fix: 合并master代码；增加百炼coding plan配置及测试

.dockerignore

@@ -0,0 +1,17 @@
+__pycache__/
+*.pyc
+*.pyo
+*.db
+.git/
+.gitignore
+*.md
+.trae/
+.vscode/
+*.egg-info/
+dist/
+build/
+.eggs/
+*.egg
+docker-compose.yml
+Dockerfile
+.dockerignore

.gitignore

@@ -0,0 +1,35 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+*.egg-info/
+dist/
+build/
+
+# Virtual environments
+venv/
+.venv/
+
+# Database files
+data/*.db
+data/*.db-journal
+
+# Config files with secrets
+config/ai_config.json
+
+# Logs
+*.log
+data/collector.log
+
+# IDE
+.vscode/
+.idea/
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Cache
+cache/*.pkl

DOCKER_DEPLOY.md

@@ -0,0 +1,224 @@
+# 数据缓冲平台 Docker 部署文档
+
+## 目录结构
+
+```
+buffer_platform/
+├── app/                    # 应用代码
+├── config/                 # 配置文件
+├── data/                   # 本地数据目录
+├── Dockerfile             # Docker 镜像构建文件
+├── docker-compose.yml     # Docker Compose 配置文件
+├── .dockerignore          # Docker 构建忽略文件
+└── requirements.txt       # Python 依赖
+```
+
+## 环境要求
+
+- Docker Desktop for Windows
+- Docker Compose v3.8+
+- Windows 10/11 或 Windows Server
+
+## 快速部署
+
+### 1. 构建并启动容器
+
+```powershell
+cd d:\alpha_workspace\buffer_platform
+docker-compose up -d --build
+```
+
+### 2. 查看容器状态
+
+```powershell
+docker-compose ps
+```
+
+### 3. 查看日志
+
+```powershell
+# 查看实时日志
+docker-compose logs -f
+
+# 查看最近 100 行日志
+docker-compose logs --tail=100
+```
+
+## 访问地址
+
+| 服务 | 地址 |
+|------|------|
+| 前端页面 | http://localhost:9600/ui |
+| API 文档 | http://localhost:9600/docs |
+| 健康检查 | http://localhost:9600/api/v1/health |
+
+## 数据持久化
+
+### 挂载路径
+
+| 宿主机路径 | 容器路径 | 说明 |
+|-----------|----------|------|
+| `E:\docker_workspace\futures_datas` | `/app/data` | SQLite 数据库及缓存数据 |
+
+### 数据目录结构
+
+容器启动后，`E:\docker_workspace\futures_datas` 目录将包含：
+
+```
+E:\docker_workspace\futures_datas\
+└── buffer.db          # SQLite 数据库文件
+```
+
+## 常用操作
+
+### 停止服务
+
+```powershell
+docker-compose stop
+```
+
+### 启动服务
+
+```powershell
+docker-compose start
+```
+
+### 重启服务
+
+```powershell
+docker-compose restart
+```
+
+### 停止并删除容器
+
+```powershell
+docker-compose down
+```
+
+### 停止并删除容器及数据卷
+
+> ⚠️ 警告：此操作将删除所有持久化数据！
+
+```powershell
+docker-compose down -v
+```
+
+### 重新构建并启动
+
+```powershell
+docker-compose up -d --build
+```
+
+### 更新镜像
+
+```powershell
+# 拉取最新代码后
+docker-compose down
+docker-compose build --no-cache
+docker-compose up -d
+```
+
+## 环境变量配置
+
+可在 `docker-compose.yml` 中修改以下环境变量：
+
+| 变量名 | 默认值 | 说明 |
+|--------|--------|------|
+| `BUFFER_DB_PATH` | `/app/data/buffer.db` | 数据库文件路径 |
+| `BUFFER_HOST` | `0.0.0.0` | 服务监听地址 |
+| `BUFFER_PORT` | `8600` | 容器内服务端口 |
+| `CACHE_TTL` | `300` | 缓存过期时间（秒） |
+| `BUFFER_LOG_LEVEL` | `INFO` | 日志级别 |
+| `MAX_WORKERS` | `2` | 并发采集数 |
+
+## 端口修改
+
+如需修改宿主机绑定端口，编辑 `docker-compose.yml`：
+
+```yaml
+ports:
+  - "9600:8600"  # 修改 9600 为其他端口
+```
+
+## 数据备份
+
+### 备份数据库
+
+```powershell
+# 停止服务
+docker-compose stop
+
+# 复制数据文件
+xcopy E:\docker_workspace\futures_datas\buffer.db E:\backup\buffer_$(Get-Date -Format 'yyyyMMdd').db
+
+# 启动服务
+docker-compose start
+```
+
+### 恢复数据库
+
+```powershell
+# 停止服务
+docker-compose stop
+
+# 复制备份文件到数据目录
+copy E:\backup\buffer_20260517.db E:\docker_workspace\futures_datas\buffer.db
+
+# 启动服务
+docker-compose start
+```
+
+## 故障排查
+
+### 容器无法启动
+
+```powershell
+# 查看详细日志
+docker-compose logs
+
+# 检查容器状态
+docker ps -a
+```
+
+### 数据库权限问题
+
+确保 `E:\docker_workspace\futures_datas` 目录存在且有写入权限：
+
+```powershell
+# 创建数据目录
+mkdir E:\docker_workspace\futures_datas
+```
+
+### 端口冲突
+
+```powershell
+# 检查端口占用
+netstat -ano | findstr "9600"
+```
+
+### 进入容器
+
+```powershell
+docker exec -it buffer-platform /bin/bash
+```
+
+## 健康检查
+
+服务内置健康检查端点：
+
+```powershell
+# PowerShell
+Invoke-WebRequest -Uri http://localhost:9600/api/v1/health
+
+# 或使用 curl
+curl http://localhost:9600/api/v1/health
+```
+
+正常响应：
+
+```json
+{
+  "status": "ok",
+  "service": "market-data-buffer"
+}
+```

Dockerfile

@@ -0,0 +1,28 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV BUFFER_DB_PATH=/app/data/buffer.db
+ENV BUFFER_HOST=0.0.0.0
+ENV BUFFER_PORT=8600
+
+RUN rm -f /etc/apt/sources.list.d/debian.sources && \
+    echo "deb http://mirrors.aliyun.com/debian trixie main" > /etc/apt/sources.list && \
+    echo "deb http://mirrors.aliyun.com/debian trixie-updates main" >> /etc/apt/sources.list && \
+    echo "deb http://mirrors.aliyun.com/debian-security trixie-security main" >> /etc/apt/sources.list && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends gcc && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
+
+COPY . .
+
+RUN mkdir -p /app/data
+
+EXPOSE 8600
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8600"]

app/analysis_db.py

@@ -0,0 +1,36 @@
+"""
+期货智析数据库 - 独立存储
+"""
+from pathlib import Path
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker, declarative_base
+from datetime import datetime
+
+# 数据目录
+DATA_DIR = Path(__file__).resolve().parent.parent / "data"
+DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+ANALYSIS_DB_PATH = DATA_DIR / "futures_analysis.db"
+
+analysis_engine = create_engine(
+    f"sqlite:///{ANALYSIS_DB_PATH}",
+    connect_args={"check_same_thread": False},
+    pool_pre_ping=True,
+)
+
+AnalysisSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=analysis_engine)
+AnalysisBase = declarative_base()
+
+
+def get_analysis_db():
+    """获取期货智析数据库会话"""
+    db = AnalysisSessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+
+def init_analysis_db():
+    """初始化期货智析数据库表"""
+    AnalysisBase.metadata.create_all(bind=analysis_engine)

app/analysis_models.py

@@ -0,0 +1,88 @@
+"""
+期货智析数据模型
+"""
+from datetime import datetime
+from sqlalchemy import Column, String, Integer, Float, Text, DateTime, Boolean, Index, UniqueConstraint, JSON
+from app.analysis_db import AnalysisBase
+
+
+class FuturesAnalysis(AnalysisBase):
+    """期货分析报告表"""
+    __tablename__ = "futures_analysis"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    symbol = Column(String(32), nullable=False, index=True, comment="品种合约代码")
+    analysis_time = Column(DateTime, nullable=False, default=datetime.now, index=True, comment="分析时间")
+    period = Column(String(16), nullable=False, default="15min", comment="分析周期")
+    # 分析结果
+    suggestion = Column(String(32), nullable=True, comment="交易建议: 逢低做多/逢高做空/观望等待")
+    suggestion_type = Column(String(16), nullable=True, comment="建议类型: up/down/neutral")
+    entry_price = Column(Float, nullable=True, comment="建议入场价")
+    target_price = Column(Float, nullable=True, comment="目标价位")
+    stop_loss = Column(Float, nullable=True, comment="止损价位")
+    risk_level = Column(String(16), nullable=True, comment="风险等级: 低/中/高")
+    # 技术指标
+    macd_signal = Column(String(16), nullable=True, comment="MACD信号")
+    rsi_value = Column(Float, nullable=True, comment="RSI值")
+    boll_signal = Column(String(16), nullable=True, comment="布林带信号")
+    kdj_signal = Column(String(16), nullable=True, comment="KDJ信号")
+    # 趋势评分
+    trend_score = Column(Integer, nullable=True, comment="趋势评分 0-100")
+    success_rate = Column(Float, nullable=True, comment="交易成功率")
+    # 关键点位
+    resistance_levels = Column(JSON, nullable=True, comment="压力位列表")
+    support_levels = Column(JSON, nullable=True, comment="支撑位列表")
+    # 多周期趋势
+    period_trends = Column(JSON, nullable=True, comment="各周期趋势")
+
+    def __repr__(self):
+        return f"<FuturesAnalysis {self.symbol} {self.analysis_time}>"
+
+
+class WatchedSymbol(AnalysisBase):
+    """用户关注品种表"""
+    __tablename__ = "watched_symbols"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    symbol = Column(String(32), nullable=False, unique=True, comment="品种合约代码")
+    name = Column(String(64), nullable=True, comment="品种名称")
+    note = Column(Text, nullable=True, comment="备注")
+    created_at = Column(DateTime, nullable=False, default=datetime.now)
+    updated_at = Column(DateTime, nullable=False, default=datetime.now, onupdate=datetime.now)
+
+    def __repr__(self):
+        return f"<WatchedSymbol {self.symbol}>"
+
+
+class AIModelConfig(AnalysisBase):
+    """AI模型配置表"""
+    __tablename__ = "ai_model_configs"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    provider = Column(String(32), nullable=False, comment="AI提供商: openai/anthropic/google等")
+    model_name = Column(String(64), nullable=False, comment="模型名称")
+    api_key = Column(String(256), nullable=False, comment="API密钥")
+    api_base = Column(String(256), nullable=True, comment="API基础URL")
+    model_id = Column(String(64), nullable=True, comment="模型ID")
+    temperature = Column(Float, nullable=True, default=0.7, comment="温度参数")
+    max_tokens = Column(Integer, nullable=True, default=2000, comment="最大输出token")
+    enabled = Column(Boolean, nullable=False, default=True, comment="是否启用")
+    is_active = Column(Boolean, nullable=False, default=False, comment="是否为当前活跃模型")
+    created_at = Column(DateTime, nullable=False, default=datetime.now)
+    updated_at = Column(DateTime, nullable=False, default=datetime.now, onupdate=datetime.now)
+
+    def __repr__(self):
+        return f"<AIModelConfig {self.provider} {self.model_name}>"
+
+
+class AnalysisSettings(AnalysisBase):
+    """分析设置表（单例配置）"""
+    __tablename__ = "analysis_settings"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    key = Column(String(64), nullable=False, unique=True, comment="配置键")
+    value = Column(JSON, nullable=False, comment="配置值")
+    updated_at = Column(DateTime, nullable=False, default=datetime.now, onupdate=datetime.now)
+
+    def __repr__(self):
+        return f"<AnalysisSettings {self.key}>"

app/api/ai_config.py

@@ -161,6 +161,18 @@ def get_ai_providers():
             "api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
             "models": ["qwen-max", "qwen-plus", "qwen-turbo"]
         },
+        {
+            "id": "aliyun_coding",
+            "name": "阿里云通义灵码",
+            "api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+            "models": ["qwen-coder-plus", "qwen-coder-turbo"]
+        },
+        {
+            "id": "bailian",
+            "name": "阿里百炼",
+            "api_base": "https://coding.dashscope.aliyuncs.com/v1",
+            "models": ["qwen3.6-plus", "qwen3.5-plus", "qwen3-max", "qwen3-coder-plus", "MiniMax-M2.5", "glm-4.7", "kimi-k2.5"]
+        },
         {
             "id": "baidu",
             "name": "百度文心一言",

app/api/futures_analysis.py

@@ -10,6 +10,8 @@ from fastapi import APIRouter, Depends, HTTPException
 from sqlalchemy.orm import Session
 
 from app.database import get_db
+from app.analysis_db import get_analysis_db
+from app.analysis_models import FuturesAnalysis, WatchedSymbol, AIModelConfig, AnalysisSettings
 from app.services.cache import get_cached_data, get_latest_cached
 
 logger = logging.getLogger(__name__)
@@ -453,3 +455,234 @@ def _calc_kdj(candles: list) -> dict:
         signal = "中性"
     
     return {"signal": signal, "detail": f"K: {k} D: {d}"}
+
+
+# ==================== 期货智析数据管理接口 ====================
+
+@router.get("/analysis/history/{symbol}")
+def get_analysis_history(symbol: str, limit: int = 10, adb: Session = Depends(get_analysis_db)):
+    """获取品种历史分析记录"""
+    records = adb.query(FuturesAnalysis).filter(
+        FuturesAnalysis.symbol == symbol
+    ).order_by(
+        FuturesAnalysis.analysis_time.desc()
+    ).limit(limit).all()
+    
+    return {
+        "success": True,
+        "data": [{
+            "id": r.id,
+            "symbol": r.symbol,
+            "analysis_time": r.analysis_time.isoformat(),
+            "suggestion": r.suggestion,
+            "suggestion_type": r.suggestion_type,
+            "trend_score": r.trend_score,
+            "entry_price": r.entry_price,
+            "target_price": r.target_price,
+            "stop_loss": r.stop_loss,
+            "risk_level": r.risk_level
+        } for r in records]
+    }
+
+
+@router.post("/analysis/save")
+def save_analysis_record(data: dict, adb: Session = Depends(get_analysis_db)):
+    """保存分析记录到数据库"""
+    try:
+        record = FuturesAnalysis(
+            symbol=data.get("symbol"),
+            suggestion=data.get("suggestion"),
+            suggestion_type=data.get("suggestion_type"),
+            entry_price=data.get("entry_price"),
+            target_price=data.get("target_price"),
+            stop_loss=data.get("stop_loss"),
+            risk_level=data.get("risk_level"),
+            macd_signal=data.get("macd", {}).get("signal") if data.get("macd") else None,
+            rsi_value=data.get("rsi", {}).get("value") if data.get("rsi") else None,
+            boll_signal=data.get("boll", {}).get("signal") if data.get("boll") else None,
+            kdj_signal=data.get("kdj", {}).get("signal") if data.get("kdj") else None,
+            trend_score=data.get("trend_score"),
+            success_rate=data.get("success_rate"),
+            resistance_levels=data.get("resistances"),
+            support_levels=data.get("supports"),
+            period_trends=data.get("periodConsistency")
+        )
+        adb.add(record)
+        adb.commit()
+        return {"success": True, "message": "分析记录已保存", "id": record.id}
+    except Exception as e:
+        adb.rollback()
+        logger.error(f"保存分析记录失败: {e}")
+        return {"success": False, "message": str(e)}
+
+
+# ==================== 关注品种管理 ====================
+
+@router.get("/watched")
+def get_watched_symbols(adb: Session = Depends(get_analysis_db)):
+    """获取关注的品种列表"""
+    symbols = adb.query(WatchedSymbol).order_by(WatchedSymbol.created_at.desc()).all()
+    return {
+        "success": True,
+        "data": [{
+            "id": s.id,
+            "symbol": s.symbol,
+            "name": s.name,
+            "note": s.note,
+            "created_at": s.created_at.isoformat()
+        } for s in symbols]
+    }
+
+
+@router.post("/watched")
+def add_watched_symbol(data: dict, adb: Session = Depends(get_analysis_db)):
+    """添加关注品种"""
+    try:
+        symbol = data.get("symbol")
+        existing = adb.query(WatchedSymbol).filter(WatchedSymbol.symbol == symbol).first()
+        if existing:
+            return {"success": False, "message": "该品种已关注"}
+        
+        new_symbol = WatchedSymbol(
+            symbol=symbol,
+            name=data.get("name"),
+            note=data.get("note")
+        )
+        adb.add(new_symbol)
+        adb.commit()
+        return {"success": True, "message": "已添加关注", "id": new_symbol.id}
+    except Exception as e:
+        adb.rollback()
+        return {"success": False, "message": str(e)}
+
+
+@router.delete("/watched/{symbol}")
+def remove_watched_symbol(symbol: str, adb: Session = Depends(get_analysis_db)):
+    """取消关注品种"""
+    try:
+        record = adb.query(WatchedSymbol).filter(WatchedSymbol.symbol == symbol).first()
+        if not record:
+            return {"success": False, "message": "未找到该品种"}
+        adb.delete(record)
+        adb.commit()
+        return {"success": True, "message": "已取消关注"}
+    except Exception as e:
+        adb.rollback()
+        return {"success": False, "message": str(e)}
+
+
+# ==================== AI模型配置管理 ====================
+
+@router.get("/ai-models")
+def get_ai_models(adb: Session = Depends(get_analysis_db)):
+    """获取AI模型配置列表"""
+    models = adb.query(AIModelConfig).order_by(AIModelConfig.created_at.desc()).all()
+    settings = adb.query(AnalysisSettings).filter(
+        AnalysisSettings.key == "analysis_settings"
+    ).first()
+    
+    return {
+        "success": True,
+        "data": {
+            "models": [{
+                "id": m.id,
+                "provider": m.provider,
+                "model_name": m.model_name,
+                "api_base": m.api_base,
+                "model_id": m.model_id,
+                "temperature": m.temperature,
+                "max_tokens": m.max_tokens,
+                "enabled": m.enabled,
+                "is_active": m.is_active,
+                "created_at": m.created_at.isoformat()
+            } for m in models],
+            "analysis_settings": settings.value if settings else {
+                "enable_technical_analysis": True,
+                "enable_fundamental_analysis": False,
+                "enable_sentiment_analysis": False,
+                "risk_tolerance": "medium",
+                "max_position_pct": 10
+            }
+        }
+    }
+
+
+@router.post("/ai-models")
+def save_ai_model(data: dict, adb: Session = Depends(get_analysis_db)):
+    """保存AI模型配置"""
+    try:
+        if data.get("action") == "save_settings":
+            settings = adb.query(AnalysisSettings).filter(
+                AnalysisSettings.key == "analysis_settings"
+            ).first()
+            if settings:
+                settings.value = data.get("settings", {})
+            else:
+                settings = AnalysisSettings(
+                    key="analysis_settings",
+                    value=data.get("settings", {})
+                )
+                adb.add(settings)
+            adb.commit()
+            return {"success": True, "message": "分析设置已保存"}
+        
+        model_data = data.get("model", {})
+        model = AIModelConfig(
+            provider=model_data.get("provider", "custom"),
+            model_name=model_data.get("model_name", ""),
+            api_key=model_data.get("api_key", ""),
+            api_base=model_data.get("api_base"),
+            model_id=model_data.get("model_id"),
+            temperature=model_data.get("temperature", 0.7),
+            max_tokens=model_data.get("max_tokens", 2000),
+            enabled=model_data.get("enabled", True),
+            is_active=model_data.get("is_active", False)
+        )
+        
+        if model.is_active:
+            adb.query(AIModelConfig).update({"is_active": False})
+        
+        adb.add(model)
+        adb.commit()
+        return {"success": True, "message": "AI模型已保存", "id": model.id}
+    except Exception as e:
+        adb.rollback()
+        return {"success": False, "message": str(e)}
+
+
+@router.put("/ai-models/{model_id}")
+def update_ai_model(model_id: int, data: dict, adb: Session = Depends(get_analysis_db)):
+    """更新AI模型配置"""
+    try:
+        model = adb.query(AIModelConfig).filter(AIModelConfig.id == model_id).first()
+        if not model:
+            return {"success": False, "message": "模型不存在"}
+        
+        if "is_active" in data and data["is_active"]:
+            adb.query(AIModelConfig).update({"is_active": False})
+            model.is_active = True
+        else:
+            for key, value in data.items():
+                if hasattr(model, key):
+                    setattr(model, key, value)
+        
+        adb.commit()
+        return {"success": True, "message": "模型已更新"}
+    except Exception as e:
+        adb.rollback()
+        return {"success": False, "message": str(e)}
+
+
+@router.delete("/ai-models/{model_id}")
+def delete_ai_model(model_id: int, adb: Session = Depends(get_analysis_db)):
+    """删除AI模型配置"""
+    try:
+        model = adb.query(AIModelConfig).filter(AIModelConfig.id == model_id).first()
+        if not model:
+            return {"success": False, "message": "模型不存在"}
+        adb.delete(model)
+        adb.commit()
+        return {"success": True, "message": "模型已删除"}
+    except Exception as e:
+        adb.rollback()
+        return {"success": False, "message": str(e)}

app/main.py

@@ -29,6 +29,9 @@ async def lifespan(app: FastAPI):
     # 启动时：建表 + 启动调度器
     logger.info("创建数据库表...")
     Base.metadata.create_all(bind=engine)
+    from app.analysis_db import init_analysis_db
+    init_analysis_db()
+    logger.info("期货智析数据库初始化完成")
 
     logger.info("启动定时调度器...")
     start_scheduler()

app/services/collector.py

@@ -10,11 +10,11 @@ from typing import Dict, List, Optional
 
 logger = logging.getLogger(__name__)
 
-# 获取原始采集脚本路径 (buffer_platform/app/services -> buffer_platform -> parent = market_data_colector_platform)
-SCRIPT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', '..'))
-if SCRIPT_DIR not in sys.path:
-    sys.path.insert(0, SCRIPT_DIR)
-    logger.info(f"已添加采集脚本路径到sys.path: {SCRIPT_DIR}")
+# 获取项目根目录 (buffer_platform/app/services -> buffer_platform)
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+if PROJECT_ROOT not in sys.path:
+    sys.path.insert(0, PROJECT_ROOT)
+    logger.info(f"已添加项目根目录到sys.path: {PROJECT_ROOT}")
 
 
 def fetch_symbol_data(
@@ -25,18 +25,6 @@ def fetch_symbol_data(
 ) -> Dict:
     """
     获取单个品种的多周期数据。
-
-    返回格式:
-    {
-        "symbol": "SN2504",
-        "type": "futures",
-        "current_price": 12345.0,
-        "timestamp": "2025-01-15T10:30:00+08:00",
-        "timeframes": {
-            "5min": [{"datetime": ..., "open": ..., ...}, ...],
-            ...
-        }
-    }
     """
     try:
         from futures_data_collector import collect_futures_data, collect_stock_data

app/static/ai_config.html

@@ -51,6 +51,8 @@
                                 <option value="anthropic">Anthropic Claude</option>
                                 <option value="google">Google Gemini</option>
                                 <option value="aliyun">阿里云通义千问</option>
+                                <option value="aliyun_coding">阿里云通义灵码</option>
+                                <option value="bailian">阿里百炼</option>
                                 <option value="baidu">百度文心一言</option>
                                 <option value="zhipu">智谱清言</option>
                                 <option value="custom">自定义</option>
@@ -77,6 +79,10 @@
                                 <option value="gpt-3.5-turbo">gpt-3.5-turbo</option>
                             </select>
                         </div>
+                        <div class="form-group">
+                            <label>自定义模型</label>
+                            <input type="text" id="custom-model" class="form-control" placeholder="输入自定义模型名称（留空使用上方选择）">
+                        </div>
                     </div>
                     <div class="form-actions">
                         <button class="btn btn-secondary" onclick="testConnection()">

app/static/ai_config.js

@@ -1,4 +1,4 @@
-const API_BASE = '/api/ai-config';
+const API_BASE = '/api/v1/ai-config';
 
 let currentConfig = null;
 let selectedProvider = 'openai';
@@ -39,6 +39,8 @@ function getDefaultProviders() {
         { id: 'anthropic', name: 'Claude', icon: 'fas fa-robot' },
         { id: 'google', name: 'Gemini', icon: 'fas fa-gem' },
         { id: 'aliyun', name: '通义千问', icon: 'fas fa-cloud' },
+        { id: 'aliyun_coding', name: '通义灵码', icon: 'fas fa-code' },
+        { id: 'bailian', name: '阿里百炼', icon: 'fas fa-flask' },
         { id: 'baidu', name: '文心一言', icon: 'fas fa-comments' },
         { id: 'zhipu', name: '智谱清言', icon: 'fas fa-lightbulb' }
     ];
@@ -51,6 +53,8 @@ function renderProviders(providers) {
         'anthropic': 'fas fa-robot',
         'google': 'fas fa-gem',
         'aliyun': 'fas fa-cloud',
+        'aliyun_coding': 'fas fa-code',
+        'bailian': 'fas fa-flask',
         'baidu': 'fas fa-comments',
         'zhipu': 'fas fa-lightbulb',
         'custom': 'fas fa-cog'
@@ -81,6 +85,8 @@ function updateProviderModels() {
         'anthropic': ['claude-3-opus', 'claude-3-sonnet', 'claude-3-haiku'],
         'google': ['gemini-pro', 'gemini-pro-vision'],
         'aliyun': ['qwen-max', 'qwen-plus', 'qwen-turbo'],
+        'aliyun_coding': ['qwen-coder-plus', 'qwen-coder-turbo'],
+        'bailian': ['qwen3.6-plus', 'qwen3.5-plus', 'qwen3-max', 'qwen3-coder-plus', 'MiniMax-M2.5', 'glm-4.7', 'kimi-k2.5', 'custom'],
         'baidu': ['ernie-4.0', 'ernie-3.5', 'ernie-speed'],
         'zhipu': ['glm-4', 'glm-3-turbo'],
         'custom': ['custom-model']
@@ -91,6 +97,8 @@ function updateProviderModels() {
         'anthropic': 'https://api.anthropic.com/v1',
         'google': 'https://generativelanguage.googleapis.com/v1beta',
         'aliyun': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
+        'aliyun_coding': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
+        'bailian': 'https://coding.dashscope.aliyuncs.com/v1',
         'baidu': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop',
         'zhipu': 'https://open.bigmodel.cn/api/paas/v4',
         'custom': ''
@@ -122,6 +130,7 @@ function populateForm(config) {
         document.getElementById('api-key').value = activeModel.api_key || '';
         document.getElementById('api-base').value = activeModel.api_base || '';
         document.getElementById('model-id').value = activeModel.model_id || 'gpt-4o';
+        document.getElementById('custom-model').value = '';
         document.getElementById('temperature').value = activeModel.temperature || 0.7;
         document.getElementById('temp-value').textContent = activeModel.temperature || 0.7;
         document.getElementById('max-tokens').value = activeModel.max_tokens || 2000;
@@ -166,6 +175,8 @@ function getProviderName(apiBase) {
         'anthropic': 'Anthropic',
         'google': 'Google',
         'aliyun': '阿里云',
+        'aliyun_coding': '阿里云通义灵码',
+        'bailian': '阿里百炼',
         'baidu': '百度',
         'zhipu': '智谱'
     };
@@ -189,11 +200,21 @@ async function testConnection() {
     resultEl.textContent = '测试中...';
     resultEl.className = 'test-result';
     
+    const customModel = document.getElementById('custom-model').value.trim();
+    const selectedModel = document.getElementById('model-id').value;
+    const modelId = customModel || (selectedModel === 'custom' ? '' : selectedModel);
+    
+    if (!modelId) {
+        resultEl.textContent = '✗ 请输入模型ID';
+        resultEl.className = 'test-result error';
+        return;
+    }
+    
     const config = {
-        model_name: document.getElementById('model-id').value,
+        model_name: modelId,
         api_key: document.getElementById('api-key').value,
         api_base: document.getElementById('api-base').value,
-        model_id: document.getElementById('model-id').value,
+        model_id: modelId,
         temperature: parseFloat(document.getElementById('temperature').value),
         max_tokens: parseInt(document.getElementById('max-tokens').value),
         enabled: true
@@ -205,17 +226,24 @@ async function testConnection() {
             headers: { 'Content-Type': 'application/json' },
             body: JSON.stringify(config)
         });
+        
+        if (!response.ok) {
+            resultEl.textContent = `✗ 请求失败: ${response.status}`;
+            resultEl.className = 'test-result error';
+            return;
+        }
+        
         const data = await response.json();
         
         if (data.success) {
             resultEl.textContent = '✓ 连接成功';
             resultEl.className = 'test-result success';
         } else {
-            resultEl.textContent = '✗ ' + data.message;
+            resultEl.textContent = '✗ ' + (data.message || '未知错误');
             resultEl.className = 'test-result error';
         }
     } catch (error) {
-        resultEl.textContent = '✗ 连接失败: ' + error.message;
+        resultEl.textContent = '✗ 连接失败: ' + (error.message || '未知错误');
         resultEl.className = 'test-result error';
     }
 }
@@ -224,12 +252,16 @@ async function saveConfig() {
     const models = currentConfig?.models || [];
     const existingIndex = models.findIndex(m => m.provider === selectedProvider);
     
+    const customModel = document.getElementById('custom-model').value.trim();
+    const selectedModel = document.getElementById('model-id').value;
+    const modelId = customModel || (selectedModel === 'custom' ? '' : selectedModel);
+    
     const newModel = {
-        model_name: document.getElementById('model-id').value,
+        model_name: modelId,
         provider: selectedProvider,
         api_key: document.getElementById('api-key').value,
         api_base: document.getElementById('api-base').value,
-        model_id: document.getElementById('model-id').value,
+        model_id: modelId,
         temperature: parseFloat(document.getElementById('temperature').value),
         max_tokens: parseInt(document.getElementById('max-tokens').value),
         enabled: true

app/static/index.html

@@ -923,6 +923,12 @@
                                 查询缓存
                             </button>
                         </div>
+                        <div class="form-group">
+                            <button class="btn btn-success" onclick="exportData()" style="height: 42px;" id="btnExportData" disabled>
+                                <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/><polyline points="7 10 12 15 17 10"/><line x1="12" y1="15" x2="12" y2="3"/></svg>
+                                导出数据
+                            </button>
+                        </div>
                     </div>
                     <div id="queryResult" class="hidden" style="margin-top: 20px;"></div>
                 </div>
@@ -1814,12 +1820,14 @@
 
                 if (!res.ok) {
                     showToast(data.detail || '未找到缓存数据', 'error');
+                    document.getElementById('btnExportData').disabled = true;
                     return;
                 }
 
                 addLog(`查询成功: ${symbol}, 缓存 ${data.timeframes ? data.timeframes.length : 0} 个周期`, 'success');
                 
                 currentQueryData = data;
+                document.getElementById('btnExportData').disabled = false;
                 
                 if (!data.timeframes || data.timeframes.length === 0) {
                     document.getElementById('queryResult').innerHTML = '<div class="empty-state"><p>暂无K线数据</p></div>';
@@ -1831,7 +1839,49 @@
                 
             } catch (e) {
                 showToast(`查询失败: ${e.message}`, 'error');
+                document.getElementById('btnExportData').disabled = true;
+            }
+        }
+
+        function exportData() {
+            if (!currentQueryData || !currentQueryData.timeframes || currentQueryData.timeframes.length === 0) {
+                showToast('暂无可导出的数据', 'error');
+                return;
             }
+
+            const symbol = document.getElementById('querySymbol').value.trim() || 'unknown';
+            const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, 19);
+            const filename = `${symbol}_多周期数据_${timestamp}.json`;
+
+            const exportObj = {
+                symbol: currentQueryData.symbol || symbol,
+                type: currentQueryData.type || 'futures',
+                current_price: currentQueryData.current_price,
+                timestamp: currentQueryData.timestamp || new Date().toISOString(),
+                timeframes: {}
+            };
+
+            currentQueryData.timeframes.forEach(tf => {
+                exportObj.timeframes[tf.period] = tf.candles || [];
+            });
+
+            const jsonStr = JSON.stringify(exportObj, null, 2);
+            const blob = new Blob([jsonStr], { type: 'application/json' });
+            const url = URL.createObjectURL(blob);
+            
+            const a = document.createElement('a');
+            a.href = url;
+            a.download = filename;
+            document.body.appendChild(a);
+            a.click();
+            document.body.removeChild(a);
+            URL.revokeObjectURL(url);
+
+            const periodCount = currentQueryData.timeframes.length;
+            const totalCandles = currentQueryData.timeframes.reduce((sum, tf) => sum + (tf.candles ? tf.candles.length : 0), 0);
+            
+            addLog(`数据导出成功: ${filename}, ${periodCount} 个周期, ${totalCandles} 条K线`, 'success');
+            showToast(`已导出 ${periodCount} 个周期数据`, 'success');
         }
 
         function renderKlineChart(timeframe, symbol) {

docker-compose.yml

@@ -0,0 +1,23 @@
+version: '3.8'
+
+services:
+  buffer-platform:
+    build: .
+    container_name: buffer-platform
+    ports:
+      - "9600:8600"
+    volumes:
+      - E:\docker_workspace\futures_datas:/app/data
+    environment:
+      - BUFFER_DB_PATH=/app/data/buffer.db
+      - BUFFER_HOST=0.0.0.0
+      - BUFFER_PORT=8600
+      - CACHE_TTL=300
+      - BUFFER_LOG_LEVEL=INFO
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8600/api/v1/health')"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 40s

futures_data_collector.py

@@ -0,0 +1,427 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+期货/股票多周期数据获取与技术指标计算脚本
+"""
+
+import akshare as ak
+import pandas as pd
+import json
+import argparse
+import os
+from datetime import datetime, timedelta
+from typing import Dict, List
+import warnings
+warnings.filterwarnings('ignore')
+ak.cache = {}
+
+DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
+os.makedirs(DATA_DIR, exist_ok=True)
+
+def calculate_ma(df: pd.DataFrame, periods: List[int] = [10, 20]) -> pd.DataFrame:
+    """计算移动平均线"""
+    for period in periods:
+        df[f'MA{period}'] = df['close'].rolling(window=period, min_periods=1).mean()
+    return df
+
+
+def calculate_macd(df: pd.DataFrame, fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame:
+    """计算MACD指标"""
+    ema_fast = df['close'].ewm(span=fast, adjust=False).mean()
+    ema_slow = df['close'].ewm(span=slow, adjust=False).mean()
+    df['macd_dif'] = ema_fast - ema_slow
+    df['macd_dea'] = df['macd_dif'].ewm(span=signal, adjust=False).mean()
+    df['macd_histogram'] = (df['macd_dif'] - df['macd_dea']) * 2
+    
+    df['macd_signal'] = df.apply(lambda row: 
+        'bullish' if row['macd_dif'] > row['macd_dea'] and row['macd_histogram'] > 0
+        else 'bearish' if row['macd_dif'] < row['macd_dea'] and row['macd_histogram'] < 0
+        else 'neutral', axis=1)
+    
+    return df
+
+
+def get_current_time() -> datetime:
+    """获取当前北京时间（去除微秒）"""
+    return datetime.now().replace(microsecond=0)
+
+
+def filter_future_data(df: pd.DataFrame, current_time: datetime = None) -> pd.DataFrame:
+    """过滤掉未来数据"""
+    if current_time is None:
+        current_time = get_current_time()
+    
+    if 'datetime' not in df.columns:
+        return df
+    
+    df['datetime'] = pd.to_datetime(df['datetime'])
+    original_count = len(df)
+    df = df[df['datetime'] <= current_time].copy()
+    filtered_count = original_count - len(df)
+    
+    if filtered_count > 0:
+        print(f"  过滤了 {filtered_count} 条未来数据")
+    
+    return df
+
+
+def extend_night_session_data(df: pd.DataFrame, symbol: str, period: str) -> pd.DataFrame:
+    """尝试获取完整的夜盘数据"""
+    if df.empty or 'datetime' not in df.columns:
+        return df
+    
+    df['datetime'] = pd.to_datetime(df['datetime'])
+    df = df.sort_values('datetime').reset_index(drop=True)
+    
+    last_time = df['datetime'].iloc[-1]
+    last_hour = last_time.hour
+    last_minute = last_time.minute
+    
+    is_night_session = (
+        (last_hour >= 21) or
+        (last_hour < 2) or
+        (last_hour == 2 and last_minute <= 30)
+    )
+    
+    if not is_night_session:
+        return df
+    
+    has_0230 = False
+    for dt in df['datetime']:
+        if dt.hour == 2 and dt.minute == 30:
+            has_0230 = True
+            break
+    
+    if has_0230:
+        return df
+    
+    print(f"  注意: 夜盘数据可能不完整（缺少02:30及之前的数据）")
+    
+    return df
+
+
+def get_minute_data(symbol: str, period: str) -> pd.DataFrame:
+    """获取期货分钟K线数据"""
+    try:
+        current_time = get_current_time()
+        df = ak.futures_zh_minute_sina(symbol=symbol, period=period)
+        
+        df = df.rename(columns={
+            'day': 'datetime',
+            'open': 'open',
+            'high': 'high',
+            'low': 'low',
+            'close': 'close',
+            'volume': 'volume'
+        })
+        
+        for col in ['open', 'high', 'low', 'close', 'volume']:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+        
+        df['datetime'] = pd.to_datetime(df['datetime'])
+        df = filter_future_data(df, current_time)
+        df = extend_night_session_data(df, symbol, period)
+        
+        if len(df) < 50:
+            print(f"  警告: {period}分钟只获取到{len(df)}根K线，建议检查数据源")
+        
+        return df
+        
+    except Exception as e:
+        print(f"  获取{period}分钟数据失败: {e}")
+        return pd.DataFrame()
+
+
+def get_daily_data(symbol: str, days: int = 60) -> pd.DataFrame:
+    """获取期货日K线数据"""
+    try:
+        current_time = get_current_time()
+        df = ak.futures_zh_daily_sina(symbol=symbol)
+        
+        df = df.rename(columns={
+            'date': 'datetime',
+            'open': 'open',
+            'high': 'high',
+            'low': 'low',
+            'close': 'close',
+            'volume': 'volume'
+        })
+        
+        for col in ['open', 'high', 'low', 'close', 'volume']:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+        
+        df['datetime'] = pd.to_datetime(df['datetime'])
+        df = df.sort_values('datetime').reset_index(drop=True)
+        df = filter_future_data(df, current_time)
+        df = df.tail(days).reset_index(drop=True)
+        
+        return df
+        
+    except Exception as e:
+        print(f"  获取日K数据失败: {e}")
+        return pd.DataFrame()
+
+
+def get_stock_minute_data(symbol: str, period: str) -> pd.DataFrame:
+    """获取股票分钟K线数据"""
+    try:
+        current_time = get_current_time()
+        
+        if symbol.startswith('6'):
+            full_symbol = f"sh{symbol}"
+        else:
+            full_symbol = f"sz{symbol}"
+        
+        df = ak.stock_zh_a_minute(symbol=full_symbol, period=period)
+        
+        df = df.rename(columns={
+            'day': 'datetime',
+            'open': 'open',
+            'high': 'high',
+            'low': 'low',
+            'close': 'close',
+            'volume': 'volume'
+        })
+        
+        for col in ['open', 'high', 'low', 'close', 'volume']:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+        
+        df['datetime'] = pd.to_datetime(df['datetime'])
+        df = filter_future_data(df, current_time)
+        
+        if len(df) < 50:
+            print(f"  警告: {period}分钟只获取到{len(df)}根K线，建议检查数据源")
+        
+        return df
+        
+    except Exception as e:
+        print(f"  获取{period}分钟数据失败: {e}")
+        return pd.DataFrame()
+
+
+def get_stock_daily_data(symbol: str, days: int = 60) -> pd.DataFrame:
+    """获取股票日K线数据"""
+    try:
+        current_time = get_current_time()
+        end_date = current_time.strftime('%Y%m%d')
+        start_date = (current_time - timedelta(days=days*2)).strftime('%Y%m%d')
+        
+        df = ak.stock_zh_a_hist(symbol=symbol, period="daily", start_date=start_date, end_date=end_date)
+        
+        df = df.rename(columns={
+            '日期': 'datetime',
+            '开盘': 'open',
+            '最高': 'high',
+            '最低': 'low',
+            '收盘': 'close',
+            '成交量': 'volume'
+        })
+        
+        for col in ['open', 'high', 'low', 'close', 'volume']:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+        
+        df['datetime'] = pd.to_datetime(df['datetime'])
+        df = df.sort_values('datetime').reset_index(drop=True)
+        df = filter_future_data(df, current_time)
+        df = df.tail(days).reset_index(drop=True)
+        
+        return df
+        
+    except Exception as e:
+        print(f"  获取日K数据失败: {e}")
+        return pd.DataFrame()
+
+
+def process_data(df: pd.DataFrame, timeframe: str) -> List[Dict]:
+    """处理数据，计算指标并格式化输出"""
+    if df.empty or len(df) < 10:
+        return []
+    
+    df = calculate_ma(df)
+    df = calculate_macd(df)
+    
+    candles = []
+    df_tail = df.tail(50) if len(df) > 50 else df
+    
+    for _, row in df_tail.iterrows():
+        candle = {
+            "time": str(row['datetime']),
+            "open": round(float(row['open']), 2),
+            "high": round(float(row['high']), 2),
+            "low": round(float(row['low']), 2),
+            "close": round(float(row['close']), 2),
+            "volume": int(row['volume']) if not pd.isna(row['volume']) else 0,
+            "ma10": round(float(row['MA10']), 2) if not pd.isna(row.get('MA10')) else None,
+            "ma20": round(float(row['MA20']), 2) if not pd.isna(row.get('MA20')) else None,
+            "macd_dif": round(float(row['macd_dif']), 4) if not pd.isna(row.get('macd_dif')) else 0,
+            "macd_dea": round(float(row['macd_dea']), 4) if not pd.isna(row.get('macd_dea')) else 0,
+            "macd_histogram": round(float(row['macd_histogram']), 4) if not pd.isna(row.get('macd_histogram')) else 0
+        }
+        candles.append(candle)
+    
+    return candles
+
+
+def collect_futures_data(symbol: str) -> Dict:
+    """收集期货多周期完整数据"""
+    print(f"\n正在获取期货 {symbol} 的多周期数据...")
+    print(f"当前时间: {get_current_time().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("-" * 50)
+    
+    result = {
+        "symbol": symbol,
+        "type": "futures",
+        "current_price": None,
+        "timestamp": datetime.now().strftime("%Y-%m-%dT%H:%M:%S+08:00"),
+        "timeframes": {}
+    }
+    
+    periods = [
+        ("60min", "60"),
+        ("30min", "30"),
+        ("15min", "15"),
+        ("5min", "5")
+    ]
+    
+    for tf_name, tf_period in periods:
+        print(f"获取 {tf_name} 数据...")
+        try:
+            df = get_minute_data(symbol, tf_period)
+            if not df.empty and len(df) >= 50:
+                candles = process_data(df, tf_name)
+                if candles:
+                    result["timeframes"][tf_name] = candles
+                    if result["current_price"] is None:
+                        result["current_price"] = candles[-1]["close"]
+                    print(f"  [OK] 成功获取 {len(candles)} 根K线")
+            else:
+                print(f"  [FAIL] 数据不足或获取失败 (获取到{len(df)}根)")
+        except Exception as e:
+            print(f"  [ERROR] 错误: {e}")
+    
+    print("获取 daily 数据...")
+    try:
+        df_daily = get_daily_data(symbol, days=60)
+        if not df_daily.empty and len(df_daily) >= 50:
+            candles = process_data(df_daily, "daily")
+            if candles:
+                result["timeframes"]["daily"] = candles
+                print(f"  [OK] 成功获取 {len(candles)} 根K线")
+        else:
+            print(f"  [FAIL] 数据不足或获取失败 (获取到{len(df_daily)}根)")
+    except Exception as e:
+        print(f"  [ERROR] 错误: {e}")
+    
+    print("-" * 50)
+    return result
+
+
+def collect_stock_data(symbol: str) -> Dict:
+    """收集股票多周期完整数据"""
+    print(f"\n正在获取股票 {symbol} 的多周期数据...")
+    print(f"当前时间: {get_current_time().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("-" * 50)
+    
+    result = {
+        "symbol": symbol,
+        "type": "stock",
+        "current_price": None,
+        "timestamp": datetime.now().strftime("%Y-%m-%dT%H:%M:%S+08:00"),
+        "timeframes": {}
+    }
+    
+    periods = [
+        ("60min", "60"),
+        ("30min", "30"),
+        ("15min", "15"),
+        ("5min", "5")
+    ]
+    
+    for tf_name, tf_period in periods:
+        print(f"获取 {tf_name} 数据...")
+        try:
+            df = get_stock_minute_data(symbol, tf_period)
+            if not df.empty and len(df) >= 50:
+                candles = process_data(df, tf_name)
+                if candles:
+                    result["timeframes"][tf_name] = candles
+                    if result["current_price"] is None:
+                        result["current_price"] = candles[-1]["close"]
+                    print(f"  [OK] 成功获取 {len(candles)} 根K线")
+            else:
+                print(f"  [FAIL] 数据不足或获取失败 (获取到{len(df)}根)")
+        except Exception as e:
+            print(f"  [ERROR] 错误: {e}")
+    
+    print("获取 daily 数据...")
+    try:
+        df_daily = get_stock_daily_data(symbol, days=60)
+        if not df_daily.empty and len(df_daily) >= 50:
+            candles = process_data(df_daily, "daily")
+            if candles:
+                result["timeframes"]["daily"] = candles
+                print(f"  [OK] 成功获取 {len(candles)} 根K线")
+        else:
+            print(f"  [FAIL] 数据不足或获取失败 (获取到{len(df_daily)}根)")
+    except Exception as e:
+        print(f"  [ERROR] 错误: {e}")
+    
+    print("-" * 50)
+    return result
+
+
+def main():
+    parser = argparse.ArgumentParser(description='期货/股票多周期数据获取与技术指标计算')
+    parser.add_argument('--symbol', type=str, required=True, 
+                        help='代码，期货如 SN2504(沪锡), 股票如 000001(平安银行)')
+    parser.add_argument('--type', type=str, default='futures', choices=['futures', 'stock'],
+                        help='数据类型：futures(期货)、stock(股票)，默认为 futures')
+    parser.add_argument('--output', type=str, default=None, 
+                        help='输出JSON文件名，默认为 代码_时间戳.json')
+    
+    args = parser.parse_args()
+    
+    if args.type == 'stock':
+        data = collect_stock_data(args.symbol)
+    else:
+        data = collect_futures_data(args.symbol)
+    
+    if not data["timeframes"]:
+        print("\n错误: 未能获取到任何数据，请检查代码是否正确")
+        if args.type == 'stock':
+            print("常见股票代码示例:")
+            print("  000001 - 平安银行")
+            print("  600000 - 浦发银行")
+            print("  000858 - 五粮液")
+            print("  600519 - 贵州茅台")
+        else:
+            print("常见期货合约代码示例:")
+            print("  SN2504 - 沪锡2504")
+            print("  AG2506 - 沪银2506")
+            print("  LC2505 - 碳酸锂2505")
+            print("  NI2505 - 沪镍2505")
+        return
+    
+    print("\n" + "="*60)
+    print("JSON 输出:")
+    print("="*60)
+    json_output = json.dumps(data, ensure_ascii=False, indent=2)
+    print(json_output)
+    
+    if args.output:
+        filename = os.path.join(DATA_DIR, args.output)
+    else:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = os.path.join(DATA_DIR, f"{data['symbol']}_{timestamp}.json")
+    
+    with open(filename, 'w', encoding='utf-8') as f:
+        f.write(json_output)
+    
+    print("\n" + "="*60)
+    print(f"[OK] 数据已保存到: {filename}")
+    print(f"[OK] 共获取 {len(data['timeframes'])} 个周期数据")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -9,3 +9,4 @@ pandas>=2.0.0
 tenacity>=8.2.0
 requests>=2.31.0
 httpx>=0.27.0
+python-multipart>=0.0.9