feat: 增加登陆注册；修复添加自选bug

qihuo_analyzer/modules/deepseek_agent.py

@@ -1,6 +1,7 @@
 # AI 研判模块
 import json
 import requests
+import datetime
 from typing import Dict, Optional, List
 from qihuo_analyzer.utils.config_manager import config_manager
 from qihuo_analyzer.core.models import AnalysisResult
@@ -52,9 +53,65 @@ class DeepseekAgent:
         self.api_url = self.current_config['api_url']
         self.headers = self.current_config['headers']
         
+        # 初始化缓存字典
+        # 缓存结构: {"date_symbol_model": {"timestamp": "2023-07-01 12:00:00", "data": {...}}}
+        self.cache = {}
+        
+    def _get_cache_key(self, market_data, model_name):
+        """获取缓存键
+        
+        Args:
+            market_data: 市场数据，包含symbol
+            model_name: 模型名称
+            
+        Returns:
+            str: 缓存键，格式为 "日期_品种_模型"
+        """
+        # 获取当前日期（交易日）
+        today = datetime.datetime.now().strftime('%Y-%m-%d')
+        # 获取品种代码
+        symbol = market_data.get('symbol', 'unknown')
+        # 构建缓存键
+        cache_key = f"{today}_{symbol}_{model_name}"
+        return cache_key
+    
+    def _get_from_cache(self, cache_key):
+        """从缓存中获取数据
+        
+        Args:
+            cache_key: 缓存键
+            
+        Returns:
+            Dict: 缓存的数据，如果不存在返回None
+        """
+        if cache_key in self.cache:
+            return self.cache[cache_key]['data']
+        return None
+    
+    def _set_to_cache(self, cache_key, data):
+        """将数据设置到缓存中
+        
+        Args:
+            cache_key: 缓存键
+            data: 要缓存的数据
+        """
+        self.cache[cache_key] = {
+            'timestamp': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+            'data': data
+        }
+    
     def analyze_market(self, market_data: Dict, technical_indicators: Dict, 
                       trend_analysis: Dict, risk_metrics: Dict) -> Dict:
         """分析市场"""
+        # 生成缓存键
+        cache_key = self._get_cache_key(market_data, self.model_name)
+        
+        # 检查缓存
+        cached_data = self._get_from_cache(cache_key)
+        if cached_data:
+            print(f"[DEBUG] 从缓存中获取分析数据: {cache_key}")
+            return cached_data
+        
         # 构建提示词
         prompt = self._build_analysis_prompt(market_data, technical_indicators, trend_analysis, risk_metrics)
         
@@ -64,10 +121,24 @@ class DeepseekAgent:
         # 解析结果
         analysis_result = self._parse_analysis_result(response)
         
+        # 缓存结果
+        self._set_to_cache(cache_key, analysis_result)
+        print(f"[DEBUG] 缓存分析数据: {cache_key}")
+        
         return analysis_result
     
-    def generate_trade_recommendation(self, analysis_result: Dict) -> Dict:
+    def generate_trade_recommendation(self, analysis_result: Dict, market_data: Optional[Dict] = None) -> Dict:
         """生成交易建议"""
+        # 生成缓存键
+        if market_data:
+            cache_key = self._get_cache_key(market_data, f"{self.model_name}_recommendation")
+            
+            # 检查缓存
+            cached_data = self._get_from_cache(cache_key)
+            if cached_data:
+                print(f"[DEBUG] 从缓存中获取交易建议: {cache_key}")
+                return cached_data
+        
         # 构建提示词
         prompt = self._build_recommendation_prompt(analysis_result)
         
@@ -77,6 +148,11 @@ class DeepseekAgent:
         # 解析结果
         recommendation = self._parse_recommendation_result(response)
         
+        # 缓存结果
+        if market_data:
+            self._set_to_cache(cache_key, recommendation)
+            print(f"[DEBUG] 缓存交易建议: {cache_key}")
+        
         return recommendation
     
     def _build_analysis_prompt(self, market_data: Dict, technical_indicators: Dict, 

requirements.txt

@@ -7,3 +7,6 @@ requests==2.31.0
 python-dotenv==1.0.0
 APScheduler==3.10.4
 pytest==7.4.4
+Flask==2.0.1
+Flask-Login==0.6.2
+Werkzeug==2.0.1

web/app.py

@@ -1,13 +1,16 @@
 #!/usr/bin/env python3
 # Flask web 应用
 
-from flask import Flask, render_template, jsonify, request
+from flask import Flask, render_template, jsonify, request, redirect, url_for, flash, session
 import sys
 import os
 
 # 添加项目根目录到 Python 路径
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
+# 导入认证模块
+from auth import login_manager, init_db, register_user, login_user_by_credentials, logout_user, login_required, current_user
+
 from qihuo_analyzer.data.data_fetcher import DataFetcher
 from qihuo_analyzer.data.data_storage import DataStorage
 from qihuo_analyzer.modules.trend_filter import TrendFilter
@@ -20,13 +23,57 @@ from qihuo_analyzer.core.models import AnalysisResult
 
 app = Flask(__name__)
 
+# 设置Flask-Login
+app.secret_key = 'your-secret-key-here'  # 实际项目中应该使用环境变量
+login_manager.init_app(app)
+login_manager.login_view = 'login'  # 设置登录页面的路由
+
+# 初始化数据库
+init_db()
+
 # 模板上下文处理器
 @app.context_processor
 def inject_functions():
     import datetime
     def now():
         return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-    return {'now': now}
+    return {'now': now, 'current_user': current_user}
+
+# 登录路由
+@app.route('/login', methods=['GET', 'POST'])
+def login():
+    if request.method == 'POST':
+        username = request.form['username']
+        password = request.form['password']
+        success, message = login_user_by_credentials(username, password)
+        if success:
+            flash(message)
+            return redirect(url_for('index'))
+        else:
+            flash(message)
+    return render_template('login.html')
+
+# 注册路由
+@app.route('/register', methods=['GET', 'POST'])
+def register():
+    if request.method == 'POST':
+        username = request.form['username']
+        password = request.form['password']
+        success, message = register_user(username, password)
+        if success:
+            flash(message)
+            return redirect(url_for('login'))
+        else:
+            flash(message)
+    return render_template('register.html')
+
+# 登出路由
+@app.route('/logout')
+@login_required
+def logout():
+    logout_user()
+    flash('已登出')
+    return redirect(url_for('login'))
 
 # 初始化组件
 data_fetcher = DataFetcher()
@@ -68,11 +115,16 @@ def get_hot_symbols(all_symbols_data):
         try:
             # 这里简化处理，实际项目中应该使用真实的成交量、振幅、涨速数据
             # 假设我们从kline_data中获取这些数据
-            # 由于我们没有这些数据，这里使用随机值模拟
-            import random
-            symbol_data['volume'] = random.randint(10000, 1000000)
-            symbol_data['amplitude'] = random.uniform(0.1, 5.0)
-            symbol_data['speed'] = random.uniform(-2.0, 2.0)
+            # 由于我们没有这些数据，这里使用基于品种代码的哈希值生成值，确保每次计算结果一致
+            import hashlib
+            # 使用品种代码生成哈希值
+            hash_obj = hashlib.md5(symbol_data['symbol'].encode())
+            hash_int = int(hash_obj.hexdigest(), 16)
+            
+            # 基于哈希值生成成交量、振幅、涨速
+            symbol_data['volume'] = 100000 + (hash_int % 900000)  # 100000-1000000
+            symbol_data['amplitude'] = 0.1 + (hash_int % 100) / 25  # 0.1-4.1
+            symbol_data['speed'] = -2.0 + (hash_int % 100) / 25  # -2.0-2.0
         except Exception:
             pass
     
@@ -88,6 +140,7 @@ def get_hot_symbols(all_symbols_data):
     return hot_symbols
 
 @app.route('/')
+@login_required
 def index():
     """首页 - 多品种分析面板"""
     # 获取所有品种的分析数据
@@ -301,6 +354,7 @@ def index():
                        data_unavailable=False)
 
 @app.route('/symbol/<symbol>')
+@login_required
 def symbol_detail(symbol):
     """品种详情页"""
     try:
@@ -451,7 +505,7 @@ def symbol_detail(symbol):
         # AI 分析 - 使用指定的模型
         ai_agent = DeepseekAgent(model_name=model_name)
         ai_analysis = ai_agent.analyze_market(market_data, technical_indicators, trend_data, risk_metrics)
-        recommendation = ai_agent.generate_trade_recommendation(ai_analysis)
+        recommendation = ai_agent.generate_trade_recommendation(ai_analysis, market_data)
         
         # 构建模板数据
         # 获取中文名称
@@ -544,6 +598,7 @@ def symbol_detail(symbol):
         return render_template('error.html', message=f"分析失败: {str(e)}")
 
 @app.route('/api/analysis/<symbol>')
+@login_required
 def api_analysis(symbol):
         """API: 获取品种分析数据"""
         try:
@@ -598,6 +653,7 @@ def api_analysis(symbol):
             return jsonify({"error": str(e)}), 500
 
 @app.route('/api/card/<symbol>')
+@login_required
 def api_card(symbol):
     """API: 获取分析卡片数据"""
     try:
@@ -695,7 +751,7 @@ def api_card(symbol):
         }
         
         ai_analysis = deepseek_agent.analyze_market(market_data, technical_indicators, trend_data, risk_metrics)
-        recommendation = deepseek_agent.generate_trade_recommendation(ai_analysis)
+        recommendation = deepseek_agent.generate_trade_recommendation(ai_analysis, market_data)
         
         # 构建卡片数据
         # 获取中文名称
@@ -727,6 +783,7 @@ def api_card(symbol):
         return jsonify({"error": str(e)}), 500
 
 @app.route('/card/<symbol>')
+@login_required
 def card(symbol):
     """分析卡片页面"""
     try:
@@ -802,6 +859,7 @@ def _get_kline_data_for_chart(kline_data):
         return []
 
 @app.route('/api/selected/add/<symbol>', methods=['POST'])
+@login_required
 def add_selected_symbol(symbol):
     """添加自选品种"""
     global selected_symbols
@@ -810,6 +868,7 @@ def add_selected_symbol(symbol):
     return jsonify({'status': 'success', 'selected_symbols': selected_symbols})
 
 @app.route('/api/selected/remove/<symbol>', methods=['POST'])
+@login_required
 def remove_selected_symbol(symbol):
     """删除自选品种"""
     global selected_symbols
@@ -818,6 +877,7 @@ def remove_selected_symbol(symbol):
     return jsonify({'status': 'success', 'selected_symbols': selected_symbols})
 
 @app.route('/api/selected/list')
+@login_required
 def get_selected_symbols():
     """获取自选品种列表"""
     return jsonify({'selected_symbols': selected_symbols})

web/templates/index.html

@@ -318,8 +318,26 @@
     <div class="container">
         <!-- 头部 -->
         <div class="header">
-            <h1>AI期货分析系统</h1>
-            <p>基于DeepSeek大模型和量化分析算法的智能决策平台</p>
+            <div style="display: flex; justify-content: space-between; align-items: center;">
+                <div>
+                    <h1>AI期货分析系统</h1>
+                    <p>基于DeepSeek大模型和量化分析算法的智能决策平台</p>
+                </div>
+                <div style="display: flex; align-items: center; gap: 16px;">
+                    {% if current_user.is_authenticated %}
+                    <div style="color: white; font-size: 14px;">
+                        欢迎, {{ current_user.username }}
+                    </div>
+                    <a href="/logout" style="padding: 8px 16px; background: rgba(255, 255, 255, 0.2); color: white; text-decoration: none; border-radius: 6px; font-size: 14px;">
+                        登出
+                    </a>
+                    {% else %}
+                    <a href="/login" style="padding: 8px 16px; background: rgba(255, 255, 255, 0.2); color: white; text-decoration: none; border-radius: 6px; font-size: 14px;">
+                        登录
+                    </a>
+                    {% endif %}
+                </div>
+            </div>
         </div>
         
         <!-- 主题切换 -->