selfrelease
2b5a32ca1e
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
546 lines
19 KiB
Python
546 lines
19 KiB
Python
"""
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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:
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logger.warning(f"获取大宗商品数据失败: {e}")
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return _neutral_result('大宗商品数据获取失败')
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# ═══════════════════════════════════════════════
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# P7: 汇率变化
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# ═══════════════════════════════════════════════
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# 汇率 → A股板块影响
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FX_SECTOR_MAP = {
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'升值': {
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'beneficiary': ['航空', '造纸', '房地产'],
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'victim': ['纺织', '家电出口', '电子代工'],
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},
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'贬值': {
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'beneficiary': ['纺织', '家电', '电子代工'],
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'victim': ['航空', '造纸'],
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},
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}
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def get_fx_overview():
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"""
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获取人民币汇率变化
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返回:
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dict: {
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'usd_cny': float, # 美元兑人民币汇率
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'change_pct': float, # 涨跌幅
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'direction': str, # 升值/贬值/稳定
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'score': int, # 评分增减（-3 ~ +3）
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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('fx')
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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': [],
|
||
}
|