国九条-新的红利因子，修正「审计意见」函数

策略作者: 水滴

```python
# 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。
# 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。
# 原文网址：https://www.joinquant.com/post/48747
# 标题：国九条-新的红利因子，修正「审计意见」函数
# 作者：LGQ_2023
# 原回测条件：2019-01-01 到 2024-06-10, ￥100000, 每天

# 原文网址：https://www.joinquant.com/post/47929
# 标题：国九条-新的红利因子，年化103.49% 回撤24.28%
# 作者：热情的刀

# 20240426  改进了红利的相关参数
# 20240429  引入了红利的超额收益因子
# 年化 103%  回撤 24%
# 作者： 热情的刀

# 原文网址：https://www.joinquant.com/post/47791
# 标题：国九小市值策略【年化100.5%|回撤25.6%】
# 作者：zycash

#enable_profile()
#本策略为www.joinquant.com/post/47346的改进版本
#根据国九条，筛选股票

#导入函数库
from jqdata import *
from jqfactor import *
import numpy as np
import pandas as pd
from datetime import time
from jqdata import finance

#import datetime
#初始化函数 
def initialize(context):
    # 开启防未来函数
    set_option('avoid_future_data', True)
    # 成交量设置
    #set_option('order_volume_ratio', 0.10)
    # 设定基准
    set_benchmark('399101.XSHE')
    # 用真实价格交易
    set_option('use_real_price', True)
    # 将滑点设置为0
    set_slippage(FixedSlippage(3/10000))
    # 设置交易成本万分之三，不同滑点影响可在归因分析中查看
    set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=2.5/10000, close_commission=2.5/10000, close_today_commission=0, min_commission=5),type='stock')
    # 过滤order中低于error级别的日志
    log.set_level('order', 'error')
    log.set_level('system', 'error')
    log.set_level('strategy', 'debug')
    
    #初始化全局变量 bool
    g.trading_signal = True  # 是否为可交易日
    g.run_stoploss = True  # 是否进行止损
    g.filter_audit = True  # 是否筛选审计意见
    g.filter_bonus= True   #是否筛选红利
    g.adjust_num = True  # 是否调整持仓数量
    
    
    #全局变量list
    g.hold_list = [] #当前持仓的全部股票    
    g.yesterday_HL_list = [] #记录持仓中昨日涨停的股票
    g.target_list = []
    g.pass_months = [1,4]  # 空仓的月份
    g.limitup_stocks = []   # 记录涨停的股票避免再次买入
    g.Expected_bonus= [5]  #设定引入超额收益的因子的月份
    
    #全局变量float/str
    g.min_mv =10  # 股票最小市值要求
    g.max_mv = 1e8  # 股票最大市值要求
    g.stock_num = 4  # 设定初始股票池的基数，经过基本面初步筛选前的股票池内的股票数量为g.stock_num*g.stock_pool_mult
    g.stock_pool_mult= 5 #原始股票池的倍率
    g.reason_to_sell = ''
    g.stoploss_strategy = 3  # 1为止损线止损，2为市场趋势止损, 3为联合1、2策略
    g.stoploss_limit = 0.09  # 止损线
    g.stoploss_market = 0.05  # 市场趋势止损参数
    g.highest =50 #股票单价上限设置
    g.bonus_year= 1  #  控制有现金分红的年限
    g.etf = '511880.XSHG'  # 空仓月份持有银华日利ETF
    
    # 设置交易运行时间
    run_daily(prepare_stock_list, '9:05')
    run_daily(trade_afternoon, time='14:00', reference_security='399101.XSHE') #检查持仓中的涨停股是否需要卖出
    run_daily(sell_stocks, time='10:00') # 止损函数
    run_daily(close_account, '14:50')
    run_weekly(weekly_adjustment,2,'10:00')
    #run_weekly(print_position_info, 5, time='15:10', reference_security='000300.XSHG')

#1-1 准备股票池
def prepare_stock_list(context):
    #获取已持有列表
    g.hold_list= []
    g.limitup_stocks = []
    for position in list(context.portfolio.positions.values()):
        stock = position.security
        g.hold_list.append(stock)
    #获取昨日涨停列表
    if g.hold_list != []:
        df = get_price(g.hold_list, end_date=context.previous_date, frequency='daily', fields=['close','high_limit','low_limit'], count=1, panel=False, fill_paused=False)
        df = df[df['close'] == df['high_limit']]
        g.yesterday_HL_list = list(df.code)
    else:
        g.yesterday_HL_list = []
    #判断今天是否为账户资金再平衡的日期
    g.trading_signal = today_is_between(context)

#1-2 选股模块
def get_stock_list(context):
    final_list = []
    MKT_index = '399101.XSHE'
    initial_list = filter_stocks(context, get_index_stocks(MKT_index))
    # 国九更新：过滤近一年净利润为负且营业收入小于1亿的
    # 国九更新：过滤近一年期末净资产为负的 (经查询没有为负数的，所以直接pass这条)
    # 国九更新：过滤近一年审计建议无法出具或者为负面建议的 (经过净利润等筛选，审计意见几乎不会存在异常)
    q = query(
        valuation.code,
        valuation.market_cap,  # 总市值 circulating_market_cap/market_cap
        income.np_parent_company_owners,  # 归属于母公司所有者的净利润
        income.net_profit,  # 净利润
        income.operating_revenue  # 营业收入
        #security_indicator.net_assets
    ).filter(
        valuation.code.in_(initial_list),
        valuation.market_cap.between(g.min_mv,g.max_mv),
        income.np_parent_company_owners > 0,
        income.net_profit > 0,
        income.operating_revenue > 1e8,
        indicator.roe>0,
        indicator.roa>0,
      
    ).order_by(valuation.market_cap.asc()).limit(g.stock_num*g.stock_pool_mult)
    
    df = get_fundamentals(q)
    final_list = list(df.code)
    
    # 过滤审计意见
    if g.filter_audit:
        final_list = filter_audit(context,final_list)
    
    #过滤红利股
    if g.filter_bonus:
        final_list = bonus_filter(context,final_list)
        
    if len(final_list) == 0:
        # 由于有时候选股条件苛刻，所以会没有股票入选，这时买入银华日利ETF
        log.info('无适合股票，买入ETF')
        return [g.etf]
    else:
        last_prices = history(1, unit='1d', field='close', security_list=final_list)
        return [stock for stock in final_list if stock in g.hold_list  or last_prices[stock][-1] <= g.highest]

#1-3 整体调整持仓
def weekly_adjustment(context):
    if g.trading_signal and g.adjust_num:
        new_num = adjust_stock_num(context)
        if new_num == 0:
            buy_security(context, [g.etf])
            log.info('MA指示指数大跌，持有银华日利ETF')
        else:
            if g.stock_num != new_num:
                g.stock_num = new_num
                log.info(f'持仓数量修改为{new_num}')
            g.target_list = get_stock_list(context)[:g.stock_num]
            log.info(str(g.target_list))
            
            sell_list = [stock for stock in g.hold_list if stock not in g.target_list and stock not in g.yesterday_HL_list]
            hold_list = [stock for stock in g.hold_list if stock in g.target_list or stock in g.yesterday_HL_list]
            log.info("已持有[%s]" % (str(hold_list)))
            log.info("卖出[%s]" % (str(sell_list)))
            
            sell_positions = [context.portfolio.positions[stock] for stock in sell_list]
            for position in sell_positions:
                close_position(position)
            
            buy_security(context, g.target_list)
            
            for position in list(context.portfolio.positions.values()):
                stock = position.security
    else:
        buy_security(context, [g.etf])
        log.info('该月份为空仓月份，持有银华日利ETF')

#1-4 调整昨日涨停股票
def check_limit_up(context):
    now_time = context.current_dt
    if g.yesterday_HL_list != []:
        #对昨日涨停股票观察到尾盘如不涨停则提前卖出，如果涨停即使不在应买入列表仍暂时持有
        for stock in g.yesterday_HL_list:
            current_data = get_price(stock, end_date=now_time, frequency='1m', fields=['close','high_limit'], skip_paused=False, fq='pre', count=1, panel=False, fill_paused=True)
            if current_data.iloc[0,0] <    current_data.iloc[0,1]:
                log.info("[%s]涨停打开，卖出" % (stock))
                position = context.portfolio.positions[stock]
                close_position(position)
                g.reason_to_sell = 'limitup'
                g.limitup_stocks.append(stock)
            else:
                log.info("[%s]涨停，继续持有" % (stock))

#1-5 如果昨天有股票卖出或者买入失败，剩余的金额今天早上买入
def check_remain_amount(context):
    if g.reason_to_sell is 'limitup': #判断提前售出原因，如果是涨停售出则次日再次交易，如果是止损售出则不交易
        g.hold_list= []
        for position in list(context.portfolio.positions.values()):
            stock = position.security
            g.hold_list.append(stock)
        if len(g.hold_list) < g.stock_num:
            # 计算需要买入的股票数量
            num_stocks_to_buy = min(len(g.limitup_stocks), g.stock_num - len(context.portfolio.positions))
            target_list = [stock for stock in g.target_list if stock not in g.limitup_stocks][:num_stocks_to_buy]
            log.info('有余额可用'+str(round((context.portfolio.cash),2))+'元。买入'+ str(target_list))
            buy_security(context,target_list)
        g.reason_to_sell = ''
    elif g.reason_to_sell is 'stoploss':
        log.info('有余额可用'+str(round((context.portfolio.cash),2))+'元。买入'+ str(g.etf))
        buy_security(context,[g.etf])
        g.reason_to_sell = ''

#1-6 下午检查交易
def trade_afternoon(context):
    if g.trading_signal == True:
        check_limit_up(context)
        check_remain_amount(context)
        
#1-7 止盈止损
def sell_stocks(context):
    if g.run_stoploss:
        current_positions = context.portfolio.positions

if g.stoploss_strategy == 1 or g.stoploss_strategy == 3:
            for stock in current_positions.keys():
                price = current_positions[stock].price
                avg_cost = current_positions[stock].avg_cost
                # 个股盈利止盈
                if price >= avg_cost * 2:
                    order_target_value(stock, 0)
                    log.debug("收益100%止盈,卖出{}".format(stock))
                # 个股止损
                elif price < avg_cost * (1 - g.stoploss_limit):
                    order_target_value(stock, 0)
                    log.debug("收益止损,卖出{}".format(stock))
                    g.reason_to_sell = 'stoploss'

if g.stoploss_strategy == 2 or g.stoploss_strategy == 3:
            stock_df = get_price(security=get_index_stocks('399101.XSHE'), end_date=context.previous_date, frequency='daily', fields=['close', 'open'], count=1, panel=False)
            down_ratio = abs((stock_df['close'] / stock_df['open'] - 1).mean())
            # 市场大跌止损
            if down_ratio >= g.stoploss_market:
                g.reason_to_sell = 'stoploss'
                log.debug("大盘惨跌,平均降幅{:.2%}".format(down_ratio))
                for stock in current_positions.keys():
                    order_target_value(stock, 0)

#1-8 动态调仓代码
def adjust_stock_num(context):
    ma_para = 10  # 设置MA参数
    today = context.previous_date
    start_date = today - datetime.timedelta(days = ma_para*2)
    index_df = get_price('399101.XSHE', start_date=start_date, end_date=today, frequency='daily')
    index_df['ma'] = index_df['close'].rolling(window=ma_para).mean()
    last_row = index_df.iloc[-1]
    diff = last_row['close'] - last_row['ma']
    # 根据差值结果返回数字
    result = 3 if diff >= 500 else \
             3 if 200 <= diff < 500 else \
             4 if -200 <= diff < 200 else \
             5 if -500 <= diff < -200 else \
             6
    return result

#2 过滤各种股票
def filter_stocks(context, stock_list):
    current_data = get_current_data()
        # 涨跌停和最近价格的判断
    last_prices = history(1, unit='1m', field='close', security_list=stock_list)
        # 过滤标准
    filtered_stocks = []
    for stock in stock_list:
        if current_data[stock].paused:  # 停牌
            continue
        if current_data[stock].is_st:  # ST
            continue
        if '退' in current_data[stock].name:  # 退市
            continue
        if stock.startswith('30') or stock.startswith('68') or stock.startswith('8') or stock.startswith('4'):  # 市场类型
            continue
        if not (stock in context.portfolio.positions or last_prices[stock][-1] < current_data[stock].high_limit):  # 涨停
            continue
        if not (stock in context.portfolio.positions or last_prices[stock][-1] > current_data[stock].low_limit):  # 跌停
            continue
        # 次新股过滤
        start_date = get_security_info(stock).start_date
        if context.previous_date - start_date < timedelta(days=375):
            continue
        filtered_stocks.append(stock)
    return filtered_stocks

#2.1 筛选审计意见
'''
审计意见类型编码
类型编码 审计意见类型
1 	     无保留
2 	     无保留带解释性说明
3        保留意见
4        拒绝/无法表示意见
5        否定意见
6 	     未经审计
7 	     保留带解释性说明
10 	     经审计（不确定具体意见类型）
11       无保留带持续经营重大不确定性
'''
def filter_audit(context,code_list):
    # 获取审计意见，近三年内如果有不合格(report_type为3、4、5、7)的审计意见则返回False，否则返回True
    final_list = []
    expection_Audit_list = []
    for stock in code_list:
        lstd = context.previous_date
        last_year = (lstd.replace(year=lstd.year - 3, month=1, day=1)).strftime('%Y-%m-%d')
        q=query(finance.STK_AUDIT_OPINION.code,finance.STK_AUDIT_OPINION.pub_date,finance.STK_AUDIT_OPINION).filter(
                                finance.STK_AUDIT_OPINION.code==stock,
                                finance.STK_AUDIT_OPINION.pub_date>=last_year,
                                finance.STK_AUDIT_OPINION.pub_date<=context.current_dt,
                                )
        df=finance.run_query(q)
        # print('\n%s'%df)
        values_to_check = [3, 4, 5, 7]
        contains_unwanted_values = df['opinion_type_id'].isin(values_to_check).any()
        if not contains_unwanted_values:
            final_list.append(stock)
        else:
            expection_Audit_list.append(stock)
    print('★★★★ 去除近三年内存在审计问题的%s只 ★★★★'%(len(expection_Audit_list)))
    print('★★★★ 存在审计问题的: %s  '%(expection_Audit_list))

return  final_list  # 返回剔除审计意见异常后的list

#2.2 #获取红利列表
def bonus_filter(context,stock_list):
    #print(f'进入红利筛选前,共{len(stock_list)}只股票')
    year=context.previous_date.year
    start_date=datetime.date(year, 1, 1)
    end_date=context.previous_date
    if end_date.month in g.Expected_bonus:
        q = query(finance.STK_XR_XD.code,finance.STK_XR_XD.company_name, finance.STK_XR_XD.board_plan_pub_date,finance.STK_XR_XD.bonus_amount_rmb,finance.STK_XR_XD.bonus_ratio_rmb
            ).filter(               
                #finance.STK_XR_XD.bonus_type !='年度分红',
                finance.STK_XR_XD.board_plan_pub_date>start_date,
                finance.STK_XR_XD.implementation_pub_date<=end_date,
                #finance.STK_XR_XD.a_xr_date < context.previous_date,
                finance.STK_XR_XD.bonus_ratio_rmb>0,
                finance.STK_XR_XD.code.in_(stock_list))
        Expected_bonus_df = finance.run_query(q)
        
        if len(Expected_bonus_df)>0:
            bonus_list=Expected_bonus_df['code'].unique().tolist()
            price_df=history(1, unit='1d', field='close', security_list=bonus_list, df=True, skip_paused=False, fq='pre')
            price_df=price_df.T
            price_df.rename(columns={price_df.columns[0]:'Close_now'},inplace=True)
            price_df['code']=price_df.index
            Expected_bonus_df=pd.merge(Expected_bonus_df,price_df,on=('code'),how='left')
            Expected_bonus_df['bonus_ratio']=(Expected_bonus_df['bonus_ratio_rmb'])/Expected_bonus_df['Close_now']
            Expected_bonus_df=Expected_bonus_df.sort_values(by='bonus_ratio',ascending=True)
            bonus_list=Expected_bonus_df['code'].unique().tolist()
        else:
            bonus_list=[]
    else:
        reprot_date = datetime.date(year-1, 12, 31)
        q = query(finance.STK_XR_XD.code,finance.STK_XR_XD.company_name,finance.STK_XR_XD.a_registration_date, finance.STK_XR_XD.bonus_amount_rmb,finance.STK_XR_XD.bonus_ratio_rmb
            ).filter(
                finance.STK_XR_XD.report_date ==reprot_date,         
                finance.STK_XR_XD.bonus_type=='年度分红' ,
                finance.STK_XR_XD.implementation_pub_date<=end_date,
                finance.STK_XR_XD.board_plan_bonusnote=='不分配不转增',
                finance.STK_XR_XD.code.in_(stock_list))
    
        no_year_bonus = finance.run_query(q)
        no_year_bonus_list=no_year_bonus['code'].unique().tolist()
        #排除今年不分红的股票
        bonus_list=[code for code in stock_list if code not in no_year_bonus_list]
        bonus_list=short_by_market_cap(context,bonus_list)
       
    print(f'进行实际红利筛选后,原有{len(stock_list)}只股票，筛选后剩余{len(bonus_list)}只股票')
    
    if len(bonus_list)< g.stock_num:
        bonus_list.extend([x for x in short_by_market_cap(context,stock_list) if x not in bonus_list ][:g.stock_num-len(bonus_list)])
    return bonus_list

#3-1 交易模块-自定义下单
def order_target_value_(security, value):
    if value == 0:
        pass
        #log.debug("Selling out %s" % (security))
    else:
        log.debug("Order %s to value %f" % (security, value))
    return order_target_value(security, value)

#3-2 交易模块-开仓
def open_position(security, value):
    order = order_target_value_(security, value)
    if order != None and order.filled > 0:
        return True
    return False

#3-3 交易模块-平仓
def close_position(position):
    security = position.security
    order = order_target_value_(security, 0)  # 可能会因停牌失败
    if order != None:
        if order.status == OrderStatus.held and order.filled == order.amount:
            return True
    return False

#3-4 买入模块
def buy_security(context,target_list):
    #调仓买入
    position_count = len(context.portfolio.positions)
    target_num = len(target_list)
    if target_num > position_count:
        value = context.portfolio.cash / (target_num - position_count)
        for stock in target_list:
            if context.portfolio.positions[stock].total_amount == 0:
            #if stock not in context.portfolio.positions:
                if open_position(stock, value):
                    log.info("买入[%s]（%s元）" % (stock,value))
                    if len(context.portfolio.positions) == target_num:
                        break

#4-1 判断今天是否跳过月份
def today_is_between(context):
    # 根据g.pass_month跳过指定月份
    today = context.current_dt
    month = today.month
    if month in g.pass_months:
        return False
    else:
        return True

#4-2 清仓后次日资金可转
def close_account(context):
    if g.trading_signal == False:
        if len(g.hold_list) != 0 and g.hold_list != [g.etf]:
            for stock in g.hold_list:
                position = context.portfolio.positions[stock]
                close_position(position)
                log.info("卖出[%s]" % (stock))

#5  公共模块
#5-1   根据市值排序
def short_by_market_cap(context,stock_list):
    short_q = query(
            valuation.code,
            valuation.market_cap,  # 总市值 circulating_market_cap/market_cap
        ).filter(
            valuation.code.in_(stock_list),
            valuation.day == context.previous_date,
        ).order_by(valuation.market_cap.asc())
    short_df=get_fundamentals(short_q)
    short_list=short_df['code'].unique().tolist()

return  short_list

def print_position_info(context):
    for position in list(context.portfolio.positions.values()):
        securities=position.security
        cost=position.avg_cost
        price=position.price
        ret=100*(price/cost-1)
        value=position.value
        amount=position.total_amount    
        print('代码:{}'.format(securities))
        print('成本价:{}'.format(format(cost,'.2f')))
        print('现价:{}'.format(price))
        print('收益率:{}%'.format(format(ret,'.2f')))
        print('持仓(股):{}'.format(amount))
        print('市值:{}'.format(format(value,'.2f')))
    print('———————————————————————————————————————分割线————————————————————————————————————————')
    
    
    
    
    
    
```

发布时间: 2025-02-26 20:58:19

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