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最新推荐文章于 2021-05-27 19:55:00 发布 · 257 阅读 · 0
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# -*- coding: utf-8 -*- """ 金银市场分析程序 - Windows Server 2008 R2 兼容版本 优化内容： 1. 移除win10toast依赖 2. 使用Agg后端，无需图形界面 3. 图表保存为文件而非显示 4. 添加更完善的错误处理 5. 优化内存使用 """ import json from datetime import datetime import matplotlib # 使用Agg后端，无需图形界面 matplotlib.use('Agg') import matplotlib.dates as mdates import matplotlib.pyplot as plt import pandas as pd import requests import os import sys import time import logging from typing import Optional, Tuple import warnings warnings.filterwarnings('ignore') # 设置Matplotlib全局参数 - 使用英文字体避免中文问题 plt.rcParams['font.sans-serif'] = ['DejaVu Sans', 'Arial', 'Helvetica'] plt.rcParams['axes.unicode_minus'] = False # 全局配置 CONFIG = { 'text_fontsize': 9, 'start_year': '2000', 'start_date': '20000101', 'timeout': 30, 'max_retries': 3, 'output_dir': 'precious_metals_analysis' } # 设置日志 def setup_logging(): """配置日志系统""" if not os.path.exists(CONFIG['output_dir']): os.makedirs(CONFIG['output_dir']) logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler(os.path.join(CONFIG['output_dir'], 'analysis.log')), logging.StreamHandler(sys.stdout) ] ) return logging.getLogger(__name__) logger = setup_logging() class NotificationManager: """通知管理器 - 控制台版本""" def send_notification(self, title: str, message: str, duration: int = 3) -> bool: """ 发送控制台通知 Args: title: 通知标题 message: 通知内容 duration: 显示时长（秒） Returns: bool: 是否成功发送 """ try: timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") print(f"\n{'=' * 60}") print(f"📢 {title} - {timestamp}") print(f"📝 {message}") print(f"{'=' * 60}") logger.info(f"{title}: {message}") # 模拟显示时长 if duration > 0: time.sleep(min(duration, 5)) return True except Exception as e: logger.error(f"通知发送失败: {e}") return True # 通知失败不影响主流程 class DataManager: """数据管理器""" def __init__(self, notification_mgr: NotificationManager): self.notification_mgr = notification_mgr self.session = requests.Session() # 设置请求头，模拟浏览器 self.session.headers.update({ 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36' }) def download_with_retry(self, url: str, filename: str, retries: int = None) -> bool: """ 带重试机制的数据下载 Args: url: 下载URL filename: 保存文件名 retries: 重试次数 Returns: bool: 是否成功 """ if retries is None: retries = CONFIG['max_retries'] for attempt in range(retries): try: self.notification_mgr.send_notification( "Download Data", f"Downloading {filename} (Attempt {attempt + 1}/{retries})" ) response = self.session.get(url, timeout=CONFIG['timeout']) response.raise_for_status() with open(filename, 'wb') as f: f.write(response.content) file_size = len(response.content) logger.info(f"Downloaded: {filename} ({file_size} bytes)") self.notification_mgr.send_notification( "Download Complete", f"{filename} downloaded successfully" ) return True except Exception as e: error_msg = f"Download failed (Attempt {attempt + 1}/{retries}): {e}" logger.error(error_msg) if attempt < retries - 1: wait_time = 2 ** attempt # 指数退避 logger.info(f"Waiting {wait_time} seconds before retry...") time.sleep(wait_time) else: self.notification_mgr.send_notification( "Download Error", f"{filename} download failed" ) return False def process_json_data(self, filename: str, column_name: str) -> Optional[pd.DataFrame]: """ 处理JSON数据文件 Args: filename: JSON文件名 column_name: 数据列名 Returns: Optional[pd.DataFrame]: 处理后的DataFrame """ try: # 检查文件是否存在 if not os.path.exists(filename): logger.error(f"File not found: {filename}") return None with open(filename, 'r', encoding='utf-8') as f: data = json.load(f) # 创建DataFrame df = pd.json_normalize(data) # 检查必要列是否存在 if 'v' not in df.columns: logger.error(f"Required column 'v' not found in {filename}") return None # 处理价格数据列 price_cols = df['v'].apply(pd.Series) price_cols.columns = ['USD', 'GBP', 'EUR'] # 清理数据 columns_to_drop = ['is_cms_locked', 'v'] for col in columns_to_drop: if col in df.columns: df.drop(col, axis=1, inplace=True) df = pd.concat([df, price_cols['USD']], axis=1) # 重命名列 df = df.rename(columns={'d': 'date', 'USD': column_name}) # 日期处理 df['date'] = pd.to_datetime(df['date']) df.set_index('date', inplace=True) # 筛选数据范围 start_date = datetime.strptime(CONFIG['start_date'], "%Y%m%d") df = df[df.index >= start_date] # 检查数据是否为空 if df.empty: logger.warning(f"No data found after filtering for {filename}") return None df.reset_index(inplace=True) logger.info(f"Processed {filename}: {len(df)} records") return df except Exception as e: error_msg = f"Error processing file {filename}: {e}" logger.error(error_msg) self.notification_mgr.send_notification("Data Processing Error", error_msg) return None def combine_metal_data(self) -> Optional[pd.DataFrame]: """ 组合金银数据 Returns: Optional[pd.DataFrame]: 组合后的数据 """ try: self.notification_mgr.send_notification("Data Processing", "Processing gold and silver data") # 处理黄金数据 gold_df = self.process_json_data('gold.json', 'gold') if gold_df is None: return None # 处理白银数据 silver_df = self.process_json_data('silver.json', 'silver') if silver_df is None: return None # 合并数据 merged_df = pd.merge(gold_df, silver_df, on='date', how='inner') # 检查合并结果 if merged_df.empty: logger.error("No common dates found between gold and silver data") return None # 数据转换和计算 initial_gold = merged_df['gold'].iloc[0] initial_silver = merged_df['silver'].iloc[0] # 白银价格标准化（基于初始值的相对变化） merged_df['silver_normalized'] = round( merged_df['silver'] * initial_gold / initial_silver, 2 ) # 计算金银比 merged_df['ratio'] = round(merged_df['gold'] / merged_df['silver'], 2) # 计算分位值 merged_df['ratio_70th'] = round(merged_df['ratio'].quantile(0.7), 2) merged_df['ratio_30th'] = round(merged_df['ratio'].quantile(0.3), 2) # 确保输出目录存在 if not os.path.exists(CONFIG['output_dir']): os.makedirs(CONFIG['output_dir']) # 保存到CSV output_path = os.path.join(CONFIG['output_dir'], "precious_metals_data.csv") merged_df.to_csv(output_path, index=False, encoding='utf-8') logger.info(f"Data combination completed and saved to {output_path}") self.notification_mgr.send_notification( "Data Processing Complete", f"Gold and silver data combined: {len(merged_df)} records" ) return merged_df except Exception as e: error_msg = f"Data combination failed: {e}" logger.error(error_msg) self.notification_mgr.send_notification("Data Combination Error", error_msg) return None class ChartManager: """图表管理器 - 保存为文件版本""" def __init__(self, notification_mgr: NotificationManager): self.notification_mgr = notification_mgr # 确保输出目录存在 if not os.path.exists(CONFIG['output_dir']): os.makedirs(CONFIG['output_dir']) def create_price_comparison_chart(self, df: pd.DataFrame) -> bool: """创建金银价格对比图并保存为文件""" try: # 创建更小的图表以适应服务器环境 fig, ax1 = plt.subplots(figsize=(10, 6)) fig.set_facecolor('#f8f9fa') ax1.set_facecolor('#ffffff') x = df['date'] gold_prices = df['gold'] silver_prices = df['silver'] # 绘制黄金价格 ax1.plot(x, gold_prices, label='Gold', color='#FFD700', linewidth=2.0, alpha=0.8) ax1.set_ylabel('Gold Price (USD/oz)', fontsize=10, color='#FFD700') ax1.tick_params(axis='y', labelcolor='#FFD700') ax1.grid(True, alpha=0.3) # 标注黄金极值 gold_max_idx = gold_prices.idxmax() gold_min_idx = gold_prices.idxmin() ax1.annotate(f'High: {gold_prices[gold_max_idx]:.0f}', xy=(x[gold_max_idx], gold_prices[gold_max_idx]), xytext=(10, 10), textcoords='offset points', bbox=dict(boxstyle='round,pad=0.3', facecolor='#FFD700', alpha=0.7), fontsize=8) # 创建第二个y轴用于白银 ax2 = ax1.twinx() ax2.plot(x, silver_prices, label='Silver', color='#C0C0C0', linewidth=2.0, alpha=0.8) ax2.set_ylabel('Silver Price (USD/oz)', fontsize=10, color='#C0C0C0') ax2.tick_params(axis='y', labelcolor='#C0C0C0') # 标注白银极值 silver_max_idx = silver_prices.idxmax() ax2.annotate(f'High: {silver_prices[silver_max_idx]:.2f}', xy=(x[silver_max_idx], silver_prices[silver_max_idx]), xytext=(10, -15), textcoords='offset points', bbox=dict(boxstyle='round,pad=0.3', facecolor='#C0C0C0', alpha=0.7), fontsize=8) # 设置标题和格式 ax1.set_title(f'{CONFIG["start_year"]}-2025 Gold vs Silver Price Comparison', fontsize=12, fontweight='bold', pad=15) ax1.set_xlabel('Date', fontsize=10) # 合并图例 lines1, labels1 = ax1.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left', framealpha=0.9) # 设置x轴刻度 ax1.xaxis.set_major_locator(mdates.YearLocator(2)) plt.xticks(rotation=45) plt.tight_layout() # 保存图表 chart_path = os.path.join(CONFIG['output_dir'], "gold_silver_comparison.png") plt.savefig(chart_path, dpi=150, bbox_inches='tight') plt.close(fig) # 关闭图表释放内存 self.notification_mgr.send_notification( "Chart Generated", f"Price comparison chart saved to {chart_path}" ) return True except Exception as e: error_msg = f"Price comparison chart generation failed: {str(e)}" logger.error(error_msg) self.notification_mgr.send_notification("Chart Error", error_msg) return False def create_ratio_chart(self, df: pd.DataFrame) -> bool: """创建金银比图表并保存为文件""" try: fig, ax = plt.subplots(figsize=(10, 6)) fig.set_facecolor('#f8f9fa') ax.set_facecolor('#ffffff') x = df['date'] ratio = df['ratio'] ratio_70 = df['ratio_70th'].iloc[0] ratio_30 = df['ratio_30th'].iloc[0] # 绘制金银比 ax.plot(x, ratio, label='Gold/Silver Ratio', color='#1f77b4', linewidth=2.0, alpha=0.8) ax.set_ylabel('Gold/Silver Ratio', fontsize=10) ax.grid(True, alpha=0.3) # 添加分位线 ax.axhline(y=ratio_70, color='#ff7f0e', linestyle='--', alpha=0.7, label=f'70th Percentile: {ratio_70:.1f}') ax.axhline(y=ratio_30, color='#2ca02c', linestyle='--', alpha=0.7, label=f'30th Percentile: {ratio_30:.1f}') # 填充分位区域 ax.fill_between(x, ratio_30, ratio_70, alpha=0.2, color='gray', label='Normal Range') # 标注极值 max_idx = ratio.idxmax() min_idx = ratio.idxmin() ax.annotate(f'Peak: {ratio[max_idx]:.1f}', xy=(x[max_idx], ratio[max_idx]), xytext=(10, 10), textcoords='offset points', bbox=dict(boxstyle='round,pad=0.3', facecolor='lightblue', alpha=0.7), fontsize=8) ax.set_title(f'{CONFIG["start_year"]}-2025 Gold/Silver Ratio Analysis', fontsize=12, fontweight='bold', pad=15) ax.set_xlabel('Date', fontsize=10) ax.legend(loc='upper left', framealpha=0.9) # 设置x轴刻度 ax.xaxis.set_major_locator(mdates.YearLocator(2)) plt.xticks(rotation=45) plt.tight_layout() # 保存图表 chart_path = os.path.join(CONFIG['output_dir'], "gold_silver_ratio.png") plt.savefig(chart_path, dpi=150, bbox_inches='tight') plt.close(fig) # 关闭图表释放内存 self.notification_mgr.send_notification( "Chart Generated", f"Ratio chart saved to {chart_path}" ) return True except Exception as e: error_msg = f"Ratio chart generation failed: {str(e)}" logger.error(error_msg) self.notification_mgr.send_notification("Chart Error", error_msg) return False def create_combined_chart(self, df: pd.DataFrame) -> bool: """创建组合图表并保存为文件""" try: fig, ax1 = plt.subplots(figsize=(12, 7)) fig.set_facecolor('#f8f9fa') ax1.set_facecolor('#ffffff') x = df['date'] gold_prices = df['gold'] silver_normalized = df['silver_normalized'] ratio = df['ratio'] # 左侧Y轴 - 价格 ax1.plot(x, gold_prices, label='Gold Price', color='#FFD700', linewidth=2.0) ax1.plot(x, silver_normalized, label='Silver (Normalized)', color='#C0C0C0', linewidth=2.0, linestyle='--') ax1.set_ylabel('Price (USD/oz)', fontsize=10) ax1.grid(True, alpha=0.3) # 右侧Y轴 - 金银比 ax2 = ax1.twinx() ax2.plot(x, ratio, label='Gold/Silver Ratio', color='#1f77b4', linewidth=1.5, alpha=0.7) ax2.set_ylabel('Gold/Silver Ratio', fontsize=10, color='#1f77b4') ax2.tick_params(axis='y', labelcolor='#1f77b4') # 添加分位线 ratio_70 = df['ratio_70th'].iloc[0] ratio_30 = df['ratio_30th'].iloc[0] ax2.axhline(y=ratio_70, color='red', linestyle=':', alpha=0.6, label='70th %') ax2.axhline(y=ratio_30, color='green', linestyle=':', alpha=0.6, label='30th %') ax1.set_title(f'{CONFIG["start_year"]}-2025 Comprehensive Precious Metals Analysis', fontsize=12, fontweight='bold', pad=15) ax1.set_xlabel('Date', fontsize=10) # 合并图例 lines1, labels1 = ax1.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left', framealpha=0.9) # 设置x轴刻度 ax1.xaxis.set_major_locator(mdates.YearLocator(2)) plt.xticks(rotation=45) plt.tight_layout() # 保存图表 chart_path = os.path.join(CONFIG['output_dir'], "comprehensive_analysis.png") plt.savefig(chart_path, dpi=150, bbox_inches='tight') plt.close(fig) # 关闭图表释放内存 self.notification_mgr.send_notification( "Chart Generated", f"Comprehensive chart saved to {chart_path}" ) return True except Exception as e: error_msg = f"Combined chart generation failed: {str(e)}" logger.error(error_msg) self.notification_mgr.send_notification("Chart Error", error_msg) return False class AnalysisReporter: """分析报告生成器""" @staticmethod def generate_report(df: pd.DataFrame) -> bool: """生成分析报告""" try: # 基本统计 gold_stats = { 'mean': df['gold'].mean(), 'max': df['gold'].max(), 'min': df['gold'].min(), 'current': df['gold'].iloc[-1], 'max_date': df.loc[df['gold'].idxmax(), 'date'].strftime('%Y-%m-%d') } silver_stats = { 'mean': df['silver'].mean(), 'max': df['silver'].max(), 'min': df['silver'].min(), 'current': df['silver'].iloc[-1], 'max_date': df.loc[df['silver'].idxmax(), 'date'].strftime('%Y-%m-%d') } ratio_stats = { 'mean': df['ratio'].mean(), 'max': df['ratio'].max(), 'min': df['ratio'].min(), 'current': df['ratio'].iloc[-1], 'max_date': df.loc[df['ratio'].idxmax(), 'date'].strftime('%Y-%m-%d') } # 生成报告 report_lines = [] report_lines.append("=" * 70) report_lines.append("PRECIOUS METALS ANALYSIS REPORT") report_lines.append("=" * 70) report_lines.append(f"\nBASIC STATISTICS ({CONFIG['start_year']}-2025):") report_lines.append(f" Gold: Avg=${gold_stats['mean']:.2f}, High=${gold_stats['max']:.2f} " f"(on {gold_stats['max_date']}), Current=${gold_stats['current']:.2f}") report_lines.append(f" Silver: Avg=${silver_stats['mean']:.2f}, High=${silver_stats['max']:.2f} " f"(on {silver_stats['max_date']}), Current=${silver_stats['current']:.2f}") report_lines.append(f" Gold/Silver Ratio: Avg={ratio_stats['mean']:.2f}, " f"Range[{ratio_stats['min']:.1f}-{ratio_stats['max']:.1f}], " f"Current={ratio_stats['current']:.2f}") report_lines.append(f"\nPERCENTILE ANALYSIS:") report_lines.append(f" 70th Percentile: {df['ratio_70th'].iloc[0]:.2f}") report_lines.append(f" 30th Percentile: {df['ratio_30th'].iloc[0]:.2f}") current_ratio = ratio_stats['current'] ratio_70 = df['ratio_70th'].iloc[0] ratio_30 = df['ratio_30th'].iloc[0] if current_ratio > ratio_70: status = "HIGH (Silver relatively undervalued)" elif current_ratio < ratio_30: status = "LOW (Gold relatively undervalued)" else: status = "NORMAL" report_lines.append(f" Current Ratio Status: {status}") report_lines.append(f"\nRECENT TREND (Last 5 trading days):") recent = df.tail()[['date', 'gold', 'silver', 'ratio']].copy() recent['date'] = recent['date'].dt.strftime('%Y-%m-%d') # 格式化近期数据表格 report_lines.append(" Date | Gold | Silver | Ratio") report_lines.append(" " + "-" * 40) for _, row in recent.iterrows(): report_lines.append( f" {row['date']} | ${row['gold']:7.2f} | ${row['silver']:6.2f} | {row['ratio']:5.1f}") report_lines.append(f"\nINVESTMENT INSIGHTS:") if status == "HIGH (Silver relatively undervalued)": report_lines.append(" Consider increasing silver allocation - silver is relatively cheap vs gold") elif status == "LOW (Gold relatively undervalued)": report_lines.append(" Consider increasing gold allocation - gold is relatively cheap vs silver") else: report_lines.append(" Current ratio is in normal range - maintain balanced allocation") report_lines.append("=" * 70) # 输出到控制台 report_text = "\n".join(report_lines) print(report_text) # 保存到文件 report_path = os.path.join(CONFIG['output_dir'], "analysis_report.txt") with open(report_path, 'w', encoding='utf-8') as f: f.write(report_text) logger.info(f"Analysis report generated and saved to {report_path}") return True except Exception as e: logger.error(f"Report generation failed: {e}") return False def check_system_compatibility(): """检查系统兼容性""" logger.info("Checking system compatibility...") # 检查Python版本 python_version = sys.version_info logger.info(f"Python version: {python_version.major}.{python_version.minor}.{python_version.micro}") # 检查必要包 required_packages = ['pandas', 'matplotlib', 'requests'] for package in required_packages: try: __import__(package) logger.info(f"✓ {package} is available") except ImportError: logger.error(f"✗ {package} is not available") return False # 检查磁盘空间 try: import shutil total, used, free = shutil.disk_usage(".") free_gb = free // (2 ** 30) logger.info(f"Disk space: {free_gb} GB free") if free_gb < 1: logger.warning("Low disk space warning") except: logger.warning("Could not check disk space") return True def main(): """主程序""" logger.info("Starting Precious Metals Analysis Program...") # 检查兼容性 if not check_system_compatibility(): logger.error("System compatibility check failed") return # 初始化管理器 notification_mgr = NotificationManager() data_mgr = DataManager(notification_mgr) chart_mgr = ChartManager(notification_mgr) # 发送启动通知 notification_mgr.send_notification( "Precious Metals Analysis", "Program starting...", duration=2 ) try: # 数据源URL data_sources = { 'gold.json': 'https://prices.lbma.org.uk/json/gold_pm.json', 'silver.json': 'https://prices.lbma.org.uk/json/silver.json' } # 添加时间戳参数避免缓存 timestamp = int(time.time()) data_sources = { 'gold.json': f'https://prices.lbma.org.uk/json/gold_pm.json?r={timestamp}', 'silver.json': f'https://prices.lbma.org.uk/json/silver.json?r={timestamp}' } # 下载数据 download_success = all( data_mgr.download_with_retry(url, filename) for filename, url in data_sources.items() ) if not download_success: logger.error("Data download failed, program exiting") return # 处理数据 combined_data = data_mgr.combine_metal_data() if combined_data is None: logger.error("Data processing failed, program exiting") return # 生成分析报告 AnalysisReporter.generate_report(combined_data) # 生成图表 charts_created = 0 if chart_mgr.create_price_comparison_chart(combined_data): charts_created += 1 if chart_mgr.create_ratio_chart(combined_data): charts_created += 1 if chart_mgr.create_combined_chart(combined_data): charts_created += 1 # 完成通知 output_dir = os.path.abspath(CONFIG['output_dir']) notification_mgr.send_notification( "Analysis Complete", f"Program finished successfully! 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