fix: 统一时区处理，准确率统计使用CST时间分组
- 增强 convert_to_cst() 函数，新增 source_tz 参数支持 UTC/ET/CST 输入 - 修改 get_monthly_stats() 使用 CONVERT_TZ 按 CST 月份统计 - 修改 get_daily_stats() 使用 CONVERT_TZ 按 CST 日期分组 - 修改 get_hourly_details() 按 CST 日期筛选和小时排序 - 交易记录使用 source_tz='CST' 避免重复转换 - 前端表头添加'(北京)'时区标注
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@@ -17,12 +17,13 @@ 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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def convert_to_cst(timestamp_val, source_tz: str = 'UTC') -> 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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timestamp_val: datetime 对象或 ISO 格式时间戳字符串
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source_tz: 输入时区 - 'UTC'(默认，币安数据), 'ET'(Polymarket), 'CST'(已是北京时间)
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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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@@ -37,9 +38,19 @@ def convert_to_cst(timestamp_val) -> str:
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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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# 根据来源时区设置时区信息
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=TZ_UTC)
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if source_tz == 'ET':
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dt = dt.replace(tzinfo=TZ_ET)
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elif source_tz == 'CST':
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# 已经是 CST，直接格式化返回
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return dt.strftime('%Y-%m-%d %H:%M:%S')
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else: # 默认 UTC
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dt = dt.replace(tzinfo=TZ_UTC)
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# 如果来源已经是 CST，直接格式化
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if source_tz == 'CST':
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return dt.strftime('%Y-%m-%d %H:%M:%S')
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# 转换为 CST
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dt_cst = dt.astimezone(TZ_CST)
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@@ -122,7 +133,8 @@ class TradeModel:
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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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# 交易记录时间已经是 CST，直接格式化
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result['timestamp_cst'] = convert_to_cst(result['timestamp'], source_tz='CST')
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return result
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@staticmethod
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@@ -173,7 +185,8 @@ class TradeModel:
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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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# 交易记录时间已经是 CST，直接格式化
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item['timestamp_cst'] = convert_to_cst(item['timestamp'], source_tz='CST')
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data_list.append(item)
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return {
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@@ -292,7 +305,7 @@ class PredictionModel:
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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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WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%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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@@ -333,16 +346,16 @@ class PredictionModel:
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sql = """
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SELECT
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date,
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DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as date_cst,
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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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WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%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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GROUP BY date_cst, model_name
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ORDER BY date_cst DESC
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"""
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cursor.execute(sql, (month,))
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@@ -352,7 +365,7 @@ class PredictionModel:
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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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date = str(row_dict['date_cst']) # CST 日期
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if date not in daily_data:
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daily_data[date] = {"date": date}
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@@ -391,28 +404,33 @@ class PredictionModel:
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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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date: 日期 (YYYY-MM-DD, CST 北京时间)
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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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# 按 CST 日期筛选，并获取 CST 小时
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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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SELECT
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*,
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HOUR(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as hour_cst,
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CONVERT_TZ(timestamp, '+00:00', '+08:00') as timestamp_cst_dt
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FROM model_predictions
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WHERE DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) = %s
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ORDER BY hour_cst, 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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# 按 CST 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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hour_cst = row_dict['hour_cst'] # 使用 CST 小时
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if hour_cst not in hourly_data:
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# 将 timestamp 转换为 CST 时间字符串
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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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hourly_data[hour_cst] = {
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"hour": hour_cst, # 返回 CST 小时
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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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@@ -422,13 +440,13 @@ class PredictionModel:
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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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hourly_data[hour_cst]["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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# 按 CST 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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@@ -259,7 +259,7 @@
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<table class="table table-hover mb-0">
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<thead class="table-light">
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<tr>
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<th>日期</th>
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<th>日期 (北京)</th>
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<th>DLinear 预测</th>
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<th>DLinear 准确率</th>
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<th>TTM-R2 预测</th>
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@@ -341,7 +341,7 @@
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<div class="modal-content">
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<div class="modal-header">
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<h5 class="modal-title">
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<i class="bi bi-clock"></i> {{ hourlyModal.date }} 每小时预测详情
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<i class="bi bi-clock"></i> {{ hourlyModal.date }} 每小时预测详情 (北京时间)
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</h5>
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<button type="button" class="btn-close" data-bs-dismiss="modal"></button>
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</div>
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