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