写了个选股策略，但是不知道为何盘中，盘后都没有触发，也没有日志显示，请指教

def daily_selection(ContextInfo): """每日选股任务 - 根据新策略修改"""

清空候选股池

clear_sector(CANDIDATE_POOL_NAME) candidate_stocks.clear()

# 从配置文件指定的通达信板块路径获取股票列表
stock_list = []
try:
    fixed_path = ContextInfo.tdx_sector_path.replace('\\', '/')
    with open(fixed_path, 'r', encoding='gbk') as f:
        raw_list = [line.strip() for line in f.readlines() if line.strip()]

    # 转换股票代码格式
    for raw_code in raw_list:
        try:
            formatted_code = get_code(raw_code)
            if formatted_code != raw_code:
                stock_list.append(formatted_code)
            else:
                logger.log(f"忽略无效代码格式: {raw_code}", level="WARNING")
        except Exception as e:
            logger.log(f"代码转换失败: {raw_code} - {str(e)}", level="WARNING")
except Exception as e:
    logger.log(f"读取通达信板块失败: {str(e)}", level="INFO")
    return

for stock_code in stock_list:
    try:
        # 获取日线数据（获取15天数据确保有足够历史数据）
        df_daily = ContextInfo.get_market_data_ex(
            fields=["close", "high", "low", "volume", "open"],
            stock_code=[stock_code],
            count=15,
            period="1d",
            dividend_type="front"
        ).get(stock_code)

        if df_daily is None or len(df_daily) < 12:  # 至少需要15天数据
            logger.log(f"股票{stock_code}数据不足，跳过", level="WARNING")
            continue

        # 计算5日均线
        close_prices = np.array(df_daily["close"])
        ma5 = mt.MA(close_prices, 5)

        # === 条件1: 倍量阳线+5日均线拐头 ===
        last_index = len(df_daily) - 1  # 最新K线索引（昨日）
        prev_index = last_index - 1     # 前一交易日

        # 检查倍量阳线
        prev_vol = df_daily["volume"].iloc[prev_index]
        curr_vol = df_daily["volume"].iloc[last_index]
        is_positive = df_daily["close"].iloc[last_index] > df_daily["open"].iloc[last_index]
        volume_ok = curr_vol > ContextInfo.volume_multiple * prev_vol

        # 检查5日均线拐头
        ma5_turn_up = ma5[last_index] > ma5[prev_index] and ma5[prev_index] < ma5[prev_index-1]

        # 排除天量/巨量
        not_extreme = not is_extreme_volume(df_daily, last_index)

        # 如果不满足条件1，跳过
        if not (volume_ok and is_positive and ma5_turn_up and not_extreme):
            continue

        # 记录倍量阳线最低价
        mark_low = df_daily["low"].iloc[last_index]

        # === 条件2: 10日内最低价K线及区间检查 ===
        # 在倍量阳线前10个交易日内寻找最低收盘价K线
        search_start = max(0, last_index - 10)  # 确保不越界
        search_end = last_index  # 搜索到倍量阳线的前一天

        # 找到最低收盘价K线
        min_close = float('inf')
        min_index = -1
        for i in range(search_start, search_end):
            if df_daily["close"].iloc[i] < min_close:
                min_close = df_daily["close"].iloc[i]
                min_index = i

        # 如果没有找到符合条件的K线，跳过
        if min_index == -1:
            continue

        # 记录最低价K线的最高价
        mark_high = df_daily["high"].iloc[min_index]

        # 检查区间内收盘价是否都低于mark_high
        # 区间：从最低价K线(min_index)到倍量阳线的前一天(last_index-1)
        condition2 = True
        # 遍历最近9天
        for offset in range(1, 10):
            # 如果最低价K线位置在offset天之前，则检查该天收盘价是否小于标记最高价
            if min_index <= (last_index - offset):
                if df_daily["close"].iloc[last_index - offset] >= mark_high:
                    condition2 = False
                    break

        # 如果不满足条件2，跳过
        if not condition2:
            continue
         # === 新增条件: RSI指标和总股本条件 ===
        # 获取周线RSI
        week_rsi_ok = False
        try:
            df_week = ContextInfo.get_market_data_ex(
                fields=["close"],
                stock_code=[stock_code],
                count=4,  # 获取7周数据
                period="1w",
                dividend_type="front"
            ).get(stock_code)

            if df_week is not None and len(df_week) >= 6:
                week_close = np.array(df_week["close"])
                week_rsi = mt.RSI(week_close, 6)
                week_rsi_ok = week_rsi[-1] > 50 if len(week_rsi) > 0 else False
        except:
            week_rsi_ok = False

        # 获取月线RSI
        month_rsi_ok = False
        try:
            df_month = ContextInfo.get_market_data_ex(
                fields=["close"],
                stock_code=[stock_code],
                count=2,  # 获取2个月数据
                period="1month",
                dividend_type="front"
            ).get(stock_code)

            if df_month is not None and len(df_month) >= 6:
                month_close = np.array(df_month["close"])
                month_rsi = mt.RSI(month_close, 6)
                month_rsi_ok = month_rsi[-1] > 50 if len(month_rsi) > 0 else False
        except:
            month_rsi_ok = False

        # 总股本条件（小于10亿股）
        total_shares = get_instrument_detail(stock_code, "total_shares")
        total_ok = total_shares is not None and total_shares < 1000000000  # 10亿股
        # 检查RSI条件
        rsi_ok = week_rsi_ok or month_rsi_ok
        # 如果所有条件满足
        if rsi_ok and total_ok :
            # 记录倍量阳线最低价
            mark_low = df_daily["low"].iloc[last_index]

            # 加入候选股池
            candidate_stocks[stock_code] = {
                "mark_low": mark_low,         # 倍量阳线最低价
                "mark_high": mark_high,       # 最低价K线的最高价
                "select_date": datetime.today().strftime("%Y%m%d")
            }
            add_stock_to_sector_custom(CANDIDATE_POOL_NAME, stock_code)
            logger.log(f"加入候选股: {stock_code}, 最低价K线最高价:{mark_high:.2f}, 倍量阳线最低价:{mark_low:.2f}")

    except Exception as e:
        logger.log(f"处理股票{stock_code}时出错: {str(e)}", level="INFO")

if len(candidate_stocks) > 0:
    logger.log(f"选股完成，新增{len(candidate_stocks)}只候选股", level="INFO")
for stock_code, info in candidate_stocks.items():
    logger.log(f"入选股票: {stock_code}, 倍量阳线最低价: {info['mark_low']:.2f}, 突破阈值: {info['mark_high']:.2f}", level="INFO")
else:
    logger.log("选股完成，候选股池为空", level="INFO")  # 修改后