feat: 新增外部因素分析模块+综合评分引擎+算法文档重构
新增模块: - fund_flow_analyzer.py: 主力资金流向分析(P0, ±20) - market_sentiment.py: 市场情绪指标(P1, ±10) - external_factors.py: 北向资金/美股/大宗商品/汇率(P2-P4,P7) - news_analyzer.py: 公告/并购/政策面LLM分析(P5-P6) - score_engine.py: 综合评分引擎，整合技术面+外部因素 路由更新: - analysis.py: deep_analyze接入综合评分，根据最终评级修正买卖建议 - market.py: 新增4个外部因素API端点 - trades.py: 交易路由更新 算法文档重构: - 章节重排: 技术面(二三)→外部因素(四)→买卖决策(五)→数据源(六)→性能(七) - 架构图更新为五层，标注章节对应 - 5.1/5.2标注纯技术面，5.3整合外部因素修正推荐
This commit is contained in:
selfrelease
@@ -5,9 +5,10 @@
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import requests
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import json
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from config import Config
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# API配置
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API_KEY = "9fd8383f-5776-4366-855d-c6f40e867940"
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API_KEY = Config.DOUBAO_API_KEY or "9fd8383f-5776-4366-855d-c6f40e867940"
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API_URL = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
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MODEL = "doubao-seed-1-6-251015"
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@@ -0,0 +1,545 @@
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"""
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外部因素分析模块（P2/P3/P4/P7）
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包含：
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- P2: 北向资金（外资动向）
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- P3: 美股隔夜板块变化
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- P4: 大宗商品价格
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- P7: 汇率变化
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数据源：AKShare（开源免费）
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所有数据采集均带超时和异常处理，失败时返回中性评分不影响主流程。
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"""
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import logging
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from datetime import datetime, timedelta
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logger = logging.getLogger(__name__)
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# 缓存（当日有效）
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_cache = {}
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_cache_date = {}
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def _get_cache(key):
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"""获取当日缓存"""
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today = datetime.now().strftime('%Y-%m-%d')
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if _cache_date.get(key) == today:
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return _cache.get(key)
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return None
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def _set_cache(key, value):
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"""设置当日缓存"""
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today = datetime.now().strftime('%Y-%m-%d')
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_cache[key] = value
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_cache_date[key] = today
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# ═══════════════════════════════════════════════
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# P2: 北向资金
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# ═══════════════════════════════════════════════
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def get_northbound_capital():
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"""
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获取北向资金净流入数据
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返回:
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dict: {
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'net_inflow': float, # 今日净流入（亿）
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'score': int, # 评分增减（-10 ~ +10）
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'summary': str, # 白话总结
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'reasons': list, # 评分原因
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}
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"""
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cached = _get_cache('northbound')
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if cached:
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return cached
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try:
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import akshare as ak
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# 获取北向资金净流入数据
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df = ak.stock_hsgt_north_net_flow_in_em(symbol="北向")
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if df is None or df.empty:
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return _neutral_result('北向资金数据为空')
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# 取最近5个交易日
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recent = df.tail(5)
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today_inflow = float(recent.iloc[-1].get('当日净流入', 0) or 0)
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# 连续流入/流出天数
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consecutive_inflow = 0
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consecutive_outflow = 0
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for _, row in recent[::-1].iterrows():
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val = float(row.get('当日净流入', 0) or 0)
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if val > 0:
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if consecutive_outflow > 0:
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break
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consecutive_inflow += 1
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elif val < 0:
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if consecutive_inflow > 0:
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break
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consecutive_outflow += 1
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# 评分
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score = 0
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reasons = []
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summary_parts = []
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if today_inflow > 50:
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score += 5
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reasons.append(f'北向今日净流入{today_inflow:.1f}亿(+5)')
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summary_parts.append(f'外资今日大幅买入{today_inflow:.1f}亿元')
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elif today_inflow > 20:
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score += 3
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reasons.append(f'北向今日净流入{today_inflow:.1f}亿(+3)')
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summary_parts.append(f'外资今日净流入{today_inflow:.1f}亿元')
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elif today_inflow < -50:
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score -= 5
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reasons.append(f'北向今日净流出{abs(today_inflow):.1f}亿(-5)')
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summary_parts.append(f'外资今日大幅卖出{abs(today_inflow):.1f}亿元')
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elif today_inflow < -20:
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score -= 3
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reasons.append(f'北向今日净流出{abs(today_inflow):.1f}亿(-3)')
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summary_parts.append(f'外资今日净流出{abs(today_inflow):.1f}亿元')
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else:
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summary_parts.append(f'外资今日净流入{today_inflow:.1f}亿元，方向不明')
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if consecutive_inflow >= 3:
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score += 3
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reasons.append(f'北向连续{consecutive_inflow}日净流入(+3)')
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summary_parts.append(f'已连续{consecutive_inflow}天买入')
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if consecutive_outflow >= 3:
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score -= 3
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reasons.append(f'北向连续{consecutive_outflow}日净流出(-3)')
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summary_parts.append(f'已连续{consecutive_outflow}天卖出')
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score = max(-10, min(10, score))
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result = {
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'net_inflow': round(today_inflow, 2),
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'consecutive_inflow': consecutive_inflow,
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'consecutive_outflow': consecutive_outflow,
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'score': score,
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'summary': '，'.join(summary_parts),
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'reasons': reasons,
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}
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_set_cache('northbound', result)
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return result
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except Exception as e:
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logger.warning(f"获取北向资金数据失败: {e}")
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return _neutral_result('北向资金数据获取失败')
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# ═══════════════════════════════════════════════
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# P3: 美股隔夜板块变化
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# ═══════════════════════════════════════════════
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# 美股板块 → A股板块映射
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US_A_SECTOR_MAP = {
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'科技': ['半导体', '软件', '消费电子', '芯片', 'IT服务'],
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'新能源车': ['锂电池', '汽车零部件', '新能源车', '充电桩'],
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'金融': ['银行', '保险', '券商'],
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'能源': ['石油开采', '化工', '页岩气'],
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'医药': ['创新药', '医疗器械', '生物制品', 'CXO'],
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'消费': ['白酒', '食品饮料', '零售', '免税'],
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'房地产': ['房地产', '建材', '家居'],
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'工业': ['机械', '军工', '工业4.0'],
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'材料': ['有色金属', '钢铁', '化工新材料'],
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'公用事业': ['电力', '环保', '水务'],
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}
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def get_us_market_overview():
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"""
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获取美股隔夜收盘数据，计算外盘情绪
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返回:
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dict: {
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'indices': dict, # 三大指数涨跌
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'sectors': dict, # 主要板块涨跌
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'score': int, # 评分增减（-10 ~ +10）
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'summary': str, # 白话总结
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'reasons': list, # 评分原因
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'affected_a_sectors': dict, # 对A股板块的影响
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}
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"""
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cached = _get_cache('us_market')
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if cached:
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return cached
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try:
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import akshare as ak
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# 获取全球主要指数
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df = ak.index_global()
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if df is None or df.empty:
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return _neutral_result('美股指数数据为空')
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# 筛选美股主要指数
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us_indices = {}
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for _, row in df.iterrows():
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name = str(row.get('名称', ''))
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if '纳斯达克' in name:
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us_indices['nasdaq'] = {
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'name': name,
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'change_pct': float(row.get('涨跌幅', 0) or 0),
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}
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elif '道琼斯' in name:
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us_indices['dow'] = {
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'name': name,
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'change_pct': float(row.get('涨跌幅', 0) or 0),
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}
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elif '标普500' in name:
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us_indices['sp500'] = {
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'name': name,
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'change_pct': float(row.get('涨跌幅', 0) or 0),
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}
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if not us_indices:
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return _neutral_result('未找到美股指数')
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# 计算综合涨跌
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avg_change = sum(v['change_pct'] for v in us_indices.values()) / len(us_indices)
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# 评分
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score = 0
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reasons = []
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summary_parts = []
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if avg_change > 2:
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score += 5
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reasons.append(f'美股三大指数平均涨幅{avg_change:.1f}%(+5)')
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summary_parts.append(f'美股大涨，平均涨幅{avg_change:.1f}%')
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elif avg_change > 0.5:
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score += 2
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reasons.append(f'美股偏强，平均涨幅{avg_change:.1f}%(+2)')
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summary_parts.append(f'美股小幅上涨，平均{avg_change:.1f}%')
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elif avg_change < -2:
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score -= 5
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reasons.append(f'美股三大指数平均跌幅{abs(avg_change):.1f}%(-5)')
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summary_parts.append(f'美股大跌，平均跌幅{abs(avg_change):.1f}%')
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elif avg_change < -0.5:
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score -= 2
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reasons.append(f'美股偏弱，平均跌幅{abs(avg_change):.1f}%(-2)')
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summary_parts.append(f'美股小幅下跌，平均{avg_change:.1f}%')
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else:
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summary_parts.append(f'美股基本平盘，平均变化{avg_change:.1f}%')
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# 对A股板块的影响
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affected_sectors = {}
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if avg_change > 1:
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for us_sector, a_sectors in US_A_SECTOR_MAP.items():
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affected_sectors[us_sector] = {
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'a_sectors': a_sectors,
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'direction': '利好',
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'note': f'美股{us_sector}板块偏强，A股{"、".join(a_sectors[:3])}可能高开',
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}
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elif avg_change < -1:
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for us_sector, a_sectors in US_A_SECTOR_MAP.items():
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affected_sectors[us_sector] = {
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'a_sectors': a_sectors,
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'direction': '利空',
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'note': f'美股{us_sector}板块偏弱，A股{"、".join(a_sectors[:3])}可能低开',
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}
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score = max(-10, min(10, score))
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result = {
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'indices': us_indices,
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'avg_change': round(avg_change, 2),
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'score': score,
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'summary': '，'.join(summary_parts),
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'reasons': reasons,
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'affected_a_sectors': affected_sectors,
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}
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_set_cache('us_market', result)
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return result
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except Exception as e:
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logger.warning(f"获取美股数据失败: {e}")
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return _neutral_result('美股数据获取失败')
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# ═══════════════════════════════════════════════
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# P4: 大宗商品价格
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# ═══════════════════════════════════════════════
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# 大宗商品 → A股板块影响映射
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COMMODITY_A_SECTOR_MAP = {
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'原油': {
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'beneficiary': ['石油开采', '油服', '化工'],
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'victim': ['航空', '物流', '化工下游'],
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'direction': '油价涨→开采受益，航空受损',
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},
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'黄金': {
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'beneficiary': ['黄金股', '珠宝', '有色'],
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'victim': [],
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'direction': '金价涨→黄金企业受益',
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},
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'铜': {
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'beneficiary': ['铜矿', '有色', '电缆'],
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'victim': [],
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'direction': '铜价涨→铜企受益',
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},
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'螺纹钢': {
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'beneficiary': ['钢铁', '钢矿'],
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'victim': ['基建', '地产'],
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'direction': '钢价涨→钢企受益，基建成本增',
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},
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'碳酸锂': {
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'beneficiary': ['锂矿', '锂电池'],
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'victim': ['新能源车'],
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'direction': '锂价涨→锂矿受益，新能源车成本增',
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},
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}
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def get_commodity_overview():
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"""
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获取主要大宗商品价格变化
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返回:
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dict: {
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'commodities': dict, # 各商品涨跌
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'score': int, # 评分增减（-5 ~ +5）
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'summary': str, # 白话总结
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'reasons': list, # 评分原因
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'affected_sectors': dict, # 对A股板块影响
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}
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"""
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cached = _get_cache('commodity')
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if cached:
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return cached
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try:
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import akshare as ak
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# 获取国内商品期货行情
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df = ak.futures_main_sina()
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if df is None or df.empty:
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return _neutral_result('大宗商品数据为空')
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# 关注的商品
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target_commodities = ['原油', '黄金', '铜', '螺纹钢', '碳酸锂']
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commodities = {}
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for _, row in df.iterrows():
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symbol = str(row.get('symbol', ''))
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for target in target_commodities:
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if target in symbol:
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change = float(row.get('change', 0) or 0)
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pct = float(row.get('change_pct', 0) or 0)
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commodities[target] = {
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'symbol': symbol,
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'change_pct': round(pct, 2),
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}
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break
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if not commodities:
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return _neutral_result('未找到关注的大宗商品')
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# 评分和影响
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score = 0
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reasons = []
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summary_parts = []
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affected = {}
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for name, data in commodities.items():
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pct = data['change_pct']
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if abs(pct) < 0.5:
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continue
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mapping = COMMODITY_A_SECTOR_MAP.get(name)
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if not mapping:
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continue
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if pct > 2:
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score += 1
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reasons.append(f'{name}涨{pct:.1f}%，利好{"、".join(mapping["beneficiary"][:2])}(+1)')
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summary_parts.append(f'{name}大涨{pct:.1f}%')
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affected[name] = {
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'direction': '利好',
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'beneficiary': mapping['beneficiary'],
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'victim': mapping['victim'],
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'note': mapping['direction'],
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}
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elif pct < -2:
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score -= 1
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reasons.append(f'{name}跌{abs(pct):.1f}%，利空{"、".join(mapping["beneficiary"][:2])}(-1)')
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summary_parts.append(f'{name}大跌{pct:.1f}%')
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affected[name] = {
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'direction': '利空',
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'beneficiary': mapping['victim'],
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'victim': mapping['beneficiary'],
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'note': mapping['direction'],
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}
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score = max(-5, min(5, score))
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result = {
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'commodities': commodities,
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'score': score,
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'summary': '，'.join(summary_parts) if summary_parts else '大宗商品整体平稳',
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'reasons': reasons,
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'affected_sectors': affected,
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}
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_set_cache('commodity', result)
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return result
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except Exception as e:
|
||||
logger.warning(f"获取大宗商品数据失败: {e}")
|
||||
return _neutral_result('大宗商品数据获取失败')
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════
|
||||
# P7: 汇率变化
|
||||
# ═══════════════════════════════════════════════
|
||||
|
||||
# 汇率 → A股板块影响
|
||||
FX_SECTOR_MAP = {
|
||||
'升值': {
|
||||
'beneficiary': ['航空', '造纸', '房地产'],
|
||||
'victim': ['纺织', '家电出口', '电子代工'],
|
||||
},
|
||||
'贬值': {
|
||||
'beneficiary': ['纺织', '家电', '电子代工'],
|
||||
'victim': ['航空', '造纸'],
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def get_fx_overview():
|
||||
"""
|
||||
获取人民币汇率变化
|
||||
|
||||
返回:
|
||||
dict: {
|
||||
'usd_cny': float, # 美元兑人民币汇率
|
||||
'change_pct': float, # 涨跌幅
|
||||
'direction': str, # 升值/贬值/稳定
|
||||
'score': int, # 评分增减（-3 ~ +3）
|
||||
'summary': str, # 白话总结
|
||||
'reasons': list, # 评分原因
|
||||
'affected_sectors': dict, # 对A股板块影响
|
||||
}
|
||||
"""
|
||||
cached = _get_cache('fx')
|
||||
if cached:
|
||||
return cached
|
||||
|
||||
try:
|
||||
import akshare as ak
|
||||
|
||||
# 获取人民币汇率
|
||||
df = ak.currency_boc_sina(symbol="美元")
|
||||
if df is None or df.empty:
|
||||
return _neutral_result('汇率数据为空')
|
||||
|
||||
# 取最近2条计算变化
|
||||
recent = df.tail(2)
|
||||
if len(recent) < 2:
|
||||
return _neutral_result('汇率数据不足')
|
||||
|
||||
today_rate = float(recent.iloc[-1].get('中行折算价', 0) or 0)
|
||||
prev_rate = float(recent.iloc[-2].get('中行折算价', 0) or 0)
|
||||
|
||||
if prev_rate == 0:
|
||||
return _neutral_result('汇率数据异常')
|
||||
|
||||
change_pct = round((today_rate / prev_rate - 1) * 100, 3)
|
||||
|
||||
# 判断方向（美元兑人民币：涨=人民币贬值，跌=人民币升值）
|
||||
if change_pct > 0.1:
|
||||
direction = '贬值'
|
||||
score = -2
|
||||
summary = f'人民币贬值{abs(change_pct):.3f}%'
|
||||
reasons = [f'人民币贬值{abs(change_pct):.3f}%(-2)']
|
||||
affected = FX_SECTOR_MAP['贬值']
|
||||
elif change_pct < -0.1:
|
||||
direction = '升值'
|
||||
score = 2
|
||||
summary = f'人民币升值{abs(change_pct):.3f}%'
|
||||
reasons = [f'人民币升值{abs(change_pct):.3f}%(+2)']
|
||||
affected = FX_SECTOR_MAP['升值']
|
||||
else:
|
||||
direction = '稳定'
|
||||
score = 0
|
||||
summary = '人民币汇率基本稳定'
|
||||
reasons = []
|
||||
affected = {}
|
||||
|
||||
score = max(-3, min(3, score))
|
||||
|
||||
result = {
|
||||
'usd_cny': round(today_rate, 4),
|
||||
'change_pct': change_pct,
|
||||
'direction': direction,
|
||||
'score': score,
|
||||
'summary': summary,
|
||||
'reasons': reasons,
|
||||
'affected_sectors': affected,
|
||||
}
|
||||
_set_cache('fx', result)
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"获取汇率数据失败: {e}")
|
||||
return _neutral_result('汇率数据获取失败')
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════
|
||||
# 综合外部因素
|
||||
# ═══════════════════════════════════════════════
|
||||
|
||||
def get_all_external_factors():
|
||||
"""
|
||||
获取所有外部因素数据，返回综合结果
|
||||
|
||||
返回:
|
||||
dict: 包含北向资金、美股、大宗商品、汇率的综合数据
|
||||
"""
|
||||
northbound = get_northbound_capital()
|
||||
us_market = get_us_market_overview()
|
||||
commodity = get_commodity_overview()
|
||||
fx = get_fx_overview()
|
||||
|
||||
total_score = (
|
||||
northbound.get('score', 0) +
|
||||
us_market.get('score', 0) +
|
||||
commodity.get('score', 0) +
|
||||
fx.get('score', 0)
|
||||
)
|
||||
|
||||
all_reasons = []
|
||||
all_reasons.extend(northbound.get('reasons', []))
|
||||
all_reasons.extend(us_market.get('reasons', []))
|
||||
all_reasons.extend(commodity.get('reasons', []))
|
||||
all_reasons.extend(fx.get('reasons', []))
|
||||
|
||||
summaries = []
|
||||
for name, data in [('北向资金', northbound), ('美股', us_market), ('大宗商品', commodity), ('汇率', fx)]:
|
||||
s = data.get('summary', '')
|
||||
if s and '失败' not in s and '为空' not in s:
|
||||
summaries.append(f'{name}：{s}')
|
||||
|
||||
return {
|
||||
'northbound_capital': northbound,
|
||||
'us_market': us_market,
|
||||
'commodity': commodity,
|
||||
'fx': fx,
|
||||
'total_score': total_score,
|
||||
'all_reasons': all_reasons,
|
||||
'summary': ' | '.join(summaries),
|
||||
}
|
||||
|
||||
|
||||
def _neutral_result(reason):
|
||||
"""返回中性结果"""
|
||||
return {
|
||||
'score': 0,
|
||||
'summary': reason,
|
||||
'reasons': [],
|
||||
}
|
||||
@@ -0,0 +1,257 @@
|
||||
"""
|
||||
主力资金流向分析模块（P0）
|
||||
|
||||
功能：
|
||||
1. 从数据库读取近N日资金流向数据
|
||||
2. 计算主力连续净流入/流出天数、累计净流入额
|
||||
3. 检测量价背离（资金流入+价格不涨 → 吸筹；资金流出+价格不跌 → 出货）
|
||||
4. 返回资金面评分和信号列表
|
||||
|
||||
数据来源：stock_fund_flow_history 表（由 sync_fund_flow.py 每日同步）
|
||||
"""
|
||||
import logging
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def get_fund_flow_history(stock_code, days=10):
|
||||
"""
|
||||
从数据库读取近N日资金流向历史数据
|
||||
|
||||
参数:
|
||||
stock_code: 股票代码
|
||||
days: 获取天数
|
||||
|
||||
返回:
|
||||
list[dict]: 每日资金流向记录，按日期升序排列
|
||||
"""
|
||||
from db import get_db, put_db
|
||||
|
||||
conn = get_db()
|
||||
if not conn:
|
||||
return []
|
||||
|
||||
try:
|
||||
cur = conn.cursor()
|
||||
start_date = (datetime.now() - timedelta(days=days + 5)).strftime('%Y-%m-%d')
|
||||
cur.execute("""
|
||||
SELECT trade_date, close_price, change_pct,
|
||||
main_net_inflow, main_net_inflow_pct,
|
||||
super_net_inflow, super_net_inflow_pct,
|
||||
big_net_inflow, big_net_inflow_pct,
|
||||
mid_net_inflow, mid_net_inflow_pct,
|
||||
small_net_inflow, small_net_inflow_pct
|
||||
FROM stock_fund_flow_history
|
||||
WHERE code = %s AND trade_date >= %s
|
||||
ORDER BY trade_date ASC
|
||||
""", (stock_code, start_date))
|
||||
rows = cur.fetchall()
|
||||
|
||||
records = []
|
||||
for row in rows:
|
||||
records.append({
|
||||
'date': row[0].strftime('%Y-%m-%d') if row[0] else '',
|
||||
'close_price': float(row[1] or 0),
|
||||
'change_pct': float(row[2] or 0),
|
||||
'main_net_inflow': float(row[3] or 0),
|
||||
'main_net_inflow_pct': float(row[4] or 0),
|
||||
'super_net_inflow': float(row[5] or 0),
|
||||
'super_net_inflow_pct': float(row[6] or 0),
|
||||
'big_net_inflow': float(row[7] or 0),
|
||||
'big_net_inflow_pct': float(row[8] or 0),
|
||||
'mid_net_inflow': float(row[9] or 0),
|
||||
'mid_net_inflow_pct': float(row[10] or 0),
|
||||
'small_net_inflow': float(row[11] or 0),
|
||||
'small_net_inflow_pct': float(row[12] or 0),
|
||||
})
|
||||
return records
|
||||
except Exception as e:
|
||||
logger.error(f"获取资金流向历史失败({stock_code}): {e}")
|
||||
return []
|
||||
finally:
|
||||
put_db(conn)
|
||||
|
||||
|
||||
def analyze_fund_flow(stock_code, days=5):
|
||||
"""
|
||||
分析主力资金流向，返回资金面评分和信号
|
||||
|
||||
参数:
|
||||
stock_code: 股票代码
|
||||
days: 分析最近几天的资金流向
|
||||
|
||||
返回:
|
||||
dict: {
|
||||
'score': int, # 资金面评分增减（-20 ~ +20）
|
||||
'signals': list, # 资金信号列表
|
||||
'summary': str, # 白话总结
|
||||
'details': dict, # 详细数据
|
||||
'reasons': list, # 评分原因列表
|
||||
}
|
||||
"""
|
||||
records = get_fund_flow_history(stock_code, days=days + 5)
|
||||
if len(records) < 2:
|
||||
return {
|
||||
'score': 0,
|
||||
'signals': [],
|
||||
'summary': '暂无资金流向数据',
|
||||
'details': {},
|
||||
'reasons': [],
|
||||
}
|
||||
|
||||
recent = records[-days:] if len(records) >= days else records
|
||||
|
||||
# 计算连续净流入/流出天数
|
||||
consecutive_inflow = 0
|
||||
consecutive_outflow = 0
|
||||
for r in reversed(recent):
|
||||
if r['main_net_inflow'] > 0:
|
||||
if consecutive_outflow > 0:
|
||||
break
|
||||
consecutive_inflow += 1
|
||||
elif r['main_net_inflow'] < 0:
|
||||
if consecutive_inflow > 0:
|
||||
break
|
||||
consecutive_outflow += 1
|
||||
|
||||
# 累计净流入
|
||||
total_main_inflow = sum(r['main_net_inflow'] for r in recent)
|
||||
avg_main_pct = sum(r['main_net_inflow_pct'] for r in recent) / len(recent) if recent else 0
|
||||
|
||||
# 超大单累计
|
||||
total_super_inflow = sum(r['super_net_inflow'] for r in recent)
|
||||
avg_super_pct = sum(r['super_net_inflow_pct'] for r in recent) / len(recent) if recent else 0
|
||||
|
||||
# 量价背离检测
|
||||
# 吸筹：主力净流入但价格不涨（涨幅<2%）
|
||||
# 出货：主力净流出但价格不跌（跌幅<2%）
|
||||
accumulation = False
|
||||
distribution = False
|
||||
if total_main_inflow > 0:
|
||||
price_changes = [r['change_pct'] for r in recent]
|
||||
avg_price_change = sum(price_changes) / len(price_changes) if price_changes else 0
|
||||
if avg_price_change < 2:
|
||||
accumulation = True
|
||||
|
||||
if total_main_inflow < 0:
|
||||
price_changes = [r['change_pct'] for r in recent]
|
||||
avg_price_change = sum(price_changes) / len(price_changes) if price_changes else 0
|
||||
if avg_price_change > -2:
|
||||
distribution = True
|
||||
|
||||
# 单日超大单突击
|
||||
big_surge = False
|
||||
big_surge_day = None
|
||||
for r in recent:
|
||||
if r['super_net_inflow_pct'] > 15:
|
||||
big_surge = True
|
||||
big_surge_day = r['date']
|
||||
break
|
||||
|
||||
# 评分计算
|
||||
score = 0
|
||||
reasons = []
|
||||
signals = []
|
||||
|
||||
if consecutive_inflow >= 3:
|
||||
score += 10
|
||||
reasons.append(f'主力连续{consecutive_inflow}日净流入(+10)')
|
||||
signals.append({
|
||||
'type': 'fund_continuous_inflow',
|
||||
'name': '主力持续流入',
|
||||
'direction': 'buy',
|
||||
'strength': 80,
|
||||
'description': f'主力资金连续{consecutive_inflow}日净流入，累计{total_main_inflow/10000:.0f}万元',
|
||||
})
|
||||
|
||||
if consecutive_outflow >= 3:
|
||||
score -= 10
|
||||
reasons.append(f'主力连续{consecutive_outflow}日净流出(-10)')
|
||||
signals.append({
|
||||
'type': 'fund_continuous_outflow',
|
||||
'name': '主力持续流出',
|
||||
'direction': 'sell',
|
||||
'strength': 75,
|
||||
'description': f'主力资金连续{consecutive_outflow}日净流出，累计{total_main_inflow/10000:.0f}万元',
|
||||
})
|
||||
|
||||
if accumulation:
|
||||
score += 8
|
||||
reasons.append('主力暗中吸筹(+8)')
|
||||
signals.append({
|
||||
'type': 'fund_accumulation',
|
||||
'name': '主力吸筹',
|
||||
'direction': 'buy',
|
||||
'strength': 85,
|
||||
'description': f'主力净流入但价格未涨，暗中吸筹，可能即将拉升',
|
||||
})
|
||||
|
||||
if distribution:
|
||||
score -= 8
|
||||
reasons.append('主力暗中出货(-8)')
|
||||
signals.append({
|
||||
'type': 'fund_distribution',
|
||||
'name': '主力出货',
|
||||
'direction': 'sell',
|
||||
'strength': 80,
|
||||
'description': f'主力净流出但价格未跌，暗中出货，需警惕',
|
||||
})
|
||||
|
||||
if big_surge:
|
||||
score += 5
|
||||
reasons.append(f'超大单突击流入({big_surge_day})(+5)')
|
||||
signals.append({
|
||||
'type': 'fund_big_surge',
|
||||
'name': '大单突击',
|
||||
'direction': 'buy',
|
||||
'strength': 70,
|
||||
'description': f'{big_surge_day}超大单净流入占比>15%，大机构突击入场',
|
||||
})
|
||||
|
||||
# 主力净流入占比评分
|
||||
if avg_main_pct > 10:
|
||||
score += 5
|
||||
reasons.append(f'主力净流入占比{avg_main_pct:.1f}%(+5)')
|
||||
elif avg_main_pct < -10:
|
||||
score -= 5
|
||||
reasons.append(f'主力净流出占比{abs(avg_main_pct):.1f}%(-5)')
|
||||
|
||||
score = max(-20, min(20, score))
|
||||
|
||||
# 白话总结
|
||||
summary_parts = []
|
||||
if consecutive_inflow >= 3:
|
||||
summary_parts.append(f'近{consecutive_inflow}天主力持续买入，累计流入{total_main_inflow/10000:.0f}万元')
|
||||
elif consecutive_outflow >= 3:
|
||||
summary_parts.append(f'近{consecutive_outflow}天主力持续卖出，累计流出{abs(total_main_inflow)/10000:.0f}万元')
|
||||
elif total_main_inflow > 0:
|
||||
summary_parts.append(f'近期主力总体净流入{total_main_inflow/10000:.0f}万元')
|
||||
elif total_main_inflow < 0:
|
||||
summary_parts.append(f'近期主力总体净流出{abs(total_main_inflow)/10000:.0f}万元')
|
||||
|
||||
if accumulation:
|
||||
summary_parts.append('但价格没怎么涨，像是在暗中吸筹')
|
||||
if distribution:
|
||||
summary_parts.append('但价格没怎么跌，像是在暗中出货，要小心')
|
||||
|
||||
summary = '，'.join(summary_parts) if summary_parts else '资金面无明显方向'
|
||||
|
||||
return {
|
||||
'score': score,
|
||||
'signals': signals,
|
||||
'summary': summary,
|
||||
'details': {
|
||||
'consecutive_inflow': consecutive_inflow,
|
||||
'consecutive_outflow': consecutive_outflow,
|
||||
'total_main_inflow': round(total_main_inflow, 2),
|
||||
'avg_main_pct': round(avg_main_pct, 2),
|
||||
'total_super_inflow': round(total_super_inflow, 2),
|
||||
'avg_super_pct': round(avg_super_pct, 2),
|
||||
'accumulation': accumulation,
|
||||
'distribution': distribution,
|
||||
'recent_days': len(recent),
|
||||
'daily_data': recent,
|
||||
},
|
||||
'reasons': reasons,
|
||||
}
|
||||
@@ -11,7 +11,8 @@ import time
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# API配置
|
||||
LICENCE = "5352ED2F-94E5-4E96-8B7F-B57BA75284E3"
|
||||
from config import Config
|
||||
LICENCE = Config.MAIRUI_LICENCE or "5352ED2F-94E5-4E96-8B7F-B57BA75284E3"
|
||||
BASE_URL = "https://api.mairuiapi.com"
|
||||
|
||||
# 缓存配置
|
||||
|
||||
@@ -0,0 +1,197 @@
|
||||
"""
|
||||
市场情绪指标模块（P1）
|
||||
|
||||
从 stock_realtime_price 表直接计算市场情绪指标，无需额外数据源。
|
||||
|
||||
指标包括：
|
||||
1. 涨停/跌停家数比
|
||||
2. 连板高度（最高连板数）
|
||||
3. 换手率中位数
|
||||
4. 两市成交额
|
||||
"""
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def calc_market_sentiment():
|
||||
"""
|
||||
从数据库实时行情表计算市场情绪指标
|
||||
|
||||
返回:
|
||||
dict: {
|
||||
'limit_up_count': int, # 涨停家数
|
||||
'limit_down_count': int, # 跌停家数
|
||||
'up_down_ratio': float, # 涨跌停比
|
||||
'sentiment': str, # 情绪标签
|
||||
'consecutive_board': int, # 最高连板数
|
||||
'turnover_median': float, # 换手率中位数
|
||||
'total_amount': float, # 两市成交额（亿）
|
||||
'market_temp': str, # 市场温度（偏热/偏冷/正常）
|
||||
'score': int, # 情绪评分增减（-10 ~ +10）
|
||||
'reasons': list, # 评分原因
|
||||
}
|
||||
"""
|
||||
from db import get_db, put_db
|
||||
|
||||
conn = get_db()
|
||||
if not conn:
|
||||
return _empty_sentiment()
|
||||
|
||||
try:
|
||||
cur = conn.cursor()
|
||||
|
||||
# 涨停跌停统计（涨停：涨幅>=9.8%，跌停：跌幅<=-9.8%）
|
||||
cur.execute("""
|
||||
SELECT
|
||||
COUNT(*) FILTER (WHERE change_pct >= 9.8) AS limit_up,
|
||||
COUNT(*) FILTER (WHERE change_pct <= -9.8) AS limit_down,
|
||||
COUNT(*) FILTER (WHERE change_pct > 0) AS up_count,
|
||||
COUNT(*) FILTER (WHERE change_pct < 0) AS down_count,
|
||||
COUNT(*) FILTER (WHERE change_pct = 0) AS flat_count,
|
||||
COUNT(*) AS total,
|
||||
COALESCE(SUM(amount), 0) AS total_amount,
|
||||
COALESCE(PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY turnover), 0) AS turnover_median
|
||||
FROM stock_realtime_price
|
||||
WHERE volume > 0 AND price > 0
|
||||
""")
|
||||
row = cur.fetchone()
|
||||
if not row:
|
||||
return _empty_sentiment()
|
||||
|
||||
limit_up = int(row[0] or 0)
|
||||
limit_down = int(row[1] or 0)
|
||||
up_count = int(row[2] or 0)
|
||||
down_count = int(row[3] or 0)
|
||||
flat_count = int(row[4] or 0)
|
||||
total = int(row[5] or 1)
|
||||
total_amount = float(row[6] or 0) / 1e8 # 转为亿
|
||||
turnover_median = float(row[7] or 0)
|
||||
|
||||
# 涨跌停比
|
||||
up_down_ratio = round(limit_up / limit_down, 1) if limit_down > 0 else float(limit_up)
|
||||
|
||||
# 情绪标签
|
||||
if limit_down == 0 and limit_up > 10:
|
||||
sentiment = '极度乐观'
|
||||
elif up_down_ratio >= 5:
|
||||
sentiment = '乐观'
|
||||
elif up_down_ratio >= 2:
|
||||
sentiment = '偏多'
|
||||
elif up_down_ratio >= 1:
|
||||
sentiment = '中性'
|
||||
elif up_down_ratio >= 0.5:
|
||||
sentiment = '偏空'
|
||||
else:
|
||||
sentiment = '悲观'
|
||||
|
||||
# 市场温度
|
||||
if total_amount > 1.2e4:
|
||||
market_temp = '偏热'
|
||||
elif total_amount < 6000:
|
||||
market_temp = '偏冷'
|
||||
else:
|
||||
market_temp = '正常'
|
||||
|
||||
# 连板高度：查找连续涨停的股票
|
||||
consecutive_board = _calc_max_consecutive_board(cur)
|
||||
|
||||
# 评分
|
||||
score = 0
|
||||
reasons = []
|
||||
|
||||
if up_down_ratio >= 5:
|
||||
score += 5
|
||||
reasons.append(f'涨跌停比{up_down_ratio}:1，情绪极度乐观(+5)')
|
||||
elif up_down_ratio >= 2:
|
||||
score += 3
|
||||
reasons.append(f'涨跌停比{up_down_ratio}:1，情绪偏多(+3)')
|
||||
elif up_down_ratio < 0.5:
|
||||
score -= 5
|
||||
reasons.append(f'涨跌停比{up_down_ratio}:1，情绪悲观(-5)')
|
||||
elif up_down_ratio < 1:
|
||||
score -= 3
|
||||
reasons.append(f'涨跌停比{up_down_ratio}:1，情绪偏空(-3)')
|
||||
|
||||
if consecutive_board >= 5:
|
||||
score += 3
|
||||
reasons.append(f'最高{consecutive_board}连板，市场热度高(+3)')
|
||||
|
||||
if total_amount > 1.2e4:
|
||||
score += 2
|
||||
reasons.append(f'两市成交额{total_amount:.0f}亿，交投活跃(+2)')
|
||||
elif total_amount < 6000:
|
||||
score -= 2
|
||||
reasons.append(f'两市成交额仅{total_amount:.0f}亿，交投清淡(-2)')
|
||||
|
||||
score = max(-10, min(10, score))
|
||||
|
||||
return {
|
||||
'limit_up_count': limit_up,
|
||||
'limit_down_count': limit_down,
|
||||
'up_count': up_count,
|
||||
'down_count': down_count,
|
||||
'up_down_ratio': up_down_ratio,
|
||||
'sentiment': sentiment,
|
||||
'consecutive_board': consecutive_board,
|
||||
'turnover_median': round(turnover_median, 2),
|
||||
'total_amount': round(total_amount, 0),
|
||||
'market_temp': market_temp,
|
||||
'score': score,
|
||||
'reasons': reasons,
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"计算市场情绪指标失败: {e}")
|
||||
return _empty_sentiment()
|
||||
finally:
|
||||
put_db(conn)
|
||||
|
||||
|
||||
def _calc_max_consecutive_board(cur):
|
||||
"""
|
||||
计算最高连板数（需要历史数据辅助判断）
|
||||
简化版：通过查找连续涨幅>=9.8%的股票
|
||||
|
||||
由于实时表只有当日数据，这里用近似方法：
|
||||
查找涨停股票数量作为市场热度参考
|
||||
"""
|
||||
try:
|
||||
# 查找涨停股票（涨幅>=9.8%）
|
||||
cur.execute("""
|
||||
SELECT COUNT(*) FROM stock_realtime_price
|
||||
WHERE change_pct >= 9.8 AND volume > 0
|
||||
""")
|
||||
limit_up_count = int(cur.fetchone()[0] or 0)
|
||||
|
||||
# 简化：涨停家数>50视为有高连板可能
|
||||
if limit_up_count > 50:
|
||||
return 5
|
||||
elif limit_up_count > 30:
|
||||
return 4
|
||||
elif limit_up_count > 15:
|
||||
return 3
|
||||
elif limit_up_count > 5:
|
||||
return 2
|
||||
elif limit_up_count > 0:
|
||||
return 1
|
||||
return 0
|
||||
except Exception:
|
||||
return 0
|
||||
|
||||
|
||||
def _empty_sentiment():
|
||||
"""返回空情绪数据"""
|
||||
return {
|
||||
'limit_up_count': 0,
|
||||
'limit_down_count': 0,
|
||||
'up_count': 0,
|
||||
'down_count': 0,
|
||||
'up_down_ratio': 0,
|
||||
'sentiment': '无数据',
|
||||
'consecutive_board': 0,
|
||||
'turnover_median': 0,
|
||||
'total_amount': 0,
|
||||
'market_temp': '无数据',
|
||||
'score': 0,
|
||||
'reasons': [],
|
||||
}
|
||||
@@ -0,0 +1,584 @@
|
||||
"""
|
||||
新闻/公告/政策分析模块（P5/P6）
|
||||
|
||||
功能：
|
||||
- P5: 上市公司公告采集 + LLM情感分析 + 异动监测
|
||||
- P6: 政策面新闻监控 + LLM政策分析
|
||||
|
||||
数据源：
|
||||
- AKShare 公告数据 (stock_notice_report)
|
||||
- 豆包LLM 做分类和情感分析
|
||||
"""
|
||||
import logging
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 当日缓存
|
||||
_news_cache = {}
|
||||
_news_cache_date = {}
|
||||
|
||||
|
||||
def _get_cache(key):
|
||||
today = datetime.now().strftime('%Y-%m-%d')
|
||||
if _news_cache_date.get(key) == today:
|
||||
return _news_cache.get(key)
|
||||
return None
|
||||
|
||||
|
||||
def _set_cache(key, value):
|
||||
today = datetime.now().strftime('%Y-%m-%d')
|
||||
_news_cache[key] = value
|
||||
_news_cache_date[key] = today
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════
|
||||
# P5: 公告/并购消息分析
|
||||
# ═══════════════════════════════════════════════
|
||||
|
||||
# 公告类型关键词映射
|
||||
ANNOUNCEMENT_KEYWORDS = {
|
||||
'并购重组': ['收购', '合并', '重组', '并购', '吸收合并'],
|
||||
'增减持': ['增持', '减持', '股份变动', '股东减持', '股东增持'],
|
||||
'业绩预告': ['业绩预告', '业绩快报', '盈利预测', '预增', '预减', '预亏', '扭亏'],
|
||||
'股权激励': ['股权激励', '限制性股票', '股票期权'],
|
||||
'定增再融资': ['定增', '非公开发行', '配股', '可转债', '再融资'],
|
||||
'分红送转': ['分红', '送转', '派息', '转增', '利润分配'],
|
||||
'重大合同': ['重大合同', '中标', '框架协议', '战略合作'],
|
||||
'停复牌': ['停牌', '复牌', '继续停牌'],
|
||||
'其他重大事项': ['重大事项', '重大投资', '资产出售', '资产剥离', '商誉减值'],
|
||||
}
|
||||
|
||||
|
||||
def classify_announcement(title):
|
||||
"""
|
||||
根据标题关键词对公告进行分类
|
||||
|
||||
参数:
|
||||
title: 公告标题
|
||||
|
||||
返回:
|
||||
str: 公告类型
|
||||
"""
|
||||
for category, keywords in ANNOUNCEMENT_KEYWORDS.items():
|
||||
for kw in keywords:
|
||||
if kw in title:
|
||||
return category
|
||||
return '其他'
|
||||
|
||||
|
||||
def get_stock_announcements(stock_code, days=7):
|
||||
"""
|
||||
获取个股近期公告
|
||||
|
||||
参数:
|
||||
stock_code: 股票代码
|
||||
days: 获取最近几天的公告
|
||||
|
||||
返回:
|
||||
list[dict]: 公告列表
|
||||
"""
|
||||
cached = _get_cache(f'announcements_{stock_code}')
|
||||
if cached:
|
||||
return cached
|
||||
|
||||
try:
|
||||
import akshare as ak
|
||||
|
||||
end_date = datetime.now().strftime('%Y%m%d')
|
||||
start_date = (datetime.now() - timedelta(days=days)).strftime('%Y%m%d')
|
||||
|
||||
df = ak.stock_notice_report(symbol=stock_code, date=start_date)
|
||||
if df is None or df.empty:
|
||||
# 尝试备用接口
|
||||
try:
|
||||
df = ak.stock_zh_a_disclosure_report_cninfo(
|
||||
symbol=stock_code, market='沪深京',
|
||||
start_date=start_date, end_date=end_date
|
||||
)
|
||||
except Exception:
|
||||
return []
|
||||
|
||||
if df is None or df.empty:
|
||||
return []
|
||||
|
||||
announcements = []
|
||||
for _, row in df.iterrows():
|
||||
title = str(row.get('标题', row.get('title', '')))
|
||||
date_str = str(row.get('公告日期', row.get('date', '')))
|
||||
|
||||
category = classify_announcement(title)
|
||||
|
||||
announcements.append({
|
||||
'title': title,
|
||||
'date': date_str[:10] if date_str else '',
|
||||
'category': category,
|
||||
'sentiment': None, # 待LLM分析
|
||||
})
|
||||
|
||||
_set_cache(f'announcements_{stock_code}', announcements)
|
||||
return announcements
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"获取公告数据失败({stock_code}): {e}")
|
||||
return []
|
||||
|
||||
|
||||
def analyze_announcement_sentiment(stock_name, stock_code, announcements):
|
||||
"""
|
||||
使用LLM分析公告情感倾向
|
||||
|
||||
参数:
|
||||
stock_name: 股票名称
|
||||
stock_code: 股票代码
|
||||
announcements: 公告列表
|
||||
|
||||
返回:
|
||||
dict: {
|
||||
'score': int, # 评分增减（-15 ~ +15）
|
||||
'summary': str, # 白话总结
|
||||
'reasons': list, # 评分原因
|
||||
'details': list, # 各公告分析结果
|
||||
}
|
||||
"""
|
||||
if not announcements:
|
||||
return {
|
||||
'score': 0,
|
||||
'summary': '近期无重要公告',
|
||||
'reasons': [],
|
||||
'details': [],
|
||||
}
|
||||
|
||||
# 先用规则快速分类
|
||||
positive_keywords = ['收购', '增持', '预增', '扭亏', '重大合同', '中标', '战略合作', '分红', '送转', '股权激励']
|
||||
negative_keywords = ['减持', '预亏', '预减', '商誉减值', '资产出售', '停牌', '重大事项']
|
||||
|
||||
details = []
|
||||
score = 0
|
||||
reasons = []
|
||||
positive_count = 0
|
||||
negative_count = 0
|
||||
|
||||
for ann in announcements:
|
||||
title = ann['title']
|
||||
category = ann['category']
|
||||
|
||||
is_positive = any(kw in title for kw in positive_keywords)
|
||||
is_negative = any(kw in title for kw in negative_keywords)
|
||||
|
||||
if is_positive and not is_negative:
|
||||
sentiment = '利好'
|
||||
ann_score = _get_category_score(category, positive=True)
|
||||
positive_count += 1
|
||||
elif is_negative and not is_positive:
|
||||
sentiment = '利空'
|
||||
ann_score = _get_category_score(category, positive=False)
|
||||
negative_count += 1
|
||||
else:
|
||||
sentiment = '中性'
|
||||
ann_score = 0
|
||||
|
||||
ann['sentiment'] = sentiment
|
||||
ann['score'] = ann_score
|
||||
score += ann_score
|
||||
details.append(ann)
|
||||
|
||||
if ann_score != 0:
|
||||
reasons.append(f'[{category}]{title[:30]}...({sentiment}{ann_score:+d})')
|
||||
|
||||
# 尝试用LLM深度分析（如果有重要公告）
|
||||
important_categories = ['并购重组', '业绩预告', '增减持', '定增再融资']
|
||||
important_anns = [a for a in announcements if a['category'] in important_categories]
|
||||
|
||||
if important_anns and len(important_anns) <= 5:
|
||||
try:
|
||||
llm_result = _llm_analyze_announcements(stock_name, stock_code, important_anns)
|
||||
if llm_result:
|
||||
# LLM分析覆盖规则评分
|
||||
score = llm_result.get('score', score)
|
||||
reasons = llm_result.get('reasons', reasons)
|
||||
except Exception as e:
|
||||
logger.warning(f"LLM公告分析失败: {e}")
|
||||
|
||||
score = max(-15, min(15, score))
|
||||
|
||||
# 白话总结
|
||||
if positive_count > negative_count:
|
||||
summary = f'近{len(announcements)}条公告中{positive_count}条利好、{negative_count}条利空，消息面偏多'
|
||||
elif negative_count > positive_count:
|
||||
summary = f'近{len(announcements)}条公告中{negative_count}条利空、{positive_count}条利好，消息面偏空'
|
||||
else:
|
||||
summary = f'近{len(announcements)}条公告，消息面中性'
|
||||
|
||||
return {
|
||||
'score': score,
|
||||
'summary': summary,
|
||||
'reasons': reasons,
|
||||
'details': details,
|
||||
}
|
||||
|
||||
|
||||
def _get_category_score(category, positive=True):
|
||||
"""根据公告类型和方向返回评分"""
|
||||
scores = {
|
||||
'并购重组': 10 if positive else -8,
|
||||
'业绩预告': 8 if positive else -10,
|
||||
'增减持': 5 if positive else -5,
|
||||
'定增再融资': 5 if positive else -3,
|
||||
'重大合同': 5 if positive else 0,
|
||||
'分红送转': 3 if positive else 0,
|
||||
'股权激励': 3 if positive else 0,
|
||||
'停复牌': 0,
|
||||
'其他重大事项': 0,
|
||||
'其他': 0,
|
||||
}
|
||||
return scores.get(category, 0)
|
||||
|
||||
|
||||
def _llm_analyze_announcements(stock_name, stock_code, announcements):
|
||||
"""
|
||||
调用豆包LLM分析公告情感
|
||||
|
||||
参数:
|
||||
stock_name: 股票名称
|
||||
stock_code: 股票代码
|
||||
announcements: 重要公告列表
|
||||
|
||||
返回:
|
||||
dict: LLM分析结果
|
||||
"""
|
||||
try:
|
||||
import requests
|
||||
import json
|
||||
from services.doubao_api import API_KEY, API_URL, MODEL
|
||||
|
||||
ann_text = '\n'.join([f"- [{a['category']}]{a['title']}" for a in announcements])
|
||||
|
||||
prompt = f"""请分析以下{stock_name}({stock_code})的近期公告，判断每条公告是利好还是利空，并给出整体消息面评分。
|
||||
|
||||
公告列表：
|
||||
{ann_text}
|
||||
|
||||
请按以下JSON格式输出（不要输出其他内容）：
|
||||
{{"score": <整数,-15到+15>, "reasons": ["原因1", "原因2"], "summary": "一句话总结"}}"""
|
||||
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {API_KEY}"
|
||||
}
|
||||
payload = {
|
||||
"model": MODEL,
|
||||
"max_completion_tokens": 1024,
|
||||
"stream": False,
|
||||
"messages": [
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
}
|
||||
|
||||
resp = requests.post(API_URL, headers=headers, json=payload, timeout=30)
|
||||
if resp.status_code == 200:
|
||||
data = resp.json()
|
||||
content = data.get('choices', [{}])[0].get('message', {}).get('content', '')
|
||||
# 尝试解析JSON
|
||||
try:
|
||||
result = json.loads(content)
|
||||
return result
|
||||
except json.JSONDecodeError:
|
||||
# 尝试提取JSON
|
||||
import re
|
||||
match = re.search(r'\{.*\}', content, re.DOTALL)
|
||||
if match:
|
||||
return json.loads(match.group())
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.warning(f"LLM公告分析失败: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def detect_price_anomaly(stock_code, df):
|
||||
"""
|
||||
检测股价异动（可能由消息面驱动）
|
||||
|
||||
参数:
|
||||
stock_code: 股票代码
|
||||
df: K线DataFrame
|
||||
|
||||
返回:
|
||||
dict: 异动检测结果
|
||||
"""
|
||||
if df is None or len(df) < 20:
|
||||
return {'anomaly': False, 'score': 0, 'reasons': []}
|
||||
|
||||
try:
|
||||
import numpy as np
|
||||
|
||||
recent = df.tail(5)
|
||||
vol_20 = float(df['volume'].tail(20).mean())
|
||||
vol_recent = float(recent['volume'].mean())
|
||||
vol_ratio = vol_recent / vol_20 if vol_20 > 0 else 1
|
||||
|
||||
change_recent = float((recent.iloc[-1]['close'] / recent.iloc[0]['close'] - 1) * 100)
|
||||
|
||||
# 异动条件：量比>3 且 涨跌幅>5%
|
||||
if vol_ratio > 3 and abs(change_recent) > 5:
|
||||
direction = '利好' if change_recent > 0 else '利空'
|
||||
score = 5 if change_recent > 0 else -5
|
||||
return {
|
||||
'anomaly': True,
|
||||
'direction': direction,
|
||||
'vol_ratio': round(vol_ratio, 1),
|
||||
'change_pct': round(change_recent, 2),
|
||||
'score': score,
|
||||
'reasons': [f'近期异动：量比{vol_ratio:.1f}倍+{direction}{abs(change_recent):.1f}%，可能有消息面催化({score:+d})'],
|
||||
'summary': f'近期量比{vol_ratio:.1f}倍，{"涨" if change_recent > 0 else "跌"}{abs(change_recent):.1f}%，可能有消息面催化',
|
||||
}
|
||||
|
||||
return {'anomaly': False, 'score': 0, 'reasons': []}
|
||||
except Exception as e:
|
||||
logger.warning(f"异动检测失败({stock_code}): {e}")
|
||||
return {'anomaly': False, 'score': 0, 'reasons': []}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════
|
||||
# P6: 政策面分析
|
||||
# ═══════════════════════════════════════════════
|
||||
|
||||
# 政策关键词
|
||||
POLICY_KEYWORDS = {
|
||||
'行业扶持': ['扶持', '支持', '补贴', '鼓励', '促进', '加快', '推动', '振兴'],
|
||||
'行业监管': ['监管', '限制', '禁止', '整顿', '规范', '处罚', '约谈'],
|
||||
'货币政策': ['降准', '降息', '逆回购', 'MLF', 'SLF', '流动性', '存款准备金'],
|
||||
'财政政策': ['减税', '降费', '基建', '专项债', '财政赤字', '以旧换新'],
|
||||
'资本市场': ['注册制', '退市', '再融资', 'IPO', '印花税', '减持新规', '分红'],
|
||||
}
|
||||
|
||||
|
||||
def get_policy_news(days=3):
|
||||
"""
|
||||
获取近期财经政策新闻
|
||||
|
||||
返回:
|
||||
list[dict]: 政策新闻列表
|
||||
"""
|
||||
cached = _get_cache('policy_news')
|
||||
if cached:
|
||||
return cached
|
||||
|
||||
try:
|
||||
import akshare as ak
|
||||
|
||||
# 获取财经新闻
|
||||
df = ak.stock_info_global_em()
|
||||
if df is None or df.empty:
|
||||
return []
|
||||
|
||||
# 筛选含政策关键词的新闻
|
||||
policy_news = []
|
||||
for _, row in df.head(50).iterrows():
|
||||
title = str(row.get('标题', row.get('title', '')))
|
||||
content = str(row.get('内容', row.get('content', '')))
|
||||
date_str = str(row.get('发布时间', row.get('date', '')))
|
||||
|
||||
for category, keywords in POLICY_KEYWORDS.items():
|
||||
if any(kw in title for kw in keywords):
|
||||
policy_news.append({
|
||||
'title': title,
|
||||
'date': date_str[:10] if date_str else '',
|
||||
'category': category,
|
||||
'content': content[:200],
|
||||
'sentiment': None,
|
||||
})
|
||||
break
|
||||
|
||||
_set_cache('policy_news', policy_news)
|
||||
return policy_news
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"获取政策新闻失败: {e}")
|
||||
return []
|
||||
|
||||
|
||||
def analyze_policy_impact(policy_news):
|
||||
"""
|
||||
分析政策面对市场的影响
|
||||
|
||||
参数:
|
||||
policy_news: 政策新闻列表
|
||||
|
||||
返回:
|
||||
dict: {
|
||||
'score': int, # 评分增减（-10 ~ +10）
|
||||
'summary': str, # 白话总结
|
||||
'reasons': list, # 评分原因
|
||||
'affected_sectors': dict, # 受影响板块
|
||||
}
|
||||
"""
|
||||
if not policy_news:
|
||||
return {
|
||||
'score': 0,
|
||||
'summary': '近期无明显政策消息',
|
||||
'reasons': [],
|
||||
'affected_sectors': {},
|
||||
}
|
||||
|
||||
# 规则评分
|
||||
sector_impact = {
|
||||
'行业扶持': {'direction': '利好', 'sectors': ['对应行业板块']},
|
||||
'行业监管': {'direction': '利空', 'sectors': ['对应行业板块']},
|
||||
'货币政策': {'direction': '利好', 'sectors': ['全市场']},
|
||||
'财政政策': {'direction': '利好', 'sectors': ['基建', '消费', '相关板块']},
|
||||
'资本市场': {'direction': '中性', 'sectors': ['券商', '全市场']},
|
||||
}
|
||||
|
||||
score = 0
|
||||
reasons = []
|
||||
affected = {}
|
||||
positive_count = 0
|
||||
negative_count = 0
|
||||
|
||||
for news in policy_news:
|
||||
category = news['category']
|
||||
impact = sector_impact.get(category, {'direction': '中性', 'sectors': []})
|
||||
|
||||
if impact['direction'] == '利好':
|
||||
score += 2
|
||||
positive_count += 1
|
||||
news['sentiment'] = '利好'
|
||||
reasons.append(f'[{category}]{news["title"][:30]}...(利好+2)')
|
||||
elif impact['direction'] == '利空':
|
||||
score -= 3
|
||||
negative_count += 1
|
||||
news['sentiment'] = '利空'
|
||||
reasons.append(f'[{category}]{news["title"][:30]}...(利空-3)')
|
||||
else:
|
||||
news['sentiment'] = '中性'
|
||||
|
||||
affected[category] = impact
|
||||
|
||||
# 尝试用LLM深度分析重大政策
|
||||
major_policies = [n for n in policy_news if n['category'] in ['行业扶持', '行业监管', '货币政策']]
|
||||
if major_policies and len(major_policies) <= 5:
|
||||
try:
|
||||
llm_result = _llm_analyze_policy(major_policies)
|
||||
if llm_result:
|
||||
score = llm_result.get('score', score)
|
||||
reasons = llm_result.get('reasons', reasons)
|
||||
except Exception as e:
|
||||
logger.warning(f"LLM政策分析失败: {e}")
|
||||
|
||||
score = max(-10, min(10, score))
|
||||
|
||||
if positive_count > negative_count:
|
||||
summary = f'近期{len(policy_news)}条政策消息，偏利好（{positive_count}条利好/{negative_count}条利空）'
|
||||
elif negative_count > positive_count:
|
||||
summary = f'近期{len(policy_news)}条政策消息，偏利空（{negative_count}条利空/{positive_count}条利好）'
|
||||
else:
|
||||
summary = f'近期{len(policy_news)}条政策消息，影响中性'
|
||||
|
||||
return {
|
||||
'score': score,
|
||||
'summary': summary,
|
||||
'reasons': reasons,
|
||||
'affected_sectors': affected,
|
||||
'details': policy_news,
|
||||
}
|
||||
|
||||
|
||||
def _llm_analyze_policy(policy_news):
|
||||
"""
|
||||
调用豆包LLM分析政策影响
|
||||
|
||||
参数:
|
||||
policy_news: 政策新闻列表
|
||||
|
||||
返回:
|
||||
dict: LLM分析结果
|
||||
"""
|
||||
try:
|
||||
import requests
|
||||
import json
|
||||
from services.doubao_api import API_KEY, API_URL, MODEL
|
||||
|
||||
news_text = '\n'.join([f"- [{n['category']}]{n['title']}" for n in policy_news])
|
||||
|
||||
prompt = f"""请分析以下财经政策新闻对A股市场的影响，判断整体是利好还是利空，并给出评分。
|
||||
|
||||
政策新闻：
|
||||
{news_text}
|
||||
|
||||
请按以下JSON格式输出（不要输出其他内容）：
|
||||
{{"score": <整数,-10到+10>, "reasons": ["原因1", "原因2"], "summary": "一句话总结", "affected_sectors": {{"板块名": "利好/利空"}}}}"""
|
||||
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {API_KEY}"
|
||||
}
|
||||
payload = {
|
||||
"model": MODEL,
|
||||
"max_completion_tokens": 1024,
|
||||
"stream": False,
|
||||
"messages": [
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
}
|
||||
|
||||
resp = requests.post(API_URL, headers=headers, json=payload, timeout=30)
|
||||
if resp.status_code == 200:
|
||||
data = resp.json()
|
||||
content = data.get('choices', [{}])[0].get('message', {}).get('content', '')
|
||||
try:
|
||||
return json.loads(content)
|
||||
except json.JSONDecodeError:
|
||||
import re
|
||||
match = re.search(r'\{.*\}', content, re.DOTALL)
|
||||
if match:
|
||||
return json.loads(match.group())
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.warning(f"LLM政策分析失败: {e}")
|
||||
return None
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════
|
||||
# 综合消息面分析
|
||||
# ═══════════════════════════════════════════════
|
||||
|
||||
def analyze_news_factors(stock_code, stock_name, df=None):
|
||||
"""
|
||||
获取个股消息面 + 政策面综合分析
|
||||
|
||||
参数:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
df: K线DataFrame（用于异动检测）
|
||||
|
||||
返回:
|
||||
dict: 综合消息面分析结果
|
||||
"""
|
||||
# 公告分析
|
||||
announcements = get_stock_announcements(stock_code, days=7)
|
||||
ann_result = analyze_announcement_sentiment(stock_name, stock_code, announcements)
|
||||
|
||||
# 异动检测
|
||||
anomaly_result = detect_price_anomaly(stock_code, df) if df is not None else {'anomaly': False, 'score': 0, 'reasons': []}
|
||||
|
||||
# 政策面
|
||||
policy_news = get_policy_news(days=3)
|
||||
policy_result = analyze_policy_impact(policy_news)
|
||||
|
||||
total_score = ann_result.get('score', 0) + anomaly_result.get('score', 0) + policy_result.get('score', 0)
|
||||
total_score = max(-20, min(20, total_score))
|
||||
|
||||
all_reasons = []
|
||||
all_reasons.extend(ann_result.get('reasons', []))
|
||||
all_reasons.extend(anomaly_result.get('reasons', []))
|
||||
all_reasons.extend(policy_result.get('reasons', []))
|
||||
|
||||
return {
|
||||
'announcements': ann_result,
|
||||
'price_anomaly': anomaly_result,
|
||||
'policy': policy_result,
|
||||
'total_score': total_score,
|
||||
'all_reasons': all_reasons,
|
||||
'summary': f"公告：{ann_result.get('summary', '')} | 政策：{policy_result.get('summary', '')}",
|
||||
}
|
||||
@@ -0,0 +1,134 @@
|
||||
"""
|
||||
综合评分引擎 — 整合所有影响因素到统一评分体系
|
||||
|
||||
将技术面（基础50%+）与外部因素（加减分项）整合为最终评分。
|
||||
|
||||
权重分配：
|
||||
- 技术面评分（compute_deep_analysis 原始分）：基础分（0-100）
|
||||
- P0 资金面：±20
|
||||
- P1 市场情绪：±10
|
||||
- P2 北向资金：±10
|
||||
- P3 美股外盘：±10
|
||||
- P4 大宗商品：±5
|
||||
- P5 公告/异动：±15
|
||||
- P6 政策面：±10
|
||||
- P7 汇率：±3
|
||||
|
||||
最终评分 = 技术面基础分 + 外部因素加减分（上限100，下限0）
|
||||
"""
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def compute_comprehensive_score(stock_code, stock_name, technical_score, df=None):
|
||||
"""
|
||||
综合评分引擎 — 整合技术面和所有外部因素
|
||||
|
||||
参数:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
technical_score: float — 技术面基础评分（0-100，来自 compute_deep_analysis）
|
||||
df: K线DataFrame（用于异动检测，可选）
|
||||
|
||||
返回:
|
||||
dict: {
|
||||
'technical_score': float, # 技术面基础分
|
||||
'external_score': int, # 外部因素总加减分
|
||||
'final_score': int, # 最终综合评分（0-100）
|
||||
'verdict': str, # 最终评级
|
||||
'factors': dict, # 各因素详细数据
|
||||
'all_reasons': list, # 所有评分原因
|
||||
'summary': str, # 综合白话总结
|
||||
}
|
||||
"""
|
||||
factors = {}
|
||||
all_reasons = []
|
||||
external_score = 0
|
||||
summaries = []
|
||||
|
||||
# ---- P0: 主力资金进出 ----
|
||||
try:
|
||||
from services.fund_flow_analyzer import analyze_fund_flow
|
||||
fund_result = analyze_fund_flow(stock_code, days=5)
|
||||
factors['fund_flow'] = fund_result
|
||||
external_score += fund_result.get('score', 0)
|
||||
all_reasons.extend(fund_result.get('reasons', []))
|
||||
s = fund_result.get('summary', '')
|
||||
if s and '暂无' not in s:
|
||||
summaries.append(f'资金面：{s}')
|
||||
except Exception as e:
|
||||
logger.warning(f"P0资金面分析失败: {e}")
|
||||
factors['fund_flow'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
|
||||
|
||||
# ---- P1: 市场情绪指标 ----
|
||||
try:
|
||||
from services.market_sentiment import calc_market_sentiment
|
||||
sentiment_result = calc_market_sentiment()
|
||||
factors['market_sentiment'] = sentiment_result
|
||||
external_score += sentiment_result.get('score', 0)
|
||||
all_reasons.extend(sentiment_result.get('reasons', []))
|
||||
s = sentiment_result.get('sentiment', '')
|
||||
if s and '无数据' not in s:
|
||||
summaries.append(f'市场情绪：{s}（涨跌停{sentiment_result.get("limit_up_count",0)}:{sentiment_result.get("limit_down_count",0)}）')
|
||||
except Exception as e:
|
||||
logger.warning(f"P1市场情绪分析失败: {e}")
|
||||
factors['market_sentiment'] = {'score': 0, 'summary': '分析失败', 'reasons': []}
|
||||
|
||||
# ---- P2/P3/P4/P7: 外部因素（北向/美股/商品/汇率）----
|
||||
try:
|
||||
from services.external_factors import get_all_external_factors
|
||||
ext_result = get_all_external_factors()
|
||||
factors['external'] = ext_result
|
||||
external_score += ext_result.get('total_score', 0)
|
||||
all_reasons.extend(ext_result.get('all_reasons', []))
|
||||
s = ext_result.get('summary', '')
|
||||
if s:
|
||||
summaries.append(s)
|
||||
except Exception as e:
|
||||
logger.warning(f"P2-P7外部因素分析失败: {e}")
|
||||
factors['external'] = {'total_score': 0, 'summary': '分析失败', 'all_reasons': []}
|
||||
|
||||
# ---- P5/P6: 公告/异动/政策 ----
|
||||
try:
|
||||
from services.news_analyzer import analyze_news_factors
|
||||
news_result = analyze_news_factors(stock_code, stock_name, df)
|
||||
factors['news'] = news_result
|
||||
external_score += news_result.get('total_score', 0)
|
||||
all_reasons.extend(news_result.get('all_reasons', []))
|
||||
s = news_result.get('summary', '')
|
||||
if s:
|
||||
summaries.append(s)
|
||||
except Exception as e:
|
||||
logger.warning(f"P5/P6消息面分析失败: {e}")
|
||||
factors['news'] = {'total_score': 0, 'summary': '分析失败', 'all_reasons': []}
|
||||
|
||||
# ---- 最终评分 ----
|
||||
# 外部因素加减分上限：±40（避免喧宾夺主）
|
||||
external_score = max(-40, min(40, external_score))
|
||||
final_score = max(0, min(100, int(technical_score + external_score)))
|
||||
|
||||
# 最终评级
|
||||
if final_score >= 80:
|
||||
verdict = '强烈看多'
|
||||
elif final_score >= 65:
|
||||
verdict = '看多'
|
||||
elif final_score >= 50:
|
||||
verdict = '中性偏多'
|
||||
elif final_score >= 35:
|
||||
verdict = '中性偏空'
|
||||
else:
|
||||
verdict = '看空'
|
||||
|
||||
# 综合总结
|
||||
summary = ' | '.join(summaries) if summaries else '暂无外部因素数据'
|
||||
|
||||
return {
|
||||
'technical_score': round(technical_score, 0),
|
||||
'external_score': external_score,
|
||||
'final_score': final_score,
|
||||
'verdict': verdict,
|
||||
'factors': factors,
|
||||
'all_reasons': all_reasons,
|
||||
'summary': summary,
|
||||
}
|
||||
@@ -168,22 +168,23 @@ def _get_kline_from_local_db(stock_code, days=120):
|
||||
"""从本地数据库读取K线（最快，毫秒级）"""
|
||||
import pandas as pd
|
||||
try:
|
||||
import psycopg2
|
||||
conn = psycopg2.connect(
|
||||
host=Config.DB_HOST, port=Config.DB_PORT,
|
||||
dbname=Config.DB_NAME, user=Config.DB_USER, password=Config.DB_PASSWORD,
|
||||
)
|
||||
from db import get_db, put_db
|
||||
conn = get_db()
|
||||
if not conn:
|
||||
return None
|
||||
conn.autocommit = True
|
||||
start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("""
|
||||
SELECT trade_date, open, high, low, close, volume
|
||||
FROM stock_kline_daily
|
||||
WHERE code = %s AND trade_date >= %s
|
||||
ORDER BY trade_date
|
||||
""", (stock_code, start_date))
|
||||
rows = cur.fetchall()
|
||||
conn.close()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("""
|
||||
SELECT trade_date, open, high, low, close, volume
|
||||
FROM stock_kline_daily
|
||||
WHERE code = %s AND trade_date >= %s
|
||||
ORDER BY trade_date
|
||||
""", (stock_code, start_date))
|
||||
rows = cur.fetchall()
|
||||
finally:
|
||||
put_db(conn)
|
||||
|
||||
if rows and len(rows) >= 30:
|
||||
df = pd.DataFrame(rows, columns=['date', 'open', 'high', 'low', 'close', 'volume'])
|
||||
@@ -625,9 +626,9 @@ def compute_recommend(signal_status, indicators, triggered_count, is_holding):
|
||||
if has_real_dragon:
|
||||
return ('watch', '关注', '真龙出现 → 趋势启动，等待龙抬头确认', 65)
|
||||
|
||||
# MACD死叉 → 卖出/回避
|
||||
# MACD死叉 → 回避（非持仓不能卖出，应为回避/观望）
|
||||
if dif is not None and dea is not None and dif < dea:
|
||||
return ('sell', '卖出', f"MACD死叉(DIF={dif:.3f}<DEA={dea:.3f})", 75)
|
||||
return ('watch', '回避', f"MACD死叉(DIF={dif:.3f}<DEA={dea:.3f})，趋势偏弱", 25)
|
||||
|
||||
# 底背离 → 关注（suanfa.md 步骤1: 纳入关注范围）
|
||||
if has_divergence:
|
||||
@@ -652,23 +653,23 @@ def get_latest_price(stock_code):
|
||||
返回:
|
||||
float: 最新价格, 失败返回 0
|
||||
"""
|
||||
from db import get_db, put_db
|
||||
conn = get_db()
|
||||
if not conn:
|
||||
return 0
|
||||
try:
|
||||
import psycopg2
|
||||
conn = psycopg2.connect(
|
||||
host=Config.DB_HOST, port=Config.DB_PORT,
|
||||
dbname=Config.DB_NAME, user=Config.DB_USER, password=Config.DB_PASSWORD,
|
||||
)
|
||||
cur = conn.cursor()
|
||||
cur.execute("""
|
||||
SELECT price FROM stock_realtime_price
|
||||
WHERE code = %s AND price > 0
|
||||
""", (stock_code,))
|
||||
row = cur.fetchone()
|
||||
conn.close()
|
||||
if row:
|
||||
return float(row[0])
|
||||
except Exception:
|
||||
pass
|
||||
finally:
|
||||
put_db(conn)
|
||||
return 0
|
||||
|
||||
|
||||
@@ -905,3 +906,393 @@ def find_bull_stocks(scan_rows, holding_codes=None):
|
||||
'total': total,
|
||||
'stage_info': BULL_STAGES,
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════
|
||||
# 9. 单股深度分析（价格位置、压力支撑、量价、空间估算）
|
||||
# ═══════════════════════════════════════════════
|
||||
|
||||
def _generate_plain_summary(price, change_pct, ma_trend, position, supports,
|
||||
resistances, vol_ratio, vol_trend, patterns,
|
||||
space, score, verdict, reasons):
|
||||
"""根据技术分析结果生成通俗易懂的中文解说"""
|
||||
parts = []
|
||||
|
||||
# 1. 当前走势概况
|
||||
if change_pct > 3:
|
||||
trend_desc = f'今天涨了{change_pct:.1f}%，涨势比较猛'
|
||||
elif change_pct > 0:
|
||||
trend_desc = f'今天小涨{change_pct:.1f}%'
|
||||
elif change_pct > -3:
|
||||
trend_desc = f'今天小跌{abs(change_pct):.1f}%'
|
||||
else:
|
||||
trend_desc = f'今天跌了{abs(change_pct):.1f}%，跌幅较大'
|
||||
|
||||
if ma_trend == 'bullish':
|
||||
trend_desc += '，均线呈多头排列，说明中短期整体向上'
|
||||
elif ma_trend == 'bearish':
|
||||
trend_desc += '，均线呈空头排列，中短期趋势偏弱'
|
||||
else:
|
||||
trend_desc += '，均线交叉纠缠，短期方向还不太明确'
|
||||
parts.append(trend_desc + '。')
|
||||
|
||||
# 2. 价格位置（用大白话）
|
||||
pos_20 = position.get('20d', {})
|
||||
pct_20 = pos_20.get('pct', 50)
|
||||
if pct_20 > 80:
|
||||
parts.append(f'当前股价处于近20天的高位区间（{pct_20:.0f}%位置），已经涨了不少，追高要小心。')
|
||||
elif pct_20 > 50:
|
||||
parts.append(f'股价在近20天的中高位置（{pct_20:.0f}%），还有一定上涨空间。')
|
||||
elif pct_20 > 20:
|
||||
parts.append(f'股价在近20天的中低位置（{pct_20:.0f}%），相对安全。')
|
||||
else:
|
||||
parts.append(f'股价处于近20天的低位区间（{pct_20:.0f}%），可能存在反弹机会。')
|
||||
|
||||
# 3. 上方压力和下方支撑
|
||||
if resistances:
|
||||
nearest_r = resistances[0]
|
||||
r_gap = round((nearest_r['level'] - price) / price * 100, 1) if price > 0 else 0
|
||||
if r_gap > 0:
|
||||
parts.append(f'往上最近的压力位在{nearest_r["level"]:.2f}元（{nearest_r["name"]}），距离约{r_gap:.1f}%。')
|
||||
if supports:
|
||||
nearest_s = supports[0]
|
||||
s_gap = round((price - nearest_s['level']) / price * 100, 1) if price > 0 else 0
|
||||
if s_gap > 0:
|
||||
parts.append(f'往下最近的支撑位在{nearest_s["level"]:.2f}元（{nearest_s["name"]}），有{s_gap:.1f}%的安全垫。')
|
||||
|
||||
# 4. 成交量情况
|
||||
if vol_ratio >= 2:
|
||||
parts.append(f'成交量明显放大（量比{vol_ratio:.1f}倍），市场关注度很高，要留意是主力进场还是出货。')
|
||||
elif vol_ratio >= 1.3:
|
||||
parts.append(f'成交量温和放大（量比{vol_ratio:.1f}倍），有资金在活跃参与。')
|
||||
elif vol_ratio < 0.6:
|
||||
parts.append(f'成交量萎缩（量比{vol_ratio:.1f}倍），市场比较冷清，短期可能震荡。')
|
||||
else:
|
||||
parts.append(f'成交量正常（量比{vol_ratio:.1f}倍）。')
|
||||
|
||||
# 5. 形态识别
|
||||
if patterns:
|
||||
pattern_names = [p['name'] for p in patterns]
|
||||
bullish_p = [p['name'] for p in patterns if p.get('bullish') is True]
|
||||
bearish_p = [p['name'] for p in patterns if p.get('bullish') is False]
|
||||
if bullish_p:
|
||||
parts.append(f'发现看涨信号：{"、".join(bullish_p)}，这是积极的技术形态。')
|
||||
if bearish_p:
|
||||
parts.append(f'注意看跌信号：{"、".join(bearish_p)}，需要警惕。')
|
||||
|
||||
# 6. 综合建议（大白话）
|
||||
action_tip = ''
|
||||
if score >= 75:
|
||||
action_tip = '综合来看比较乐观，可以考虑逢低关注或适量参与，但注意控制仓位。'
|
||||
elif score >= 60:
|
||||
action_tip = '整体偏积极，可以少量关注，等回调到支撑位附近再考虑。'
|
||||
elif score >= 45:
|
||||
action_tip = '目前多空力量比较均衡，建议观望为主，等方向更明确再做决定。'
|
||||
elif score >= 30:
|
||||
action_tip = '目前偏弱势，不建议急于买入。如果持有，可以在反弹时适当减仓。'
|
||||
else:
|
||||
action_tip = '当前走势比较弱，建议回避。已经持有的可以考虑止损或等待反弹减仓。'
|
||||
|
||||
# 7. 空间估算
|
||||
rr = space.get('risk_reward', 0)
|
||||
if rr and rr > 0:
|
||||
if rr >= 2:
|
||||
parts.append(f'从空间来看，潜在收益是风险的{rr:.1f}倍，性价比不错。')
|
||||
elif rr >= 1:
|
||||
parts.append(f'收益风险比{rr:.1f}:1，性价比一般。')
|
||||
else:
|
||||
parts.append(f'收益风险比仅{rr:.1f}:1，下行风险大于上涨空间，不太划算。')
|
||||
|
||||
summary_text = ''.join(parts)
|
||||
|
||||
return {
|
||||
'text': summary_text,
|
||||
'action_tip': action_tip,
|
||||
'confidence': '高' if score >= 70 or score <= 30 else '中',
|
||||
}
|
||||
|
||||
|
||||
def compute_deep_analysis(df, signal_result=None, realtime_info=None):
|
||||
"""
|
||||
对单只股票进行深度分析，返回结构化的分析报告。
|
||||
|
||||
参数:
|
||||
df: DataFrame (含技术指标的K线数据)
|
||||
signal_result: dict (detect_all_signals 返回的结果，可选)
|
||||
realtime_info: dict (stock_realtime_price 行数据，可选)
|
||||
|
||||
返回:
|
||||
dict: 完整的深度分析报告
|
||||
"""
|
||||
import numpy as np
|
||||
if df is None or len(df) < 30:
|
||||
return {'error': 'K线数据不足（需要至少30天）'}
|
||||
|
||||
last = df.iloc[-1]
|
||||
cl = float(last['close'])
|
||||
n = len(df)
|
||||
|
||||
# ---- 1. 均线系统 ----
|
||||
ma_data = {}
|
||||
for period in [5, 10, 20, 60]:
|
||||
col = f'ma{period}'
|
||||
if col in df.columns and n >= period:
|
||||
ma_data[f'ma{period}'] = round(float(df[col].iloc[-1]), 2)
|
||||
|
||||
ma_list = sorted(ma_data.items(), key=lambda x: x[1], reverse=True)
|
||||
ma_trend = 'bullish' if all(
|
||||
ma_data.get(f'ma{a}', 0) >= ma_data.get(f'ma{b}', 0)
|
||||
for a, b in [(5, 10), (10, 20)]
|
||||
) else 'bearish' if all(
|
||||
ma_data.get(f'ma{a}', 0) <= ma_data.get(f'ma{b}', 0)
|
||||
for a, b in [(5, 10), (10, 20)]
|
||||
) else 'mixed'
|
||||
|
||||
ma_trend_label = {'bullish': '多头排列', 'bearish': '空头排列', 'mixed': '交叉整理'}
|
||||
|
||||
# ---- 2. 价格位置分析 ----
|
||||
position = {}
|
||||
for days in [20, 60, 120]:
|
||||
subset = df.tail(days) if n >= days else df
|
||||
h = float(subset['high'].max())
|
||||
l = float(subset['low'].min())
|
||||
rng = h - l
|
||||
pct = round((cl - l) / rng * 100, 0) if rng > 0 else 50
|
||||
position[f'd{days}'] = {
|
||||
'high': round(h, 2), 'low': round(l, 2),
|
||||
'range_pct': pct,
|
||||
'up_space': round((h / cl - 1) * 100, 1),
|
||||
'down_risk': round((1 - l / cl) * 100, 1),
|
||||
}
|
||||
|
||||
# ---- 3. 支撑与压力位 ----
|
||||
supports = []
|
||||
resistances = []
|
||||
|
||||
for name, val in ma_data.items():
|
||||
if val < cl:
|
||||
supports.append({'level': val, 'type': 'ma', 'name': name.upper()})
|
||||
elif val > cl:
|
||||
resistances.append({'level': val, 'type': 'ma', 'name': name.upper()})
|
||||
|
||||
for days_key in ['d20', 'd60', 'd120']:
|
||||
p = position.get(days_key, {})
|
||||
label = days_key.replace('d', '') + '日'
|
||||
if p.get('low', 0) < cl:
|
||||
supports.append({'level': p['low'], 'type': 'low', 'name': f'{label}低点'})
|
||||
if p.get('high', 0) > cl:
|
||||
resistances.append({'level': p['high'], 'type': 'high', 'name': f'{label}高点'})
|
||||
|
||||
supports.sort(key=lambda x: x['level'], reverse=True)
|
||||
resistances.sort(key=lambda x: x['level'])
|
||||
|
||||
# ---- 4. 成交量分析 ----
|
||||
vol = float(last['volume'])
|
||||
vol_5 = float(df['volume'].tail(5).mean()) if n >= 5 else vol
|
||||
vol_20 = float(df['volume'].tail(20).mean()) if n >= 20 else vol
|
||||
vol_ratio = round(vol / vol_20, 1) if vol_20 > 0 else 1.0
|
||||
|
||||
vol_trend = '缩量' if vol_ratio < 0.7 else '平量' if vol_ratio < 1.3 else '温和放量' if vol_ratio < 2.0 else '大幅放量'
|
||||
|
||||
# ---- 5. 形态识别（增强版） ----
|
||||
patterns = []
|
||||
closes_10 = [float(x) for x in df['close'].tail(10)]
|
||||
if n >= 10:
|
||||
std_10 = np.std(closes_10)
|
||||
mean_10 = np.mean(closes_10)
|
||||
cv_10 = std_10 / mean_10 if mean_10 > 0 else 0
|
||||
|
||||
if cv_10 < 0.015 and cl > max(closes_10[:-1]):
|
||||
patterns.append({'name': '平台突破', 'bullish': True,
|
||||
'desc': f'近10日波动率仅{cv_10*100:.1f}%，今日突破平台'})
|
||||
elif cv_10 < 0.015:
|
||||
patterns.append({'name': '窄幅整理', 'bullish': None,
|
||||
'desc': f'近10日波动率{cv_10*100:.1f}%，蓄势待变'})
|
||||
|
||||
if n >= 20:
|
||||
h20 = float(df.tail(20)['high'].max())
|
||||
if cl >= h20 * 0.99:
|
||||
patterns.append({'name': '创20日新高', 'bullish': True,
|
||||
'desc': f'触及20日高点{h20:.2f}'})
|
||||
|
||||
# 双底形态：近30日内两个低点价格接近（差异<3%），且当前价格高于两低点之间的高点
|
||||
if n >= 30:
|
||||
lows_30 = [float(x) for x in df['low'].tail(30)]
|
||||
# 找最低点和次低点
|
||||
min_idx = int(np.argmin(lows_30))
|
||||
min_val = lows_30[min_idx]
|
||||
# 在最低点之前找次低点
|
||||
if min_idx > 5:
|
||||
before_lows = lows_30[:min_idx]
|
||||
second_min_idx = int(np.argmin(before_lows))
|
||||
second_min_val = before_lows[second_min_idx]
|
||||
if abs(min_val - second_min_val) / min_val < 0.03:
|
||||
# 两低点之间的高点
|
||||
between_high = max(lows_30[second_min_idx:min_idx])
|
||||
if cl > between_high:
|
||||
patterns.append({'name': '双底突破', 'bullish': True,
|
||||
'desc': f'双底形态（低点{min_val:.2f}和{second_min_val:.2f}），已突破颈线{between_high:.2f}'})
|
||||
|
||||
# 量价齐升：近5日成交量递增且价格递增
|
||||
if n >= 5:
|
||||
vols_5 = [float(x) for x in df['volume'].tail(5)]
|
||||
closes_5 = [float(x) for x in df['close'].tail(5)]
|
||||
if all(vols_5[i] <= vols_5[i+1] for i in range(len(vols_5)-1)) and \
|
||||
all(closes_5[i] <= closes_5[i+1] for i in range(len(closes_5)-1)):
|
||||
patterns.append({'name': '量价齐升', 'bullish': True,
|
||||
'desc': '近5日成交量与价格同步递增，强势特征'})
|
||||
|
||||
# 均线粘合后发散：MA5/10/20 三线粘合后开始发散
|
||||
if n >= 20:
|
||||
ma5_val = ma_data.get('ma5', 0)
|
||||
ma10_val = ma_data.get('ma10', 0)
|
||||
ma20_val = ma_data.get('ma20', 0)
|
||||
if ma5_val and ma10_val and ma20_val:
|
||||
ma_spread = max(ma5_val, ma10_val, ma20_val) - min(ma5_val, ma10_val, ma20_val)
|
||||
ma_pct = ma_spread / cl * 100
|
||||
if ma_pct < 1.0 and ma5_val > ma10_val > ma20_val:
|
||||
patterns.append({'name': '均线粘合发散', 'bullish': True,
|
||||
'desc': f'MA5/10/20粘合（离散{ma_pct:.1f}%）后多头排列'})
|
||||
|
||||
# 涨跌幅计算：如果最后一条是今天（可能未收盘），用前一日收盘价计算
|
||||
from datetime import date
|
||||
last_date_str = str(df['date'].values[-1])[:10]
|
||||
today_str = date.today().isoformat()
|
||||
if last_date_str == today_str and n >= 3:
|
||||
# 今天未收盘，用倒数第二根K线的收盘价对比倒数第三根
|
||||
change_today = round((cl / float(df.iloc[-2]['close']) - 1) * 100, 2)
|
||||
else:
|
||||
change_today = round((cl / float(df.iloc[-2]['close']) - 1) * 100, 2) if n >= 2 else 0
|
||||
if change_today >= 5:
|
||||
patterns.append({'name': '大阳线', 'bullish': True,
|
||||
'desc': f'涨幅{change_today:.1f}%'})
|
||||
elif change_today <= -5:
|
||||
patterns.append({'name': '大阴线', 'bullish': False,
|
||||
'desc': f'跌幅{change_today:.1f}%'})
|
||||
|
||||
# ---- 6. 空间估算 ----
|
||||
first_resist = resistances[0] if resistances else None
|
||||
first_support = supports[0] if supports else None
|
||||
|
||||
space = {
|
||||
'nearest_resist': first_resist,
|
||||
'nearest_support': first_support,
|
||||
'risk_reward': None,
|
||||
}
|
||||
if first_resist and first_support:
|
||||
upside = first_resist['level'] - cl
|
||||
downside = cl - first_support['level']
|
||||
space['risk_reward'] = round(upside / downside, 1) if downside > 0 else 99
|
||||
|
||||
# ---- 7. 综合评估 ----
|
||||
score = 50
|
||||
reasons = []
|
||||
|
||||
if ma_trend == 'bullish':
|
||||
score += 10
|
||||
reasons.append('均线多头排列(+10)')
|
||||
elif ma_trend == 'bearish':
|
||||
score -= 10
|
||||
reasons.append('均线空头排列(-10)')
|
||||
|
||||
if vol_ratio >= 1.3:
|
||||
score += 5
|
||||
reasons.append(f'放量{vol_ratio}倍(+5)')
|
||||
elif vol_ratio < 0.6:
|
||||
score -= 3
|
||||
reasons.append(f'缩量{vol_ratio}倍(-3)')
|
||||
|
||||
any_breakout = any(p['name'] == '平台突破' for p in patterns)
|
||||
if any_breakout:
|
||||
score += 10
|
||||
reasons.append('平台突破(+10)')
|
||||
|
||||
any_new_high = any(p['name'] == '创20日新高' for p in patterns)
|
||||
if any_new_high:
|
||||
score += 5
|
||||
reasons.append('创20日新高(+5)')
|
||||
|
||||
any_double_bottom = any(p['name'] == '双底突破' for p in patterns)
|
||||
if any_double_bottom:
|
||||
score += 10
|
||||
reasons.append('双底突破(+10)')
|
||||
|
||||
any_vol_price_rise = any(p['name'] == '量价齐升' for p in patterns)
|
||||
if any_vol_price_rise:
|
||||
score += 8
|
||||
reasons.append('量价齐升(+8)')
|
||||
|
||||
any_ma_converge = any(p['name'] == '均线粘合发散' for p in patterns)
|
||||
if any_ma_converge:
|
||||
score += 7
|
||||
reasons.append('均线粘合发散(+7)')
|
||||
|
||||
pos_120 = position.get('d120', {}).get('range_pct', 50)
|
||||
if pos_120 < 30:
|
||||
score += 5
|
||||
reasons.append(f'120日位置偏低{pos_120}%(+5)')
|
||||
elif pos_120 > 80:
|
||||
score -= 5
|
||||
reasons.append(f'120日位置偏高{pos_120}%(-5)')
|
||||
|
||||
# 20日位置也纳入评分
|
||||
pos_20 = position.get('d20', {}).get('range_pct', 50)
|
||||
if pos_20 < 25:
|
||||
score += 3
|
||||
reasons.append(f'20日位置偏低{pos_20}%(+3)')
|
||||
elif pos_20 > 85:
|
||||
score -= 3
|
||||
reasons.append(f'20日位置偏高{pos_20}%(-3)')
|
||||
|
||||
if signal_result:
|
||||
sig_count = signal_result.get('signal_summary', {}).get('total_signals', 0)
|
||||
if sig_count >= 3:
|
||||
score += 15
|
||||
reasons.append(f'{sig_count}信号共振(+15)')
|
||||
elif sig_count >= 2:
|
||||
score += 10
|
||||
reasons.append(f'{sig_count}信号叠加(+10)')
|
||||
elif sig_count >= 1:
|
||||
score += 5
|
||||
reasons.append(f'{sig_count}个信号(+5)')
|
||||
|
||||
if space.get('risk_reward') and space['risk_reward'] >= 2:
|
||||
score += 5
|
||||
reasons.append(f'风险收益比{space["risk_reward"]}:1(+5)')
|
||||
elif space.get('risk_reward') and space['risk_reward'] < 0.8:
|
||||
score -= 5
|
||||
reasons.append(f'风险收益比{space["risk_reward"]}:1(-5)')
|
||||
|
||||
score = max(0, min(100, score))
|
||||
|
||||
verdict = '强烈看多' if score >= 80 else '看多' if score >= 65 else '中性偏多' if score >= 50 else '中性偏空' if score >= 35 else '看空'
|
||||
|
||||
ai_summary = _generate_plain_summary(
|
||||
cl, change_today, ma_trend, position, supports, resistances,
|
||||
vol_ratio, vol_trend, patterns, space, score, verdict, reasons
|
||||
)
|
||||
|
||||
return {
|
||||
'price': cl,
|
||||
'change_pct': change_today,
|
||||
'ma': ma_data,
|
||||
'ma_trend': ma_trend,
|
||||
'ma_trend_label': ma_trend_label[ma_trend],
|
||||
'position': position,
|
||||
'supports': supports[:5],
|
||||
'resistances': resistances[:5],
|
||||
'volume': {
|
||||
'today': vol,
|
||||
'avg_5': round(vol_5),
|
||||
'avg_20': round(vol_20),
|
||||
'ratio': vol_ratio,
|
||||
'trend': vol_trend,
|
||||
},
|
||||
'patterns': patterns,
|
||||
'space': space,
|
||||
'deep_score': score,
|
||||
'verdict': verdict,
|
||||
'score_reasons': reasons,
|
||||
'ai_summary': ai_summary,
|
||||
'kline_days': n,
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user