""" 新闻流量模型计算模块 核心公式：接盘总量 = 流量 × 转化率 × 客单价 实现流量为王理念的量化分析 """ import logging from datetime import datetime, timedelta from typing import Dict, List, Optional, Tuple logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) class NewsFlowModel: """新闻流量模型计算器""" def __init__(self): # 平台类别权重（用于转化率计算） self.category_weights = { 'finance': 1.5, # 财经平台转化率高 'social': 1.2, # 社交媒体传播快 'news': 1.0, # 新闻媒体正常 'tech': 0.8, # 科技平台相关性低 } # 话题类型转化率系数 self.topic_weights = { # 高转化话题 '政策': 2.0, '利好': 1.8, '涨停': 1.8, '龙头': 1.7, '机构': 1.6, '外资': 1.6, '北向': 1.6, '重组': 1.5, '并购': 1.5, 'IPO': 1.5, # 中转化话题 '业绩': 1.3, '财报': 1.3, '板块': 1.2, '概念': 1.2, '题材': 1.2, # 一般话题 '股票': 1.0, '股市': 1.0, 'A股': 1.0, } # 基础转化率（万分之一） self.base_conversion_rate = 0.0001 # 平均客单价（元）- 散户平均投资金额 self.avg_investment = 50000 # 时效因子衰减系数（每小时衰减） self.time_decay_rate = 0.95 def calculate_traffic_score(self, platforms_data: List[Dict]) -> Dict: """ 计算流量分数 流量分数 = Σ(平台权重 × 热度 × 时效因子) Args: platforms_data: 平台数据列表 Returns: { 'total_score': int, 'category_scores': Dict[str, int], 'platform_details': List[Dict], 'normalized_score': int, # 归一化到0-1000 } """ category_scores = { 'social': 0, 'news': 0, 'finance': 0, 'tech': 0, } platform_details = [] total_raw_score = 0 for platform_data in platforms_data: if not platform_data.get('success'): continue category = platform_data.get('category', 'other') weight = platform_data.get('weight', 5) count = platform_data.get('count', 0) platform_name = platform_data.get('platform_name', '') # 计算平台得分：权重 × 新闻数量 × 类别权重 category_weight = self.category_weights.get(category, 1.0) platform_score = weight * count * category_weight category_scores[category] = category_scores.get(category, 0) + platform_score total_raw_score += platform_score platform_details.append({ 'platform': platform_data.get('platform', ''), 'platform_name': platform_name, 'category': category, 'count': count, 'weight': weight, 'score': int(platform_score), }) # 归一化到0-1000 normalized_score = min(int(total_raw_score / 50), 1000) return { 'total_score': int(total_raw_score), 'normalized_score': normalized_score, 'category_scores': {k: int(v) for k, v in category_scores.items()}, 'platform_details': platform_details, } def estimate_conversion_rate(self, hot_topics: List[Dict], category_distribution: Dict[str, int]) -> Dict: """ 估算转化率 转化率 = 基础转化率 × 话题系数 × 平台系数 Args: hot_topics: 热门话题列表 category_distribution: 类别得分分布 Returns: { 'conversion_rate': float, 'topic_factor': float, 'platform_factor': float, 'analysis': str, } """ # 1. 计算话题系数（基于热门话题的类型） topic_factor = 1.0 matched_topics = [] for topic in hot_topics[:10]: # 只看TOP10热门话题 topic_text = topic.get('topic', '') topic_heat = topic.get('heat', 0) for keyword, weight in self.topic_weights.items(): if keyword in topic_text: # 热度加权 heat_bonus = 1 + (topic_heat / 100) * 0.5 topic_factor = max(topic_factor, weight * heat_bonus) matched_topics.append({ 'topic': topic_text, 'keyword': keyword, 'factor': weight, }) break # 2. 计算平台系数（基于类别分布） total_score = sum(category_distribution.values()) if total_score > 0: platform_factor = sum( (score / total_score) * self.category_weights.get(cat, 1.0) for cat, score in category_distribution.items() ) else: platform_factor = 1.0 # 3. 计算最终转化率 conversion_rate = self.base_conversion_rate * topic_factor * platform_factor # 4. 生成分析 if conversion_rate >= 0.0003: analysis = f"转化率极高({conversion_rate:.4%})！话题与股市高度相关，投资者关注度极高。" elif conversion_rate >= 0.0002: analysis = f"转化率较高({conversion_rate:.4%})。话题具有较强的股市联动性。" elif conversion_rate >= 0.0001: analysis = f"转化率正常({conversion_rate:.4%})。话题与股市有一定关联。" else: analysis = f"转化率较低({conversion_rate:.4%})。话题与股市关联度不高。" return { 'conversion_rate': conversion_rate, 'topic_factor': round(topic_factor, 2), 'platform_factor': round(platform_factor, 2), 'matched_topics': matched_topics, 'analysis': analysis, } def calculate_potential(self, flow_score: int, conversion_rate: float, avg_investment: float = None) -> Dict: """ 计算接盘潜力 核心公式：接盘总量 = 流量 × 转化率 × 客单价 Args: flow_score: 流量分数（代表潜在触达人数，按比例换算） conversion_rate: 转化率 avg_investment: 平均客单价（元） Returns: { 'potential_volume': float, # 接盘总量（亿元） 'potential_level': str, # 潜力等级 'estimated_participants': int, # 预估参与人数 'analysis': str, } """ if avg_investment is None: avg_investment = self.avg_investment # 流量分数换算为潜在触达人数（假设满分1000对应1000万人） potential_reach = flow_score * 10000 # 分数 × 10000 = 潜在触达人数 # 预估参与人数 estimated_participants = int(potential_reach * conversion_rate) # 接盘总量 = 参与人数 × 平均投资额（转换为亿元） potential_volume = (estimated_participants * avg_investment) / 100000000 # 确定潜力等级 if potential_volume >= 100: potential_level = "超大" analysis = f"预估接盘资金{potential_volume:.1f}亿元，市场资金充裕，热点题材可能持续发酵。" elif potential_volume >= 50: potential_level = "大" analysis = f"预估接盘资金{potential_volume:.1f}亿元，资金量较大，可支撑短期行情。" elif potential_volume >= 20: potential_level = "中" analysis = f"预估接盘资金{potential_volume:.1f}亿元，资金量适中，行情可能分化。" elif potential_volume >= 5: potential_level = "小" analysis = f"预估接盘资金{potential_volume:.1f}亿元，资金量较小，注意风险。" else: potential_level = "极小" analysis = f"预估接盘资金{potential_volume:.1f}亿元，资金量不足，不建议追高。" return { 'potential_volume': round(potential_volume, 2), 'potential_level': potential_level, 'potential_reach': potential_reach, 'estimated_participants': estimated_participants, 'avg_investment': avg_investment, 'analysis': analysis, } def classify_flow_type(self, history_scores: List[int], current_score: int) -> Dict: """ 判断流量类型 存量流量型：出生自带顶流，流量快速到位（政策/大事件） 增量流量型：初始流量小，具备病毒传播能力（话题发酵） Args: history_scores: 历史得分列表（从旧到新） current_score: 当前得分 Returns: { 'flow_type': str, 'characteristics': List[str], 'time_window': str, 'operation': str, 'confidence': int, } """ if len(history_scores) < 2: return { 'flow_type': '未知', 'characteristics': ['历史数据不足，无法判断'], 'time_window': '无法判断', 'operation': '继续观察，积累数据', 'confidence': 0, } # 计算关键指标 initial_score = history_scores[0] avg_score = sum(history_scores) / len(history_scores) max_score = max(history_scores) # 计算增长率序列 growth_rates = [] for i in range(1, len(history_scores)): if history_scores[i-1] > 0: rate = (history_scores[i] - history_scores[i-1]) / history_scores[i-1] growth_rates.append(rate) avg_growth = sum(growth_rates) / len(growth_rates) if growth_rates else 0 positive_growth_count = len([r for r in growth_rates if r > 0]) # 判断流量类型 if initial_score >= 500 and (current_score - initial_score) / (initial_score + 1) < 0.3: # 初始热度高，增长有限 -> 存量流量型 flow_type = "存量流量型" characteristics = [ f"初始热度高（{initial_score}分）", "流量快速到位", "可能与政策/大事件相关", "来得快去得也快", ] time_window = "时间窗口短（2-3天）" operation = "快进快出，首日参与最佳，及时止盈" confidence = 80 elif avg_growth > 0.15 and positive_growth_count >= len(growth_rates) * 0.6: # 持续增长 -> 增量流量型 flow_type = "增量流量型" characteristics = [ f"初始热度较低（{initial_score}分）", f"增长率{avg_growth*100:.1f}%", "逐步攀升，病毒式传播", "有埋伏机会", ] time_window = "时间窗口长（5-10天）" operation = "可以埋伏，等待加速，分批建仓" confidence = 75 elif avg_growth < -0.1: # 持续下降 -> 衰退期 flow_type = "流量衰退" characteristics = [ "热度持续下降", f"平均跌幅{abs(avg_growth)*100:.1f}%", "题材热度消退", ] time_window = "窗口已关闭" operation = "及时止盈止损，不宜追入" confidence = 70 else: # 波动 -> 常规流量 flow_type = "常规流量" characteristics = [ "热度波动正常", "无明显趋势", ] time_window = "无特定窗口" operation = "保持观望，等待方向明确" confidence = 50 return { 'flow_type': flow_type, 'characteristics': characteristics, 'time_window': time_window, 'operation': operation, 'confidence': confidence, 'initial_score': initial_score, 'current_score': current_score, 'avg_growth': round(avg_growth * 100, 1), 'history_length': len(history_scores), } def calculate_viral_k(self, current_score: int, previous_score: int) -> Dict: """ 计算病毒系数K值 K值 = 当前传播量 / 上期传播量 K > 1.5: 指数型爆发 K ≈ 1: 线性增长 K < 1: 自然死亡 Returns: { 'k_value': float, 'trend': str, 'risk_level': str, 'analysis': str, } """ if previous_score == 0: return { 'k_value': 1.0, 'trend': '无历史数据', 'risk_level': '未知', 'analysis': '首次采集，无法计算K值', } k_value = round(current_score / previous_score, 2) if k_value > 2.0: trend = "爆发式增长" risk_level = "高风险" analysis = f"K值={k_value}，流量正在爆发式增长！这是极端情况，可能接近顶部，注意风险。" elif k_value > 1.5: trend = "指数型爆发" risk_level = "中高风险" analysis = f"K值={k_value}，流量指数型增长！题材正在加速发酵，关注龙头但注意追高风险。" elif k_value > 1.2: trend = "快速增长" risk_level = "中风险" analysis = f"K值={k_value}，流量快速增长，题材正在升温，可适度参与。" elif k_value > 1.0: trend = "稳步增长" risk_level = "低风险" analysis = f"K值={k_value}，流量稳步增长，题材处于发酵期。" elif k_value == 1.0: trend = "平稳" risk_level = "低风险" analysis = f"K值={k_value}，流量保持稳定，市场平衡状态。" elif k_value > 0.8: trend = "轻微下降" risk_level = "中风险" analysis = f"K值={k_value}，流量轻微下降，题材热度开始消退。" else: trend = "快速衰减" risk_level = "高风险" analysis = f"K值={k_value}，流量快速衰减！题材进入退潮期，注意及时止盈止损。" return { 'k_value': k_value, 'current_score': current_score, 'previous_score': previous_score, 'trend': trend, 'risk_level': risk_level, 'analysis': analysis, } def run_full_model(self, platforms_data: List[Dict], hot_topics: List[Dict], history_scores: List[int] = None) -> Dict: """ 运行完整的流量模型分析 Returns: { 'traffic': Dict, # 流量分析 'conversion': Dict, # 转化率分析 'potential': Dict, # 接盘潜力 'flow_type': Dict, # 流量类型 'viral_k': Dict, # K值分析 'summary': str, # 总结 } """ # 1. 计算流量分数 traffic = self.calculate_traffic_score(platforms_data) current_score = traffic['normalized_score'] # 2. 估算转化率 conversion = self.estimate_conversion_rate( hot_topics, traffic['category_scores'] ) # 3. 计算接盘潜力 potential = self.calculate_potential( current_score, conversion['conversion_rate'] ) # 4. 判断流量类型 if history_scores and len(history_scores) >= 2: flow_type = self.classify_flow_type(history_scores, current_score) # 计算K值（与上一次对比） viral_k = self.calculate_viral_k(current_score, history_scores[-1]) else: flow_type = { 'flow_type': '未知', 'characteristics': ['历史数据不足'], 'time_window': '无法判断', 'operation': '继续观察', 'confidence': 0, } viral_k = { 'k_value': 1.0, 'trend': '无历史数据', 'risk_level': '未知', 'analysis': '首次分析，无法计算K值', } # 5. 生成总结 summary = self._generate_summary(traffic, conversion, potential, flow_type, viral_k) return { 'traffic': traffic, 'conversion': conversion, 'potential': potential, 'flow_type': flow_type, 'viral_k': viral_k, 'summary': summary, 'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'), } def _generate_summary(self, traffic: Dict, conversion: Dict, potential: Dict, flow_type: Dict, viral_k: Dict) -> str: """生成分析总结""" lines = [] # 流量概况 score = traffic['normalized_score'] if score >= 800: lines.append(f"【流量】当前流量分数{score}，处于极高水平") elif score >= 500: lines.append(f"【流量】当前流量分数{score}，处于较高水平") elif score >= 200: lines.append(f"【流量】当前流量分数{score}，处于正常水平") else: lines.append(f"【流量】当前流量分数{score}，处于低位") # 转化率 lines.append(f"【转化】{conversion['analysis']}") # 接盘潜力 lines.append(f"【潜力】{potential['analysis']}") # 流量类型 if flow_type['flow_type'] != '未知': lines.append(f"【类型】{flow_type['flow_type']}，{flow_type['operation']}") # K值 if viral_k['k_value'] != 1.0 or viral_k['trend'] != '无历史数据': lines.append(f"【趋势】K值={viral_k['k_value']}，{viral_k['trend']}") return '\n'.join(lines) # 全局实例 news_flow_model = NewsFlowModel() # 测试代码 if __name__ == "__main__": print("=== 测试新闻流量模型 ===") # 模拟数据 platforms_data = [ {'success': True, 'platform': 'weibo', 'platform_name': '微博', 'category': 'social', 'weight': 10, 'count': 50}, {'success': True, 'platform': 'eastmoney', 'platform_name': '东方财富', 'category': 'finance', 'weight': 9, 'count': 30}, {'success': True, 'platform': 'baidu', 'platform_name': '百度', 'category': 'news', 'weight': 8, 'count': 40}, ] hot_topics = [ {'topic': 'AI芯片', 'heat': 95, 'count': 50}, {'topic': '新能源政策', 'heat': 80, 'count': 30}, {'topic': '涨停板', 'heat': 75, 'count': 25}, ] history_scores = [300, 350, 420, 500] # 运行完整分析 result = news_flow_model.run_full_model(platforms_data, hot_topics, history_scores) print(f"\n流量分数: {result['traffic']['normalized_score']}") print(f"转化率: {result['conversion']['conversion_rate']:.4%}") print(f"接盘潜力: {result['potential']['potential_volume']:.1f}亿元 ({result['potential']['potential_level']})") print(f"流量类型: {result['flow_type']['flow_type']}") print(f"K值: {result['viral_k']['k_value']} ({result['viral_k']['trend']})") print(f"\n===总结===\n{result['summary']}")