hahaju/aiagents-stock
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

增加新闻流量监测板块

Browse Source
This commit is contained in:
oficcejo
2026-01-25 16:53:55 +08:00
parent be76e444c7
commit 73491aa22d
18 changed files with 8365 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files
news_flow_model.py
+557
View File
@@ -0,0 +1,557 @@
"""
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']}")