ADD: 增加港股支持 增加Dockerfile

Dockerfile

@@ -0,0 +1,27 @@
+# 使用 Python 3.9 作为基础镜像
+FROM python:3.10-slim
+
+# 设置工作目录
+WORKDIR /app
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    ca-certificates \ 
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制项目文件
+COPY . /app/
+
+# 安装 Python 依赖
+RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install akshare --upgrade -i https://pypi.org/simple
+
+# 设置环境变量
+ENV PYTHONPATH=/app
+
+# 暴露端口（如果需要）
+EXPOSE 8888
+
+# 启动命令
+CMD ["python", "web_server.py"]

gui.py

@@ -1,218 +0,0 @@
-import sys
-from PyQt6.QtWidgets import (QApplication, QMainWindow, QWidget, QVBoxLayout, 
-                           QHBoxLayout, QLineEdit, QPushButton, QTextBrowser,
-                           QLabel, QTextEdit, QMessageBox, QProgressBar)
-from PyQt6.QtCore import Qt, QThread, pyqtSignal
-from PyQt6.QtGui import QFont
-import markdown2
-from stock_analyzer import StockAnalyzer  # 导入股票分析器
-
-class AnalysisWorker(QThread):
-    """后台工作线程，用于执行分析任务"""
-    finished = pyqtSignal(dict)
-    error = pyqtSignal(str)
-    progress = pyqtSignal(int)
-
-    def __init__(self, analyzer, stock_code):
-        super().__init__()
-        self.analyzer = analyzer
-        self.stock_code = stock_code
-
-    def run(self):
-        try:
-            report = self.analyzer.analyze_stock(self.stock_code)
-            self.finished.emit(report)
-        except Exception as e:
-            self.error.emit(str(e))
-
-class BatchAnalysisWorker(QThread):
-    """后台工作线程，用于执行批量分析任务"""
-    finished = pyqtSignal(list)
-    error = pyqtSignal(str)
-    progress = pyqtSignal(int)
-
-    def __init__(self, analyzer, stock_list):
-        super().__init__()
-        self.analyzer = analyzer
-        self.stock_list = stock_list
-
-    def run(self):
-        try:
-            results = []
-            total = len(self.stock_list)
-            for i, stock_code in enumerate(self.stock_list):
-                report = self.analyzer.analyze_stock(stock_code)
-                results.append(report)
-                self.progress.emit(int((i + 1) / total * 100))
-            self.finished.emit(results)
-        except Exception as e:
-            self.error.emit(str(e))
-
-class StockAnalyzerGUI(QMainWindow):
-    def __init__(self):
-        super().__init__()
-        self.analyzer = StockAnalyzer()
-        self.initUI()
-        
-    def initUI(self):
-        self.setWindowTitle('股票分析系统')
-        self.setGeometry(100, 100, 1200, 800)
-        
-        # 创建中央部件和布局
-        central_widget = QWidget()
-        self.setCentralWidget(central_widget)
-        layout = QVBoxLayout(central_widget)
-        
-        # 创建输入区域
-        input_layout = QHBoxLayout()
-        
-        # 单只股票分析部分
-        single_stock_layout = QVBoxLayout()
-        single_label = QLabel('单只股票分析:')
-        single_label.setFont(QFont('Arial', 12, QFont.Weight.Bold))
-        self.single_stock_input = QLineEdit()
-        self.single_stock_input.setPlaceholderText('输入股票代码（如：600000）')
-        self.single_stock_input.setFont(QFont('Arial', 10))
-        self.analyze_btn = QPushButton('分析')
-        self.analyze_btn.setFont(QFont('Arial', 10, QFont.Weight.Bold))
-        self.analyze_btn.clicked.connect(self.analyze_single_stock)
-        single_stock_layout.addWidget(single_label)
-        single_stock_layout.addWidget(self.single_stock_input)
-        single_stock_layout.addWidget(self.analyze_btn)
-        
-        # 批量分析部分
-        batch_stock_layout = QVBoxLayout()
-        batch_label = QLabel('批量股票分析:')
-        batch_label.setFont(QFont('Arial', 12, QFont.Weight.Bold))
-        self.batch_stock_input = QTextEdit()
-        self.batch_stock_input.setPlaceholderText('输入多个股票代码，每行一个')
-        self.batch_stock_input.setFont(QFont('Arial', 10))
-        self.batch_stock_input.setMaximumHeight(100)
-        self.batch_analyze_btn = QPushButton('批量分析')
-        self.batch_analyze_btn.setFont(QFont('Arial', 10, QFont.Weight.Bold))
-        self.batch_analyze_btn.clicked.connect(self.analyze_multiple_stocks)
-        batch_stock_layout.addWidget(batch_label)
-        batch_stock_layout.addWidget(self.batch_stock_input)
-        batch_stock_layout.addWidget(self.batch_analyze_btn)
-        
-        input_layout.addLayout(single_stock_layout)
-        input_layout.addLayout(batch_stock_layout)
-        
-        # 添加进度条
-        self.progress_bar = QProgressBar()
-        self.progress_bar.setVisible(False)
-        
-        # 添加结果显示区域
-        self.result_browser = QTextBrowser()
-        self.result_browser.setOpenExternalLinks(True)
-        self.result_browser.setFont(QFont('Arial', 10))
-        
-        layout.addLayout(input_layout)
-        layout.addWidget(self.progress_bar)
-        layout.addWidget(self.result_browser)
-        
-    def format_report(self, report, is_single=True):
-        """将分析报告格式化为Markdown格式"""
-        md = f"""# 股票分析报告 - {report['stock_code']}
-
-## 基本信息
-- 分析日期：{report['analysis_date']}
-- 当前价格：{report['price']:.2f}
-- 价格变动：{report['price_change']:.2f}%
-
-## 技术指标
-- 均线趋势：{report['ma_trend']}
-- RSI指标：{report['rsi']:.2f}
-- MACD信号：{report['macd_signal']}
-- 成交量状态：{report['volume_status']}
-
-## 评分与建议
-- 综合评分：{report['score']}分
-- 投资建议：{report['recommendation']}
-
-## AI分析
-{report['ai_analysis']}
-
----
-"""
-        return md
-        
-    def analyze_single_stock(self):
-        """分析单只股票"""
-        stock_code = self.single_stock_input.text().strip()
-        if not stock_code:
-            QMessageBox.warning(self, '警告', '请输入股票代码')
-            return
-            
-        self.analyze_btn.setEnabled(False)
-        self.progress_bar.setVisible(True)
-        self.progress_bar.setValue(0)
-        
-        # 创建工作线程
-        self.worker = AnalysisWorker(self.analyzer, stock_code)
-        self.worker.finished.connect(self.handle_single_analysis_result)
-        self.worker.error.connect(self.handle_analysis_error)
-        self.worker.progress.connect(self.progress_bar.setValue)
-        self.worker.start()
-        
-    def handle_single_analysis_result(self, report):
-        """处理单只股票分析结果"""
-        markdown_text = self.format_report(report)
-        html_content = markdown2.markdown(markdown_text)
-        self.result_browser.setHtml(html_content)
-        self.analyze_btn.setEnabled(True)
-        self.progress_bar.setVisible(False)
-            
-    def analyze_multiple_stocks(self):
-        """批量分析股票"""
-        text = self.batch_stock_input.toPlainText().strip()
-        if not text:
-            QMessageBox.warning(self, '警告', '请输入股票代码')
-            return
-            
-        stock_list = [code.strip() for code in text.split('\n') if code.strip()]
-        
-        self.batch_analyze_btn.setEnabled(False)
-        self.progress_bar.setVisible(True)
-        self.progress_bar.setValue(0)
-        
-        # 创建工作线程
-        self.batch_worker = BatchAnalysisWorker(self.analyzer, stock_list)
-        self.batch_worker.finished.connect(self.handle_batch_analysis_result)
-        self.batch_worker.error.connect(self.handle_analysis_error)
-        self.batch_worker.progress.connect(self.progress_bar.setValue)
-        self.batch_worker.start()
-        
-    def handle_batch_analysis_result(self, recommendations):
-        """处理批量分析结果"""
-        # 生成markdown格式的报告
-        markdown_text = "# 批量股票分析报告\n\n"
-        for rec in recommendations:
-            markdown_text += self.format_report(rec, False)
-            
-        html_content = markdown2.markdown(markdown_text)
-        self.result_browser.setHtml(html_content)
-        self.batch_analyze_btn.setEnabled(True)
-        self.progress_bar.setVisible(False)
-        
-    def handle_analysis_error(self, error_message):
-        """处理分析错误"""
-        QMessageBox.critical(self, '错误', f'分析过程中出现错误：{error_message}')
-        self.analyze_btn.setEnabled(True)
-        self.batch_analyze_btn.setEnabled(True)
-        self.progress_bar.setVisible(False)
-
-def main():
-    app = QApplication(sys.argv)
-    
-    # 设置应用程序样式
-    app.setStyle('Fusion')
-    
-    # 创建并显示主窗口
-    window = StockAnalyzerGUI()
-    window.show()
-    
-    sys.exit(app.exec())
-
-if __name__ == '__main__':
-    main()

gui2.py

@@ -1,497 +0,0 @@
-import sys
-from PyQt6.QtWidgets import (QApplication, QMainWindow, QWidget, QVBoxLayout, 
-                           QHBoxLayout, QLineEdit, QPushButton, QTextBrowser,
-                           QLabel, QTextEdit, QMessageBox, QProgressBar, 
-                           QFrame, QSizePolicy)
-from PyQt6.QtCore import Qt, QThread, pyqtSignal
-from PyQt6.QtGui import QFont, QPalette, QColor
-import markdown2
-from stock_analyzer import StockAnalyzer
-
-class ModernFrame(QFrame):
-    """现代化的面板组件"""
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.setFrameStyle(QFrame.Shape.StyledPanel | QFrame.Shadow.Raised)
-        self.setStyleSheet("""
-            ModernFrame {
-                background-color: #ffffff;
-                border-radius: 10px;
-                border: 1px solid #e0e0e0;
-            }
-        """)
-
-class ModernButton(QPushButton):
-    """现代化的按钮组件"""
-    def __init__(self, text, parent=None, primary=True):
-        super().__init__(text, parent)
-        self.setMinimumHeight(40)
-        if primary:
-            self.setStyleSheet("""
-                QPushButton {
-                    background-color: #1a73e8;
-                    color: white;
-                    border: none;
-                    border-radius: 5px;
-                    padding: 8px 16px;
-                    font-weight: bold;
-                }
-                QPushButton:hover {
-                    background-color: #1557b0;
-                }
-                QPushButton:pressed {
-                    background-color: #0d47a1;
-                }
-                QPushButton:disabled {
-                    background-color: #cccccc;
-                }
-            """)
-        else:
-            self.setStyleSheet("""
-                QPushButton {
-                    background-color: #f8f9fa;
-                    color: #1a73e8;
-                    border: 1px solid #dadce0;
-                    border-radius: 5px;
-                    padding: 8px 16px;
-                    font-weight: bold;
-                }
-                QPushButton:hover {
-                    background-color: #f1f3f4;
-                    border-color: #d2e3fc;
-                }
-                QPushButton:pressed {
-                    background-color: #e8eaed;
-                }
-                QPushButton:disabled {
-                    color: #5f6368;
-                    border-color: #e0e0e0;
-                }
-            """)
-
-class ModernLineEdit(QLineEdit):
-    """现代化的输入框组件"""
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.setMinimumHeight(40)
-        self.setStyleSheet("""
-            QLineEdit {
-                border: 2px solid #e0e0e0;
-                border-radius: 5px;
-                padding: 8px 12px;
-                background-color: white;
-                selection-background-color: #cce0ff;
-            }
-            QLineEdit:focus {
-                border-color: #1a73e8;
-            }
-            QLineEdit:hover {
-                border-color: #999999;
-            }
-        """)
-
-class ModernTextEdit(QTextEdit):
-    """现代化的多行文本输入框组件"""
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.setStyleSheet("""
-            QTextEdit {
-                border: 2px solid #e0e0e0;
-                border-radius: 5px;
-                padding: 8px;
-                background-color: white;
-                selection-background-color: #cce0ff;
-            }
-            QTextEdit:focus {
-                border-color: #1a73e8;
-            }
-            QTextEdit:hover {
-                border-color: #999999;
-            }
-        """)
-
-class ModernProgressBar(QProgressBar):
-    """现代化的进度条组件"""
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.setStyleSheet("""
-            QProgressBar {
-                border: none;
-                border-radius: 3px;
-                background-color: #f0f0f0;
-                height: 6px;
-                text-align: center;
-            }
-            QProgressBar::chunk {
-                background-color: #1a73e8;
-                border-radius: 3px;
-            }
-        """)
-        self.setTextVisible(False)
-
-class AnalysisWorker(QThread):
-    """后台工作线程，用于执行分析任务"""
-    finished = pyqtSignal(dict)
-    error = pyqtSignal(str)
-    progress = pyqtSignal(int)
-
-    def __init__(self, analyzer, stock_code):
-        super().__init__()
-        self.analyzer = analyzer
-        self.stock_code = stock_code
-
-    def run(self):
-        try:
-            report = self.analyzer.analyze_stock(self.stock_code)
-            self.finished.emit(report)
-        except Exception as e:
-            self.error.emit(str(e))
-
-class BatchAnalysisWorker(QThread):
-    """后台工作线程，用于执行批量分析任务"""
-    finished = pyqtSignal(list)
-    error = pyqtSignal(str)
-    progress = pyqtSignal(int)
-
-    def __init__(self, analyzer, stock_list):
-        super().__init__()
-        self.analyzer = analyzer
-        self.stock_list = stock_list
-
-    def run(self):
-        try:
-            results = []
-            total = len(self.stock_list)
-            for i, stock_code in enumerate(self.stock_list):
-                report = self.analyzer.analyze_stock(stock_code)
-                results.append(report)
-                self.progress.emit(int((i + 1) / total * 100))
-            self.finished.emit(results)
-        except Exception as e:
-            self.error.emit(str(e))
-
-class ModernStockAnalyzerGUI(QMainWindow):
-    def __init__(self):
-        super().__init__()
-        self.analyzer = StockAnalyzer()
-        self.init_ui()
-        self.adjust_size_and_position()
-
-    def adjust_size_and_position(self):
-        """调整窗口大小和位置以适应不同分辨率"""
-        screen = QApplication.primaryScreen()
-        if screen:
-            geometry = screen.availableGeometry()
-            # 设置窗口大小为屏幕的75%
-            width = int(geometry.width() * 0.75)
-            height = int(geometry.height() * 0.75)
-            self.resize(width, height)
-            
-            # 居中显示
-            center = geometry.center()
-            frame = self.frameGeometry()
-            frame.moveCenter(center)
-            self.move(frame.topLeft())
-
-    def init_ui(self):
-        self.setWindowTitle('现代股票分析系统')
-        self.setStyleSheet("""
-            QMainWindow {
-                background-color: #f8f9fa;
-            }
-        """)
-
-        # 创建中央部件和主布局
-        central_widget = QWidget()
-        self.setCentralWidget(central_widget)
-        main_layout = QVBoxLayout(central_widget)
-        main_layout.setSpacing(20)
-        main_layout.setContentsMargins(20, 20, 20, 20)
-
-        # 创建标题
-        title_label = QLabel('股票分析系统')
-        title_label.setStyleSheet("""
-            QLabel {
-                color: #202124;
-                font-size: 24px;
-                font-weight: bold;
-                margin-bottom: 20px;
-            }
-        """)
-        main_layout.addWidget(title_label, alignment=Qt.AlignmentFlag.AlignTop)
-
-        # 创建输入区域容器
-        input_container = ModernFrame()
-        input_layout = QHBoxLayout(input_container)
-        input_layout.setSpacing(20)
-        input_layout.setContentsMargins(20, 20, 20, 20)
-
-        # 单只股票分析部分
-        single_stock_frame = self.create_single_stock_section()
-        input_layout.addWidget(single_stock_frame)
-
-        # 分隔线
-        separator = QFrame()
-        separator.setFrameShape(QFrame.Shape.VLine)
-        separator.setStyleSheet("background-color: #e0e0e0;")
-        input_layout.addWidget(separator)
-
-        # 批量分析部分
-        batch_stock_frame = self.create_batch_stock_section()
-        input_layout.addWidget(batch_stock_frame)
-
-        main_layout.addWidget(input_container)
-
-        # 进度条
-        self.progress_bar = ModernProgressBar()
-        self.progress_bar.setVisible(False)
-        main_layout.addWidget(self.progress_bar)
-
-        # 结果显示区域
-        result_frame = ModernFrame()
-        result_layout = QVBoxLayout(result_frame)
-        result_layout.setContentsMargins(15, 15, 15, 15)
-
-        result_label = QLabel('分析结果')
-        result_label.setStyleSheet("""
-            QLabel {
-                color: #202124;
-                font-size: 18px;
-                font-weight: bold;
-            }
-        """)
-        result_layout.addWidget(result_label)
-
-        self.result_browser = QTextBrowser()
-        self.result_browser.setOpenExternalLinks(True)
-        self.result_browser.setStyleSheet("""
-            QTextBrowser {
-                border: none;
-                background-color: white;
-                font-size: 14px;
-                line-height: 1.5;
-            }
-        """)
-        result_layout.addWidget(self.result_browser)
-
-        main_layout.addWidget(result_frame)
-
-    def create_single_stock_section(self):
-        """创建单只股票分析部分"""
-        frame = QFrame()
-        layout = QVBoxLayout(frame)
-        layout.setSpacing(15)
-
-        label = QLabel('单只股票分析')
-        label.setStyleSheet("""
-            QLabel {
-                color: #202124;
-                font-size: 16px;
-                font-weight: bold;
-            }
-        """)
-        layout.addWidget(label)
-
-        self.single_stock_input = ModernLineEdit()
-        self.single_stock_input.setPlaceholderText('输入股票代码（如：600000）')
-        layout.addWidget(self.single_stock_input)
-
-        self.analyze_btn = ModernButton('分析')
-        self.analyze_btn.clicked.connect(self.analyze_single_stock)
-        layout.addWidget(self.analyze_btn)
-
-        layout.addStretch()
-        return frame
-
-    def create_batch_stock_section(self):
-        """创建批量分析部分"""
-        frame = QFrame()
-        layout = QVBoxLayout(frame)
-        layout.setSpacing(15)
-
-        label = QLabel('批量股票分析')
-        label.setStyleSheet("""
-            QLabel {
-                color: #202124;
-                font-size: 16px;
-                font-weight: bold;
-            }
-        """)
-        layout.addWidget(label)
-
-        self.batch_stock_input = ModernTextEdit()
-        self.batch_stock_input.setPlaceholderText('输入多个股票代码，每行一个')
-        self.batch_stock_input.setMinimumHeight(100)
-        layout.addWidget(self.batch_stock_input)
-
-        self.batch_analyze_btn = ModernButton('批量分析')
-        self.batch_analyze_btn.clicked.connect(self.analyze_multiple_stocks)
-        layout.addWidget(self.batch_analyze_btn)
-
-        layout.addStretch()
-        return frame
-
-    def format_report(self, report, is_single=True):
-        """将分析报告格式化为现代化的Markdown格式"""
-        md = f"""# 股票分析报告 - {report['stock_code']}
-
-## 基本信息
-- **分析日期：** {report['analysis_date']}
-- **当前价格：** ¥{report['price']:.2f}
-- **价格变动：** {report['price_change']:.2f}%
-
-## 技术指标
-- **均线趋势：** {report['ma_trend']}
-- **RSI指标：** {report['rsi']:.2f}
-- **MACD信号：** {report['macd_signal']}
-- **成交量状态：** {report['volume_status']}
-
-## 评分与建议
-- **综合评分：** {report['score']}分
-- **投资建议：** {report['recommendation']}
-
-## AI分析
-{report['ai_analysis']}
-
----
-"""
-        return md
-
-    def analyze_single_stock(self):
-        """分析单只股票"""
-        stock_code = self.single_stock_input.text().strip()
-        if not stock_code:
-            self.show_warning('请输入股票代码')
-            return
-
-        self.analyze_btn.setEnabled(False)
-        self.progress_bar.setVisible(True)
-        self.progress_bar.setValue(0)
-
-        self.worker = AnalysisWorker(self.analyzer, stock_code)
-        self.worker.finished.connect(self.handle_single_analysis_result)
-        self.worker.error.connect(self.handle_analysis_error)
-        self.worker.progress.connect(self.progress_bar.setValue)
-        self.worker.start()
-
-    def analyze_multiple_stocks(self):
-        """批量分析股票"""
-        text = self.batch_stock_input.toPlainText().strip()
-        if not text:
-            self.show_warning('请输入股票代码')
-            return
-
-        stock_list = [code.strip() for code in text.split('\n') if code.strip()]
-        
-        self.batch_analyze_btn.setEnabled(False)
-        self.progress_bar.setVisible(True)
-        self.progress_bar.setValue(0)
-
-        self.batch_worker = BatchAnalysisWorker(self.analyzer, stock_list)
-        self.batch_worker.finished.connect(self.handle_batch_analysis_result)
-        self.batch_worker.error.connect(self.handle_analysis_error)
-        self.batch_worker.progress.connect(self.progress_bar.setValue)
-        self.batch_worker.start()
-
-    def handle_single_analysis_result(self, report):
-        """处理单只股票分析结果"""
-        markdown_text = self.format_report(report)
-        html_content = markdown2.markdown(markdown_text, extras=['tables', 'fenced-code-blocks'])
-        self.result_browser.setHtml(html_content)
-        self.analyze_btn.setEnabled(True)
-        self.progress_bar.setVisible(False)
-
-    def handle_batch_analysis_result(self, recommendations):
-        """处理批量分析结果"""
-        markdown_text = "# 批量股票分析报告\n\n"
-        for rec in recommendations:
-            markdown_text += self.format_report(rec, False)
-            
-        html_content = markdown2.markdown(markdown_text, extras=['tables', 'fenced-code-blocks'])
-        self.result_browser.setHtml(html_content)
-        self.batch_analyze_btn.setEnabled(True)
-        self.progress_bar.setVisible(False)
-        
-    def handle_analysis_error(self, error_message):
-        """处理分析错误"""
-        self.show_error(f'分析过程中出现错误：{error_message}')
-        self.analyze_btn.setEnabled(True)
-        self.batch_analyze_btn.setEnabled(True)
-        self.progress_bar.setVisible(False)
-
-    def show_warning(self, message):
-        """显示警告对话框"""
-        warning = QMessageBox(self)
-        warning.setIcon(QMessageBox.Icon.Warning)
-        warning.setWindowTitle('警告')
-        warning.setText(message)
-        warning.setStandardButtons(QMessageBox.StandardButton.Ok)
-        warning.setStyleSheet("""
-            QMessageBox {
-                background-color: white;
-            }
-            QMessageBox QLabel {
-                color: #202124;
-                min-width: 200px;
-            }
-            QPushButton {
-                background-color: #1a73e8;
-                color: white;
-                border: none;
-                border-radius: 4px;
-                padding: 8px 16px;
-                min-width: 80px;
-            }
-            QPushButton:hover {
-                background-color: #1557b0;
-            }
-            QPushButton:pressed {
-                background-color: #0d47a1;
-            }
-        """)
-        warning.exec()
-
-    def show_error(self, message):
-        """显示错误对话框"""
-        error = QMessageBox(self)
-        error.setIcon(QMessageBox.Icon.Critical)
-        error.setWindowTitle('错误')
-        error.setText(message)
-        error.setStandardButtons(QMessageBox.StandardButton.Ok)
-        error.setStyleSheet("""
-            QMessageBox {
-                background-color: white;
-            }
-            QMessageBox QLabel {
-                color: #202124;
-                min-width: 200px;
-            }
-            QPushButton {
-                background-color: #1a73e8;
-                color: white;
-                border: none;
-                border-radius: 4px;
-                padding: 8px 16px;
-                min-width: 80px;
-            }
-            QPushButton:hover {
-                background-color: #1557b0;
-            }
-            QPushButton:pressed {
-                background-color: #0d47a1;
-            }
-        """)
-        error.exec()
-
-def main():
-    app = QApplication(sys.argv)
-    app.setStyle('Fusion')
-    
-    # 创建并显示主窗口
-    window = ModernStockAnalyzerGUI()
-    window.show()
-    
-    sys.exit(app.exec())
-
-if __name__ == '__main__':
-    main()

requirements.txt

@@ -1,5 +1,5 @@
 # 基础科学计算和数据处理库
-numpy==2.0.0
+numpy==2.1.2
 pandas==2.2.2
 scipy==1.15.1
 
@@ -7,13 +7,11 @@ scipy==1.15.1
 akshare==1.15.87
 tqdm==4.67.1
 
-# GUI库
-PyQt6==6.8.1
-markdown2==2.5.3
 
 # 网络和API请求
 requests==2.32.3
 python-dotenv==1.0.1
+flask==3.1.0
 
 # 日志和系统工具
 loguru==0.7.2

stock_analyzer.py

@@ -18,7 +18,7 @@ class StockAnalyzer:
         load_dotenv()
         
         # 设置 Gemini API
-        self.gemini_api_url = "https://api.xxx.xxx"
+        self.gemini_api_url = os.getenv('GEMINI_API_URL')
         self.gemini_api_key = os.getenv('GEMINI_API_KEY')
         
         # 配置参数
@@ -31,7 +31,14 @@ class StockAnalyzer:
             'atr_period': 14
         }
 
-    def get_stock_data(self, stock_code, start_date=None, end_date=None):
+         # 添加市场类型枚举
+        self.MARKET_TYPES = {
+            'A': 'A股',
+            'HK': '港股',
+            'CRYPTO': '加密货币'
+        }
+        
+    def get_stock_data(self, stock_code, market_type='A', start_date=None, end_date=None, ):
         """获取股票数据"""
         import akshare as ak
         
@@ -41,11 +48,33 @@ class StockAnalyzer:
             end_date = datetime.now().strftime('%Y%m%d')
             
         try:
-            # 使用 akshare 获取股票数据
+            # 根据市场类型获取数据
-            df = ak.stock_zh_a_hist(symbol=stock_code, 
+            if market_type == 'A':
-                                  start_date=start_date, 
+                df = ak.stock_zh_a_hist(
-                                  end_date=end_date,
+                    symbol=stock_code,
-                                  adjust="qfq")
+                    start_date=start_date,
+                    end_date=end_date,
+                    adjust="qfq"
+                )
+                # A股数据列名映射
+            elif market_type == 'HK':
+                df = ak.stock_hk_daily(
+                    symbol=stock_code,
+                    adjust="qfq"
+                )
+            elif market_type == 'US':
+                df = ak.stock_us_hist(
+                    symbol=stock_code,
+                    start_date=start_date,
+                    end_date=end_date,
+                    adjust="qfq"
+                )
+            elif market_type == 'CRYPTO':
+                df = ak.crypto_js_spot(
+                    symbol=stock_code
+                )
+            else:
+                raise ValueError(f"不支持的市场类型: {market_type}")
             
             # 重命名列名以匹配分析需求
             df = df.rename(columns={
@@ -225,7 +254,7 @@ class StockAnalyzer:
             }
             
             data = {
-                "model": "gemini-1.5-flash",
+                "model": os.getenv('GEMINI_API_MODEL'),
                 "messages": [{"role": "user", "content": prompt}]
             }
             
@@ -233,8 +262,11 @@ class StockAnalyzer:
                 f"{self.gemini_api_url}/v1/chat/completions",
                 headers=headers,
                 json=data,
-                timeout=10
+                timeout=30
             )
+            print(headers)
+            print(data)
+            print(response.json())
 
             if response.status_code == 200:
                 return response.json()['choices'][0]['message']['content']
@@ -258,11 +290,11 @@ class StockAnalyzer:
         else:
             return '强烈建议卖出'
             
-    def analyze_stock(self, stock_code):
+    def analyze_stock(self, stock_code, market_type='A'):
         """分析单个股票"""
         try:
             # 获取股票数据
-            df = self.get_stock_data(stock_code)
+            df = self.get_stock_data(stock_code, market_type)
             
             # 计算技术指标
             df = self.calculate_indicators(df)
@@ -295,13 +327,13 @@ class StockAnalyzer:
             self.logger.error(f"分析股票时出错: {str(e)}")
             raise
             
-    def scan_market(self, stock_list, min_score=60):
+    def scan_market(self, stock_list, min_score=60, market_type='A'):
         """扫描市场，寻找符合条件的股票"""
         recommendations = []
         
         for stock_code in stock_list:
             try:
-                report = self.analyze_stock(stock_code)
+                report = self.analyze_stock(stock_code, market_type)
                 if report['score'] >= min_score:
                     recommendations.append(report)
             except Exception as e:

templates/index.html

@@ -10,130 +10,206 @@
     <div class="container mx-auto px-4 py-8">
         <h1 class="text-3xl font-bold mb-8 text-center">股票分析系统</h1>
         
-        <div class="grid grid-cols-1 md:grid-cols-2 gap-8">
+        <div class="max-w-4xl mx-auto">  <!-- 将 max-w-2xl 改为 max-w-4xl -->
-            <!-- 单只股票分析 -->
-            <div class="bg-white p-6 rounded-lg shadow-md">
-                <h2 class="text-xl font-semibold mb-4">单只股票分析</h2>
-                <div class="mb-4">
-                    <input type="text" id="singleStock" 
-                           class="w-full p-2 border rounded"
-                           placeholder="输入股票代码（如：600000）">
-                </div>
-                <button onclick="analyzeSingleStock()"
-                        class="w-full bg-blue-600 text-white py-2 rounded hover:bg-blue-700">
-                    分析
-                </button>
-            </div>
-
             <!-- 批量分析 -->
             <div class="bg-white p-6 rounded-lg shadow-md">
-                <h2 class="text-xl font-semibold mb-4">批量股票分析</h2>
+                <h2 class="text-xl font-semibold mb-4">股票批量分析</h2>
+                
+                <!-- 添加市场类型选择 -->
                 <div class="mb-4">
+                    <label for="marketType" class="block text-sm font-medium text-gray-700 mb-2">
+                        选择市场类型
+                    </label>
+                    <select id="marketType" 
+                            class="w-full p-2 border rounded bg-white focus:ring-2 focus:ring-blue-500 focus:border-blue-500">
+                        <option value="A">A股</option>
+                        <option value="HK">港股</option>
+                        <!-- <option value="US">美股</option> -->
+                        <!-- <option value="CRYPTO">加密货币</option> -->
+                    </select>
+                </div>
+
+                <div class="mb-4">
+                    <label for="batchStocks" class="block text-sm font-medium text-gray-700 mb-2">
+                        输入代码
+                    </label>
                     <textarea id="batchStocks" 
                               class="w-full p-2 border rounded h-32"
-                              placeholder="输入多个股票代码，每行一个"></textarea>
+                              placeholder="输入代码，支持多个代码（用回车或逗号分隔）"></textarea>
                 </div>
-                <button onclick="analyzeBatchStocks()"
+                
-                        class="w-full bg-blue-600 text-white py-2 rounded hover:bg-blue-700">
+                <button id="analyzeBtn" onclick="analyzeStocks()"
-                    批量分析
+                        class="w-full bg-blue-600 text-white py-2 rounded hover:bg-blue-700 flex items-center justify-center">
+                    <span>开始分析</span>
+                    <div id="loadingSpinner" class="hidden ml-2">
+                        <svg class="animate-spin h-5 w-5 text-white" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24">
+                            <circle class="opacity-25" cx="12" cy="12" r="10" stroke="currentColor" stroke-width="4"></circle>
+                            <path class="opacity-75" fill="currentColor" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path>
+                        </svg>
+                    </div>
                 </button>
             </div>
-        </div>
 
-        <!-- 结果展示 -->
+            <!-- 结果展示 -->
-        <div id="results" class="mt-8 bg-white p-6 rounded-lg shadow-md">
+            <div id="results" class="mt-8">
-            <h2 class="text-xl font-semibold mb-4">分析结果</h2>
+                <h2 class="text-2xl font-bold mb-6 text-gray-800">分析结果</h2>
-            <div id="resultContent" class="prose"></div>
+                <div id="resultContent" class="space-y-8"></div>
-        </div>
+            </div>
-    </div>
 
     <script>
-        async function analyzeSingleStock() {
+        let isAnalyzing = false;
-            const stockCode = document.getElementById('singleStock').value.trim();
+
-            if (!stockCode) {
+        async function analyzeStocks() {
-                alert('请输入股票代码');
+            if (isAnalyzing) return;  // 防止重复点击
+
+            const stockInput = document.getElementById('batchStocks').value.trim();
+            const marketType = document.getElementById('marketType').value;
+            const analyzeBtn = document.getElementById('analyzeBtn');
+            const loadingSpinner = document.getElementById('loadingSpinner');
+            
+            if (!stockInput) {
+                alert('请输入代码');
                 return;
             }
 
+            const stockCodes = stockInput.split(/[\n,]/)
+                                      .map(code => code.trim())
+                                      .filter(code => code.length > 0);
+
             try {
-                const response = await fetch('/api/analyze', {
+                isAnalyzing = true;
+                analyzeBtn.disabled = true;
+                loadingSpinner.classList.remove('hidden');
+                analyzeBtn.querySelector('span').textContent = '分析中...';
+
+                const response = await fetch('/analyze', {
                     method: 'POST',
                     headers: {
                         'Content-Type': 'application/json',
                     },
-                    body: JSON.stringify({ stock_code: stockCode })
+                    body: JSON.stringify({ 
+                        stock_codes: stockCodes,
+                        market_type: marketType  // 添加市场类型参数
+                    })
                 });
 
-                const result = await response.json();
+                const data = await response.json();
-                if (response.ok) {
+                
-                    displayResults([result]);
+                if (!response.ok) {
-                } else {
+                    throw new Error(data.error || '分析失败');
-                    alert(result.error || '分析失败');
-                }
-            } catch (error) {
-                alert('请求失败: ' + error.message);
-            }
-        }
-
-        async function analyzeBatchStocks() {
-            const stocksText = document.getElementById('batchStocks').value.trim();
-            if (!stocksText) {
-                alert('请输入股票代码');
-                return;
-            }
-
-            const stockList = stocksText.split('\n').map(s => s.trim()).filter(s => s);
-
-            try {
-                const response = await fetch('/api/batch-analyze', {
-                    method: 'POST',
-                    headers: {
-                        'Content-Type': 'application/json',
-                    },
-                    body: JSON.stringify({ stock_list: stockList })
-                });
-
-                const results = await response.json();
-                if (response.ok) {
-                    displayResults(results);
-                } else {
-                    alert(results.error || '分析失败');
                 }
+
+                const results = Array.isArray(data.results) ? data.results : [data];
+                displayResults(results);
             } catch (error) {
                 alert('请求失败: ' + error.message);
+                document.getElementById('resultContent').innerHTML = `
+                    <div class="p-4 bg-red-50 text-red-600 rounded">
+                        分析出错：${error.message}
+                    </div>
+                `;
+            } finally {
+                isAnalyzing = false;
+                analyzeBtn.disabled = false;
+                loadingSpinner.classList.add('hidden');
+                analyzeBtn.querySelector('span').textContent = '开始分析';
             }
         }
 
         function displayResults(results) {
             const resultContent = document.getElementById('resultContent');
-            let html = '';
+            if (!results || results.length === 0) {
+                resultContent.innerHTML = '<div class="p-6 bg-yellow-50 text-yellow-600 rounded-lg text-center">没有分析结果</div>';
+                return;
+            }
 
+            let html = '';
             results.forEach(result => {
                 html += `
-                    <div class="mb-8 p-4 border rounded">
+                    <div class="bg-white rounded-lg shadow-lg overflow-hidden">
-                        <h3 class="text-lg font-semibold mb-2">股票代码: ${result.stock_code}</h3>
+                        <!-- 头部信息 -->
-                        <div class="grid grid-cols-2 gap-4">
+                        <div class="bg-gradient-to-r from-blue-600 to-blue-700 px-6 py-4">
-                            <div>
+                            <h3 class="text-xl font-bold text-white">
-                                <p><strong>分析日期:</strong> ${result.analysis_date}</p>
+                                ${result.stock_code}
-                                <p><strong>当前价格:</strong> ¥${result.price.toFixed(2)}</p>
+                            </h3>
-                                <p><strong>价格变动:</strong> ${result.price_change.toFixed(2)}%</p>
-                            </div>
-                            <div>
-                                <p><strong>综合评分:</strong> ${result.score}分</p>
-                                <p><strong>投资建议:</strong> ${result.recommendation}</p>
-                                <p><strong>RSI指标:</strong> ${result.rsi.toFixed(2)}</p>
-                            </div>
                         </div>
-                        <div class="mt-4">
+                        
-                            <h4 class="font-semibold mb-2">AI分析:</h4>
+                        <!-- 主要指标 -->
-                            <p class="whitespace-pre-line">${result.ai_analysis}</p>
+                        <div class="p-6">
+                            <div class="grid grid-cols-2 gap-6 mb-6">
+                                <div class="space-y-3">
+                                    <div class="flex justify-between items-center p-3 bg-gray-50 rounded">
+                                        <span class="text-gray-600">分析时间</span>
+                                        <span class="font-medium">${result.analysis_date}</span>
+                                    </div>
+                                    <div class="flex justify-between items-center p-3 bg-gray-50 rounded">
+                                        <span class="text-gray-600">当前价格</span>
+                                        <span class="font-medium">¥${result.price.toFixed(2)}</span>
+                                    </div>
+                                    <div class="flex justify-between items-center p-3 bg-gray-50 rounded">
+                                        <span class="text-gray-600">价格变动</span>
+                                        <span class="font-medium ${result.price_change >= 0 ? 'text-red-500' : 'text-green-500'}">
+                                            ${result.price_change.toFixed(2)}%
+                                        </span>
+                                    </div>
+                                </div>
+                                <div class="space-y-3">
+                                    <div class="flex justify-between items-center p-3 bg-gray-50 rounded">
+                                        <span class="text-gray-600">综合评分</span>
+                                        <span class="font-medium text-blue-600">${result.score}分</span>
+                                    </div>
+                                    <div class="flex justify-between items-center p-3 bg-gray-50 rounded">
+                                        <span class="text-gray-600">投资建议</span>
+                                        <span class="font-medium text-purple-600">${result.recommendation}</span>
+                                    </div>
+                                    <div class="flex justify-between items-center p-3 bg-gray-50 rounded">
+                                        <span class="text-gray-600">RSI指标</span>
+                                        <span class="font-medium">${result.rsi.toFixed(2)}</span>
+                                    </div>
+                                </div>
+                            </div>
+
+                            <!-- AI分析部分 -->
+                            <div class="mt-6">
+                                <h4 class="text-lg font-semibold text-gray-800 mb-3">AI分析</h4>
+                                <div class="prose prose-blue max-w-none bg-gray-50 p-4 rounded-lg">
+                                    ${marked.parse(result.ai_analysis)}
+                                </div>
+                            </div>
+
+                            <!-- 免责声明 -->
+                            <div class="mt-6 border-t border-gray-100 pt-4">
+                                <div class="bg-blue-50 p-4 rounded-lg">
+                                    <p class="text-sm text-blue-800 font-semibold mb-1">声明：</p>
+                                    <p class="text-sm text-blue-600">本分析仅基于技术指标和历史数据，不构成投资建议。股市有风险，投资需谨慎。</p>
+                                </div>
+                            </div>
                         </div>
                     </div>
                 `;
             });
 
             resultContent.innerHTML = html;
+
+            // 添加 Markdown 样式
+            const style = document.createElement('style');
+            style.textContent = `
+                .prose h1 { font-size: 1.5em; margin-top: 1em; margin-bottom: 0.5em; font-weight: bold; }
+                .prose h2 { font-size: 1.3em; margin-top: 1em; margin-bottom: 0.5em; font-weight: bold; }
+                .prose h3 { font-size: 1.1em; margin-top: 1em; margin-bottom: 0.5em; font-weight: bold; }
+                .prose p { margin-bottom: 1em; line-height: 1.6; }
+                .prose ul { list-style-type: disc; padding-left: 1.5em; margin-bottom: 1em; }
+                .prose ol { list-style-type: decimal; padding-left: 1.5em; margin-bottom: 1em; }
+                .prose li { margin-bottom: 0.5em; }
+                .prose strong { font-weight: 600; color: #1a56db; }
+                .prose em { font-style: italic; }
+                .prose blockquote { border-left: 4px solid #e5e7eb; padding-left: 1em; margin: 1em 0; color: #4b5563; }
+                .prose code { background-color: #f3f4f6; padding: 0.2em 0.4em; border-radius: 0.25em; font-size: 0.9em; }
+            `;
+            document.head.appendChild(style);
         }
     </script>
+    <!-- 添加 marked.js 用于解析 Markdown -->
+    <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
 </body>
 </html>

web_app.py

@@ -1,39 +0,0 @@
-from flask import Flask, request, jsonify, render_template
-from stock_analyzer import StockAnalyzer
-import os
-
-app = Flask(__name__)
-analyzer = StockAnalyzer()
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/api/analyze', methods=['POST'])
-def analyze():
-    try:
-        data = request.json
-        stock_code = data.get('stock_code')
-        if not stock_code:
-            return jsonify({'error': '请提供股票代码'}), 400
-
-        result = analyzer.analyze_stock(stock_code)
-        return jsonify(result)
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-@app.route('/api/batch-analyze', methods=['POST'])
-def batch_analyze():
-    try:
-        data = request.json
-        stock_list = data.get('stock_list', [])
-        if not stock_list:
-            return jsonify({'error': '请提供股票代码列表'}), 400
-
-        results = analyzer.scan_market(stock_list)
-        return jsonify(results)
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=8443)

web_server.py

@@ -0,0 +1,62 @@
+from flask import Flask, render_template, request, jsonify
+from stock_analyzer import StockAnalyzer
+from us_stock_service import USStockService
+import threading
+import logging
+from logging.handlers import RotatingFileHandler
+import traceback
+
+app = Flask(__name__)
+analyzer = StockAnalyzer()
+us_stock_service = USStockService()
+
+# 配置日志
+logging.basicConfig(level=logging.INFO)
+handler = RotatingFileHandler('flask_app.log', maxBytes=10000000, backupCount=5)
+handler.setFormatter(logging.Formatter(
+    '[%(asctime)s] %(levelname)s in %(module)s: %(message)s'
+))
+app.logger.addHandler(handler)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    try:
+        data = request.json
+        stock_codes = data.get('stock_codes', [])
+        market_type = data.get('market_type', 'A') 
+        
+        if not stock_codes:
+            return jsonify({'error': '请输入代码'}), 400
+            
+        results = []
+        for stock_code in stock_codes:
+            result = analyzer.analyze_stock(stock_code.strip(), market_type)
+            results.append(result)
+            
+        return jsonify({'results': results})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/search_us_stocks', methods=['GET'])
+def search_us_stocks():
+    try:
+        keyword = request.args.get('keyword', '')
+        if not keyword:
+            return jsonify({'error': '请输入搜索关键词'}), 400
+            
+        results = us_stock_service.search_us_stocks(keyword)
+        return jsonify({'results': results})
+        
+    except Exception as e:
+        app.logger.error(f"搜索美股代码时出错: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    # 将 host 设置为 '0.0.0.0' 使其支持所有网络接口访问
+    app.run(host='0.0.0.0', port=8888, debug=True)
+
+    

全部股票分析推荐1.py

@@ -1,602 +0,0 @@
-""""
-Stock Analysis System
-优化后的全盘股票技术分析系统——用于A股市场股票的全面分析，已加速分析并增加额外指标。
-"""
-
-import os
-import time
-import random
-import logging
-import traceback
-from datetime import datetime, timedelta
-from concurrent.futures import ThreadPoolExecutor
-from typing import Dict, List, Optional, Tuple, Union
-from dataclasses import dataclass
-
-import numpy as np
-import pandas as pd
-import akshare as ak
-from tqdm import tqdm
-
-# -------------------------------
-# **技术指标配置**
-# -------------------------------
-@dataclass
-class TechnicalParams:
-    """技术指标参数配置"""
-    ma_periods: Dict[str, int]
-    rsi_period: int
-    bollinger_period: int
-    bollinger_std: int
-    volume_ma_period: int
-    atr_period: int
-
-    @classmethod
-    def default(cls) -> 'TechnicalParams':
-        """返回默认的技术指标参数"""
-        return cls(
-            ma_periods={'short': 5, 'medium': 20, 'long': 60},
-            rsi_period=14,
-            bollinger_period=20,
-            bollinger_std=2,
-            volume_ma_period=20,
-            atr_period=14
-        )
-
-# -------------------------------
-# **股票分析引擎**
-# -------------------------------
-class StockAnalyzer:
-    """股票分析引擎，计算各类技术指标"""
-
-    def __init__(self, params: Optional[TechnicalParams] = None):
-        """
-        初始化股票分析引擎
-
-        Args:
-            params: 技术指标配置参数
-        """
-        self._setup_logging()
-        self.params = params or TechnicalParams.default()
-
-    def _setup_logging(self) -> None:
-        """配置日志记录"""
-        logging.basicConfig(
-            level=logging.INFO,
-            format='%(asctime)s - %(levelname)s - %(message)s',
-            datefmt='%Y-%m-%d %H:%M:%S'
-        )
-        self.logger = logging.getLogger(__name__)
-
-    def get_stock_data(self, stock_code: str,
-                       start_date: Optional[str] = None,
-                       end_date: Optional[str] = None) -> pd.DataFrame:
-        """
-        获取单只股票历史数据，默认使用前一年的数据。
-
-        Args:
-            stock_code: 股票代码（可以带市场前缀或纯代码）
-            start_date: 开始日期(格式YYYYMMDD)
-            end_date: 结束日期(格式YYYYMMDD)
-
-        Returns:
-            包含日期、开盘、收盘、最高、最低、成交量的 DataFrame
-        """
-        try:
-            if not start_date:
-                start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
-            if not end_date:
-                end_date = datetime.now().strftime('%Y%m%d')
-
-            code = stock_code[2:] if stock_code.startswith(('sz', 'sh')) else stock_code
-
-            df = ak.stock_zh_a_hist(
-                symbol=code,
-                start_date=start_date,
-                end_date=end_date,
-                adjust="qfq"
-            )
-
-            self.logger.info(f"获取到 {len(df)} 行数据，列名：{df.columns.tolist()}")
-
-            df = df.rename(columns={
-                "日期": "date",
-                "开盘": "open",
-                "收盘": "close",
-                "最高": "high",
-                "最低": "low",
-                "成交量": "volume",
-                "trade_date": "date"
-            })
-
-            required_columns = {'date', 'open', 'close', 'high', 'low', 'volume'}
-            missing_columns = required_columns - set(df.columns)
-            if missing_columns:
-                raise ValueError(f"缺失必须字段: {missing_columns}")
-
-            df['date'] = pd.to_datetime(df['date'], errors='coerce')
-            numeric_columns = ['open', 'close', 'high', 'low', 'volume']
-            df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce')
-            df_cleaned = df.dropna(subset=['date'] + numeric_columns).sort_values('date')
-
-            if len(df_cleaned) < 60:
-                raise ValueError(f"数据不足（仅 {len(df_cleaned)} 行），无法计算至少60日均线")
-
-            return df_cleaned
-
-        except Exception as e:
-            self.logger.error(f"获取股票数据失败，股票代码 {stock_code}，错误信息：{str(e)}")
-            raise ValueError(f"股票 {stock_code} 数据获取出错: {str(e)}")
-
-    @staticmethod
-    def calculate_ema(series: pd.Series, period: int) -> pd.Series:
-        """计算 EMA"""
-        return series.ewm(span=period, adjust=False).mean()
-
-    @staticmethod
-    def calculate_rsi(series: pd.Series, period: int) -> pd.Series:
-        """
-        计算 RSI（指数加权移动平均法）
-        """
-        delta = series.diff()
-        gain = delta.clip(lower=0)
-        loss = -delta.clip(upper=0)
-        avg_gain = gain.ewm(com=period - 1, adjust=False).mean()
-        avg_loss = loss.ewm(com=period - 1, adjust=False).mean()
-        rs = avg_gain / (avg_loss + 1e-10)
-        return 100 - (100 / (1 + rs))
-
-    @staticmethod
-    def calculate_macd(series: pd.Series) -> Tuple[pd.Series, pd.Series, pd.Series]:
-        """计算 MACD、信号线和直方图"""
-        exp1 = series.ewm(span=12, adjust=False).mean()
-        exp2 = series.ewm(span=26, adjust=False).mean()
-        macd = exp1 - exp2
-        signal = macd.ewm(span=9, adjust=False).mean()
-        return macd, signal, macd - signal
-
-    @staticmethod
-    def calculate_bollinger_bands(series: pd.Series, period: int, std_dev: int) -> Tuple[pd.Series, pd.Series, pd.Series]:
-        """计算 Bollinger 通道"""
-        middle = series.rolling(window=period, min_periods=period).mean()
-        std = series.rolling(window=period, min_periods=period).std()
-        upper = middle + std * std_dev
-        lower = middle - std * std_dev
-        return upper, middle, lower
-
-    def calculate_atr(self, df: pd.DataFrame, period: int) -> pd.Series:
-        """计算 ATR"""
-        high = df['high']
-        low = df['low']
-        prev_close = df['close'].shift(1)
-        tr = pd.concat([
-            high - low,
-            (high - prev_close).abs(),
-            (low - prev_close).abs()
-        ], axis=1).max(axis=1)
-        return tr.rolling(window=period, min_periods=period).mean()
-
-    @staticmethod
-    def calculate_obv(series_close: pd.Series, series_volume: pd.Series) -> pd.Series:
-        """计算 OBV（能量潮指标）"""
-        diff = series_close.diff().fillna(0)
-        obv = np.where(diff > 0, series_volume, np.where(diff < 0, -series_volume, 0))
-        return pd.Series(obv, index=series_close.index).cumsum()
-
-    @staticmethod
-    def calculate_stochastic(series_close: pd.Series, window: int = 14) -> Tuple[pd.Series, pd.Series]:
-        """
-        计算随机指标（Stochastic Oscillator）
-           %K = (close - lowest_low) / (highest_high - lowest_low)*100
-           %D 为 %K 的3日简单移动平均
-        """
-        lowest = series_close.rolling(window=window, min_periods=window).min()
-        highest = series_close.rolling(window=window, min_periods=window).max()
-        percentK = (series_close - lowest) / (highest - lowest + 1e-10) * 100
-        percentD = percentK.rolling(window=3, min_periods=3).mean()
-        return percentK, percentD
-
-    def calculate_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
-        """计算所有技术指标，并增加 OBV 和随机指标"""
-        try:
-            # 移动均线
-            for key, period in self.params.ma_periods.items():
-                df[f'MA{period}'] = self.calculate_ema(df['close'], period)
-            df['RSI'] = self.calculate_rsi(df['close'], self.params.rsi_period)
-            df['MACD'], df['Signal'], df['MACD_hist'] = self.calculate_macd(df['close'])
-            df['BB_upper'], df['BB_middle'], df['BB_lower'] = self.calculate_bollinger_bands(
-                df['close'], self.params.bollinger_period, self.params.bollinger_std)
-            df['Volume_MA'] = df['volume'].rolling(window=self.params.volume_ma_period,
-                                                   min_periods=self.params.volume_ma_period).mean()
-            df['Volume_Ratio'] = df['volume'] / (df['Volume_MA'] + 1e-10)
-            df['ATR'] = self.calculate_atr(df, self.params.atr_period)
-            df['Volatility'] = df['ATR'] / df['close'] * 100
-            df['ROC'] = df['close'].pct_change(periods=10) * 100
-
-            # 增加 OBV 指标
-            df['OBV'] = self.calculate_obv(df['close'], df['volume'])
-            df['OBV_MA10'] = df['OBV'].rolling(window=10, min_periods=10).mean()
-
-            # 增加随机指标 Stochastic
-            df['%K'], df['%D'] = self.calculate_stochastic(df['close'], window=14)
-
-            return df
-
-        except Exception as e:
-            self.logger.error(f"指标计算出错：{str(e)}")
-            raise
-
-    def calculate_score(self, df: pd.DataFrame) -> float:
-        """
-        计算股票综合打分（基本打分满分100分），并根据 OBV 与随机指标调整±5分，
-        使量化结果更全面。
-        基本打分逻辑不变：
-          趋势（30分）、RSI（20分）、MACD（20分）、成交量（30分）
-        附加指标调整：
-          OBV：OBV > OBV_MA10，+5分；反之，-5分；
-          随机指标：%K < 20为超卖，+5分；%K > 80为超买，-5分。
-        """
-        try:
-            score = 0
-            latest = df.iloc[-1]
-            # 趋势打分
-            if latest['MA5'] > latest['MA20'] and latest['MA20'] > latest['MA60']:
-                score += 30
-            else:
-                if latest['MA5'] > latest['MA20']:
-                    score += 15
-                if latest['MA20'] > latest['MA60']:
-                    score += 15
-            # RSI 打分
-            if 30 <= latest['RSI'] <= 70:
-                score += 20
-            elif latest['RSI'] < 30:
-                score += 15
-            # MACD 打分
-            if latest['MACD'] > latest['Signal']:
-                score += 20
-            # 成交量打分
-            if latest['Volume_Ratio'] > 1.5:
-                score += 30
-            elif latest['Volume_Ratio'] > 1:
-                score += 15
-
-            # 附加 OBV 调整
-            if latest['OBV'] > latest['OBV_MA10']:
-                score += 5
-            else:
-                score -= 5
-
-            # 附加随机指标调整
-            if latest['%K'] < 20:
-                score += 5
-            elif latest['%K'] > 80:
-                score -= 5
-
-            return score
-
-        except Exception as e:
-            self.logger.error(f"计算打分失败：{str(e)}")
-            raise
-
-    @staticmethod
-    def get_recommendation(score: float) -> str:
-        """根据最终打分给出投资建议"""
-        if score >= 80:
-            return '强烈推荐买入'
-        elif score >= 60:
-            return '建议买入'
-        elif score >= 40:
-            return '建议观望'
-        elif score >= 20:
-            return '建议卖出'
-        else:
-            return '强烈建议卖出'
-
-    def analyze_stock(self, stock_code: str) -> Dict:
-        """针对单只股票执行完整的技术分析流程"""
-        try:
-            df = self.get_stock_data(stock_code)
-            df = self.calculate_indicators(df)
-            score = self.calculate_score(df)
-            latest = df.iloc[-1]
-            prev = df.iloc[-2]
-            return {
-                'stock_code': stock_code,
-                'analysis_date': datetime.now().strftime('%Y-%m-%d'),
-                'score': score,
-                'price': latest['close'],
-                'price_change': (latest['close'] - prev['close']) / prev['close'] * 100,
-                'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
-                'rsi': latest['RSI'],
-                'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
-                'volume_status': 'HIGH' if latest['Volume_Ratio'] > 1.5 else 'NORMAL',
-                'recommendation': self.get_recommendation(score)
-            }
-
-        except Exception as e:
-            self.logger.error(f"分析股票 {stock_code} 失败：{str(e)}")
-            raise
-
-# -------------------------------
-# **全盘股票扫描器**
-# -------------------------------
-class TopStockScanner:
-    """全盘筛选高打分股票的扫描器"""
-
-    def __init__(self, max_workers: int = 20, min_score: float = 85):
-        """
-        初始化扫描器
-
-        Args:
-            max_workers: 并发线程数量（已增至20以加速分析）
-            min_score: 高分最低阈值
-        """
-        self.analyzer = StockAnalyzer()
-        self.max_workers = max_workers
-        self.min_score = min_score
-        self.logger = logging.getLogger(__name__)
-
-    def get_all_stocks(self) -> List[str]:
-        """
-        获取所有上市 A 股股票代码（全盘版）。
-        使用 ak.stock_info_sh_name_code(symbol="主板A股") 与 ak.stock_info_sz_name_code(symbol="A股列表")，
-        候选字段列表为：['A股代码', '证券代码', '股票代码', 'code'] 。
-        """
-        try:
-            sh_df = ak.stock_info_sh_name_code(symbol="主板A股")
-            sz_df = ak.stock_info_sz_name_code(symbol="A股列表")
-            candidate_cols = ['A股代码', '证券代码', '股票代码', 'code']
-
-            def get_codes(df: pd.DataFrame) -> set:
-                for col in candidate_cols:
-                    if col in df.columns:
-                        return {str(code).zfill(6) for code in df[col]}
-                raise KeyError(f"未能找到股票代码字段，现有字段：{df.columns.tolist()}")
-
-            sh_codes = get_codes(sh_df)
-            sz_codes = get_codes(sz_df)
-            all_codes = sorted(sh_codes | sz_codes)
-            self.logger.info(f"完整股票列表获取到 {len(all_codes)} 支股票信息")
-            print(f"\n开始分析 {len(all_codes)} 支股票...")
-            return all_codes
-
-        except Exception as e:
-            self.logger.error(f"获取股票列表失败：{str(e)}")
-            raise
-
-    def analyze_stock_safe(self, stock_code: str, max_retries: int = 3) -> Optional[Dict]:
-        """
-        安全分析单只股票（加入重试机制），数据异常则跳过。
-        """
-        for attempt in range(max_retries):
-            try:
-                return self.analyzer.analyze_stock(stock_code)
-            except ValueError as e:
-                self.logger.warning(f"跳过股票 {stock_code}: {str(e)}")
-                return None
-            except Exception as e:
-                if attempt == max_retries - 1:
-                    self.logger.error(f"股票 {stock_code} 分析尝试 {max_retries} 次后失败：{str(e)}")
-                    return None
-                self.logger.warning(f"股票 {stock_code} 第 {attempt+1} 次分析失败：{str(e)}")
-                time.sleep(random.uniform(2, 5))
-
-    def process_batch(self, stock_codes: List[str]) -> List[Dict]:
-        """利用多线程并行处理一批股票的分析任务"""
-        results = []
-        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-            futures = {executor.submit(self.analyze_stock_safe, code): code for code in stock_codes}
-            for future in tqdm(futures, desc="分析进度", ncols=80):
-                try:
-                    result = future.result()
-                    if result is not None:
-                        results.append(result)
-                except Exception as e:
-                    stock = futures[future]
-                    self.logger.error(f"处理股票 {stock} 时出错：{str(e)}")
-        return results
-
-    def save_intermediate_results(self, results: List[Dict]) -> None:
-        """周期性保存中间结果，便于后续查看进度"""
-        try:
-            df = pd.DataFrame(results)
-            high_score_stocks = df[df['score'] >= self.min_score].sort_values('score', ascending=False)
-            output_lines = [
-                "=" * 80,
-                f"股票扫描中间结果 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
-                f"共分析 {len(results)} 支股票",
-                "=" * 80,
-                f"\n发现 {len(high_score_stocks)} 支高分股票（得分≥{self.min_score}）："
-            ]
-            for _, row in high_score_stocks.iterrows():
-                output_lines.extend([
-                    f"\n股票代码: {row['stock_code']}",
-                    f"得分: {row['score']:.1f} | 价格: ¥{row['price']:.2f} | 涨跌幅: {row['price_change']:.2f}%"
-                ])
-
-            os.makedirs('scanner', exist_ok=True)
-            with open('scanner/temp_results.txt', 'w', encoding='utf-8') as f:
-                f.write('\n'.join(output_lines))
-
-        except Exception as e:
-            self.logger.error(f"保存中间结果失败：{str(e)}")
-
-    def get_high_score_stocks(self, batch_size: int = 20) -> List[Dict]:
-        """扫描全盘股票，返回高打分结果列表"""
-        try:
-            all_stocks = self.get_all_stocks()
-            total_stocks = len(all_stocks)
-            print(f"\n开始扫描 {total_stocks} 支股票……")
-            results = []
-            total_batches = (total_stocks + batch_size - 1) // batch_size
-
-            for i in range(0, total_stocks, batch_size):
-                batch_number = i // batch_size + 1
-                print(f"\r当前进度: 批次 {batch_number}/{total_batches}", end="")
-                batch = all_stocks[i:i + batch_size]
-                batch_results = self.process_batch(batch)
-                results.extend(batch_results)
-                if i + batch_size < total_stocks:
-                    time.sleep(random.uniform(3, 5))
-                if results and ((len(results) % 100 == 0) or (i + batch_size >= total_stocks)):
-                    self.save_intermediate_results(results)
-            print("\n扫描结束！")
-
-            if results:
-                df_results = pd.DataFrame(results)
-                high_score_stocks = df_results[df_results['score'] >= self.min_score].sort_values('score', ascending=False)
-                formatted_results = []
-                for _, row in high_score_stocks.iterrows():
-                    formatted_results.append({
-                        '股票代码': row['stock_code'],
-                        '评分': f"{row['score']:.1f}",
-                        '当前价格': f"¥{row['price']:.2f}",
-                        '涨跌幅': f"{row['price_change']:.2f}%",
-                        'RSI指标': f"{row['rsi']:.2f}",
-                        '均线趋势': '上升' if row['ma_trend'] == 'UP' else '下降',
-                        'MACD信号': '买入' if row['macd_signal'] == 'BUY' else '卖出',
-                        '成交量状态': '放量' if row['volume_status'] == 'HIGH' else '正常',
-                        '投资建议': row['recommendation']
-                    })
-                return formatted_results
-            return []
-
-        except Exception as e:
-            self.logger.error(f"全盘扫描失败：{str(e)}")
-            raise
-
-# -------------------------------
-# **结果分组与报告生成**
-# -------------------------------
-def format_price_category(price: float) -> str:
-    """将价格划分为区间（例如 32.5 -> '30-40'）"""
-    base = (price // 10) * 10
-    return f"{int(base)}-{int(base+10)}"
-
-def save_results_by_price(results: List[Dict]) -> None:
-    """按价格区间保存分析结果至文件"""
-    try:
-        os.makedirs('scanner', exist_ok=True)
-        price_groups = {}
-        for stock in results:
-            price = float(stock['当前价格'].replace('¥', ''))
-            category = format_price_category(price)
-            price_groups.setdefault(category, []).append(stock)
-
-        for category, stocks in price_groups.items():
-            output_lines = [
-                "=" * 80,
-                f"股票分析结果 - 价格区间: {category}元",
-                f"分析时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
-                "=" * 80,
-                f"\n该区间共发现 {len(stocks)} 支高分股票（得分≥85）：",
-                "-" * 80
-            ]
-            stocks.sort(key=lambda x: float(x['评分']), reverse=True)
-            for i, stock in enumerate(stocks, 1):
-                output_lines.extend([
-                    f"\n{i}. 股票代码: {stock['股票代码']}",
-                    f"   评分: {stock['评分']} | 价格: {stock['当前价格']} | 涨跌幅: {stock['涨跌幅']}",
-                    f"   RSI指标: {stock['RSI指标']} | 均线趋势: {stock['均线趋势']} | MACD信号: {stock['MACD信号']}",
-                    f"   成交量状态: {stock['成交量状态']}",
-                    f"   投资建议: {stock['投资建议']}",
-                    "-" * 80
-                ])
-            output_lines.extend([
-                f"\n价格区间 {category}元 分析汇总：",
-                f"1. 股票数量: {len(stocks)}",
-                f"2. 平均评分: {np.mean([float(stock['评分']) for stock in stocks]):.1f}",
-                f"3. 买入信号股票数: {sum(1 for stock in stocks if stock['MACD信号'] == '买入')}",
-                f"4. 放量股票数: {sum(1 for stock in stocks if stock['成交量状态'] == '放量')}"
-            ])
-
-            filename = f'scanner/price_{category.replace("-", "_")}.txt'
-            with open(filename, 'w', encoding='utf-8') as f:
-                f.write('\n'.join(output_lines))
-        create_summary_file(price_groups)
-    except Exception as e:
-        logging.error(f"保存结果时发生错误: {str(e)}")
-        raise
-
-def create_summary_file(price_groups: Dict[str, List[Dict]]) -> None:
-    """生成综合汇总报告"""
-    try:
-        output_lines = [
-            "=" * 80,
-            "A股市场优质股票筛选报告",
-            f"分析时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
-            "=" * 80
-        ]
-        total_stocks = sum(len(stocks) for stocks in price_groups.values())
-        all_scores = [float(stock['评分']) for stocks in price_groups.values() for stock in stocks]
-
-        output_lines.extend([
-            "\n整体统计：",
-            f"1. 共筛选出 {total_stocks} 支高分股票（得分≥85）",
-            f"2. 平均评分: {np.mean(all_scores):.1f}",
-            f"3. 最高评分: {max(all_scores):.1f}",
-            "\n各价格区间分布：",
-            "-" * 80
-        ])
-        for category, stocks in sorted(price_groups.items(), key=lambda x: float(x[0].split('-')[0])):
-            output_lines.extend([
-                f"\n价格区间 {category}元：",
-                f"  - 股票数量: {len(stocks)}",
-                f"  - 平均评分: {np.mean([float(stock['评分']) for stock in stocks]):.1f}"
-            ])
-
-        with open('scanner/summary.txt', 'w', encoding='utf-8') as f:
-            f.write('\n'.join(output_lines))
-    except Exception as e:
-        logging.error(f"生成汇总报告失败：{str(e)}")
-        raise
-
-# -------------------------------
-# **主程序入口**
-# -------------------------------
-def main():
-    """程序主入口"""
-    print("\n" + "=" * 80)
-    print("Market-Wide High-Score Stock Scanner".center(76))
-    print("=" * 80)
-
-    scanner = TopStockScanner(max_workers=20)  # 已提升至20线程
-    try:
-        print("\n开始全盘扫描股票……")
-        high_score_stocks = scanner.get_high_score_stocks(batch_size=20)
-        if not high_score_stocks:
-            print("\n未找到得分大于等于85分的股票。")
-            return
-
-        save_results_by_price(high_score_stocks)
-
-        print(f"\n分析完成！结果已保存至 scanner 文件夹中：")
-        print("1. 按价格区间保存的详细分析文件（price_XX_YY.txt）")
-        print("2. 汇总报告（summary.txt）")
-
-        temp_file = 'scanner/temp_results.txt'
-        if os.path.exists(temp_file):
-            os.remove(temp_file)
-
-        print("\n" + "=" * 80)
-        input("\n按Enter键退出……")
-
-    except Exception as e:
-        error_msg = f"\n程序错误：{str(e)}\n"
-        print("=" * 80)
-        print(error_msg)
-        print("=" * 80)
-        os.makedirs('scanner', exist_ok=True)
-        with open('scanner/error_log.txt', 'w', encoding='utf-8') as f:
-            f.write("Stock Analysis System Error Report\n")
-            f.write("=" * 80 + "\n")
-            f.write(f"Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
-            f.write(f"Error: {str(e)}\n")
-            f.write("=" * 80 + "\n")
-            f.write(f"详细堆栈信息:\n{traceback.format_exc()}")
-        print("错误日志已保存至 scanner/error_log.txt")
-        input("\n按Enter键退出……")
-
-if __name__ == "__main__":
-    main()