fix: 精简 requirements.txt，移除未使用的依赖

移除的依赖：
- gluonts: 项目中未使用
- pytorch-lightning: 非必需
- torchmetrics: 非必需
- onnx/onnxruntime: 非核心功能
- sqlalchemy: 使用 pymysql 直连
- jupyter/matplotlib/seaborn/pytest: 开发工具，非运行必需

保留的核心依赖：
- pandas, numpy, scikit-learn: 数据处理
- granite-tsfm: TTM-R2 模型
- pandas-ta: 技术指标
- fastapi, uvicorn: API 服务
- httpx, python-binance: 数据获取
- schedule: 任务调度
- pymysql, cryptography: 数据库

注意：PyTorch 需要单独安装（Windows 用 CPU 版本）

.env

@@ -1,32 +0,0 @@
-# Binance API (public data - no keys needed)
-BINANCE_API_URL=https://api.binance.com
-
-# 代理配置 (SOCKS5)
-SOCKS5_PROXY=socks5://127.0.0.1:7890
-USE_PROXY=true
-
-# Data Configuration
-DATA_CACHE_DIR=./data/cache
-LOOKBACK_1H=168      # 1 week for 1h data
-LOOKBACK_15M=672     # 1 week for 15m data
-FORECAST_HORIZON=24  # Predict 24 periods ahead
-
-# Training Configuration
-BATCH_SIZE=64
-LEARNING_RATE=0.001
-MAX_EPOCHS=100
-EARLY_STOPPING_PATIENCE=10
-TRAIN_SPLIT=0.7
-VAL_SPLIT=0.15
-TEST_SPLIT=0.15
-
-# Model Paths
-MODEL_DIR=./models/checkpoints
-ONNX_DIR=./models/onnx
-
-# API Configuration
-API_PORT=5000
-API_HOST=0.0.0.0
-
-# Classification Threshold
-PRICE_CHANGE_THRESHOLD=0.015  # 1.5% threshold for binary labels

.env.example

@@ -30,3 +30,10 @@ API_HOST=0.0.0.0
 
 # Classification Threshold
 PRICE_CHANGE_THRESHOLD=0.015  # 1.5% threshold for binary labels
+
+# MySQL Configuration
+MYSQL_HOST=localhost
+MYSQL_PORT=3306
+MYSQL_USER=user
+MYSQL_PASSWORD=password
+MYSQL_DB=crypto_predictor

.gitignore

@@ -9,9 +9,11 @@ venv/
 env/
 ENV/
 
+# Sensitive
+.env
+
 # 数据和缓存
 data/cache/*.csv
-*.db
 
 # 模型检查点（保留最佳模型，忽略训练过程文件）
 models/checkpoints/*/epoch_*.pt
@@ -38,3 +40,4 @@ logs/
 # 临时文件
 *.tmp
 .cache/
+test_*.py

AGENTS.md

@@ -0,0 +1,73 @@
+# AGENTS.md - 智能代理开发指南
+
+本文件定义了在该项目中进行开发的 AI 代理（Agent）的行为准则、技术规范和操作流程。请严格遵守。
+
+## 1. 核心行为准则 (Core Mandates)
+
+**最高优先级原则：**
+*   **语言要求**：所有回答必须使用**中文**。
+*   **称呼要求**：每次回答必须加上**“主人”**。
+*   **最小改动 (Minimal Change)**：只修改完成任务所必须的代码。**禁止**为了“最佳实践”或“代码美观”而进行未被请求的重构。
+*   **环境约束**：本项目严格使用 **Conda** 环境 (`crypto_pred_312`)。禁止使用 venv/poetry/pipenv。
+*   **复用优先**：在编写新功能前，必须检查 `data/`, `models/`, `api/` 下是否已有类似实现。
+
+## 2. 环境与构建 (Environment & Build)
+
+*   **Python 环境**: `conda activate crypto_pred_312`
+*   **依赖安装**: `pip install -r requirements.txt`
+*   **数据库**:
+    *   **仅支持 MySQL**。禁止引入 SQLite 代码。
+    *   配置：通过 `.env` 文件配置 `MYSQL_HOST`, `MYSQL_USER` 等参数。
+    *   连接管理：使用 `data/database.py` 中的 `DatabaseManager` 或 `api/models.py` 中的 `get_db_connection`。
+
+## 3. 项目结构说明 (Project Structure)
+
+*   **`api/`**: 核心服务层
+    *   `main.py`: FastAPI 主应用入口。
+    *   `models.py`: 数据库模型与操作（MySQL），包含时区转换逻辑。
+    *   `prediction_service.py`: 预测业务逻辑。
+    *   `static/`: Vue 3 前端静态资源（Dashboard）。
+*   **`data/`**: 数据层
+    *   `database.py`: 市场数据 (OHLCV) 数据库管理。
+    *   `trading_db.py`: 交易历史数据库管理。
+*   **`models/`**: 机器学习模型定义 (DLinear, TTM-R2 等) 及训练产物。
+*   **`scripts/`**: 辅助工具脚本。
+*   **根目录**:
+    *   `start_system.py`: **系统统一启动脚本**（同时启动 API 和自动交易）。
+    *   `auto_trade_polymarket.py`: 自动交易主程序。
+
+## 4. 开发规范 (Development Standards)
+
+### 4.1 时区处理 (Timezone)
+*   **存储**: 数据库中时间戳通常为 **UTC** 或 **ET (美东时间)**。
+*   **展示**: 前端及日志输出必须转换为 **CST (北京时间)**。
+*   **工具**: 使用 `api.models.convert_to_cst` 进行转换。
+
+### 4.2 代码风格
+*   **注释**: 必须使用**中文**解释“为什么这样做”。
+*   **命名**: 变量/函数 `snake_case`，类名 `PascalCase`。
+*   **类型提示**: 推荐在关键接口使用 Type Hints。
+
+### 4.3 数据库操作
+*   **禁止**使用 SQLite。
+*   使用 `pymysql` 或 `sqlalchemy` 进行操作。
+*   确保所有数据库连接都使用上下文管理器 (`with` 语句) 以自动关闭。
+
+## 5. 测试与验证 (Testing & QA)
+
+*   **运行测试**: `pytest`
+*   **代码检查**: `flake8 .`
+*   **本地验证**:
+    *   修改 API 或前端后，运行 `./start_system.py` 并访问 `http://localhost:8000` 验证。
+    *   修改模型后，运行 `python ensemble_predict.py` 验证预测逻辑。
+
+## 6. 禁止事项 (Constraints)
+
+*   ❌ **禁止**猜测业务逻辑。
+*   ❌ **禁止**删除看似无用但可能被外部脚本调用的代码。
+*   ❌ **禁止**在未确认的情况下升级核心库版本 (如 torch, pandas)。
+*   ❌ **禁止**输出英语回复 (代码变量名除外)。
+*   ❌ **禁止**恢复已删除的 SQLite 相关代码。
+
+---
+*遵循此指南，确保高效、安全地协助主人完成任务。*

api/__init__.py

@@ -0,0 +1 @@
+# api模块初始化

api/main.py

@@ -0,0 +1,191 @@
+"""
+FastAPI 主应用 - 交易看板 API
+"""
+import os
+import sys
+from datetime import datetime
+from typing import Optional
+from zoneinfo import ZoneInfo
+
+from fastapi import FastAPI, Query, HTTPException
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+# 添加项目路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from api.models import TradeModel, PredictionModel
+from api.prediction_service import get_prediction_service
+
+# 创建 FastAPI 应用
+app = FastAPI(
+    title="Crypto Predictor Dashboard API",
+    description="BTC 预测交易看板 API",
+    version="1.0.0"
+)
+
+# 启用 CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 静态文件目录
+STATIC_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'static')
+
+# 挂载静态文件
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+
+# ==================== 页面路由 ====================
+
+@app.get("/")
+async def index():
+    """返回主页"""
+    return FileResponse(os.path.join(STATIC_DIR, 'index.html'))
+
+
+# ==================== 交易 API ====================
+
+@app.get("/api/trades/current")
+async def get_current_trade():
+    """获取当前/最新交易"""
+    try:
+        trade = TradeModel.get_current_trade()
+        if trade:
+            return {"success": True, "data": trade}
+        return {"success": True, "data": None, "message": "暂无交易记录"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/api/trades/history")
+async def get_trade_history(
+    page: int = Query(1, ge=1, description="页码"),
+    limit: int = Query(10, ge=1, le=100, description="每页条数"),
+    month: Optional[str] = Query(None, description="月份筛选 (YYYY-MM)"),
+    day: Optional[str] = Query(None, description="日期筛选 (YYYY-MM-DD)")
+):
+    """获取历史交易记录"""
+    try:
+        result = TradeModel.get_history(page=page, limit=limit, month=month, day=day)
+        return {"success": True, **result}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ==================== 准确率 API ====================
+
+@app.get("/api/accuracy/current")
+async def get_current_prediction():
+    """获取当前小时的预测"""
+    try:
+        service = get_prediction_service()
+        prediction = service.predict_current_hour()
+        return {"success": True, "data": prediction}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/api/accuracy/monthly")
+async def get_monthly_accuracy(
+    month: Optional[str] = Query(None, description="月份 (YYYY-MM)，默认当月")
+):
+    """获取月度准确率统计"""
+    try:
+        # 默认当月
+        if not month:
+            et_tz = ZoneInfo('America/New_York')
+            month = datetime.now(et_tz).strftime('%Y-%m')
+        
+        # 确保有数据
+        service = get_prediction_service()
+        service.ensure_predictions_for_month(month)
+        
+        # 获取统计
+        stats = PredictionModel.get_monthly_stats(month)
+        
+        return {
+            "success": True,
+            "month": month,
+            "data": stats
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/api/accuracy/daily")
+async def get_daily_accuracy(
+    month: Optional[str] = Query(None, description="月份 (YYYY-MM)，默认当月"),
+    page: int = Query(1, ge=1, description="页码"),
+    limit: int = Query(10, ge=1, le=100, description="每页条数")
+):
+    """获取每日准确率统计"""
+    try:
+        # 默认当月
+        if not month:
+            et_tz = ZoneInfo('America/New_York')
+            month = datetime.now(et_tz).strftime('%Y-%m')
+        
+        # 确保有数据
+        service = get_prediction_service()
+        service.ensure_predictions_for_month(month)
+        
+        # 获取统计
+        result = PredictionModel.get_daily_stats(month=month, page=page, limit=limit)
+        
+        return {
+            "success": True,
+            "month": month,
+            **result
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/api/accuracy/hourly/{date}")
+async def get_hourly_accuracy(date: str):
+    """获取指定日期的每小时准确率详情"""
+    try:
+        # 确保有数据
+        service = get_prediction_service()
+        service.ensure_predictions_for_date(date)
+        
+        # 获取详情
+        details = PredictionModel.get_hourly_details(date)
+        
+        return {
+            "success": True,
+            "date": date,
+            "data": details
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ==================== 状态 API ====================
+
+@app.get("/api/status")
+async def get_status():
+    """获取服务状态"""
+    et_tz = ZoneInfo('America/New_York')
+    now_et = datetime.now(et_tz)
+    
+    return {
+        "success": True,
+        "status": "running",
+        "server_time": datetime.now().isoformat(),
+        "server_time_et": now_et.isoformat(),
+        "version": "1.0.0"
+    }
+
+
+# ==================== 启动入口 ====================
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

api/models.py

@@ -0,0 +1,475 @@
+"""
+数据库模型和操作类 - MySQL
+"""
+import pymysql
+import os
+from typing import Optional, List, Dict
+from datetime import datetime
+from contextlib import contextmanager
+from zoneinfo import ZoneInfo
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# 时区定义
+TZ_UTC = ZoneInfo('UTC')
+TZ_ET = ZoneInfo('America/New_York')  # 美东时间 (Polymarket)
+TZ_CST = ZoneInfo('Asia/Shanghai')     # 中国标准时间 (前端显示)
+
+
+def convert_to_cst(timestamp_val, source_tz: str = 'UTC') -> str:
+    """
+    将时间戳转换为 CST 时间字符串
+    
+    Args:
+        timestamp_val: datetime 对象或 ISO 格式时间戳字符串
+        source_tz: 输入时区 - 'UTC'(默认，币安数据), 'ET'(Polymarket), 'CST'(已是北京时间)
+    
+    Returns:
+        CST 时间字符串 (YYYY-MM-DD HH:MM:SS)
+    """
+    if not timestamp_val:
+        return ""
+    try:
+        # 如果是 datetime 对象 (MySQL 返回)
+        if isinstance(timestamp_val, datetime):
+            dt = timestamp_val
+        else:
+            # 字符串格式
+            dt = datetime.fromisoformat(str(timestamp_val).replace('Z', '+00:00'))
+        
+        # 根据来源时区设置时区信息
+        if dt.tzinfo is None:
+            if source_tz == 'ET':
+                dt = dt.replace(tzinfo=TZ_ET)
+            elif source_tz == 'CST':
+                # 已经是 CST，直接格式化返回
+                return dt.strftime('%Y-%m-%d %H:%M:%S')
+            else:  # 默认 UTC
+                dt = dt.replace(tzinfo=TZ_UTC)
+        
+        # 如果来源已经是 CST，直接格式化
+        if source_tz == 'CST':
+            return dt.strftime('%Y-%m-%d %H:%M:%S')
+        
+        # 转换为 CST
+        dt_cst = dt.astimezone(TZ_CST)
+        return dt_cst.strftime('%Y-%m-%d %H:%M:%S')
+    except Exception:
+        return str(timestamp_val)
+
+
+@contextmanager
+def get_db_connection():
+    """获取 MySQL 数据库连接"""
+    conn = pymysql.connect(
+        host=os.getenv('MYSQL_HOST'),
+        port=int(os.getenv('MYSQL_PORT', 3306)),
+        user=os.getenv('MYSQL_USER'),
+        password=os.getenv('MYSQL_PASSWORD'),
+        database=os.getenv('MYSQL_DB'),
+        charset='utf8mb4',
+        cursorclass=pymysql.cursors.DictCursor
+    )
+    try:
+        yield conn
+    finally:
+        conn.close()
+
+
+def _dict_from_row(row):
+    """将数据库行转换为字典"""
+    if row is None:
+        return None
+    if isinstance(row, dict):
+        return row
+    return dict(row)
+
+
+def init_model_predictions_table():
+    """初始化 model_predictions 表"""
+    with get_db_connection() as conn:
+        cursor = conn.cursor()
+        cursor.execute('''
+            CREATE TABLE IF NOT EXISTS model_predictions (
+                id INT AUTO_INCREMENT PRIMARY KEY,
+                timestamp DATETIME NOT NULL,
+                timestamp_et VARCHAR(50),
+                date VARCHAR(10),
+                hour INT,
+                model_name VARCHAR(50) NOT NULL,
+                prediction INT,
+                probability DOUBLE,
+                actual_result INT,
+                is_correct INT,
+                open_price DOUBLE,
+                close_price DOUBLE,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                UNIQUE KEY uk_date_hour_model (date, hour, model_name),
+                INDEX idx_date (date),
+                INDEX idx_model (model_name)
+            ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
+        ''')
+        conn.commit()
+
+
+# 初始化表
+init_model_predictions_table()
+
+
+class TradeModel:
+    """交易记录模型"""
+    
+    @staticmethod
+    def get_current_trade() -> Optional[Dict]:
+        """获取最新的交易记录"""
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT * FROM trades 
+                ORDER BY timestamp DESC 
+                LIMIT 1
+            """)
+            row = cursor.fetchone()
+            result = _dict_from_row(row)
+            if result and result.get('timestamp'):
+                # 交易记录时间已经是 CST，直接格式化
+                result['timestamp_cst'] = convert_to_cst(result['timestamp'], source_tz='CST')
+            return result
+    
+    @staticmethod
+    def get_history(page: int = 1, limit: int = 10, 
+                    month: Optional[str] = None,
+                    day: Optional[str] = None) -> Dict:
+        """
+        获取历史交易记录
+        
+        Args:
+            page: 页码
+            limit: 每页条数
+            month: 月份筛选 (YYYY-MM)
+            day: 日期筛选 (YYYY-MM-DD)
+        """
+        offset = (page - 1) * limit
+        
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            
+            # 构建查询条件
+            where_clauses = []
+            params = []
+            
+            if month:
+                where_clauses.append("DATE_FORMAT(timestamp, '%%Y-%%m') = %s")
+                params.append(month)
+            
+            if day:
+                where_clauses.append("DATE(timestamp) = %s")
+                params.append(day)
+            
+            where_sql = " AND ".join(where_clauses) if where_clauses else "1=1"
+            
+            # 查询数据
+            sql = f"SELECT * FROM trades WHERE {where_sql} ORDER BY timestamp DESC LIMIT %s OFFSET %s"
+            cursor.execute(sql, params + [limit, offset])
+            rows = cursor.fetchall()
+            
+            # 查询总数
+            count_sql = f"SELECT COUNT(*) as cnt FROM trades WHERE {where_sql}"
+            cursor.execute(count_sql, params)
+            result = cursor.fetchone()
+            total = result['cnt'] if result else 0
+            
+            # 转换每条记录的时间戳
+            data_list = []
+            for row in rows:
+                item = _dict_from_row(row)
+                if item and item.get('timestamp'):
+                    # 交易记录时间已经是 CST，直接格式化
+                    item['timestamp_cst'] = convert_to_cst(item['timestamp'], source_tz='CST')
+                data_list.append(item)
+            
+            return {
+                "data": data_list,
+                "page": page,
+                "limit": limit,
+                "total": total,
+                "total_pages": (total + limit - 1) // limit if limit > 0 else 0
+            }
+
+
+class PredictionModel:
+    """预测记录模型"""
+    
+    @staticmethod
+    def get_prediction(date: str, hour: int, model_name: str) -> Optional[Dict]:
+        """获取指定时间和模型的预测记录"""
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT * FROM model_predictions 
+                WHERE date = %s AND hour = %s AND model_name = %s
+            """, (date, hour, model_name))
+            row = cursor.fetchone()
+            return _dict_from_row(row)
+    
+    @staticmethod
+    def save_prediction(data: Dict) -> int:
+        """
+        保存预测记录
+        
+        Args:
+            data: 预测数据字典
+        
+        Returns:
+            插入的记录ID
+        """
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            sql = """
+                INSERT INTO model_predictions 
+                (timestamp, timestamp_et, date, hour, model_name, 
+                 prediction, probability, actual_result, is_correct,
+                 open_price, close_price)
+                VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+                ON DUPLICATE KEY UPDATE
+                    prediction = VALUES(prediction),
+                    probability = VALUES(probability),
+                    actual_result = VALUES(actual_result),
+                    is_correct = VALUES(is_correct),
+                    open_price = VALUES(open_price),
+                    close_price = VALUES(close_price)
+            """
+            cursor.execute(sql, (
+                data.get('timestamp'),
+                data.get('timestamp_et'),
+                data.get('date'),
+                data.get('hour'),
+                data.get('model_name'),
+                data.get('prediction'),
+                data.get('probability'),
+                data.get('actual_result'),
+                data.get('is_correct'),
+                data.get('open_price'),
+                data.get('close_price')
+            ))
+            conn.commit()
+            return cursor.lastrowid or 0
+    
+    @staticmethod
+    def update_actual_result(date: str, hour: int, model_name: str,
+                             actual_result: int, close_price: float) -> bool:
+        """更新实际结果"""
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            
+            # 先获取预测值
+            cursor.execute("""
+                SELECT prediction FROM model_predictions 
+                WHERE date = %s AND hour = %s AND model_name = %s
+            """, (date, hour, model_name))
+            row = cursor.fetchone()
+            
+            if not row:
+                return False
+            
+            prediction = row['prediction']
+            is_correct = 1 if prediction == actual_result else 0
+            
+            cursor.execute("""
+                UPDATE model_predictions 
+                SET actual_result = %s, is_correct = %s, close_price = %s
+                WHERE date = %s AND hour = %s AND model_name = %s
+            """, (actual_result, is_correct, close_price, date, hour, model_name))
+            conn.commit()
+            return True
+    
+    @staticmethod
+    def get_monthly_stats(month: str) -> Dict:
+        """
+        获取月度统计
+        
+        Args:
+            month: 月份 (YYYY-MM)
+        
+        Returns:
+            各模型的统计数据
+        """
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            
+            sql = """
+                SELECT 
+                    model_name,
+                    COUNT(*) as total,
+                    SUM(CASE WHEN is_correct = 1 THEN 1 ELSE 0 END) as correct,
+                    SUM(CASE WHEN is_correct = 0 THEN 1 ELSE 0 END) as incorrect
+                FROM model_predictions 
+                WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
+                  AND actual_result IS NOT NULL
+                GROUP BY model_name
+            """
+            cursor.execute(sql, (month,))
+            
+            rows = cursor.fetchall()
+            result = {}
+            
+            for row in rows:
+                row_dict = _dict_from_row(row)
+                model = row_dict['model_name']
+                total = row_dict['total']
+                correct = row_dict['correct'] or 0
+                incorrect = row_dict['incorrect'] or 0
+                accuracy = (correct / total * 100) if total > 0 else 0
+                
+                result[model] = {
+                    "total": total,
+                    "correct": correct,
+                    "incorrect": incorrect,
+                    "accuracy": round(accuracy, 2)
+                }
+            
+            return result
+    
+    @staticmethod
+    def get_daily_stats(month: str, page: int = 1, limit: int = 10) -> Dict:
+        """
+        获取每日统计
+        
+        Args:
+            month: 月份 (YYYY-MM)
+            page: 页码
+            limit: 每页条数
+        """
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            
+            sql = """
+                SELECT 
+                    DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as date_cst,
+                    model_name,
+                    COUNT(*) as total,
+                    SUM(CASE WHEN is_correct = 1 THEN 1 ELSE 0 END) as correct,
+                    SUM(CASE WHEN is_correct = 0 THEN 1 ELSE 0 END) as incorrect
+                FROM model_predictions 
+                WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
+                  AND actual_result IS NOT NULL
+                GROUP BY date_cst, model_name
+                ORDER BY date_cst DESC
+            """
+            cursor.execute(sql, (month,))
+            
+            rows = cursor.fetchall()
+            
+            # 重组数据: 按日期合并不同模型的统计
+            daily_data = {}
+            for row in rows:
+                row_dict = _dict_from_row(row)
+                date = str(row_dict['date_cst'])  # CST 日期
+                if date not in daily_data:
+                    daily_data[date] = {"date": date}
+                
+                model = row_dict['model_name']
+                total = row_dict['total']
+                correct = row_dict['correct'] or 0
+                incorrect = row_dict['incorrect'] or 0
+                accuracy = (correct / total * 100) if total > 0 else 0
+                
+                daily_data[date][model] = {
+                    "total": total,
+                    "correct": correct,
+                    "incorrect": incorrect,
+                    "accuracy": round(accuracy, 2)
+                }
+            
+            # 排序并分页
+            sorted_dates = sorted(daily_data.keys(), reverse=True)
+            total_count = len(sorted_dates)
+            offset = (page - 1) * limit
+            paginated_dates = sorted_dates[offset:offset + limit]
+            
+            data = [daily_data[d] for d in paginated_dates]
+            
+            return {
+                "data": data,
+                "page": page,
+                "limit": limit,
+                "total": total_count,
+                "total_pages": (total_count + limit - 1) // limit if limit > 0 else 0
+            }
+    
+    @staticmethod
+    def get_hourly_details(date: str) -> List[Dict]:
+        """
+        获取指定日期的每小时详情
+        
+        Args:
+            date: 日期 (YYYY-MM-DD, CST 北京时间)
+        """
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            # 按 CST 日期筛选，并获取 CST 小时
+            cursor.execute("""
+                SELECT 
+                    *,
+                    HOUR(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as hour_cst,
+                    CONVERT_TZ(timestamp, '+00:00', '+08:00') as timestamp_cst_dt
+                FROM model_predictions 
+                WHERE DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) = %s
+                ORDER BY hour_cst, model_name
+            """, (date,))
+            
+            rows = cursor.fetchall()
+            
+            # 按 CST 小时重组数据
+            hourly_data = {}
+            for row in rows:
+                row_dict = _dict_from_row(row)
+                hour_cst = row_dict['hour_cst']  # 使用 CST 小时
+                if hour_cst not in hourly_data:
+                    # 将 timestamp 转换为 CST 时间字符串
+                    timestamp_cst = convert_to_cst(row_dict.get('timestamp', ''))
+                    hourly_data[hour_cst] = {
+                        "hour": hour_cst,  # 返回 CST 小时
+                        "timestamp_et": row_dict['timestamp_et'],
+                        "timestamp_cst": timestamp_cst,
+                        "open_price": row_dict['open_price'],
+                        "close_price": row_dict['close_price'],
+                        "actual_result": row_dict['actual_result'],
+                        "models": {}
+                    }
+                
+                model = row_dict['model_name']
+                hourly_data[hour_cst]["models"][model] = {
+                    "prediction": row_dict['prediction'],
+                    "probability": row_dict['probability'],
+                    "is_correct": row_dict['is_correct']
+                }
+            
+            # 按 CST 小时排序返回
+            return [hourly_data[h] for h in sorted(hourly_data.keys())]
+    
+    @staticmethod
+    def get_missing_hours(date: str, model_name: str) -> List[int]:
+        """
+        获取指定日期缺少预测的小时列表
+        
+        Args:
+            date: 日期 (YYYY-MM-DD)
+            model_name: 模型名称
+        
+        Returns:
+            缺少预测的小时列表
+        """
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT hour FROM model_predictions 
+                WHERE date = %s AND model_name = %s
+            """, (date, model_name))
+            
+            rows = cursor.fetchall()
+            existing_hours = {row['hour'] for row in rows}
+            all_hours = set(range(24))
+            
+            return sorted(list(all_hours - existing_hours))

api/polymarket_client.py

@@ -0,0 +1,390 @@
+"""
+Polymarket API 客户端
+
+用于与Polymarket CLOB API交互，支持：
+- 搜索BTC相关市场
+- 获取订单簿和价格
+- 下单交易
+"""
+
+import os
+import httpx
+from typing import Optional, Dict, List, Any
+from dataclasses import dataclass
+from dotenv import load_dotenv
+
+# 加载环境变量
+load_dotenv()
+
+
+@dataclass
+class MarketInfo:
+    """市场信息"""
+    condition_id: str
+    question: str
+    outcomes: List[str]
+    outcome_prices: List[float]
+    tokens: List[Dict]
+    volume: float
+    liquidity: float
+    end_date: str
+
+
+class PolymarketClient:
+    """Polymarket API客户端"""
+    
+    GAMMA_API = "https://gamma-api.polymarket.com"
+    CLOB_API = "https://clob.polymarket.com"
+    
+    def __init__(self, private_key: Optional[str] = None):
+        """
+        初始化客户端
+        
+        Args:
+            private_key: 钱包私钥(用于交易)，如不提供则只能读取数据
+        """
+        self.private_key = private_key or os.getenv("POLYMARKET_PRIVATE_KEY")
+        
+        # 配置代理
+        proxy = os.getenv("SOCKS5_PROXY") if os.getenv("USE_PROXY", "false").lower() == "true" else None
+        
+        # HTTP客户端 - 禁用压缩避免brotli问题
+        self.http = httpx.Client(
+            timeout=30.0,
+            proxy=proxy,
+            headers={'Accept-Encoding': 'identity'}  # 禁用压缩
+        )
+        
+        # 如果有私钥，初始化CLOB客户端
+        self.clob_client = None
+        if self.private_key:
+            self._init_clob_client()
+    
+    def _init_clob_client(self):
+        """初始化CLOB交易客户端"""
+        try:
+            from py_clob_client.client import ClobClient
+            from py_clob_client.clob_types import ApiCreds
+            
+            # Polygon链ID
+            chain_id = 137
+            
+            # 初始化客户端
+            self.clob_client = ClobClient(
+                host=self.CLOB_API,
+                key=self.private_key,
+                chain_id=chain_id,
+            )
+            
+            # 获取或创建API凭证
+            self.clob_client.set_api_creds(self.clob_client.create_or_derive_api_creds())
+            
+            print("✓ CLOB客户端初始化成功")
+        except Exception as e:
+            print(f"⚠️ CLOB客户端初始化失败: {e}")
+            self.clob_client = None
+    
+    def search_markets(self, query: str = "BTC", limit: int = 20) -> List[Dict]:
+        """
+        搜索市场
+        
+        Args:
+            query: 搜索关键词
+            limit: 返回数量限制
+            
+        Returns:
+            市场列表
+        """
+        try:
+            response = self.http.get(
+                f"{self.GAMMA_API}/markets",
+                params={
+                    "active": "true",
+                    "closed": "false",
+                    "limit": limit,
+                }
+            )
+            response.raise_for_status()
+            markets = response.json()
+            
+            # 过滤包含关键词的市场
+            filtered = [m for m in markets if query.lower() in m.get("question", "").lower()]
+            return filtered
+        except Exception as e:
+            print(f"搜索市场失败: {e}")
+            return []
+    
+    def search_btc_1h_markets(self) -> List[Dict]:
+        """
+        搜索BTC 1小时价格预测市场
+        
+        Returns:
+            BTC 1H相关市场列表
+        """
+        try:
+            response = self.http.get(
+                f"{self.GAMMA_API}/markets",
+                params={
+                    "active": "true",
+                    "closed": "false",
+                    "limit": 100,
+                }
+            )
+            response.raise_for_status()
+            markets = response.json()
+            
+            # 过滤BTC 1小时相关市场
+            btc_1h_markets = []
+            keywords = ["btc", "bitcoin"]
+            time_keywords = ["1h", "1 hour", "hourly", "hour"]
+            
+            for m in markets:
+                question = m.get("question", "").lower()
+                # 检查是否包含BTC关键词
+                has_btc = any(kw in question for kw in keywords)
+                # 检查是否包含1小时关键词
+                has_1h = any(kw in question for kw in time_keywords)
+                
+                if has_btc and has_1h:
+                    btc_1h_markets.append(m)
+            
+            return btc_1h_markets
+        except Exception as e:
+            print(f"搜索BTC 1H市场失败: {e}")
+            return []
+    
+    def get_market_info(self, condition_id: str) -> Optional[MarketInfo]:
+        """
+        获取市场详细信息
+        
+        Args:
+            condition_id: 市场条件ID
+            
+        Returns:
+            市场信息对象
+        """
+        try:
+            response = self.http.get(f"{self.GAMMA_API}/markets/{condition_id}")
+            response.raise_for_status()
+            data = response.json()
+            
+            return MarketInfo(
+                condition_id=data.get("conditionId", ""),
+                question=data.get("question", ""),
+                outcomes=data.get("outcomes", []),
+                outcome_prices=data.get("outcomePrices", []),
+                tokens=data.get("tokens", []),
+                volume=float(data.get("volume", 0)),
+                liquidity=float(data.get("liquidity", 0)),
+                end_date=data.get("endDate", ""),
+            )
+        except Exception as e:
+            print(f"获取市场信息失败: {e}")
+            return None
+    
+    def get_orderbook(self, token_id: str) -> Dict:
+        """
+        获取订单簿
+        
+        Args:
+            token_id: 代币ID
+            
+        Returns:
+            订单簿数据
+        """
+        try:
+            response = self.http.get(
+                f"{self.CLOB_API}/book",
+                params={"token_id": token_id}
+            )
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            print(f"获取订单簿失败: {e}")
+            return {}
+    
+    def get_price(self, token_id: str) -> Optional[float]:
+        """
+        获取代币当前价格
+        
+        Args:
+            token_id: 代币ID
+            
+        Returns:
+            当前价格(0-1之间)
+        """
+        try:
+            response = self.http.get(
+                f"{self.CLOB_API}/price",
+                params={"token_id": token_id, "side": "buy"}
+            )
+            response.raise_for_status()
+            data = response.json()
+            return float(data.get("price", 0))
+        except Exception as e:
+            print(f"获取价格失败: {e}")
+            return None
+    
+    def place_market_order(
+        self, 
+        token_id: str, 
+        side: str,  # "BUY" or "SELL"
+        amount: float,  # USDC金额
+    ) -> Optional[Dict]:
+        """
+        下市价单
+        
+        Args:
+            token_id: 代币ID
+            side: 买卖方向 "BUY" 或 "SELL"
+            amount: 下注金额(USDC)
+            
+        Returns:
+            订单响应
+        """
+        if not self.clob_client:
+            print("❌ CLOB客户端未初始化，无法下单")
+            return None
+        
+        try:
+            from py_clob_client.order_builder.constants import BUY, SELL
+            
+            order_side = BUY if side.upper() == "BUY" else SELL
+            
+            # 创建市价单
+            order = self.clob_client.create_market_order(
+                token_id=token_id,
+                amount=amount,
+                side=order_side,
+            )
+            
+            # 提交订单
+            response = self.clob_client.post_order(order)
+            print(f"✓ 订单已提交: {response}")
+            return response
+        except Exception as e:
+            print(f"❌ 下单失败: {e}")
+            return None
+    
+    def place_limit_order(
+        self,
+        token_id: str,
+        side: str,  # "BUY" or "SELL"
+        price: float,  # 价格(0-1)
+        size: float,  # 数量
+    ) -> Optional[Dict]:
+        """
+        下限价单
+        
+        Args:
+            token_id: 代币ID
+            side: 买卖方向 "BUY" 或 "SELL"
+            price: 价格(0-1之间)
+            size: 购买数量
+            
+        Returns:
+            订单响应
+        """
+        if not self.clob_client:
+            print("❌ CLOB客户端未初始化，无法下单")
+            return None
+        
+        try:
+            from py_clob_client.order_builder.constants import BUY, SELL
+            
+            order_side = BUY if side.upper() == "BUY" else SELL
+            
+            # 创建限价单
+            order = self.clob_client.create_limit_order(
+                token_id=token_id,
+                price=price,
+                size=size,
+                side=order_side,
+            )
+            
+            # 提交订单
+            response = self.clob_client.post_order(order)
+            print(f"✓ 限价单已提交: {response}")
+            return response
+        except Exception as e:
+            print(f"❌ 下单失败: {e}")
+            return None
+    
+    def get_positions(self) -> List[Dict]:
+        """
+        获取当前仓位
+        
+        Returns:
+            仓位列表
+        """
+        if not self.clob_client:
+            print("❌ CLOB客户端未初始化")
+            return []
+        
+        try:
+            positions = self.clob_client.get_positions()
+            return positions
+        except Exception as e:
+            print(f"获取仓位失败: {e}")
+            return []
+    
+    def cancel_order(self, order_id: str) -> bool:
+        """
+        取消订单
+        
+        Args:
+            order_id: 订单ID
+            
+        Returns:
+            是否成功
+        """
+        if not self.clob_client:
+            print("❌ CLOB客户端未初始化")
+            return False
+        
+        try:
+            self.clob_client.cancel(order_id)
+            print(f"✓ 订单已取消: {order_id}")
+            return True
+        except Exception as e:
+            print(f"❌ 取消订单失败: {e}")
+            return False
+    
+    def close(self):
+        """关闭客户端"""
+        self.http.close()
+
+
+# 测试函数
+def test_search_markets():
+    """测试搜索市场功能"""
+    client = PolymarketClient()
+    
+    print("搜索BTC相关市场...")
+    markets = client.search_markets("BTC", limit=10)
+    
+    if markets:
+        print(f"\n找到 {len(markets)} 个BTC相关市场:\n")
+        for m in markets[:5]:
+            print(f"  问题: {m.get('question', 'N/A')}")
+            print(f"  条件ID: {m.get('conditionId', 'N/A')}")
+            print(f"  结果: {m.get('outcomes', [])}")
+            print(f"  价格: {m.get('outcomePrices', [])}")
+            print("-" * 50)
+    else:
+        print("未找到BTC相关市场")
+    
+    print("\n搜索BTC 1H市场...")
+    btc_1h = client.search_btc_1h_markets()
+    if btc_1h:
+        print(f"找到 {len(btc_1h)} 个BTC 1H市场")
+        for m in btc_1h:
+            print(f"  {m.get('question', 'N/A')}")
+    else:
+        print("未找到BTC 1H市场")
+    
+    client.close()
+
+
+if __name__ == "__main__":
+    test_search_markets()

api/prediction_service.py

@@ -0,0 +1,483 @@
+"""
+预测服务 - DLinear 和 TTM-R2 模型预测
+"""
+import os
+import sys
+import pickle
+from datetime import datetime, timedelta
+from typing import Optional, List, Dict, Tuple
+from zoneinfo import ZoneInfo
+
+import torch
+import numpy as np
+import pandas as pd
+import pandas_ta as ta
+from sklearn.preprocessing import StandardScaler
+
+# 添加项目路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from data.fetcher import BinanceDataFetcher
+from api.models import PredictionModel
+
+
+class ClassificationPreprocessor:
+    """DLinear分类预处理器"""
+    
+    def __init__(self, lookback: int = 168, forecast_horizon: int = 1):
+        self.lookback = lookback
+        self.forecast_horizon = forecast_horizon
+        self.scaler = StandardScaler()
+        self.is_fitted = False
+
+    def add_features(self, df: pd.DataFrame) -> pd.DataFrame:
+        df = df.copy()
+        df['rsi'] = ta.rsi(df['close'], length=14)
+        macd = ta.macd(df['close'])
+        if macd is not None and not macd.empty:
+            df['macd'] = macd.iloc[:, 0]
+            df['macd_hist'] = macd.iloc[:, 1]
+            df['macd_signal'] = macd.iloc[:, 2]
+        return df
+
+    def create_labels(self, df: pd.DataFrame) -> pd.DataFrame:
+        df = df.copy()
+        df['future_price'] = df['close'].shift(-self.forecast_horizon)
+        df['future_return'] = (df['future_price'] - df['close']) / df['close']
+        df['label'] = (df['future_return'] > 0).astype(int)
+        return df[:-self.forecast_horizon]
+
+    def preprocess(self, df: pd.DataFrame, fit: bool = True):
+        df = self.add_features(df)
+        df = self.create_labels(df)
+        df = df.dropna().reset_index(drop=True)
+        
+        feature_cols = ['open', 'high', 'low', 'close', 'volume', 
+                       'rsi', 'macd', 'macd_hist', 'macd_signal']
+        
+        if fit:
+            scaled_features = self.scaler.fit_transform(df[feature_cols])
+            self.is_fitted = True
+            self.feature_columns = feature_cols
+        else:
+            scaled_features = self.scaler.transform(df[feature_cols])
+        
+        X, y = [], []
+        for i in range(len(scaled_features) - self.lookback):
+            X.append(scaled_features[i:i + self.lookback])
+            y.append(df['label'].iloc[i + self.lookback])
+        
+        return np.array(X), np.array(y), df
+
+
+class CustomUnpickler(pickle.Unpickler):
+    """自定义反序列化器，用于处理 __main__ 模块的类"""
+    def find_class(self, module, name):
+        if module == "__main__":
+            if name == "ClassificationPreprocessor":
+                return ClassificationPreprocessor
+        return super().find_class(module, name)
+
+
+class PredictionService:
+    """预测服务 - 管理 DLinear 和 TTM-R2 模型"""
+    
+    # 类级别的锁，确保多个请求不会同时生成预测
+    _prediction_lock = None
+    
+    def __init__(self):
+        import threading
+        import platform
+        # 初始化锁
+        if PredictionService._prediction_lock is None:
+            PredictionService._prediction_lock = threading.Lock()
+        
+        # 设备选择：macOS 用 MPS，Windows/Linux 用 CPU
+        if platform.system() == 'Darwin':  # macOS
+            self.device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
+        else:  # Windows / Linux
+            self.device = torch.device('cpu')
+        print(f"使用设备: {self.device}")
+        
+        self.et_tz = ZoneInfo('America/New_York')
+        self.fetcher = BinanceDataFetcher()
+        
+        # 模型和预处理器
+        self.dlinear_model = None
+        self.dlinear_preprocessor = None
+        self.ttm_model = None
+        
+        # 记录正在处理的日期和月份，避免重复处理
+        self._processing_dates = set()
+        self._processing_months = set()
+        
+        self._load_models()
+    
+    def _load_models(self):
+        """加载模型"""
+        print("正在加载预测模型...")
+        
+        # 加载 DLinear
+        from models.dlinear.model import DLinear
+        
+        dlinear_path = './models/checkpoints/dlinear_classification_2026/best_model.pt'
+        preprocessor_path = './models/checkpoints/dlinear_classification_2026/preprocessor.pkl'
+        
+        checkpoint = torch.load(dlinear_path, map_location=self.device, weights_only=False)
+        self.dlinear_model = DLinear(checkpoint['config'])
+        self.dlinear_model.load_state_dict(checkpoint['model_state_dict'])
+        self.dlinear_model = self.dlinear_model.to(self.device).eval()
+        
+        with open(preprocessor_path, 'rb') as f:
+            try:
+                self.dlinear_preprocessor = pickle.load(f)
+            except AttributeError:
+                f.seek(0)
+                self.dlinear_preprocessor = CustomUnpickler(f).load()
+        
+        print(f"  ✓ DLinear 已加载")
+        
+        # 加载 TTM-R2
+        from tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction
+        
+        self.ttm_model = TinyTimeMixerForPrediction.from_pretrained(
+            "ibm-granite/granite-timeseries-ttm-r2",
+            trust_remote_code=True
+        )
+        self.ttm_model = self.ttm_model.to(self.device).eval()
+        
+        print(f"  ✓ TTM-R2 已加载")
+        print(f"  设备: {self.device}")
+    
+    def _get_btc_data(self, days: int = 30) -> pd.DataFrame:
+        """获取BTC历史数据"""
+        end_date = (datetime.now() + timedelta(days=1)).strftime('%Y-%m-%d')
+        start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
+        
+        df = self.fetcher.fetch_ohlcv(
+            symbol='BTC',
+            timeframe='1h',
+            start_date=start_date,
+            end_date=end_date,
+            use_cache=False
+        )
+        return df
+    
+    def predict_dlinear(self, df: pd.DataFrame) -> Tuple[int, float]:
+        """
+        使用 DLinear 预测下一小时的涨跌
+        
+        输入: df 包含历史K线数据 (最后一行是最新的已完成K线)
+        输出: 预测下一小时相对于 df 最后一行收盘价的涨跌
+        
+        Returns:
+            (prediction, probability): 预测结果(0=跌, 1=涨)和概率
+        """
+        # 预处理: 使用全部数据，不去掉最后一行
+        X, _, _ = self.dlinear_preprocessor.preprocess(df, fit=False)
+        
+        if len(X) == 0:
+            return 0, 0.5
+        
+        # 取最后一个窗口进行预测
+        X_last = torch.FloatTensor(X[-1:]).to(self.device)
+        
+        with torch.no_grad():
+            logits = self.dlinear_model(X_last).cpu().numpy()
+            prob = 1 / (1 + np.exp(-logits))
+            prediction = int(prob.item() > 0.5)
+            probability = float(prob.item())
+        
+        return prediction, probability
+    
+    def predict_ttm(self, df: pd.DataFrame) -> Tuple[int, float]:
+        """
+        使用 TTM-R2 预测下一小时的涨跌
+        
+        输入: df 包含历史K线数据 (最后一行是最新的已完成K线)
+        输出: 预测下一小时相对于 df 最后一行收盘价的涨跌
+        
+        Returns:
+            (prediction, probability): 预测结果(0=跌, 1=涨)和概率
+        """
+        context_length = 512
+        all_prices = df['close'].values
+        
+        # 使用最后 context_length 个价格
+        if len(all_prices) >= context_length:
+            context_data = all_prices[-context_length:].reshape(1, -1, 1)
+        else:
+            # 数据不足，用最后一个价格填充
+            padded = np.full(context_length, all_prices[-1])
+            padded[-len(all_prices):] = all_prices
+            context_data = padded.reshape(1, -1, 1)
+        
+        # 基准价格: 最后一行的收盘价 (即预测目标小时的前一小时收盘价)
+        base_price = float(df['close'].iloc[-1])
+        
+        context_tensor = torch.FloatTensor(context_data).to(self.device)
+        
+        with torch.no_grad():
+            output = self.ttm_model(context_tensor)
+            pred_price = output.prediction_outputs.cpu().numpy()[0, 0, 0]
+        
+        # 判断涨跌: 预测价格 vs 基准价格
+        prediction = 1 if pred_price > base_price else 0
+        
+        # 计算伪概率: 基于价格变化百分比
+        price_pct = (pred_price - base_price) / base_price
+        # 将价格变化百分比映射到概率 (sigmoid)
+        probability = 1 / (1 + np.exp(-price_pct * 100))
+        
+        return prediction, float(probability)
+    
+    def predict_current_hour(self) -> Dict:
+        """
+        预测当前小时的涨跌
+        
+        逻辑:
+        - 获取最新的K线数据 (最后一行是最新已完成的K线)
+        - 预测下一小时相对于最后一行收盘价的涨跌
+        
+        Returns:
+            预测结果字典
+        """
+        df = self._get_btc_data()
+        
+        # 获取当前时间 (美东)
+        now_et = datetime.now(self.et_tz)
+        current_date = now_et.strftime('%Y-%m-%d')
+        current_hour = now_et.hour
+        
+        # 最后一个已完成的K线信息 (作为预测基准)
+        last_timestamp = df['timestamp'].iloc[-1]
+        last_close = float(df['close'].iloc[-1])
+        
+        # 当前正在形成的K线信息
+        current_open = float(df['open'].iloc[-1])
+        current_price = float(df['close'].iloc[-1])  # 当前价格
+        
+        results = {
+            "date": current_date,
+            "hour": current_hour,
+            "timestamp": str(last_timestamp),
+            "base_price": last_close,  # 预测基准价格
+            "current_price": current_price,
+            "models": {}
+        }
+        
+        # DLinear 预测 (预测下一小时相对于 last_close 的涨跌)
+        dlinear_pred, dlinear_prob = self.predict_dlinear(df)
+        results["models"]["DLinear"] = {
+            "prediction": dlinear_pred,
+            "probability": round(dlinear_prob, 4),
+            "direction": "UP" if dlinear_pred == 1 else "DOWN"
+        }
+        
+        # TTM-R2 预测
+        ttm_pred, ttm_prob = self.predict_ttm(df)
+        results["models"]["TTM-R2"] = {
+            "prediction": ttm_pred,
+            "probability": round(ttm_prob, 4),
+            "direction": "UP" if ttm_pred == 1 else "DOWN"
+        }
+        
+        return results
+    
+    def ensure_predictions_for_date(self, date: str) -> None:
+        """
+        确保指定日期有预测数据，如果没有则生成
+        
+        Args:
+            date: 日期 (YYYY-MM-DD)
+        """
+        # 使用锁避免并发冲突
+        with PredictionService._prediction_lock:
+            # 检查是否正在处理该日期
+            if date in self._processing_dates:
+                return
+            
+            # 标记正在处理
+            self._processing_dates.add(date)
+        
+        try:
+            # 检查是否有缺失的小时
+            for model_name in ["DLinear", "TTM-R2"]:
+                missing_hours = PredictionModel.get_missing_hours(date, model_name)
+                
+                if not missing_hours:
+                    continue
+                
+                print(f"为 {date} 生成 {model_name} 预测 (缺失 {len(missing_hours)} 小时)...")
+                
+                # 获取足够的历史数据
+                target_date = datetime.strptime(date, '%Y-%m-%d')
+                df = self._get_historical_data(target_date)
+                
+                if df is None or len(df) < 200:
+                    print(f"  警告: 数据不足，无法为 {date} 生成预测")
+                    continue
+                
+                # 为每个缺失的小时生成预测
+                for hour in missing_hours:
+                    self._generate_prediction_for_hour(df, date, hour, model_name)
+        finally:
+            # 移除处理标记
+            with PredictionService._prediction_lock:
+                self._processing_dates.discard(date)
+    
+    def _get_historical_data(self, target_date: datetime) -> Optional[pd.DataFrame]:
+        """获取指定日期的历史数据"""
+        try:
+            start_date = (target_date - timedelta(days=30)).strftime('%Y-%m-%d')
+            end_date = (target_date + timedelta(days=2)).strftime('%Y-%m-%d')
+            
+            df = self.fetcher.fetch_ohlcv(
+                symbol='BTC',
+                timeframe='1h',
+                start_date=start_date,
+                end_date=end_date,
+                use_cache=True
+            )
+            return df
+        except Exception as e:
+            print(f"获取历史数据失败: {e}")
+            return None
+    
+    def _generate_prediction_for_hour(self, df: pd.DataFrame, date: str, 
+                                      hour: int, model_name: str) -> None:
+        """
+        为指定小时生成预测
+        
+        预测逻辑:
+        - 预测目标: {date} {hour}:00 的K线收盘价涨跌
+        - 输入数据: {hour-1}:00 及以前的K线数据 (不包含目标小时)
+        - 涨跌判断: 预测价格 vs 前一小时收盘价 (即 {hour-1}:00 的 close)
+        - 正确率验证: 目标小时的 close vs 前一小时的 close
+        
+        Args:
+            df: 历史K线数据 (UTC时间戳)
+            date: 目标日期 (美东时间 YYYY-MM-DD)
+            hour: 目标小时 (美东时间 0-23)
+            model_name: 模型名称
+        """
+        try:
+            # 1. 构建目标时间 (美东时间 -> UTC)
+            target_time_et = pd.Timestamp(f"{date} {hour:02d}:00:00", tz='America/New_York')
+            target_time_utc = target_time_et.tz_convert('UTC').tz_localize(None)
+            
+            # 2. 确保 timestamp 列是 datetime 类型
+            if 'timestamp_dt' not in df.columns:
+                df = df.copy()
+                df['timestamp_dt'] = pd.to_datetime(df['timestamp'])
+            
+            # 3. 找到目标小时的K线 (用于验证)
+            target_mask = df['timestamp_dt'] == target_time_utc
+            if not target_mask.any():
+                # 目标小时数据不存在，跳过
+                return
+            
+            target_row = df[target_mask].iloc[0]
+            target_open = float(target_row['open'])
+            target_close = float(target_row['close'])
+            
+            # 4. 找到前一小时的K线 (用于预测输入和基准价格)
+            prev_time_utc = target_time_utc - pd.Timedelta(hours=1)
+            prev_mask = df['timestamp_dt'] == prev_time_utc
+            if not prev_mask.any():
+                # 前一小时数据不存在，跳过
+                return
+            
+            prev_row = df[prev_mask].iloc[0]
+            prev_close = float(prev_row['close'])
+            
+            # 5. 获取预测输入数据: 前一小时及以前的所有数据
+            input_mask = df['timestamp_dt'] <= prev_time_utc
+            df_input = df[input_mask].copy()
+            
+            if len(df_input) < 200:
+                # 数据不足，跳过
+                return
+            
+            # 6. 调用模型预测
+            if model_name == "DLinear":
+                prediction, probability = self.predict_dlinear(df_input)
+            else:  # TTM-R2
+                prediction, probability = self.predict_ttm(df_input)
+            
+            # 7. 判断实际涨跌 (目标小时收盘价 vs 前一小时收盘价)
+            actual_result = 1 if target_close > prev_close else 0
+            
+            # 8. 判断预测是否正确
+            is_correct = 1 if prediction == actual_result else 0
+            
+            # 9. 保存预测结果
+            prediction_data = {
+                "timestamp": target_time_utc.isoformat(),
+                "timestamp_et": target_time_et.isoformat(),
+                "date": date,
+                "hour": hour,
+                "model_name": model_name,
+                "prediction": prediction,
+                "probability": probability,
+                "actual_result": actual_result,
+                "is_correct": is_correct,
+                "open_price": target_open,
+                "close_price": target_close
+            }
+            
+            PredictionModel.save_prediction(prediction_data)
+            
+        except Exception as e:
+            print(f"生成预测失败 ({date} {hour}:00 {model_name}): {e}")
+    
+    def ensure_predictions_for_month(self, month: str) -> None:
+        """
+        确保指定月份有预测数据
+        
+        Args:
+            month: 月份 (YYYY-MM)
+        """
+        # 使用锁避免并发冲突
+        with PredictionService._prediction_lock:
+            # 检查是否正在处理该月份
+            if month in self._processing_months:
+                return
+            
+            # 标记正在处理
+            self._processing_months.add(month)
+        
+        try:
+            # 解析月份
+            year, mon = map(int, month.split('-'))
+            
+            # 计算该月的日期范围
+            from calendar import monthrange
+            _, last_day = monthrange(year, mon)
+            
+            # 对于过去的日期，确保有预测数据
+            today = datetime.now(self.et_tz).date()
+            
+            for day in range(1, last_day + 1):
+                date = f"{year}-{mon:02d}-{day:02d}"
+                date_obj = datetime.strptime(date, '%Y-%m-%d').date()
+                
+                # 只处理过去和今天的日期
+                if date_obj <= today:
+                    self.ensure_predictions_for_date(date)
+        finally:
+            # 移除处理标记
+            with PredictionService._prediction_lock:
+                self._processing_months.discard(month)
+
+
+# 全局单例
+_prediction_service: Optional[PredictionService] = None
+
+
+def get_prediction_service() -> PredictionService:
+    """获取预测服务单例"""
+    global _prediction_service
+    if _prediction_service is None:
+        _prediction_service = PredictionService()
+    return _prediction_service

api/static/app.js

@@ -0,0 +1,252 @@
+/**
+ * BTC 预测交易看板 - Vue 3 应用
+ */
+const { createApp } = Vue;
+
+const app = createApp({
+    data() {
+        return {
+            // 状态
+            loading: false,
+            activeTab: 'trades',
+            serverTime: '',
+            
+            // 当前交易
+            currentTrade: null,
+            
+            // 交易历史
+            tradeHistory: {
+                data: [],
+                page: 1,
+                limit: 10,
+                total: 0,
+                total_pages: 0
+            },
+            tradeFilter: {
+                month: '',
+                day: ''
+            },
+            
+            // 准确率
+            accuracyFilter: {
+                month: this.getCurrentMonth()
+            },
+            monthlyStats: {},
+            dailyStats: {
+                data: [],
+                page: 1,
+                limit: 10,
+                total: 0,
+                total_pages: 0
+            },
+            
+            // 弹窗数据
+            hourlyModal: {
+                date: '',
+                data: []
+            }
+        };
+    },
+    
+    mounted() {
+        // 初始化月份筛选
+        this.accuracyFilter.month = this.getCurrentMonth();
+        
+        // 加载数据
+        this.loadCurrentTrade();
+        this.loadTradeHistory(1);
+        
+        // 每分钟更新时间
+        this.updateServerTime();
+        setInterval(() => this.updateServerTime(), 60000);
+    },
+    
+    methods: {
+        // ==================== 工具方法 ====================
+        
+        getCurrentMonth() {
+            const now = new Date();
+            return `${now.getFullYear()}-${String(now.getMonth() + 1).padStart(2, '0')}`;
+        },
+        
+        updateServerTime() {
+            const now = new Date();
+            this.serverTime = now.toLocaleString('zh-CN', {
+                year: 'numeric',
+                month: '2-digit',
+                day: '2-digit',
+                hour: '2-digit',
+                minute: '2-digit'
+            });
+        },
+        
+        formatTime(timestamp, timestamp_cst) {
+            // 优先使用后端返回的 CST 时间
+            if (timestamp_cst) {
+                return timestamp_cst;
+            }
+            if (!timestamp) return '-';
+            const date = new Date(timestamp);
+            return date.toLocaleString('zh-CN', {
+                month: '2-digit',
+                day: '2-digit',
+                hour: '2-digit',
+                minute: '2-digit'
+            });
+        },
+        
+        formatPercent(value) {
+            if (value === null || value === undefined) return '-';
+            return `${(value * 100).toFixed(1)}%`;
+        },
+        
+        getPredictionClass(prediction) {
+            if (prediction === 'UP') return 'text-success fw-bold';
+            if (prediction === 'DOWN') return 'text-danger fw-bold';
+            return '';
+        },
+        
+        getStatusClass(status) {
+            switch (status) {
+                case 'success': return 'text-success';
+                case 'test': return 'text-info';
+                case 'failure': return 'text-danger';
+                default: return 'text-muted';
+            }
+        },
+        
+        getStatusText(status) {
+            switch (status) {
+                case 'success': return '成功';
+                case 'test': return '测试';
+                case 'failure': return '失败';
+                case 'in_progress': return '进行中';
+                default: return status || '-';
+            }
+        },
+        
+        getAccuracyClass(accuracy) {
+            if (accuracy >= 60) return 'text-success fw-bold';
+            if (accuracy >= 50) return 'text-warning';
+            return 'text-danger';
+        },
+        
+        // ==================== API 调用 ====================
+        
+        async fetchAPI(url) {
+            try {
+                const response = await fetch(url);
+                const data = await response.json();
+                if (!data.success) {
+                    console.error('API Error:', data);
+                }
+                return data;
+            } catch (error) {
+                console.error('Fetch Error:', error);
+                return { success: false, error: error.message };
+            }
+        },
+        
+        async loadCurrentTrade() {
+            const data = await this.fetchAPI('/api/trades/current');
+            if (data.success) {
+                this.currentTrade = data.data;
+            }
+        },
+        
+        async loadTradeHistory(page) {
+            if (page < 1) return;
+            
+            this.loading = true;
+            
+            let url = `/api/trades/history?page=${page}&limit=${this.tradeHistory.limit}`;
+            if (this.tradeFilter.month) {
+                url += `&month=${this.tradeFilter.month}`;
+            }
+            if (this.tradeFilter.day) {
+                url += `&day=${this.tradeFilter.day}`;
+            }
+            
+            const data = await this.fetchAPI(url);
+            if (data.success) {
+                this.tradeHistory = {
+                    data: data.data,
+                    page: data.page,
+                    limit: data.limit,
+                    total: data.total,
+                    total_pages: data.total_pages
+                };
+            }
+            
+            this.loading = false;
+        },
+        
+        clearTradeFilter() {
+            this.tradeFilter.month = '';
+            this.tradeFilter.day = '';
+            this.loadTradeHistory(1);
+        },
+        
+        async loadAccuracyData() {
+            this.loading = true;
+            
+            // 加载月度统计
+            const monthlyData = await this.fetchAPI(
+                `/api/accuracy/monthly?month=${this.accuracyFilter.month}`
+            );
+            if (monthlyData.success) {
+                this.monthlyStats = monthlyData.data || {};
+            }
+            
+            // 加载每日统计
+            await this.loadDailyStats(1);
+            
+            this.loading = false;
+        },
+        
+        async loadDailyStats(page) {
+            if (page < 1) return;
+            
+            const data = await this.fetchAPI(
+                `/api/accuracy/daily?month=${this.accuracyFilter.month}&page=${page}&limit=10`
+            );
+            
+            if (data.success) {
+                this.dailyStats = {
+                    data: data.data,
+                    page: data.page,
+                    limit: data.limit,
+                    total: data.total,
+                    total_pages: data.total_pages
+                };
+            }
+        },
+        
+        async showHourlyDetails(date) {
+            this.loading = true;
+            this.hourlyModal.date = date;
+            
+            const data = await this.fetchAPI(`/api/accuracy/hourly/${date}`);
+            
+            if (data.success) {
+                this.hourlyModal.data = data.data || [];
+            }
+            
+            this.loading = false;
+            
+            // 显示弹窗
+            const modal = new bootstrap.Modal(document.getElementById('hourlyModal'));
+            modal.show();
+        }
+    },
+    
+    watch: {
+        activeTab(newTab) {
+            if (newTab === 'accuracy' && !this.dailyStats.data.length) {
+                this.loadAccuracyData();
+            }
+        }
+    }
+});
+
+app.mount('#app');

api/static/index.html

@@ -0,0 +1,414 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>BTC 预测交易看板</title>
+    <!-- Bootstrap 5 CSS -->
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.2/dist/css/bootstrap.min.css" rel="stylesheet">
+    <!-- Bootstrap Icons -->
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap-icons@1.11.1/font/bootstrap-icons.css" rel="stylesheet">
+    <!-- Custom CSS -->
+    <link href="/static/style.css" rel="stylesheet">
+</head>
+<body>
+    <div id="app">
+        <!-- 导航栏 -->
+        <nav class="navbar navbar-expand-lg navbar-dark bg-dark">
+            <div class="container">
+                <a class="navbar-brand" href="#">
+                    <i class="bi bi-currency-bitcoin"></i> BTC 预测交易看板
+                </a>
+                <span class="navbar-text text-light">
+                    <i class="bi bi-clock"></i> {{ serverTime }}
+                </span>
+            </div>
+        </nav>
+
+        <!-- 主容器 -->
+        <div class="container mt-4">
+            <!-- Tab 导航 -->
+            <ul class="nav nav-tabs mb-4" role="tablist">
+                <li class="nav-item">
+                    <button class="nav-link" :class="{ active: activeTab === 'trades' }" 
+                            @click="activeTab = 'trades'">
+                        <i class="bi bi-graph-up"></i> 交易详情
+                    </button>
+                </li>
+                <li class="nav-item">
+                    <button class="nav-link" :class="{ active: activeTab === 'accuracy' }" 
+                            @click="activeTab = 'accuracy'">
+                        <i class="bi bi-bullseye"></i> 模型准确率
+                    </button>
+                </li>
+            </ul>
+
+            <!-- 交易详情 Tab -->
+            <div v-show="activeTab === 'trades'">
+                <!-- 当前交易卡片 -->
+                <div class="card mb-4">
+                    <div class="card-header bg-primary text-white">
+                        <i class="bi bi-lightning-charge"></i> 当前小时交易
+                    </div>
+                    <div class="card-body">
+                        <div v-if="currentTrade" class="row">
+                            <div class="col-md-3">
+                                <div class="stat-card">
+                                    <div class="stat-label">市场</div>
+                                    <div class="stat-value small">{{ currentTrade.market_title || '-' }}</div>
+                                </div>
+                            </div>
+                            <div class="col-md-2">
+                                <div class="stat-card">
+                                    <div class="stat-label">预测方向</div>
+                                    <div class="stat-value" :class="getPredictionClass(currentTrade.prediction)">
+                                        {{ currentTrade.prediction || '-' }}
+                                    </div>
+                                </div>
+                            </div>
+                            <div class="col-md-2">
+                                <div class="stat-card">
+                                    <div class="stat-label">概率</div>
+                                    <div class="stat-value">{{ formatPercent(currentTrade.probability) }}</div>
+                                </div>
+                            </div>
+                            <div class="col-md-2">
+                                <div class="stat-card">
+                                    <div class="stat-label">价格</div>
+                                    <div class="stat-value">${{ currentTrade.price?.toFixed(3) || '-' }}</div>
+                                </div>
+                            </div>
+                            <div class="col-md-3">
+                                <div class="stat-card">
+                                    <div class="stat-label">状态</div>
+                                    <div class="stat-value" :class="getStatusClass(currentTrade.status)">
+                                        {{ getStatusText(currentTrade.status) }}
+                                    </div>
+                                </div>
+                            </div>
+                        </div>
+                        <div v-else class="text-center text-muted py-4">
+                            <i class="bi bi-inbox"></i> 暂无交易记录
+                        </div>
+                    </div>
+                </div>
+
+                <!-- 历史交易 -->
+                <div class="card">
+                    <div class="card-header d-flex justify-content-between align-items-center">
+                        <span><i class="bi bi-clock-history"></i> 历史交易记录</span>
+                        <div class="d-flex gap-2">
+                            <input type="month" class="form-control form-control-sm" 
+                                   v-model="tradeFilter.month" @change="loadTradeHistory(1)"
+                                   style="width: 150px;">
+                            <input type="date" class="form-control form-control-sm" 
+                                   v-model="tradeFilter.day" @change="loadTradeHistory(1)"
+                                   style="width: 150px;">
+                            <button class="btn btn-sm btn-outline-secondary" @click="clearTradeFilter">
+                                <i class="bi bi-x-circle"></i> 清除
+                            </button>
+                        </div>
+                    </div>
+                    <div class="card-body p-0">
+                        <div class="table-responsive">
+                            <table class="table table-hover table-striped mb-0">
+                                <thead class="table-light">
+                                    <tr>
+                                        <th>时间 (北京)</th>
+                                        <th>市场</th>
+                                        <th>预测</th>
+                                        <th>概率</th>
+                                        <th>价格</th>
+                                        <th>状态</th>
+                                    </tr>
+                                </thead>
+                                <tbody>
+                                    <tr v-for="trade in tradeHistory.data" :key="trade.id">
+                                        <td>{{ formatTime(trade.timestamp, trade.timestamp_cst) }}</td>
+                                        <td class="text-truncate" style="max-width: 200px;">
+                                            {{ trade.market_title }}
+                                        </td>
+                                        <td :class="getPredictionClass(trade.prediction)">
+                                            <i :class="trade.prediction === 'UP' ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
+                                            {{ trade.prediction }}
+                                        </td>
+                                        <td>{{ formatPercent(trade.probability) }}</td>
+                                        <td>${{ trade.price?.toFixed(3) }}</td>
+                                        <td :class="getStatusClass(trade.status)">
+                                            {{ getStatusText(trade.status) }}
+                                        </td>
+                                    </tr>
+                                    <tr v-if="!tradeHistory.data?.length">
+                                        <td colspan="6" class="text-center text-muted py-4">
+                                            暂无数据
+                                        </td>
+                                    </tr>
+                                </tbody>
+                            </table>
+                        </div>
+                    </div>
+                    <!-- 分页 -->
+                    <div class="card-footer d-flex justify-content-between align-items-center">
+                        <span class="text-muted">共 {{ tradeHistory.total }} 条</span>
+                        <nav>
+                            <ul class="pagination pagination-sm mb-0">
+                                <li class="page-item" :class="{ disabled: tradeHistory.page <= 1 }">
+                                    <a class="page-link" href="#" @click.prevent="loadTradeHistory(tradeHistory.page - 1)">
+                                        上一页
+                                    </a>
+                                </li>
+                                <li class="page-item disabled">
+                                    <span class="page-link">{{ tradeHistory.page }} / {{ tradeHistory.total_pages }}</span>
+                                </li>
+                                <li class="page-item" :class="{ disabled: tradeHistory.page >= tradeHistory.total_pages }">
+                                    <a class="page-link" href="#" @click.prevent="loadTradeHistory(tradeHistory.page + 1)">
+                                        下一页
+                                    </a>
+                                </li>
+                            </ul>
+                        </nav>
+                    </div>
+                </div>
+            </div>
+
+            <!-- 模型准确率 Tab -->
+            <div v-show="activeTab === 'accuracy'">
+                <!-- 月份筛选 -->
+                <div class="mb-4">
+                    <div class="d-flex gap-2 align-items-center">
+                        <label class="form-label mb-0">选择月份:</label>
+                        <input type="month" class="form-control" v-model="accuracyFilter.month" 
+                               @change="loadAccuracyData" style="width: 180px;">
+                    </div>
+                </div>
+
+                <!-- 当月汇总卡片 -->
+                <div class="row mb-4">
+                    <!-- DLinear 卡片 -->
+                    <div class="col-md-6">
+                        <div class="card h-100 border-primary">
+                            <div class="card-header bg-primary text-white">
+                                <i class="bi bi-graph-up-arrow"></i> DLinear 模型
+                            </div>
+                            <div class="card-body">
+                                <div class="row text-center" v-if="monthlyStats.DLinear">
+                                    <div class="col-3">
+                                        <div class="stat-number">{{ monthlyStats.DLinear.total }}</div>
+                                        <div class="stat-label">预测次数</div>
+                                    </div>
+                                    <div class="col-3">
+                                        <div class="stat-number text-success">{{ monthlyStats.DLinear.correct }}</div>
+                                        <div class="stat-label">正确</div>
+                                    </div>
+                                    <div class="col-3">
+                                        <div class="stat-number text-danger">{{ monthlyStats.DLinear.incorrect }}</div>
+                                        <div class="stat-label">错误</div>
+                                    </div>
+                                    <div class="col-3">
+                                        <div class="stat-number text-primary">{{ monthlyStats.DLinear.accuracy }}%</div>
+                                        <div class="stat-label">准确率</div>
+                                    </div>
+                                </div>
+                                <div v-else class="text-center text-muted py-3">
+                                    暂无数据
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+
+                    <!-- TTM-R2 卡片 -->
+                    <div class="col-md-6">
+                        <div class="card h-100 border-success">
+                            <div class="card-header bg-success text-white">
+                                <i class="bi bi-cpu"></i> TTM-R2 模型
+                            </div>
+                            <div class="card-body">
+                                <div class="row text-center" v-if="monthlyStats['TTM-R2']">
+                                    <div class="col-3">
+                                        <div class="stat-number">{{ monthlyStats['TTM-R2'].total }}</div>
+                                        <div class="stat-label">预测次数</div>
+                                    </div>
+                                    <div class="col-3">
+                                        <div class="stat-number text-success">{{ monthlyStats['TTM-R2'].correct }}</div>
+                                        <div class="stat-label">正确</div>
+                                    </div>
+                                    <div class="col-3">
+                                        <div class="stat-number text-danger">{{ monthlyStats['TTM-R2'].incorrect }}</div>
+                                        <div class="stat-label">错误</div>
+                                    </div>
+                                    <div class="col-3">
+                                        <div class="stat-number text-primary">{{ monthlyStats['TTM-R2'].accuracy }}%</div>
+                                        <div class="stat-label">准确率</div>
+                                    </div>
+                                </div>
+                                <div v-else class="text-center text-muted py-3">
+                                    暂无数据
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- 每日准确率表格 -->
+                <div class="card">
+                    <div class="card-header">
+                        <i class="bi bi-calendar3"></i> 每日准确率
+                    </div>
+                    <div class="card-body p-0">
+                        <div class="table-responsive">
+                            <table class="table table-hover mb-0">
+                                <thead class="table-light">
+                                    <tr>
+                                        <th>日期 (北京)</th>
+                                        <th>DLinear 预测</th>
+                                        <th>DLinear 准确率</th>
+                                        <th>TTM-R2 预测</th>
+                                        <th>TTM-R2 准确率</th>
+                                        <th>操作</th>
+                                    </tr>
+                                </thead>
+                                <tbody>
+                                    <tr v-for="day in dailyStats.data" :key="day.date">
+                                        <td>{{ day.date }}</td>
+                                        <td>
+                                            <span v-if="day.DLinear">
+                                                {{ day.DLinear.correct }}/{{ day.DLinear.total }}
+                                            </span>
+                                            <span v-else class="text-muted">-</span>
+                                        </td>
+                                        <td>
+                                            <span v-if="day.DLinear" :class="getAccuracyClass(day.DLinear.accuracy)">
+                                                {{ day.DLinear.accuracy }}%
+                                            </span>
+                                            <span v-else class="text-muted">-</span>
+                                        </td>
+                                        <td>
+                                            <span v-if="day['TTM-R2']">
+                                                {{ day['TTM-R2'].correct }}/{{ day['TTM-R2'].total }}
+                                            </span>
+                                            <span v-else class="text-muted">-</span>
+                                        </td>
+                                        <td>
+                                            <span v-if="day['TTM-R2']" :class="getAccuracyClass(day['TTM-R2'].accuracy)">
+                                                {{ day['TTM-R2'].accuracy }}%
+                                            </span>
+                                            <span v-else class="text-muted">-</span>
+                                        </td>
+                                        <td>
+                                            <button class="btn btn-sm btn-outline-primary" 
+                                                    @click="showHourlyDetails(day.date)">
+                                                <i class="bi bi-eye"></i> 查看
+                                            </button>
+                                        </td>
+                                    </tr>
+                                    <tr v-if="!dailyStats.data?.length">
+                                        <td colspan="6" class="text-center text-muted py-4">
+                                            暂无数据
+                                        </td>
+                                    </tr>
+                                </tbody>
+                            </table>
+                        </div>
+                    </div>
+                    <!-- 分页 -->
+                    <div class="card-footer d-flex justify-content-between align-items-center">
+                        <span class="text-muted">共 {{ dailyStats.total }} 天</span>
+                        <nav>
+                            <ul class="pagination pagination-sm mb-0">
+                                <li class="page-item" :class="{ disabled: dailyStats.page <= 1 }">
+                                    <a class="page-link" href="#" @click.prevent="loadDailyStats(dailyStats.page - 1)">
+                                        上一页
+                                    </a>
+                                </li>
+                                <li class="page-item disabled">
+                                    <span class="page-link">{{ dailyStats.page }} / {{ dailyStats.total_pages }}</span>
+                                </li>
+                                <li class="page-item" :class="{ disabled: dailyStats.page >= dailyStats.total_pages }">
+                                    <a class="page-link" href="#" @click.prevent="loadDailyStats(dailyStats.page + 1)">
+                                        下一页
+                                    </a>
+                                </li>
+                            </ul>
+                        </nav>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- 每小时详情弹窗 -->
+        <div class="modal fade" id="hourlyModal" tabindex="-1">
+            <div class="modal-dialog modal-lg modal-dialog-scrollable">
+                <div class="modal-content">
+                    <div class="modal-header">
+                        <h5 class="modal-title">
+                            <i class="bi bi-clock"></i> {{ hourlyModal.date }} 每小时预测详情 (北京时间)
+                        </h5>
+                        <button type="button" class="btn-close" data-bs-dismiss="modal"></button>
+                    </div>
+                    <div class="modal-body p-0">
+                        <table class="table table-sm table-hover mb-0">
+                            <thead class="table-light sticky-top">
+                                <tr>
+                                    <th>时间 (北京)</th>
+                                    <th>实际涨跌</th>
+                                    <th>DLinear</th>
+                                    <th>TTM-R2</th>
+                                </tr>
+                            </thead>
+                            <tbody>
+                                <tr v-for="hour in hourlyModal.data" :key="hour.hour">
+                                    <td>{{ hour.timestamp_cst || (hour.hour + ':00') }}</td>
+                                    <td :class="hour.actual_result === 1 ? 'text-success' : 'text-danger'">
+                                        <i :class="hour.actual_result === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
+                                        {{ hour.actual_result === 1 ? 'UP' : 'DOWN' }}
+                                    </td>
+                                    <td>
+                                        <span v-if="hour.models?.DLinear" 
+                                              :class="hour.models.DLinear.is_correct ? 'text-success' : 'text-danger'">
+                                            <i :class="hour.models.DLinear.prediction === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
+                                            {{ hour.models.DLinear.prediction === 1 ? 'UP' : 'DOWN' }}
+                                            <i :class="hour.models.DLinear.is_correct ? 'bi-check-circle' : 'bi-x-circle'"></i>
+                                        </span>
+                                        <span v-else class="text-muted">-</span>
+                                    </td>
+                                    <td>
+                                        <span v-if="hour.models?.['TTM-R2']" 
+                                              :class="hour.models['TTM-R2'].is_correct ? 'text-success' : 'text-danger'">
+                                            <i :class="hour.models['TTM-R2'].prediction === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
+                                            {{ hour.models['TTM-R2'].prediction === 1 ? 'UP' : 'DOWN' }}
+                                            <i :class="hour.models['TTM-R2'].is_correct ? 'bi-check-circle' : 'bi-x-circle'"></i>
+                                        </span>
+                                        <span v-else class="text-muted">-</span>
+                                    </td>
+                                </tr>
+                                <tr v-if="!hourlyModal.data?.length">
+                                    <td colspan="4" class="text-center text-muted py-4">
+                                        暂无数据
+                                    </td>
+                                </tr>
+                            </tbody>
+                        </table>
+                    </div>
+                    <div class="modal-footer">
+                        <button type="button" class="btn btn-secondary" data-bs-dismiss="modal">关闭</button>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Loading 遮罩 -->
+        <div v-if="loading" class="loading-overlay">
+            <div class="spinner-border text-primary" role="status">
+                <span class="visually-hidden">加载中...</span>
+            </div>
+        </div>
+    </div>
+
+    <!-- Bootstrap 5 JS -->
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.2/dist/js/bootstrap.bundle.min.js"></script>
+    <!-- Vue 3 -->
+    <script src="https://cdn.jsdelivr.net/npm/vue@3.4.15/dist/vue.global.prod.js"></script>
+    <!-- App JS -->
+    <script src="/static/app.js"></script>
+</body>
+</html>

api/static/style.css

@@ -0,0 +1,202 @@
+/**
+ * BTC 预测交易看板 - 自定义样式
+ */
+
+/* 全局样式 */
+body {
+    background-color: #f8f9fa;
+    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;
+}
+
+/* 导航栏 */
+.navbar-brand {
+    font-weight: 600;
+}
+
+.navbar-brand i {
+    color: #f7931a;
+}
+
+/* Tab 样式 */
+.nav-tabs .nav-link {
+    color: #6c757d;
+    border: none;
+    padding: 0.75rem 1.5rem;
+}
+
+.nav-tabs .nav-link.active {
+    color: #0d6efd;
+    border-bottom: 3px solid #0d6efd;
+    background: transparent;
+}
+
+.nav-tabs .nav-link:hover:not(.active) {
+    border-bottom: 3px solid #dee2e6;
+}
+
+/* 卡片样式 */
+.card {
+    box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);
+    border-radius: 0.5rem;
+}
+
+.card-header {
+    font-weight: 600;
+    border-radius: 0.5rem 0.5rem 0 0 !important;
+}
+
+/* 统计卡片 */
+.stat-card {
+    padding: 1rem;
+    text-align: center;
+    background: #f8f9fa;
+    border-radius: 0.5rem;
+    height: 100%;
+}
+
+.stat-label {
+    font-size: 0.75rem;
+    color: #6c757d;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    margin-bottom: 0.25rem;
+}
+
+.stat-value {
+    font-size: 1.25rem;
+    font-weight: 600;
+}
+
+.stat-number {
+    font-size: 1.75rem;
+    font-weight: 700;
+}
+
+/* 表格样式 */
+.table {
+    margin-bottom: 0;
+}
+
+.table th {
+    font-weight: 600;
+    font-size: 0.875rem;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    color: #6c757d;
+    border-bottom: 2px solid #dee2e6;
+}
+
+.table td {
+    vertical-align: middle;
+}
+
+.table-hover tbody tr:hover {
+    background-color: rgba(13, 110, 253, 0.05);
+}
+
+/* 涨跌颜色 */
+.text-success {
+    color: #198754 !important;
+}
+
+.text-danger {
+    color: #dc3545 !important;
+}
+
+/* 分页 */
+.pagination {
+    margin-bottom: 0;
+}
+
+.page-link {
+    color: #0d6efd;
+}
+
+.page-item.disabled .page-link {
+    color: #6c757d;
+}
+
+/* Loading 遮罩 */
+.loading-overlay {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: rgba(255, 255, 255, 0.8);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    z-index: 9999;
+}
+
+/* 弹窗 */
+.modal-content {
+    border-radius: 0.5rem;
+}
+
+.modal-header {
+    background: #f8f9fa;
+    border-radius: 0.5rem 0.5rem 0 0;
+}
+
+/* 响应式调整 */
+@media (max-width: 768px) {
+    .stat-card {
+        margin-bottom: 1rem;
+    }
+    
+    .card-header .d-flex {
+        flex-direction: column;
+        gap: 0.5rem !important;
+    }
+    
+    .nav-tabs .nav-link {
+        padding: 0.5rem 1rem;
+        font-size: 0.875rem;
+    }
+}
+
+/* 准确率颜色 */
+.accuracy-high {
+    color: #198754;
+    font-weight: 600;
+}
+
+.accuracy-medium {
+    color: #fd7e14;
+    font-weight: 600;
+}
+
+.accuracy-low {
+    color: #dc3545;
+    font-weight: 600;
+}
+
+/* 边框颜色增强 */
+.border-primary {
+    border-color: #0d6efd !important;
+    border-width: 2px !important;
+}
+
+.border-success {
+    border-color: #198754 !important;
+    border-width: 2px !important;
+}
+
+/* 按钮样式 */
+.btn-outline-primary:hover {
+    color: #fff;
+}
+
+/* 截断文本 */
+.text-truncate {
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+/* 间距调整 */
+.gap-2 {
+    gap: 0.5rem !important;
+}

auto_trade_polymarket.py

@@ -0,0 +1,944 @@
+"""
+BTC Hourly 自动交易脚本
+
+基于DLinear模型预测，在Polymarket自动下单BTC小时涨跌
+支持:
+- 每小时自动寻找对应市场
+- 实时价格刷新
+- 定时下单
+- 详细日志记录
+"""
+
+import os
+import sys
+import argparse
+import json
+import time
+import logging
+from logging.handlers import RotatingFileHandler
+import schedule
+from datetime import datetime, timedelta
+from typing import Optional, Tuple, Dict
+import pickle
+from zoneinfo import ZoneInfo
+
+import torch
+import numpy as np
+import pandas as pd
+import pandas_ta as ta
+from sklearn.preprocessing import StandardScaler
+from dotenv import load_dotenv
+import httpx
+
+# 添加项目路径
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from data.fetcher import BinanceDataFetcher
+from data.trading_db import TradingDatabase
+
+load_dotenv()
+
+
+def setup_logging(log_dir: str = "logs", log_level: int = logging.INFO) -> logging.Logger:
+    """
+    配置日志系统
+    
+    Args:
+        log_dir: 日志目录
+        log_level: 日志级别
+        
+    Returns:
+        配置好的logger
+    """
+    # 创建日志目录
+    os.makedirs(log_dir, exist_ok=True)
+    
+    # 创建logger
+    logger = logging.getLogger("polymarket_trader")
+    logger.setLevel(log_level)
+    
+    # 避免重复添加handler
+    if logger.handlers:
+        return logger
+    
+    # 日志格式
+    formatter = logging.Formatter(
+        "%(asctime)s | %(levelname)-8s | %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S"
+    )
+    
+    # 文件handler (按大小轮转, 最大10MB, 保留5个备份)
+    log_file = os.path.join(log_dir, "polymarket_trading.log")
+    file_handler = RotatingFileHandler(
+        log_file,
+        maxBytes=10*1024*1024,  # 10MB
+        backupCount=5,
+        encoding='utf-8'
+    )
+    file_handler.setLevel(log_level)
+    file_handler.setFormatter(formatter)
+    logger.addHandler(file_handler)
+    
+    # 控制台handler
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(log_level)
+    console_handler.setFormatter(formatter)
+    logger.addHandler(console_handler)
+    
+    return logger
+
+
+# 初始化全局logger
+logger = setup_logging()
+
+
+
+class ClassificationPreprocessor:
+    """DLinear分类预处理器"""
+    
+    def __init__(self, lookback: int = 168, forecast_horizon: int = 1):
+        self.lookback = lookback
+        self.forecast_horizon = forecast_horizon
+        self.scaler = StandardScaler()
+        self.is_fitted = False
+
+    def add_features(self, df: pd.DataFrame) -> pd.DataFrame:
+        df = df.copy()
+        df['rsi'] = ta.rsi(df['close'], length=14)
+        macd = ta.macd(df['close'])
+        if macd is not None and not macd.empty:
+            df['macd'] = macd.iloc[:, 0]
+            df['macd_hist'] = macd.iloc[:, 1]
+            df['macd_signal'] = macd.iloc[:, 2]
+        return df
+
+    def create_labels(self, df: pd.DataFrame) -> pd.DataFrame:
+        df = df.copy()
+        df['future_price'] = df['close'].shift(-self.forecast_horizon)
+        df['future_return'] = (df['future_price'] - df['close']) / df['close']
+        df['label'] = (df['future_return'] > 0).astype(int)
+        return df[:-self.forecast_horizon]
+
+    def preprocess(self, df: pd.DataFrame, fit: bool = True):
+        df = self.add_features(df)
+        df = self.create_labels(df)
+        df = df.dropna().reset_index(drop=True)
+        
+        feature_cols = ['open', 'high', 'low', 'close', 'volume', 
+                       'rsi', 'macd', 'macd_hist', 'macd_signal']
+        
+        if fit:
+            scaled_features = self.scaler.fit_transform(df[feature_cols])
+            self.is_fitted = True
+            self.feature_columns = feature_cols
+        else:
+            scaled_features = self.scaler.transform(df[feature_cols])
+        
+        X, y = [], []
+        for i in range(len(scaled_features) - self.lookback):
+            X.append(scaled_features[i:i + self.lookback])
+            y.append(df['label'].iloc[i + self.lookback])
+        
+        return np.array(X), np.array(y), df
+
+
+class PolymarketBTCTrader:
+    """Polymarket BTC Hourly交易器"""
+    
+    GAMMA_API = "https://gamma-api.polymarket.com"
+    CLOB_API = "https://clob.polymarket.com"
+    
+    # 月份名称映射
+    MONTH_NAMES = {
+        1: 'january', 2: 'february', 3: 'march', 4: 'april',
+        5: 'may', 6: 'june', 7: 'july', 8: 'august',
+        9: 'september', 10: 'october', 11: 'november', 12: 'december'
+    }
+    
+    def __init__(
+        self,
+        private_key: Optional[str] = None,
+        max_bet: float = 1.0,
+        max_price: float = 0.50,
+        dry_run: bool = True,
+    ):
+        self.private_key = private_key or os.getenv("POLYMARKET_PRIVATE_KEY")
+        self.max_bet = max_bet
+        self.max_price = max_price
+        self.dry_run = dry_run
+        
+        # HTTP客户端
+        proxy = os.getenv("SOCKS5_PROXY") if os.getenv("USE_PROXY", "false").lower() == "true" else None
+        self.http = httpx.Client(
+            timeout=30.0,
+            proxy=proxy,
+            headers={'Accept-Encoding': 'identity'}
+        )
+        
+        # 设备选择：macOS 用 MPS，Windows/Linux 用 CPU
+        import platform
+        if platform.system() == 'Darwin':  # macOS
+            self.device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
+        else:  # Windows / Linux
+            self.device = torch.device('cpu')
+        print(f"使用设备: {self.device}")
+        
+        # 加载模型
+        self.model = None
+        self.preprocessor = None
+        self._load_model()
+        
+        # 数据获取器
+        self.fetcher = BinanceDataFetcher()
+        
+        # 数据库
+        self.db = TradingDatabase()
+        
+        # CLOB客户端(用于下单)
+        self.clob_client = None
+        if self.private_key and not self.dry_run:
+            self._init_clob_client()
+        
+        # 当前市场缓存
+        self.current_market = None
+        self.last_price_update = None
+    
+    def _load_model(self):
+        """加载DLinear模型"""
+        logger.info("加载DLinear模型...")
+        
+        from models.dlinear.model import DLinear
+        
+        checkpoint_path = './models/checkpoints/dlinear_classification_2026/best_model.pt'
+        preprocessor_path = './models/checkpoints/dlinear_classification_2026/preprocessor.pkl'
+        
+        checkpoint = torch.load(checkpoint_path, map_location=self.device, weights_only=False)
+        self.model = DLinear(checkpoint['config'])
+        self.model.load_state_dict(checkpoint['model_state_dict'])
+        self.model = self.model.to(self.device).eval()
+        
+        with open(preprocessor_path, 'rb') as f:
+            self.preprocessor = pickle.load(f)
+        
+        logger.info(f"✓ 模型已加载 (设备: {self.device})")
+    
+    def _init_clob_client(self):
+        """初始化CLOB交易客户端"""
+        try:
+            from py_clob_client.client import ClobClient
+            
+            self.clob_client = ClobClient(
+                host=self.CLOB_API,
+                key=self.private_key,
+                chain_id=137,  # Polygon
+            )
+            self.clob_client.set_api_creds(self.clob_client.create_or_derive_api_creds())
+            logger.info("✓ CLOB客户端初始化成功")
+        except Exception as e:
+            logger.warning(f"⚠️ CLOB客户端初始化失败: {e}")
+            self.clob_client = None
+
+    def _log_to_db(self, result: Dict, market: Optional[Dict]):
+        """记录交易结果到数据库"""
+        try:
+            # 确定状态
+            status = 'failure'
+            if self.dry_run:
+                status = 'test'
+            elif result.get('success'):
+                status = 'success'
+            elif result.get('error'):
+                status = 'failure'
+            elif result.get('prediction'): # 有预测但未交易（如市场未找到）
+                status = 'in_progress' # 或者 failure? 未找到市场算 failure
+                if result.get('error'):
+                    status = 'failure'
+            
+            log_data = {
+                'timestamp': result.get('timestamp'),
+                'market_title': result.get('market'),
+                'market_slug': market.get('slug') if market else result.get('market_slug'),
+                'model_name': 'DLinear',
+                'prediction': result.get('prediction'),
+                'probability': result.get('probability'),
+                'action': result.get('side'),
+                'price': result.get('price'),
+                'amount': result.get('amount'),
+                'size': result.get('size'),
+                'status': status,
+                'error_message': result.get('error'),
+                'raw_data': result
+            }
+            self.db.log_trade(log_data)
+            logger.info(f"💾 交易记录已保存到数据库")
+        except Exception as e:
+            logger.error(f"❌ 保存数据库失败: {e}")
+    
+    def generate_market_slug(self, target_time: Optional[datetime] = None) -> str:
+        """
+        生成市场slug
+        
+        格式: bitcoin-up-or-down-{month}-{day}-{hour}am/pm-et
+        例如: bitcoin-up-or-down-february-3-8am-et
+        
+        Args:
+            target_time: 目标时间(ET时区)
+            for_current_hour: 如果True，使用当前整点；否则使用下一个整点
+            
+        Returns:
+            市场slug
+        """
+        # 获取美东时间
+        et_tz = ZoneInfo('America/New_York')
+        
+        if target_time is None:
+            now_et = datetime.now(et_tz)
+            target_time = now_et.replace(minute=0, second=0, microsecond=0)
+        
+        month_name = self.MONTH_NAMES[target_time.month]
+        day = target_time.day
+        
+        # 转换为12小时制
+        hour_24 = target_time.hour
+        if hour_24 == 0:
+            hour_12 = 12
+            am_pm = 'am'
+        elif hour_24 < 12:
+            hour_12 = hour_24
+            am_pm = 'am'
+        elif hour_24 == 12:
+            hour_12 = 12
+            am_pm = 'pm'
+        else:
+            hour_12 = hour_24 - 12
+            am_pm = 'pm'
+        
+        slug = f"bitcoin-up-or-down-{month_name}-{day}-{hour_12}{am_pm}-et"
+        return slug
+    
+    def get_market_by_slug(self, slug: str) -> Optional[Dict]:
+        """
+        通过slug获取市场信息
+        
+        Args:
+            slug: 市场slug
+            
+        Returns:
+            市场信息
+        """
+        try:
+            response = self.http.get(
+                f"{self.GAMMA_API}/events",
+                params={'slug': slug}
+            )
+            response.raise_for_status()
+            events = response.json()
+            
+            if not events:
+                return None
+            
+            event = events[0]
+            markets = event.get('markets', [])
+            
+            if not markets:
+                return None
+            
+            market = markets[0]
+            
+            # outcomePrices可能是JSON字符串或列表
+            outcome_prices_raw = market.get('outcomePrices', ['0', '0'])
+            if isinstance(outcome_prices_raw, str):
+                import json as _json
+                try:
+                    outcome_prices_raw = _json.loads(outcome_prices_raw)
+                except:
+                    outcome_prices_raw = ['0', '0']
+            
+            # clobTokenIds也可能是JSON字符串
+            clob_tokens_raw = market.get('clobTokenIds', [])
+            if isinstance(clob_tokens_raw, str):
+                import json as _json
+                try:
+                    clob_tokens_raw = _json.loads(clob_tokens_raw)
+                except:
+                    clob_tokens_raw = []
+            
+            return {
+                'title': event.get('title', ''),
+                'slug': slug,
+                'question': market.get('question', ''),
+                'condition_id': market.get('conditionId', ''),
+                'outcomes': market.get('outcomes', []),
+                'outcome_prices': [float(p) for p in outcome_prices_raw],
+                'tokens': clob_tokens_raw,
+                'end_date': event.get('endDate', ''),
+                'closed': market.get('closed', False),
+            }
+        except Exception as e:
+            logger.error(f"获取市场失败: {e}")
+            return None
+    
+    def get_current_hourly_market(self) -> Optional[Dict]:
+        """
+        获取BTC市场
+        
+        Args:
+            for_current_hour: 如果True，获取当前整点市场；否则获取下一个整点市场
+            
+        Returns:
+            市场信息
+        """
+        slug = self.generate_market_slug()
+        logger.info(f"搜索市场: {slug}")
+        
+        market = self.get_market_by_slug(slug)
+        
+        if market:
+            self.current_market = market
+            self.last_price_update = datetime.now()
+            logger.info(f"✓ 找到市场: {market['title']}")
+            logger.info(f"  Up价格: ${market['outcome_prices'][0]:.3f}")
+            logger.info(f"  Down价格: ${market['outcome_prices'][1]:.3f}")
+        else:
+            logger.warning(f"❌ 未找到市场: {slug}")
+        
+        return market
+    
+    def refresh_prices(self) -> Optional[Dict]:
+        """
+        刷新当前市场价格
+        
+        Returns:
+            更新后的市场信息
+        """
+        if not self.current_market:
+            self.get_current_hourly_market()
+        
+        # 使用CLOB API获取实时价格
+        tokens = self.current_market.get('tokens', [])
+        if len(tokens) >= 2:
+            realtime_prices = self.get_realtime_prices(tokens[0], tokens[1])
+            if realtime_prices:
+                old_prices = self.current_market['outcome_prices']
+                new_prices = realtime_prices
+                
+                self.current_market['outcome_prices'] = new_prices
+                self.last_price_update = datetime.now()
+                
+                # 显示价格变化
+                up_change = new_prices[0] - old_prices[0]
+                down_change = new_prices[1] - old_prices[1]
+                
+                logger.info(f"📊 实时价格 [{datetime.now().strftime('%H:%M:%S')}]")
+                logger.info(f"   Up: ${new_prices[0]:.3f} ({up_change:+.3f})")
+                logger.info(f"   Down: ${new_prices[1]:.3f} ({down_change:+.3f})")
+                
+                return self.current_market
+        
+        # 备用: 使用Gamma API
+        slug = self.current_market['slug']
+        market = self.get_market_by_slug(slug)
+        
+        if market:
+            old_prices = self.current_market['outcome_prices']
+            new_prices = market['outcome_prices']
+            
+            self.current_market = market
+            self.last_price_update = datetime.now()
+            
+            # 显示价格变化
+            up_change = new_prices[0] - old_prices[0]
+            down_change = new_prices[1] - old_prices[1]
+            
+            logger.info(f"📊 价格更新(Gamma) [{datetime.now().strftime('%H:%M:%S')}]")
+            logger.info(f"   Up: ${new_prices[0]:.3f} ({up_change:+.3f})")
+            logger.info(f"   Down: ${new_prices[1]:.3f} ({down_change:+.3f})")
+        
+        return market
+    
+    def get_realtime_prices(self, up_token_id: str, down_token_id: str) -> Optional[list]:
+        """
+        从CLOB API获取实时买入价格
+        
+        使用/price?side=BUY端点获取真实买入价格 (与页面显示一致)
+        
+        Args:
+            up_token_id: Up代币ID
+            down_token_id: Down代币ID
+            
+        Returns:
+            [up_price, down_price] 或 None
+        """
+        try:
+            # 使用/price端点获取买入价格
+            up_response = self.http.get(
+                f"{self.CLOB_API}/price",
+                params={'token_id': up_token_id, 'side': 'BUY'}
+            )
+            up_response.raise_for_status()
+            up_data = up_response.json()
+            
+            down_response = self.http.get(
+                f"{self.CLOB_API}/price",
+                params={'token_id': down_token_id, 'side': 'BUY'}
+            )
+            down_response.raise_for_status()
+            down_data = down_response.json()
+            
+            # 解析买入价格
+            up_price = float(up_data.get('price', 0.5))
+            down_price = float(down_data.get('price', 0.5))
+            
+            return [up_price, down_price]
+        except Exception as e:
+            logger.debug(f"获取实时价格失败: {e}")
+            return None
+    
+    def execute_trade(self, prediction: int, market: Dict) -> Dict:
+        """
+        执行交易
+        
+        Args:
+            prediction: 预测结果(0=跌, 1=涨)
+            market: 市场信息
+            
+        Returns:
+            交易结果
+        """
+        result = {
+            'timestamp': datetime.now().isoformat(),
+            'market': market['title'],
+            'prediction': 'UP' if prediction == 1 else 'DOWN',
+            'success': False,
+            'order': None,
+            'error': None,
+        }
+        
+        if prediction == 1:  # 预测涨 -> 买UP
+            token_id = market['tokens'][0]
+            price = market['outcome_prices'][0]
+            side = "UP"
+        else:  # 预测跌 -> 买DOWN
+            token_id = market['tokens'][1]
+            price = market['outcome_prices'][1]
+            side = "DOWN"
+        
+        result['side'] = side
+        result['price'] = price
+        
+        # 检查价格限制
+        if price > self.max_price:
+            result['error'] = f"价格${price:.2f}超过限制${self.max_price:.2f}"
+            logger.error(f"❌ {result['error']}")
+            return result
+        
+        # 计算数量
+        size = self.max_bet / price
+        result['size'] = size
+        result['amount'] = self.max_bet
+        
+        logger.info("📋 交易详情:")
+        logger.info(f"   方向: {side}")
+        logger.info(f"   价格: ${price:.3f}")
+        logger.info(f"   金额: ${self.max_bet:.2f} USDC")
+        logger.info(f"   数量: {size:.2f} 股")
+        
+        if self.dry_run:
+            logger.info("🔸 [模拟模式] 未实际下单")
+            result['success'] = True
+            result['order'] = 'DRY_RUN'
+            return result
+        
+        # 实际下单
+        if not self.clob_client:
+            result['error'] = "CLOB客户端未初始化"
+            logger.error(f"❌ {result['error']}")
+            return result
+        
+        try:
+            from py_clob_client.order_builder.constants import BUY
+            
+            order = self.clob_client.create_limit_order(
+                token_id=token_id,
+                price=price,
+                size=size,
+                side=BUY,
+            )
+            
+            response = self.clob_client.post_order(order)
+            logger.info(f"✓ 订单已提交: {response}")
+            result['success'] = True
+            result['order'] = response
+        except Exception as e:
+            result['error'] = str(e)
+            logger.error(f"❌ 下单失败: {e}")
+        
+        return result
+    
+    def get_prediction_with_delay(self, delay_seconds: float = 2.0) -> Tuple[int, float]:
+        """
+        延迟后获取K线并预测当前小时的涨跌
+        
+        注意: 使用上一小时收盘的K线(倒数第二根)来预测当前小时
+        例如: 22:00整点时，用21:00收盘的K线预测22:00这个小时的涨跌
+        
+        Args:
+            delay_seconds: 延迟秒数(等待K线更新)
+            
+        Returns:
+            (prediction, probability): 预测结果(0=跌, 1=涨)和概率
+        """
+        logger.info(f"⏳ 等待{delay_seconds}秒获取最新K线...")
+        time.sleep(delay_seconds)
+        
+        logger.info("获取最新BTC数据...")
+        
+        end_date = (datetime.now() + timedelta(days=1)).strftime('%Y-%m-%d')
+        start_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')
+        
+        df = self.fetcher.fetch_ohlcv(
+            symbol='BTC',
+            timeframe='1h',
+            start_date=start_date,
+            end_date=end_date,
+            use_cache=False  # 不使用缓存，获取最新数据
+        )
+        
+        # 使用倒数第二根K线(上一小时收盘)进行预测
+        # 例如22:00时，最新K线是14:00 UTC(22:00北京)，我们用13:00 UTC(21:00北京)的数据
+        prev_time = df['timestamp'].iloc[-2]
+        prev_close = df['close'].iloc[-2]
+        current_time = df['timestamp'].iloc[-1]
+        current_close = df['close'].iloc[-1]
+        
+        logger.info(f"  上一K线时间: {prev_time} (用于预测)")
+        logger.info(f"  上一收盘价: ${prev_close:,.2f}")
+        logger.info(f"  当前K线时间: {current_time} (预测目标)")
+        logger.info(f"  当前收盘价: ${current_close:,.2f} (实际结果)")
+        
+        # 用除最后一根外的数据进行预测
+        df_for_prediction = df.iloc[:-1]
+        X, _, _ = self.preprocessor.preprocess(df_for_prediction, fit=False)
+        X_last = torch.FloatTensor(X[-1:]).to(self.device)
+        
+        with torch.no_grad():
+            logits = self.model(X_last).cpu().numpy()
+            prob = 1 / (1 + np.exp(-logits))
+            prediction = int(prob.item() > 0.5)
+            probability = float(prob.item())
+        
+        direction = "📈 UP (涨)" if prediction == 1 else "📉 DOWN (跌)"
+        confidence = probability if prediction == 1 else 1 - probability
+        logger.info(f"🤖 DLinear预测: {direction}")
+        logger.info(f"   置信度: {confidence:.2%}")
+        
+        # 显示预测是否正确 (用于验证)
+        actual_up = current_close > prev_close
+        if prediction == 1:
+            correct = actual_up
+        else:
+            correct = not actual_up
+        logger.info(f"   实际涨跌: {'📈 涨' if actual_up else '📉 跌'} ({'✅ 正确' if correct else '❌ 错误'})")
+        
+        return prediction, probability
+    
+    def run_once(self) -> Dict:
+        """运行一次交易流程（不等待整点）"""
+        logger.info("运行单次交易...")
+        return self.run_at_hour_start(delay_seconds=0)
+
+    def run_at_hour_start(self, delay_seconds: float = 2.0, timeout_minutes: int = 50) -> Dict:
+        """
+        整点时执行的交易流程
+        
+        1. 延迟delay_seconds秒等待K线更新
+        2. 获取最新K线
+        3. DLinear预测当前整点的收盘涨跌
+        4. 获取对应的Polymarket市场
+        5. 循环检查价格:
+           - 如果价格 <= max_price: 下单并结束
+           - 如果价格 > max_price: 等待并重试(直到超时)
+        
+        Args:
+            delay_seconds: 整点后延迟秒数
+            timeout_minutes: 价格等待超时时间(分钟)
+            
+        Returns:
+            执行结果
+        """
+        logger.info("=" * 60)
+        logger.info(f"⏰ 整点交易触发 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+        logger.info("=" * 60)
+        logger.info(f"  模式: {'🔸 模拟' if self.dry_run else '💰 真实交易'}")
+        logger.info(f"  最大下注: ${self.max_bet:.2f} USDC")
+        logger.info(f"  最高价格: ${self.max_price:.2f}")
+        logger.info(f"  等待超时: {timeout_minutes}分钟")
+        
+        result = {
+            'timestamp': datetime.now().isoformat(),
+            'success': False,
+            'error': None,
+        }
+        
+        # 1. 延迟后获取预测
+        prediction, probability = self.get_prediction_with_delay(delay_seconds)
+        result['prediction'] = 'UP' if prediction == 1 else 'DOWN'
+        result['probability'] = probability if prediction == 1 else 1 - probability
+        
+        # 2. 获取初始市场
+        market = self.get_current_hourly_market()
+        if not market:
+            result['error'] = '未找到市场'
+            logger.error(f"❌ {result['error']}")
+            self._log_to_db(result, None)
+            return result
+        
+        result['market'] = market['title']
+        result['market_slug'] = market['slug']
+        
+        if market['closed']:
+            result['error'] = '市场已关闭'
+            logger.error(f"❌ {result['error']}")
+            self._log_to_db(result, market)
+            return result
+            
+        # 检查是否已交易过该市场
+        check_status = 'test' if self.dry_run else 'success'
+        if self.db.check_existing_trade(market['slug'], status=check_status):
+            result['error'] = '该市场已存在成功交易记录'
+            logger.warning(f"⚠️ {result['error']}, 跳过交易")
+            return result
+
+        # 3. 循环检查价格并交易
+        start_time = datetime.now()
+        end_time = start_time + timedelta(minutes=timeout_minutes)
+        
+        logger.info(f"🔄 开始价格监控 (预测: {'UP' if prediction == 1 else 'DOWN'})...")
+        
+        while datetime.now() < end_time:
+            # 刷新价格
+            market = self.refresh_prices()
+            if not market:
+                logger.warning("无法获取市场价格，重试中...")
+                time.sleep(10)
+                continue
+            
+            # 检查价格
+            price_idx = 0 if prediction == 1 else 1
+            current_price = market['outcome_prices'][price_idx]
+            
+            logger.info(f"   当前价格: ${current_price:.3f} (目标: <= ${self.max_price:.2f})")
+            
+            if current_price <= self.max_price:
+                # 价格符合，执行交易
+                logger.info("✅ 价格满足条件，正在下单...")
+                trade_result = self.execute_trade(prediction, market)
+                result.update(trade_result)
+                
+                # 记录数据库
+                self._log_to_db(result, market)
+                
+                logger.info("=" * 60)
+                return result
+            else:
+                # 价格过高，等待
+                wait_time = 30  # 等待30秒
+                elapsed = int((datetime.now() - start_time).total_seconds())
+                logger.info(f"⏳ 价格过高，继续等待... (已耗时: {elapsed}s)")
+                time.sleep(wait_time)
+        
+        # 超时
+        result['error'] = f'等待价格超时 ({timeout_minutes}分钟)'
+        result['price'] = market['outcome_prices'][0] if prediction == 1 else market['outcome_prices'][1]
+        result['side'] = 'UP' if prediction == 1 else 'DOWN'
+        logger.error(f"❌ {result['error']}")
+        
+        self._log_to_db(result, market)
+        logger.info("=" * 60)
+        
+        return result
+    
+    def _show_initial_prediction(self):
+        """
+        启动时显示当前预测（不下单）
+        """
+        logger.info("\n📊 当前预测状态:")
+        
+        end_date = (datetime.now() + timedelta(days=1)).strftime('%Y-%m-%d')
+        start_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')
+        
+        df = self.fetcher.fetch_ohlcv(
+            symbol='BTC',
+            timeframe='1h',
+            start_date=start_date,
+            end_date=end_date,
+            use_cache=False
+        )
+        
+        # 显示K线信息
+        prev_time = df['timestamp'].iloc[-2]
+        prev_close = df['close'].iloc[-2]
+        current_time = df['timestamp'].iloc[-1]
+        current_close = df['close'].iloc[-1]
+        
+        logger.info(f"  上一K线: {prev_time} 收盘价: ${prev_close:,.2f}")
+        logger.info(f"  当前K线: {current_time} 收盘价: ${current_close:,.2f}")
+        
+        # 进行预测
+        df_for_prediction = df.iloc[:-1]
+        X, _, _ = self.preprocessor.preprocess(df_for_prediction, fit=False)
+        X_last = torch.FloatTensor(X[-1:]).to(self.device)
+        
+        with torch.no_grad():
+            logits = self.model(X_last).cpu().numpy()
+            prob = 1 / (1 + np.exp(-logits))
+            prediction = int(prob.item() > 0.5)
+            probability = float(prob.item())
+        
+        direction = "📈 UP (涨)" if prediction == 1 else "📉 DOWN (跌)"
+        confidence = probability if prediction == 1 else 1 - probability
+        
+        logger.info(f"  🤖 预测: {direction} (置信度: {confidence:.2%})")
+        
+        # 显示市场价格
+        if self.current_market:
+            up_price = self.current_market['outcome_prices'][0]
+            down_price = self.current_market['outcome_prices'][1]
+            if prediction == 1:
+                logger.info(f"  💰 买入Up价格: ${up_price:.3f} {'✅ 可下单' if up_price <= self.max_price else '❌ 超过限价'}")
+            else:
+                logger.info(f"  💰 买入Down价格: ${down_price:.3f} {'✅ 可下单' if down_price <= self.max_price else '❌ 超过限价'}")
+        
+        # 计算距下一整点时间
+        now = datetime.now()
+        next_hour = now.replace(minute=0, second=0, microsecond=0) + timedelta(hours=1)
+        wait_minutes = int((next_hour - now).total_seconds() / 60)
+        logger.info(f"  ⏰ 距离下一整点: {wait_minutes}分钟")
+        logger.info("")
+    
+    def wait_for_next_hour(self) -> datetime:
+        """
+        等待到下一个整点
+        
+        Returns:
+            下一个整点时间
+        """
+        now = datetime.now()
+        next_hour = now.replace(minute=0, second=0, microsecond=0) + timedelta(hours=1)
+        wait_seconds = (next_hour - now).total_seconds()
+        
+        logger.info(f"⏳ 等待下一个整点: {next_hour.strftime('%H:%M:%S')}")
+        logger.info(f"   剩余等待: {int(wait_seconds)}秒")
+        
+        return next_hour
+    
+    def run_scheduler(self, interval_minutes: int = 5, delay_seconds: float = 2.0):
+        """
+        定时运行交易器
+        
+        工作流程:
+        1. 每个整点+delay_seconds秒触发交易
+        2. 期间每interval_minutes分钟刷新价格
+        
+        Args:
+            interval_minutes: 价格刷新间隔(分钟)
+            delay_seconds: 整点后延迟秒数
+        """
+        logger.info("=" * 60)
+        logger.info("🤖 BTC Hourly自动交易已启动")
+        logger.info("=" * 60)
+        logger.info(f"  模式: {'🔸 模拟' if self.dry_run else '💰 真实交易'}")
+        logger.info(f"  下单时机: 每个整点后延迟{delay_seconds}秒")
+        logger.info(f"  价格刷新: 每{interval_minutes}分钟")
+        logger.info(f"  最大下注: ${self.max_bet:.2f} USDC")
+        logger.info(f"  最高价格: ${self.max_price:.2f}")
+        logger.info("  按 Ctrl+C 停止")
+        logger.info("=" * 60)
+        
+        # 立即获取整点的市场
+        self.get_current_hourly_market()
+        
+        # 启动时进行一次预测（不下单，仅显示）
+        self._show_initial_prediction()
+        
+        # 每小时整点后执行交易
+        def hourly_trade():
+            self.run_at_hour_start(delay_seconds=delay_seconds)
+        
+        schedule.every().hour.at(":00").do(lambda: time.sleep(delay_seconds) or hourly_trade())
+        
+        try:
+            while True:
+                schedule.run_pending()
+                time.sleep(1)
+        except KeyboardInterrupt:
+            logger.info("✓ 交易器已停止")
+    
+    def close(self):
+        """关闭资源"""
+        self.http.close()
+
+
+def main():
+    parser = argparse.ArgumentParser(description='BTC Hourly自动交易 - 基于DLinear模型')
+    parser.add_argument('--dry-run', action='store_true', default=True,
+                       help='模拟模式(不实际下单)')
+    parser.add_argument('--real', action='store_true',
+                       help='真实交易模式')
+    parser.add_argument('--max-bet', type=float, default=1.0,
+                       help='最大下注金额(USDC)')
+    parser.add_argument('--max-price', type=float, default=0.50,
+                       help='最高可接受价格')
+    parser.add_argument('--once', action='store_true',
+                       help='只执行一次(不启动定时器)')
+    parser.add_argument('--interval', type=int, default=1,
+                       help='价格刷新间隔(分钟)')
+    parser.add_argument('--check-market', action='store_true',
+                       help='只检查当前市场信息')
+    
+    args = parser.parse_args()
+    
+    dry_run = not args.real
+    
+    # 真实交易确认
+    if not dry_run:
+        logger.warning("⚠️  警告: 即将进行真实交易!")
+        logger.warning(f"   最大下注: ${args.max_bet:.2f} USDC")
+        confirm = input("确认继续? (yes/no): ")
+        if confirm.lower() != 'yes':
+            logger.info("已取消")
+            return
+    
+    trader = PolymarketBTCTrader(
+        max_bet=args.max_bet,
+        max_price=args.max_price,
+        dry_run=dry_run,
+    )
+    
+    try:
+        if args.check_market:
+            # 只检查市场
+            trader.get_current_hourly_market()
+        elif args.once:
+            # 执行一次
+            result = trader.run_once()
+            
+            # 保存日志
+            log_file = 'trading_log.json'
+            logs = []
+            if os.path.exists(log_file):
+                with open(log_file, 'r') as f:
+                    logs = json.load(f)
+            logs.append(result)
+            with open(log_file, 'w') as f:
+                json.dump(logs, f, indent=2, ensure_ascii=False)
+            logger.info(f"结果已保存到 {log_file}")
+        else:
+            # 定时运行
+            trader.run_scheduler(interval_minutes=args.interval)
+    finally:
+        trader.close()
+
+
+if __name__ == "__main__":
+    main()

data/database.py

@@ -0,0 +1,157 @@
+import pymysql
+import pandas as pd
+from typing import Optional
+from contextlib import contextmanager
+import os
+from dotenv import load_dotenv
+from sqlalchemy import create_engine, text
+
+load_dotenv()
+
+
+class DatabaseManager:
+    """管理 OHLCV 数据的 MySQL 数据库"""
+
+    def __init__(self, db_path: str = None):
+        """初始化数据库管理器 (db_path 参数已废弃，仅为兼容性保留)"""
+        self._engine = None
+        self.initialize_db()
+    
+    def _get_sqlalchemy_engine(self):
+        """获取 SQLAlchemy 引擎（用于 pandas read_sql）"""
+        if self._engine is None:
+            host = os.getenv('MYSQL_HOST')
+            port = os.getenv('MYSQL_PORT', 3306)
+            user = os.getenv('MYSQL_USER')
+            password = os.getenv('MYSQL_PASSWORD')
+            database = os.getenv('MYSQL_DB')
+            self._engine = create_engine(
+                f"mysql+pymysql://{user}:{password}@{host}:{port}/{database}?charset=utf8mb4"
+            )
+        return self._engine
+
+    @contextmanager
+    def get_connection(self):
+        """获取 MySQL 数据库连接"""
+        conn = pymysql.connect(
+            host=os.getenv('MYSQL_HOST'),
+            port=int(os.getenv('MYSQL_PORT', 3306)),
+            user=os.getenv('MYSQL_USER'),
+            password=os.getenv('MYSQL_PASSWORD'),
+            database=os.getenv('MYSQL_DB'),
+            charset='utf8mb4',
+        )
+        try:
+            yield conn
+        finally:
+            conn.close()
+
+    def initialize_db(self):
+        """创建表（如果不存在）"""
+        with self.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                CREATE TABLE IF NOT EXISTS ohlcv (
+                    timestamp DATETIME,
+                    symbol VARCHAR(20),
+                    timeframe VARCHAR(10),
+                    open DOUBLE,
+                    high DOUBLE,
+                    low DOUBLE,
+                    close DOUBLE,
+                    volume DOUBLE,
+                    PRIMARY KEY (symbol, timeframe, timestamp),
+                    INDEX idx_ohlcv_time (symbol, timeframe, timestamp)
+                ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
+            """)
+            conn.commit()
+
+    def save_ohlcv(self, df: pd.DataFrame, symbol: str, timeframe: str):
+        """保存 OHLCV 数据到数据库 (Upsert)"""
+        if df.empty:
+            return
+
+        # 确保 timestamp 是 datetime 类型
+        df = df.copy()
+        if not pd.api.types.is_datetime64_any_dtype(df['timestamp']):
+            df['timestamp'] = pd.to_datetime(df['timestamp'])
+            
+        # 转换为字符串格式
+        df['timestamp'] = df['timestamp'].dt.strftime('%Y-%m-%d %H:%M:%S')
+        
+        # 添加元数据列
+        if 'symbol' not in df.columns:
+            df['symbol'] = symbol
+        if 'timeframe' not in df.columns:
+            df['timeframe'] = timeframe
+        
+        cols = ['timestamp', 'symbol', 'timeframe', 'open', 'high', 'low', 'close', 'volume']
+        data = [tuple(row) for row in df[cols].values]
+        
+        with self.get_connection() as conn:
+            cursor = conn.cursor()
+            sql = """
+                REPLACE INTO ohlcv 
+                (timestamp, symbol, timeframe, open, high, low, close, volume)
+                VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
+            """
+            cursor.executemany(sql, data)
+            conn.commit()
+
+    def get_ohlcv(self, symbol: str, timeframe: str, start_date: Optional[str] = None, end_date: Optional[str] = None) -> pd.DataFrame:
+        """从数据库获取 OHLCV 数据"""
+        
+        query = """
+            SELECT timestamp, open, high, low, close, volume 
+            FROM ohlcv 
+            WHERE symbol = :p0 AND timeframe = :p1
+        """
+        params = {'p0': symbol, 'p1': timeframe}
+        param_idx = 2
+
+        if start_date:
+            query += f" AND timestamp >= :p{param_idx}"
+            params[f'p{param_idx}'] = start_date
+            param_idx += 1
+        
+        if end_date:
+            # 处理 'YYYY-MM-DD' 格式
+            if len(str(end_date)) == 10:
+                query += f" AND timestamp < DATE_ADD(:p{param_idx}, INTERVAL 1 DAY)"
+            else:
+                query += f" AND timestamp <= :p{param_idx}"
+            params[f'p{param_idx}'] = end_date
+            
+        query += " ORDER BY timestamp ASC"
+
+        engine = self._get_sqlalchemy_engine()
+        with engine.connect() as conn:
+            df = pd.read_sql_query(text(query), conn, params=params, parse_dates=['timestamp'])
+            
+        return df
+
+    def get_latest_timestamp(self, symbol: str, timeframe: str) -> Optional[pd.Timestamp]:
+        """获取指定 symbol/timeframe 的最新时间戳"""
+        with self.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT MAX(timestamp) 
+                FROM ohlcv 
+                WHERE symbol = %s AND timeframe = %s
+            """, (symbol, timeframe))
+            result = cursor.fetchone()
+            
+        return pd.to_datetime(result[0]) if result and result[0] else None
+
+    def get_earliest_timestamp(self, symbol: str, timeframe: str) -> Optional[pd.Timestamp]:
+        """获取指定 symbol/timeframe 的最早时间戳"""
+        with self.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT MIN(timestamp) 
+                FROM ohlcv 
+                WHERE symbol = %s AND timeframe = %s
+            """, (symbol, timeframe))
+            result = cursor.fetchone()
+            
+        return pd.to_datetime(result[0]) if result and result[0] else None

data/fetcher.py

@@ -10,6 +10,8 @@ from binance.client import Client
 import pytz
 from dotenv import load_dotenv
 
+from data.database import DatabaseManager
+
 # 加载环境变量
 load_dotenv()
 
@@ -23,10 +25,10 @@ class BinanceDataFetcher:
     }
 
     def __init__(self, cache_dir: str = "./data/cache"):
-        """初始化Binance客户端和缓存目录
-
+        """初始化Binance客户端和数据库管理器
+        
         Args:
-            cache_dir: 缓存下载数据的目录
+            cache_dir: 缓存/数据库目录
         """
         # 配置代理
         proxies = None
@@ -58,8 +60,8 @@ class BinanceDataFetcher:
             print("  3. 或者使用已缓存的数据")
             self.client = None
 
-        self.cache_dir = Path(cache_dir)
-        self.cache_dir.mkdir(parents=True, exist_ok=True)
+        # 初始化数据库管理器
+        self.db_manager = DatabaseManager(os.path.join(cache_dir, "market_data.db"))
 
     def fetch_ohlcv(
         self,
@@ -81,21 +83,7 @@ class BinanceDataFetcher:
         Returns:
             包含以下列的DataFrame: timestamp, open, high, low, close, volume
         """
-        # 生成缓存文件名
-        cache_file = self._get_cache_filename(symbol, timeframe, start_date, end_date)
-
-        # 检查缓存
-        if use_cache and cache_file.exists():
-            print(f"从缓存加载数据：{cache_file}")
-            return pd.read_csv(cache_file, parse_dates=['timestamp'])
-
-        # 检查客户端是否可用
-        if self.client is None:
-            raise ConnectionError(
-                "Binance客户端不可用。请使用VPN或使用已缓存的数据。"
-            )
-
-        # 设置默认日期
+        # 设置日期范围
         if end_date is None:
             end_dt = datetime.now(pytz.UTC)
         else:
@@ -106,6 +94,34 @@ class BinanceDataFetcher:
         else:
             start_dt = datetime.strptime(start_date, '%Y-%m-%d').replace(tzinfo=pytz.UTC)
 
+        start_str = start_dt.strftime('%Y-%m-%d')
+        end_str = end_dt.strftime('%Y-%m-%d')
+
+        # 1. 尝试从数据库获取数据
+        df = pd.DataFrame()
+        if use_cache:
+            df = self.db_manager.get_ohlcv(symbol, timeframe, start_str, end_str)
+            
+            # 检查数据是否足够
+            if not df.empty:
+                min_ts = df['timestamp'].min().replace(tzinfo=pytz.UTC)
+                max_ts = df['timestamp'].max().replace(tzinfo=pytz.UTC)
+                
+                # 简单判断覆盖范围 (容忍少量缺失)
+                if min_ts <= start_dt + timedelta(days=2) and max_ts >= end_dt - timedelta(days=2):
+                    print(f"从数据库加载数据：{symbol} {timeframe} ({len(df)} rows)")
+                    return df
+
+        # 2. 从API获取数据
+        if self.client is None:
+            if not df.empty:
+                 print(f"⚠ API不可用，返回数据库现有数据 ({len(df)} rows)")
+                 return df
+            
+            raise ConnectionError(
+                "Binance客户端不可用。请使用VPN或确保数据库有数据。"
+            )
+
         # Binance使用USDT交易对
         pair = f"{symbol}USDT"
         interval = self.INTERVALS[timeframe]
@@ -113,53 +129,47 @@ class BinanceDataFetcher:
         print(f"获取 {symbol} {timeframe} 数据，时间范围：{start_dt.date()} 至 {end_dt.date()}...")
 
         # 获取K线数据
-        klines = self.client.get_historical_klines(
-            symbol=pair,
-            interval=interval,
-            start_str=str(int(start_dt.timestamp() * 1000)),
-            end_str=str(int(end_dt.timestamp() * 1000))
-        )
+        try:
+            klines = self.client.get_historical_klines(
+                symbol=pair,
+                interval=interval,
+                start_str=str(int(start_dt.timestamp() * 1000)),
+                end_str=str(int(end_dt.timestamp() * 1000))
+            )
+        except Exception as e:
+            if not df.empty:
+                print(f"⚠ API请求失败 ({e})，返回数据库现有数据")
+                return df
+            raise e
 
         if not klines:
-            raise ValueError(f"未获取到 {symbol} {timeframe} 的数据")
+             if not df.empty:
+                 return df
+             raise ValueError(f"未获取到 {symbol} {timeframe} 的数据")
 
         # 转换为DataFrame
-        df = pd.DataFrame(klines, columns=[
+        new_df = pd.DataFrame(klines, columns=[
             'timestamp', 'open', 'high', 'low', 'close', 'volume',
             'close_time', 'quote_volume', 'trades', 'taker_buy_base',
             'taker_buy_quote', 'ignore'
         ])
 
         # 选择并转换列
-        df = df[['timestamp', 'open', 'high', 'low', 'close', 'volume']].copy()
-        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+        new_df = new_df[['timestamp', 'open', 'high', 'low', 'close', 'volume']].copy()
+        new_df['timestamp'] = pd.to_datetime(new_df['timestamp'], unit='ms')
 
         for col in ['open', 'high', 'low', 'close', 'volume']:
-            df[col] = df[col].astype(float)
+            new_df[col] = new_df[col].astype(float)
 
         # 按时间排序
-        df = df.sort_values('timestamp').reset_index(drop=True)
+        new_df = new_df.sort_values('timestamp').reset_index(drop=True)
 
-        print(f"已获取 {len(df)} 行数据，时间范围：{df['timestamp'].min()} 至 {df['timestamp'].max()}")
+        print(f"已获取 {len(new_df)} 行数据")
 
-        # 保存到缓存
-        df.to_csv(cache_file, index=False)
-        print(f"已缓存数据至：{cache_file}")
-
-        return df
-
-    def _get_cache_filename(
-        self,
-        symbol: str,
-        timeframe: str,
-        start_date: Optional[str],
-        end_date: Optional[str]
-    ) -> Path:
-        """根据参数生成缓存文件名"""
-        start = start_date or "1y"
-        end = end_date or "now"
-        filename = f"{symbol}_{timeframe}_{start}_{end}.csv"
-        return self.cache_dir / filename
+        # 保存到数据库
+        self.db_manager.save_ohlcv(new_df, symbol, timeframe)
+        
+        return new_df
 
     def get_latest_price(self, symbol: Literal['BTC', 'ETH', 'SOL']) -> float:
         """获取符号的当前价格
@@ -187,5 +197,8 @@ if __name__ == "__main__":
     print(f"日期范围：{df['timestamp'].min()} 至 {df['timestamp'].max()}")
 
     # 获取当前价格
-    current_price = fetcher.get_latest_price('BTC')
-    print(f"\n当前BTC价格：${current_price:,.2f}")
+    try:
+        current_price = fetcher.get_latest_price('BTC')
+        print(f"\n当前BTC价格：${current_price:,.2f}")
+    except:
+        print("\n无法获取当前价格")

data/trading_db.py

@@ -0,0 +1,110 @@
+import pymysql
+from datetime import datetime
+import json
+import os
+from contextlib import contextmanager
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+class TradingDatabase:
+    """交易数据库管理类 - 用于记录交易历史和预测结果 (MySQL)"""
+    
+    def __init__(self, db_path=None):
+        """初始化 (db_path 参数已废弃，仅为兼容性保留)"""
+        self._init_db()
+
+    @contextmanager
+    def get_connection(self):
+        """获取 MySQL 数据库连接"""
+        conn = pymysql.connect(
+            host=os.getenv('MYSQL_HOST'),
+            port=int(os.getenv('MYSQL_PORT', 3306)),
+            user=os.getenv('MYSQL_USER'),
+            password=os.getenv('MYSQL_PASSWORD'),
+            database=os.getenv('MYSQL_DB'),
+            charset='utf8mb4',
+            cursorclass=pymysql.cursors.DictCursor
+        )
+        try:
+            yield conn
+        finally:
+            conn.close()
+
+    def _init_db(self):
+        """初始化数据库表结构"""
+        with self.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute('''
+                CREATE TABLE IF NOT EXISTS trades (
+                    id INT AUTO_INCREMENT PRIMARY KEY,
+                    timestamp DATETIME,
+                    market_title VARCHAR(255),
+                    market_slug VARCHAR(255),
+                    model_name VARCHAR(50),
+                    prediction VARCHAR(10),
+                    probability DOUBLE,
+                    action VARCHAR(10),
+                    price DOUBLE,
+                    amount DOUBLE,
+                    size DOUBLE,
+                    status VARCHAR(20),
+                    error_message TEXT,
+                    raw_data JSON,
+                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                    INDEX idx_market_slug (market_slug),
+                    INDEX idx_status (status)
+                ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
+            ''')
+            conn.commit()
+
+    def log_trade(self, trade_data: dict):
+        """记录交易信息"""
+        try:
+            with self.get_connection() as conn:
+                cursor = conn.cursor()
+                
+                timestamp = trade_data.get('timestamp', datetime.now().isoformat())
+                raw_data_json = json.dumps(trade_data.get('raw_data', {}), ensure_ascii=False)
+                
+                sql = '''
+                    INSERT INTO trades (
+                        timestamp, market_title, market_slug, model_name,
+                        prediction, probability, action, price, amount, size,
+                        status, error_message, raw_data
+                    ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+                '''
+                
+                cursor.execute(sql, (
+                    timestamp,
+                    trade_data.get('market_title'),
+                    trade_data.get('market_slug'),
+                    trade_data.get('model_name'),
+                    trade_data.get('prediction'),
+                    trade_data.get('probability'),
+                    trade_data.get('action'),
+                    trade_data.get('price'),
+                    trade_data.get('amount'),
+                    trade_data.get('size'),
+                    trade_data.get('status'),
+                    trade_data.get('error_message'),
+                    raw_data_json
+                ))
+                conn.commit()
+        except Exception as e:
+            print(f"写入数据库失败: {e}")
+
+    def check_existing_trade(self, market_slug: str, status: str = 'success') -> bool:
+        """检查指定市场是否已有成功交易记录"""
+        try:
+            with self.get_connection() as conn:
+                cursor = conn.cursor()
+                sql = 'SELECT COUNT(*) as cnt FROM trades WHERE market_slug = %s AND status = %s'
+                cursor.execute(sql, (market_slug, status))
+                result = cursor.fetchone()
+                count = result['cnt'] if result else 0
+                return count > 0
+        except Exception as e:
+            print(f"检查已存在交易失败: {e}")
+            return False

docs/WINDOWS_INSTALL.md

@@ -0,0 +1,295 @@
+# Windows 环境安装指南
+
+本指南帮助您在 Windows 系统上部署加密货币预测系统。
+
+---
+
+## 一、前置要求
+
+- Windows 10/11 (64位)
+- 至少 8GB 内存
+- 至少 10GB 可用磁盘空间
+- 网络连接（需访问 GitHub、PyPI、Binance API）
+
+---
+
+## 二、安装 Miniconda
+
+### 1. 下载 Miniconda
+
+访问 [Miniconda 官网](https://docs.conda.io/en/latest/miniconda.html) 下载 Windows 64-bit 安装包，或直接下载：
+
+```
+https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe
+```
+
+### 2. 安装
+
+1. 双击运行安装程序
+2. 选择 "Just Me" (仅为当前用户安装)
+3. 安装路径建议使用默认值或 `C:\Miniconda3`
+4. **重要**：勾选 "Add Miniconda3 to my PATH environment variable"
+5. 完成安装
+
+### 3. 验证安装
+
+打开 **命令提示符 (CMD)** 或 **PowerShell**：
+
+```powershell
+conda --version
+# 应显示类似: conda 24.x.x
+```
+
+---
+
+## 三、创建 Conda 环境
+
+### 1. 创建 Python 3.12 环境
+
+```powershell
+conda create -n crypto_pred_312 python=3.12 -y
+```
+
+### 2. 激活环境
+
+```powershell
+conda activate crypto_pred_312
+```
+
+激活后，命令行前缀应显示 `(crypto_pred_312)`
+
+---
+
+## 四、克隆项目
+
+### 1. 安装 Git (如未安装)
+
+下载并安装 [Git for Windows](https://git-scm.com/download/win)
+
+### 2. 克隆仓库
+
+```powershell
+cd C:\Projects  # 或您喜欢的目录
+git clone http://192.168.50.24:3000/subuhui/crpto_predictor.git
+cd crpto_predictor
+```
+
+---
+
+## 五、安装依赖
+
+### 1. 安装 PyTorch (CPU 版本)
+
+```powershell
+# CPU 版本 PyTorch（Windows 不使用 GPU）
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+```
+
+### 2. 安装项目依赖
+
+```powershell
+pip install -r requirements.txt
+```
+
+### 3. 安装 IBM 时序模型 (TTM-R2)
+
+```powershell
+# PyPI 包名是 granite-tsfm，导入时用 tsfm_public
+pip install granite-tsfm
+```
+
+验证安装：
+```powershell
+python -c "from tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction; print('TTM-R2 安装成功')"
+```
+
+---
+
+## 六、配置环境变量
+
+### 1. 复制环境变量模板
+
+```powershell
+copy .env.example .env
+```
+
+### 2. 编辑 .env 文件
+
+使用记事本或 VSCode 编辑 `.env` 文件：
+
+```ini
+# MySQL 数据库配置
+MYSQL_HOST=your_mysql_host
+MYSQL_PORT=3306
+MYSQL_USER=your_username
+MYSQL_PASSWORD=your_password
+MYSQL_DB=crypto_predictor
+
+# Binance API (可选，用于实时数据)
+BINANCE_API_KEY=your_api_key
+BINANCE_API_SECRET=your_api_secret
+
+# Polymarket 配置 (可选，用于自动交易)
+POLYMARKET_PRIVATE_KEY=your_private_key
+DRY_RUN=true  # 设为 true 进行模拟交易
+
+# 代理配置 (可选)
+USE_PROXY=false
+SOCKS5_PROXY=socks5://127.0.0.1:1080
+```
+
+---
+
+## 七、数据库配置
+
+### 1. 安装 MySQL
+
+下载并安装 [MySQL Community Server](https://dev.mysql.com/downloads/mysql/)
+
+或使用 Docker:
+```powershell
+docker run -d --name mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=yourpassword mysql:8.0
+```
+
+### 2. 创建数据库
+
+```sql
+CREATE DATABASE crypto_predictor CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
+```
+
+### 3. 初始化表结构
+
+首次运行时会自动创建表，或手动执行：
+```powershell
+python -c "from api.models import init_model_predictions_table; from data.trading_db import TradingDatabase; TradingDatabase()"
+```
+
+---
+
+## 八、验证安装
+
+### 1. 测试 PyTorch
+
+```powershell
+python -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'Device: cpu')"
+```
+
+### 2. 测试模型加载
+
+```powershell
+python -c "from training.config import TrainingConfig; c = TrainingConfig(); print(f'Device: {c.get_device()}')"
+# 应输出: Device: cpu
+```
+
+### 3. 测试数据库连接
+
+```powershell
+python -c "from api.models import get_db_connection; conn = get_db_connection(); print('数据库连接成功!')"
+```
+
+---
+
+## 九、运行系统
+
+### 方式一：双击批处理脚本 (推荐)
+
+双击项目根目录的 `start_system.bat`
+
+### 方式二：PowerShell 脚本
+
+```powershell
+.\start_system.ps1
+```
+
+### 方式三：Python 脚本
+
+```powershell
+python start_system.py
+```
+
+### 方式四：分别启动
+
+```powershell
+# 终端 1: API 服务
+python -m uvicorn api.main:app --host 0.0.0.0 --port 8000
+
+# 终端 2: 自动交易
+python auto_trade_polymarket.py
+```
+
+---
+
+## 十、访问系统
+
+- **前端看板**: http://localhost:8000
+- **API 文档**: http://localhost:8000/docs
+
+---
+
+## 常见问题
+
+### Q1: `conda activate` 不生效
+
+在 PowerShell 中需要先初始化 conda：
+```powershell
+conda init powershell
+# 重启 PowerShell
+```
+
+### Q2: pip 安装超时
+
+使用国内镜像：
+```powershell
+pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+### Q3: PyTorch 安装失败
+
+确保使用 CPU 版本的安装命令，不要安装 CUDA 版本。
+
+### Q4: 数据库连接失败
+
+1. 检查 MySQL 服务是否运行
+2. 检查 `.env` 中的数据库配置
+3. 检查防火墙是否允许 3306 端口
+
+### Q5: 模型文件缺失
+
+确保 `models/checkpoints/` 目录包含预训练模型文件。如缺失，联系管理员获取。
+
+---
+
+## 目录结构
+
+```
+crpto_predictor/
+├── api/                    # API 服务
+│   ├── main.py            # FastAPI 入口
+│   ├── models.py          # 数据库模型
+│   ├── prediction_service.py  # 预测服务
+│   └── static/            # 前端静态文件
+├── models/                 # 模型定义和权重
+│   ├── checkpoints/       # 模型权重文件
+│   └── dlinear/           # DLinear 模型
+├── data/                   # 数据层
+├── training/               # 训练配置
+├── start_system.py        # 跨平台启动脚本
+├── start_system.bat       # Windows CMD 脚本
+├── start_system.ps1       # Windows PowerShell 脚本
+├── auto_trade_polymarket.py  # 自动交易程序
+├── requirements.txt       # 依赖列表
+└── .env                   # 环境变量 (需自行创建)
+```
+
+---
+
+## 更新项目
+
+```powershell
+git pull origin main
+pip install -r requirements.txt
+```
+
+---
+
+*如有问题，请联系管理员。*

ensemble_three_models.py

@@ -205,12 +205,13 @@ def main():
     # 还原真实价格
     nbeats_pred_prices = nbeats_preprocessor.inverse_transform_price(nbeats_pred_scaled)
 
-    # 【修改】获取当前K线的open价格（未来可交易价格）用于判断方向
-    # 第i个样本对应的是第168+i根K线，其open价格是真正可以交易的价格
-    current_open_prices = df_nbeats['open'].iloc[168:168+len(nbeats_pred_prices)].values
-    nbeats_preds = (nbeats_pred_prices > current_open_prices).astype(int)  # 1=涨, 0=跌
+    # 【修复】使用当前K线的close价格判断涨跌（与标签定义一致）
+    # 第i个样本对应的是第168+i根K线，其close价格是当前收盘价
+    # 标签定义: label = (future_close > current_close) 
+    current_close_prices = df_nbeats['close'].iloc[168:168+len(nbeats_pred_prices)].values
+    nbeats_preds = (nbeats_pred_prices > current_close_prices).astype(int)  # 1=涨, 0=跌
 
-    print(f"✓ N-BEATS预测完成 (使用当前K线open价格判断)")
+    print(f"✓ N-BEATS预测完成 (使用当前K线close价格判断)")
     print(f"  预测上涨: {(nbeats_preds==1).sum()} ({(nbeats_preds==1).sum()/len(nbeats_preds)*100:.1f}%)")
     print(f"  预测下跌: {(nbeats_preds==0).sum()} ({(nbeats_preds==0).sum()/len(nbeats_preds)*100:.1f}%)")
 
@@ -223,7 +224,6 @@ def main():
 
     # 从df_2026获取价格数据
     all_prices = df_2026['close'].values
-    all_open_prices = df_2026['open'].values
 
     # 为每个样本创建预测
     for i in range(min_samples):
@@ -235,12 +235,12 @@ def main():
         if end_idx > len(all_prices):
             # 如果超出范围，使用最后512个价格
             context_data = all_prices[-context_length:].reshape(1, -1, 1)
-            # 【修改】使用对应K线的open价格作为当前可交易价格
-            current_open_price = all_open_prices[min(168 + i, len(all_open_prices) - 1)]
+            # 【修复】使用对应K线的close价格作为当前收盘价
+            current_close_price = all_prices[min(168 + i, len(all_prices) - 1)]
         else:
             context_data = all_prices[start_idx:end_idx].reshape(1, -1, 1)
-            # 【修改】使用第end_idx根K线的open价格（即当前K线的开盘价）
-            current_open_price = all_open_prices[end_idx] if end_idx < len(all_open_prices) else all_open_prices[-1]
+            # 【修复】使用第168+i根K线的close价格（即当前K线的收盘价）
+            current_close_price = all_prices[168 + i] if 168 + i < len(all_prices) else all_prices[-1]
 
         context_tensor = torch.FloatTensor(context_data).to(device)
 
@@ -249,12 +249,12 @@ def main():
             # 预测第一个时间步的值
             pred_price = output.prediction_outputs.cpu().numpy()[0, 0, 0]
 
-        # 【修改】判断方向: 预测价格 vs 当前K线open价格
-        ttm_preds_list.append(1 if pred_price > current_open_price else 0)
+        # 【修复】判断方向: 预测价格 vs 当前K线close价格（与标签定义一致）
+        ttm_preds_list.append(1 if pred_price > current_close_price else 0)
 
     ttm_preds = np.array(ttm_preds_list)
 
-    print(f"✓ TTM-R2预测完成 (使用当前K线open价格判断)")
+    print(f"✓ TTM-R2预测完成 (使用当前K线close价格判断)")
     print(f"  预测上涨: {(ttm_preds==1).sum()} ({(ttm_preds==1).sum()/len(ttm_preds)*100:.1f}%)")
     print(f"  预测下跌: {(ttm_preds==0).sum()} ({(ttm_preds==0).sum()/len(ttm_preds)*100:.1f}%)")
 
@@ -358,38 +358,22 @@ def main():
 
     print(f"\n集成策略性能:")
     print(f"  双模型交集 (DLinear+NBEATS): {two_model_acc*100:.2f}% (覆盖率: {two_model_valid.sum()/len(dlinear_preds)*100:.1f}%)")
+    print(f"  三模型多数投票: {ensemble_accuracy*100:.2f}% (覆盖率: 100%)")
 
-    if len(valid_preds) > 0:
-        print(f"  三模型交集 (ALL): {ensemble_accuracy*100:.2f}% (覆盖率: {valid_mask.sum()/len(dlinear_preds)*100:.1f}%)")
+    # 性能评估
+    print("\n" + "="*80)
+    print("🎯 策略评估")
+    print("="*80)
 
-        # 性能评估
-        print("\n" + "="*80)
-        print("🎯 策略评估")
-        print("="*80)
+    best_single = max(dlinear_acc, nbeats_acc, ttm_acc)
 
-        best_single = max(dlinear_acc, nbeats_acc, ttm_acc)
-
-        if ensemble_accuracy > best_single:
-            improvement = ensemble_accuracy - best_single
-            print(f"\n  ✅ 三模型交集优于所有单模型!")
-            print(f"     准确率提升: {improvement*100:.2f}个百分点")
-            print(f"     代价: 弃权{(~valid_mask).sum()}次预测 ({(~valid_mask).sum()/len(dlinear_preds)*100:.1f}%)")
-        elif ensemble_accuracy > two_model_acc:
-            improvement = ensemble_accuracy - two_model_acc
-            print(f"\n  ✅ 三模型交集优于双模型交集!")
-            print(f"     准确率提升: {improvement*100:.2f}个百分点")
-            print(f"     但覆盖率下降: {(two_model_valid.sum() - valid_mask.sum())/len(dlinear_preds)*100:.1f}%")
-        else:
-            print(f"\n  ⚠️  三模型交集未能提升准确率")
-            print(f"     可能原因: TTM-R2表现太差({ttm_acc*100:.2f}%)，拖累了整体性能")
-            print(f"     建议: 使用双模型交集(DLinear+NBEATS)")
-
-        # 实际收益分析
-        print(f"\n💰 实际收益分析:")
-        print(f"  如果只在三个模型都同意时交易:")
-        print(f"    - 交易次数: {valid_mask.sum()} ({valid_mask.sum()/len(dlinear_preds)*100:.1f}%)")
-        print(f"    - 准确率: {ensemble_accuracy*100:.2f}%")
-        print(f"    - 避免交易: {(~valid_mask).sum()}次 (规避不确定性)")
+    if ensemble_accuracy > best_single:
+        improvement = ensemble_accuracy - best_single
+        print(f"\n  ✅ 三模型多数投票优于所有单模型!")
+        print(f"     准确率提升: {improvement*100:.2f}个百分点")
+    else:
+        print(f"\n  ⚠️  三模型多数投票未能超越最佳单模型")
+        print(f"     可能原因: 某个模型表现太差，拖累整体性能")
 
     print("\n" + "="*80)
     print("✓ 三模型集成预测分析完成!")

environment.yml

@@ -1,63 +0,0 @@
-name: crypto_pred
-channels:
-  - pytorch
-  - conda-forge
-  - defaults
-dependencies:
-  # Python
-  - python=3.10
-
-  # PyTorch (Mac M1 支持)
-  - pytorch
-  - torchvision
-  - torchaudio
-
-  # 数据处理
-  - pandas>=2.0.0
-  - numpy>=1.24.0
-  - scikit-learn>=1.3.0
-
-  # 开发工具
-  - jupyter
-  - ipython
-  - tqdm
-
-  # 可视化
-  - matplotlib
-  - seaborn
-
-  # 通过pip安装的包
-  - pip
-  - pip:
-    # Binance API
-    - python-binance>=1.0.19
-
-    # 环境变量
-    - python-dotenv>=1.0.0
-
-    # 时区处理
-    - pytz
-
-    # ONNX
-    - onnx>=1.14.0
-    - onnxruntime>=1.15.0
-
-    # 技术指标
-    - pandas-ta>=0.3.14b
-
-    # 模型库
-    - darts>=0.27.0
-    - u8darts[torch]>=0.27.0
-    - tsfm_public>=0.2.0
-    - gluonts[torch]>=0.14.0
-
-    # FastAPI
-    - fastapi>=0.104.0
-    - uvicorn[standard]>=0.24.0
-    - pydantic>=2.0.0
-    - python-multipart>=0.0.6
-
-    # 测试
-    - pytest>=7.4.0
-    - black>=23.0.0
-    - flake8>=6.0.0

install_windows.bat

@@ -0,0 +1,58 @@
+@echo off
+chcp 65001 >nul
+REM ============================================
+REM  Windows 环境安装脚本
+REM  安装所有依赖和模型
+REM ============================================
+
+echo ==========================================
+echo   加密货币预测系统 - Windows 安装脚本
+echo ==========================================
+echo.
+
+REM 检查 conda
+where conda >nul 2>&1
+if %errorlevel% neq 0 (
+    echo [错误] 未找到 conda
+    echo 请先安装 Miniconda: https://docs.conda.io/en/latest/miniconda.html
+    pause
+    exit /b 1
+)
+
+echo [1/6] 创建 conda 环境...
+call conda create -n crypto_pred_312 python=3.12 -y
+if %errorlevel% neq 0 (
+    echo [警告] 环境可能已存在，继续...
+)
+
+echo.
+echo [2/6] 激活环境...
+call conda activate crypto_pred_312
+
+echo.
+echo [3/6] 安装 PyTorch (CPU 版本)...
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+
+echo.
+echo [4/5] 安装项目依赖...
+pip install -r requirements.txt
+
+echo.
+echo [5/5] 验证安装...
+python -c "import torch; print(f'PyTorch: {torch.__version__}')"
+python -c "from tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction; print('TTM-R2: 已安装')"
+python -c "import pandas_ta; print('pandas-ta: 已安装')"
+python -c "from training.config import TrainingConfig; c=TrainingConfig(); print(f'设备: {c.get_device()}')"
+
+echo.
+echo ==========================================
+echo   安装完成!
+echo ==========================================
+echo.
+echo 下一步:
+echo   1. 复制 .env.example 为 .env 并配置数据库
+echo   2. 双击 start_system.bat 启动系统
+echo   3. 访问 http://localhost:8000
+echo.
+
+pause

requirements.txt

@@ -1,40 +1,69 @@
-# Core dependencies
-torch>=2.0.0
-torchvision
-torchaudio
-python-binance>=1.0.19
+# ==============================================
+# 加密货币预测系统依赖
+# Python 3.12 | 支持 macOS (MPS) 和 Windows (CPU)
+# ==============================================
+
+# ----------------------------------------------
+# PyTorch (根据平台单独安装)
+# ----------------------------------------------
+# macOS: pip install torch torchvision torchaudio
+# Windows CPU: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+# 注意：requirements.txt 中不包含 torch，需要手动安装
+
+# ----------------------------------------------
+# 数据处理
+# ----------------------------------------------
 pandas>=2.0.0
 numpy>=1.24.0
 scikit-learn>=1.3.0
 pytz
-python-dotenv>=1.0.0
 
-# ONNX
-onnx>=1.14.0
-onnxruntime>=1.15.0
+# ----------------------------------------------
+# 时序模型
+# ----------------------------------------------
+# IBM Granite TTM-R2 (导入名: tsfm_public)
+granite-tsfm>=0.3.0
 
-# Model-specific dependencies
-darts>=0.27.0
-u8darts[torch]>=0.27.0
-tsfm_public>=0.2.0
-gluonts[torch]>=0.14.0
+# ----------------------------------------------
+# 技术指标
+# ----------------------------------------------
+pandas-ta>=0.3.14b
 
-# API dependencies
+# ----------------------------------------------
+# API 服务
+# ----------------------------------------------
 fastapi>=0.104.0
 uvicorn[standard]>=0.24.0
 pydantic>=2.0.0
 python-multipart>=0.0.6
 
-# Technical indicators
-pandas-ta>=0.3.14b
+# ----------------------------------------------
+# HTTP 客户端
+# ----------------------------------------------
+httpx>=0.25.0
 
-# Development tools
-jupyter>=1.0.0
-matplotlib>=3.7.0
-seaborn>=0.12.0
-pytest>=7.4.0
-black>=23.0.0
-flake8>=6.0.0
+# ----------------------------------------------
+# 数据源
+# ----------------------------------------------
+python-binance>=1.0.19
 
-# Utilities
+# ----------------------------------------------
+# 任务调度
+# ----------------------------------------------
+schedule>=1.2.0
+
+# ----------------------------------------------
+# 数据库
+# ----------------------------------------------
+pymysql>=1.1.0
+cryptography>=41.0.0
+
+# ----------------------------------------------
+# 环境变量
+# ----------------------------------------------
+python-dotenv>=1.0.0
+
+# ----------------------------------------------
+# 进度条
+# ----------------------------------------------
 tqdm>=4.66.0

scripts/generate_sample_data.py

@@ -1,10 +1,17 @@
 """生成测试数据以便在无法连接Binance时使用"""
 
+import sys
+import os
+from pathlib import Path
+
+# 添加项目根目录 to path
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+
 import pandas as pd
 import numpy as np
 from datetime import datetime, timedelta
-from pathlib import Path
 
+from data.database import DatabaseManager
 
 def generate_sample_data(
     symbol: str = 'BTC',
@@ -76,14 +83,13 @@ def generate_sample_data(
 
     df = pd.DataFrame(data)
 
-    # 保存到缓存
-    cache_dir = Path('./data/cache')
-    cache_dir.mkdir(parents=True, exist_ok=True)
+    # 保存到数据库
+    # 注意：我们这里假设DatabaseManager的默认路径在脚本运行时是相对于CWD的
+    # 如果从根目录运行，./data/cache/market_data.db 是正确的
+    db_manager = DatabaseManager()
+    db_manager.save_ohlcv(df, symbol, timeframe)
 
-    cache_file = cache_dir / f"{symbol}_{timeframe}_1y_now.csv"
-    df.to_csv(cache_file, index=False)
-
-    print(f"✓ 已生成 {len(df)} 行模拟数据并保存到：{cache_file}")
+    print(f"✓ 已生成 {len(df)} 行模拟数据并保存到数据库")
     return df
 
 

start_system.bat

@@ -0,0 +1,47 @@
+@echo off
+chcp 65001 >nul
+REM ============================================
+REM  系统启动脚本 (Windows)
+REM  启动 API 服务和自动交易程序
+REM ============================================
+
+echo [%date% %time%] 正在启动系统...
+
+REM 检查 conda 环境
+where conda >nul 2>&1
+if %errorlevel% neq 0 (
+    echo [错误] 未找到 conda，请先安装 Anaconda 或 Miniconda
+    pause
+    exit /b 1
+)
+
+REM 激活 conda 环境
+echo [%date% %time%] 激活 conda 环境: crypto_pred_312
+call conda activate crypto_pred_312
+if %errorlevel% neq 0 (
+    echo [错误] 无法激活 conda 环境 crypto_pred_312
+    echo 请先创建环境: conda create -n crypto_pred_312 python=3.12
+    pause
+    exit /b 1
+)
+
+REM 启动 API 服务 (后台运行)
+echo [%date% %time%] 启动 API 服务...
+start "API服务" cmd /c "python -m uvicorn api.main:app --host 0.0.0.0 --port 8000"
+
+REM 等待 API 启动
+timeout /t 3 /nobreak >nul
+
+REM 启动自动交易程序 (后台运行)
+echo [%date% %time%] 启动自动交易程序...
+start "自动交易" cmd /c "python -u auto_trade_polymarket.py"
+
+echo.
+echo ============================================
+echo   系统启动完成!
+echo   前端访问: http://localhost:8000
+echo   关闭窗口可停止对应服务
+echo ============================================
+echo.
+
+pause

start_system.ps1

@@ -0,0 +1,90 @@
+# ============================================
+#  系统启动脚本 (Windows PowerShell)
+#  启动 API 服务和自动交易程序
+# ============================================
+
+$ErrorActionPreference = "Stop"
+
+function Write-Log {
+    param([string]$Message, [string]$Color = "White")
+    $timestamp = Get-Date -Format "yyyy-MM-dd HH:mm:ss"
+    Write-Host "[$timestamp] $Message" -ForegroundColor $Color
+}
+
+Write-Log "正在启动系统..." "Yellow"
+
+# 检查 conda
+$condaPath = Get-Command conda -ErrorAction SilentlyContinue
+if (-not $condaPath) {
+    Write-Log "错误: 未找到 conda，请先安装 Anaconda 或 Miniconda" "Red"
+    Read-Host "按回车键退出"
+    exit 1
+}
+
+# 激活 conda 环境
+Write-Log "激活 conda 环境: crypto_pred_312" "Yellow"
+try {
+    conda activate crypto_pred_312
+} catch {
+    Write-Log "错误: 无法激活 conda 环境 crypto_pred_312" "Red"
+    Write-Log "请先创建环境: conda create -n crypto_pred_312 python=3.12" "Yellow"
+    Read-Host "按回车键退出"
+    exit 1
+}
+
+# 启动 API 服务
+Write-Log "启动 API 服务..." "Yellow"
+$apiProcess = Start-Process -FilePath "python" `
+    -ArgumentList "-m uvicorn api.main:app --host 0.0.0.0 --port 8000" `
+    -PassThru -NoNewWindow
+
+Write-Log "API 服务已启动 (PID: $($apiProcess.Id))" "Green"
+
+# 等待 API 启动
+Start-Sleep -Seconds 3
+
+# 启动自动交易程序
+Write-Log "启动自动交易程序..." "Yellow"
+$tradeProcess = Start-Process -FilePath "python" `
+    -ArgumentList "-u auto_trade_polymarket.py" `
+    -PassThru -NoNewWindow
+
+Write-Log "自动交易程序已启动 (PID: $($tradeProcess.Id))" "Green"
+
+Write-Host ""
+Write-Host "============================================" -ForegroundColor Cyan
+Write-Host "  系统启动完成!" -ForegroundColor Green
+Write-Host "  前端访问: http://localhost:8000" -ForegroundColor Green
+Write-Host "  按 Ctrl+C 停止所有服务" -ForegroundColor Yellow
+Write-Host "============================================" -ForegroundColor Cyan
+Write-Host ""
+
+# 等待用户中断
+try {
+    while ($true) {
+        # 检查进程状态
+        if ($apiProcess.HasExited) {
+            Write-Log "API 服务意外退出" "Red"
+            break
+        }
+        if ($tradeProcess.HasExited) {
+            Write-Log "自动交易程序意外退出" "Red"
+            break
+        }
+        Start-Sleep -Seconds 1
+    }
+} finally {
+    Write-Log "正在停止服务..." "Yellow"
+    
+    if (-not $apiProcess.HasExited) {
+        Stop-Process -Id $apiProcess.Id -Force -ErrorAction SilentlyContinue
+        Write-Log "API 服务已停止" "Green"
+    }
+    
+    if (-not $tradeProcess.HasExited) {
+        Stop-Process -Id $tradeProcess.Id -Force -ErrorAction SilentlyContinue
+        Write-Log "自动交易程序已停止" "Green"
+    }
+    
+    Write-Log "所有服务已停止" "Green"
+}

start_system.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""
+系统启动脚本
+同时启动：
+1. FastAPI 后端服务 (提供前端看板 API)
+2. Polymarket 自动交易程序
+
+支持平台：macOS, Linux, Windows
+"""
+import subprocess
+import sys
+import time
+import signal
+import os
+import platform
+from datetime import datetime
+
+# 检测操作系统
+IS_WINDOWS = platform.system() == 'Windows'
+
+# 颜色代码 (Windows CMD 默认不支持 ANSI，但现代 Windows Terminal 支持)
+if IS_WINDOWS:
+    # 尝试启用 Windows ANSI 支持
+    try:
+        import ctypes
+        kernel32 = ctypes.windll.kernel32
+        kernel32.SetConsoleMode(kernel32.GetStdHandle(-11), 7)
+        ANSI_SUPPORTED = True
+    except Exception:
+        ANSI_SUPPORTED = False
+else:
+    ANSI_SUPPORTED = True
+
+if ANSI_SUPPORTED:
+    GREEN = '\033[92m'
+    YELLOW = '\033[93m'
+    RED = '\033[91m'
+    RESET = '\033[0m'
+else:
+    GREEN = YELLOW = RED = RESET = ''
+
+def log(message, color=RESET):
+    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    print(f"{color}[{timestamp}] {message}{RESET}")
+
+def start_process(command, name):
+    """启动子进程（跨平台兼容）"""
+    log(f"正在启动 {name}...", YELLOW)
+    try:
+        if IS_WINDOWS:
+            # Windows: 不使用 preexec_fn，使用 CREATE_NEW_PROCESS_GROUP
+            process = subprocess.Popen(
+                command,
+                shell=True,
+                stdout=sys.stdout,
+                stderr=sys.stderr,
+                creationflags=subprocess.CREATE_NEW_PROCESS_GROUP
+            )
+        else:
+            # macOS / Linux: 使用 setsid 创建新进程组
+            process = subprocess.Popen(
+                command,
+                shell=True,
+                stdout=sys.stdout,
+                stderr=sys.stderr,
+                preexec_fn=os.setsid
+            )
+        log(f"{name} 已启动 (PID: {process.pid})", GREEN)
+        return process
+    except Exception as e:
+        log(f"启动 {name} 失败: {e}", RED)
+        return None
+
+def terminate_process(process, name):
+    """终止进程（跨平台兼容）"""
+    if process is None:
+        return
+    try:
+        if IS_WINDOWS:
+            # Windows: 使用 terminate() 或发送 CTRL_BREAK_EVENT
+            process.terminate()
+        else:
+            # macOS / Linux: 终止整个进程组
+            os.killpg(os.getpgid(process.pid), signal.SIGTERM)
+        log(f"{name} 已停止", GREEN)
+    except Exception as e:
+        log(f"停止 {name} 时出错: {e}", RED)
+        # 强制杀死
+        try:
+            process.kill()
+        except Exception:
+            pass
+
+def main():
+    # 激活 Conda 环境
+    # 注意：在子进程中直接使用当前已激活的 python 解释器路径，避免 conda activate 问题
+    python_exec = sys.executable
+    
+    # 1. 启动 API 服务 (Uvicorn)
+    # 使用 --host 0.0.0.0 允许局域网访问
+    api_cmd = f"{python_exec} -m uvicorn api.main:app --host 0.0.0.0 --port 8000"
+    api_process = start_process(api_cmd, "API 服务 & 前端看板")
+    
+    # 等待几秒确保 API 启动
+    time.sleep(3)
+    
+    # 2. 启动自动交易程序
+    # 使用 -u 参数禁用输出缓冲
+    trade_cmd = f"{python_exec} -u auto_trade_polymarket.py"
+    trade_process = start_process(trade_cmd, "Polymarket 自动交易程序")
+    
+    log("系统启动完成！", GREEN)
+    log("前端访问地址: http://localhost:8000", GREEN)
+    log("按 Ctrl+C 停止所有服务...", YELLOW)
+    
+    try:
+        # 监控子进程状态
+        while True:
+            if api_process and api_process.poll() is not None:
+                log("API 服务意外退出", RED)
+                break
+            if trade_process and trade_process.poll() is not None:
+                log("自动交易程序意外退出", RED)
+                break
+            time.sleep(1)
+            
+    except KeyboardInterrupt:
+        log("\n正在停止服务...", YELLOW)
+    finally:
+        # 优雅退出
+        terminate_process(api_process, "API 服务")
+        terminate_process(trade_process, "自动交易程序")
+        
+        log("所有服务已停止", GREEN)
+
+if __name__ == "__main__":
+    main()

trading_log.json

@@ -0,0 +1,13 @@
+[
+  {
+    "timestamp": "2026-02-03T21:18:33.773774",
+    "market": "Bitcoin Up or Down - February 3, 9AM ET",
+    "prediction": "DOWN",
+    "success": false,
+    "order": null,
+    "error": "价格$0.51超过限制$0.50",
+    "side": "DOWN",
+    "price": 0.505,
+    "probability": 0.5563886165618896
+  }
+]

training/config.py

@@ -38,18 +38,23 @@ class TrainingConfig:
 
         Returns:
             Device string ('mps', 'cuda', or 'cpu')
+        
+        Note:
+            macOS 使用 MPS，Windows/Linux 使用 CPU
         """
         import torch
+        import platform
 
         if self.device != 'auto':
             return self.device
 
-        # Auto-detect best available device
-        if torch.backends.mps.is_available():
-            return 'mps'
-        elif torch.cuda.is_available():
-            return 'cuda'
-        else:
+        # 根据操作系统选择设备：macOS 用 MPS，Windows/Linux 用 CPU
+        if platform.system() == 'Darwin':  # macOS
+            if torch.backends.mps.is_available():
+                return 'mps'
+            else:
+                return 'cpu'
+        else:  # Windows / Linux
             return 'cpu'