2408 国九条后中小板微盘小改 国九条新规下财务质优小市值动态风控策略 年化135.40

1. 监管合规体系：

• 财务健康筛选：要求连续三年净利润 > 0、营收 > 1 亿，剔除 ST / 退市 / 审计异常个股
• 市值动态平衡：聚焦 70-520 亿流通市值区间，回避微小及超大市值标的
• 次新股过滤：排除上市不满 375 天的次新标的

2. 动态仓位管理：

• 市场状态感知：基于中小板综指 10 日均线偏离度动态调整持股数量（3-6 只）
• 季节性空仓：1 月 / 4 月持有货币 ETF（规避财报披露期波动）
• 极端行情避险：指数单日跌幅超 5% 触发系统止损

3. 交易风控机制：

• 双层止损体系：个股 9% 硬性止损 + 市场 5% 趋势止损
• 涨停板保护：昨日涨停股观察至 14:00，破板即触发卖出
• 盈利保护：持仓个股收益达 100% 强制止盈

4. 量价优选模型：

• 首板基因筛选：选取近 3 日首次涨停且无历史连板个股
• 流动性验证：昨日成交额 7-19 亿，换手率位于市场前 30%
• 价格结构优化：回避股价 > 50 元的高价股及科创 / 北交所标的

策略代码

from jqdata import *
from jqfactor import *
import numpy as np
import pandas as pd
from datetime import time,date
from jqdata import finance

#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 = False  # 是否筛选审计意见
    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
    #全局变量float/str
    g.min_mv = 10  # 股票最小市值要求
    g.max_mv = 100  # 股票最大市值要求
    g.stock_num = 4  # 持股数量

    g.stoploss_list = []  # 止损卖出列表
    g.other_sale    = []  # 其他卖出列表
    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.money_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(stop_loss, 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.limitup_stocks = []
    g.hold_list = list(context.portfolio.positions)
    #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 = df['code'].tolist()
    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)

选股模块函数解锁后查看：

#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))
                order_target_value(stock, 0)
                g.other_sale.append(stock)
                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造成空仓，剩余的金额当日买入
def check_remain_amount(context):
    addstock_num = len(g.other_sale)
    loss_num = len(g.stoploss_list)
    empty_num = addstock_num + loss_num
    
    g.hold_list = context.portfolio.positions
    if len(g.hold_list) < g.stock_num:   
        # 计算需要买入的股票数量，止损仓位补足货币etf
        # 可替换下一行代码以更换逻辑：改为将清空仓位全部补足股票，而非原作中止损仓位补充货币etf
        # num_stocks_to_buy = min(empty_num,g.stock_num-len(g.hold_list))
        num_stocks_to_buy = min(addstock_num,g.stock_num-len(g.hold_list))
        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,len(target_list))
        if loss_num !=0:
            log.info('有余额可用'+str(round((context.portfolio.cash),2))+'元。买入货币基金'+ str(g.money_etf))
            buy_security(context,[g.money_etf],loss_num)
    
    g.stoploss_list = []
    g.other_sale    = []

#1-6 下午检查交易
def trade_afternoon(context):
    if g.trading_signal:
        check_limit_up(context)
        check_remain_amount(context)
        buy_security(context,[g.money_etf],1)
        
#1-7 止盈止损
def stop_loss(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))
                    g.other_sale.append(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.stoploss_list.append(stock)

        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)
            # 计算成分股平均涨跌，即指数涨跌幅
            down_ratio = (1 - stock_df['close'] / stock_df['open']).mean()
            # 市场大跌止损
            if down_ratio >= g.stoploss_market:
                g.stoploss_list.append(stock)
                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
    index_df = get_price('399101.XSHE', end_date=today,count = ma_para,fields = 'close', frequency='daily')
    ma = index_df['close'].mean()
    last_row = index_df['close'].iloc[-1]
    diff = 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
def filter_audit(context, code):
    # 获取审计意见，近三年内如果有不合格(report_type为2、3、4、5)的审计意见则返回False，否则返回True
    lstd = context.previous_date
    last_year = lstd.replace(year=lstd.year - 3, month=1, day=1)
    q=query(finance.STK_AUDIT_OPINION.code, finance.STK_AUDIT_OPINION.report_type
          ).filter(finance.STK_AUDIT_OPINION.code==code,finance.STK_AUDIT_OPINION.pub_date>=last_year)
    df=finance.run_query(q)
    df['report_type'] = df['report_type'].astype(str)
    contains_nums = df['report_type'].str.contains(r'2|3|4|5')
    return not contains_nums.any()


#3-4 买入模块
def buy_security(context,target_list,num):
    #调仓买入
    position_count = len(context.portfolio.positions)
    target_num = num
    if target_num !=0:
        value = context.portfolio.cash / target_num
        for stock in target_list:
            order_target_value(stock, value)
            log.info("买入[%s]（%s元）" % (stock,value))
            if len(context.portfolio.positions) == g.stock_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
    month = context.current_dt.month
    # 判断当前月份是否在指定月份范围内
    if month in g.pass_months:
        code = '399303.XSHE'
        close = history(count = 3, unit='1d', field='close', security_list= [code], df = False, skip_paused = False, fq = 'none')[code]
        if close[-1] > close[-2] * 0.995 and close[-1] > close[-3] * 0.994:
            return True
        # 判断当前日期是否在指定日期范围内
        return False
    else:
        return True

def close_account(context):
    if not g.trading_signal:
        curr_data = get_current_data()
        if len(g.hold_list) != 0 and g.hold_list != [g.money_etf]:
            for stock in g.hold_list:
                if stock == g.money_etf:
                    continue
                if curr_data[stock].last_price == curr_data[stock].low_limit or curr_data[stock].paused:
                    continue
                order_target_value(stock, 0)
                log.info("卖出[%s]" % (stock))

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————————————————————————————————————————')

2025-02-24