前言
本人不是金融相关专业，下述的程序是由deepseek生成， 对于K线图啥的其实也不是很了解，所以有什么问题希望有了解的同学可以在评论区或者私信进行联系。
文章的目的主要是想进行快速的绘制图表，然后想扩展一下就想到了金融行业，目前来说可能还存在绘制失败的问题，只能接收标准的行列的Excel表格（有些的Excel表格在表头前还有一个大的提示信息），还有对于太多数据的情况坐标轴会进行重叠。
安装相关的库
pip install pandas numpy matplotlib PyQt5 chardet mplfinance核心的绘图代码
if chart_type == "折线图":
ax.plot(self.df[x_col], self.df[y_col], marker='o', color='green', linestyle='-', linewidth=1,
markersize=3)
elif chart_type == "柱状图":
ax.bar(self.df[x_col], self.df[y_col], color='orange', alpha=0.7)
elif chart_type == "条形图":
ax.barh(self.df[x_col], self.df[y_col], color='purple', alpha=0.7)
elif chart_type == "面积图":
ax.fill_between(self.df[x_col], self.df[y_col], color='skyblue', alpha=0.5)
elif chart_type == "散点图":
ax.scatter(self.df[x_col], self.df[y_col], color='blue', alpha=0.7) if indicator == "K线图":
self.plot_candlestick(df, date_col, open_col, high_col, low_col, close_col, volume=False)
elif indicator == "K线+成交量":
self.plot_candlestick(df, date_col, open_col, high_col, low_col, close_col, volume=True)
elif indicator == "MACD":
self.plot_macd(df, date_col, close_col)
elif indicator == "布林带":
self.plot_bollinger_bands(df, date_col, close_col)
elif indicator == "RSI":
self.plot_rsi(df, date_col, close_col)相关参考链接
Python绘图库及图像类型之基础图表_ax.axvline(x, 0, 1, color = "k", ls = ':', lw = 0.-CSDN博客
https://blog.csdn.net/weixin_64066303/article/details/148433762?spm=1001.2014.3001.5502Python绘图库及图像类型之高级可视化_统计学疾病地理热图举例-CSDN博客https://blog.csdn.net/weixin_64066303/article/details/148450750?spm=1001.2014.3001.5502Python绘图库及图像类型之特殊领域可视化_fluent中创建注释选项annotate-CSDN博客https://blog.csdn.net/weixin_64066303/article/details/148450970?spm=1001.2014.3001.5502
生成金融行业测试数据
利用pandas和numpy库创建包含股票交易数据（如开盘价、收盘价、成交量等）和财务指标（市盈率、市净率等）的模拟数据集。用户可以指定生成的天数和公司数量，数据会被保存为Excel文件并包含说明文字。脚本通过设置随机种子确保结果可重复，适用于金融数据分析和可视化程序的测试场景。
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import random
def generate_financial_test_data(num_days=30, num_companies=5):
"""
生成金融行业测试数据
参数:
num_days: 生成多少天的数据
num_companies: 生成多少家公司的数据
返回:
DataFrame: 包含生成的金融测试数据
"""
# 设置随机种子以确保可重复性
np.random.seed(42)
random.seed(42)
# 生成日期序列
end_date = datetime.now()
start_date = end_date - timedelta(days=num_days - 1)
dates = pd.date_range(start_date, end_date, freq='D')
# 公司列表
companies = [
"阿里巴巴", "腾讯控股", "中国平安", "贵州茅台", "招商银行",
"美团点评", "京东集团", "中国移动", "比亚迪", "宁德时代"
][:num_companies]
# 行业分类
industries = ["科技", "金融", "消费", "能源", "医疗"]
# 生成数据
data = []
for date in dates:
for company in companies:
# 基础价格在50-500之间
base_price = random.uniform(50, 500)
# 生成股票数据
open_price = round(base_price * random.uniform(0.95, 1.05), 2)
close_price = round(open_price * random.uniform(0.97, 1.03), 2)
high_price = round(max(open_price, close_price) * random.uniform(1.0, 1.05), 2)
low_price = round(min(open_price, close_price) * random.uniform(0.95, 1.0), 2)
volume = random.randint(100000, 5000000)
# 生成财务指标
pe_ratio = round(random.uniform(5, 50), 2)
pb_ratio = round(random.uniform(0.8, 8), 2)
dividend_yield = round(random.uniform(0, 0.05), 4)
market_cap = round(random.uniform(1e9, 1e12), 2)
# 随机涨跌
change = round(close_price - open_price, 2)
change_percent = round(change / open_price * 100, 2)
# 行业分类
industry = random.choice(industries)
data.append([
date.strftime('%Y-%m-%d'),
company,
industry,
open_price,
close_price,
high_price,
low_price,
volume,
change,
change_percent,
pe_ratio,
pb_ratio,
dividend_yield,
market_cap
])
# 创建DataFrame
columns = [
'日期', '公司名称', '行业', '开盘价', '收盘价', '最高价', '最低价',
'成交量', '涨跌额', '涨跌幅(%)', '市盈率', '市净率', '股息率', '市值'
]
df = pd.DataFrame(data, columns=columns)
return df
def save_to_excel(df, filename="金融测试数据.xlsx"):
"""将数据保存为Excel文件"""
# 设置Excel写入引擎
writer = pd.ExcelWriter(filename, engine='openpyxl')
# 写入数据
df.to_excel(writer, index=False, sheet_name='股票数据')
# 添加一些说明性文字
workbook = writer.book
worksheet = writer.sheets['股票数据']
# 添加说明
worksheet.cell(row=1, column=len(df.columns) + 2, value="数据说明:")
worksheet.cell(row=2, column=len(df.columns) + 2, value="1. 本数据为随机生成的金融测试数据")
worksheet.cell(row=3, column=len(df.columns) + 2, value="2. 可用于测试数据可视化程序")
worksheet.cell(row=4, column=len(df.columns) + 2, value="3. 数据包含多家公司的股票价格和财务指标")
# 保存文件
writer.close()
print(f"数据已保存到 {filename}")
if __name__ == "__main__":
# 生成30天、5家公司的数据
financial_data = generate_financial_test_data(num_days=30, num_companies=5)
# 保存为Excel文件
save_to_excel(financial_data)
# 打印前几行数据
print("\n生成的数据样例:")
print(financial_data.head())
Excel数据可视化工具
基于PyQt5的GUI分析工具，支持加载CSV/Excel文件，提供基础图表(折线图、柱状图等)和技术分析指标(K线、MACD、布林带、RSI等)的可视化功能。工具采用模块化设计，包含数据自动检测、图表交互和保存功能，适用于金融数据分析和可视化测试场景。
import sys
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from PyQt5.QtWidgets import (QApplication, QMainWindow, QVBoxLayout, QHBoxLayout,
QWidget, QPushButton, QLabel, QComboBox, QFileDialog,
QLineEdit, QMessageBox, QCheckBox, QTabWidget)
from PyQt5.QtCore import Qt
import chardet
from mplfinance.original_flavor import candlestick_ohlc
import matplotlib.dates as mdates
from matplotlib.gridspec import GridSpec
# 设置matplotlib支持中文显示
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
class FinancialPlotter(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("金融数据可视化工具")
self.setWindowFlags(Qt.WindowCloseButtonHint | Qt.WindowMinimizeButtonHint)
# 初始化变量
self.df = None
self.file_path = None
self.date_col = None
self.ohlc_cols = {}
# 创建主部件和布局
main_widget = QWidget()
self.setCentralWidget(main_widget)
main_layout = QVBoxLayout(main_widget)
# 文件选择区域
file_layout = QHBoxLayout()
self.file_label = QLabel("未选择文件")
self.file_label.setStyleSheet("color: #666; font-style: italic;")
file_button = QPushButton("选择金融数据文件")
file_button.setStyleSheet("QPushButton {padding: 5px 10px;}")
file_button.clicked.connect(self.load_file)
file_layout.addWidget(self.file_label, stretch=1)
file_layout.addWidget(file_button)
main_layout.addLayout(file_layout)
# 创建选项卡
self.tabs = QTabWidget()
main_layout.addWidget(self.tabs)
# 基本图表选项卡
self.basic_tab = QWidget()
self.tabs.addTab(self.basic_tab, "基本图表")
self.setup_basic_tab()
# 技术分析选项卡
self.tech_tab = QWidget()
self.tabs.addTab(self.tech_tab, "技术分析")
self.setup_tech_tab()
# 设置窗口大小并居中
self.resize(1200, 800)
self.center_window()
def center_window(self):
"""将窗口居中显示"""
screen = QApplication.primaryScreen().geometry()
size = self.geometry()
self.move(
(screen.width() - size.width()) // 2,
(screen.height() - size.height()) // 2
)
def setup_basic_tab(self):
"""设置基本图表选项卡"""
layout = QVBoxLayout(self.basic_tab)
# 图表控制区域
control_layout = QHBoxLayout()
# 图表类型选择
control_layout.addWidget(QLabel("图表类型:"))
self.chart_type = QComboBox()
self.chart_type.addItems(["折线图", "柱状图", "条形图", "面积图", "散点图"])
self.chart_type.setStyleSheet("QComboBox {padding: 3px;}")
control_layout.addWidget(self.chart_type)
# X轴选择
control_layout.addWidget(QLabel("X轴:"))
self.x_axis = QComboBox()
self.x_axis.setStyleSheet("QComboBox {padding: 3px;}")
control_layout.addWidget(self.x_axis)
# Y轴选择
control_layout.addWidget(QLabel("Y轴:"))
self.y_axis = QComboBox()
self.y_axis.setStyleSheet("QComboBox {padding: 3px;}")
control_layout.addWidget(self.y_axis)
# 标题输入
control_layout.addWidget(QLabel("标题:"))
self.title_input = QLineEdit()
self.title_input.setPlaceholderText("输入图表标题")
self.title_input.setStyleSheet("QLineEdit {padding: 3px;}")
control_layout.addWidget(self.title_input)
# 绘制按钮
plot_button = QPushButton("绘制图表")
plot_button.setStyleSheet("QPushButton {padding: 5px 10px; background-color: #4CAF50; color: white;}")
plot_button.clicked.connect(self.plot_basic_chart)
control_layout.addWidget(plot_button)
layout.addLayout(control_layout)
# 图表区域
self.basic_figure = plt.figure(figsize=(10, 6), dpi=100)
self.basic_canvas = FigureCanvas(self.basic_figure)
layout.addWidget(self.basic_canvas, stretch=1)
# 保存按钮
save_button = QPushButton("保存图表")
save_button.setStyleSheet("QPushButton {padding: 5px 10px; background-color: #2196F3; color: white;}")
save_button.clicked.connect(lambda: self.save_chart(self.basic_figure))
layout.addWidget(save_button, alignment=Qt.AlignRight)
def setup_tech_tab(self):
"""设置技术分析选项卡"""
layout = QVBoxLayout(self.tech_tab)
# 技术指标选择区域
tech_control_layout = QHBoxLayout()
# 日期列选择
tech_control_layout.addWidget(QLabel("日期列:"))
self.date_column = QComboBox()
self.date_column.setStyleSheet("QComboBox {padding: 3px;}")
tech_control_layout.addWidget(self.date_column)
# OHLC列选择
tech_control_layout.addWidget(QLabel("开盘价:"))
self.open_column = QComboBox()
self.open_column.setStyleSheet("QComboBox {padding: 3px;}")
tech_control_layout.addWidget(self.open_column)
tech_control_layout.addWidget(QLabel("最高价:"))
self.high_column = QComboBox()
self.high_column.setStyleSheet("QComboBox {padding: 3px;}")
tech_control_layout.addWidget(self.high_column)
tech_control_layout.addWidget(QLabel("最低价:"))
self.low_column = QComboBox()
self.low_column.setStyleSheet("QComboBox {padding: 3px;}")
tech_control_layout.addWidget(self.low_column)
tech_control_layout.addWidget(QLabel("收盘价:"))
self.close_column = QComboBox()
self.close_column.setStyleSheet("QComboBox {padding: 3px;}")
tech_control_layout.addWidget(self.close_column)
tech_control_layout.addWidget(QLabel("成交量:"))
self.volume_column = QComboBox()
self.volume_column.setStyleSheet("QComboBox {padding: 3px;}")
tech_control_layout.addWidget(self.volume_column)
layout.addLayout(tech_control_layout)
# 技术指标选择
indicator_layout = QHBoxLayout()
# 技术指标选择
indicator_layout.addWidget(QLabel("技术指标:"))
self.tech_indicator = QComboBox()
self.tech_indicator.addItems(["K线图", "K线+成交量", "MACD", "布林带", "RSI"])
self.tech_indicator.setStyleSheet("QComboBox {padding: 3px;}")
indicator_layout.addWidget(self.tech_indicator)
# 移动平均线选择（新增子布局，使MA相关控件更紧凑）
ma_layout = QHBoxLayout()
ma_layout.setSpacing(5) # 设置控件之间的间距为5像素
self.ma_check = QCheckBox("显示均线")
self.ma_check.setChecked(True)
ma_layout.addWidget(self.ma_check)
# MA1
ma1_label = QLabel("MA1:")
ma1_label.setFixedWidth(30) # 固定标签宽度，避免文字过长导致间距变大
ma_layout.addWidget(ma1_label)
self.ma1 = QLineEdit("5")
self.ma1.setFixedWidth(30) # 固定输入框宽度
ma_layout.addWidget(self.ma1)
# MA2
ma2_label = QLabel("MA2:")
ma2_label.setFixedWidth(30)
ma_layout.addWidget(ma2_label)
self.ma2 = QLineEdit("10")
self.ma2.setFixedWidth(30)
ma_layout.addWidget(self.ma2)
# MA3
ma3_label = QLabel("MA3:")
ma3_label.setFixedWidth(30)
ma_layout.addWidget(ma3_label)
self.ma3 = QLineEdit("20")
self.ma3.setFixedWidth(30)
ma_layout.addWidget(self.ma3)
# 将MA子布局添加到主布局
indicator_layout.addLayout(ma_layout)
# 绘制按钮
tech_plot_button = QPushButton("绘制技术图表")
tech_plot_button.setStyleSheet("QPushButton {padding: 5px 10px; background-color: #4CAF50; color: white;}")
tech_plot_button.clicked.connect(self.plot_tech_chart)
indicator_layout.addWidget(tech_plot_button)
layout.addLayout(indicator_layout)
# 技术图表区域
self.tech_figure = plt.figure(figsize=(10, 8), dpi=100)
self.tech_canvas = FigureCanvas(self.tech_figure)
layout.addWidget(self.tech_canvas, stretch=1)
# 保存按钮
tech_save_button = QPushButton("保存技术图表")
tech_save_button.setStyleSheet("QPushButton {padding: 5px 10px; background-color: #2196F3; color: white;}")
tech_save_button.clicked.connect(lambda: self.save_chart(self.tech_figure))
layout.addWidget(tech_save_button, alignment=Qt.AlignRight)
def detect_encoding(self, file_path):
"""检测文件编码"""
with open(file_path, 'rb') as f:
rawdata = f.read(10000) # 读取前10000字节用于检测编码
result = chardet.detect(rawdata)
return result['encoding']
def load_file(self):
"""加载金融数据文件"""
file_path, _ = QFileDialog.getOpenFileName(
self, "选择金融数据文件", "", "数据文件 (*.xlsx *.xls *.csv)"
)
if file_path:
self.file_path = file_path
self.file_label.setText(f"已选择: {file_path.split('/')[-1]}")
self.file_label.setStyleSheet("color: #006400; font-style: normal;")
try:
if file_path.endswith('.csv'):
# 检测文件编码
encoding = self.detect_encoding(file_path)
# 对于CSV文件，尝试自动检测标题行
with open(file_path, 'r', encoding=encoding) as f:
lines = f.readlines()
# 寻找第一个看起来像标题的行（包含多个非空列）
header_row = 0
for i, line in enumerate(lines):
cols = line.strip().split(',')
if len(cols) > 1 and any(col.strip() for col in cols):
header_row = i
break
# 重新读取文件，指定编码和标题行
self.df = pd.read_csv(file_path, header=header_row, encoding=encoding)
else:
# 对于Excel文件，使用pandas自动检测标题行
self.df = pd.read_excel(file_path, header=None)
# 寻找第一个看起来像标题的行（包含多个非空值）
header_row = 0
for i in range(len(self.df)):
if self.df.iloc[i].count() > 1: # 如果一行中有多个非空值
header_row = i
break
# 重新读取文件，指定标题行
self.df = pd.read_excel(file_path, header=header_row)
# 清理数据：删除全空的行和列
self.df.dropna(how='all', inplace=True)
self.df.dropna(axis=1, how='all', inplace=True)
# 确保列名为字符串格式
self.df.columns = self.df.columns.astype(str)
# 更新轴选择下拉框
self.update_axis_comboboxes()
QMessageBox.information(self, "成功", f"已成功加载数据，共 {len(self.df)} 行")
except Exception as e:
QMessageBox.critical(self, "错误", f"无法读取文件:\n{str(e)}")
self.df = None
def update_axis_comboboxes(self):
"""更新所有轴选择下拉选项"""
if self.df is not None:
columns = [str(col) for col in self.df.columns.tolist()]
# 更新基本图表选项卡的下拉框
self.x_axis.clear()
self.y_axis.clear()
self.x_axis.addItems(columns)
self.y_axis.addItems(columns)
# 默认选择第一列和第二列
if len(columns) >= 1:
self.x_axis.setCurrentIndex(0)
if len(columns) >= 2:
self.y_axis.setCurrentIndex(1)
# 更新技术分析选项卡的下拉框
self.date_column.clear()
self.open_column.clear()
self.high_column.clear()
self.low_column.clear()
self.close_column.clear()
self.volume_column.clear()
self.date_column.addItems(columns)
self.open_column.addItems(columns)
self.high_column.addItems(columns)
self.low_column.addItems(columns)
self.close_column.addItems(columns)
self.volume_column.addItems(columns)
# 尝试自动识别OHLC列
for col in columns:
col_lower = col.lower()
if 'date' in col_lower or '时间' in col or '日期' in col:
self.date_column.setCurrentText(col)
elif 'open' in col_lower or '开盘' in col:
self.open_column.setCurrentText(col)
elif 'high' in col_lower or '最高' in col:
self.high_column.setCurrentText(col)
elif 'low' in col_lower or '最低' in col:
self.low_column.setCurrentText(col)
elif 'close' in col_lower or '收盘' in col:
self.close_column.setCurrentText(col)
elif 'volume' in col_lower or '成交量' in col or '交易量' in col:
self.volume_column.setCurrentText(col)
def plot_basic_chart(self):
"""绘制基本图表"""
if self.df is None:
QMessageBox.warning(self, "警告", "请先选择数据文件")
return
x_col = self.x_axis.currentText()
y_col = self.y_axis.currentText()
chart_type = self.chart_type.currentText()
title = self.title_input.text() or f"{y_col} vs {x_col}"
# 清除之前的图表
self.basic_figure.clear()
ax = self.basic_figure.add_subplot(111)
try:
# 根据选择的图表类型绘制
if chart_type == "折线图":
ax.plot(self.df[x_col], self.df[y_col], marker='o', color='green', linestyle='-', linewidth=1,
markersize=3)
elif chart_type == "柱状图":
ax.bar(self.df[x_col], self.df[y_col], color='orange', alpha=0.7)
elif chart_type == "条形图":
ax.barh(self.df[x_col], self.df[y_col], color='purple', alpha=0.7)
elif chart_type == "面积图":
ax.fill_between(self.df[x_col], self.df[y_col], color='skyblue', alpha=0.5)
elif chart_type == "散点图":
ax.scatter(self.df[x_col], self.df[y_col], color='blue', alpha=0.7)
# 设置标题和标签
ax.set_title(title, fontsize=12, pad=20)
ax.set_xlabel(x_col, fontsize=10)
ax.set_ylabel(y_col, fontsize=10)
# 自动调整布局
self.basic_figure.tight_layout()
# 旋转x轴标签以避免重叠
if len(self.df[x_col]) > 5:
plt.xticks(rotation=45, ha='right')
# 刷新画布
self.basic_canvas.draw()
except Exception as e:
QMessageBox.critical(self, "错误", f"绘制图表时出错:\n{str(e)}")
def plot_tech_chart(self):
"""绘制技术分析图表"""
if self.df is None:
QMessageBox.warning(self, "警告", "请先选择数据文件")
return
# 获取选择的列
date_col = self.date_column.currentText()
open_col = self.open_column.currentText()
high_col = self.high_column.currentText()
low_col = self.low_column.currentText()
close_col = self.close_column.currentText()
volume_col = self.volume_column.currentText() if self.volume_column.currentText() else None
indicator = self.tech_indicator.currentText()
try:
# 准备数据
df = self.df.copy()
df[date_col] = pd.to_datetime(df[date_col])
df = df.sort_values(date_col)
# 计算移动平均线
ma1 = int(self.ma1.text()) if self.ma1.text().isdigit() else 5
ma2 = int(self.ma2.text()) if self.ma2.text().isdigit() else 10
ma3 = int(self.ma3.text()) if self.ma3.text().isdigit() else 20
df[f'MA{ma1}'] = df[close_col].rolling(ma1).mean()
df[f'MA{ma2}'] = df[close_col].rolling(ma2).mean()
df[f'MA{ma3}'] = df[close_col].rolling(ma3).mean()
# 清除之前的图表
self.tech_figure.clear()
# 根据选择的指标绘制图表
if indicator == "K线图":
self.plot_candlestick(df, date_col, open_col, high_col, low_col, close_col, volume=False)
elif indicator == "K线+成交量":
self.plot_candlestick(df, date_col, open_col, high_col, low_col, close_col, volume=True)
elif indicator == "MACD":
self.plot_macd(df, date_col, close_col)
elif indicator == "布林带":
self.plot_bollinger_bands(df, date_col, close_col)
elif indicator == "RSI":
self.plot_rsi(df, date_col, close_col)
# 刷新画布
self.tech_canvas.draw()
except Exception as e:
QMessageBox.critical(self, "错误", f"绘制技术图表时出错:\n{str(e)}")
def plot_candlestick(self, df, date_col, open_col, high_col, low_col, close_col, volume=True):
"""绘制K线图"""
# 设置图表布局
if volume:
gs = GridSpec(2, 1, height_ratios=[3, 1])
ax1 = self.tech_figure.add_subplot(gs[0])
ax2 = self.tech_figure.add_subplot(gs[1], sharex=ax1)
else:
ax1 = self.tech_figure.add_subplot(111)
# 准备K线图数据
df['date_num'] = mdates.date2num(df[date_col])
ohlc = df[['date_num', open_col, high_col, low_col, close_col]].values
# 绘制K线图
candlestick_ohlc(ax1, ohlc, width=0.6, colorup='r', colordown='g')
# 绘制移动平均线
if self.ma_check.isChecked():
ma1 = int(self.ma1.text()) if self.ma1.text().isdigit() else 5
ma2 = int(self.ma2.text()) if self.ma2.text().isdigit() else 10
ma3 = int(self.ma3.text()) if self.ma3.text().isdigit() else 20
ax1.plot(df[date_col], df[f'MA{ma1}'], label=f'MA{ma1}', linewidth=1)
ax1.plot(df[date_col], df[f'MA{ma2}'], label=f'MA{ma2}', linewidth=1)
ax1.plot(df[date_col], df[f'MA{ma3}'], label=f'MA{ma3}', linewidth=1)
ax1.legend()
# 设置K线图标题和标签
ax1.set_title("K线图", fontsize=12)
ax1.set_ylabel("价格", fontsize=10)
ax1.grid(True)
# 格式化x轴日期
ax1.xaxis_date()
self.tech_figure.autofmt_xdate()
# 绘制成交量
if volume and hasattr(self, 'volume_column'):
volume_col = self.volume_column.currentText()
if volume_col in df.columns:
# 计算涨跌颜色
colors = ['r' if close >= open else 'g' for close, open in zip(df[close_col], df[open_col])]
ax2.bar(df[date_col], df[volume_col], color=colors, width=0.6)
ax2.set_ylabel("成交量", fontsize=10)
ax2.grid(True)
self.tech_figure.tight_layout()
def plot_macd(self, df, date_col, close_col):
"""绘制MACD指标"""
ax1 = self.tech_figure.add_subplot(111)
# 计算MACD
exp12 = df[close_col].ewm(span=12, adjust=False).mean()
exp26 = df[close_col].ewm(span=26, adjust=False).mean()
macd = exp12 - exp26
signal = macd.ewm(span=9, adjust=False).mean()
histogram = macd - signal
# 绘制价格和移动平均线
ax1.plot(df[date_col], df[close_col], label='收盘价', color='black', linewidth=1)
if self.ma_check.isChecked():
ma1 = int(self.ma1.text()) if self.ma1.text().isdigit() else 5
ma2 = int(self.ma2.text()) if self.ma2.text().isdigit() else 10
ma3 = int(self.ma3.text()) if self.ma3.text().isdigit() else 20
ax1.plot(df[date_col], df[f'MA{ma1}'], label=f'MA{ma1}', linewidth=1)
ax1.plot(df[date_col], df[f'MA{ma2}'], label=f'MA{ma2}', linewidth=1)
ax1.plot(df[date_col], df[f'MA{ma3}'], label=f'MA{ma3}', linewidth=1)
ax1.set_title("MACD指标", fontsize=12)
ax1.set_ylabel("价格", fontsize=10)
ax1.grid(True)
ax1.legend()
# 创建MACD子图
ax2 = ax1.twinx()
ax2.plot(df[date_col], macd, label='MACD', color='blue', linewidth=1)
ax2.plot(df[date_col], signal, label='信号线', color='red', linewidth=1)
# 绘制柱状图
colors = ['g' if val >= 0 else 'r' for val in histogram]
ax2.bar(df[date_col], histogram, color=colors, width=0.6, alpha=0.5)
ax2.axhline(0, color='gray', linestyle='--', linewidth=0.5)
ax2.set_ylabel("MACD", fontsize=10)
ax2.legend(loc='upper right')
self.tech_figure.tight_layout()
def plot_bollinger_bands(self, df, date_col, close_col):
"""绘制布林带"""
ax = self.tech_figure.add_subplot(111)
# 计算布林带
window = 20
sma = df[close_col].rolling(window).mean()
std = df[close_col].rolling(window).std()
upper_band = sma + 2 * std
lower_band = sma - 2 * std
# 绘制价格和布林带
ax.plot(df[date_col], df[close_col], label='收盘价', color='black', linewidth=1)
ax.plot(df[date_col], sma, label=f'{window}日均线', color='blue', linewidth=1)
ax.plot(df[date_col], upper_band, label='上轨', color='red', linewidth=1, linestyle='--')
ax.plot(df[date_col], lower_band, label='下轨', color='green', linewidth=1, linestyle='--')
# 填充布林带区域
ax.fill_between(df[date_col], upper_band, lower_band, color='gray', alpha=0.1)
ax.set_title("布林带", fontsize=12)
ax.set_ylabel("价格", fontsize=10)
ax.grid(True)
ax.legend()
self.tech_figure.tight_layout()
def plot_rsi(self, df, date_col, close_col):
"""绘制RSI指标"""
# 设置图表布局
gs = GridSpec(2, 1, height_ratios=[3, 1])
ax1 = self.tech_figure.add_subplot(gs[0])
ax2 = self.tech_figure.add_subplot(gs[1], sharex=ax1)
# 绘制价格和移动平均线
ax1.plot(df[date_col], df[close_col], label='收盘价', color='black', linewidth=1)
if self.ma_check.isChecked():
ma1 = int(self.ma1.text()) if self.ma1.text().isdigit() else 5
ma2 = int(self.ma2.text()) if self.ma2.text().isdigit() else 10
ma3 = int(self.ma3.text()) if self.ma3.text().isdigit() else 20
ax1.plot(df[date_col], df[f'MA{ma1}'], label=f'MA{ma1}', linewidth=1)
ax1.plot(df[date_col], df[f'MA{ma2}'], label=f'MA{ma2}', linewidth=1)
ax1.plot(df[date_col], df[f'MA{ma3}'], label=f'MA{ma3}', linewidth=1)
ax1.set_title("RSI指标", fontsize=12)
ax1.set_ylabel("价格", fontsize=10)
ax1.grid(True)
ax1.legend()
# 计算RSI
delta = df[close_col].diff()
gain = delta.where(delta > 0, 0)
loss = -delta.where(delta < 0, 0)
avg_gain = gain.rolling(14).mean()
avg_loss = loss.rolling(14).mean()
rs = avg_gain / avg_loss
rsi = 100 - (100 / (1 + rs))
# 绘制RSI
ax2.plot(df[date_col], rsi, label='RSI(14)', color='purple', linewidth=1)
ax2.axhline(70, color='red', linestyle='--', linewidth=0.5)
ax2.axhline(30, color='green', linestyle='--', linewidth=0.5)
ax2.set_ylabel("RSI", fontsize=10)
ax2.set_ylim(0, 100)
ax2.grid(True)
ax2.legend()
self.tech_figure.tight_layout()
def save_chart(self, figure):
"""保存图表为图片"""
if self.df is None:
QMessageBox.warning(self, "警告", "没有可保存的图表")
return
file_path, _ = QFileDialog.getSaveFileName(
self, "保存图表", "financial_chart",
"PNG 图片 (*.png);;JPEG 图片 (*.jpg);;PDF 文件 (*.pdf);;SVG 矢量图 (*.svg)"
)
if file_path:
try:
figure.savefig(file_path, bbox_inches='tight', dpi=300)
QMessageBox.information(self, "成功", f"图表已保存到:\n{file_path}")
except Exception as e:
QMessageBox.critical(self, "错误", f"保存图表时出错:\n{str(e)}")
if __name__ == "__main__":
app = QApplication(sys.argv)
app.setStyle('Fusion') # 使用Fusion样式，看起来更现代
# 设置全局字体
font = app.font()
font.setPointSize(10)
app.setFont(font)
window = FinancialPlotter()
window.show()
sys.exit(app.exec_())
