JUKUAN/Lib/future/MAPatternStrategy_v002.py

# 导入函数库
from jqdata import *
from jqdata import finance
import pandas as pd
import numpy as np
import math
from datetime import date, datetime, timedelta, time
import re

MA_ADDITIONAL_HISTORY_DAYS = 30  # 计算均线时额外获取的交易日数量，用于支持更长周期

# 顺势交易策略 v001
# 基于均线走势（前提条件）+ K线形态（开盘价差、当天价差）的期货交易策略
#
# 核心逻辑：
# 1. 开盘时检查均线走势（MA30<=MA20<=MA10<=MA5为多头，反之为空头）
# 2. 检查开盘价差是否符合方向要求（多头>=0.5%，空头<=-0.5%）
# 3. 14:35和14:55检查当天价差（多头>0，空头<0），满足条件则开仓
# 4. 应用固定止损和动态追踪止盈
# 5. 自动换月移仓

# 设置以便完整打印 DataFrame
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', 20)

## 初始化函数，设定基准等等
def initialize(context):
    # 设定沪深300作为基准
    set_benchmark('000300.XSHG')
    # 开启动态复权模式(真实价格)
    set_option('use_real_price', True)
    # 输出内容到日志
    log.info('=' * 60)
    log.info('均线形态交易策略 v001 初始化开始')
    log.info('策略类型: 均线走势 + K线形态')
    log.info('=' * 60)

    ### 期货相关设定 ###
    # 设定账户为金融账户
    set_subportfolios([SubPortfolioConfig(cash=context.portfolio.starting_cash, type='index_futures')])
    # 期货类每笔交易时的手续费是: 买入时万分之0.23,卖出时万分之0.23,平今仓为万分之23
    set_order_cost(OrderCost(open_commission=0.000023, close_commission=0.000023, close_today_commission=0.0023), type='index_futures')
    
    # 设置期货交易的滑点
    set_slippage(StepRelatedSlippage(2))
    
    # 初始化全局变量
    g.usage_percentage = 0.8  # 最大资金使用比例
    g.max_margin_per_position = 30000  # 单个标的最大持仓保证金（元）
    
    # 均线策略参数
    g.ma_periods = [5, 10, 20, 30, 60]  # 均线周期（新增MA60）
    g.ma_cross_periods = [5, 10, 20, 30]  # 参与均线穿越判断的均线周期
    g.ma_historical_days = 60 + MA_ADDITIONAL_HISTORY_DAYS  # 额外增加30天，确保MA60计算稳定
    g.ma_open_gap_threshold = 0.001  # 方案1开盘价差阈值（0.2%）
    g.ma_pattern_lookback_days = 10  # 历史均线模式一致性检查的天数
    g.ma_pattern_consistency_threshold = 0.8  # 历史均线模式一致性阈值（80%）
    g.check_intraday_spread = False  # 是否检查日内价差（True: 检查, False: 跳过）
    g.ma_proximity_min_threshold = 8  # MA5与MA10贴近计数和的最低阈值
    g.ma_pattern_extreme_days_threshold = 4  # 极端趋势天数阈值
    g.ma_distribution_lookback_days = 5  # MA5分布过滤回溯天数
    g.ma_distribution_min_ratio = 0.4  # MA5分布满足比例阈值
    g.enable_ma_distribution_filter = True  # 是否启用MA5分布过滤
    g.ma_cross_threshold = 1  # 均线穿越数量阈值
    g.enable_open_gap_filter = True  # 是否启用开盘价差过滤
    
    # 均线价差策略方案选择
    g.ma_gap_strategy_mode = 3  # 策略模式选择（1: 原方案, 2: 新方案, 3: 方案3）
    g.ma_open_gap_threshold2 = 0.001  # 方案2开盘价差阈值（0.2%）
    g.ma_intraday_threshold_scheme2 = 0.005  # 方案2日内变化阈值（0.5%）
    
    # 止损止盈策略参数
    g.fixed_stop_loss_rate = 0.01  # 固定止损比率（1%）
    g.ma_offset_ratio_normal = 0.003  # 均线跟踪止盈常规偏移量（0.3%）
    g.ma_offset_ratio_close = 0.01  # 均线跟踪止盈收盘前偏移量（1%）
    g.days_for_adjustment = 4  # 持仓天数调整阈值
    
    # 输出策略参数
    log.info("均线形态策略参数:")
    log.info(f"  均线周期: {g.ma_periods}")
    log.info(f"  均线穿越判定周期: {g.ma_cross_periods}")
    log.info(f"  策略模式: 方案{g.ma_gap_strategy_mode}")
    log.info(f"  方案1开盘价差阈值: {g.ma_open_gap_threshold:.1%}")
    log.info(f"  方案2开盘价差阈值: {g.ma_open_gap_threshold2:.1%}")
    log.info(f"  方案2日内变化阈值: {g.ma_intraday_threshold_scheme2:.1%}")
    log.info(f"  历史均线模式检查天数: {g.ma_pattern_lookback_days}天")
    log.info(f"  历史均线模式一致性阈值: {g.ma_pattern_consistency_threshold:.1%}")
    log.info(f"  极端趋势天数阈值: {g.ma_pattern_extreme_days_threshold}")
    log.info(f"  均线贴近计数阈值: {g.ma_proximity_min_threshold}")
    log.info(f"  MA5分布过滤天数: {g.ma_distribution_lookback_days}")
    log.info(f"  MA5分布最低比例: {g.ma_distribution_min_ratio:.0%}")
    log.info(f"  启用MA5分布过滤: {g.enable_ma_distribution_filter}")
    log.info(f"  是否检查日内价差: {g.check_intraday_spread}")
    log.info(f"  均线穿越阈值: {g.ma_cross_threshold}")
    log.info(f"  是否启用开盘价差过滤: {g.enable_open_gap_filter}")
    log.info(f"  固定止损: {g.fixed_stop_loss_rate:.1%}")
    log.info(f"  均线跟踪止盈常规偏移: {g.ma_offset_ratio_normal:.1%}")
    log.info(f"  均线跟踪止盈收盘前偏移: {g.ma_offset_ratio_close:.1%}")
    log.info(f"  持仓天数调整阈值: {g.days_for_adjustment}天")
    
    # 期货品种完整配置字典
    g.futures_config = {
        # 贵金属
        'AU': {'has_night_session': True, 'margin_rate': {'long': 0.21, 'short': 0.21}, 'multiplier': 1000, 'trading_start_time': '21:00'},
        'AG': {'has_night_session': True, 'margin_rate': {'long': 0.22, 'short': 0.22}, 'multiplier': 15, 'trading_start_time': '21:00'},
        
        # 有色金属
        'CU': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'AL': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'ZN': {'has_night_session': True, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'PB': {'has_night_session': True, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'NI': {'has_night_session': True, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 1, 'trading_start_time': '21:00'},
        'SN': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 1, 'trading_start_time': '21:00'},
        'SS': {'has_night_session': True, 'margin_rate': {'long': 0.07, 'short': 0.07}, 'multiplier': 5, 'trading_start_time': '21:00'},
        
        # 黑色系
        'RB': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'HC': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'I': {'has_night_session': True, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 100, 'trading_start_time': '21:00'},
        'JM': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 100, 'trading_start_time': '21:00'},
        'J': {'has_night_session': True, 'margin_rate': {'long': 0.25, 'short': 0.25}, 'multiplier': 60, 'trading_start_time': '21:00'},
        
        # 能源化工
        'SP': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'FU': {'has_night_session': True, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'BU': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'RU': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'BR': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'SC': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 1000, 'trading_start_time': '21:00'},
        'NR': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'LU': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'LC': {'has_night_session': False, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 1, 'trading_start_time': '09:00'},
        
        # 化工
        'FG': {'has_night_session': True, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 20, 'trading_start_time': '21:00'},
        'TA': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'MA': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'SA': {'has_night_session': True, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 20, 'trading_start_time': '21:00'},
        'L': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'V': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'EG': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'PP': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'EB': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'PG': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 20, 'trading_start_time': '21:00'},
        'PX': {'has_night_session': True, 'margin_rate': {'long': 0.1, 'short': 0.1}, 'multiplier': 5, 'trading_start_time': '21:00'},
        
        # 农产品
        'RM': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'OI': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'CF': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'SR': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'PF': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'C': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'CS': {'has_night_session': True, 'margin_rate': {'long': 0.11, 'short': 0.11}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'CY': {'has_night_session': True, 'margin_rate': {'long': 0.11, 'short': 0.11}, 'multiplier': 5, 'trading_start_time': '21:00'},
        'A': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'B': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'M': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'Y': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'P': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '21:00'},
        
        # 无夜盘品种
        'IF': {'has_night_session': False, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 300, 'trading_start_time': '09:30'},
        'IH': {'has_night_session': False, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 300, 'trading_start_time': '09:30'},
        'IC': {'has_night_session': False, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 200, 'trading_start_time': '09:30'},
        'IM': {'has_night_session': False, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 200, 'trading_start_time': '09:30'},
        'AP': {'has_night_session': False, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 10, 'trading_start_time': '09:00'},
        'CJ': {'has_night_session': False, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 5, 'trading_start_time': '09:00'},
        'PK': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 5, 'trading_start_time': '09:00'},
        'JD': {'has_night_session': False, 'margin_rate': {'long': 0.11, 'short': 0.11}, 'multiplier': 10, 'trading_start_time': '09:00'},
        'LH': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 16, 'trading_start_time': '09:00'},
        'T': {'has_night_session': False, 'margin_rate': {'long': 0.03, 'short': 0.03}, 'multiplier': 1000000, 'trading_start_time': '09:30'},
        'PS': {'has_night_session': False, 'margin_rate': {'long': 0.16, 'short': 0.16}, 'multiplier': 3, 'trading_start_time': '09:00'},
        'UR': {'has_night_session': False, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 20, 'trading_start_time': '09:00'},
        'MO': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 100, 'trading_start_time': '21:00'},
        # 'LF': {'has_night_session': False, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 1, 'trading_start_time': '09:30'},
        'HO': {'has_night_session': False, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 100, 'trading_start_time': '09:30'},
        # 'LR': {'has_night_session': True, 'margin_rate': {'long': 0.21, 'short': 0.21}, 'multiplier': 20, 'trading_start_time': '21:00'},
        'LG': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 90, 'trading_start_time': '21:00'},
        # 'FB': {'has_night_session': True, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 10, 'trading_start_time': '21:00'},
        # 'PM': {'has_night_session': True, 'margin_rate': {'long': 0.2, 'short': 0.2}, 'multiplier': 50, 'trading_start_time': '21:00'},
        'EC': {'has_night_session': False, 'margin_rate': {'long': 0.23, 'short': 0.23}, 'multiplier': 50, 'trading_start_time': '09:00'},
        # 'RR': {'has_night_session': True, 'margin_rate': {'long': 0.11, 'short': 0.11}, 'multiplier': 10, 'trading_start_time': '21:00'},
        'OP': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 40, 'trading_start_time': '09:00'},
        # 'IO': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 1, 'trading_start_time': '21:00'},
        'BC': {'has_night_session': True, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '21:00'},
        # 'WH': {'has_night_session': False, 'margin_rate': {'long': 0.2, 'short': 0.2}, 'multiplier': 20, 'trading_start_time': '09:00'},
        'SH': {'has_night_session': True, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 30, 'trading_start_time': '21:00'},
        # 'RI': {'has_night_session': False, 'margin_rate': {'long': 0.21, 'short': 0.21}, 'multiplier': 20, 'trading_start_time': '09:00'},
        'TS': {'has_night_session': False, 'margin_rate': {'long': 0.015, 'short': 0.015}, 'multiplier': 2000000, 'trading_start_time': '09:30'},
        # 'JR': {'has_night_session': False, 'margin_rate': {'long': 0.21, 'short': 0.21}, 'multiplier': 20, 'trading_start_time': '09:00'},
        'AD': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 10, 'trading_start_time': '09:00'},
        # 'BB': {'has_night_session': False, 'margin_rate': {'long': 0.19, 'short': 0.19}, 'multiplier': 500, 'trading_start_time': '09:00'},
        'PL': {'has_night_session': False, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 20, 'trading_start_time': '09:00'},
        # 'RS': {'has_night_session': False, 'margin_rate': {'long': 0.26, 'short': 0.26}, 'multiplier': 10, 'trading_start_time': '09:00'},
        'SI': {'has_night_session': False, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '09:00'},
        # 'ZC': {'has_night_session': True, 'margin_rate': {'long': 0.56, 'short': 0.56}, 'multiplier': 100, 'trading_start_time': '21:00'},
        'SM': {'has_night_session': False, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 5, 'trading_start_time': '09:00'},
        'AO': {'has_night_session': True, 'margin_rate': {'long': 0.17, 'short': 0.17}, 'multiplier': 20, 'trading_start_time': '21:00'},
        'TL': {'has_night_session': False, 'margin_rate': {'long': 0.045, 'short': 0.045}, 'multiplier': 1000000, 'trading_start_time': '09:00'},
        'SF': {'has_night_session': False, 'margin_rate': {'long': 0.14, 'short': 0.14}, 'multiplier': 5, 'trading_start_time': '09:00'},
        # 'WR': {'has_night_session': False, 'margin_rate': {'long': 0.15, 'short': 0.15}, 'multiplier': 10, 'trading_start_time': '09:00'},
        'PR': {'has_night_session': True, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 15, 'trading_start_time': '21:00'},
        'TF': {'has_night_session': False, 'margin_rate': {'long': 0.022, 'short': 0.022}, 'multiplier': 1000000, 'trading_start_time': '09:00'},
        # 'VF': {'has_night_session': False, 'margin_rate': {'long': 0.12, 'short': 0.12}, 'multiplier': 1, 'trading_start_time': '09:00'},
        'BZ': {'has_night_session': False, 'margin_rate': {'long': 0.13, 'short': 0.13}, 'multiplier': 30, 'trading_start_time': '09:00'},
    }
    
    # 策略品种选择策略配置
    # 方案1：全品种策略 - 考虑所有配置的期货品种
    g.strategy_focus_symbols = []  # 空列表表示考虑所有品种
    
    # 方案2：精选品种策略 - 只交易流动性较好的特定品种（如需使用请取消下行注释）
    # g.strategy_focus_symbols = ['RM', 'CJ', 'CY', 'JD', 'L', 'LC', 'SF', 'SI']
    
    log.info(f"品种选择策略: {'全品种策略（覆盖所有配置品种）' if not g.strategy_focus_symbols else '精选品种策略（' + str(len(g.strategy_focus_symbols)) + '个品种）'}")
    
    # 交易记录和数据存储
    g.trade_history = {}  # 持仓记录 {symbol: {'entry_price': xxx, 'direction': xxx, ...}}
    g.daily_ma_candidates = {}  # 通过均线和开盘价差检查的候选品种 {symbol: {'direction': 'long'/'short', 'open_price': xxx, ...}}
    g.today_trades = []  # 当日交易记录
    g.excluded_contracts = {}  # 每日排除的合约缓存 {dominant_future: {'reason': 'ma_trend'/'open_gap', 'trading_day': xxx}}
    g.ma_checked_underlyings = {}  # 记录各品种在交易日的均线检查状态 {symbol: trading_day}
    g.last_ma_trading_day = None  # 最近一次均线检查所属交易日
    
    # 夜盘禁止操作标志
    g.night_session_blocked = False  # 标记是否禁止当晚操作
    g.night_session_blocked_trading_day = None  # 记录被禁止的交易日
    
    # 定时任务设置
    # 夜盘开始（21:05） - 均线和开盘价差检查
    run_daily(check_ma_trend_and_open_gap, time='21:05:00', reference_security='IF1808.CCFX')
    
    # 日盘开始 - 均线和开盘价差检查
    run_daily(check_ma_trend_and_open_gap, time='09:05:00', reference_security='IF1808.CCFX')
    run_daily(check_ma_trend_and_open_gap, time='09:35:00', reference_security='IF1808.CCFX')
    
    # 夜盘开仓和止损止盈检查
    run_daily(check_open_and_stop, time='21:05:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='21:35:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='22:05:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='22:35:00', reference_security='IF1808.CCFX')
    
    # 日盘开仓和止损止盈检查
    run_daily(check_open_and_stop, time='09:05:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='09:35:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='10:05:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='10:35:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='11:05:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='11:25:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='13:35:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='14:05:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='14:35:00', reference_security='IF1808.CCFX')
    run_daily(check_open_and_stop, time='14:55:00', reference_security='IF1808.CCFX')
    run_daily(check_ma_trailing_reactivation, time='14:55:00', reference_security='IF1808.CCFX')
    
    # 收盘后
    run_daily(after_market_close, time='15:30:00', reference_security='IF1808.CCFX')
    
    log.info('=' * 60)

############################ 主程序执行函数 ###################################

def get_current_trading_day(current_dt):
    """根据当前时间推断对应的期货交易日"""
    current_date = current_dt.date()
    current_time = current_dt.time()

    trade_days = get_trade_days(end_date=current_date, count=1)
    if trade_days and trade_days[0] == current_date:
        trading_day = current_date
    else:
        next_days = get_trade_days(start_date=current_date, count=1)
        trading_day = next_days[0] if next_days else current_date

    if current_time >= time(20, 59):
        next_trade_days = get_trade_days(start_date=trading_day, count=2)
        if len(next_trade_days) >= 2:
            return next_trade_days[1]
        if len(next_trade_days) == 1:
            return next_trade_days[0]
    return trading_day


def normalize_trade_day_value(value):
    """将交易日对象统一转换为 datetime.date"""
    if isinstance(value, date) and not isinstance(value, datetime):
        return value
    if isinstance(value, datetime):
        return value.date()
    if hasattr(value, 'to_pydatetime'):
        return value.to_pydatetime().date()
    try:
        return pd.Timestamp(value).date()
    except Exception:
        return value


def check_ma_trend_and_open_gap(context):
    """阶段一：开盘时均线走势和开盘价差检查（一天一次）"""
    log.info("=" * 60)
    current_trading_day = get_current_trading_day(context.current_dt)
    log.info(f"执行均线走势和开盘价差检查 - 时间: {context.current_dt}, 交易日: {current_trading_day}")
    log.info("=" * 60)
    
    # 换月移仓检查（在所有部分之前）
    position_auto_switch(context)
    
    # ==================== 第一部分：基础数据获取 ====================
    
    # 步骤1：交易日检查和缓存清理
    if g.last_ma_trading_day != current_trading_day:
        if g.excluded_contracts:
            log.info(f"交易日切换至 {current_trading_day}，清空上一交易日的排除缓存")
        g.excluded_contracts = {}
        g.ma_checked_underlyings = {}
        g.last_ma_trading_day = current_trading_day

    # 步骤2：获取当前时间和筛选可交易品种
    current_time = str(context.current_dt.time())[:5]  # HH:MM格式
    focus_symbols = g.strategy_focus_symbols if g.strategy_focus_symbols else list(g.futures_config.keys())
    tradable_symbols = []
    
    # 根据当前时间确定可交易的时段
    # 21:05 -> 仅接受21:00开盘的合约
    # 09:05 -> 接受09:00或21:00开盘的合约
    # 09:35 -> 接受所有时段（21:00, 09:00, 09:30）的合约
    for symbol in focus_symbols:
        trading_start_time = get_futures_config(symbol, 'trading_start_time', '09:05')
        should_trade = False
        
        if current_time == '21:05':
            should_trade = trading_start_time.startswith('21:00')
        elif current_time == '09:05':
            should_trade = trading_start_time.startswith('21:00') or trading_start_time.startswith('09:00')
        elif current_time == '09:35':
            should_trade = True
        
        if should_trade:
            tradable_symbols.append(symbol)
    
    if not tradable_symbols:
        log.info(f"当前时间 {current_time} 无品种开盘，跳过检查")
        return
    
    log.info(f"当前时间 {current_time} 开盘品种: {tradable_symbols}")
    
    # 步骤3：对每个品种循环处理
    for symbol in tradable_symbols:
        # 步骤3.1：检查是否已处理过
        if g.ma_checked_underlyings.get(symbol) == current_trading_day:
            log.info(f"{symbol} 已在交易日 {current_trading_day} 完成均线检查，跳过本次执行")
            continue

        try:
            g.ma_checked_underlyings[symbol] = current_trading_day
            
            # 步骤3.2：获取主力合约
            dominant_future = get_dominant_future(symbol)
            if not dominant_future:
                log.info(f"{symbol} 未找到主力合约，跳过")
                continue
            
            # 步骤3.3：检查排除缓存
            if dominant_future in g.excluded_contracts:
                excluded_info = g.excluded_contracts[dominant_future]
                if excluded_info['trading_day'] == current_trading_day:
                    continue
                else:
                    # 新的一天，从缓存中移除（会在after_market_close统一清理，这里也做兜底）
                    del g.excluded_contracts[dominant_future]
            
            # 步骤3.4：检查是否已有持仓
            if check_symbol_prefix_match(dominant_future, context, set(g.trade_history.keys())):
                log.info(f"{symbol} 已有持仓，跳过")
                continue
            
            # 步骤3.5：获取历史数据和前一交易日数据（合并优化）
            # 获取历史数据（需要足够计算MA30）
            historical_data = get_price(dominant_future, end_date=context.current_dt, 
                                       frequency='1d', fields=['open', 'close', 'high', 'low'], 
                                       count=g.ma_historical_days)
            
            if historical_data is None or len(historical_data) < max(g.ma_periods):
                log.info(f"{symbol} 历史数据不足，跳过")
                continue

            # 获取前一交易日并在历史数据中匹配
            previous_trade_days = get_trade_days(end_date=current_trading_day, count=2)
            previous_trade_days = [normalize_trade_day_value(d) for d in previous_trade_days]
            previous_trading_day = None
            if len(previous_trade_days) >= 2:
                previous_trading_day = previous_trade_days[-2]
            elif len(previous_trade_days) == 1 and previous_trade_days[0] < current_trading_day:
                previous_trading_day = previous_trade_days[0]

            if previous_trading_day is None:
                log.info(f"{symbol} 无法确定前一交易日，跳过")
                continue

            # 在历史数据中匹配前一交易日
            historical_dates = historical_data.index.date
            match_indices = np.where(historical_dates == previous_trading_day)[0]
            if len(match_indices) == 0:
                earlier_indices = np.where(historical_dates < previous_trading_day)[0]
                if len(earlier_indices) == 0:
                    log.info(f"{symbol} 历史数据缺少 {previous_trading_day} 之前的记录，跳过")
                    continue
                match_indices = [earlier_indices[-1]]

            # 提取截至前一交易日的数据，并一次性提取所有需要的字段
            data_upto_yesterday = historical_data.iloc[:match_indices[-1] + 1]
            yesterday_data = data_upto_yesterday.iloc[-1]
            yesterday_close = yesterday_data['close']
            yesterday_open = yesterday_data['open']
            
            # 步骤3.6：获取当前价格数据
            current_data = get_current_data()[dominant_future]
            today_open = current_data.day_open
            
            # ==================== 第二部分：核心指标计算 ====================
            
            # 步骤4：计算均线相关指标（合并优化）
            ma_values = calculate_ma_values(data_upto_yesterday, g.ma_periods)
            ma_proximity_counts = calculate_ma_proximity_counts(data_upto_yesterday, g.ma_periods, g.ma_pattern_lookback_days)
            
            log.info(f"{symbol}({dominant_future}) 均线检查:")
            log.info(f"  均线贴近统计: {ma_proximity_counts}")
            
            # 检查均线贴近计数
            proximity_sum = ma_proximity_counts.get('MA5', 0) + ma_proximity_counts.get('MA10', 0)
            if proximity_sum < g.ma_proximity_min_threshold:
                log.info(f"  {symbol}({dominant_future}) ✗ 均线贴近计数不足，MA5+MA10={proximity_sum} < {g.ma_proximity_min_threshold}，跳过")
                add_to_excluded_contracts(dominant_future, 'ma_proximity', current_trading_day)
                continue
            
            # 步骤5：计算极端趋势天数
            extreme_above_count, extreme_below_count = calculate_extreme_trend_days(
                data_upto_yesterday,
                g.ma_periods,
                g.ma_pattern_lookback_days
            )
            extreme_total = extreme_above_count + extreme_below_count
            min_extreme = min(extreme_above_count, extreme_below_count)
            filter_threshold = max(2, g.ma_pattern_extreme_days_threshold)
            log.info(
                f"  极端趋势天数统计: 收盘在所有均线上方 {extreme_above_count} 天, 收盘在所有均线下方 {extreme_below_count} 天, "
                f"合计 {extreme_total} 天, min(A,B)={min_extreme} (过滤阈值: {filter_threshold})"
            )
            if extreme_above_count > 0 and extreme_below_count > 0 and min_extreme >= filter_threshold:
                log.info(
                    f"  {symbol}({dominant_future}) ✗ 极端趋势多空同时出现且 min(A,B)={min_extreme} ≥ {filter_threshold}，跳过"
                )
                add_to_excluded_contracts(dominant_future, 'ma_extreme_trend', current_trading_day)
                continue

            # 步骤6：判断均线走势
            direction = None
            if check_ma_pattern(ma_values, 'long'):
                direction = 'long'
            elif check_ma_pattern(ma_values, 'short'):
                direction = 'short'
            else:
                add_to_excluded_contracts(dominant_future, 'ma_trend', current_trading_day)
                continue
            
            # 步骤7：检查MA5分布过滤
            if g.enable_ma_distribution_filter:
                distribution_passed, distribution_stats = check_ma5_distribution_filter(
                    data_upto_yesterday,
                    g.ma_distribution_lookback_days,
                    direction,
                    g.ma_distribution_min_ratio
                )
                log.info(
                    f"  MA5分布过滤: 方向 {direction}, 有效天数 "
                    f"{distribution_stats['valid_days']}/{distribution_stats['lookback_days']}，"
                    f"满足天数 {distribution_stats['qualified_days']}/{distribution_stats['required_days']}"
                )
                if not distribution_passed:
                    insufficiency = distribution_stats['valid_days'] < distribution_stats['lookback_days']
                    reason = "有效数据不足" if insufficiency else "满足天数不足"
                    log.info(
                        f"  {symbol}({dominant_future}) ✗ MA5分布过滤未通过（{reason}）"
                    )
                    add_to_excluded_contracts(dominant_future, 'ma5_distribution', current_trading_day)
                    continue
            
            # 步骤8：检查历史均线模式一致性
            consistency_passed, consistency_ratio = check_historical_ma_pattern_consistency(
                historical_data, direction, g.ma_pattern_lookback_days, g.ma_pattern_consistency_threshold
            )
            
            if not consistency_passed:
                log.info(f"  {symbol}({dominant_future}) ✗ 历史均线模式一致性不足 "
                        f"({consistency_ratio:.1%} < {g.ma_pattern_consistency_threshold:.1%})，跳过")
                add_to_excluded_contracts(dominant_future, 'ma_consistency', current_trading_day)
                continue
            else:
                log.info(f"  {symbol}({dominant_future}) ✓ 历史均线模式一致性检查通过 "
                        f"({consistency_ratio:.1%} >= {g.ma_pattern_consistency_threshold:.1%})")
            
            # 步骤9：检查开盘价差（可配置开关）
            if g.enable_open_gap_filter:
                gap_check_passed = check_open_gap_filter(
                    symbol=symbol,
                    dominant_future=dominant_future,
                    direction=direction,
                    yesterday_close=yesterday_close,
                    today_open=today_open,
                    current_trading_day=current_trading_day
                )
                if not gap_check_passed:
                    continue
            else:
                log.info("  已关闭开盘价差过滤（enable_open_gap_filter=False），跳过该检查")
            
            # 步骤10：将通过检查的品种加入候选列表
            g.daily_ma_candidates[dominant_future] = {
                'symbol': symbol,
                'direction': direction,
                'open_price': today_open,
                'yesterday_close': yesterday_close,
                'yesterday_open': yesterday_open,
                'ma_values': ma_values
            }
            
            log.info(f"  ✓✓ {symbol} 通过均线和开盘价差检查，加入候选列表")
            
        except Exception as e:
            g.ma_checked_underlyings.pop(symbol, None)
            log.warning(f"{symbol} 检查时出错: {str(e)}")
            continue
    
    log.info(f"候选列表更新完成，当前候选品种: {list(g.daily_ma_candidates.keys())}")
    log.info("=" * 60)

def check_open_and_stop(context):
    """统一的开仓和止损止盈检查函数"""
    # 先检查换月移仓
    log.info("=" * 60)
    current_trading_day = get_current_trading_day(context.current_dt)
    log.info(f"执行开仓和止损止盈检查 - 时间: {context.current_dt}, 交易日: {current_trading_day}")
    log.info("=" * 60)
    log.info(f"先检查换月：")
    position_auto_switch(context)
    
    # 获取当前时间
    current_time = str(context.current_dt.time())[:2]
    
    # 判断是否为夜盘时间
    is_night_session = (current_time in ['21', '22', '23', '00', '01', '02'])
    
    # 检查是否禁止当晚操作
    if is_night_session and g.night_session_blocked:
        blocked_trading_day = normalize_trade_day_value(g.night_session_blocked_trading_day) if g.night_session_blocked_trading_day else None
        current_trading_day_normalized = normalize_trade_day_value(current_trading_day)
        if blocked_trading_day == current_trading_day_normalized:
            log.info(f"当晚操作已被禁止（订单状态为'new'，无夜盘），跳过所有操作")
            return

    # 得到当前未完成订单
    orders = get_open_orders()
    # 循环，撤销订单
    if len(orders) == 0:
        log.debug(f"无未完成订单")
    else:
        for _order in orders.values():
            log.debug(f"order: {_order}")
            cancel_order(_order)
    
    # 第一步：检查开仓条件
    log.info(f"检查开仓条件：")
    if g.daily_ma_candidates:
        log.info("=" * 60)
        log.info(f"执行开仓检查 - 时间: {context.current_dt}, 候选品种数量: {len(g.daily_ma_candidates)}")
        
        # 遍历候选品种
        candidates_to_remove = []
        
        for dominant_future, candidate_info in g.daily_ma_candidates.items():
            try:
                symbol = candidate_info['symbol']
                direction = candidate_info['direction']
                open_price = candidate_info['open_price']
                yesterday_close = candidate_info.get('yesterday_close')
                yesterday_open = candidate_info.get('yesterday_open')
                
                # 检查是否已有持仓
                if check_symbol_prefix_match(dominant_future, context, set(g.trade_history.keys())):
                    log.info(f"{symbol} 已有持仓，从候选列表移除")
                    candidates_to_remove.append(dominant_future)
                    continue
                
                # 获取当前价格
                current_data = get_current_data()[dominant_future]
                current_price = current_data.last_price
                
                # 计算当天价差
                intraday_diff = current_price - open_price
                intraday_diff_ratio = intraday_diff / open_price
                
                log.info(f"{symbol}({dominant_future}) 开仓条件检查:")
                log.info(f"  方向: {direction}, 开盘价: {open_price:.2f}, 当前价: {current_price:.2f}, "
                        f"当天价差: {intraday_diff:.2f}, 变化比例: {intraday_diff_ratio:.2%}")
                
                # 判断是否满足开仓条件 - 仅检查均线穿越得分
                should_open = True
                log.info(f"  开仓条件简化：仅检查均线穿越得分，跳过策略1,2,3的判断")
                
                if should_open:
                    ma_values = candidate_info.get('ma_values') or {}
                    avg_5day_change = calculate_recent_average_change(dominant_future, days=5)
                    entry_snapshot = {
                        'yesterday_close': yesterday_close,
                        'today_open': open_price,
                        'ma_values': ma_values.copy() if ma_values else {},
                        'avg_5day_change': avg_5day_change
                    }
                    cross_score, score_details = calculate_ma_cross_score(open_price, current_price, ma_values, direction)

                    # 根据当前时间调整所需的均线穿越得分阈值
                    current_time_str = str(context.current_dt.time())[:5]  # HH:MM格式
                    required_cross_score = g.ma_cross_threshold
                    if current_time_str != '14:55':
                        # 在14:55以外的时间，需要更高的得分阈值
                        required_cross_score = g.ma_cross_threshold + 1

                    log.info(f"  均线穿越得分: {cross_score}, 得分详情: {score_details}, 当前时间: {current_time_str}, 所需阈值: {required_cross_score}")

                    # 检查得分是否满足条件
                    score_passed = False
                    if cross_score >= required_cross_score:
                        # 如果得分达到阈值，检查特殊情况：只有1分且来自MA5
                        if cross_score == 1 and required_cross_score == 1:
                            # 检查这一分是否来自MA5
                            from_ma5_only = len(score_details) == 1 and score_details[0]['period'] == 5
                            if not from_ma5_only:
                                score_passed = True
                                log.info(f"  ✓ 均线穿越得分检查通过：1分且非来自MA5")
                            else:
                                log.info(f"  ✗ 均线穿越得分检查未通过：1分且仅来自MA5")
                        else:
                            score_passed = True
                            log.info(f"  ✓ 均线穿越得分检查通过")

                    if not score_passed:
                        log.info(f"  ✗ 均线穿越得分不足或不符合条件（{cross_score} < {required_cross_score} 或 1分来自MA5），跳过开仓")
                        continue
                    
                    if current_time_str == '14:55':
                        positive_cross_periods = {detail['period'] for detail in score_details if detail.get('delta', 0) > 0}
                        if positive_cross_periods == {30}:
                            log.info("  ✗ 尾盘仅穿越MA30，跳过开仓")
                            continue
                    # 执行开仓
                    log.info(f"  准备开仓: {symbol} {direction}")
                    target_hands, single_hand_margin = calculate_target_hands(context, dominant_future, direction)
                    
                    if target_hands > 0:
                        success = open_position(context, dominant_future, target_hands, direction, single_hand_margin,
                                              f'均线形态开仓', crossed_ma_details=score_details, entry_snapshot=entry_snapshot)
                        if success:
                            log.info(f"  ✓✓ {symbol} 开仓成功，从候选列表移除")
                            candidates_to_remove.append(dominant_future)
                        else:
                            log.warning(f"  ✗ {symbol} 开仓失败")
                    else:
                        log.warning(f"  ✗ {symbol} 计算目标手数为0，跳过开仓")
                        
            except Exception as e:
                log.warning(f"{dominant_future} 处理时出错: {str(e)}")
                continue
        
        # 从候选列表中移除已开仓的品种
        for future in candidates_to_remove:
            if future in g.daily_ma_candidates:
                del g.daily_ma_candidates[future]
        
        log.info(f"剩余候选品种: {list(g.daily_ma_candidates.keys())}")
        log.info("=" * 60)
    
    # 第二步：检查止损止盈
    log.info(f"检查止损止盈条件：")
    subportfolio = context.subportfolios[0]
    long_positions = list(subportfolio.long_positions.values())
    short_positions = list(subportfolio.short_positions.values())
    
    closed_count = 0
    skipped_count = 0
    
    for position in long_positions + short_positions:
        security = position.security
        underlying_symbol = security.split('.')[0][:-4]
        
        # 检查交易时间适配性
        has_night_session = get_futures_config(underlying_symbol, 'has_night_session', False)
        
        # 如果是夜盘时间，但品种不支持夜盘交易，则跳过
        if is_night_session and not has_night_session:
            skipped_count += 1
            continue
        
        # 执行止损止盈检查
        if check_position_stop_loss_profit(context, position):
            closed_count += 1
    
    if closed_count > 0:
        log.info(f"执行了 {closed_count} 次止损止盈")
    
    if skipped_count > 0:
        log.info(f"夜盘时间跳过 {skipped_count} 个日间品种的止损止盈检查")


def check_ma_trailing_reactivation(context):
    """检查是否需要恢复均线跟踪止盈"""
    subportfolio = context.subportfolios[0]
    positions = list(subportfolio.long_positions.values()) + list(subportfolio.short_positions.values())
    
    if not positions:
        return
    
    reenabled_count = 0
    current_data = get_current_data()
    
    for position in positions:
        security = position.security
        trade_info = g.trade_history.get(security)
        
        if not trade_info or trade_info.get('ma_trailing_enabled', True):
            continue
        
        direction = trade_info['direction']
        ma_values = calculate_realtime_ma_values(security, [5])
        ma5_value = ma_values.get('ma5')
        
        if ma5_value is None or security not in current_data:
            continue
        
        today_price = current_data[security].last_price
        
        if direction == 'long' and today_price > ma5_value:
            trade_info['ma_trailing_enabled'] = True
            reenabled_count += 1
            log.info(f"恢复均线跟踪止盈: {security} {direction}, 当前价 {today_price:.2f} > MA5 {ma5_value:.2f}")
        elif direction == 'short' and today_price < ma5_value:
            trade_info['ma_trailing_enabled'] = True
            reenabled_count += 1
            log.info(f"恢复均线跟踪止盈: {security} {direction}, 当前价 {today_price:.2f} < MA5 {ma5_value:.2f}")
    
    if reenabled_count > 0:
        log.info(f"恢复均线跟踪止盈持仓数量: {reenabled_count}")


def check_position_stop_loss_profit(context, position):
    """检查单个持仓的止损止盈"""
    log.info(f"检查持仓: {position.security}")
    security = position.security
    
    if security not in g.trade_history:
        return False
    
    trade_info = g.trade_history[security]
    direction = trade_info['direction']
    entry_price = trade_info['entry_price']
    entry_time = trade_info['entry_time']
    entry_trading_day = trade_info.get('entry_trading_day')
    if entry_trading_day is None:
        entry_trading_day = get_current_trading_day(entry_time)
        trade_info['entry_trading_day'] = entry_trading_day
    if entry_trading_day is not None:
        entry_trading_day = normalize_trade_day_value(entry_trading_day)
    current_trading_day = normalize_trade_day_value(get_current_trading_day(context.current_dt))
    current_price = position.price
    
    # 计算当前盈亏比率
    if direction == 'long':
        profit_rate = (current_price - entry_price) / entry_price
    else:
        profit_rate = (entry_price - current_price) / entry_price
    
    # 检查固定止损
    log.info("=" * 60)
    log.info(f"检查固定止损：")
    log.info("=" * 60)
    if profit_rate <= -g.fixed_stop_loss_rate:
        log.info(f"{security} {direction} 触发固定止损 {g.fixed_stop_loss_rate:.3%}, 当前亏损率: {profit_rate:.3%}, "
                f"成本价: {entry_price:.2f}, 当前价格: {current_price:.2f}")
        close_position(context, security, direction)
        return True
    else:
        log.debug(f"{security} {direction} 未触发固定止损 {g.fixed_stop_loss_rate:.3%}, 当前亏损率: {profit_rate:.3%}, "
                f"成本价: {entry_price:.2f}, 当前价格: {current_price:.2f}")

    if entry_trading_day is not None and entry_trading_day == current_trading_day:
        log.info(f"{security} 建仓交易日内跳过动态止盈检查")
        return False

    # 检查是否启用均线跟踪止盈
    log.info("=" * 60)
    log.info(f"检查是否启用均线跟踪止盈：")
    log.info("=" * 60)
    if not trade_info.get('ma_trailing_enabled', True):
        log.debug(f"{security} {direction} 未启用均线跟踪止盈")
        return False

    # 检查均线跟踪止盈
    # 获取持仓天数
    entry_date = entry_time.date()
    current_date = context.current_dt.date()
    all_trade_days = get_all_trade_days()
    holding_days = sum((entry_date <= d <= current_date) for d in all_trade_days)
    
    # 计算变化率
    today_price = get_current_data()[security].last_price
    avg_daily_change_rate = calculate_average_daily_change_rate(security)
    historical_data = attribute_history(security, 1, '1d', ['close'])
    yesterday_close = historical_data['close'].iloc[-1]
    today_change_rate = abs((today_price - yesterday_close) / yesterday_close)
    
    # 根据时间判断使用的偏移量
    current_time = context.current_dt.time()
    target_time = datetime.strptime('14:55:00', '%H:%M:%S').time()
    if current_time > target_time:
        offset_ratio = g.ma_offset_ratio_close
        log.debug(f"当前时间是：{current_time}，使用偏移量: {offset_ratio:.3%}")
    else:
        offset_ratio = g.ma_offset_ratio_normal
        log.debug(f"当前时间是：{current_time}，使用偏移量: {offset_ratio:.3%}")
    # 选择止损均线
    close_line = None
    if today_change_rate >= 1.5 * avg_daily_change_rate:
        close_line = 'ma5'  # 波动剧烈时用短周期
    elif holding_days <= g.days_for_adjustment:
        close_line = 'ma5'  # 持仓初期用短周期
    else:
        close_line = 'ma5' if today_change_rate >= 1.2 * avg_daily_change_rate else 'ma10'
    
    # 计算实时均线值
    ma_values = calculate_realtime_ma_values(security, [5, 10])
    ma_value = ma_values[close_line]
    
    # 应用偏移量
    if direction == 'long':
        adjusted_ma_value = ma_value * (1 - offset_ratio)
    else:
        adjusted_ma_value = ma_value * (1 + offset_ratio)
    
    # 判断是否触发均线止损
    if (direction == 'long' and today_price < adjusted_ma_value) or \
       (direction == 'short' and today_price > adjusted_ma_value):
        log.info(f"触发均线跟踪止盈 {security} {direction}, 止损均线: {close_line}, "
                f"均线值: {ma_value:.2f}, 调整后: {adjusted_ma_value:.2f}, "
                f"当前价: {today_price:.2f}, 持仓天数: {holding_days}")
        close_position(context, security, direction)
        return True
    else:
        log.debug(f"未触发均线跟踪止盈 {security} {direction}, 止损均线: {close_line}, "
                f"均线值: {ma_value:.2f}, 调整后: {adjusted_ma_value:.2f}, "
                f"当前价: {today_price:.2f}, 持仓天数: {holding_days}")
    
    return False

############################ 核心辅助函数 ###################################

def calculate_ma_values(data, periods):
    """计算均线值
    
    Args:
        data: DataFrame，包含'close'列的历史数据（最后一行是最新的数据）
        periods: list，均线周期列表，如[5, 10, 20, 30]
    
    Returns:
        dict: {'MA5': value, 'MA10': value, 'MA20': value, 'MA30': value}
        返回最后一行（最新日期）的各周期均线值
    """
    ma_values = {}
    
    for period in periods:
        if len(data) >= period:
            # 计算最后period天的均线值
            ma_values[f'MA{period}'] = data['close'].iloc[-period:].mean()
        else:
            ma_values[f'MA{period}'] = None
    
    return ma_values


def calculate_ma_cross_score(open_price, current_price, ma_values, direction):
    """根据开盘价与当前价统计多周期均线穿越得分
    返回: (总得分, 得分详情列表)
    得分详情: [{'period': 5, 'delta': 1}, {'period': 10, 'delta': 1}, ...]
    """
    if not ma_values:
        return 0, []
    assert direction in ('long', 'short')
    score = 0
    score_details = []
    periods = getattr(g, 'ma_cross_periods', g.ma_periods)
    for period in periods:
        key = f'MA{period}'
        ma_value = ma_values.get(key)
        if ma_value is None:
            continue
        cross_up = open_price < ma_value and current_price > ma_value
        cross_down = open_price > ma_value and current_price < ma_value
        if not (cross_up or cross_down):
            continue
        if direction == 'long':
            delta = 1 if cross_up else -1
        else:
            delta = -1 if cross_up else 1
        score += delta
        score_details.append({'period': period, 'delta': delta})
        log.debug(
            f"  均线穿越[{key}] - 开盘 {open_price:.2f}, 当前 {current_price:.2f}, "
            f"均线 {ma_value:.2f}, 方向 {direction}, 增量 {delta}, 当前得分 {score}"
        )
    return score, score_details


def calculate_ma_proximity_counts(data, periods, lookback_days):
    """统计近 lookback_days 天收盘价贴近各均线的次数"""
    proximity_counts = {f'MA{period}': 0 for period in periods}

    if len(data) < lookback_days:
        return proximity_counts

    closes = data['close'].iloc[-lookback_days:]
    ma_series = {
        period: data['close'].rolling(window=period).mean().iloc[-lookback_days:]
        for period in periods
    }

    for idx, close_price in enumerate(closes):
        min_diff = None
        closest_period = None

        for period in periods:
            ma_value = ma_series[period].iloc[idx]
            if pd.isna(ma_value):
                continue
            diff = abs(close_price - ma_value)
            if min_diff is None or diff < min_diff:
                min_diff = diff
                closest_period = period

        if closest_period is not None:
            proximity_counts[f'MA{closest_period}'] += 1

    return proximity_counts


def calculate_extreme_trend_days(data, periods, lookback_days):
    """统计过去 lookback_days 天收盘价相对所有均线的极端趋势天数"""
    if len(data) < lookback_days:
        return 0, 0

    recent_closes = data['close'].iloc[-lookback_days:]
    ma_series = {
        period: data['close'].rolling(window=period).mean().iloc[-lookback_days:]
        for period in periods
    }

    above_count = 0
    below_count = 0

    for idx, close_price in enumerate(recent_closes):
        ma_values = []
        valid = True

        for period in periods:
            ma_value = ma_series[period].iloc[idx]
            if pd.isna(ma_value):
                valid = False
                break
            ma_values.append(ma_value)

        if not valid or not ma_values:
            continue

        if all(close_price > ma_value for ma_value in ma_values):
            above_count += 1
        elif all(close_price < ma_value for ma_value in ma_values):
            below_count += 1

    return above_count, below_count


def check_ma5_distribution_filter(data, lookback_days, direction, min_ratio):
    """检查近 lookback_days 天收盘价相对于MA5的分布情况"""
    stats = {
        'lookback_days': lookback_days,
        'valid_days': 0,
        'qualified_days': 0,
        'required_days': max(0, math.ceil(lookback_days * min_ratio))
    }

    if lookback_days <= 0:
        return True, stats

    if len(data) < max(lookback_days, 5):
        return False, stats

    recent_closes = data['close'].iloc[-lookback_days:]
    ma5_series = data['close'].rolling(window=5).mean().iloc[-lookback_days:]

    for close_price, ma5_value in zip(recent_closes, ma5_series):
        log.debug(f"close_price: {close_price}, ma5_value: {ma5_value}")
        if pd.isna(ma5_value):
            continue
        stats['valid_days'] += 1
        if direction == 'long' and close_price < ma5_value:
            stats['qualified_days'] += 1
        elif direction == 'short' and close_price > ma5_value:
            stats['qualified_days'] += 1

    if stats['valid_days'] < lookback_days:
        return False, stats

    return stats['qualified_days'] >= stats['required_days'], stats


def check_open_gap_filter(symbol, dominant_future, direction, yesterday_close, today_open, current_trading_day):
    """开盘价差过滤辅助函数

    根据当前策略模式(`g.ma_gap_strategy_mode`)和对应阈值检查开盘价差是否符合方向要求。

    Args:
        symbol: 品种代码（如 'AO'）
        dominant_future: 主力合约代码（如 'AO2502.XSGE'）
        direction: 方向，'long' 或 'short'
        yesterday_close: 前一交易日收盘价
        today_open: 当日开盘价
        current_trading_day: 当前期货交易日

    Returns:
        bool: True 表示通过开盘价差过滤，False 表示未通过（并已加入排除缓存）
    """
    open_gap_ratio = (today_open - yesterday_close) / yesterday_close
    log.info(
        f"  开盘价差检查: 昨收 {yesterday_close:.2f}, 今开 {today_open:.2f}, "
        f"价差比例 {open_gap_ratio:.2%}"
    )

    gap_check_passed = False

    if g.ma_gap_strategy_mode == 1:
        # 方案1：多头检查上跳，空头检查下跳
        if direction == 'long' and open_gap_ratio >= g.ma_open_gap_threshold:
            log.info(
                f"  {symbol}({dominant_future}) ✓ 方案1多头开盘价差检查通过 "
                f"({open_gap_ratio:.2%} >= {g.ma_open_gap_threshold:.2%})"
            )
            gap_check_passed = True
        elif direction == 'short' and open_gap_ratio <= -g.ma_open_gap_threshold:
            log.info(
                f"  {symbol}({dominant_future}) ✓ 方案1空头开盘价差检查通过 "
                f"({open_gap_ratio:.2%} <= {-g.ma_open_gap_threshold:.2%})"
            )
            gap_check_passed = True
    elif g.ma_gap_strategy_mode == 2 or g.ma_gap_strategy_mode == 3:
        # 方案2和方案3：多头检查下跳，空头检查上跳
        if direction == 'long' and open_gap_ratio <= -g.ma_open_gap_threshold2:
            log.info(
                f"  {symbol}({dominant_future}) ✓ 方案{g.ma_gap_strategy_mode}多头开盘价差检查通过 "
                f"({open_gap_ratio:.2%} <= {-g.ma_open_gap_threshold2:.2%})"
            )
            gap_check_passed = True
        elif direction == 'short' and open_gap_ratio >= g.ma_open_gap_threshold2:
            log.info(
                f"  {symbol}({dominant_future}) ✓ 方案{g.ma_gap_strategy_mode}空头开盘价差检查通过 "
                f"({open_gap_ratio:.2%} >= {g.ma_open_gap_threshold2:.2%})"
            )
            gap_check_passed = True

    if not gap_check_passed:
        add_to_excluded_contracts(dominant_future, 'open_gap', current_trading_day)
        return False

    return True



def check_ma_pattern(ma_values, direction):
    """检查均线排列模式是否符合方向要求
    
    Args:
        ma_values: dict，包含MA5, MA10, MA20, MA30的均线值
        direction: str，'long'或'short'
    
    Returns:
        bool: 是否符合均线排列要求
    """
    ma5 = ma_values['MA5']
    ma10 = ma_values['MA10']
    ma20 = ma_values['MA20']
    ma30 = ma_values['MA30']
    
    if direction == 'long':
        # 多头模式：MA30 <= MA20 <= MA10 <= MA5 或 MA30 <= MA20 <= MA5 <= MA10
        # 或者：MA20 <= MA30 <= MA10 <= MA5 或 MA20 <= MA30 <= MA5 <= MA10
        pattern1 = (ma30 <= ma20 <= ma10 <= ma5)
        pattern2 = (ma30 <= ma20 <= ma5 <= ma10)
        pattern3 = (ma20 <= ma30 <= ma10 <= ma5)
        pattern4 = (ma20 <= ma30 <= ma5 <= ma10)
        return pattern1 or pattern2 or pattern3 or pattern4
    elif direction == 'short':
        # 空头模式：MA10 <= MA5 <= MA20 <= MA30 或 MA5 <= MA10 <= MA20 <= MA30
        # 或者：MA10 <= MA5 <= MA30 <= MA20 或 MA5 <= MA10 <= MA30 <= MA20
        pattern1 = (ma10 <= ma5 <= ma20 <= ma30)
        pattern2 = (ma5 <= ma10 <= ma20 <= ma30)
        pattern3 = (ma10 <= ma5 <= ma30 <= ma20)
        pattern4 = (ma5 <= ma10 <= ma30 <= ma20)
        return pattern1 or pattern2 or pattern3 or pattern4
    else:
        return False

def check_historical_ma_pattern_consistency(historical_data, direction, lookback_days, consistency_threshold):
    """检查历史均线模式的一致性
    
    Args:
        historical_data: DataFrame，包含足够天数的历史数据
        direction: str，'long'或'short'
        lookback_days: int，检查过去多少天
        consistency_threshold: float，一致性阈值（0-1之间）
    
    Returns:
        tuple: (bool, float) - (是否通过一致性检查, 实际一致性比例)
    """
    if len(historical_data) < max(g.ma_periods) + lookback_days:
        # 历史数据不足
        return False, 0.0
    
    match_count = 0
    total_count = lookback_days
    # log.debug(f"历史均线模式一致性检查: {direction}, 检查过去{lookback_days}天的数据")
    # log.debug(f"历史数据: {historical_data}")
    
    # 检查过去lookback_days天的均线模式
    for i in range(lookback_days):
        # 获取倒数第(i+1)天的数据（i=0时是昨天，i=1时是前天，依此类推）
        end_idx = -(i + 1)
        # 获取这一天的具体日期
        date = historical_data.index[end_idx].date()
        # 获取到该天（包括该天）为止的所有数据
        if i == 0:
            data_slice = historical_data
        else:
            data_slice = historical_data.iloc[:-i]
        
        # 计算该天的均线值
        # log.debug(f"对于倒数第{i+1}天，end_idx: {end_idx}，日期: {date}，计算均线值: {data_slice}")
        ma_values = calculate_ma_values(data_slice, g.ma_periods)
        # log.debug(f"end_idx: {end_idx}，日期: {date}，倒数第{i+1}天的均线值: {ma_values}")
        
        # 检查是否符合模式
        if check_ma_pattern(ma_values, direction):
            match_count += 1
            # log.debug(f"日期: {date}，对于倒数第{i+1}天，历史均线模式一致性检查: {direction} 符合模式")
        # else:
            # log.debug(f"日期: {date}，对于倒数第{i+1}天，历史均线模式一致性检查: {direction} 不符合模式")
    
    consistency_ratio = match_count / total_count
    passed = consistency_ratio >= consistency_threshold
    
    return passed, consistency_ratio

############################ 交易执行函数 ###################################

def open_position(context, security, target_hands, direction, single_hand_margin, reason='',
                  crossed_ma_details=None, entry_snapshot=None):
    """开仓并可选地在成交后校验保证金"""
    try:
        # 记录交易前的可用资金
        cash_before = context.portfolio.available_cash
        
        # 使用order_target按手数开仓
        order = order_target(security, target_hands, side=direction)
        log.debug(f"order: {order}")
        
        # 检查订单状态，如果为'new'说明当晚没有夜盘
        if order is not None:
            order_status = str(order.status).lower()
            if order_status == 'new':
                # 取消订单
                cancel_order(order)
                current_trading_day = get_current_trading_day(context.current_dt)
                g.night_session_blocked = True
                g.night_session_blocked_trading_day = current_trading_day
                log.warning(f"订单状态为'new'，说明{current_trading_day}当晚没有夜盘，已取消订单: {security} {direction} {target_hands}手，并禁止当晚所有操作")
                return False
        
        if order is not None and order.filled > 0:
            # 记录交易后的可用资金
            cash_after = context.portfolio.available_cash
            
            # 计算实际资金变化
            cash_change = cash_before - cash_after

            # 计算保证金变化
            margin_change = single_hand_margin * target_hands
            
            # 获取订单价格和数量
            order_price = order.avg_cost if order.avg_cost else order.price
            order_amount = order.filled
            
            # 记录当日交易
            underlying_symbol = security.split('.')[0][:-4]
            g.today_trades.append({
                'security': security, # 合约代码
                'underlying_symbol': underlying_symbol, # 标的代码
                'direction': direction, # 方向
                'order_amount': order_amount, # 订单数量
                'order_price': order_price, # 订单价格
                'cash_change': cash_change, # 资金变化
                'margin_change': margin_change, # 保证金
                'time': context.current_dt # 时间
            })
            
            # 记录交易信息
            entry_trading_day = get_current_trading_day(context.current_dt)
            
            # 处理穿越均线信息
            crossed_ma_lines = []
            if crossed_ma_details:
                crossed_ma_lines = [f"MA{detail['period']}" for detail in crossed_ma_details if detail.get('delta', 0) > 0]
            
            snapshot_copy = None
            if entry_snapshot:
                snapshot_copy = {
                    'yesterday_close': entry_snapshot.get('yesterday_close'),
                    'today_open': entry_snapshot.get('today_open'),
                    'ma_values': (entry_snapshot.get('ma_values') or {}).copy(),
                    'avg_5day_change': entry_snapshot.get('avg_5day_change')
                }
            g.trade_history[security] = {
                'entry_price': order_price,
                'target_hands': target_hands,
                'actual_hands': order_amount,
                'actual_margin': margin_change,
                'direction': direction,
                'entry_time': context.current_dt,
                'entry_trading_day': entry_trading_day,
                'crossed_ma_lines': crossed_ma_lines,  # 记录穿越的均线
                'entry_snapshot': snapshot_copy
            }

            ma_trailing_enabled = True
            ma_values_at_entry = calculate_realtime_ma_values(security, [5])
            ma5_value = ma_values_at_entry.get('ma5')
            if ma5_value is not None:
                if direction == 'long' and order_price < ma5_value:
                    ma_trailing_enabled = False
                    log.info(f"禁用均线跟踪止盈: {security} {direction}, 开仓价 {order_price:.2f} < MA5 {ma5_value:.2f}")
                elif direction == 'short' and order_price > ma5_value:
                    ma_trailing_enabled = False
                    log.info(f"禁用均线跟踪止盈: {security} {direction}, 开仓价 {order_price:.2f} > MA5 {ma5_value:.2f}")

            g.trade_history[security]['ma_trailing_enabled'] = ma_trailing_enabled
            
            crossed_ma_str = ', '.join(crossed_ma_lines) if crossed_ma_lines else '无'
            log.info(f"开仓成功: {security} {direction} {order_amount}手 @{order_price:.2f}, "
                    f"保证金: {margin_change:.0f}, 资金变化: {cash_change:.0f}, 原因: {reason}, "
                    f"穿越均线: {crossed_ma_str}")
            
            log.debug(f"开仓后检查并补足保证金占用，使其满足最小要求")
            verify_position_margin_requirement(context, security)
            
            return True
            
    except Exception as e:
        log.warning(f"开仓失败 {security}: {str(e)}")
    
    return False

def verify_position_margin_requirement(context, security):
    """开仓后检查并补足保证金占用，使其满足最小要求"""
    try:
        min_margin = getattr(g, 'max_margin_per_position', 0)
        trade_info = g.trade_history.get(security)
        
        if min_margin <= 0 or not trade_info:
            return True
        
        actual_margin = trade_info.get('actual_margin', 0)
        actual_hands = trade_info.get('actual_hands', 0)
        
        if actual_hands <= 0:
            log.warning(f"保证金校验跳过: {security} 无有效持仓信息")
            return False
        
        if actual_margin >= min_margin:
            log.debug(f"{security} 实际保证金 {actual_margin:.0f} 已满足要求 {min_margin:.0f}")
            return True
        
        underlying_symbol = security.split('.')[0][:-4]
        direction = trade_info.get('direction')
        multiplier = get_multiplier(underlying_symbol)
        
        current_data = get_current_data()
        entry_price = trade_info.get('entry_price')
        
        def _safe_price(data, symbol):
            try:
                return data[symbol].last_price
            except Exception:
                return None
        
        market_price = _safe_price(current_data, security)
        price_for_rate = entry_price if entry_price and entry_price > 0 else market_price
        
        if not price_for_rate or price_for_rate <= 0 or multiplier <= 0:
            log.warning(f"保证金校验失败: {security} 缺少价格或乘数数据")
            return False
        
        contract_value = price_for_rate * multiplier * actual_hands
        if contract_value <= 0:
            log.warning(f"保证金校验失败: {security} 名义价值无效")
            return False
        
        actual_margin_rate = actual_margin / contract_value
        if actual_margin_rate <= 0:
            log.warning(f"保证金校验失败: {security} 无法计算保证金比例")
            return False
        
        # 更新对应方向的保证金率配置
        if underlying_symbol in g.futures_config and direction:
            margin_cfg = g.futures_config[underlying_symbol].setdefault('margin_rate', {})
            prev_rate = margin_cfg.get(direction)
            margin_cfg[direction] = actual_margin_rate
            prev_rate_display = f"{prev_rate:.4f}" if prev_rate else "0.0000"
            log.info(f"{underlying_symbol} {direction} 保证金率校准: {prev_rate_display} -> {actual_margin_rate:.4f}")
        
        max_iterations = 3
        while trade_info['actual_margin'] < min_margin and max_iterations > 0:
            max_iterations -= 1
            market_data = get_current_data()
            latest_price = _safe_price(market_data, security) or price_for_rate
            
            if not latest_price or latest_price <= 0:
                log.warning(f"保证金补仓失败: {security} 缺少最新价格")
                break
            
            single_hand_margin = latest_price * multiplier * actual_margin_rate
            if single_hand_margin <= 0:
                log.warning(f"保证金补仓失败: {security} 无法计算单手保证金")
                break
            
            ideal_total_hands = math.ceil(min_margin / single_hand_margin)
            if ideal_total_hands * single_hand_margin > min_margin:
                ideal_total_hands = max(int(min_margin / single_hand_margin), trade_info['actual_hands'])
            
            if ideal_total_hands <= trade_info['actual_hands']:
                log.info(f"{security} 无法进一步增仓，单手保证金 {single_hand_margin:.0f} 限制")
                break
            
            available_cash = context.portfolio.available_cash * g.usage_percentage
            if available_cash <= 0:
                log.warning(f"{security} 可用资金不足，无法补仓")
                break
            
            max_additional_by_cash = int(available_cash / single_hand_margin)
            if max_additional_by_cash <= 0:
                log.warning(f"{security} 可用资金不足以增加1手")
                break
            
            target_hands = min(ideal_total_hands, trade_info['actual_hands'] + max_additional_by_cash)
            additional_hands = target_hands - trade_info['actual_hands']
            
            if additional_hands <= 0:
                log.info(f"{security} 目标手数未增加，跳过补仓")
                break
            
            cash_before = context.portfolio.available_cash
            order = order_target(security, target_hands, side=direction)
            
            if not order or order.filled <= 0:
                log.warning(f"{security} 保证金补仓订单未成交")
                break
            
            cash_after = context.portfolio.available_cash
            additional_margin_used = max(0, cash_before - cash_after)
            if additional_margin_used <= 0:
                additional_margin_used = order.filled * single_hand_margin
            
            trade_info['actual_hands'] += order.filled
            trade_info['target_hands'] = target_hands
            trade_info['actual_margin'] += additional_margin_used
            
            g.today_trades.append({
                'security': security,
                'underlying_symbol': underlying_symbol,
                'direction': direction,
                'order_amount': order.filled,
                'order_price': order.avg_cost if order.avg_cost else order.price,
                'cash_change': additional_margin_used,
                'margin_change': additional_margin_used,
                'time': context.current_dt,
                'reason': 'margin_adjustment'
            })
            
            log.info(f"{security} 保证金补仓成功: +{order.filled}手, 总手数 {trade_info['actual_hands']}手, "
                     f"总保证金 {trade_info['actual_margin']:.0f}/{min_margin:.0f}")
        
        if trade_info['actual_margin'] < min_margin:
            log.warning(f"{security} 补仓后仍未达到保证金要求 {trade_info['actual_margin']:.0f}/{min_margin:.0f}")
            return False
        
        return True
    
    except Exception as e:
        log.warning(f"保证金校验异常 {security}: {str(e)}")
        return False

def close_position(context, security, direction):
    """平仓"""
    try:
        # 使用order_target平仓到0手
        order = order_target(security, 0, side=direction)
        
        if order is not None and order.filled > 0:
            underlying_symbol = security.split('.')[0][:-4]
            
            # 记录当日交易（平仓）
            g.today_trades.append({
                'security': security,
                'underlying_symbol': underlying_symbol,
                'direction': direction,
                'order_amount': -order.filled,
                'order_price': order.avg_cost if order.avg_cost else order.price,
                'cash_change': 0,
                'time': context.current_dt
            })
            
            log.info(f"平仓成功: {underlying_symbol} {direction} {order.filled}手")
            
            # 从交易历史中移除
            if security in g.trade_history:
                del g.trade_history[security]
                log.debug(f"从交易历史中移除: {security}")
        else:
            log.info(f"平仓失败: {security} {direction} {order.filled}手")
            return True
            
    except Exception as e:
        log.warning(f"平仓失败 {security}: {str(e)}")
    
    return False

############################ 辅助函数 ###################################

def get_futures_config(underlying_symbol, config_key=None, default_value=None):
    """获取期货品种配置信息的辅助函数"""
    if underlying_symbol not in g.futures_config:
        if config_key and default_value is not None:
            return default_value
        return {}
    
    if config_key is None:
        return g.futures_config[underlying_symbol]
    
    return g.futures_config[underlying_symbol].get(config_key, default_value)

def get_margin_rate(underlying_symbol, direction, default_rate=0.10):
    """获取保证金比例的辅助函数"""
    return g.futures_config.get(underlying_symbol, {}).get('margin_rate', {}).get(direction, default_rate)

def get_multiplier(underlying_symbol, default_multiplier=10):
    """获取合约乘数的辅助函数"""
    return g.futures_config.get(underlying_symbol, {}).get('multiplier', default_multiplier)

def add_to_excluded_contracts(dominant_future, reason, current_trading_day):
    """将合约添加到排除缓存"""
    g.excluded_contracts[dominant_future] = {
        'reason': reason,
        'trading_day': current_trading_day
    }

def has_reached_trading_start(current_dt, trading_start_time_str, has_night_session=False):
    """判断当前是否已到达合约允许交易的起始时间"""
    if not trading_start_time_str:
        return True

    try:
        hour, minute = [int(part) for part in trading_start_time_str.split(':')[:2]]
    except Exception:
        return True

    start_time = time(hour, minute)
    current_time = current_dt.time()

    if has_night_session:
        if current_time >= start_time:
            return True
        if current_time < time(12, 0):
            return True
        if time(8, 30) <= current_time <= time(15, 30):
            return True
        return False

    if current_time < start_time:
        return False
    if current_time >= time(20, 0):
        return False
    return True

def calculate_target_hands(context, security, direction):
    """计算目标开仓手数"""
    current_price = get_current_data()[security].last_price
    underlying_symbol = security.split('.')[0][:-4]
    
    # 使用保证金比例
    margin_rate = get_margin_rate(underlying_symbol, direction)
    multiplier = get_multiplier(underlying_symbol)
    
    # 计算单手保证金
    single_hand_margin = current_price * multiplier * margin_rate
    log.debug(f"计算单手保证金: {current_price:.2f} * {multiplier:.2f} * {margin_rate:.2f} = {single_hand_margin:.2f}")
    
    # 还要考虑可用资金限制
    available_cash = context.portfolio.available_cash * g.usage_percentage
    
    # 根据单个标的最大持仓保证金限制计算开仓数量
    max_margin = g.max_margin_per_position
    
    if single_hand_margin <= max_margin:
        # 如果单手保证金不超过最大限制，计算最大可开仓手数
        max_hands = int(max_margin / single_hand_margin)
        max_hands_by_cash = int(available_cash / single_hand_margin)
        
        # 取两者较小值
        actual_hands = min(max_hands, max_hands_by_cash)
        
        # 确保至少开1手
        actual_hands = max(1, actual_hands)
        
        log.info(f"单手保证金: {single_hand_margin:.0f}, 目标开仓手数: {actual_hands}")
        
        return actual_hands, single_hand_margin
    else:
        # 如果单手保证金超过最大限制，默认开仓1手
        actual_hands = 1
        
        log.info(f"单手保证金: {single_hand_margin:.0f} 超过最大限制: {max_margin}, 默认开仓1手")
        
        return actual_hands, single_hand_margin

def check_symbol_prefix_match(symbol, context, hold_symbols):
    """检查是否有相似的持仓品种"""
    log.debug(f"检查持仓")
    symbol_prefix = symbol[:-9]

    long_positions = context.subportfolios[0].long_positions
    short_positions = context.subportfolios[0].short_positions
    log.debug(f"long_positions: {long_positions}, short_positions: {short_positions}")
    
    for hold_symbol in hold_symbols:
        hold_symbol_prefix = hold_symbol[:-9] if len(hold_symbol) > 9 else hold_symbol
        
        if symbol_prefix == hold_symbol_prefix:
            return True
    return False

def calculate_average_daily_change_rate(security, days=30):
    """计算日均变化率"""
    historical_data = attribute_history(security, days + 1, '1d', ['close'])
    daily_change_rates = abs(historical_data['close'].pct_change()).iloc[1:]
    return daily_change_rates.mean()

def calculate_realtime_ma_values(security, ma_periods):
    """计算包含当前价格的实时均线值"""
    if not ma_periods:
        return {}
    max_period = max(ma_periods)
    history_days = max_period + MA_ADDITIONAL_HISTORY_DAYS
    historical_data = attribute_history(security, history_days, '1d', ['close'])
    today_price = get_current_data()[security].last_price
    close_prices = historical_data['close'].tolist() + [today_price]
    ma_values = {}
    for period in ma_periods:
        if len(close_prices) >= period:
            ma_values[f'ma{period}'] = sum(close_prices[-period:]) / period
        else:
            ma_values[f'ma{period}'] = None
    return ma_values


def calculate_recent_average_change(security, days=5):
    """计算最近days天日间收盘涨幅的平均值"""
    if days <= 0:
        return None
    try:
        history = attribute_history(security, days + 1, '1d', ['close'])
    except Exception as e:
        log.warning(f"{security} 获取历史数据失败（均值涨幅计算）: {str(e)}")
        return None

    closes = history['close']
    if len(closes) < days + 1:
        return None
    pct_changes = closes.pct_change().dropna().abs() # 取绝对值，更关心涨跌幅度而不是方向
    if len(pct_changes) < days:
        return None
    return pct_changes.iloc[-days:].mean()


def format_entry_details(entry_snapshot):
    """格式化记录到CSV的价格、均线及扩展信息"""
    if not entry_snapshot:
        return ''

    def fmt(value):
        return f"{value:.2f}" if value is not None else "NA"

    ma_values = entry_snapshot.get('ma_values') or {}
    snapshot_parts = [
        f"prev_close:{fmt(entry_snapshot.get('yesterday_close'))}",
        f"open:{fmt(entry_snapshot.get('today_open'))}"
    ]

    for period in [5, 10, 20, 30, 60]:
        key_upper = f"MA{period}"
        key_lower = key_upper.lower()
        value = ma_values.get(key_upper)
        if value is None:
            value = ma_values.get(key_lower)
        snapshot_parts.append(f"{key_upper}:{fmt(value)}")

    avg_change = entry_snapshot.get('avg_5day_change')
    if avg_change is not None:
        snapshot_parts.append(f"AVG5:{avg_change:.4%}")
    else:
        snapshot_parts.append("AVG5:NA")

    return '|'.join(snapshot_parts)

def save_today_new_positions_to_csv(context):
    """将当天新增的持仓记录追加保存到CSV文件"""
    log.info(f"保存当天新增的持仓记录到CSV文件")
    if not g.trade_history:
        return
    
    current_trading_day = normalize_trade_day_value(get_current_trading_day(context.current_dt))
    
    # 筛选当天新开仓的记录
    today_new_positions = []
    for security, trade_info in g.trade_history.items():
        entry_trading_day = normalize_trade_day_value(trade_info.get('entry_trading_day'))
        if entry_trading_day == current_trading_day:
            underlying_symbol = security.split('.')[0][:-4]
            crossed_ma_lines = trade_info.get('crossed_ma_lines', [])
            # 使用分号分隔多条均线，避免CSV格式问题
            crossed_ma_str = ';'.join(crossed_ma_lines) if crossed_ma_lines else ''
            details = format_entry_details(trade_info.get('entry_snapshot'))

            today_new_positions.append({
                'security': security,
                'underlying_symbol': underlying_symbol,
                'direction': trade_info['direction'],
                'entry_price': trade_info['entry_price'],
                'actual_hands': trade_info['actual_hands'],
                'actual_margin': trade_info['actual_margin'],
                'entry_time': trade_info['entry_time'].strftime('%H:%M:%S'),  # 只保留时间
                'entry_trading_day': str(entry_trading_day),
                'crossed_ma_lines': crossed_ma_str,
                'details': details
            })
    
    # 如果没有当天新增的记录，直接返回
    if not today_new_positions:
        log.debug("当天无新增持仓记录，跳过保存")
        return
    
    try:
        filename = 'trade_history.csv'
        
        # 尝试读取现有文件
        existing_content = ''
        file_exists = False
        try:
            existing_content_bytes = read_file(filename)
            if existing_content_bytes:
                existing_content = existing_content_bytes.decode('utf-8')
                file_exists = True
        except:
            pass
        
        # 准备CSV内容
        csv_lines = []
        
        # 如果文件不存在，添加表头
        if not file_exists:
            header = 'security,underlying_symbol,direction,entry_price,actual_hands,actual_margin,entry_time,entry_trading_day,crossed_ma_lines,details'
            csv_lines.append(header)
        
        # 添加新记录
        for pos in today_new_positions:
            line = f"{pos['security']},{pos['underlying_symbol']},{pos['direction']},{pos['entry_price']:.2f},{pos['actual_hands']},{pos['actual_margin']:.2f},{pos['entry_time']},{pos['entry_trading_day']},{pos['crossed_ma_lines']},{pos['details']}"
            csv_lines.append(line)
        
        # 合并内容：现有内容 + 新内容
        new_content = '\n'.join(csv_lines)
        if file_exists:
            final_content = existing_content.rstrip('\n') + '\n' + new_content
        else:
            final_content = new_content
        
        # 写入文件 - 将字符串编码为bytes以符合JoinQuant API要求
        write_file(filename, final_content.encode('utf-8'))
        
        log.info(f"已追加{len(today_new_positions)}条当天新增持仓记录到文件: {filename}")
        
    except Exception as e:
        log.warning(f"保存持仓记录到CSV文件时出错: {str(e)}")

def sync_trade_history_with_positions(context):
    """同步g.trade_history与实际持仓，清理已平仓但记录未删除的持仓"""
    if not g.trade_history:
        return
    
    subportfolio = context.subportfolios[0]
    actual_positions = set(subportfolio.long_positions.keys()) | set(subportfolio.short_positions.keys())
    
    # 找出g.trade_history中有记录但实际已平仓的合约
    stale_records = []
    for security in g.trade_history.keys():
        if security not in actual_positions:
            stale_records.append(security)
    
    # 清理这些过期记录
    if stale_records:
        log.info("=" * 60)
        log.info("发现持仓记录与实际持仓不同步，进行清理：")
        for security in stale_records:
            trade_info = g.trade_history[security]
            underlying_symbol = security.split('.')[0][:-4]
            log.info(f"  清理过期记录: {underlying_symbol}({security}) {trade_info['direction']}, "
                    f"成本价: {trade_info['entry_price']:.2f}, "
                    f"入场时间: {trade_info['entry_time']}")
            del g.trade_history[security]
        log.info(f"共清理 {len(stale_records)} 条过期持仓记录")
        log.info("=" * 60)

def after_market_close(context):
    """收盘后运行函数"""
    log.info(str('函数运行时间(after_market_close):'+str(context.current_dt.time())))
    
    # 保存当天新增的持仓记录到CSV文件
    save_today_new_positions_to_csv(context)
    
    # 同步检查：清理g.trade_history中已平仓但记录未删除的持仓
    sync_trade_history_with_positions(context)
    
    # 清空候选列表（每天重新检查）
    g.daily_ma_candidates = {}
    
    # 清空排除缓存（每天重新检查）
    excluded_count = len(g.excluded_contracts)
    if excluded_count > 0:
        log.info(f"清空排除缓存，共 {excluded_count} 个合约")
        g.excluded_contracts = {}
    
    # 重置夜盘禁止操作标志
    if g.night_session_blocked:
        log.info(f"重置夜盘禁止操作标志")
        g.night_session_blocked = False
        g.night_session_blocked_trading_day = None
    
    # 只有当天有交易时才打印统计信息
    if g.today_trades:
        print_daily_trading_summary(context)
        
        # 清空当日交易记录
        g.today_trades = []
    
    log.info('##############################################################')

def print_daily_trading_summary(context):
    """打印当日交易汇总"""
    if not g.today_trades:
        return
    
    log.info("\n=== 当日交易汇总 ===")
    total_margin = 0
    
    for trade in g.today_trades:
        if trade['order_amount'] > 0:  # 开仓
            log.info(f"开仓 {trade['underlying_symbol']} {trade['direction']} {trade['order_amount']}手 "
                  f"价格:{trade['order_price']:.2f} 保证金:{trade['cash_change']:.0f}")
            total_margin += trade['cash_change']
        else:  # 平仓
            log.info(f"平仓 {trade['underlying_symbol']} {trade['direction']} {abs(trade['order_amount'])}手 "
                  f"价格:{trade['order_price']:.2f}")
    
    log.info(f"当日保证金占用: {total_margin:.0f}")
    log.info("==================\n")

########################## 自动移仓换月函数 #################################
def position_auto_switch(context, pindex=0, switch_func=None, callback=None):
    """期货自动移仓换月"""
    import re
    subportfolio = context.subportfolios[pindex]
    symbols = set(subportfolio.long_positions.keys()) | set(subportfolio.short_positions.keys())
    switch_result = []
    for symbol in symbols:
        match = re.match(r"(?P<underlying_symbol>[A-Z]{1,})", symbol)
        if not match:
            raise ValueError("未知期货标的: {}".format(symbol))
        else:
            underlying_symbol = match.groupdict()["underlying_symbol"]
            trading_start = get_futures_config(underlying_symbol, 'trading_start_time', None)
            has_night_session = get_futures_config(underlying_symbol, 'has_night_session', False)
            # log.debug(f"移仓换月: {symbol}, 交易开始时间: {trading_start}, 夜盘: {has_night_session}")
            if trading_start and not has_reached_trading_start(context.current_dt, trading_start, has_night_session):
                # log.info("{} 当前时间 {} 未到达交易开始时间 {} (夜盘:{} )，跳过移仓".format(
                #     symbol,
                #     context.current_dt.strftime('%H:%M:%S'),
                #     trading_start,
                #     has_night_session
                # ))
                continue
            dominant = get_dominant_future(underlying_symbol)
            cur = get_current_data()
            symbol_last_price = cur[symbol].last_price
            dominant_last_price = cur[dominant].last_price
            
            if dominant > symbol:
                for positions_ in (subportfolio.long_positions, subportfolio.short_positions):
                    if symbol not in positions_.keys():
                        continue
                    else :
                        p = positions_[symbol]

                    if switch_func is not None:
                        switch_func(context, pindex, p, dominant)
                    else:
                        amount = p.total_amount
                        # 跌停不能开空和平多，涨停不能开多和平空
                        if p.side == "long":
                            symbol_low_limit = cur[symbol].low_limit
                            dominant_high_limit = cur[dominant].high_limit
                            if symbol_last_price <= symbol_low_limit:
                                log.warning("标的{}跌停，无法平仓。移仓换月取消。".format(symbol))
                                continue
                            elif dominant_last_price >= dominant_high_limit:
                                log.warning("标的{}涨停，无法开仓。移仓换月取消。".format(dominant))
                                continue
                            else:
                                log.info("进行移仓换月: ({0},long) -> ({1},long)".format(symbol, dominant))
                                order_old = order_target(symbol, 0, side='long')
                                if order_old != None and order_old.filled > 0:
                                    order_new = order_target(dominant, amount, side='long')
                                    if order_new != None and order_new.filled > 0:
                                        switch_result.append({"before": symbol, "after": dominant, "side": "long"})
                                        # 换月成功，更新交易记录
                                        if symbol in g.trade_history:
                                            # 复制旧的交易记录作为基础
                                            old_entry_price = g.trade_history[symbol]['entry_price']
                                            g.trade_history[dominant] = g.trade_history[symbol].copy()

                                            # 更新成本价为新合约的实际开仓价
                                            new_entry_price = None
                                            if order_new.avg_cost and order_new.avg_cost > 0:
                                                new_entry_price = order_new.avg_cost
                                                g.trade_history[dominant]['entry_price'] = order_new.avg_cost
                                            elif order_new.price and order_new.price > 0:
                                                new_entry_price = order_new.price
                                                g.trade_history[dominant]['entry_price'] = order_new.price
                                            else:
                                                # 如果订单价格无效，使用当前价格作为成本价
                                                new_entry_price = dominant_last_price
                                                g.trade_history[dominant]['entry_price'] = dominant_last_price

                                            # 更新入场时间
                                            g.trade_history[dominant]['entry_time'] = context.current_dt
                                            # 更新入场交易日
                                            g.trade_history[dominant]['entry_trading_day'] = get_current_trading_day(context.current_dt)
                                            # 删除旧合约的交易记录
                                            del g.trade_history[symbol]

                                            log.info(f"移仓换月成本价更新: {symbol} -> {dominant}, "
                                                   f"旧成本价: {old_entry_price:.2f}, 新成本价: {new_entry_price:.2f}")
                                    else:
                                        log.warning("标的{}交易失败，无法开仓。移仓换月失败。".format(dominant))
                        if p.side == "short":
                            symbol_high_limit = cur[symbol].high_limit
                            dominant_low_limit = cur[dominant].low_limit
                            if symbol_last_price >= symbol_high_limit:
                                log.warning("标的{}涨停，无法平仓。移仓换月取消。".format(symbol))
                                continue
                            elif dominant_last_price <= dominant_low_limit:
                                log.warning("标的{}跌停，无法开仓。移仓换月取消。".format(dominant))
                                continue
                            else:
                                log.info("进行移仓换月: ({0},short) -> ({1},short)".format(symbol, dominant))
                                order_old = order_target(symbol, 0, side='short')
                                if order_old != None and order_old.filled > 0:
                                    order_new = order_target(dominant, amount, side='short')
                                    if order_new != None and order_new.filled > 0:
                                        switch_result.append({"before": symbol, "after": dominant, "side": "short"})
                                        # 换月成功，更新交易记录
                                        if symbol in g.trade_history:
                                            # 复制旧的交易记录作为基础
                                            old_entry_price = g.trade_history[symbol]['entry_price']
                                            g.trade_history[dominant] = g.trade_history[symbol].copy()

                                            # 更新成本价为新合约的实际开仓价
                                            new_entry_price = None
                                            if order_new.avg_cost and order_new.avg_cost > 0:
                                                new_entry_price = order_new.avg_cost
                                                g.trade_history[dominant]['entry_price'] = order_new.avg_cost
                                            elif order_new.price and order_new.price > 0:
                                                new_entry_price = order_new.price
                                                g.trade_history[dominant]['entry_price'] = order_new.price
                                            else:
                                                # 如果订单价格无效，使用当前价格作为成本价
                                                new_entry_price = dominant_last_price
                                                g.trade_history[dominant]['entry_price'] = dominant_last_price

                                            # 更新入场时间
                                            g.trade_history[dominant]['entry_time'] = context.current_dt
                                            # 更新入场交易日
                                            g.trade_history[dominant]['entry_trading_day'] = get_current_trading_day(context.current_dt)
                                            # 删除旧合约的交易记录
                                            del g.trade_history[symbol]

                                            log.info(f"移仓换月成本价更新: {symbol} -> {dominant}, "
                                                   f"旧成本价: {old_entry_price:.2f}, 新成本价: {new_entry_price:.2f}")
                                    else:
                                        log.warning("标的{}交易失败，无法开仓。移仓换月失败。".format(dominant))
                        if callback:
                            callback(context, pindex, p, dominant)
    return switch_result