commit 6a4110920411f2095a95822602f8f8bcb9f79843 Author: DR-lin-eng <52230594+DR-lin-eng@users.noreply.github.com> Date: Thu Feb 27 22:50:22 2025 +0800 Add files via upload diff --git a/gui.py b/gui.py new file mode 100644 index 0000000..57b16f1 --- /dev/null +++ b/gui.py @@ -0,0 +1,218 @@ +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() diff --git a/gui2.py b/gui2.py new file mode 100644 index 0000000..4124871 --- /dev/null +++ b/gui2.py @@ -0,0 +1,497 @@ +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() diff --git a/stock_analyzer.py b/stock_analyzer.py new file mode 100644 index 0000000..f5bb47c --- /dev/null +++ b/stock_analyzer.py @@ -0,0 +1,313 @@ +import pandas as pd +import numpy as np +from datetime import datetime, timedelta +import os +import requests +from typing import Dict, List, Optional, Tuple +from dotenv import load_dotenv +import logging + +class StockAnalyzer: + def __init__(self, initial_cash=1000000): + # 设置日志 + logging.basicConfig(level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s') + self.logger = logging.getLogger(__name__) + + # 加载环境变量 + load_dotenv() + + # 设置 Gemini API + self.gemini_api_url = "https://api.linzefeng.top" + self.gemini_api_key = os.getenv('GEMINI_API_KEY') + + # 配置参数 + self.params = { + 'ma_periods': {'short': 5, 'medium': 20, 'long': 60}, + 'rsi_period': 14, + 'bollinger_period': 20, + 'bollinger_std': 2, + 'volume_ma_period': 20, + 'atr_period': 14 + } + + def get_stock_data(self, stock_code, start_date=None, end_date=None): + """获取股票数据""" + import akshare as ak + + if start_date is None: + start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d') + if end_date is None: + end_date = datetime.now().strftime('%Y%m%d') + + try: + # 使用 akshare 获取股票数据 + df = ak.stock_zh_a_hist(symbol=stock_code, + start_date=start_date, + end_date=end_date, + adjust="qfq") + + # 重命名列名以匹配分析需求 + df = df.rename(columns={ + "日期": "date", + "开盘": "open", + "收盘": "close", + "最高": "high", + "最低": "low", + "成交量": "volume" + }) + + # 确保日期格式正确 + df['date'] = pd.to_datetime(df['date']) + + # 数据类型转换 + numeric_columns = ['open', 'close', 'high', 'low', 'volume'] + df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce') + + # 删除空值 + df = df.dropna() + + return df.sort_values('date') + + except Exception as e: + self.logger.error(f"获取股票数据失败: {str(e)}") + raise Exception(f"获取股票数据失败: {str(e)}") + + def calculate_ema(self, series, period): + """计算指数移动平均线""" + return series.ewm(span=period, adjust=False).mean() + + def calculate_rsi(self, series, period): + """计算RSI指标""" + delta = series.diff() + gain = (delta.where(delta > 0, 0)).rolling(window=period).mean() + loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean() + rs = gain / loss + return 100 - (100 / (1 + rs)) + + def calculate_macd(self, series): + """计算MACD指标""" + 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() + hist = macd - signal + return macd, signal, hist + + def calculate_bollinger_bands(self, series, period, std_dev): + """计算布林带""" + middle = series.rolling(window=period).mean() + std = series.rolling(window=period).std() + upper = middle + (std * std_dev) + lower = middle - (std * std_dev) + return upper, middle, lower + + def calculate_atr(self, df, period): + """计算ATR指标""" + high = df['high'] + low = df['low'] + close = df['close'].shift(1) + + tr1 = high - low + tr2 = abs(high - close) + tr3 = abs(low - close) + + tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1) + return tr.rolling(window=period).mean() + + def calculate_indicators(self, df): + """计算技术指标""" + try: + # 计算移动平均线 + df['MA5'] = self.calculate_ema(df['close'], self.params['ma_periods']['short']) + df['MA20'] = self.calculate_ema(df['close'], self.params['ma_periods']['medium']) + df['MA60'] = self.calculate_ema(df['close'], self.params['ma_periods']['long']) + + # 计算RSI + df['RSI'] = self.calculate_rsi(df['close'], self.params['rsi_period']) + + # 计算MACD + df['MACD'], df['Signal'], df['MACD_hist'] = self.calculate_macd(df['close']) + + # 计算布林带 + df['BB_upper'], df['BB_middle'], df['BB_lower'] = self.calculate_bollinger_bands( + df['close'], + self.params['bollinger_period'], + self.params['bollinger_std'] + ) + + # 成交量分析 + df['Volume_MA'] = df['volume'].rolling(window=self.params['volume_ma_period']).mean() + df['Volume_Ratio'] = df['volume'] / df['Volume_MA'] + + # 计算ATR和波动率 + df['ATR'] = self.calculate_atr(df, self.params['atr_period']) + df['Volatility'] = df['ATR'] / df['close'] * 100 + + # 动量指标 + df['ROC'] = df['close'].pct_change(periods=10) * 100 + + return df + + except Exception as e: + self.logger.error(f"计算技术指标时出错: {str(e)}") + raise + + def calculate_score(self, df): + """计算股票评分""" + try: + score = 0 + latest = df.iloc[-1] + + # 趋势得分 (30分) + if latest['MA5'] > latest['MA20']: + score += 15 + if latest['MA20'] > latest['MA60']: + score += 15 + + # RSI得分 (20分) + if 30 <= latest['RSI'] <= 70: + score += 20 + elif latest['RSI'] < 30: # 超卖 + score += 15 + + # MACD得分 (20分) + if latest['MACD'] > latest['Signal']: + score += 20 + + # 成交量得分 (30分) + if latest['Volume_Ratio'] > 1.5: + score += 30 + elif latest['Volume_Ratio'] > 1: + score += 15 + + return score + + except Exception as e: + self.logger.error(f"计算评分时出错: {str(e)}") + raise + + def get_ai_analysis(self, df, stock_code): + """使用 Gemini 进行 AI 分析""" + try: + recent_data = df.tail(14).to_dict('records') + + technical_summary = { + 'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward', + 'volatility': f"{df.iloc[-1]['Volatility']:.2f}%", + 'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing', + 'rsi_level': df.iloc[-1]['RSI'] + } + + prompt = f""" + 分析股票 {stock_code}： + + 技术指标概要： + {technical_summary} + + 近14日交易数据： + {recent_data} + + 请提供： + 1. 趋势分析（包含支撑位和压力位） + 2. 成交量分析及其含义 + 3. 风险评估（包含波动率分析） + 4. 短期和中期目标价位 + 5. 关键技术位分析 + 6. 具体交易建议（包含止损位） + + 请基于技术指标和市场动态进行分析，给出具体数据支持。 + """ + + headers = { + "Authorization": f"Bearer {self.gemini_api_key}", + "Content-Type": "application/json" + } + + data = { + "model": "gemini-1.5-flash", + "messages": [{"role": "user", "content": prompt}] + } + + response = requests.post( + f"{self.gemini_api_url}/v1/chat/completions", + headers=headers, + json=data, + timeout=10 + ) + + if response.status_code == 200: + return response.json()['choices'][0]['message']['content'] + else: + return "AI 分析暂时无法使用" + + except Exception as e: + self.logger.error(f"AI 分析发生错误: {str(e)}") + return "AI 分析过程中发生错误" + + def get_recommendation(self, score): + """根据得分给出建议""" + if score >= 80: + return '强烈推荐买入' + elif score >= 60: + return '建议买入' + elif score >= 40: + return '观望' + elif score >= 20: + return '建议卖出' + else: + return '强烈建议卖出' + + def analyze_stock(self, stock_code): + """分析单个股票""" + try: + # 获取股票数据 + df = self.get_stock_data(stock_code) + + # 计算技术指标 + df = self.calculate_indicators(df) + + # 评分系统 + score = self.calculate_score(df) + + # 获取最新数据 + latest = df.iloc[-1] + prev = df.iloc[-2] + + # 生成报告（保持原有格式） + report = { + '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), + 'ai_analysis': self.get_ai_analysis(df, stock_code) + } + + return report + + except Exception as e: + self.logger.error(f"分析股票时出错: {str(e)}") + raise + + def scan_market(self, stock_list, min_score=60): + """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) + if report['score'] >= min_score: + recommendations.append(report) + except Exception as e: + self.logger.error(f"分析股票 {stock_code} 时出错: {str(e)}") + continue + + # 按得分排序 + recommendations.sort(key=lambda x: x['score'], reverse=True) + return recommendations diff --git a/全部股票分析推荐1.py b/全部股票分析推荐1.py new file mode 100644 index 0000000..3190e70 --- /dev/null +++ b/全部股票分析推荐1.py @@ -0,0 +1,602 @@ +"""" +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()