crypto_predictor
7af21bed6a
- 新增 daily_report_sender.py 定时任务程序 - 北京时间每天 8 点推送昨天 24 小时预测统计 - 包含各模型准确率、逐小时预测详情 - 失败交易订单汇总 - start_system.py 集成启动报告推送服务
380 lines
14 KiB
Python
380 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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每日数据报告定时推送程序
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- 每天北京时间 8 点推送昨天的 24 小时预测统计
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- 包含各模型准确率、逐小时预测结果详情
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"""
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import schedule
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import time
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import logging
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from datetime import datetime, timedelta
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from zoneinfo import ZoneInfo
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from typing import Dict, List
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from utils.send_msg import lark_notifier
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from api.models import PredictionModel, convert_to_cst, get_db_connection, TZ_CST, TZ_UTC
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler('logs/daily_report.log'),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger(__name__)
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class DailyReportSender:
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"""每日数据报告发送器"""
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def __init__(self):
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self.model = PredictionModel()
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self.lark = lark_notifier
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def get_yesterday_failed_trades(self) -> List[Dict]:
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"""
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获取昨天的失败交易订单
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Returns:
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[
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{
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'timestamp': '2026-02-27 10:30:00',
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'market_title': 'BTC 价格',
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'action': 'BUY',
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'status': 'failed',
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'error_message': '余额不足'
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},
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...
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]
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"""
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try:
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# 获取昨天的日期（CST）
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now_cst = datetime.now(TZ_CST)
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yesterday_cst = now_cst - timedelta(days=1)
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yesterday_date = yesterday_cst.strftime('%Y-%m-%d')
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# 计算 CST 时间范围
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start_cst = datetime.strptime(f"{yesterday_date} 00:00:00", '%Y-%m-%d %H:%M:%S').replace(tzinfo=TZ_CST)
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end_cst = start_cst + timedelta(days=1)
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start_str = start_cst.strftime('%Y-%m-%d %H:%M:%S')
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end_str = end_cst.strftime('%Y-%m-%d %H:%M:%S')
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# 从 trades 表查询失败订单
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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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timestamp,
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market_title,
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action,
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status,
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error_message
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FROM trades
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WHERE timestamp >= %s AND timestamp < %s
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AND status != 'MATCHED'
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ORDER BY timestamp DESC
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"""
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cursor.execute(sql, (start_str, end_str))
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rows = cursor.fetchall()
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return rows if rows else []
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except Exception as e:
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logger.error(f"获取昨天失败交易失败: {e}", exc_info=True)
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return []
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def get_yesterday_statistics(self) -> Dict:
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"""
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获取昨天的 24 小时预测统计和详细数据
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Returns:
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{
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'date': 'YYYY-MM-DD',
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'models': {
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'DLinear': {'total': 24, 'correct': 14, 'accuracy': 58.3},
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'N-BEATS': {...},
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'TTM-R2': {...},
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'Ensemble': {...}
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},
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'hourly_details': [
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{
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'hour': '00:00-01:00',
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'actual': '涨',
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'dlinear': {'pred': '涨', 'correct': True},
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'nbeats': {...},
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'ttm': {...},
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'ensemble': {...}
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},
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...
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]
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}
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"""
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try:
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# 获取昨天的日期（CST）
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now_cst = datetime.now(TZ_CST)
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yesterday_cst = now_cst - timedelta(days=1)
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yesterday_date = yesterday_cst.strftime('%Y-%m-%d')
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# 计算 UTC 时间范围
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start_cst = datetime.strptime(f"{yesterday_date} 00:00:00", '%Y-%m-%d %H:%M:%S').replace(tzinfo=TZ_CST)
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end_cst = start_cst + timedelta(days=1)
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start_utc = start_cst.astimezone(TZ_UTC)
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end_utc = end_cst.astimezone(TZ_UTC)
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start_str = start_utc.strftime('%Y-%m-%d %H:%M:%S')
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end_str = end_utc.strftime('%Y-%m-%d %H:%M:%S')
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# 查询数据库
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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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timestamp,
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actual_result,
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dlinear_prediction, dlinear_correct,
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nbeats_prediction, nbeats_correct,
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ttm_prediction, ttm_correct,
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ensemble_prediction, ensemble_correct
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FROM hourly_model_predictions
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WHERE timestamp >= %s AND timestamp < %s
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ORDER BY timestamp ASC
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"""
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cursor.execute(sql, (start_str, end_str))
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rows = cursor.fetchall()
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# 整理统计数据
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models = ['dlinear', 'nbeats', 'ttm', 'ensemble']
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model_names = {
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'dlinear': 'DLinear',
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'nbeats': 'N-BEATS',
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'ttm': 'TTM-R2',
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'ensemble': 'Ensemble'
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}
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# 初始化统计
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stats = {
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'date': yesterday_date,
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'models': {},
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'hourly_details': []
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}
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for model in models:
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stats['models'][model_names[model]] = {
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'total': 0,
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'correct': 0,
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'accuracy': 0.0
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}
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# 处理每行数据
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for idx, row in enumerate(rows):
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if row.get('actual_result') is None:
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continue
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# 获取时间戳（转为 CST 显示）
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ts_utc = row['timestamp']
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if isinstance(ts_utc, str):
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ts_utc = datetime.fromisoformat(str(ts_utc).replace('Z', '+00:00'))
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if ts_utc.tzinfo is None:
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ts_utc = ts_utc.replace(tzinfo=TZ_UTC)
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ts_cst = ts_utc.astimezone(TZ_CST)
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hour_start = ts_cst.strftime('%H:%M')
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hour_end = (ts_cst + timedelta(hours=1)).strftime('%H:%M')
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hour_label = f"{hour_start}-{hour_end}"
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# 实际结果
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actual_result = row['actual_result']
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actual_label = '涨' if actual_result == 1 else '跌'
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# 每小时预测详情
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hourly_item = {
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'hour': hour_label,
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'actual': actual_label
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}
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# 逐模型处理预测结果
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for model in models:
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pred = row[f"{model}_prediction"]
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correct = row[f"{model}_correct"]
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# 更新统计
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if pred is not None:
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stats['models'][model_names[model]]['total'] += 1
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if correct == 1:
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stats['models'][model_names[model]]['correct'] += 1
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# 添加到小时详情
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pred_label = '涨' if pred == 1 else ('跌' if pred == 0 else '无')
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correct_flag = correct == 1 if correct is not None else None
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hourly_item[model] = {
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'pred': pred_label,
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'correct': '✓' if correct_flag else ('✗' if correct_flag is False else '-')
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}
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stats['hourly_details'].append(hourly_item)
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# 计算准确率百分比
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for model_name in stats['models']:
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total = stats['models'][model_name]['total']
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correct = stats['models'][model_name]['correct']
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if total > 0:
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stats['models'][model_name]['accuracy'] = round(correct / total * 100, 2)
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return stats
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except Exception as e:
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logger.error(f"获取昨天统计数据失败: {e}", exc_info=True)
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return None
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def format_report(self, stats: Dict, failed_trades: List[Dict] = None) -> str:
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"""
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格式化报告为飞书 Markdown 格式
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Args:
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stats: get_yesterday_statistics 返回的数据字典
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failed_trades: 失败订单列表
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Returns:
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Markdown 格式的报告文本
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"""
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if not stats:
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return "❌ 无法获取昨天的统计数据，请检查数据库连接"
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date = stats['date']
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models = stats['models']
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# 构建报告头
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report = f"**【BTC 日报 - {date}】**\n\n"
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# 模型准确率统计部分
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report += "**📊 模型准确率统计（24小时）**\n"
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for model_name in ['DLinear', 'N-BEATS', 'TTM-R2', 'Ensemble']:
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data = models.get(model_name, {})
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total = data.get('total', 0)
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correct = data.get('correct', 0)
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accuracy = data.get('accuracy', 0)
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# 进度条可视化
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bar_length = 10
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filled = int(accuracy / 100 * bar_length)
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bar = '█' * filled + '░' * (bar_length - filled)
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report += f"• **{model_name}**: {correct}/{total} {bar} {accuracy}%\n"
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report += "\n"
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# 交易失败订单详情
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report += "**⚠️ 交易失败订单**\n"
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if failed_trades and len(failed_trades) > 0:
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report += f"昨天共有 {len(failed_trades)} 个失败订单\n"
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for idx, trade in enumerate(failed_trades[:10], 1): # 仅显示前10个
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timestamp = trade.get('timestamp', '未知')
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market_title = trade.get('market_title', '未知市场')
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action = trade.get('action', '未知')
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status = trade.get('status', '未知')
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error_msg = trade.get('error_message', '无详细信息')
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# 简化错误信息 - 仅取第一行
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if error_msg:
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error_msg = error_msg.split('\n')[0][:50] # 仅取前50字
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else:
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error_msg = '无错误信息'
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report += f"{idx}. [{timestamp}] {market_title} ({action}) → {error_msg}\n"
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if len(failed_trades) > 10:
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report += f"... 还有 {len(failed_trades) - 10} 个失败订单\n"
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else:
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report += "昨日无失败订单，运营正常 ✓\n"
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report += "\n生成时间: " + datetime.now(TZ_CST).strftime('%Y-%m-%d %H:%M:%S CST')
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return report
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def send_daily_report(self):
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"""获取昨天统计并推送"""
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try:
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logger.info("开始推送每日报告...")
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stats = self.get_yesterday_statistics()
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if not stats:
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logger.warning("无法获取昨天统计数据")
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self.lark.send_alert(
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title="⚠️ 每日报告生成失败",
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content="无法获取昨天的统计数据，请检查数据库连接"
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)
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return
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# 获取失败订单
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failed_trades = self.get_yesterday_failed_trades()
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# 格式化报告
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report = self.format_report(stats, failed_trades)
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# 推送到飞书
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self.lark.send_alert(
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title="📊 BTC 每日数据报告",
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content=report
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)
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logger.info(f"每日报告已送出: {stats['date']}")
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except Exception as e:
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logger.error(f"推送每日报告异常: {e}", exc_info=True)
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self.lark.send_alert(
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title="❌ 每日报告推送异常",
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content=f"错误详情: {str(e)}"
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)
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def schedule_daily_report():
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"""
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启动每日报告定时任务
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北京时间每天 8 点执行
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"""
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sender = DailyReportSender()
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sender.send_daily_report() # 启动时立即发送一次（可选）
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# 使用 schedule 库在北京时间 8 点触发
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# 由于 schedule 库基于本地时间，当前环境如果是 macOS 默认时间，
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# 我们使用一个包装器来处理 CST 时间转换
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def cst_8am_job():
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"""北京时间 8 点执行的任务"""
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now_cst = datetime.now(TZ_CST)
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if now_cst.hour == 8 and now_cst.minute < 1:
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# 确保只在 08:00-08:01 间隔执行一次（避免重复）
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sender.send_daily_report()
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# 每分钟检查一次是否需要执行 (确保能在 8 点执行)
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schedule.every(1).minutes.do(cst_8am_job)
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logger.info("每日报告定时任务已启动（北京时间每天 8 点执行）")
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# 启动调度循环
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while True:
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schedule.run_pending()
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time.sleep(1)
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def run_scheduler_background():
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"""
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在后台线程中运行调度器（供 start_system.py 调用）
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"""
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import threading
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scheduler_thread = threading.Thread(target=schedule_daily_report, daemon=True)
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scheduler_thread.start()
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logger.info("每日报告调度器已在后台启动")
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return scheduler_thread
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if __name__ == '__main__':
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# 支持单独运行以及从其他模块导入
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logger.info("启动每日报告系统...")
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schedule_daily_report()
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