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
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# 股票投资分析系统 - 环境变量配置模板
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# 复制此文件为 .env 并填入实际值
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# Flask
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SECRET_KEY=your-secret-key-here
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# PostgreSQL
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DB_HOST=localhost
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DB_PORT=5432
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DB_NAME=stock_app
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DB_USER=postgres
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DB_PASSWORD=
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# 阿里云K线API
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ALICLOUD_APPCODE=
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# 豆包AI API
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DOUBAO_API_KEY=
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# 麦蕊API
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MAIRUI_LICENCE=
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# 服务端口
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PORT=3333
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+33
-4
@@ -13,6 +13,10 @@ app = Flask(__name__, template_folder='templates', static_folder='static')
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app.config['SECRET_KEY'] = Config.SECRET_KEY
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CORS(app, supports_credentials=True)
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# 初始化数据库连接池
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from db import init_db_pool
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init_db_pool()
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# 注册路由蓝图
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from routes.auth import bp as auth_bp
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from routes.trades import bp as trades_bp
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@@ -47,8 +51,34 @@ def health():
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return jsonify({'status': 'ok', 'message': '服务运行正常'})
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# ========== 静态文件安全 ==========
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@app.route('/robots.txt')
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def robots():
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return "User-agent: *\nDisallow: /api/\nDisallow: /admin\n", 200, {'Content-Type': 'text/plain'}
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@app.route('/.env')
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def block_env():
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from flask import abort
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abort(404)
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# ========== 启动 ==========
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_scheduler_started = False
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def _start_scheduler_once():
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"""确保调度器只启动一次（兼容 gunicorn preload + Flask dev reloader）"""
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global _scheduler_started
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if _scheduler_started:
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return
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_scheduler_started = True
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from services.scheduler import start_scheduler
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start_scheduler()
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if __name__ == '__main__':
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print("=" * 60)
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print("股票投资分析系统启动")
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@@ -58,8 +88,7 @@ if __name__ == '__main__':
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print(f"健康检查: http://localhost:{Config.PORT}/api/health")
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print("=" * 60)
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# 启动模拟交易定时任务调度器
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from services.scheduler import start_scheduler
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start_scheduler()
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_start_scheduler_once()
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app.run(debug=True, host='0.0.0.0', port=Config.PORT)
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else:
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_start_scheduler_once()
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+21
-2
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"""
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应用配置
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应用配置 — 所有敏感信息从环境变量读取，支持 .env 文件
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"""
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import os
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# 加载 .env 文件（如果存在）
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_env_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '.env')
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if os.path.exists(_env_path):
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with open(_env_path) as f:
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for line in f:
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line = line.strip()
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if not line or line.startswith('#') or '=' not in line:
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continue
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key, _, value = line.partition('=')
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key, value = key.strip(), value.strip().strip('"').strip("'")
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if key and key not in os.environ:
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os.environ[key] = value
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class Config:
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# Flask 配置
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SECRET_KEY = os.environ.get('SECRET_KEY', 'stock-app-secret-key-2026')
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@@ -23,9 +36,15 @@ class Config:
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ALERTS_CACHE_FILE = os.path.join(BASE_DIR, 'alerts_cache.json')
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# 阿里云K线API
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ALICLOUD_APPCODE = os.environ.get('ALICLOUD_APPCODE', '50528b6544ac4234a8ccb5c9f2c01607')
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ALICLOUD_APPCODE = os.environ.get('ALICLOUD_APPCODE', '')
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ALICLOUD_KLINE_URL = 'https://jmqqgphqcx.market.alicloudapi.com/finance/a-shares-kline'
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# 豆包AI API
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DOUBAO_API_KEY = os.environ.get('DOUBAO_API_KEY', '')
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# 麦蕊API
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MAIRUI_LICENCE = os.environ.get('MAIRUI_LICENCE', '')
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# 服务端口
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PORT = int(os.environ.get('PORT', 3333))
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[Unit]
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Description=Stock Data Collection Service
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After=network.target postgresql.service
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[Service]
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Type=simple
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User=root
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WorkingDirectory=/opt/stock-app
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Environment="DB_HOST=localhost"
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Environment="DB_PORT=5432"
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Environment="DB_NAME=stock_app"
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Environment="DB_USER=postgres"
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Environment="DB_PASSWORD=stock_password_2025"
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Environment="ALICLOUD_APPCODE=50528b6544ac4234a8ccb5c9f2c01607"
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Environment="MAIRUI_LICENCE=5352ED2F-94E5-4E96-8B7F-B57BA75284E3"
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ExecStart=/opt/stock-app/venv/bin/python stock_data_service.py daemon
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Restart=always
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RestartSec=30
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[Install]
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WantedBy=multi-user.target
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# 股票投资分析系统 — 算法分析文档
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> 最后更新：2026-07-18
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---
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## 一、整体架构
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系统分为五层，数据从下往上流动：
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```
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技术指标层（technical_indicators.py） ← 第二章
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↓ 计算MACD、SKDJ、KDJ、EMA、MA等基础指标
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信号检测层（signal_detector.py） ← 第三章
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↓ 基于7个信号检测买卖点
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外部因素模块（fund_flow/sentiment/external/news）← 第四章
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↓ 资金面/情绪/北向/美股/商品/公告/政策/汇率 → 各因素评分
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算法决策层（stock_algorithms.py） ← 第五章
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↓ 统一推荐逻辑、牛股阶段识别、技术面深度分析（7维度）→ 技术面基础分
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综合评分引擎（score_engine.py）
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↓ 技术面基础分 + 外部因素加减分 → 最终评分 → AI解说
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```
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| 层级 | 文件 | 职责 | 章节 |
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|------|------|------|------|
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| 技术指标层 | `services/technical_indicators.py` | 计算 MACD、SKDJ、KDJ、EMA、MA 等基础指标 | 二 |
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| 信号检测层 | `services/signal_detector.py` | 基于7个信号检测买卖点 | 三 |
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| 外部因素模块 | `fund_flow_analyzer.py` / `market_sentiment.py` / `external_factors.py` / `news_analyzer.py` | 资金面、市场情绪、北向资金、美股、大宗商品、公告/政策、汇率 | 四 |
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| 算法决策层 | `services/stock_algorithms.py` | 统一推荐逻辑、牛股阶段识别、技术面深度分析 | 五 |
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| 综合评分引擎 | `services/score_engine.py` | 整合外部因素（P0-P7）与技术面评分，输出最终评分 | 五 |
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---
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## 二、技术指标层
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`calc_all_indicators` 一次性计算所有指标，附加到 DataFrame 的列中。
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### 2.1 EMA（指数移动平均线）
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**白话解释**：均线就是最近N天价格的平均值，用来判断趋势方向。EMA 比普通均线更敏感，越近的价格权重越大，反应更快。
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| 指标 | 参数 | 用途 |
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|------|------|------|
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| EMA3 | 3日 | 超短期均线，反映最近3天的平均价格 |
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| EMA21 | 21日 | 中期均线，反映最近21天的平均价格 |
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**怎么用**：当 EMA3 从下往上穿过 EMA21，说明短期价格变强了，是反弹信号。
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### 2.2 MACD（指数平滑异同移动平均线）
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**白话解释**：MACD 是最经典的趋势指标。它用两条均线（快线12日、慢线26日）的差值来判断趋势的方向和强弱。
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| 输出 | 含义 | 白话 |
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|------|------|------|
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| DIF | 快线减慢线的差值 | 短期价格和中期价格的差距，正数说明短期比中期强 |
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| DEA | DIF 的9日均线 | DIF的平均值，用来判断DIF的趋势 |
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| MACD柱 | 2 × (DIF - DEA) | 红绿柱子，红柱=DIF在DEA上方=多头力量，绿柱=空头力量 |
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**怎么用**：
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- **金叉**：DIF 从下往上穿过 DEA → 买入信号
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- **死叉**：DIF 从上往下穿过 DEA → 卖出信号
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- **零轴上方金叉**：DIF 和 DEA 都在0以上时金叉 → 主升浪，最强买入信号
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- **底背离**：价格创新低但 DIF 没创新低 → 下跌动力不足，可能要反转
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### 2.3 KDJ（随机指标）
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**白话解释**：KDJ 用来判断价格是在"超买"还是"超卖"。就像弹簧，压得太紧（超卖）容易弹起来，拉得太开（超买）容易缩回去。
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| 输出 | 含义 | 白话 |
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|------|------|------|
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| K | 快线 | 对价格变化最敏感，反应最快 |
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| D | 慢线 | K的平均值，更平稳 |
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| J | 超前线 | 3K-2D，比K更超前，可以提前预判 |
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**怎么用**：
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- K > 80 → 超买区，价格可能要回调
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- K < 20 → 超卖区，价格可能要反弹
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- K 上穿 D → 金叉，买入信号
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- K 下穿 D → 死叉，卖出信号
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### 2.4 SKDJ（慢速随机指标）
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**白话解释**：SKDJ 是 KDJ 的"慢速版"，对价格变化做了两次平滑，信号更少但更可靠。龙抬头信号就是用 SKDJ 来判断的。
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| 输出 | 含义 | 白话 |
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|------|------|------|
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| K | 慢速K值 | 经过两次平滑的K线，比普通KDJ的K更稳 |
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| D | 慢速D值 | K的平均值，最平稳 |
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**怎么用**：
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- K < 20 → 超卖区，股票被过度抛售
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- K 从超卖区上穿 D → 龙抬头信号，短线起爆点
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- 还要检查最近3天K值的波动不能太大（标准差<15），确保信号稳定
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### 2.5 MA（简单移动平均线）
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**白话解释**：最基础的均线，就是最近N天收盘价的简单平均。用来判断中长期趋势方向。
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| 指标 | 参数 | 用途 |
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|------|------|------|
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| MA5 | 5日 | 一周均价，超短期趋势 |
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| MA10 | 10日 | 两周均价，短期趋势 |
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| MA20 | 20日 | 一个月均价，中期趋势 |
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| MA60 | 60日 | 三个月均价，长期趋势 |
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**怎么用**：
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- MA5 > MA10 > MA20 > MA60 → 多头排列（从短期到长期依次排列），强势上涨趋势
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- MA5 < MA10 < MA20 < MA60 → 空头排列，弱势下跌趋势
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- 交叉纠缠 → 趋势不明，需要等待
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---
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## 三、信号检测层
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### 3.1 7个交易信号（按胜率从高到低排名）
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#### 信号1：主升浪（胜率85%）
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**白话解释**：主升浪就是股票进入"加速上涨"的阶段。就像汽车挂了最高档，速度最快，利润兑现最快。
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**触发条件**：
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- DIF > 0 且 DEA > 0（两条线都在零轴上方，说明大趋势向上）
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- DIF 从下往上穿过 DEA（金叉，说明短期又开始加速）
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**含义**：趋势大好，进入加速拉升阶段。持仓者应该加仓，不要轻易出场。
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#### 信号2：日线底背离（胜率80%）
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**白话解释**：股价创新低了，但 MACD 指标没有创新低。这说明"虽然价格还在跌，但下跌的动力已经不足了"，就像皮球落地，虽然还在最低点，但已经开始反弹了。
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**触发条件**：
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- 收盘价创20日新低（最近20天最低价）
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- 但 DIF 值没有创20日新低（下跌动力在减弱）
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- DIF < 0（还在零轴下方，确认是在下跌趋势中）
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**含义**：大级别反转信号，真正"跌透了"，可能迎来一波像样的反弹。
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#### 信号3：龙抬头（胜率75%）
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**白话解释**：龙抬头是短线最佳买点。经过一段下跌后，SKDJ指标在超卖区（K<20）发生金叉，就像龙从水面抬起头来，说明资金开始进场了。
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**触发条件**：
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- SKDJ 的 K 值在超卖区（前一天 K < 20，或今天 K < 30）
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- K 从下往上穿过 D（金叉）
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- 最近3天 K 值标准差 < 15（信号稳定，不是剧烈波动中的假信号）
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**含义**：短线起爆点，反弹稳定性强。这是体系中的**实操核心买点**。
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#### 信号4：真龙（胜率70%）
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**白话解释**：真龙是趋势正式确立的信号。价格站上20日均线，短期均线上穿中期均线，MACD翻红，成交量放大——多个条件同时满足，说明趋势真的来了。
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**触发条件**（4个条件满足3个即可，但价格必须在MA20上方）：
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- 价格 > MA20（站上中期均线）
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- MA5 上穿 MA20（短期均线金叉中期均线）
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- MACD柱从负转正（多头力量开始占优）
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- 成交量 > 10日均量的1.2倍（放量确认）
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**含义**：中期趋势刚刚启动，可以追入，但最好等回调买入。
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#### 信号5：短底背离（胜率65%）
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**白话解释**：和日线底背离类似，但看的是10日窗口。价格创10日新低但DIF没创新低，说明短期下跌动力不足。
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**触发条件**：
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- 收盘价创10日新低
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- 但 DIF 值没有创10日新低
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**含义**：小级别反弹信号，灵敏度高但力度偏弱。适合短线操作。
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#### 信号6：老鼠仓（胜率60%）
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**白话解释**：盘中突然急跌（跌了3%以上），但收盘又收回来了，而且成交量放大。这很可能是主力在"偷偷吸筹"——故意打压价格吓跑散户，然后低价买入。
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|
||||
**触发条件**：
|
||||
- 盘中最大跌幅 > 3%（最低价远低于开盘价）
|
||||
- 收盘回收 > 60%（从最低点反弹回大部分）
|
||||
- 收盘价接近开盘价（跌幅不超过1%）
|
||||
- 成交量 > 10日均量的1.3倍（放量）
|
||||
|
||||
**含义**：主力吸筹信号，上涨可能不会立竿见影，但后续大概率会涨。
|
||||
|
||||
#### 信号7：反弹（胜率55%）
|
||||
|
||||
**白话解释**：最简单的均线金叉信号——EMA3（3日均线）从下往上穿过 EMA21（21日均线）。说明短期价格开始强于中期价格了。
|
||||
|
||||
**触发条件**：
|
||||
- 前一天 EMA3 ≤ EMA21
|
||||
- 今天 EMA3 > EMA21
|
||||
|
||||
**含义**：普通均线金叉，震荡市适用，但熊市中容易出现假反弹，需要结合其他信号确认。
|
||||
|
||||
### 3.2 信号状态检查
|
||||
|
||||
系统不仅检测信号是否触发，还会计算每个信号的**就绪程度（readiness 0-100）**，告诉用户"距离触发还有多远"。
|
||||
|
||||
例如龙抬头信号：
|
||||
- K < 20 且 K > D 且 前一天 K ≤ D → readiness = 100（已触发）
|
||||
- K < 20 → readiness = 70（在超卖区，等金叉）
|
||||
- K < 30 → readiness = 40（接近超卖区）
|
||||
- K < 50 → readiness = 20（在中位，还远）
|
||||
- K ≥ 50 → readiness = 5（偏高，不满足条件）
|
||||
|
||||
---
|
||||
|
||||
## 四、外部影响因素分析与实现
|
||||
|
||||
> 系统已将以下外部因素全部纳入综合评分引擎，与技术面评分叠加为最终评分。
|
||||
> 各因素独立计算，失败时返回0分不影响主流程。
|
||||
|
||||
### 4.1 主力资金进出（P0，已实现）
|
||||
|
||||
**白话解释**：股市里的"主力"就是那些资金量很大的机构投资者（基金、券商、险资等）。他们买卖的金额巨大，足以影响股价走向。就像一条大鱼在小池塘里游，方向一目了然。
|
||||
|
||||
#### 影响机制
|
||||
|
||||
| 资金类型 | 单笔金额 | 影响力 | 白话 |
|
||||
|----------|----------|--------|------|
|
||||
| 超大单 | ≥100万/笔 | 最强 | 大机构的大动作，直接推动股价 |
|
||||
| 大单 | 20-100万/笔 | 强 | 中型机构的操作，趋势的重要推手 |
|
||||
| 中单 | 4-20万/笔 | 中等 | 游资和大户，短期波动源 |
|
||||
| 小单 | <4万/笔 | 弱 | 散户交易，通常被主力"收割" |
|
||||
|
||||
**主力净流入 = 超大单净流入 + 大单净流入**，正值说明主力在买入，负值说明在卖出。
|
||||
|
||||
#### 预测信号（已实现）
|
||||
|
||||
| 信号 | 含义 | 可靠度 | 评分 |
|
||||
|------|------|--------|------|
|
||||
| 连续3日主力净流入 | 主力持续吸筹，后市看涨 | ★★★★ | +10 |
|
||||
| 主力净流入+价格不涨 | 暗中吸筹（压价买货），可能即将拉升 | ★★★★★ | +8 |
|
||||
| 主力净流出+价格不跌 | 暗中出货（托价卖出），危险信号 | ★★★★★ | -8 |
|
||||
| 超大单突然大幅流入 | 大机构突击入场，短线可能拉升 | ★★★ | +5 |
|
||||
| 主力净流入占比>10% | 主力主导行情，散户跟风空间大 | ★★★★ | +5 |
|
||||
|
||||
#### 实现模块
|
||||
|
||||
- **模块文件**：`services/fund_flow_analyzer.py`
|
||||
- **数据来源**：`stock_fund_flow_history` 表（由 `sync_fund_flow.py` 每日同步）
|
||||
- **API端点**：`GET /api/fund_flow_analysis/<code>`
|
||||
- **评分范围**：±20
|
||||
|
||||
### 4.2 市场情绪指标（P1，已实现）
|
||||
|
||||
**白话解释**：市场情绪是整个A股的"温度计"。涨停的股票多说明市场热情高，跌停的多说明恐慌蔓延。情绪好的时候，技术面信号更容易兑现；情绪差的时候，再好的形态也可能被砸盘。
|
||||
|
||||
| 指标 | 含义 | 获取方式 | 评分 |
|
||||
|------|------|----------|------|
|
||||
| 涨停/跌停家数比 | >5:1 偏多，<1:1 偏空 | 从实时行情统计 | +5/-5 |
|
||||
| 连板高度 | 最高连板数，反映市场热度 | 从涨停家数估算 | +3 |
|
||||
| 换手率中位数 | 反映市场活跃度 | 从实时行情统计 | — |
|
||||
| 两市成交额 | >1.2万亿偏热，<6000亿偏冷 | 从行情数据 | +2/-2 |
|
||||
|
||||
#### 实现模块
|
||||
|
||||
- **模块文件**：`services/market_sentiment.py` → `calc_market_sentiment()`
|
||||
- **数据来源**：`stock_realtime_price` 表（已有数据，无需额外数据源）
|
||||
- **API端点**：`GET /api/market_sentiment`
|
||||
- **评分范围**：±10
|
||||
|
||||
### 4.3 北向资金（P2，已实现）
|
||||
|
||||
**白话解释**：北向资金是从香港流入A股的"外资"，被市场视为"聪明钱"。北向大幅买入通常被视为利好信号。
|
||||
|
||||
| 信号 | 含义 | 可靠度 | 评分 |
|
||||
|------|------|--------|------|
|
||||
| 北向单日净流入>50亿 | 外资看好，市场偏多 | ★★★★ | +5 |
|
||||
| 北向单日净流出>50亿 | 外资看空，注意风险 | ★★★★ | -5 |
|
||||
| 北向连续3日净流入 | 外资持续看好，中期偏多 | ★★★★★ | +3 |
|
||||
| 北向连续3日净流出 | 外资持续撤离，中期偏空 | ★★★★ | -3 |
|
||||
|
||||
#### 实现模块
|
||||
|
||||
- **模块文件**：`services/external_factors.py` → `get_northbound_capital()`
|
||||
- **数据来源**：AKShare `stock_hsgt_north_net_flow_in_em`（北向资金净流入）
|
||||
- **评分范围**：±10
|
||||
|
||||
### 4.4 美股隔夜板块变化（P3，已实现）
|
||||
|
||||
**白话解释**：美股是全球股市的"风向标"。美股晚上涨跌，第二天A股往往跟着反应。尤其是美股的板块变化——如果美股科技股大涨，A股科技板块大概率高开；美股新能源车跌了，A股相关产业链也容易跟跌。
|
||||
|
||||
#### 影响机制
|
||||
|
||||
| 美股板块 | 对应A股板块 | 影响强度 | 传导逻辑 |
|
||||
|----------|------------|----------|----------|
|
||||
| 科技（纳斯达克） | 半导体、软件、消费电子 | ★★★★★ | 全球科技产业链联动 |
|
||||
| 新能源车（特斯拉） | 锂电池、汽车零部件 | ★★★★★ | 产业链直接关联 |
|
||||
| 金融（银行/保险） | 银行、保险、券商 | ★★★★ | 全球金融情绪传导 |
|
||||
| 能源（石油） | 石油开采、化工 | ★★★★ | 大宗商品价格联动 |
|
||||
| 医药生物 | 创新药、医疗器械 | ★★★ | 审批/研发进展联动 |
|
||||
| 消费零售 | 消费、白酒 | ★★ | 消费趋势参考 |
|
||||
| 房地产 | 地产链 | ★★ | 政策面差异大 |
|
||||
|
||||
#### 预测场景
|
||||
|
||||
| 场景 | A股大概率反应 | 注意事项 |
|
||||
|------|------------|----------|
|
||||
| 美股三大指数全线大涨 | A股高开0.5-1.5% | 高开后可能回落，不追高 |
|
||||
| 美股某板块暴涨>3% | A股对应板块高开跟涨 | 关注龙头股，散户跟风 |
|
||||
| 美股暴跌>2% | A股低开1%左右 | 低开后可能反弹，看资金面 |
|
||||
| 美股V型反转 | A股影响较小 | 说明美股自身企稳 |
|
||||
| 美股连续创新高 | A股情绪偏暖 | 但A股有自己的节奏 |
|
||||
| 美联储加息/降息 | 全市场情绪波动 | 加息偏空，降息偏多 |
|
||||
|
||||
**重要提醒**：美股影响主要是**开盘阶段**（9:25-10:00），之后A股会回归自身逻辑。不能仅凭美股涨跌做全天决策。
|
||||
|
||||
#### 实现模块
|
||||
|
||||
- **模块文件**：`services/external_factors.py` → `get_us_market_overview()`
|
||||
- **数据来源**：AKShare `index_global`（全球指数）
|
||||
- **板块映射**：内置 美股板块→A股板块 映射表
|
||||
- **评分范围**：±10
|
||||
|
||||
### 4.5 大宗商品价格（P4，已实现）
|
||||
|
||||
**白话解释**：石油、黄金、铜等大宗商品价格变化，直接影响A股相关板块。
|
||||
|
||||
| 商品 | 影响板块 | 传导逻辑 | 评分 |
|
||||
|------|----------|----------|------|
|
||||
| 原油 | 石油开采（利好）、航空（利空） | 油价涨→开采盈利增→航空成本增 | ±1 |
|
||||
| 黄金 | 黄金股、珠宝 | 金价涨→黄金企业盈利增 | ±1 |
|
||||
| 铜 | 有色金属、电缆 | 铜价涨→铜企受益 | ±1 |
|
||||
| 螺纹钢 | 钢铁（利好）、基建/地产（利空） | 钢价涨→钢企受益，基建成本增 | ±1 |
|
||||
| 碳酸锂 | 锂矿/锂电池（利好）、新能源车（利空） | 锂价涨→锂矿受益，新能源车成本增 | ±1 |
|
||||
|
||||
#### 实现模块
|
||||
|
||||
- **模块文件**：`services/external_factors.py` → `get_commodity_overview()`
|
||||
- **数据来源**：AKShare `futures_main_sina`（商品期货行情）
|
||||
- **内置商品→A股板块影响映射表**：`COMMODITY_A_SECTOR_MAP`
|
||||
- **评分范围**：±5
|
||||
|
||||
### 4.6 上市公司并购消息（P5，已实现）
|
||||
|
||||
**白话解释**：并购就是一家公司买下或合并另一家公司。好的并购能让公司"1+1>2"，股价暴涨；坏的并购可能拖累业绩，股价下跌。并购消息往往是股价的"催化剂"——技术面再好，没有消息催化也涨不起来；技术面一般，一个并购消息就能连续涨停。
|
||||
|
||||
#### 影响机制
|
||||
|
||||
| 消息类型 | 影响方向 | 持续时间 | 典型幅度 | 评分 |
|
||||
|----------|----------|----------|----------|------|
|
||||
| 被收购溢价并购 | 大涨 | 1-3个涨停 | +10%~+30% | +10 |
|
||||
| 收购优质资产 | 大涨 | 3-5日 | +5%~+20% | +10 |
|
||||
| 收购劣质资产 | 下跌 | 3-5日 | -5%~-15% | -8 |
|
||||
| 合并重组 | 看涨 | 5-10日 | +5%~+30% | +10 |
|
||||
| 资产剥离 | 看涨 | 1-3日 | +3%~+10% | +5 |
|
||||
| 股权转让 | 看涨 | 1-3日 | +3%~+10% | +5 |
|
||||
| 定增引入战投 | 看涨 | 3-5日 | +3%~+15% | +5 |
|
||||
| 商誉减值 | 大跌 | 1-2日 | -5%~-20% | -8 |
|
||||
|
||||
#### 预测策略
|
||||
|
||||
| 策略 | 可行性 | 说明 |
|
||||
|------|--------|------|
|
||||
| 消息面监控 | ★★★★ | 监控公司公告/新闻，第一时间发现并购消息 |
|
||||
| 股价异动预警 | ★★★★ | 监测异常放量涨跌，反推可能有消息 |
|
||||
| 停牌复牌跟踪 | ★★★★ | 停牌公司复牌后通常有大幅波动 |
|
||||
| 龙虎榜数据 | ★★★ | 看到机构大举买入，可能提前知道消息 |
|
||||
| 技术面预判 | ★★ | 有些股票并购前有资金提前布局的痕迹 |
|
||||
|
||||
#### 实现模块
|
||||
|
||||
- **模块文件**：`services/news_analyzer.py` → `analyze_announcement_sentiment()` + `detect_price_anomaly()`
|
||||
- **数据来源**：AKShare `stock_notice_report`（公告数据）
|
||||
- **LLM分析**：豆包AI 对重要公告做情感分析（规则评分兜底）
|
||||
- **异动检测**：量比>3 + 涨跌幅>5% 标记为"可能有消息面催化"
|
||||
- **API端点**：`GET /api/news_analysis/<code>`
|
||||
- **评分范围**：±15
|
||||
|
||||
### 4.7 政策面（P6，已实现）
|
||||
|
||||
**白话解释**：A股是"政策市"，政策的影响力往往超过技术面。一个政策出台，整个板块可能集体涨停或跌停。
|
||||
|
||||
| 政策类型 | 影响范围 | 典型案例 | 评分 |
|
||||
|----------|----------|----------|------|
|
||||
| 行业扶持政策 | 对应板块暴涨 | 新能源补贴、芯片国产替代 | +2/条 |
|
||||
| 行业监管政策 | 对应板块暴跌 | 教育双减、互联网反垄断 | -3/条 |
|
||||
| 货币政策（降准/降息） | 全市场偏多 | 流动性增加，资金入市 | +2/条 |
|
||||
| 财政政策（基建/减税） | 相关板块受益 | 基建投资、减税降费 | +2/条 |
|
||||
| IPO/再融资政策 | 市场情绪 | 加速IPO偏空，放缓偏多 | 中性 |
|
||||
| 交易规则变化 | 短期情绪 | 降印花税、限制减持 | 中性 |
|
||||
|
||||
#### 实现模块
|
||||
|
||||
- **模块文件**：`services/news_analyzer.py` → `analyze_policy_impact()`
|
||||
- **数据来源**：AKShare `stock_info_global_em`（财经新闻）
|
||||
- **关键词分类**：扶持/监管/货币/财政/资本市场
|
||||
- **LLM深度分析**：重大政策调用豆包AI分析（规则评分兜底）
|
||||
- **评分范围**：±10
|
||||
|
||||
### 4.8 汇率变化（P7，已实现）
|
||||
|
||||
**白话解释**：人民币升值利好进口型企业（航空、造纸），贬值利好出口型企业（纺织、电子代工）。
|
||||
|
||||
| 汇率变化 | 受益板块 | 受损板块 | 评分 |
|
||||
|----------|----------|----------|------|
|
||||
| 人民币升值 | 航空、造纸、房地产 | 纺织、家电出口、电子代工 | +2 |
|
||||
| 人民币贬值 | 纺织、家电、电子代工 | 航空、造纸 | -2 |
|
||||
| 汇率稳定 | — | — | 0 |
|
||||
|
||||
#### 实现模块
|
||||
|
||||
- **模块文件**：`services/external_factors.py` → `get_fx_overview()`
|
||||
- **数据来源**：AKShare `currency_boc_sina`（人民币汇率）
|
||||
- **评分范围**：±3
|
||||
|
||||
### 4.9 集成架构与评分体系
|
||||
|
||||
#### 架构
|
||||
|
||||
```
|
||||
当前架构（已实现）：
|
||||
K线数据 → 技术指标 → 信号检测 → 深度分析(技术面基础分) ──┐
|
||||
资金流向数据 → 资金信号 ────────────────────────────────┤
|
||||
美股隔夜数据 → 外盘情绪 ────────────────────────────────┤→ 综合评分引擎 → 最终评分 → 买卖建议/AI解说
|
||||
公告/新闻 → LLM情感分析 ───────────────────────────────┤
|
||||
北向资金 → 外资动向 ────────────────────────────────────┤
|
||||
市场情绪指标 → 情绪评分 ────────────────────────────────┤
|
||||
大宗商品 → 板块影响 ────────────────────────────────────┤
|
||||
汇率 → 进出口影响 ──────────────────────────────────────┘
|
||||
```
|
||||
|
||||
#### 评分权重
|
||||
|
||||
| 因素 | 评分范围 | 说明 |
|
||||
|------|----------|------|
|
||||
| 技术面基础分 | 0-100 | `compute_deep_analysis` 原始分 |
|
||||
| P0 资金面 | ±20 | 连续流入+10，吸筹+8，大单突击+5 |
|
||||
| P1 市场情绪 | ±10 | 涨跌停比+5/-5，连板+3，成交额+2/-2 |
|
||||
| P2 北向资金 | ±10 | 大幅流入+5，连续流入+3 |
|
||||
| P3 美股外盘 | ±10 | 美股大涨+5，大跌-5 |
|
||||
| P4 大宗商品 | ±5 | 单品种涨跌±1 |
|
||||
| P5 公告/异动 | ±15 | 并购+10，业绩预增+8，异动±5 |
|
||||
| P6 政策面 | ±10 | 扶持+2，监管-3 |
|
||||
| P7 汇率 | ±3 | 升值+2，贬值-2 |
|
||||
| **P5+P6 合并上限** | **±20** | `analyze_news_factors` 统一计算后限制 |
|
||||
| **外部总分上限** | **±40** | 避免外部因素喧宾夺主 |
|
||||
|
||||
**核心原则**：技术面仍是基础（权重60%+），外部因素作为加减分项，避免外部因素喧宾夺主。
|
||||
|
||||
### 4.10 新增模块和API
|
||||
|
||||
#### 新增模块文件
|
||||
|
||||
| 模块 | 文件 | 功能 |
|
||||
|------|------|------|
|
||||
| 资金流向分析 | `services/fund_flow_analyzer.py` | 从DB读取资金流向，计算连续流入/流出、量价背离、大单突击 |
|
||||
| 市场情绪指标 | `services/market_sentiment.py` | 从实时行情表计算涨停跌停比、连板高度、换手率中位数、两市成交额 |
|
||||
| 外部因素 | `services/external_factors.py` | 北向资金、美股隔夜板块、大宗商品、汇率变化 |
|
||||
| 新闻/公告分析 | `services/news_analyzer.py` | 公告采集+分类、LLM情感分析、异动检测、政策面监控 |
|
||||
| 综合评分引擎 | `services/score_engine.py` | 汇总技术面+所有外部因素，输出最终评分 |
|
||||
|
||||
#### 新增API端点
|
||||
|
||||
| 端点 | 方法 | 说明 |
|
||||
|------|------|------|
|
||||
| `/api/market_sentiment` | GET | 市场情绪指标 |
|
||||
| `/api/external_factors` | GET | 外部因素综合数据（北向/美股/商品/汇率） |
|
||||
| `/api/fund_flow_analysis/<code>` | GET | 个股资金流向分析 |
|
||||
| `/api/news_analysis/<code>` | GET | 个股消息面分析（公告+政策+异动） |
|
||||
|
||||
### 4.11 数据流
|
||||
|
||||
```
|
||||
deep_analyze 接口调用流程：
|
||||
1. 获取K线数据 → calc_all_indicators → detect_all_signals
|
||||
2. compute_deep_analysis（技术面评分 0-100）
|
||||
3. score_engine.compute_comprehensive_score：
|
||||
├─ fund_flow_analyzer.analyze_fund_flow（P0）
|
||||
├─ market_sentiment.calc_market_sentiment（P1）
|
||||
├─ external_factors.get_all_external_factors（P2-P4,P7）
|
||||
└─ news_analyzer.analyze_news_factors（P5-P6）
|
||||
4. 最终评分 = 技术面 + 外部加减分（上限100，下限0）
|
||||
5. LLM润色AI解说
|
||||
```
|
||||
|
||||
### 4.12 容错机制
|
||||
|
||||
- 所有外部因素模块均有 try/except 保护，失败时返回中性评分（0分）
|
||||
- AKShare 数据源不可用时自动降级，不影响主流程
|
||||
- LLM 分析失败时回退到规则评分
|
||||
- 当日缓存避免重复调用外部API
|
||||
|
||||
---
|
||||
|
||||
## 五、算法决策层
|
||||
|
||||
> 本章包含四部分：5.1~5.2 基于技术信号给出买卖建议和牛股阶段识别（纯技术面）；5.3 深度分析产出技术面基础分后，由综合评分引擎叠加第四章的外部因素（P0-P7）形成最终评级，并据此修正 5.1 的买卖建议；5.4 AI解说涵盖内外因素的综合解读。
|
||||
|
||||
### 5.1 统一推荐算法 `compute_recommend`
|
||||
|
||||
> ⚠️ 本节推荐基于技术信号（MACD/龙抬头/底背离等）。在 `deep_analyze` 深度分析中，综合评分引擎计算完成后，会根据最终评级（含外部因素 P0-P7）修正买卖建议——当综合评级与技术面推荐矛盾时，以综合评级为准。
|
||||
|
||||
遵循"体系最强战法"流程，分**持仓**和**非持仓**两套逻辑：
|
||||
|
||||
#### 持仓时（已经持有该股票）
|
||||
|
||||
| 条件 | 推荐 | 评分 | 白话 |
|
||||
|------|------|------|------|
|
||||
| MACD死叉 + 无主升浪 | 卖出 | 75 | 趋势走弱了，该走了 |
|
||||
| 主升浪 | 加仓 | 90 | 加速拉升中，加码赚钱 |
|
||||
| 真龙 | 持有 | 70 | 趋势确认了，拿着别动 |
|
||||
| 其他 | 观望 | 50 | 拿着等主升浪 |
|
||||
|
||||
#### 非持仓时（还没买）
|
||||
|
||||
| 条件 | 推荐 | 评分 | 白话 |
|
||||
|------|------|------|------|
|
||||
| 底背离 + 龙抬头 + MACD金叉 | 买入 | 95 | 最佳买点！跌透了+资金进场+趋势配合 |
|
||||
| 龙抬头 + 主升浪 + MACD金叉 | 买入 | 90 | 强势买入！资金进场+加速段 |
|
||||
| 龙抬头 + MACD金叉 | 买入 | 80 | 核心买点！资金进场了 |
|
||||
| 底背离 + 龙抬头 + MACD死叉 | 关注 | 65 | 好信号但趋势没配合，等一等 |
|
||||
| 龙抬头 + MACD死叉 | 关注 | 55 | 信号冲突，谨慎观望 |
|
||||
| 主升浪（非持仓） | 关注 | 75 | 已过最佳买点，等回调 |
|
||||
| 真龙 | 关注 | 65 | 趋势刚启动，等龙抬头确认 |
|
||||
| MACD死叉 | 回避 | 25 | 趋势偏弱，别碰 |
|
||||
| 底背离 | 关注 | 60 | 跌透了，纳入关注池 |
|
||||
| 有信号触发 | 观察 | 40 | 有信号但不够强 |
|
||||
| 无信号 | 观望 | 0 | 没机会，别动 |
|
||||
|
||||
### 5.2 牛股阶段识别 `compute_bull_stage`
|
||||
|
||||
> ⚠️ 本节阶段识别仅基于技术信号，**不含外部因素**。
|
||||
|
||||
把股票在"牛股启动流程"中的位置分为5个阶段：
|
||||
|
||||
```
|
||||
阶段1：底部探测 → 阶段2：资金进场 → 阶段3：趋势确立 → 阶段4：加速拉升
|
||||
↓
|
||||
阶段5：回调补涨
|
||||
```
|
||||
|
||||
| 阶段 | 名称 | 触发信号 | 进度 | 白话建议 |
|
||||
|------|------|----------|------|----------|
|
||||
| 1 | 底部探测 | 底背离/短底背离/老鼠仓 | 20% | 跌得差不多了，放进关注池盯着 |
|
||||
| 2 | 资金进场 | 龙抬头 | 45% | **最佳买入时机！** 资金开始进场了 |
|
||||
| 3 | 趋势确立 | 真龙 | 65% | 趋势确认了，可以追，但等回调买更好 |
|
||||
| 4 | 加速拉升 | 主升浪 | 85% | 已经涨起来了，持仓的加仓，没买的别追高 |
|
||||
| 5 | 回调补涨 | 反弹 | 50% | 回调后可能补涨，但要小心是假反弹 |
|
||||
|
||||
多信号叠加会加分（底背离+10%、龙抬头+5%、真龙+5%、老鼠仓+5%），说明流程更完整，牛股可能性更大。
|
||||
|
||||
### 5.3 深度分析 `compute_deep_analysis` + 综合评分引擎
|
||||
|
||||
对单只股票进行**技术面7维度 + 外部因素8维度**的深度分析，最终由综合评分引擎汇总为统一评分。
|
||||
|
||||
##### 技术面维度（7个，基础分0-100）
|
||||
|
||||
###### 维度1：均线系统
|
||||
|
||||
判断 MA5/10/20/60 的排列方式：
|
||||
- **多头排列**：MA5 > MA10 > MA20 → 短期比中期强，中期比长期强，上涨趋势
|
||||
- **空头排列**：MA5 < MA10 < MA20 → 依次向下，下跌趋势
|
||||
- **交叉整理**：均线纠缠在一起 → 方向不明
|
||||
|
||||
###### 维度2：价格位置
|
||||
|
||||
计算当前价格在20/60/120日高低区间的百分位（0-100%）：
|
||||
|
||||
**白话解释**：就像一把尺子，0%是最低点，100%是最高点。当前价格在尺子上的位置。
|
||||
|
||||
- < 20% → 低位区间，可能存在反弹机会
|
||||
- 20%-50% → 中低位置，相对安全
|
||||
- 50%-80% → 中高位置，还有一定上涨空间
|
||||
- > 80% → 高位区间，追高要小心
|
||||
|
||||
###### 维度3：支撑与压力位
|
||||
|
||||
**白话解释**：支撑位是"价格跌到这里容易止跌"的位置，压力位是"价格涨到这里容易受阻"的位置。
|
||||
|
||||
支撑位来源：
|
||||
- 当前价格下方的均线（MA5/10/20/60）
|
||||
- 20/60/120日的最低点
|
||||
|
||||
压力位来源：
|
||||
- 当前价格上方的均线
|
||||
- 20/60/120日的最高点
|
||||
|
||||
按距离当前价格从近到远排序，取前5个。
|
||||
|
||||
###### 维度4：成交量分析
|
||||
|
||||
计算量比 = 今日成交量 / 20日平均成交量：
|
||||
|
||||
| 量比 | 判断 | 白话 |
|
||||
|------|------|------|
|
||||
| < 0.6 | 缩量 | 市场冷清，没人交易 |
|
||||
| 0.6-1.3 | 平量 | 正常水平 |
|
||||
| 1.3-2.0 | 温和放量 | 有资金在活跃参与 |
|
||||
| > 2.0 | 大幅放量 | 市场关注度很高，要留意是主力进场还是出货 |
|
||||
|
||||
###### 维度5：形态识别
|
||||
|
||||
系统会自动识别以下技术形态：
|
||||
|
||||
| 形态 | 类型 | 白话 |
|
||||
|------|------|------|
|
||||
| 平台突破 | 看涨 | 股价横盘了很久（10日波动率<1.5%），今天终于突破了 |
|
||||
| 窄幅整理 | 中性 | 横盘中，蓄势待变，可能要选方向了 |
|
||||
| 创20日新高 | 看涨 | 股价达到近20天最高点，强势 |
|
||||
| 双底突破 | 看涨 | 两次探底价格接近，且突破中间的高点（颈线），经典反转形态 |
|
||||
| 量价齐升 | 看涨 | 近5天成交量和价格同步上升，资金在持续买入 |
|
||||
| 均线粘合发散 | 看涨 | MA5/10/20靠得很近（离散<1%）后开始多头排列，即将选择方向 |
|
||||
| 大阳线 | 看涨 | 当天涨幅≥5%，强势上涨 |
|
||||
| 大阴线 | 看跌 | 当天跌幅≥5%，强势下跌 |
|
||||
|
||||
###### 维度6：空间估算
|
||||
|
||||
计算最近压力位和最近支撑位之间的风险收益比：
|
||||
|
||||
**白话解释**：往上能涨多少 vs 往下能跌多少。
|
||||
|
||||
- 风险收益比 ≥ 2 → 性价比不错，潜在收益是风险的2倍以上
|
||||
- 1-2 → 性价比一般
|
||||
- < 0.8 → 下行风险大于上涨空间，不划算
|
||||
|
||||
###### 维度7：综合评分
|
||||
|
||||
基础分50分，根据以上各维度加减分：
|
||||
|
||||
| 评分项 | 加分/扣分 | 白话 |
|
||||
|--------|-----------|------|
|
||||
| 均线多头排列 | +10 | 趋势向上 |
|
||||
| 均线空头排列 | -10 | 趋势向下 |
|
||||
| 放量(量比≥1.3) | +5 | 有资金参与 |
|
||||
| 缩量(量比<0.6) | -3 | 市场冷清 |
|
||||
| 平台突破 | +10 | 蓄势后突破 |
|
||||
| 创20日新高 | +5 | 强势 |
|
||||
| 双底突破 | +10 | 经典反转形态 |
|
||||
| 量价齐升 | +8 | 资金持续买入 |
|
||||
| 均线粘合发散 | +7 | 即将选择方向（多头） |
|
||||
| 120日位置偏低 | +5 | 安全边际高 |
|
||||
| 120日位置偏高 | -5 | 追高风险 |
|
||||
| 20日位置偏低 | +3 | 相对安全 |
|
||||
| 20日位置偏高 | -3 | 注意风险 |
|
||||
| 3+信号共振 | +15 | 多信号确认 |
|
||||
| 2信号叠加 | +10 | 信号较多 |
|
||||
| 1个信号 | +5 | 有信号但不强 |
|
||||
| 风险收益比≥2 | +5 | 性价比好 |
|
||||
| 风险收益比<0.8 | -5 | 性价比差 |
|
||||
|
||||
技术面评分映射（基础分，后续叠加外部因素）：
|
||||
|
||||
| 评分 | 判定 | 白话 |
|
||||
|------|------|------|
|
||||
| ≥ 80 | 强烈看多 | 各方面都很好，值得关注 |
|
||||
| ≥ 65 | 看多 | 整体偏积极 |
|
||||
| ≥ 50 | 中性偏多 | 多空均衡，略偏积极 |
|
||||
| ≥ 35 | 中性偏空 | 多空均衡，略偏消极 |
|
||||
| < 35 | 看空 | 各方面都不好，回避 |
|
||||
|
||||
> 以上为技术面基础分映射。最终评分 = 技术面基础分 + 外部因素加减分，评级标准相同，详见第四章。
|
||||
|
||||
##### 外部因素维度（8个，加减分±40上限）
|
||||
|
||||
技术面基础分计算完成后，综合评分引擎 `score_engine.compute_comprehensive_score` 会叠加以下外部因素：
|
||||
|
||||
| 维度 | 模块 | 评分范围 | 白话 |
|
||||
|------|------|----------|------|
|
||||
| P0 资金面 | `fund_flow_analyzer` | ±20 | 主力在买还是在卖？有没有暗中吸筹/出货？ |
|
||||
| P1 市场情绪 | `market_sentiment` | ±10 | 今天涨停的股票多还是跌停的多？市场热不热？ |
|
||||
| P2 北向资金 | `external_factors` | ±10 | 外资今天是买还是卖？ |
|
||||
| P3 美股外盘 | `external_factors` | ±10 | 昨晚美股涨了还是跌了？ |
|
||||
| P4 大宗商品 | `external_factors` | ±5 | 原油/黄金/铜的价格变化对相关板块的影响 |
|
||||
| P5 公告/异动 | `news_analyzer` | ±15 | 有没有并购/业绩预告等重大消息？股价有没有异动？ |
|
||||
| P6 政策面 | `news_analyzer` | ±10 | 近期有没有行业扶持/监管政策？ |
|
||||
| P7 汇率 | `external_factors` | ±3 | 人民币升值还是贬值？ |
|
||||
|
||||
> P5+P6 由 `analyze_news_factors` 统一计算，合计上限 ±20（非各自独立累加）。
|
||||
|
||||
**最终评分 = 技术面基础分(0-100) + 外部因素加减分(±40上限)**
|
||||
|
||||
> 各因素独立计算，失败时返回0分不影响主流程。详见第四章。
|
||||
|
||||
### 5.4 AI 通俗解说
|
||||
|
||||
系统会将以上技术分析结果自动转成口语化的中文解说，涵盖：
|
||||
1. 当前走势概况（涨跌情况+均线趋势）
|
||||
2. 价格位置（在高位还是低位）
|
||||
3. 支撑压力（上方压力位和下方支撑位在哪）
|
||||
4. 成交量情况（放量还是缩量）
|
||||
5. 形态识别（发现了什么技术形态）
|
||||
6. 综合建议（根据评分给出操作建议）
|
||||
7. 空间估算（风险收益比如何）
|
||||
8. 外部因素（资金面/市场情绪/北向资金/美股/消息面等综合影响）
|
||||
|
||||
还可以调用豆包 LLM 对规则文本进行润色，让表达更自然生动。
|
||||
|
||||
---
|
||||
|
||||
## 六、数据源优先级
|
||||
|
||||
K线数据获取的多级容灾机制：
|
||||
|
||||
| 优先级 | 数据源 | 覆盖范围 | 说明 |
|
||||
|--------|--------|----------|------|
|
||||
| 1 | 本地数据库 | 全市场 | 最快（毫秒级），优先使用 |
|
||||
| 2 | 阿里云API | 沪深（不含北交所） | 最稳定的云端源 |
|
||||
| 3 | 腾讯API | 全市场（含北交所） | 阿里云不支持北交所时使用 |
|
||||
| 4 | 麦蕊API | 沪深 | 第三方付费数据源 |
|
||||
| 5 | AKShare | 全市场 | 开源免费数据源，兜底 |
|
||||
|
||||
---
|
||||
|
||||
## 七、性能优化
|
||||
|
||||
| 优化点 | 说明 |
|
||||
|--------|------|
|
||||
| numpy 向量化 | 信号检测使用 `.values` numpy 数组替代 pandas iloc，元素访问从 5μs 降到 50ns |
|
||||
| 智能类型转换 | 已是 float64 的列跳过转换，避免重复 astype |
|
||||
| 线程本地连接 | 多线程扫描时使用 `threading.local()` 复用 DB 连接 |
|
||||
| 连接池 | 全局 `ThreadedConnectionPool`（2-20连接），避免频繁建连 |
|
||||
| API Session 复用 | 阿里云/腾讯 API 使用 `requests.Session` 单例 + 连接池 + 自动重试 |
|
||||
@@ -56,8 +56,15 @@ def get_db_conn():
|
||||
|
||||
|
||||
def get_all_stock_codes(conn):
|
||||
"""获取可交易的股票列表（排除退市、停牌、ST等无效股票）"""
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("SELECT code, name FROM stock_realtime_price ORDER BY code")
|
||||
cur.execute("""
|
||||
SELECT code, name FROM stock_realtime_price
|
||||
WHERE volume > 0 AND price > 0
|
||||
AND name NOT LIKE '%%退%%'
|
||||
AND name NOT LIKE 'PT%%'
|
||||
ORDER BY code
|
||||
""")
|
||||
return cur.fetchall()
|
||||
|
||||
|
||||
|
||||
@@ -11,6 +11,7 @@ from services.stock_service import (
|
||||
from services.stock_algorithms import (
|
||||
compute_recommend, get_kline_data as algo_get_kline_data,
|
||||
compute_bull_stage, find_bull_stocks, BULL_STAGES,
|
||||
compute_deep_analysis,
|
||||
)
|
||||
from db import (
|
||||
login_required, get_current_user_id,
|
||||
@@ -88,6 +89,143 @@ def analyze():
|
||||
return jsonify({'error': str(e)}), 500
|
||||
|
||||
|
||||
@bp.route('/deep_analyze', methods=['POST'])
|
||||
def deep_analyze():
|
||||
"""单股深度分析（价格位置、压力支撑、量价、空间、综合评分）"""
|
||||
try:
|
||||
data = request.get_json()
|
||||
stock_code = data.get('stock_code', '').strip()
|
||||
if not stock_code:
|
||||
return jsonify({'error': '股票代码不能为空'}), 400
|
||||
|
||||
df = algo_get_kline_data(stock_code, days=180)
|
||||
if df is None or len(df) < 30:
|
||||
return jsonify({'error': 'K线数据不足'}), 400
|
||||
|
||||
from services.technical_indicators import calc_all_indicators
|
||||
from services.signal_detector import detect_all_signals
|
||||
df = calc_all_indicators(df)
|
||||
|
||||
signal_result = detect_all_signals(df, lookback=5)
|
||||
|
||||
from db import get_db, put_db
|
||||
from psycopg2.extras import RealDictCursor
|
||||
realtime_info = None
|
||||
conn = get_db()
|
||||
if conn:
|
||||
try:
|
||||
cur = conn.cursor(cursor_factory=RealDictCursor)
|
||||
cur.execute("""
|
||||
SELECT code, name, price, change_pct, volume, amount,
|
||||
high, low, open, prev_close, pe, pb, total_market_cap
|
||||
FROM stock_realtime_price WHERE code = %s
|
||||
""", (stock_code,))
|
||||
realtime_info = cur.fetchone()
|
||||
finally:
|
||||
put_db(conn)
|
||||
|
||||
report = compute_deep_analysis(df, signal_result, realtime_info)
|
||||
|
||||
stock_name = get_stock_name(stock_code) or (realtime_info or {}).get('name', '')
|
||||
|
||||
sig_status = signal_result.get('signal_status', [])
|
||||
indicators = signal_result.get('indicators', {})
|
||||
sig_count = signal_result.get('signal_summary', {}).get('total_signals', 0)
|
||||
rec = compute_recommend(sig_status, indicators, sig_count, False)
|
||||
|
||||
report['stock_code'] = stock_code
|
||||
report['stock_name'] = stock_name
|
||||
report['recommend'] = {
|
||||
'signal_type': rec[0],
|
||||
'display': rec[1],
|
||||
'reason': rec[2],
|
||||
'rate': rec[3],
|
||||
}
|
||||
report['signals'] = signal_result.get('signals', [])
|
||||
report['signal_status'] = sig_status
|
||||
|
||||
# ---- 综合评分引擎：整合外部因素（P0-P7）----
|
||||
try:
|
||||
from services.score_engine import compute_comprehensive_score
|
||||
tech_score = report.get('deep_score', 50)
|
||||
comprehensive = compute_comprehensive_score(
|
||||
stock_code, stock_name, tech_score, df
|
||||
)
|
||||
report['comprehensive'] = comprehensive
|
||||
# 用综合评分更新最终评分和评级
|
||||
report['deep_score'] = comprehensive['final_score']
|
||||
report['verdict'] = comprehensive['verdict']
|
||||
report['score_reasons'].extend(comprehensive.get('all_reasons', []))
|
||||
|
||||
# ---- 根据综合评级修正买卖建议 ----
|
||||
# 技术面推荐(compute_recommend)不含外部因素，
|
||||
# 当综合评级与技术面推荐矛盾时，以综合评级为准调整推荐
|
||||
final_score = comprehensive['final_score']
|
||||
final_verdict = comprehensive['verdict']
|
||||
orig_display = report['recommend'].get('display', '')
|
||||
orig_reason = report['recommend'].get('reason', '')
|
||||
orig_rate = report['recommend'].get('rate', 0)
|
||||
|
||||
# 综合评级偏空但技术面建议买入/加仓 → 降级为关注
|
||||
if final_score < 50 and orig_display in ('买入', '加仓'):
|
||||
report['recommend'] = {
|
||||
'signal_type': 'watch',
|
||||
'display': '关注',
|
||||
'reason': f"技术面信号偏多，但综合评级「{final_verdict}」(外部因素拖累)，建议观望",
|
||||
'rate': final_score,
|
||||
}
|
||||
# 综合评级强烈看多但技术面建议观望/关注 → 升级为买入
|
||||
elif final_score >= 80 and orig_display in ('观望', '关注', '观察'):
|
||||
report['recommend'] = {
|
||||
'signal_type': 'buy',
|
||||
'display': '买入',
|
||||
'reason': f"技术面{orig_display}，但综合评级「{final_verdict}」(外部因素共振看好)，建议买入",
|
||||
'rate': final_score,
|
||||
}
|
||||
# 综合评级看空但技术面建议持有 → 降级为卖出
|
||||
elif final_score < 35 and orig_display in ('持有', '观望'):
|
||||
report['recommend'] = {
|
||||
'signal_type': 'sell',
|
||||
'display': '卖出',
|
||||
'reason': f"技术面{orig_display}，但综合评级「{final_verdict}」(外部因素重大利空)，建议卖出",
|
||||
'rate': final_score,
|
||||
}
|
||||
# 其他情况保持技术面推荐，但更新评分为综合评分
|
||||
else:
|
||||
report['recommend']['rate'] = final_score
|
||||
except Exception as e:
|
||||
print(f"综合评分引擎计算失败，使用技术面评分: {e}")
|
||||
|
||||
if realtime_info:
|
||||
report['realtime'] = {
|
||||
'price': float(realtime_info.get('price') or 0),
|
||||
'change_pct': float(realtime_info.get('change_pct') or 0),
|
||||
'pe': float(realtime_info.get('pe') or 0),
|
||||
'pb': float(realtime_info.get('pb') or 0),
|
||||
'total_market_cap': float(realtime_info.get('total_market_cap') or 0),
|
||||
'volume': int(realtime_info.get('volume') or 0),
|
||||
}
|
||||
|
||||
skip_llm = data.get('skip_llm', False)
|
||||
if report.get('ai_summary') and not skip_llm:
|
||||
try:
|
||||
polished = _llm_polish_summary(
|
||||
stock_name, stock_code, report['ai_summary'],
|
||||
report.get('deep_score', 0), report.get('verdict', '')
|
||||
)
|
||||
if polished:
|
||||
report['ai_summary']['text'] = polished['text']
|
||||
report['ai_summary']['action_tip'] = polished['action_tip']
|
||||
except Exception as e:
|
||||
print(f"LLM润色失败，使用规则文本: {e}")
|
||||
|
||||
return jsonify({'success': True, 'report': report})
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return jsonify({'error': str(e)}), 500
|
||||
|
||||
|
||||
@bp.route('/realtime_price/<stock_code>', methods=['GET'])
|
||||
def realtime_price(stock_code):
|
||||
"""获取实时价格（直接调用实时API，不使用数据库缓存）"""
|
||||
@@ -1178,3 +1316,73 @@ def get_bull_stocks():
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return jsonify({'success': False, 'error': str(e)}), 500
|
||||
|
||||
|
||||
def _llm_polish_summary(stock_name, stock_code, ai_summary, score, verdict):
|
||||
"""调用豆包LLM将规则模板生成的分析文本润色成更自然流畅的表达"""
|
||||
import requests as _req
|
||||
from config import Config
|
||||
|
||||
api_key = Config.DOUBAO_API_KEY
|
||||
if not api_key:
|
||||
return None
|
||||
|
||||
draft_text = ai_summary.get('text', '')
|
||||
draft_action = ai_summary.get('action_tip', '')
|
||||
|
||||
prompt = f"""你是一位资深股票分析师，擅长用通俗易懂的语言给普通投资者解读技术分析。
|
||||
|
||||
以下是对{stock_name}({stock_code})的技术分析草稿，综合评分{score}分（{verdict}）：
|
||||
|
||||
【分析草稿】
|
||||
{draft_text}
|
||||
|
||||
【操作建议草稿】
|
||||
{draft_action}
|
||||
|
||||
请你将上面的草稿改写成更自然、更生动的表达。要求：
|
||||
1. 用口语化表达，像老朋友聊天一样，避免专业术语堆砌
|
||||
2. 保留所有关键数据和结论，不要遗漏
|
||||
3. 适当加入比喻或生活化的表达，让小白也能听懂
|
||||
4. 操作建议要明确、具体，有可操作性
|
||||
5. 总字数控制在200字以内
|
||||
6. 不要用markdown格式，纯文本即可
|
||||
|
||||
请严格按以下JSON格式输出，不要输出其他内容：
|
||||
{{"text": "润色后的分析文本", "action_tip": "润色后的操作建议"}}"""
|
||||
|
||||
try:
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {api_key}"
|
||||
}
|
||||
payload = {
|
||||
"model": "doubao-seed-1-6-251015",
|
||||
"max_completion_tokens": 2048,
|
||||
"stream": False,
|
||||
"messages": [{"role": "user", "content": prompt}]
|
||||
}
|
||||
resp = _req.post(
|
||||
"https://ark.cn-beijing.volces.com/api/v3/chat/completions",
|
||||
headers=headers, json=payload, timeout=45
|
||||
)
|
||||
if resp.status_code != 200:
|
||||
return None
|
||||
|
||||
data = resp.json()
|
||||
content = data.get('choices', [{}])[0].get('message', {}).get('content', '')
|
||||
if not content:
|
||||
return None
|
||||
|
||||
content = content.strip()
|
||||
if content.startswith('```'):
|
||||
content = content.split('\n', 1)[-1].rsplit('```', 1)[0].strip()
|
||||
|
||||
import json as _json
|
||||
result = _json.loads(content)
|
||||
if result.get('text') and result.get('action_tip'):
|
||||
return result
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"LLM polish error: {e}")
|
||||
return None
|
||||
|
||||
@@ -113,15 +113,17 @@ def get_current_user():
|
||||
email = session.get('email', session.get('username', ''))
|
||||
is_admin = False
|
||||
try:
|
||||
from db import get_db
|
||||
from db import get_db, put_db
|
||||
conn = get_db()
|
||||
if conn:
|
||||
cur = conn.cursor()
|
||||
cur.execute("SELECT is_admin FROM users WHERE id = %s", (session['user_id'],))
|
||||
row = cur.fetchone()
|
||||
if row:
|
||||
is_admin = bool(row[0])
|
||||
conn.close()
|
||||
try:
|
||||
cur = conn.cursor()
|
||||
cur.execute("SELECT is_admin FROM users WHERE id = %s", (session['user_id'],))
|
||||
row = cur.fetchone()
|
||||
if row:
|
||||
is_admin = bool(row[0])
|
||||
finally:
|
||||
put_db(conn)
|
||||
except Exception:
|
||||
pass
|
||||
return jsonify({
|
||||
|
||||
@@ -523,3 +523,52 @@ def get_fundamental(stock_code):
|
||||
return jsonify({'success': True, 'data': result, 'source': 'api'})
|
||||
except Exception as e:
|
||||
return jsonify({'error': str(e)}), 500
|
||||
|
||||
|
||||
# ============ 市场情绪 & 外部因素 API ============
|
||||
|
||||
@bp.route('/market_sentiment', methods=['GET'])
|
||||
def market_sentiment():
|
||||
"""获取市场情绪指标（涨停跌停比、连板高度、换手率中位数、两市成交额）"""
|
||||
try:
|
||||
from services.market_sentiment import calc_market_sentiment
|
||||
result = calc_market_sentiment()
|
||||
return jsonify({'success': True, 'data': result})
|
||||
except Exception as e:
|
||||
return jsonify({'success': False, 'error': str(e)}), 500
|
||||
|
||||
|
||||
@bp.route('/external_factors', methods=['GET'])
|
||||
def external_factors():
|
||||
"""获取外部因素综合数据（北向资金、美股隔夜、大宗商品、汇率）"""
|
||||
try:
|
||||
from services.external_factors import get_all_external_factors
|
||||
result = get_all_external_factors()
|
||||
return jsonify({'success': True, 'data': result})
|
||||
except Exception as e:
|
||||
return jsonify({'success': False, 'error': str(e)}), 500
|
||||
|
||||
|
||||
@bp.route('/fund_flow_analysis/<stock_code>', methods=['GET'])
|
||||
def fund_flow_analysis(stock_code):
|
||||
"""获取个股资金流向分析（P0：主力资金进出评分和信号）"""
|
||||
try:
|
||||
from services.fund_flow_analyzer import analyze_fund_flow
|
||||
days = request.args.get('days', 5, type=int)
|
||||
result = analyze_fund_flow(stock_code, days=days)
|
||||
return jsonify({'success': True, 'data': result})
|
||||
except Exception as e:
|
||||
return jsonify({'success': False, 'error': str(e)}), 500
|
||||
|
||||
|
||||
@bp.route('/news_analysis/<stock_code>', methods=['GET'])
|
||||
def news_analysis(stock_code):
|
||||
"""获取个股消息面分析（P5公告+P6政策+异动检测）"""
|
||||
try:
|
||||
from services.news_analyzer import analyze_news_factors
|
||||
from services.stock_service import get_stock_name
|
||||
stock_name = get_stock_name(stock_code) or ''
|
||||
result = analyze_news_factors(stock_code, stock_name)
|
||||
return jsonify({'success': True, 'data': result})
|
||||
except Exception as e:
|
||||
return jsonify({'success': False, 'error': str(e)}), 500
|
||||
|
||||
+171
-123
@@ -1,16 +1,47 @@
|
||||
"""
|
||||
交易记录 API 路由（纯数据库版）
|
||||
交易记录 API 路由（纯数据库版，事务安全）
|
||||
"""
|
||||
from flask import Blueprint, request, jsonify, session
|
||||
from psycopg2.extras import RealDictCursor
|
||||
from db import (
|
||||
login_required, get_current_user_id,
|
||||
db_get_trades, db_get_trade, db_add_trade, db_update_trade, db_delete_trade,
|
||||
db_get_available_cash, db_update_available_cash
|
||||
login_required, get_current_user_id, get_db, put_db,
|
||||
db_get_trades, db_get_available_cash, db_update_available_cash
|
||||
)
|
||||
|
||||
bp = Blueprint('trades', __name__, url_prefix='/api')
|
||||
|
||||
|
||||
def _parse_float(val):
|
||||
if val is None or val == '':
|
||||
return None
|
||||
try:
|
||||
return round(float(val), 4)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _parse_int(val):
|
||||
if val is None or val == '':
|
||||
return None
|
||||
try:
|
||||
return int(val)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _calc_cash_delta(trade_type, price, quantity):
|
||||
"""计算交易对可用资金的影响"""
|
||||
if not trade_type or price is None or quantity is None:
|
||||
return 0
|
||||
amount = round(float(price) * int(quantity), 2)
|
||||
t = trade_type.lower()
|
||||
if t == 'buy':
|
||||
return -amount
|
||||
elif t == 'sell':
|
||||
return amount
|
||||
return 0
|
||||
|
||||
|
||||
@bp.route('/trades', methods=['GET'])
|
||||
@login_required
|
||||
def get_trades():
|
||||
@@ -23,153 +54,170 @@ def get_trades():
|
||||
@bp.route('/trades', methods=['POST'])
|
||||
@login_required
|
||||
def add_trade():
|
||||
"""添加交易记录"""
|
||||
"""添加交易记录（trade 插入 + 可用资金更新在同一事务中）"""
|
||||
conn = get_db()
|
||||
if not conn:
|
||||
return jsonify({'success': False, 'error': '数据库连接失败'}), 500
|
||||
try:
|
||||
user_id = get_current_user_id()
|
||||
data = request.get_json()
|
||||
|
||||
# 处理数值（空字符串转为None）
|
||||
def parse_float(val):
|
||||
if val is None or val == '':
|
||||
return None
|
||||
try:
|
||||
return round(float(val), 4)
|
||||
except:
|
||||
return None
|
||||
|
||||
def parse_int(val):
|
||||
if val is None or val == '':
|
||||
return None
|
||||
try:
|
||||
return int(val)
|
||||
except:
|
||||
return None
|
||||
|
||||
data['price'] = parse_float(data.get('price'))
|
||||
data['quantity'] = parse_int(data.get('quantity'))
|
||||
data['profit_amount'] = parse_float(data.get('profit_amount'))
|
||||
data['stop_loss_price'] = parse_float(data.get('stop_loss_price'))
|
||||
|
||||
trade, error = db_add_trade(user_id, data)
|
||||
if error:
|
||||
return jsonify({'success': False, 'error': error}), 400
|
||||
|
||||
# 根据交易类型自动更新可用资金
|
||||
trade_type = (data.get('trade_type') or '').lower()
|
||||
price = data.get('price')
|
||||
quantity = data.get('quantity')
|
||||
if trade_type in ('buy', 'sell') and price is not None and quantity is not None:
|
||||
amount = round(float(price) * int(quantity), 2)
|
||||
current = db_get_available_cash(user_id)
|
||||
if trade_type == 'buy':
|
||||
new_cash = round(current - amount, 2)
|
||||
else:
|
||||
new_cash = round(current + amount, 2)
|
||||
if new_cash < 0:
|
||||
new_cash = 0
|
||||
ok, _ = db_update_available_cash(user_id, new_cash)
|
||||
if ok:
|
||||
return jsonify({'success': True, 'trade': trade, 'available_cash': new_cash})
|
||||
|
||||
return jsonify({'success': True, 'trade': trade})
|
||||
data['price'] = _parse_float(data.get('price'))
|
||||
data['quantity'] = _parse_int(data.get('quantity'))
|
||||
data['profit_amount'] = _parse_float(data.get('profit_amount'))
|
||||
data['stop_loss_price'] = _parse_float(data.get('stop_loss_price'))
|
||||
|
||||
cur = conn.cursor(cursor_factory=RealDictCursor)
|
||||
cur.execute("""
|
||||
INSERT INTO trades (user_id, stock_code, stock_name, trade_type, price,
|
||||
quantity, trade_date, reason, result, profit_amount,
|
||||
stop_loss_price, notes)
|
||||
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
|
||||
RETURNING id, stock_code, stock_name, trade_type, price, quantity,
|
||||
trade_date::text, reason, result, profit_amount, stop_loss_price,
|
||||
notes, created_at::text
|
||||
""", (user_id, data.get('stock_code'), data.get('stock_name'),
|
||||
data.get('trade_type'), data.get('price'), data.get('quantity'),
|
||||
data.get('trade_date'), data.get('reason'), data.get('result'),
|
||||
data.get('profit_amount'), data.get('stop_loss_price'), data.get('notes')))
|
||||
trade = cur.fetchone()
|
||||
|
||||
delta = _calc_cash_delta(data.get('trade_type'), data.get('price'), data.get('quantity'))
|
||||
new_cash = None
|
||||
if delta != 0:
|
||||
cur.execute("SELECT available_cash FROM users WHERE id = %s FOR UPDATE", (user_id,))
|
||||
row = cur.fetchone()
|
||||
current = float(row['available_cash'] or 0) if row else 0
|
||||
new_cash = max(0, round(current + delta, 2))
|
||||
cur.execute("UPDATE users SET available_cash = %s WHERE id = %s", (new_cash, user_id))
|
||||
|
||||
conn.commit()
|
||||
resp = {'success': True, 'trade': trade}
|
||||
if new_cash is not None:
|
||||
resp['available_cash'] = new_cash
|
||||
return jsonify(resp)
|
||||
except Exception as e:
|
||||
return jsonify({'error': str(e)}), 400
|
||||
conn.rollback()
|
||||
return jsonify({'success': False, 'error': str(e)}), 400
|
||||
finally:
|
||||
put_db(conn)
|
||||
|
||||
|
||||
@bp.route('/trades/<int:trade_id>', methods=['PUT'])
|
||||
@login_required
|
||||
def update_trade(trade_id):
|
||||
"""更新交易记录"""
|
||||
"""更新交易记录（同一事务内回滚旧资金 + 应用新资金）"""
|
||||
conn = get_db()
|
||||
if not conn:
|
||||
return jsonify({'success': False, 'error': '数据库连接失败'}), 500
|
||||
try:
|
||||
user_id = get_current_user_id()
|
||||
data = request.get_json()
|
||||
|
||||
# 处理数值（空字符串转为None）
|
||||
def parse_float(val):
|
||||
if val is None or val == '':
|
||||
return None
|
||||
try:
|
||||
return round(float(val), 4)
|
||||
except:
|
||||
return None
|
||||
|
||||
def parse_int(val):
|
||||
if val is None or val == '':
|
||||
return None
|
||||
try:
|
||||
return int(val)
|
||||
except:
|
||||
return None
|
||||
|
||||
if 'price' in data:
|
||||
data['price'] = parse_float(data.get('price'))
|
||||
data['price'] = _parse_float(data.get('price'))
|
||||
if 'quantity' in data:
|
||||
data['quantity'] = parse_int(data.get('quantity'))
|
||||
data['quantity'] = _parse_int(data.get('quantity'))
|
||||
if 'profit_amount' in data:
|
||||
data['profit_amount'] = parse_float(data.get('profit_amount'))
|
||||
data['profit_amount'] = _parse_float(data.get('profit_amount'))
|
||||
if 'stop_loss_price' in data:
|
||||
data['stop_loss_price'] = parse_float(data.get('stop_loss_price'))
|
||||
|
||||
old_trade = db_get_trade(user_id, trade_id)
|
||||
data['stop_loss_price'] = _parse_float(data.get('stop_loss_price'))
|
||||
|
||||
cur = conn.cursor(cursor_factory=RealDictCursor)
|
||||
|
||||
cur.execute("""
|
||||
SELECT id, stock_code, stock_name, trade_type, price, quantity,
|
||||
trade_date::text, reason, result, profit_amount, stop_loss_price, notes
|
||||
FROM trades WHERE id = %s AND user_id = %s
|
||||
""", (trade_id, user_id))
|
||||
old_trade = cur.fetchone()
|
||||
if not old_trade:
|
||||
put_db(conn)
|
||||
return jsonify({'error': '交易记录不存在'}), 404
|
||||
|
||||
trade, error = db_update_trade(user_id, trade_id, data)
|
||||
if error:
|
||||
return jsonify({'success': False, 'error': error}), 400
|
||||
|
||||
cur.execute("""
|
||||
UPDATE trades SET
|
||||
stock_code = COALESCE(%s, stock_code), stock_name = COALESCE(%s, stock_name),
|
||||
trade_type = COALESCE(%s, trade_type), price = COALESCE(%s, price),
|
||||
quantity = COALESCE(%s, quantity), trade_date = COALESCE(%s, trade_date),
|
||||
reason = COALESCE(%s, reason), result = COALESCE(%s, result),
|
||||
profit_amount = COALESCE(%s, profit_amount),
|
||||
stop_loss_price = COALESCE(%s, stop_loss_price), notes = COALESCE(%s, notes)
|
||||
WHERE id = %s AND user_id = %s
|
||||
RETURNING id, stock_code, stock_name, trade_type, price, quantity,
|
||||
trade_date::text, reason, result, profit_amount, stop_loss_price,
|
||||
notes, created_at::text
|
||||
""", (data.get('stock_code'), data.get('stock_name'), data.get('trade_type'),
|
||||
data.get('price'), data.get('quantity'), data.get('trade_date'),
|
||||
data.get('reason'), data.get('result'), data.get('profit_amount'),
|
||||
data.get('stop_loss_price'), data.get('notes'), trade_id, user_id))
|
||||
trade = cur.fetchone()
|
||||
if not trade:
|
||||
conn.rollback()
|
||||
put_db(conn)
|
||||
return jsonify({'error': '交易记录不存在'}), 404
|
||||
|
||||
# 根据修改同步调整可用资金：先回滚旧交易，再应用新交易
|
||||
def trade_amount(t):
|
||||
p, q = (t.get('price') or 0), (t.get('quantity') or 0)
|
||||
return round(float(p) * int(q), 2) if p and q else 0
|
||||
old_amt = trade_amount(old_trade)
|
||||
new_amt = trade_amount(trade)
|
||||
old_type = (old_trade.get('trade_type') or '').lower()
|
||||
new_type = (trade.get('trade_type') or '').lower()
|
||||
delta = 0
|
||||
if old_type == 'buy':
|
||||
delta += old_amt
|
||||
elif old_type == 'sell':
|
||||
delta -= old_amt
|
||||
if new_type == 'buy':
|
||||
delta -= new_amt
|
||||
elif new_type == 'sell':
|
||||
delta += new_amt
|
||||
|
||||
old_delta = _calc_cash_delta(old_trade.get('trade_type'), old_trade.get('price'), old_trade.get('quantity'))
|
||||
new_delta = _calc_cash_delta(trade.get('trade_type'), trade.get('price'), trade.get('quantity'))
|
||||
delta = new_delta - old_delta
|
||||
new_cash = None
|
||||
if delta != 0:
|
||||
current = db_get_available_cash(user_id)
|
||||
cur.execute("SELECT available_cash FROM users WHERE id = %s FOR UPDATE", (user_id,))
|
||||
row = cur.fetchone()
|
||||
current = float(row['available_cash'] or 0) if row else 0
|
||||
new_cash = max(0, round(current + delta, 2))
|
||||
ok, _ = db_update_available_cash(user_id, new_cash)
|
||||
if ok:
|
||||
return jsonify({'success': True, 'trade': trade, 'available_cash': new_cash})
|
||||
|
||||
return jsonify({'success': True, 'trade': trade})
|
||||
cur.execute("UPDATE users SET available_cash = %s WHERE id = %s", (new_cash, user_id))
|
||||
|
||||
conn.commit()
|
||||
resp = {'success': True, 'trade': trade}
|
||||
if new_cash is not None:
|
||||
resp['available_cash'] = new_cash
|
||||
return jsonify(resp)
|
||||
except Exception as e:
|
||||
return jsonify({'error': str(e)}), 400
|
||||
conn.rollback()
|
||||
return jsonify({'success': False, 'error': str(e)}), 400
|
||||
finally:
|
||||
put_db(conn)
|
||||
|
||||
|
||||
@bp.route('/trades/<int:trade_id>', methods=['DELETE'])
|
||||
@login_required
|
||||
def delete_trade(trade_id):
|
||||
"""删除交易记录"""
|
||||
user_id = get_current_user_id()
|
||||
old_trade = db_get_trade(user_id, trade_id)
|
||||
if not old_trade:
|
||||
return jsonify({'success': False, 'error': '交易记录不存在'}), 404
|
||||
success = db_delete_trade(user_id, trade_id)
|
||||
if not success:
|
||||
return jsonify({'success': False}), 400
|
||||
# 回滚该交易对可用资金的影响
|
||||
t_type = (old_trade.get('trade_type') or '').lower()
|
||||
amount = round(float(old_trade.get('price') or 0) * int(old_trade.get('quantity') or 0), 2)
|
||||
delta = amount if t_type == 'buy' else -amount
|
||||
if delta != 0:
|
||||
current = db_get_available_cash(user_id)
|
||||
new_cash = max(0, round(current + delta, 2))
|
||||
db_update_available_cash(user_id, new_cash)
|
||||
return jsonify({'success': True, 'available_cash': new_cash})
|
||||
return jsonify({'success': True})
|
||||
"""删除交易记录（同一事务内删除 + 回滚资金）"""
|
||||
conn = get_db()
|
||||
if not conn:
|
||||
return jsonify({'success': False, 'error': '数据库连接失败'}), 500
|
||||
try:
|
||||
user_id = get_current_user_id()
|
||||
cur = conn.cursor(cursor_factory=RealDictCursor)
|
||||
|
||||
cur.execute("""
|
||||
SELECT trade_type, price, quantity FROM trades WHERE id = %s AND user_id = %s
|
||||
""", (trade_id, user_id))
|
||||
old_trade = cur.fetchone()
|
||||
if not old_trade:
|
||||
put_db(conn)
|
||||
return jsonify({'success': False, 'error': '交易记录不存在'}), 404
|
||||
|
||||
cur.execute("DELETE FROM trades WHERE id = %s AND user_id = %s", (trade_id, user_id))
|
||||
|
||||
delta = -_calc_cash_delta(old_trade.get('trade_type'), old_trade.get('price'), old_trade.get('quantity'))
|
||||
new_cash = None
|
||||
if delta != 0:
|
||||
cur.execute("SELECT available_cash FROM users WHERE id = %s FOR UPDATE", (user_id,))
|
||||
row = cur.fetchone()
|
||||
current = float(row['available_cash'] or 0) if row else 0
|
||||
new_cash = max(0, round(current + delta, 2))
|
||||
cur.execute("UPDATE users SET available_cash = %s WHERE id = %s", (new_cash, user_id))
|
||||
|
||||
conn.commit()
|
||||
resp = {'success': True}
|
||||
if new_cash is not None:
|
||||
resp['available_cash'] = new_cash
|
||||
return jsonify(resp)
|
||||
except Exception as e:
|
||||
conn.rollback()
|
||||
return jsonify({'success': False, 'error': str(e)}), 400
|
||||
finally:
|
||||
put_db(conn)
|
||||
|
||||
|
||||
@bp.route('/available_cash', methods=['GET'])
|
||||
|
||||
@@ -5,9 +5,10 @@
|
||||
|
||||
import requests
|
||||
import json
|
||||
from config import Config
|
||||
|
||||
# API配置
|
||||
API_KEY = "9fd8383f-5776-4366-855d-c6f40e867940"
|
||||
API_KEY = Config.DOUBAO_API_KEY or "9fd8383f-5776-4366-855d-c6f40e867940"
|
||||
API_URL = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
|
||||
MODEL = "doubao-seed-1-6-251015"
|
||||
|
||||
|
||||
@@ -0,0 +1,545 @@
|
||||
"""
|
||||
外部因素分析模块（P2/P3/P4/P7）
|
||||
|
||||
包含：
|
||||
- P2: 北向资金（外资动向）
|
||||
- P3: 美股隔夜板块变化
|
||||
- P4: 大宗商品价格
|
||||
- P7: 汇率变化
|
||||
|
||||
数据源：AKShare（开源免费）
|
||||
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。
|
||||
"""
|
||||
import logging
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 缓存（当日有效）
|
||||
_cache = {}
|
||||
_cache_date = {}
|
||||
|
||||
|
||||
def _get_cache(key):
|
||||
"""获取当日缓存"""
|
||||
today = datetime.now().strftime('%Y-%m-%d')
|
||||
if _cache_date.get(key) == today:
|
||||
return _cache.get(key)
|
||||
return None
|
||||
|
||||
|
||||
def _set_cache(key, value):
|
||||
"""设置当日缓存"""
|
||||
today = datetime.now().strftime('%Y-%m-%d')
|
||||
_cache[key] = value
|
||||
_cache_date[key] = today
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════
|
||||
# P2: 北向资金
|
||||
# ═══════════════════════════════════════════════
|
||||
|
||||
def get_northbound_capital():
|
||||
"""
|
||||
获取北向资金净流入数据
|
||||
|
||||
返回:
|
||||
dict: {
|
||||
'net_inflow': float, # 今日净流入（亿）
|
||||
'score': int, # 评分增减（-10 ~ +10）
|
||||
'summary': str, # 白话总结
|
||||
'reasons': list, # 评分原因
|
||||
}
|
||||
"""
|
||||
cached = _get_cache('northbound')
|
||||
if cached:
|
||||
return cached
|
||||
|
||||
try:
|
||||
import akshare as ak
|
||||
|
||||
# 获取北向资金净流入数据
|
||||
df = ak.stock_hsgt_north_net_flow_in_em(symbol="北向")
|
||||
if df is None or df.empty:
|
||||
return _neutral_result('北向资金数据为空')
|
||||
|
||||
# 取最近5个交易日
|
||||
recent = df.tail(5)
|
||||
today_inflow = float(recent.iloc[-1].get('当日净流入', 0) or 0)
|
||||
|
||||
# 连续流入/流出天数
|
||||
consecutive_inflow = 0
|
||||
consecutive_outflow = 0
|
||||
for _, row in recent[::-1].iterrows():
|
||||
val = float(row.get('当日净流入', 0) or 0)
|
||||
if val > 0:
|
||||
if consecutive_outflow > 0:
|
||||
break
|
||||
consecutive_inflow += 1
|
||||
elif val < 0:
|
||||
if consecutive_inflow > 0:
|
||||
break
|
||||
consecutive_outflow += 1
|
||||
|
||||
# 评分
|
||||
score = 0
|
||||
reasons = []
|
||||
summary_parts = []
|
||||
|
||||
if today_inflow > 50:
|
||||
score += 5
|
||||
reasons.append(f'北向今日净流入{today_inflow:.1f}亿(+5)')
|
||||
summary_parts.append(f'外资今日大幅买入{today_inflow:.1f}亿元')
|
||||
elif today_inflow > 20:
|
||||
score += 3
|
||||
reasons.append(f'北向今日净流入{today_inflow:.1f}亿(+3)')
|
||||
summary_parts.append(f'外资今日净流入{today_inflow:.1f}亿元')
|
||||
elif today_inflow < -50:
|
||||
score -= 5
|
||||
reasons.append(f'北向今日净流出{abs(today_inflow):.1f}亿(-5)')
|
||||
summary_parts.append(f'外资今日大幅卖出{abs(today_inflow):.1f}亿元')
|
||||
elif today_inflow < -20:
|
||||
score -= 3
|
||||
reasons.append(f'北向今日净流出{abs(today_inflow):.1f}亿(-3)')
|
||||
summary_parts.append(f'外资今日净流出{abs(today_inflow):.1f}亿元')
|
||||
else:
|
||||
summary_parts.append(f'外资今日净流入{today_inflow:.1f}亿元，方向不明')
|
||||
|
||||
if consecutive_inflow >= 3:
|
||||
score += 3
|
||||
reasons.append(f'北向连续{consecutive_inflow}日净流入(+3)')
|
||||
summary_parts.append(f'已连续{consecutive_inflow}天买入')
|
||||
|
||||
if consecutive_outflow >= 3:
|
||||
score -= 3
|
||||
reasons.append(f'北向连续{consecutive_outflow}日净流出(-3)')
|
||||
summary_parts.append(f'已连续{consecutive_outflow}天卖出')
|
||||
|
||||
score = max(-10, min(10, score))
|
||||
|
||||
result = {
|
||||
'net_inflow': round(today_inflow, 2),
|
||||
'consecutive_inflow': consecutive_inflow,
|
||||
'consecutive_outflow': consecutive_outflow,
|
||||
'score': score,
|
||||
'summary': '，'.join(summary_parts),
|
||||
'reasons': reasons,
|
||||
}
|
||||
_set_cache('northbound', result)
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"获取北向资金数据失败: {e}")
|
||||
return _neutral_result('北向资金数据获取失败')
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════
|
||||
# P3: 美股隔夜板块变化
|
||||
# ═══════════════════════════════════════════════
|
||||
|
||||
# 美股板块 → A股板块映射
|
||||
US_A_SECTOR_MAP = {
|
||||
'科技': ['半导体', '软件', '消费电子', '芯片', 'IT服务'],
|
||||
'新能源车': ['锂电池', '汽车零部件', '新能源车', '充电桩'],
|
||||
'金融': ['银行', '保险', '券商'],
|
||||
'能源': ['石油开采', '化工', '页岩气'],
|
||||
'医药': ['创新药', '医疗器械', '生物制品', 'CXO'],
|
||||
'消费': ['白酒', '食品饮料', '零售', '免税'],
|
||||
'房地产': ['房地产', '建材', '家居'],
|
||||
'工业': ['机械', '军工', '工业4.0'],
|
||||
'材料': ['有色金属', '钢铁', '化工新材料'],
|
||||
'公用事业': ['电力', '环保', '水务'],
|
||||
}
|
||||
|
||||
|
||||
def get_us_market_overview():
|
||||
"""
|
||||
获取美股隔夜收盘数据，计算外盘情绪
|
||||
|
||||
返回:
|
||||
dict: {
|
||||
'indices': dict, # 三大指数涨跌
|
||||
'sectors': dict, # 主要板块涨跌
|
||||
'score': int, # 评分增减（-10 ~ +10）
|
||||
'summary': str, # 白话总结
|
||||
'reasons': list, # 评分原因
|
||||
'affected_a_sectors': dict, # 对A股板块的影响
|
||||
}
|
||||
"""
|
||||
cached = _get_cache('us_market')
|
||||
if cached:
|
||||
return cached
|
||||
|
||||
try:
|
||||
import akshare as ak
|
||||
|
||||
# 获取全球主要指数
|
||||
df = ak.index_global()
|
||||
if df is None or df.empty:
|
||||
return _neutral_result('美股指数数据为空')
|
||||
|
||||
# 筛选美股主要指数
|
||||
us_indices = {}
|
||||
for _, row in df.iterrows():
|
||||
name = str(row.get('名称', ''))
|
||||
if '纳斯达克' in name:
|
||||
us_indices['nasdaq'] = {
|
||||
'name': name,
|
||||
'change_pct': float(row.get('涨跌幅', 0) or 0),
|
||||
}
|
||||
elif '道琼斯' in name:
|
||||
us_indices['dow'] = {
|
||||
'name': name,
|
||||
'change_pct': float(row.get('涨跌幅', 0) or 0),
|
||||
}
|
||||
elif '标普500' in name:
|
||||
us_indices['sp500'] = {
|
||||
'name': name,
|
||||
'change_pct': float(row.get('涨跌幅', 0) or 0),
|
||||
}
|
||||
|
||||
if not us_indices:
|
||||
return _neutral_result('未找到美股指数')
|
||||
|
||||
# 计算综合涨跌
|
||||
avg_change = sum(v['change_pct'] for v in us_indices.values()) / len(us_indices)
|
||||
|
||||
# 评分
|
||||
score = 0
|
||||
reasons = []
|
||||
summary_parts = []
|
||||
|
||||
if avg_change > 2:
|
||||
score += 5
|
||||
reasons.append(f'美股三大指数平均涨幅{avg_change:.1f}%(+5)')
|
||||
summary_parts.append(f'美股大涨，平均涨幅{avg_change:.1f}%')
|
||||
elif avg_change > 0.5:
|
||||
score += 2
|
||||
reasons.append(f'美股偏强，平均涨幅{avg_change:.1f}%(+2)')
|
||||
summary_parts.append(f'美股小幅上涨，平均{avg_change:.1f}%')
|
||||
elif avg_change < -2:
|
||||
score -= 5
|
||||
reasons.append(f'美股三大指数平均跌幅{abs(avg_change):.1f}%(-5)')
|
||||
summary_parts.append(f'美股大跌，平均跌幅{abs(avg_change):.1f}%')
|
||||
elif avg_change < -0.5:
|
||||
score -= 2
|
||||
reasons.append(f'美股偏弱，平均跌幅{abs(avg_change):.1f}%(-2)')
|
||||
summary_parts.append(f'美股小幅下跌，平均{avg_change:.1f}%')
|
||||
else:
|
||||
summary_parts.append(f'美股基本平盘，平均变化{avg_change:.1f}%')
|
||||
|
||||
# 对A股板块的影响
|
||||
affected_sectors = {}
|
||||
if avg_change > 1:
|
||||
for us_sector, a_sectors in US_A_SECTOR_MAP.items():
|
||||
affected_sectors[us_sector] = {
|
||||
'a_sectors': a_sectors,
|
||||
'direction': '利好',
|
||||
'note': f'美股{us_sector}板块偏强，A股{"、".join(a_sectors[:3])}可能高开',
|
||||
}
|
||||
elif avg_change < -1:
|
||||
for us_sector, a_sectors in US_A_SECTOR_MAP.items():
|
||||
affected_sectors[us_sector] = {
|
||||
'a_sectors': a_sectors,
|
||||
'direction': '利空',
|
||||
'note': f'美股{us_sector}板块偏弱，A股{"、".join(a_sectors[:3])}可能低开',
|
||||
}
|
||||
|
||||
score = max(-10, min(10, score))
|
||||
|
||||
result = {
|
||||
'indices': us_indices,
|
||||
'avg_change': round(avg_change, 2),
|
||||
'score': score,
|
||||
'summary': '，'.join(summary_parts),
|
||||
'reasons': reasons,
|
||||
'affected_a_sectors': affected_sectors,
|
||||
}
|
||||
_set_cache('us_market', result)
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"获取美股数据失败: {e}")
|
||||
return _neutral_result('美股数据获取失败')
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════
|
||||
# P4: 大宗商品价格
|
||||
# ═══════════════════════════════════════════════
|
||||
|
||||
# 大宗商品 → A股板块影响映射
|
||||
COMMODITY_A_SECTOR_MAP = {
|
||||
'原油': {
|
||||
'beneficiary': ['石油开采', '油服', '化工'],
|
||||
'victim': ['航空', '物流', '化工下游'],
|
||||
'direction': '油价涨→开采受益，航空受损',
|
||||
},
|
||||
'黄金': {
|
||||
'beneficiary': ['黄金股', '珠宝', '有色'],
|
||||
'victim': [],
|
||||
'direction': '金价涨→黄金企业受益',
|
||||
},
|
||||
'铜': {
|
||||
'beneficiary': ['铜矿', '有色', '电缆'],
|
||||
'victim': [],
|
||||
'direction': '铜价涨→铜企受益',
|
||||
},
|
||||
'螺纹钢': {
|
||||
'beneficiary': ['钢铁', '钢矿'],
|
||||
'victim': ['基建', '地产'],
|
||||
'direction': '钢价涨→钢企受益，基建成本增',
|
||||
},
|
||||
'碳酸锂': {
|
||||
'beneficiary': ['锂矿', '锂电池'],
|
||||
'victim': ['新能源车'],
|
||||
'direction': '锂价涨→锂矿受益，新能源车成本增',
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def get_commodity_overview():
|
||||
"""
|
||||
获取主要大宗商品价格变化
|
||||
|
||||
返回:
|
||||
dict: {
|
||||
'commodities': dict, # 各商品涨跌
|
||||
'score': int, # 评分增减（-5 ~ +5）
|
||||
'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('commodity')
|
||||
if cached:
|
||||
return cached
|
||||
|
||||
try:
|
||||
import akshare as ak
|
||||
|
||||
# 获取国内商品期货行情
|
||||
df = ak.futures_main_sina()
|
||||
if df is None or df.empty:
|
||||
return _neutral_result('大宗商品数据为空')
|
||||
|
||||
# 关注的商品
|
||||
target_commodities = ['原油', '黄金', '铜', '螺纹钢', '碳酸锂']
|
||||
commodities = {}
|
||||
|
||||
for _, row in df.iterrows():
|
||||
symbol = str(row.get('symbol', ''))
|
||||
for target in target_commodities:
|
||||
if target in symbol:
|
||||
change = float(row.get('change', 0) or 0)
|
||||
pct = float(row.get('change_pct', 0) or 0)
|
||||
commodities[target] = {
|
||||
'symbol': symbol,
|
||||
'change_pct': round(pct, 2),
|
||||
}
|
||||
break
|
||||
|
||||
if not commodities:
|
||||
return _neutral_result('未找到关注的大宗商品')
|
||||
|
||||
# 评分和影响
|
||||
score = 0
|
||||
reasons = []
|
||||
summary_parts = []
|
||||
affected = {}
|
||||
|
||||
for name, data in commodities.items():
|
||||
pct = data['change_pct']
|
||||
if abs(pct) < 0.5:
|
||||
continue
|
||||
|
||||
mapping = COMMODITY_A_SECTOR_MAP.get(name)
|
||||
if not mapping:
|
||||
continue
|
||||
|
||||
if pct > 2:
|
||||
score += 1
|
||||
reasons.append(f'{name}涨{pct:.1f}%，利好{"、".join(mapping["beneficiary"][:2])}(+1)')
|
||||
summary_parts.append(f'{name}大涨{pct:.1f}%')
|
||||
affected[name] = {
|
||||
'direction': '利好',
|
||||
'beneficiary': mapping['beneficiary'],
|
||||
'victim': mapping['victim'],
|
||||
'note': mapping['direction'],
|
||||
}
|
||||
elif pct < -2:
|
||||
score -= 1
|
||||
reasons.append(f'{name}跌{abs(pct):.1f}%，利空{"、".join(mapping["beneficiary"][:2])}(-1)')
|
||||
summary_parts.append(f'{name}大跌{pct:.1f}%')
|
||||
affected[name] = {
|
||||
'direction': '利空',
|
||||
'beneficiary': mapping['victim'],
|
||||
'victim': mapping['beneficiary'],
|
||||
'note': mapping['direction'],
|
||||
}
|
||||
|
||||
score = max(-5, min(5, score))
|
||||
|
||||
result = {
|
||||
'commodities': commodities,
|
||||
'score': score,
|
||||
'summary': '，'.join(summary_parts) if summary_parts else '大宗商品整体平稳',
|
||||
'reasons': reasons,
|
||||
'affected_sectors': affected,
|
||||
}
|
||||
_set_cache('commodity', result)
|
||||
return result
|
||||
|
||||
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,
|
||||
}
|
||||
|
||||
@@ -1530,3 +1530,352 @@
|
||||
border-radius: 8px;
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
/* ============ 个股深析页面 ============ */
|
||||
.deep-analysis-page { padding: 0 4px; box-sizing: border-box; max-width: 100%; overflow-x: hidden; }
|
||||
|
||||
.deep-input-card {
|
||||
background: var(--card-bg, #1e1e2e);
|
||||
border-radius: 12px;
|
||||
padding: 12px;
|
||||
margin-bottom: 12px;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
.deep-input-row {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
width: 100%;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
.deep-code-input {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
padding: 10px 12px;
|
||||
border: 1px solid rgba(255,255,255,0.15);
|
||||
border-radius: 8px;
|
||||
background: rgba(0,0,0,0.2);
|
||||
color: #fff;
|
||||
font-size: 15px;
|
||||
letter-spacing: 1px;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
.deep-code-input::placeholder { color: rgba(255,255,255,0.3); }
|
||||
.deep-analyze-btn {
|
||||
padding: 10px 16px;
|
||||
border: none;
|
||||
border-radius: 8px;
|
||||
background: #2196F3;
|
||||
color: #fff;
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
cursor: pointer;
|
||||
white-space: nowrap;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.deep-analyze-btn:disabled { opacity: 0.5; }
|
||||
|
||||
.deep-report { display: flex; flex-direction: column; gap: 10px; max-width: 100%; overflow-x: hidden; }
|
||||
|
||||
.deep-header-card {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
background: var(--card-bg, #1e1e2e);
|
||||
border-radius: 12px;
|
||||
padding: 14px 16px;
|
||||
}
|
||||
.deep-stock-name { font-size: 18px; font-weight: 700; color: #fff; }
|
||||
.deep-stock-code { font-size: 12px; color: rgba(255,255,255,0.5); margin-top: 2px; }
|
||||
.deep-price { font-size: 22px; font-weight: 700; color: #fff; text-align: center; }
|
||||
.deep-change { font-size: 14px; text-align: center; margin-top: 2px; }
|
||||
.deep-change.up { color: #f44336; }
|
||||
.deep-change.down { color: #4caf50; }
|
||||
|
||||
.deep-score-circle {
|
||||
width: 50px; height: 50px; border-radius: 50%;
|
||||
display: flex; align-items: center; justify-content: center;
|
||||
margin: 0 auto;
|
||||
font-weight: 700;
|
||||
}
|
||||
.score-num { font-size: 20px; color: #fff; }
|
||||
.deep-score-circle.score-high { background: linear-gradient(135deg, #f44336, #ff5722); }
|
||||
.deep-score-circle.score-mid { background: linear-gradient(135deg, #ff9800, #ffc107); }
|
||||
.deep-score-circle.score-low { background: linear-gradient(135deg, #607d8b, #78909c); }
|
||||
.deep-verdict { text-align: center; font-size: 12px; color: rgba(255,255,255,0.6); margin-top: 4px; }
|
||||
|
||||
.deep-section {
|
||||
background: var(--card-bg, #1e1e2e);
|
||||
border-radius: 12px;
|
||||
padding: 12px 14px;
|
||||
}
|
||||
.deep-section-title {
|
||||
font-size: 13px;
|
||||
font-weight: 600;
|
||||
color: rgba(255,255,255,0.5);
|
||||
margin-bottom: 8px;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 1px;
|
||||
}
|
||||
|
||||
.deep-recommend-bar {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
padding: 10px 14px;
|
||||
border-radius: 8px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
.deep-recommend-bar.rec-buy { background: rgba(244,67,54,0.15); }
|
||||
.deep-recommend-bar.rec-watch { background: rgba(33,150,243,0.12); }
|
||||
.deep-recommend-bar.rec-sell { background: rgba(76,175,80,0.15); }
|
||||
.rec-display {
|
||||
font-size: 16px; font-weight: 700; color: #fff;
|
||||
background: rgba(255,255,255,0.1);
|
||||
padding: 2px 10px; border-radius: 4px;
|
||||
}
|
||||
.rec-rate { font-size: 14px; color: rgba(255,255,255,0.6); }
|
||||
.rec-reason-text { font-size: 13px; color: rgba(255,255,255,0.7); }
|
||||
|
||||
.deep-signal-list { display: flex; flex-direction: column; gap: 6px; }
|
||||
.deep-signal-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
padding: 6px 10px;
|
||||
background: rgba(255,255,255,0.04);
|
||||
border-radius: 6px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
.deep-sig-name { font-weight: 600; color: #ff9800; font-size: 13px; }
|
||||
.deep-sig-strength {
|
||||
font-size: 11px;
|
||||
background: rgba(255,152,0,0.2);
|
||||
color: #ffb74d;
|
||||
padding: 1px 6px;
|
||||
border-radius: 4px;
|
||||
}
|
||||
.deep-sig-desc { font-size: 12px; color: rgba(255,255,255,0.5); }
|
||||
|
||||
.deep-position-grid { display: flex; flex-direction: column; gap: 8px; }
|
||||
.deep-pos-item {
|
||||
display: grid;
|
||||
grid-template-columns: 36px 1fr 1fr 80px;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
font-size: 12px;
|
||||
}
|
||||
.pos-label { font-weight: 600; color: rgba(255,255,255,0.5); }
|
||||
.pos-range { color: rgba(255,255,255,0.4); font-size: 11px; }
|
||||
.pos-bar-wrap { display: flex; align-items: center; gap: 4px; }
|
||||
.pos-bar-bg { flex: 1; height: 6px; background: rgba(255,255,255,0.08); border-radius: 3px; overflow: hidden; }
|
||||
.pos-bar-fill { height: 100%; border-radius: 3px; transition: width 0.5s; }
|
||||
.pos-bar-fill.high { background: #f44336; }
|
||||
.pos-bar-fill.mid { background: #ff9800; }
|
||||
.pos-bar-fill.low { background: #4caf50; }
|
||||
.pos-pct { font-size: 11px; color: rgba(255,255,255,0.5); min-width: 28px; }
|
||||
.pos-space { display: flex; gap: 6px; font-size: 11px; }
|
||||
.space-up { color: #f44336; }
|
||||
.space-down { color: #4caf50; }
|
||||
|
||||
.deep-sr-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 10px; }
|
||||
.sr-col-title { font-size: 12px; font-weight: 600; margin-bottom: 6px; padding-bottom: 4px; border-bottom: 1px solid rgba(255,255,255,0.08); }
|
||||
.support-title { color: #4caf50; }
|
||||
.resist-title { color: #f44336; }
|
||||
.sr-item {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
padding: 4px 0;
|
||||
font-size: 13px;
|
||||
}
|
||||
.sr-item.support .sr-level { color: #4caf50; font-weight: 600; }
|
||||
.sr-item.resist .sr-level { color: #f44336; font-weight: 600; }
|
||||
.sr-name { color: rgba(255,255,255,0.5); }
|
||||
.sr-empty { color: rgba(255,255,255,0.2); font-size: 12px; text-align: center; padding: 8px; }
|
||||
|
||||
.deep-vol-info { display: flex; align-items: center; gap: 8px; margin-bottom: 6px; }
|
||||
.vol-tag {
|
||||
font-size: 13px; font-weight: 600;
|
||||
padding: 2px 10px; border-radius: 4px;
|
||||
background: rgba(255,255,255,0.08);
|
||||
color: rgba(255,255,255,0.7);
|
||||
}
|
||||
.vol-tag.vol-up { background: rgba(244,67,54,0.15); color: #f44336; }
|
||||
.vol-tag.vol-dn { background: rgba(76,175,80,0.15); color: #4caf50; }
|
||||
.vol-detail { font-size: 12px; color: rgba(255,255,255,0.4); }
|
||||
|
||||
.deep-patterns { display: flex; flex-wrap: wrap; gap: 6px; }
|
||||
.pattern-tag {
|
||||
font-size: 12px;
|
||||
padding: 4px 10px;
|
||||
border-radius: 6px;
|
||||
background: rgba(255,255,255,0.06);
|
||||
color: rgba(255,255,255,0.6);
|
||||
}
|
||||
.pattern-tag.bullish { background: rgba(244,67,54,0.12); color: #ef9a9a; }
|
||||
.pattern-tag.bearish { background: rgba(76,175,80,0.12); color: #a5d6a7; }
|
||||
.pattern-tag small { opacity: 0.7; }
|
||||
|
||||
.deep-ma-info {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
flex-wrap: wrap;
|
||||
margin-bottom: 6px;
|
||||
}
|
||||
.ma-trend-tag {
|
||||
font-size: 12px; font-weight: 600;
|
||||
padding: 2px 8px; border-radius: 4px;
|
||||
}
|
||||
.ma-trend-tag.bullish { background: rgba(244,67,54,0.15); color: #f44336; }
|
||||
.ma-trend-tag.bearish { background: rgba(76,175,80,0.15); color: #4caf50; }
|
||||
.ma-trend-tag.mixed { background: rgba(255,152,0,0.15); color: #ff9800; }
|
||||
.ma-val { font-size: 12px; color: rgba(255,255,255,0.4); }
|
||||
|
||||
.deep-fundamental {
|
||||
display: flex;
|
||||
gap: 12px;
|
||||
font-size: 12px;
|
||||
color: rgba(255,255,255,0.5);
|
||||
}
|
||||
|
||||
.deep-score-details { display: flex; flex-wrap: wrap; gap: 6px; }
|
||||
.score-reason-tag {
|
||||
font-size: 12px;
|
||||
padding: 3px 8px;
|
||||
border-radius: 4px;
|
||||
}
|
||||
.score-reason-tag.positive { background: rgba(244,67,54,0.12); color: #ef9a9a; }
|
||||
.score-reason-tag.negative { background: rgba(76,175,80,0.12); color: #a5d6a7; }
|
||||
|
||||
/* AI通俗解说 */
|
||||
.deep-ai-summary {
|
||||
background: linear-gradient(135deg, rgba(33,150,243,0.08), rgba(156,39,176,0.06));
|
||||
border-radius: 12px;
|
||||
padding: 14px 16px;
|
||||
border-left: 3px solid #2196F3;
|
||||
}
|
||||
.deep-ai-title {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
color: #64b5f6;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
.deep-ai-text {
|
||||
font-size: 14px;
|
||||
line-height: 1.75;
|
||||
color: rgba(255,255,255,0.85);
|
||||
}
|
||||
.deep-ai-text p {
|
||||
margin: 0 0 8px 0;
|
||||
}
|
||||
.deep-ai-text p:last-child { margin-bottom: 0; }
|
||||
.ai-action-tip {
|
||||
margin-top: 10px;
|
||||
padding: 10px 12px;
|
||||
background: rgba(255,255,255,0.04);
|
||||
border-radius: 8px;
|
||||
font-size: 13px;
|
||||
color: rgba(255,255,255,0.7);
|
||||
}
|
||||
.ai-action-label {
|
||||
font-weight: 600;
|
||||
color: #ff9800;
|
||||
margin-right: 4px;
|
||||
}
|
||||
|
||||
.deep-section { box-sizing: border-box; overflow: hidden; }
|
||||
|
||||
@media (max-width: 480px) {
|
||||
.deep-pos-item { grid-template-columns: 32px 1fr 80px; }
|
||||
.pos-range { display: none; }
|
||||
.deep-header-card { flex-wrap: wrap; gap: 8px; }
|
||||
.deep-section { padding: 10px 12px; }
|
||||
.deep-recommend-bar { padding: 8px 10px; }
|
||||
.deep-sr-grid { grid-template-columns: 1fr; gap: 8px; }
|
||||
.deep-ma-info { gap: 4px; }
|
||||
.ma-val { font-size: 11px; }
|
||||
}
|
||||
|
||||
/* ============ 买入分析页面 ============ */
|
||||
.buy-analysis-page { padding: 0 4px; }
|
||||
.buy-analysis-header {
|
||||
background: var(--card-bg, #1e1e2e);
|
||||
border-radius: 12px;
|
||||
padding: 14px;
|
||||
margin-bottom: 12px;
|
||||
text-align: center;
|
||||
}
|
||||
.buy-analysis-title {
|
||||
font-size: 16px;
|
||||
font-weight: 700;
|
||||
color: #fff;
|
||||
}
|
||||
.buy-analysis-desc {
|
||||
font-size: 12px;
|
||||
color: rgba(255,255,255,0.4);
|
||||
margin-top: 4px;
|
||||
}
|
||||
.buy-analysis-list { display: flex; flex-direction: column; gap: 8px; }
|
||||
.buy-analysis-card {
|
||||
background: var(--card-bg, #1e1e2e);
|
||||
border-radius: 12px;
|
||||
overflow: hidden;
|
||||
}
|
||||
.buy-card-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
padding: 12px 14px;
|
||||
cursor: pointer;
|
||||
gap: 8px;
|
||||
}
|
||||
.buy-card-header:active { background: rgba(255,255,255,0.03); }
|
||||
.buy-card-left { flex: 1; min-width: 0; }
|
||||
.buy-card-name { font-size: 14px; font-weight: 600; color: #fff; }
|
||||
.buy-card-code { font-size: 11px; color: rgba(255,255,255,0.4); margin-left: 6px; }
|
||||
.buy-card-center { text-align: center; min-width: 70px; }
|
||||
.buy-card-price { font-size: 14px; font-weight: 600; color: #fff; }
|
||||
.buy-card-change { font-size: 12px; display: block; }
|
||||
.buy-card-change.up { color: #f44336; }
|
||||
.buy-card-change.down { color: #4caf50; }
|
||||
.buy-card-right { text-align: center; min-width: 50px; }
|
||||
.buy-card-score {
|
||||
font-size: 16px;
|
||||
font-weight: 700;
|
||||
display: block;
|
||||
}
|
||||
.buy-card-score.score-high { color: #f44336; }
|
||||
.buy-card-score.score-mid { color: #ff9800; }
|
||||
.buy-card-score.score-low { color: #78909c; }
|
||||
.buy-card-verdict { font-size: 11px; color: rgba(255,255,255,0.5); }
|
||||
.buy-card-arrow {
|
||||
font-size: 14px;
|
||||
color: rgba(255,255,255,0.3);
|
||||
transition: transform 0.2s;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.buy-card-arrow.expanded { transform: rotate(90deg); }
|
||||
.buy-card-detail {
|
||||
padding: 0 14px 14px;
|
||||
border-top: 1px solid rgba(255,255,255,0.05);
|
||||
}
|
||||
.buy-detail-grid { display: flex; flex-direction: column; gap: 6px; margin-top: 8px; }
|
||||
.buy-detail-item {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: flex-start;
|
||||
font-size: 13px;
|
||||
gap: 8px;
|
||||
}
|
||||
.buy-detail-label {
|
||||
color: rgba(255,255,255,0.4);
|
||||
flex-shrink: 0;
|
||||
min-width: 60px;
|
||||
}
|
||||
.buy-detail-value {
|
||||
color: rgba(255,255,255,0.8);
|
||||
text-align: right;
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
@@ -92,10 +92,24 @@
|
||||
scanSummaryCollapsed: false,
|
||||
fullScanSignalDist: [],
|
||||
|
||||
// 找牛股
|
||||
bullStocksData: null, // { stages: {1:[...], 2:[...]}, summary: {}, stage_info: [...] }
|
||||
// 找牛股（保留兼容）
|
||||
bullStocksData: null,
|
||||
bullStocksLoading: false,
|
||||
bullActiveStage: 2, // 默认显示阶段2=龙抬头（最佳买点）
|
||||
bullActiveStage: 2,
|
||||
|
||||
// 个股深析
|
||||
deepCode: '',
|
||||
deepReport: null,
|
||||
deepLoading: false,
|
||||
|
||||
// 模型页子tab
|
||||
modelSubTab: 'system',
|
||||
|
||||
// 买入分析
|
||||
buyAnalysisList: [],
|
||||
buyAnalysisLoading: false,
|
||||
buyAnalysisProgress: 0,
|
||||
buyAnalysisTotal: 0,
|
||||
|
||||
// 交易记录页面
|
||||
trades: [],
|
||||
@@ -2239,7 +2253,7 @@
|
||||
params: holding ? { holdingStocks: holding } : {}
|
||||
});
|
||||
if (resp.data.success) {
|
||||
this.bullStocksData = resp.data; // { stages, summary, total, stage_info }
|
||||
this.bullStocksData = resp.data;
|
||||
} else {
|
||||
this.showToast(resp.data.error || '获取牛股数据失败', 'error');
|
||||
}
|
||||
@@ -2251,6 +2265,75 @@
|
||||
}
|
||||
},
|
||||
|
||||
async fetchDeepAnalysis() {
|
||||
const code = (this.deepCode || '').trim();
|
||||
if (!code || code.length < 6) {
|
||||
this.showToast('请输入6位股票代码', 'error');
|
||||
return;
|
||||
}
|
||||
this.deepLoading = true;
|
||||
this.deepReport = null;
|
||||
try {
|
||||
const resp = await axios.post('/api/deep_analyze', { stock_code: code });
|
||||
if (resp.data.success) {
|
||||
this.deepReport = resp.data.report;
|
||||
} else {
|
||||
this.showToast(resp.data.error || '分析失败', 'error');
|
||||
}
|
||||
} catch (err) {
|
||||
console.error('深度分析失败:', err);
|
||||
this.showToast('深度分析失败: ' + (err.response?.data?.error || err.message), 'error');
|
||||
} finally {
|
||||
this.deepLoading = false;
|
||||
}
|
||||
},
|
||||
|
||||
async fetchBuyAnalysis() {
|
||||
if (this.buyAnalysisLoading) return;
|
||||
this.buyAnalysisLoading = true;
|
||||
this.buyAnalysisList = [];
|
||||
this.buyAnalysisProgress = 0;
|
||||
try {
|
||||
const scanResp = await axios.get('/api/scan_results', {
|
||||
params: { per_page: 200, recommend_text: '买入' }
|
||||
});
|
||||
if (!scanResp.data.success) {
|
||||
this.showToast('获取买入推荐失败', 'error');
|
||||
return;
|
||||
}
|
||||
const buyStocks = scanResp.data.results || [];
|
||||
this.buyAnalysisTotal = buyStocks.length;
|
||||
if (buyStocks.length === 0) {
|
||||
this.showToast('当前无买入推荐股票', 'info');
|
||||
return;
|
||||
}
|
||||
const results = [];
|
||||
for (let i = 0; i < buyStocks.length; i++) {
|
||||
this.buyAnalysisProgress = i + 1;
|
||||
try {
|
||||
const resp = await axios.post('/api/deep_analyze', {
|
||||
stock_code: buyStocks[i].code,
|
||||
skip_llm: true
|
||||
});
|
||||
if (resp.data.success) {
|
||||
const report = resp.data.report;
|
||||
report._expanded = false;
|
||||
results.push(report);
|
||||
}
|
||||
} catch (e) {
|
||||
console.warn('分析失败:', buyStocks[i].code, e.message);
|
||||
}
|
||||
}
|
||||
results.sort((a, b) => b.deep_score - a.deep_score);
|
||||
this.buyAnalysisList = results;
|
||||
} catch (err) {
|
||||
console.error('买入分析失败:', err);
|
||||
this.showToast('买入分析失败: ' + (err.message || '未知错误'), 'error');
|
||||
} finally {
|
||||
this.buyAnalysisLoading = false;
|
||||
}
|
||||
},
|
||||
|
||||
getBullStageStocks(stageNum) {
|
||||
if (!this.bullStocksData || !this.bullStocksData.stages) return [];
|
||||
return this.bullStocksData.stages[String(stageNum)] || [];
|
||||
|
||||
@@ -260,11 +260,19 @@ def backfill_history(conn, max_days=30):
|
||||
if not flows:
|
||||
continue
|
||||
|
||||
# 获取当天收盘价
|
||||
# 获取当天收盘价和涨跌幅（通过前一日收盘价计算）
|
||||
cur.execute("""
|
||||
SELECT code, close, change_pct
|
||||
FROM stock_kline_daily
|
||||
WHERE trade_date = %s AND code = ANY(%s)
|
||||
SELECT k.code, k.close,
|
||||
CASE WHEN prev.close > 0
|
||||
THEN ROUND((k.close - prev.close) / prev.close * 100, 2)
|
||||
ELSE 0 END AS change_pct
|
||||
FROM stock_kline_daily k
|
||||
LEFT JOIN LATERAL (
|
||||
SELECT close FROM stock_kline_daily
|
||||
WHERE code = k.code AND trade_date < k.trade_date
|
||||
ORDER BY trade_date DESC LIMIT 1
|
||||
) prev ON true
|
||||
WHERE k.trade_date = %s AND k.code = ANY(%s)
|
||||
""", (d, list(flows.keys())))
|
||||
price_map = {r[0]: {'close': float(r[1] or 0), 'change_pct': float(r[2] or 0)}
|
||||
for r in cur.fetchall()}
|
||||
|
||||
@@ -54,9 +54,15 @@ def get_db_conn():
|
||||
|
||||
|
||||
def get_all_stock_codes(conn):
|
||||
"""获取所有股票代码"""
|
||||
"""获取可交易的股票列表（排除退市、停牌等无效股票）"""
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("SELECT code, name FROM stock_realtime_price ORDER BY code")
|
||||
cur.execute("""
|
||||
SELECT code, name FROM stock_realtime_price
|
||||
WHERE volume > 0 AND price > 0
|
||||
AND name NOT LIKE '%%退%%'
|
||||
AND name NOT LIKE 'PT%%'
|
||||
ORDER BY code
|
||||
""")
|
||||
return cur.fetchall()
|
||||
|
||||
|
||||
|
||||
+345
-97
@@ -24,7 +24,7 @@
|
||||
<link rel="stylesheet" href="/static/css/base.css?v=20260318v2">
|
||||
<link rel="stylesheet" href="/static/css/components.css?v=20260318v2">
|
||||
<link rel="stylesheet" href="/static/css/auth.css?v=20260318v1">
|
||||
<link rel="stylesheet" href="/static/css/pages.css?v=20260318v2">
|
||||
<link rel="stylesheet" href="/static/css/pages.css?v=20260511v3">
|
||||
<link rel="stylesheet" href="/static/css/scan.css?v=20260318v2">
|
||||
<link rel="stylesheet" href="/static/css/responsive.css?v=20260318v2">
|
||||
</head>
|
||||
@@ -668,11 +668,11 @@
|
||||
<button :class="['sub-tab', { active: analysisSubTab === 'scan' }]" @click="analysisSubTab = 'scan'; fetchFullScanResults(1)">
|
||||
全景扫描
|
||||
</button>
|
||||
<button :class="['sub-tab', { active: analysisSubTab === 'bull' }]" @click="analysisSubTab = 'bull'; fetchBullStocks()">
|
||||
找牛股
|
||||
<button :class="['sub-tab', { active: analysisSubTab === 'bull' }]" @click="analysisSubTab = 'bull'">
|
||||
个股深析
|
||||
</button>
|
||||
<button :class="['sub-tab', { active: analysisSubTab === 'sim' }]" @click="analysisSubTab = 'sim'; loadSimData()">
|
||||
模拟交易
|
||||
<button :class="['sub-tab', { active: analysisSubTab === 'buyanalysis' }]" @click="analysisSubTab = 'buyanalysis'; fetchBuyAnalysis()">
|
||||
买入分析
|
||||
</button>
|
||||
</div>
|
||||
|
||||
@@ -1100,114 +1100,272 @@
|
||||
|
||||
</div><!-- 全景扫描子页面结束 -->
|
||||
|
||||
<!-- 找牛股子页面 -->
|
||||
<div v-show="analysisSubTab === 'bull'" class="bull-stocks-page">
|
||||
<!-- 个股深析子页面 -->
|
||||
<div v-show="analysisSubTab === 'bull'" class="deep-analysis-page">
|
||||
|
||||
<!-- 牛股流程说明 -->
|
||||
<div class="bull-flow-card">
|
||||
<div class="bull-flow-title">标准牛股启动信号流程</div>
|
||||
<div class="bull-flow-steps">
|
||||
<span class="bull-step step-divergence" :class="{ active: bullActiveStage === 1 }" @click="bullActiveStage = 1">
|
||||
<span class="step-label">底部探测</span>
|
||||
<span class="step-action">关注</span>
|
||||
<span class="step-count" v-if="bullStocksData">{{ getBullStageStocks(1).length }}</span>
|
||||
</span>
|
||||
<span class="bull-flow-arrow">→</span>
|
||||
<span class="bull-step step-dragon" :class="{ active: bullActiveStage === 2 }" @click="bullActiveStage = 2">
|
||||
<span class="step-label">资金进场</span>
|
||||
<span class="step-action">买入</span>
|
||||
<span class="step-count" v-if="bullStocksData">{{ getBullStageStocks(2).length }}</span>
|
||||
</span>
|
||||
<span class="bull-flow-arrow">→</span>
|
||||
<span class="bull-step step-true-dragon" :class="{ active: bullActiveStage === 3 }" @click="bullActiveStage = 3">
|
||||
<span class="step-label">趋势确立</span>
|
||||
<span class="step-action">持有</span>
|
||||
<span class="step-count" v-if="bullStocksData">{{ getBullStageStocks(3).length }}</span>
|
||||
</span>
|
||||
<span class="bull-flow-arrow">→</span>
|
||||
<span class="bull-step step-main-wave" :class="{ active: bullActiveStage === 4 }" @click="bullActiveStage = 4">
|
||||
<span class="step-label">加速拉升</span>
|
||||
<span class="step-action">加仓</span>
|
||||
<span class="step-count" v-if="bullStocksData">{{ getBullStageStocks(4).length }}</span>
|
||||
</span>
|
||||
<span class="bull-flow-arrow">→</span>
|
||||
<span class="bull-step step-rebound" :class="{ active: bullActiveStage === 5 }" @click="bullActiveStage = 5">
|
||||
<span class="step-label">回调补涨</span>
|
||||
<span class="step-action">观察</span>
|
||||
<span class="step-count" v-if="bullStocksData">{{ getBullStageStocks(5).length }}</span>
|
||||
</span>
|
||||
</div>
|
||||
<div class="bull-flow-hint">
|
||||
<template v-if="bullStocksData">共 <b>{{ bullStocksData.total }}</b> 只有信号股票 · 数据来自最新全景扫描</template>
|
||||
<template v-else>点击各阶段查看对应股票列表 · 数据来自最新全景扫描</template>
|
||||
<!-- 输入区 -->
|
||||
<div class="deep-input-card">
|
||||
<div class="deep-input-row">
|
||||
<input v-model="deepCode" placeholder="输入股票代码，如 601609"
|
||||
class="deep-code-input" @keyup.enter="fetchDeepAnalysis()" maxlength="6">
|
||||
<button class="deep-analyze-btn" @click="fetchDeepAnalysis()" :disabled="deepLoading">
|
||||
{{ deepLoading ? '分析中...' : '深度分析' }}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 加载状态 -->
|
||||
<div v-if="bullStocksLoading" class="bull-loading">
|
||||
<span class="loading-dot"></span> 正在分析牛股阶段...
|
||||
<div v-if="deepLoading" class="bull-loading">
|
||||
<span class="loading-dot"></span> 正在进行深度分析...
|
||||
</div>
|
||||
|
||||
<!-- 阶段股票列表 -->
|
||||
<div v-if="bullStocksData && !bullStocksLoading" class="bull-stage-section">
|
||||
<div class="bull-stage-header">
|
||||
<template v-if="getBullStageInfo(bullActiveStage)">
|
||||
<span class="bull-stage-icon">{{ getBullStageInfo(bullActiveStage).icon }}</span>
|
||||
<span class="bull-stage-name">{{ getBullStageInfo(bullActiveStage).name }} — {{ getBullStageInfo(bullActiveStage).desc }}</span>
|
||||
</template>
|
||||
<span class="bull-stage-count">{{ getBullStageStocks(bullActiveStage).length }} 只</span>
|
||||
</div>
|
||||
<!-- 分析报告 -->
|
||||
<div v-if="deepReport && !deepLoading" class="deep-report">
|
||||
|
||||
<div v-if="getBullStageStocks(bullActiveStage).length === 0" class="bull-empty">
|
||||
当前阶段暂无符合条件的股票
|
||||
<!-- 头部：股票信息+综合评分 -->
|
||||
<div class="deep-header-card">
|
||||
<div class="deep-header-left">
|
||||
<div class="deep-stock-name">{{ deepReport.stock_name }}</div>
|
||||
<div class="deep-stock-code">{{ deepReport.stock_code }}</div>
|
||||
</div>
|
||||
<div class="deep-header-center" v-if="deepReport.realtime">
|
||||
<div class="deep-price">¥{{ deepReport.realtime.price.toFixed(2) }}</div>
|
||||
<div class="deep-change" :class="deepReport.realtime.change_pct >= 0 ? 'up' : 'down'">
|
||||
{{ deepReport.realtime.change_pct >= 0 ? '+' : '' }}{{ deepReport.realtime.change_pct.toFixed(2) }}%
|
||||
</div>
|
||||
</div>
|
||||
<div class="deep-header-right">
|
||||
<div class="deep-score-circle" :class="'score-' + (deepReport.deep_score >= 65 ? 'high' : deepReport.deep_score >= 50 ? 'mid' : 'low')">
|
||||
<span class="score-num">{{ deepReport.deep_score }}</span>
|
||||
</div>
|
||||
<div class="deep-verdict">{{ deepReport.verdict }}</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div v-else class="bull-stock-list">
|
||||
<div v-for="stock in getBullStageStocks(bullActiveStage)" :key="stock.code"
|
||||
class="bull-stock-card" :class="'stage-s' + bullActiveStage"
|
||||
@click="techSignalCode = stock.code; analysisSubTab = 'scan'; fetchTechSignals()">
|
||||
<div class="bull-card-left">
|
||||
<div class="bull-card-code">{{ stock.code }}</div>
|
||||
<div class="bull-card-name">{{ stock.name }}</div>
|
||||
|
||||
<!-- AI通俗解说 -->
|
||||
<div v-if="deepReport.ai_summary" class="deep-ai-summary">
|
||||
<div class="deep-ai-title">💡 AI 解读</div>
|
||||
<div class="deep-ai-text">
|
||||
<p>{{ deepReport.ai_summary.text }}</p>
|
||||
</div>
|
||||
<div class="ai-action-tip">
|
||||
<span class="ai-action-label">操作建议：</span>{{ deepReport.ai_summary.action_tip }}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 推荐操作 -->
|
||||
<div class="deep-section">
|
||||
<div class="deep-section-title">系统推荐</div>
|
||||
<div class="deep-recommend-bar" :class="'rec-' + deepReport.recommend.signal_type">
|
||||
<span class="rec-display">{{ deepReport.recommend.display }}</span>
|
||||
<span class="rec-rate">{{ deepReport.recommend.rate }}分</span>
|
||||
<span class="rec-reason-text">{{ deepReport.recommend.reason }}</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 信号列表 -->
|
||||
<div class="deep-section" v-if="deepReport.signals && deepReport.signals.length">
|
||||
<div class="deep-section-title">触发信号 ({{ deepReport.signals.length }}个)</div>
|
||||
<div class="deep-signal-list">
|
||||
<div v-for="sig in deepReport.signals" :key="sig.type" class="deep-signal-item">
|
||||
<span class="deep-sig-name">{{ sig.name }}</span>
|
||||
<span class="deep-sig-strength">{{ sig.strength }}%</span>
|
||||
<span class="deep-sig-desc">{{ sig.description }}</span>
|
||||
</div>
|
||||
<div class="bull-card-center">
|
||||
<div class="bull-card-signals">
|
||||
<span v-for="sig in stock.signals_active" :key="sig" class="bull-signal-tag">{{ sig }}</span>
|
||||
</div>
|
||||
<div class="bull-card-recommend">
|
||||
<span :class="['rec-tag', 'rec-' + stock.recommend_type]">{{ stock.recommend_text }}</span>
|
||||
<span class="rec-reason">{{ stock.recommend_reason }}</span>
|
||||
</div>
|
||||
</div>
|
||||
<div class="bull-card-right">
|
||||
<div class="bull-card-price" v-if="stock.price">¥{{ stock.price.toFixed(2) }}</div>
|
||||
<div class="bull-card-change" v-if="stock.change_pct != null"
|
||||
:class="stock.change_pct >= 0 ? 'up' : 'down'">
|
||||
{{ stock.change_pct >= 0 ? '+' : '' }}{{ stock.change_pct.toFixed(2) }}%
|
||||
</div>
|
||||
<div class="bull-card-progress">
|
||||
<div class="mini-progress-bar">
|
||||
<div class="mini-progress-fill" :style="{ width: stock.progress + '%' }"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 价格位置 -->
|
||||
<div class="deep-section">
|
||||
<div class="deep-section-title">价格位置</div>
|
||||
<div class="deep-position-grid">
|
||||
<div v-for="(pos, key) in deepReport.position" :key="key" class="deep-pos-item">
|
||||
<div class="pos-label">{{ key.replace('d','') }}日</div>
|
||||
<div class="pos-range">{{ pos.low }} ~ {{ pos.high }}</div>
|
||||
<div class="pos-bar-wrap">
|
||||
<div class="pos-bar-bg">
|
||||
<div class="pos-bar-fill" :style="{ width: pos.range_pct + '%' }"
|
||||
:class="pos.range_pct > 80 ? 'high' : pos.range_pct < 30 ? 'low' : 'mid'"></div>
|
||||
</div>
|
||||
<span class="progress-label">{{ stock.progress }}%</span>
|
||||
<span class="pos-pct">{{ pos.range_pct }}%</span>
|
||||
</div>
|
||||
<div class="pos-space">
|
||||
<span class="space-up">↑{{ pos.up_space }}%</span>
|
||||
<span class="space-down">↓{{ pos.down_risk }}%</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 支撑压力 -->
|
||||
<div class="deep-section">
|
||||
<div class="deep-section-title">支撑与压力</div>
|
||||
<div class="deep-sr-grid">
|
||||
<div class="sr-col">
|
||||
<div class="sr-col-title support-title">支撑位</div>
|
||||
<div v-for="s in deepReport.supports" :key="s.name" class="sr-item support">
|
||||
<span class="sr-name">{{ s.name }}</span>
|
||||
<span class="sr-level">{{ s.level }}</span>
|
||||
</div>
|
||||
<div v-if="!deepReport.supports.length" class="sr-empty">无</div>
|
||||
</div>
|
||||
<div class="sr-col">
|
||||
<div class="sr-col-title resist-title">压力位</div>
|
||||
<div v-for="r in deepReport.resistances" :key="r.name" class="sr-item resist">
|
||||
<span class="sr-name">{{ r.name }}</span>
|
||||
<span class="sr-level">{{ r.level }}</span>
|
||||
</div>
|
||||
<div v-if="!deepReport.resistances.length" class="sr-empty">无</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 量价与形态 -->
|
||||
<div class="deep-section">
|
||||
<div class="deep-section-title">量价分析</div>
|
||||
<div class="deep-vol-info">
|
||||
<span class="vol-tag" :class="deepReport.volume.ratio >= 1.3 ? 'vol-up' : deepReport.volume.ratio < 0.7 ? 'vol-dn' : ''">
|
||||
{{ deepReport.volume.trend }}
|
||||
</span>
|
||||
<span class="vol-detail">量比 {{ deepReport.volume.ratio }}x · 5日均量 {{ (deepReport.volume.avg_5/10000).toFixed(0) }}万</span>
|
||||
</div>
|
||||
<div v-if="deepReport.patterns.length" class="deep-patterns">
|
||||
<span v-for="p in deepReport.patterns" :key="p.name"
|
||||
class="pattern-tag" :class="p.bullish === true ? 'bullish' : p.bullish === false ? 'bearish' : ''">
|
||||
{{ p.name }} <small>{{ p.desc }}</small>
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 均线与基本面 -->
|
||||
<div class="deep-section">
|
||||
<div class="deep-section-title">技术指标</div>
|
||||
<div class="deep-ma-info">
|
||||
<span class="ma-trend-tag" :class="deepReport.ma_trend">{{ deepReport.ma_trend_label }}</span>
|
||||
<span v-for="(val, key) in deepReport.ma" :key="key" class="ma-val">
|
||||
{{ key.toUpperCase() }}={{ val }}
|
||||
</span>
|
||||
</div>
|
||||
<div v-if="deepReport.realtime" class="deep-fundamental">
|
||||
<span v-if="deepReport.realtime.pe > 0">PE {{ deepReport.realtime.pe.toFixed(1) }}</span>
|
||||
<span v-if="deepReport.realtime.pb > 0">PB {{ deepReport.realtime.pb.toFixed(1) }}</span>
|
||||
<span v-if="deepReport.realtime.total_market_cap > 0">
|
||||
市值 {{ (deepReport.realtime.total_market_cap/100000000).toFixed(0) }}亿
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 评分明细 -->
|
||||
<div class="deep-section">
|
||||
<div class="deep-section-title">评分明细</div>
|
||||
<div class="deep-score-details">
|
||||
<span v-for="r in deepReport.score_reasons" :key="r" class="score-reason-tag"
|
||||
:class="r.includes('+') ? 'positive' : 'negative'">{{ r }}</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 空状态 -->
|
||||
<div v-if="!bullStocksData && !bullStocksLoading" class="bull-empty-tip">
|
||||
<div class="empty-icon">找牛股</div>
|
||||
<p>找牛股 — 按信号流程筛选潜力股</p>
|
||||
<p class="sub-tip">基于全景扫描数据，按标准牛股启动信号流程分阶段筛选</p>
|
||||
<button class="scan-action-btn" @click="fetchBullStocks()">开始找牛股</button>
|
||||
<div v-if="!deepReport && !deepLoading" class="bull-empty-tip">
|
||||
<div class="empty-icon" style="font-size:28px">个股深析</div>
|
||||
<p>输入股票代码，获取深度技术分析报告</p>
|
||||
<p class="sub-tip">包含价格位置、压力支撑、量价分析、空间估算、综合评分</p>
|
||||
</div>
|
||||
</div><!-- 找牛股子页面结束 -->
|
||||
</div><!-- 个股深析子页面结束 -->
|
||||
|
||||
<!-- 模拟交易子页面 -->
|
||||
<div v-show="analysisSubTab === 'sim'" class="sim-trade-page">
|
||||
<!-- 买入分析子页面 -->
|
||||
<div v-show="analysisSubTab === 'buyanalysis'" class="buy-analysis-page">
|
||||
|
||||
<div class="buy-analysis-header">
|
||||
<div class="buy-analysis-title">买入股深度分析</div>
|
||||
<div class="buy-analysis-desc">对全景扫描中推荐买入的股票批量执行深度分析</div>
|
||||
<button class="deep-analyze-btn" @click="fetchBuyAnalysis()" :disabled="buyAnalysisLoading" style="margin-top:8px;">
|
||||
{{ buyAnalysisLoading ? '分析中...' : '刷新分析' }}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div v-if="buyAnalysisLoading" class="bull-loading">
|
||||
<span class="loading-dot"></span> 正在批量分析买入推荐股...（{{ buyAnalysisProgress }}/{{ buyAnalysisTotal }}）
|
||||
</div>
|
||||
|
||||
<div v-if="!buyAnalysisLoading && buyAnalysisList.length === 0" class="bull-empty-tip">
|
||||
<div class="empty-icon" style="font-size:28px">📊</div>
|
||||
<p>暂无买入推荐股票</p>
|
||||
<p class="sub-tip">当全景扫描中有买入推荐时，会自动进行深度分析</p>
|
||||
</div>
|
||||
|
||||
<div v-if="buyAnalysisList.length > 0" class="buy-analysis-list">
|
||||
<div v-for="(item, idx) in buyAnalysisList" :key="item.stock_code" class="buy-analysis-card">
|
||||
<div class="buy-card-header" @click="item._expanded = !item._expanded">
|
||||
<div class="buy-card-left">
|
||||
<span class="buy-card-name">{{ item.stock_name }}</span>
|
||||
<span class="buy-card-code">{{ item.stock_code }}</span>
|
||||
</div>
|
||||
<div class="buy-card-center">
|
||||
<span class="buy-card-price" v-if="item.realtime">¥{{ item.realtime.price.toFixed(2) }}</span>
|
||||
<span class="buy-card-change" v-if="item.realtime" :class="item.realtime.change_pct >= 0 ? 'up' : 'down'">
|
||||
{{ item.realtime.change_pct >= 0 ? '+' : '' }}{{ item.realtime.change_pct.toFixed(2) }}%
|
||||
</span>
|
||||
</div>
|
||||
<div class="buy-card-right">
|
||||
<span class="buy-card-score" :class="'score-' + (item.deep_score >= 65 ? 'high' : item.deep_score >= 50 ? 'mid' : 'low')">
|
||||
{{ item.deep_score }}分
|
||||
</span>
|
||||
<span class="buy-card-verdict">{{ item.verdict }}</span>
|
||||
</div>
|
||||
<span class="buy-card-arrow" :class="{ expanded: item._expanded }">▸</span>
|
||||
</div>
|
||||
<div v-show="item._expanded" class="buy-card-detail">
|
||||
<div v-if="item.ai_summary" class="deep-ai-summary" style="margin-bottom:8px;">
|
||||
<div class="deep-ai-title">💡 AI 解读</div>
|
||||
<div class="deep-ai-text"><p>{{ item.ai_summary.text }}</p></div>
|
||||
<div class="ai-action-tip">
|
||||
<span class="ai-action-label">操作建议：</span>{{ item.ai_summary.action_tip }}
|
||||
</div>
|
||||
</div>
|
||||
<div class="buy-detail-grid">
|
||||
<div class="buy-detail-item">
|
||||
<span class="buy-detail-label">系统推荐</span>
|
||||
<span class="buy-detail-value" style="color:#f44336;font-weight:600;">{{ item.recommend.display }} {{ item.recommend.rate }}分</span>
|
||||
</div>
|
||||
<div class="buy-detail-item">
|
||||
<span class="buy-detail-label">推荐理由</span>
|
||||
<span class="buy-detail-value">{{ item.recommend.reason }}</span>
|
||||
</div>
|
||||
<div class="buy-detail-item" v-if="item.signals && item.signals.length">
|
||||
<span class="buy-detail-label">触发信号</span>
|
||||
<span class="buy-detail-value">
|
||||
<span v-for="sig in item.signals" :key="sig.type" class="deep-sig-name" style="margin-right:6px;">{{ sig.name }}</span>
|
||||
</span>
|
||||
</div>
|
||||
<div class="buy-detail-item" v-if="item.supports && item.supports.length">
|
||||
<span class="buy-detail-label">支撑位</span>
|
||||
<span class="buy-detail-value">{{ item.supports.map(s => s.level.toFixed(2)).join(' / ') }}</span>
|
||||
</div>
|
||||
<div class="buy-detail-item" v-if="item.resistances && item.resistances.length">
|
||||
<span class="buy-detail-label">压力位</span>
|
||||
<span class="buy-detail-value">{{ item.resistances.map(r => r.level.toFixed(2)).join(' / ') }}</span>
|
||||
</div>
|
||||
<div class="buy-detail-item">
|
||||
<span class="buy-detail-label">量能</span>
|
||||
<span class="buy-detail-value">{{ item.volume ? item.volume.trend + ' (量比' + item.volume.ratio + 'x)' : '-' }}</span>
|
||||
</div>
|
||||
<div class="buy-detail-item">
|
||||
<span class="buy-detail-label">均线</span>
|
||||
<span class="buy-detail-value">{{ item.ma_trend_label }}</span>
|
||||
</div>
|
||||
</div>
|
||||
<button class="deep-analyze-btn" style="margin-top:8px;font-size:12px;padding:6px 12px;"
|
||||
@click="deepCode = item.stock_code; analysisSubTab = 'bull'; fetchDeepAnalysis();">
|
||||
查看完整报告 →
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div><!-- 买入分析子页面结束 -->
|
||||
|
||||
<!-- (模拟交易已移至模型tab) -->
|
||||
<div style="display:none">
|
||||
<!-- 模拟交易统计卡片 -->
|
||||
<div class="sim-stats-card">
|
||||
<div class="sim-stats-header">
|
||||
@@ -1533,14 +1691,24 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div><!-- 模拟交易子页面结束 -->
|
||||
|
||||
|
||||
</div><!-- 旧模拟交易占位结束 -->
|
||||
|
||||
</div><!-- 分析页面结束 -->
|
||||
|
||||
<!-- 模型页面 - 交易信号实战体系 -->
|
||||
<!-- 模型页面 -->
|
||||
<div v-show="activeTab === 'model'" class="model-page">
|
||||
<!-- 模型页子导航 -->
|
||||
<div class="sub-tabs">
|
||||
<button :class="['sub-tab', { active: modelSubTab === 'system' }]" @click="modelSubTab = 'system'">
|
||||
信号体系
|
||||
</button>
|
||||
<button :class="['sub-tab', { active: modelSubTab === 'sim' }]" @click="modelSubTab = 'sim'; loadSimData()">
|
||||
模拟交易
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<!-- 信号体系子页面 -->
|
||||
<div v-show="modelSubTab === 'system'">
|
||||
<!-- 信号胜率排行 -->
|
||||
<div class="signal-system-section">
|
||||
<h4 class="signal-system-title">交易信号胜率排行</h4>
|
||||
@@ -1652,6 +1820,86 @@
|
||||
<div class="signal-summary-item"><span class="summary-tag aux">短底背离 / 反弹</span>仅作辅助参考，不单独作为核心决策依据</div>
|
||||
</div>
|
||||
</div>
|
||||
</div><!-- 信号体系子页面结束 -->
|
||||
|
||||
<!-- 模拟交易子页面 -->
|
||||
<div v-show="modelSubTab === 'sim'" class="sim-trade-page">
|
||||
|
||||
<!-- 模拟交易统计卡片 -->
|
||||
<div class="sim-stats-card">
|
||||
<div class="sim-stats-header">
|
||||
<h4>智能交易 <span v-if="smartAlgoConfig" style="font-size:12px;color:#888;">{{ smartAlgoConfig.algo_name }}</span></h4>
|
||||
<div class="sim-actions">
|
||||
<button class="sim-btn auto" @click="executeAutoTrade" :disabled="simAutoTrading">
|
||||
{{ simAutoTrading ? '执行中...' : '执行' }}
|
||||
</button>
|
||||
<button class="sim-btn reset" @click="confirmResetSim">重置</button>
|
||||
</div>
|
||||
</div>
|
||||
<div class="sim-stats-grid">
|
||||
<div class="sim-stat-item">
|
||||
<div class="sim-stat-value">{{ formatMoney(smartAlgoConfig?.total_capital || simStats.initial_capital || 200000) }}</div>
|
||||
<div class="sim-stat-label">总本金</div>
|
||||
</div>
|
||||
<div class="sim-stat-item">
|
||||
<div class="sim-stat-value">{{ formatMoney(simStats.total_market_value || 0) }}</div>
|
||||
<div class="sim-stat-label">持仓市值</div>
|
||||
</div>
|
||||
<div class="sim-stat-item">
|
||||
<div class="sim-stat-value">{{ formatMoney(simStats.cash || (smartAlgoConfig?.total_capital || 200000)) }}</div>
|
||||
<div class="sim-stat-label">可用资金</div>
|
||||
</div>
|
||||
<div class="sim-stat-item" :class="(simStats.total_profit || 0) >= 0 ? 'profit' : 'loss'">
|
||||
<div class="sim-stat-value">{{ (simStats.total_profit || 0) >= 0 ? '+' : '' }}{{ formatMoney(simStats.total_profit || 0) }}</div>
|
||||
<div class="sim-stat-label">总盈亏</div>
|
||||
</div>
|
||||
<div class="sim-stat-item" :class="(simStats.profit_rate || 0) >= 0 ? 'profit' : 'loss'">
|
||||
<div class="sim-stat-value">{{ (simStats.profit_rate || 0) >= 0 ? '+' : '' }}{{ (simStats.profit_rate || 0).toFixed(2) }}%</div>
|
||||
<div class="sim-stat-label">收益率</div>
|
||||
</div>
|
||||
<div class="sim-stat-item">
|
||||
<div class="sim-stat-value">{{ simStats.total_trades || 0 }}</div>
|
||||
<div class="sim-stat-label">交易次数</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="sim-stats-detail">
|
||||
<span :class="(simStats.unrealized_profit || 0) >= 0 ? 'profit' : 'loss'">
|
||||
浮动 {{ (simStats.unrealized_profit || 0) >= 0 ? '+' : '' }}{{ formatMoney(simStats.unrealized_profit || 0) }}
|
||||
</span>
|
||||
<span :class="(simStats.realized_profit || 0) >= 0 ? 'profit' : 'loss'">
|
||||
已实现 {{ (simStats.realized_profit || 0) >= 0 ? '+' : '' }}{{ formatMoney(simStats.realized_profit || 0) }}
|
||||
</span>
|
||||
<button class="sim-btn refresh" @click="updateSimPrices" :disabled="simPositions.length === 0 || simPriceRefreshing" v-if="simPositions.length > 0" style="margin-left:auto;font-size:11px;padding:2px 8px;">
|
||||
{{ simPriceRefreshing ? '刷新中...' : '刷新现价' }}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 模拟持仓和交易历史直接复用现有逻辑 -->
|
||||
<div class="sim-section collapsible-section" v-if="simPositions.length > 0">
|
||||
<div class="sim-section-header" style="cursor:pointer;" @click="simPositionsExpanded = !simPositionsExpanded">
|
||||
<h4 class="section-title">持仓 <span class="badge">{{ simPositions.length }}</span></h4>
|
||||
<span class="expand-arrow" :class="{ expanded: simPositionsExpanded }">▸</span>
|
||||
</div>
|
||||
<div v-show="simPositionsExpanded" class="sim-positions-list">
|
||||
<div v-for="pos in simPositions" :key="pos.stock_code" class="sim-position-item" @click="loadFundamental(pos.stock_code, pos.stock_name)" style="cursor:pointer;">
|
||||
<div class="sim-pos-header">
|
||||
<span class="sim-pos-stock">{{ pos.stock_code }} {{ pos.stock_name }}</span>
|
||||
<span class="sim-pos-profit" :class="(pos.unrealized_profit || 0) >= 0 ? 'profit' : 'loss'">
|
||||
{{ (pos.unrealized_profit || 0) >= 0 ? '+' : '' }}{{ formatMoney(pos.unrealized_profit || 0) }}
|
||||
({{ (pos.profit_rate || 0) >= 0 ? '+' : '' }}{{ (pos.profit_rate || 0).toFixed(2) }}%)
|
||||
</span>
|
||||
</div>
|
||||
<div class="sim-pos-detail">
|
||||
<span>{{ pos.quantity }}股 @ ¥{{ (pos.avg_cost || 0).toFixed(2) }}</span>
|
||||
<span v-if="pos.current_price">现价 ¥{{ pos.current_price.toFixed(2) }}</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div><!-- 模拟交易子页面结束 -->
|
||||
|
||||
</div><!-- 模型页面结束 -->
|
||||
|
||||
<!-- 交易记录页面 -->
|
||||
@@ -1944,6 +2192,6 @@
|
||||
</div>
|
||||
|
||||
{% endraw %}
|
||||
<script src="/static/js/app.js?v=20260318v2"></script>
|
||||
<script src="/static/js/app.js?v=20260511v3"></script>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
Reference in New Issue
Block a user