_stats[combo_stats['出现次数'] >= 10]
|
|
|
+
|
|
|
+ # 按保证金收益率排序，显示前10
|
|
|
+ combo_stats_sorted = combo_stats.sort_values('平均保证金收益率', ascending=False).head(10)
|
|
|
+ print("\n保证金收益率最高的10个组合:")
|
|
|
+ print(combo_stats_sorted.to_string())
|
|
|
+
|
|
|
+ # 按胜率排序，显示前10
|
|
|
+ combo_stats_sorted_winrate = combo_stats.sort_values('胜率', ascending=False).head(10)
|
|
|
+ print("\n胜率最高的10个组合:")
|
|
|
+ print(combo_stats_sorted_winrate.to_string())
|
|
|
+
|
|
|
+ return {
|
|
|
+ 'amount_stats': amount_stats,
|
|
|
+ 'session_stats': session_stats,
|
|
|
+ 'ma_count_stats': ma_count_stats,
|
|
|
+ 'long_short_stats': long_short_stats,
|
|
|
+ 'night_session_stats': night_session_stats,
|
|
|
+ 'combo_stats': combo_stats
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+def create_visualizations(df, ma_stats, time_stats, cross_winrate, cross_profit, cross_return, output_dir):
|
|
|
+ """
|
|
|
+ 创建数据可视化图表
|
|
|
+ """
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("生成可视化图表...")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ # 创建输出目录
|
|
|
+ os.makedirs(output_dir, exist_ok=True)
|
|
|
+
|
|
|
+ def annotate_barh(ax, bars, formatter=lambda v: f"{v:.0f}", offset_ratio=0.01):
|
|
|
+ """
|
|
|
+ 为水平柱状图添加数值标注
|
|
|
+ """
|
|
|
+ if bars is None or len(bars) == 0:
|
|
|
+ return
|
|
|
+ max_width = max((bar.get_width() for bar in bars), default=0)
|
|
|
+ offset = max(max_width * offset_ratio, 0.5)
|
|
|
+ for bar in bars:
|
|
|
+ width = bar.get_width()
|
|
|
+ if np.isnan(width):
|
|
|
+ continue
|
|
|
+ ha = 'left'
|
|
|
+ x = width + offset
|
|
|
+ if width < 0:
|
|
|
+ ha = 'right'
|
|
|
+ x = width - offset
|
|
|
+ y = bar.get_y() + bar.get_height() / 2
|
|
|
+ ax.text(x, y, formatter(width), va='center', ha=ha, fontsize=9)
|
|
|
+
|
|
|
+ def annotate_bar(ax, bars, formatter=lambda v: f"{v:.0f}", offset_ratio=0.01):
|
|
|
+ """
|
|
|
+ 为垂直柱状图添加数值标注
|
|
|
+ """
|
|
|
+ if bars is None or len(bars) == 0:
|
|
|
+ return
|
|
|
+ max_height = max((bar.get_height() for bar in bars), default=0)
|
|
|
+ offset = max(max_height * offset_ratio, 0.5)
|
|
|
+ for bar in bars:
|
|
|
+ height = bar.get_height()
|
|
|
+ if np.isnan(height):
|
|
|
+ continue
|
|
|
+ va = 'bottom'
|
|
|
+ y = height + offset
|
|
|
+ if height < 0:
|
|
|
+ va = 'top'
|
|
|
+ y = height - offset
|
|
|
+ x = bar.get_x() + bar.get_width() / 2
|
|
|
+ ax.text(x, y, formatter(height), va=va, ha='center', fontsize=9)
|
|
|
+
|
|
|
+ # 1. 均线组合表现对比（前15个）
|
|
|
+ fig, axes = plt.subplots(2, 2, figsize=(16, 12))
|
|
|
+
|
|
|
+ top_ma = ma_stats.head(15)
|
|
|
+
|
|
|
+ # 出现次数
|
|
|
+ bars = axes[0, 0].barh(range(len(top_ma)), top_ma['出现次数'])
|
|
|
+ axes[0, 0].set_yticks(range(len(top_ma)))
|
|
|
+ axes[0, 0].set_yticklabels(top_ma.index)
|
|
|
+ axes[0, 0].set_xlabel('出现次数')
|
|
|
+ axes[0, 0].set_title('均线组合出现次数（Top 15）')
|
|
|
+ axes[0, 0].invert_yaxis()
|
|
|
+ annotate_barh(axes[0, 0], bars)
|
|
|
+
|
|
|
+ # 胜率
|
|
|
+ colors = ['green' if x > 0.5 else 'red' for x in top_ma['胜率']]
|
|
|
+ bars = axes[0, 1].barh(range(len(top_ma)), top_ma['胜率'], color=colors)
|
|
|
+ axes[0, 1].set_yticks(range(len(top_ma)))
|
|
|
+ axes[0, 1].set_yticklabels(top_ma.index)
|
|
|
+ axes[0, 1].set_xlabel('胜率')
|
|
|
+ axes[0, 1].set_title('均线组合胜率（Top 15）')
|
|
|
+ axes[0, 1].axvline(x=0.5, color='black', linestyle='--', alpha=0.5)
|
|
|
+ axes[0, 1].invert_yaxis()
|
|
|
+ annotate_barh(axes[0, 1], bars, formatter=lambda v: f"{v:.1%}", offset_ratio=0.02)
|
|
|
+
|
|
|
+ # 平均盈亏
|
|
|
+ colors = ['green' if x > 0 else 'red' for x in top_ma['平均盈亏']]
|
|
|
+ bars = axes[1, 0].barh(range(len(top_ma)), top_ma['平均盈亏'], color=colors)
|
|
|
+ axes[1, 0].set_yticks(range(len(top_ma)))
|
|
|
+ axes[1, 0].set_yticklabels(top_ma.index)
|
|
|
+ axes[1, 0].set_xlabel('平均盈亏（元）')
|
|
|
+ axes[1, 0].set_title('均线组合平均盈亏（Top 15）')
|
|
|
+ axes[1, 0].axvline(x=0, color='black', linestyle='--', alpha=0.5)
|
|
|
+ axes[1, 0].invert_yaxis()
|
|
|
+ annotate_barh(axes[1, 0], bars, formatter=lambda v: f"{v:,.0f}", offset_ratio=0.015)
|
|
|
+
|
|
|
+ # 保证金收益率
|
|
|
+ colors = ['green' if x > 0 else 'red' for x in top_ma['平均保证金收益率']]
|
|
|
+ bars = axes[1, 1].barh(range(len(top_ma)), top_ma['平均保证金收益率'], color=colors)
|
|
|
+ axes[1, 1].set_yticks(range(len(top_ma)))
|
|
|
+ axes[1, 1].set_yticklabels(top_ma.index)
|
|
|
+ axes[1, 1].set_xlabel('平均保证金收益率（%）')
|
|
|
+ axes[1, 1].set_title('均线组合平均保证金收益率（Top 15）')
|
|
|
+ axes[1, 1].axvline(x=0, color='black', linestyle='--', alpha=0.5)
|
|
|
+ axes[1, 1].invert_yaxis()
|
|
|
+ annotate_barh(axes[1, 1], bars, formatter=lambda v: f"{v:.2f}%", offset_ratio=0.02)
|
|
|
+
|
|
|
+ plt.tight_layout()
|
|
|
+ plt.savefig(os.path.join(output_dir, 'ma_lines_analysis.png'), dpi=150, bbox_inches='tight')
|
|
|
+ print(f"已保存: {os.path.join(output_dir, 'ma_lines_analysis.png')}")
|
|
|
+ plt.close()
|
|
|
+
|
|
|
+ # 2. 开盘后时间段表现
|
|
|
+ fig, axes = plt.subplots(1, 2, figsize=(14, 6))
|
|
|
+ order = ['<30分钟', '30-60分钟', '>1小时']
|
|
|
+ time_stats_filtered = time_stats[time_stats.index.isin(order)]
|
|
|
+ time_stats_filtered = time_stats_filtered.loc[[idx for idx in order if idx in time_stats_filtered.index]]
|
|
|
+
|
|
|
+ profit_colors = ['green' if val >= 0 else 'red' for val in time_stats_filtered['平均盈亏']]
|
|
|
+ bars = axes[0].bar(range(len(time_stats_filtered)), time_stats_filtered['平均盈亏'], color=profit_colors)
|
|
|
+ axes[0].set_xticks(range(len(time_stats_filtered)))
|
|
|
+ axes[0].set_xticklabels(time_stats_filtered.index)
|
|
|
+ axes[0].set_ylabel('平均盈亏（元）')
|
|
|
+ axes[0].set_title('不同时间段平均盈亏')
|
|
|
+ axes[0].axhline(y=0, color='black', linestyle='--', alpha=0.5)
|
|
|
+ annotate_bar(axes[0], bars, formatter=lambda v: f"{v:,.0f}")
|
|
|
+
|
|
|
+ margin_colors = ['green' if val >= 0 else 'red' for val in time_stats_filtered['平均保证金收益率']]
|
|
|
+ bars = axes[1].bar(range(len(time_stats_filtered)), time_stats_filtered['平均保证金收益率'], color=margin_colors)
|
|
|
+ axes[1].set_xticks(range(len(time_stats_filtered)))
|
|
|
+ axes[1].set_xticklabels(time_stats_filtered.index)
|
|
|
+ axes[1].set_ylabel('平均保证金收益率（%）')
|
|
|
+ axes[1].set_title('不同时间段平均保证金收益率')
|
|
|
+ axes[1].axhline(y=0, color='black', linestyle='--', alpha=0.5)
|
|
|
+ annotate_bar(axes[1], bars, formatter=lambda v: f"{v:.2f}%")
|
|
|
+
|
|
|
+ plt.tight_layout()
|
|
|
+ plt.savefig(os.path.join(output_dir, 'time_segment_analysis.png'), dpi=150, bbox_inches='tight')
|
|
|
+ print(f"已保存: {os.path.join(output_dir, 'time_segment_analysis.png')}")
|
|
|
+ plt.close()
|
|
|
+
|
|
|
+ # 3. 交叉分析热力图
|
|
|
+ fig, axes = plt.subplots(1, 3, figsize=(22, 10))
|
|
|
+
|
|
|
+ # 选择前15个均线组合
|
|
|
+ top_ma_lines = ma_stats.head(15).index
|
|
|
+ heatmap_cols = ['<30分钟', '30-60分钟', '>1小时']
|
|
|
+
|
|
|
+ def prepare_heatmap(table):
|
|
|
+ filtered = table.reindex(index=[idx for idx in top_ma_lines if idx in table.index])
|
|
|
+ if filtered.empty:
|
|
|
+ return filtered
|
|
|
+ cols = [col for col in heatmap_cols if col in filtered.columns]
|
|
|
+ if cols:
|
|
|
+ filtered = filtered[cols]
|
|
|
+ return filtered
|
|
|
+
|
|
|
+ cross_winrate_filtered = prepare_heatmap(cross_winrate)
|
|
|
+ cross_profit_filtered = prepare_heatmap(cross_profit)
|
|
|
+ cross_return_filtered = prepare_heatmap(cross_return)
|
|
|
+
|
|
|
+ # 胜率热力图
|
|
|
+ sns.heatmap(cross_winrate_filtered, annot=True, fmt='.2f', cmap='RdYlGn',
|
|
|
+ center=0.5, vmin=0, vmax=1, ax=axes[0], cbar_kws={'label': '胜率'})
|
|
|
+ axes[0].set_title('均线组合 × 时间段 胜率热力图（Top 15）')
|
|
|
+ axes[0].set_xlabel('开盘后时间段')
|
|
|
+ axes[0].set_ylabel('均线组合')
|
|
|
+
|
|
|
+ # 平均盈亏热力图
|
|
|
+ sns.heatmap(cross_profit_filtered, annot=True, fmt='.0f', cmap='RdYlGn', center=0,
|
|
|
+ ax=axes[1], cbar_kws={'label': '平均盈亏（元）'})
|
|
|
+ axes[1].set_title('均线组合 × 时间段 平均盈亏热力图（Top 15）')
|
|
|
+ axes[1].set_xlabel('开盘后时间段')
|
|
|
+ axes[1].set_ylabel('均线组合')
|
|
|
+
|
|
|
+ # 平均保证金收益率热力图
|
|
|
+ sns.heatmap(cross_return_filtered, annot=True, fmt='.2f', cmap='RdYlGn', center=0,
|
|
|
+ ax=axes[2], cbar_kws={'label': '平均保证金收益率（%）'})
|
|
|
+ axes[2].set_title('均线组合 × 时间段 平均保证金收益率热力图（Top 15）')
|
|
|
+ axes[2].set_xlabel('开盘后时间段')
|
|
|
+ axes[2].set_ylabel('均线组合')
|
|
|
+
|
|
|
+ plt.tight_layout()
|
|
|
+ plt.savefig(os.path.join(output_dir, 'cross_analysis_heatmap.png'), dpi=150, bbox_inches='tight')
|
|
|
+ print(f"已保存: {os.path.join(output_dir, 'cross_analysis_heatmap.png')}")
|
|
|
+ plt.close()
|
|
|
+
|
|
|
+ # 4. 品种表现分析
|
|
|
+ fig, axes = plt.subplots(2, 2, figsize=(16, 12))
|
|
|
+
|
|
|
+ # 交易类型对比
|
|
|
+ trade_type_stats = df.groupby('交易类型').apply(calculate_statistics)
|
|
|
+ axes[0, 0].bar(trade_type_stats.index, trade_type_stats['胜率'],
|
|
|
+ color=['green', 'red'])
|
|
|
+ axes[0, 0].set_ylabel('胜率')
|
|
|
+ axes[0, 0].set_title('交易类型胜率对比')
|
|
|
+ axes[0, 0].axhline(y=0.5, color='black', linestyle='--', alpha=0.5)
|
|
|
+
|
|
|
+ axes[0, 1].bar(trade_type_stats.index, trade_type_stats['平均保证金收益率'],
|
|
|
+ color=['green', 'red'])
|
|
|
+ axes[0, 1].set_ylabel('平均保证金收益率（%）')
|
|
|
+ axes[0, 1].set_title('交易类型保证金收益率对比')
|
|
|
+ axes[0, 1].axhline(y=0, color='black', linestyle='--', alpha=0.5)
|
|
|
+
|
|
|
+ # 品种类型对比
|
|
|
+ variety_stats = df.groupby('品种').apply(calculate_statistics)
|
|
|
+ axes[1, 0].bar(variety_stats.index, variety_stats['胜率'])
|
|
|
+ axes[1, 0].set_ylabel('胜率')
|
|
|
+ axes[1, 0].set_title('品种类型胜率对比')
|
|
|
+ axes[1, 0].axhline(y=0.5, color='black', linestyle='--', alpha=0.5)
|
|
|
+
|
|
|
+ axes[1, 1].bar(variety_stats.index, variety_stats['平均保证金收益率'])
|
|
|
+ axes[1, 1].set_ylabel('平均保证金收益率（%）')
|
|
|
+ axes[1, 1].set_title('品种类型保证金收益率对比')
|
|
|
+ axes[1, 1].axhline(y=0, color='black', linestyle='--', alpha=0.5)
|
|
|
+
|
|
|
+ plt.tight_layout()
|
|
|
+ plt.savefig(os.path.join(output_dir, 'variety_analysis.png'), dpi=150, bbox_inches='tight')
|
|
|
+ print(f"已保存: {os.path.join(output_dir, 'variety_analysis.png')}")
|
|
|
+ plt.close()
|
|
|
+
|
|
|
+ print("\n所有图表已生成！")
|
|
|
+
|
|
|
+
|
|
|
+def save_results_to_csv(df, ma_stats, time_stats, output_dir):
|
|
|
+ """
|
|
|
+ 保存分析结果到CSV
|
|
|
+ """
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("保存分析结果到CSV...")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ # 保存增强后的原始数据
|
|
|
+ output_file = os.path.join(output_dir, 'records_enhanced.csv')
|
|
|
+ df.to_csv(output_file, index=False, encoding='utf-8-sig')
|
|
|
+ print(f"已保存增强数据: {output_file}")
|
|
|
+
|
|
|
+ # 保存均线组合统计
|
|
|
+ output_file = os.path.join(output_dir, 'ma_lines_stats.csv')
|
|
|
+ ma_stats.to_csv(output_file, encoding='utf-8-sig')
|
|
|
+ print(f"已保存均线组合统计: {output_file}")
|
|
|
+
|
|
|
+ # 保存时间段统计
|
|
|
+ output_file = os.path.join(output_dir, 'time_segment_stats.csv')
|
|
|
+ time_stats.to_csv(output_file, encoding='utf-8-sig')
|
|
|
+ print(f"已保存时间段统计: {output_file}")
|
|
|
+
|
|
|
+ # 保存品种统计
|
|
|
+ symbol_stats = df.groupby('品种代码').apply(calculate_statistics)
|
|
|
+ output_file = os.path.join(output_dir, 'symbol_stats.csv')
|
|
|
+ symbol_stats.to_csv(output_file, encoding='utf-8-sig')
|
|
|
+ print(f"已保存品种统计: {output_file}")
|
|
|
+
|
|
|
+ # 保存组合策略统计
|
|
|
+ combo_stats = df.groupby(['crossed_ma_lines', '开盘后时间段', '交易类型']).apply(
|
|
|
+ calculate_statistics
|
|
|
+ )
|
|
|
+ combo_stats = combo_stats[combo_stats['出现次数'] >= 5]
|
|
|
+ combo_stats = combo_stats.sort_values('平均保证金收益率', ascending=False)
|
|
|
+ output_file = os.path.join(output_dir, 'combo_strategy_stats.csv')
|
|
|
+ combo_stats.to_csv(output_file, encoding='utf-8-sig')
|
|
|
+ print(f"已保存组合策略统计: {output_file}")
|
|
|
+
|
|
|
+
|
|
|
+def main():
|
|
|
+ """
|
|
|
+ 主函数
|
|
|
+ """
|
|
|
+ # 设置路径
|
|
|
+ script_dir = os.path.dirname(os.path.abspath(__file__))
|
|
|
+ csv_path = os.path.join(script_dir, 'records.csv')
|
|
|
+ output_dir = os.path.join('data', 'future', 'analysis_results')
|
|
|
+
|
|
|
+ # 检查文件是否存在
|
|
|
+ if not os.path.exists(csv_path):
|
|
|
+ print(f"错误: 找不到文件 {csv_path}")
|
|
|
+ return
|
|
|
+
|
|
|
+ print("="*80)
|
|
|
+ print("期货开仓记录分析工具")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+ # 加载和预处理数据
|
|
|
+ df = load_and_preprocess_data(csv_path)
|
|
|
+
|
|
|
+ # 进行各维度分析
|
|
|
+ ma_stats = analyze_ma_lines(df)
|
|
|
+ time_stats = analyze_time_segment(df)
|
|
|
+ cross_count, cross_winrate, cross_profit, cross_return = analyze_cross_dimension(df)
|
|
|
+ trade_type_stats, variety_stats, symbol_stats = analyze_trade_type_and_variety(df)
|
|
|
+ additional_stats = analyze_additional_dimensions(df)
|
|
|
+
|
|
|
+ # 生成可视化图表
|
|
|
+ create_visualizations(df, ma_stats, time_stats, cross_winrate, cross_profit, cross_return, output_dir)
|
|
|
+
|
|
|
+ # 保存结果到CSV
|
|
|
+ save_results_to_csv(df, ma_stats, time_stats, output_dir)
|
|
|
+
|
|
|
+ print("\n" + "="*80)
|
|
|
+ print("分析完成！")
|
|
|
+ print(f"结果保存在: {output_dir}")
|
|
|
+ print("="*80)
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == '__main__':
|
|
|
+ main()
|
|
|
+
|
maxfeng