456 lines
16 KiB
Python
456 lines
16 KiB
Python
import requests
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import pandas as pd
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import json
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import re
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import time
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import matplotlib.pyplot as plt
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import datetime
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import numpy as np
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from matplotlib.patches import Rectangle
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def get_fund_basic_info(fund_code):
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"""
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获取基金综合信息，包括实时估值、基本资料、持仓等。
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合并了原 realtmfundinfo 和 get_fund_basic_info 的功能。
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Args:
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fund_code (str): 基金代码
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Returns:
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dict: 包含基金信息的字典，包含以下字段：
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- fundcode: 基金代码
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- name: 基金名称
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- jzrq: 净值日期
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- dwjz: 单位净值
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- gsz: 估算净值
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- gszzl: 估算增长率
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- gztime: 估算时间
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- fund_rate: 现费率
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- fund_min_subscription: 最小申购金额
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- stock_codes: 前十大持仓股票代码列表
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- fund_size: 基金规模（亿元）
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"""
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info = {}
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# 1. 获取实时估值信息 (原 realtmfundinfo)
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try:
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# 天天基金实时估值接口
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real_time_url = f"http://fundgz.1234567.com.cn/js/{fund_code}.js"
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response = requests.get(real_time_url, timeout=5)
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if response.status_code == 200:
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content = response.text
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# 提取 jsonpgz({...}) 中的 json 部分
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match = re.search(r"jsonpgz\((.*?)\);", content)
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if match:
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real_time_data = json.loads(match.group(1))
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info.update(real_time_data)
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except Exception as e:
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print(f"获取实时估值信息失败: {e}")
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# 2. 获取基本资料
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try:
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basic_url = f"https://fund.eastmoney.com/pingzhongdata/{fund_code}.js"
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response = requests.get(basic_url, timeout=5)
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if response.status_code == 200:
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content = response.text
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# 基金名称 (如果实时接口没获取到，尝试在这里获取)
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if 'name' not in info:
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name_match = re.search(r'var fS_name\s*=\s*"(.*?)";', content)
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if name_match:
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info['name'] = name_match.group(1)
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# 现费率
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rate_match = re.search(r'var fund_Rate\s*=\s*"(.*?)";', content)
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if rate_match:
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info['fund_rate'] = rate_match.group(1)
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# 最小申购金额
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min_match = re.search(r'var fund_minsg\s*=\s*"(.*?)";', content)
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if min_match:
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info['fund_min_subscription'] = min_match.group(1)
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# 前十大持仓
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stock_codes_match = re.search(r'var stockCodes\s*=\s*(\[.*?\]);', content)
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if stock_codes_match:
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try:
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codes_raw = json.loads(stock_codes_match.group(1))
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# 天天基金返回的code有时候带有交易所后缀（如0025580），取前6位
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info['stock_codes'] = [code[:6] for code in codes_raw]
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except:
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info['stock_codes'] = []
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# 基金规模
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asset_alloc_match = re.search(r'var Data_assetAllocation\s*=\s*(\{.*?\});', content, re.DOTALL)
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if asset_alloc_match:
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try:
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asset_data = json.loads(asset_alloc_match.group(1))
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net_asset_series = next((s for s in asset_data.get('series', []) if s.get('name') == '净资产'), None)
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if net_asset_series and net_asset_series.get('data'):
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info['fund_size'] = net_asset_series['data'][-1]
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except:
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info['fund_size'] = None
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except Exception as e:
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print(f"获取基本资料失败: {e}")
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return info
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def get_fund_net_worth_history(fund_code, years=None):
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"""
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获取基金历史净值数据
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Args:
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fund_code (str): 基金代码
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years (int, optional): 显示最近多少年的数据，None表示显示全部数据
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Returns:
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pd.DataFrame: 包含日期和净值的DataFrame
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"""
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url = f"https://fund.eastmoney.com/pingzhongdata/{fund_code}.js"
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try:
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response = requests.get(url)
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response.raise_for_status()
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content = response.text
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# 使用正则表达式提取 Data_netWorthTrend
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match = re.search(r'var Data_netWorthTrend\s*=\s*(\[.*?\]);', content, re.DOTALL)
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if not match:
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print(f"无法在响应中找到 Data_netWorthTrend 数据 (基金代码: {fund_code})")
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return None
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json_str = match.group(1)
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data = json.loads(json_str)
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# 转换为DataFrame
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df = pd.DataFrame(data)
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# 处理数据
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df['date'] = pd.to_datetime(df['x'], unit='ms')
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df['net_worth'] = df['y']
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# 只保留需要的列
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result_df = df[['date', 'net_worth']].copy()
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result_df.set_index('date', inplace=True)
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result_df.sort_index(inplace=True)
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# 如果指定了年份，筛选最近的数据
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if years is not None:
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cutoff_date = result_df.index[-1] - pd.DateOffset(years=years)
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result_df = result_df[result_df.index >= cutoff_date]
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return result_df
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except requests.RequestException as e:
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print(f"网络请求错误: {e}")
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return None
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except json.JSONDecodeError as e:
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print(f"JSON解析错误: {e}")
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return None
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except Exception as e:
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print(f"发生未知错误: {e}")
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return None
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def calculate_max_drawdown(net_worth_series):
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"""
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计算最大回撤
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Args:
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net_worth_series: 净值序列
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Returns:
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dict: 包含最大回撤信息的字典
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"""
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if len(net_worth_series) < 2:
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return {
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'max_drawdown': 0,
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'drawdown_period': 0,
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'drawdown_start': None,
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'drawdown_end': None,
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'drawdown_start_value': 0,
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'drawdown_end_value': 0
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}
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# 计算累积最大值
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cumulative_max = net_worth_series.expanding().max()
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# 计算回撤
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drawdown = (net_worth_series - cumulative_max) / cumulative_max * 100
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drawdown_abs = (cumulative_max - net_worth_series) / cumulative_max * 100
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# 最大回撤值（负值表示回撤）
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max_dd_value = drawdown.min()
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max_dd_idx = drawdown.idxmin()
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if pd.isna(max_dd_value) or max_dd_value == 0:
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return {
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'max_drawdown': 0,
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'drawdown_period': 0,
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'drawdown_start': None,
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'drawdown_end': None,
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'drawdown_start_value': 0,
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'drawdown_end_value': 0
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}
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# 找到最大回撤开始点（最高点）
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max_dd_end = max_dd_idx
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temp_series = net_worth_series[:max_dd_end]
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if len(temp_series) > 0:
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max_dd_start = temp_series.idxmax()
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max_dd_start_value = net_worth_series.loc[max_dd_start]
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max_dd_end_value = net_worth_series.loc[max_dd_end]
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max_dd_period = (max_dd_end - max_dd_start).days
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else:
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max_dd_start = None
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max_dd_start_value = 0
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max_dd_end_value = 0
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max_dd_period = 0
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return {
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'max_drawdown': -max_dd_value, # 转为正数
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'drawdown_period': max_dd_period,
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'drawdown_start': max_dd_start,
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'drawdown_end': max_dd_end,
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'drawdown_start_value': max_dd_start_value,
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'drawdown_end_value': max_dd_end_value
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}
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def calculate_technical_indicators(df, window=20):
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"""
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计算技术指标
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Args:
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df: 包含net_worth的DataFrame
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window: 移动平均窗口
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Returns:
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DataFrame: 包含技术指标的DataFrame
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"""
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result_df = df.copy()
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# 计算移动平均线
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if len(result_df) >= window:
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result_df[f'MA{window}'] = result_df['net_worth'].rolling(window=window, min_periods=1).mean()
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else:
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result_df[f'MA{window}'] = result_df['net_worth']
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# 计算收益率
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if len(result_df) > 1:
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result_df['daily_return'] = result_df['net_worth'].pct_change() * 100
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return result_df
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def plot_fund_vector_graph(fund_code, years=None, save_path=None):
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"""
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绘制基金历史表现矢量图
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Args:
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fund_code (str): 基金代码
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years (int, optional): 显示最近多少年的数据，None表示显示全部数据，1表示最近1年的数据，依此类推.
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save_path (str, optional): 保存路径，例如 'fund_plot.svg'. 默认为 None (显示图表).
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"""
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# 首先获取基金的基本信息用于标题
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try:
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fund_info = get_fund_basic_info(fund_code)
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fund_name = fund_info.get('name', f'基金{fund_code}')
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except:
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fund_name = f'基金{fund_code}'
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# 获取数据
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df = get_fund_net_worth_history(fund_code, years)
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if df is None or df.empty:
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print("没有数据可供绘图")
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return
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if len(df) < 20:
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print(f"数据不足，至少需要20个交易日的数据，当前只有{len(df)}个")
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return
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# 计算技术指标
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df = calculate_technical_indicators(df, window=20)
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# 计算最大回撤
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max_dd_info = calculate_max_drawdown(df['net_worth'])
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# 计算收益和风险指标
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if 'daily_return' in df.columns:
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returns = df['daily_return'].dropna()
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if len(returns) > 0:
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mean_return = returns.mean()
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std_return = returns.std()
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total_return = (df['net_worth'].iloc[-1] / df['net_worth'].iloc[0] - 1) * 100
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positive_days = (returns > 0).sum()
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total_days = len(returns)
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positive_ratio = positive_days / total_days * 100
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# 年化收益率（假设一年252个交易日）
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if len(df) > 252:
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annual_return = ((1 + total_return/100) ** (252/len(df)) - 1) * 100
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else:
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annual_return = total_return
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else:
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mean_return = 0
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std_return = 0
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total_return = 0
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positive_days = 0
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total_days = 0
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positive_ratio = 0
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annual_return = 0
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else:
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mean_return = 0
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std_return = 0
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total_return = 0
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positive_days = 0
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total_days = 0
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positive_ratio = 0
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annual_return = 0
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# 设置绘图风格
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plt.style.use('seaborn-v0_8-darkgrid')
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# 支持中文显示
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try:
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Arial Unicode MS', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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except:
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pass
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# 创建图形
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fig = plt.figure(figsize=(14, 10))
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# 创建子图网格
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gs = fig.add_gridspec(3, 2, height_ratios=[2, 1, 0.8], width_ratios=[3, 1])
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# 主图 - 净值走势
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ax1 = fig.add_subplot(gs[0, :])
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# 绘制单位净值走势
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ax1.plot(df.index, df['net_worth'],
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linewidth=2,
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color='#1f77b4',
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label='单位净值',
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alpha=0.8)
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# 绘制20日移动平均线
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ax1.plot(df.index, df['MA20'],
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'orange',
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linewidth=1.5,
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alpha=0.7,
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label='20日均线')
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# 标记最大回撤区间
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if (max_dd_info['drawdown_start'] is not None and
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max_dd_info['drawdown_end'] is not None):
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# 找到最大回撤区间内的数据
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mask = (df.index >= max_dd_info['drawdown_start']) & (df.index <= max_dd_info['drawdown_end'])
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if mask.any():
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# 填充最大回撤区间
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ax1.fill_between(df.index[mask],
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df.loc[mask, 'net_worth'].min(),
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df.loc[mask, 'net_worth'].max(),
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color='red', alpha=0.2, label='最大回撤区间')
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# 标注最大回撤
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ax1.annotate(f"最大回撤: {max_dd_info['max_drawdown']:.2f}%\n"
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f"持续: {max_dd_info['drawdown_period']}天",
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xy=(max_dd_info['drawdown_end'],
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max_dd_info['drawdown_end_value']),
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xytext=(10, -30), textcoords='offset points',
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bbox=dict(boxstyle="round,pad=0.3", facecolor="red", alpha=0.7),
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fontsize=9,
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arrowprops=dict(arrowstyle="->", color='red', alpha=0.7))
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# 标记当前点
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last_date = df.index[-1]
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last_value = df['net_worth'].iloc[-1]
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ax1.scatter([last_date], [last_value], color='green', s=100, zorder=5)
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ax1.annotate(f'当前: {last_value:.3f}',
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xy=(last_date, last_value),
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xytext=(10, 10), textcoords='offset points',
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bbox=dict(boxstyle="round,pad=0.3", facecolor="green", alpha=0.7))
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# 设置主图属性
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year_text = f' ({years}年)' if years else ' (全部数据)'
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ax1.set_title(f'{fund_name} {year_text}', fontsize=16, fontweight='bold', pad=20)
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ax1.set_ylabel('单位净值 (元)', fontsize=12)
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ax1.grid(True, alpha=0.3)
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ax1.legend(loc='upper left', fontsize=11)
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# 收益率子图
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ax2 = fig.add_subplot(gs[1, :], sharex=ax1)
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if 'daily_return' in df.columns:
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returns = df['daily_return'].dropna()
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if len(returns) > 0:
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# 绘制收益率柱状图
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colors = ['red' if r >= 0 else 'green' for r in returns]
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ax2.bar(returns.index, returns, color=colors, alpha=0.7, width=1.0)
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# 添加零线
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ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5, alpha=0.5)
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ax2.set_ylabel('日收益率 (%)', fontsize=12)
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ax2.grid(True, alpha=0.3)
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# 统计信息子图
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ax3 = fig.add_subplot(gs[2, 0])
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ax3.axis('off')
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# 创建统计信息表格
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stats_data = [
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['总收益率', f'{total_return:+.2f}%'],
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['年化收益率', f'{annual_return:+.2f}%'],
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['平均日收益率', f'{mean_return:+.2f}%'],
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['波动率(日)', f'{std_return:.2f}%'],
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['最大回撤', f'{max_dd_info["max_drawdown"]:.2f}%'],
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['回撤天数', f'{max_dd_info["drawdown_period"]}天'],
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['上涨天数', f'{positive_days}/{total_days} ({positive_ratio:.1f}%)'],
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['数据点数', f'{len(df)}']
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]
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# 绘制表格
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table = ax3.table(cellText=stats_data,
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colLabels=['指标', '数值'],
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cellLoc='center',
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loc='center',
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colWidths=[0.3, 0.3])
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table.auto_set_font_size(False)
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table.set_fontsize(10)
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table.scale(1, 2)
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# 基本信息子图
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ax4 = fig.add_subplot(gs[2, 1])
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ax4.axis('off')
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info_text = (f'基金代码: {fund_code}\n'
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f'数据范围:\n{df.index[0].strftime("%Y-%m-%d")}\n至\n{df.index[-1].strftime("%Y-%m-%d")}\n'
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f'净值范围:\n{df["net_worth"].min():.3f} - {df["net_worth"].max():.3f}')
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ax4.text(0.5, 0.5, info_text,
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transform=ax4.transAxes,
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fontsize=9,
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verticalalignment='center',
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horizontalalignment='center',
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bbox=dict(boxstyle="round,pad=0.5", facecolor="lightblue", alpha=0.7))
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# 旋转日期标签
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plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45, ha='right')
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plt.tight_layout()
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if save_path:
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if not save_path.lower().endswith('.svg'):
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save_path += '.svg'
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plt.savefig(save_path, format='svg', dpi=300, bbox_inches='tight')
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print(f"图表已保存至: {save_path}")
|
||
plt.close()
|
||
else:
|
||
plt.show()
|
||
|
||
|