胜率65%之缩量分歧反包战法

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/34829
# 标题：胜率65%之缩量分歧反包战法
# 作者：游资小码哥
# 选择分钟回测

# 导入函数库
from jqdata import *

# 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()
    log.info('初始函数开始运行且全局只运行一次')
    # 过滤掉order系列API产生的比error级别低的log
    # log.set_level('order', 'error')
    # g 内置全局变量
    g.my_security = '510300.XSHG'
    set_universe([g.my_security])
    g.help_stock = []
    ### 股票相关设定 ###
    # 股票类每笔交易时的手续费是：买入时佣金万分之三，卖出时佣金万分之三加千分之一印花税, 每笔交易佣金最低扣5块钱
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')

## 运行函数（reference_security为运行时间的参考标的；传入的标的只做种类区分，因此传入'000300.XSHG'或'510300.XSHG'是一样的）
      # 开盘前运行
    run_daily(before_market_open, time='before_open', reference_security='000300.XSHG')
      # 开盘时运行
    run_daily(market_open, time='every_bar', reference_security='000300.XSHG')
    #run_daily(market_run_sell, time='every_bar', reference_security='000300.XSHG')

# 收盘后运行before_open
    run_daily(after_market_close, time='after_close', reference_security='000300.XSHG')

## 开盘时运行函数
def market_open(context):
    time_buy = context.current_dt.strftime('%H:%M:%S')
    aday = datetime.datetime.strptime('13:00:00', '%H:%M:%S').strftime('%H:%M:%S')
    if len(g.help_stock) > 0 :
        for stock in g.help_stock:
            #log.info("当前时间 %s" % (context.current_dt))
            #log.info("股票 %s 的最新价: %f" % (stock, get_current_data()[stock].last_price))
            cash = context.portfolio.available_cash
            #print(cash)
            day_open_price = get_current_data()[stock].day_open
            current_price = get_current_data()[stock].last_price
            high_limit_price = get_current_data()[stock].high_limit
            pre_date =  (context.current_dt + timedelta(days = -1)).strftime("%Y-%m-%d")
            df_panel = get_price(stock, count = 1,end_date=pre_date, frequency='daily', fields=['open', 'high', 'close','low', 'high_limit','money','pre_close'])
            pre_high_limit = df_panel['high_limit'].values
            pre_close = df_panel['close'].values
            pre_open = df_panel['open'].values
            pre_high = df_panel['high'].values
            pre_low = df_panel['low'].values
            pre_pre_close = df_panel['pre_close'].values
            
            now = context.current_dt
            zeroToday = now - datetime.timedelta(hours=now.hour, minutes=now.minute, seconds=now.second,microseconds=now.microsecond)
            lastToday = zeroToday + datetime.timedelta(hours=9, minutes=31, seconds=00)
            df_panel_allday = get_price(stock, start_date=lastToday, end_date=context.current_dt, frequency='minute', fields=['high','low','close','high_limit','money'])
            low_allday = df_panel_allday.loc[:,"low"].min()
            high_allday = df_panel_allday.loc[:,"high"].max()
            sum_plus_num_two = (df_panel_allday.loc[:,'close'] == df_panel_allday.loc[:,'high_limit']).sum()
            
            ##当前持仓有哪些股票
            if cash > 5000 :
                if current_price > pre_close * 1.05 and day_open_price > pre_close and current_price > day_open_price and current_price > low_allday * 1.03 and current_price < high_limit_price:
                    print("1."+stock+"买入金额"+str(cash))
                    orders = order_value(stock, cash)
                    if str(orders.status) == 'held':
                        g.help_stock.remove(stock)

time_sell = context.current_dt.strftime('%H:%M:%S')
    cday = datetime.datetime.strptime('14:40:00', '%H:%M:%S').strftime('%H:%M:%S')
    sell_day = datetime.datetime.strptime('11:10:00', '%H:%M:%S').strftime('%H:%M:%S')
    sell_day_10 = datetime.datetime.strptime('13:30:00', '%H:%M:%S').strftime('%H:%M:%S')
    if time_sell > cday:
        stock_owner = context.portfolio.positions
        if len(stock_owner) > 0:
            for stock_two in stock_owner:
                if context.portfolio.positions[stock_two].closeable_amount > 0:
                    current_price_list = get_ticks(stock_two,start_dt=None, end_dt=context.current_dt, count=1, fields=['time', 'current', 'high', 'low', 'volume', 'money'])
                    current_price = current_price_list['current'][0]
                    day_open_price = get_current_data()[stock_two].day_open
                    day_high_limit = get_current_data()[stock_two].high_limit 
                    
                    now = context.current_dt
                    zeroToday = now - datetime.timedelta(hours=now.hour, minutes=now.minute, seconds=now.second,microseconds=now.microsecond)
                    lastToday = zeroToday + datetime.timedelta(hours=9, minutes=31, seconds=00)
                    df_panel_allday = get_price(stock_two, start_date=lastToday, end_date=context.current_dt, frequency='minute', fields=['high','low','close','high_limit','money'])
                    low_allday = df_panel_allday.loc[:,"low"].min()
                    high_allday = df_panel_allday.loc[:,"high"].max()
                    sum_plus_num_two = (df_panel_allday.loc[:,'close'] == df_panel_allday.loc[:,'high_limit']).sum()
                    
                    ##获取前一天的收盘价
                    pre_date =  (context.current_dt + timedelta(days = -1)).strftime("%Y-%m-%d")
                    df_panel = get_price(stock_two, count = 1,end_date=pre_date, frequency='daily', fields=['open', 'close','high_limit','money','low',])
                    pre_low_price =df_panel['low'].values
                    pre_close_price =df_panel['close'].values
                    
                    #平均持仓成本
                    cost = context.portfolio.positions[stock_two].avg_cost
                    if current_price < high_allday * 0.92 and day_open_price > pre_close_price:
                        print("1.卖出股票：小于最高价0.869倍"+str(stock_two))
                        order_target(stock_two, 0)
                    elif current_price > cost * 1.3 and sum_plus_num_two < 80:
                        print("2.卖出股票：亏8个点"+str(stock_two))
                        order_target(stock_two, 0)
                    elif day_open_price < pre_close_price * 0.98 and current_price < pre_close_price * 0.93:
                        print("3.卖出股票：1.3以下"+str(stock_two))
                        order_target(stock_two, 0)
    else:
        stock_owner = context.portfolio.positions
        if len(stock_owner) > 0:
            for stock_two in stock_owner:
                if context.portfolio.positions[stock_two].closeable_amount > 0:
                    current_price_list = get_ticks(stock_two,start_dt=None, end_dt=context.current_dt, count=1, fields=['time', 'current', 'high', 'low', 'volume', 'money'])
                    current_price = current_price_list['current'][0]
                    day_open_price = get_current_data()[stock_two].day_open
                    day_high_limit = get_current_data()[stock_two].high_limit 
                    
                    ##获取前一天的收盘价
                    pre_date =  (context.current_dt + timedelta(days = -1)).strftime("%Y-%m-%d")
                    df_panel = get_price(stock_two, count = 1,end_date=pre_date, frequency='daily', fields=['open', 'close','high_limit','money','low',])
                    pre_low_price =df_panel['low'].values
                    pre_close_price =df_panel['close'].values
                    now = context.current_dt
                    zeroToday = now - datetime.timedelta(hours=now.hour, minutes=now.minute, seconds=now.second,microseconds=now.microsecond)
                    lastToday = zeroToday + datetime.timedelta(hours=9, minutes=31, seconds=00)
                    df_panel_allday = get_price(stock_two, start_date=lastToday, end_date=context.current_dt, frequency='minute', fields=['high','low','close','high_limit','money'])
                    low_allday = df_panel_allday.loc[:,"low"].min()
                    high_allday = df_panel_allday.loc[:,"high"].max()
                    sum_plus_num_two = (df_panel_allday.loc[:,'close'] == df_panel_allday.loc[:,'high_limit']).sum()
                    
                    current_price = context.portfolio.positions[stock_two].price #持仓股票的当前价 
                    cost = context.portfolio.positions[stock_two].avg_cost
                    
                    df_panel_three = get_price(stock_two, count = 2, end_date=context.current_dt, frequency='minute', fields=['high','low','close','pre_close','high_limit','money'])
                    
                    df_min_close_three = df_panel_three['close'].iloc[0]
                    df_max_close_three = df_panel_three['close'].iloc[1]
                    
                    
                    if current_price < cost * 0.91:
                        print("6.卖出股票：亏5个点"+str(stock_two))
                        order_target(stock_two, 0)
                    elif day_open_price < pre_close_price * 0.92 and current_price > pre_close_price * 0.97:
                        print("add.高位放量，请走！"+str(day_open_price))
                        order_target(stock_two, 0)
                    elif high_allday > pre_close_price * 1.09 and current_price < day_open_price and day_open_price < day_high_limit * 0.95 and current_price < cost * 1.2:
                        print("8.高位放量，请走！"+str(day_open_price))
                        order_target(stock_two, 0)
                    elif current_price > cost * 1.25 and current_price < day_high_limit * 0.95 and time_sell > sell_day:
                        print("9.挣够25%，高位放量，请走！"+str(day_open_price))
                        order_target(stock_two, 0)
                    elif current_price < high_allday * 0.93 and high_allday > pre_close_price * 1.06 and time_sell > sell_day_10:
                        print("11.挣够25%，高位放量，请走！"+str(day_open_price))
                        order_target(stock_two, 0)
                    elif df_max_close_three < df_min_close_three * 0.95 and current_price < pre_close_price * 0.98:
                        print("11.挣够25%，高位放量，请走！"+str(day_open_price))
                        order_target(stock_two, 0)

def send_micromessage(result_in):
   #调用send_message
   send_message(result_in)

## 选出连续涨停超过3天的，最近一天是阴板的
def before_market_open(context):
    date_now =  (context.current_dt + timedelta(days = -1)).strftime("%Y-%m-%d")#'2021-01-15'#datetime.datetime.now()
    yesterday = (context.current_dt + timedelta(days = -90)).strftime("%Y-%m-%d")
    trade_date = get_trade_days(start_date=yesterday, end_date=date_now, count=None)
    stocks = list(get_all_securities(['stock']).index)
    end_date = trade_date[trade_date.size-1]
    pre_date = trade_date[trade_date.size-3]
    #选出昨天是涨停板的个股
    continuous_price_limit = pick_high_limit(stocks,end_date,pre_date)

filter_st_stock = filter_st(continuous_price_limit)
    templist = filter_stock_by_days(context,filter_st_stock,150)
    print("选出的连扳股票")
    print(templist)
    for stock in templist:
        ##查询昨天的股票是否阴板
        stock_date=trade_date[trade_date.size-2]
        df_panel = get_price(stock, count = 1,end_date=stock_date, frequency='daily', fields=['open', 'close','high_limit','money','low','high','pre_close'])
        df_close = df_panel['close'].values
        df_high = df_panel['high'].values
        df_low = df_panel['low'].values
        df_open = df_panel['open'].values
        df_pre_close = df_panel['pre_close'].values
        df_high_limit = df_panel['high_limit'].values
        df_money = df_panel['money'].values
        #查询昨天的close价格
        df_panel_yes = get_price(stock, count = 1,end_date=end_date, frequency='daily', fields=['open', 'close','high_limit','money','low','high','pre_close'],skip_paused=True)
        df_close_yes = df_panel_yes['close'].values
        df_money_yes = df_panel_yes['money'].values
        # if stock == '600956.XSHG':
        #     print(df_open)
        #     print(df_close)
        #     print(df_high)
        #     print(df_money_yes)
        #     print(df_money * 0.65)
        pre_date_two = trade_date[trade_date.size-8]
        if df_close < df_high_limit * 0.98:
            g.help_stock.append(stock)
    print("被选出的股票为:")
    print(g.help_stock)            
            
##选出打板的股票
def pick_high_limit(stocks,end_date,pre_date):
    df_panel = get_price(stocks, count = 1,end_date=end_date, frequency='daily', fields=['open', 'close','high_limit','money','pre_close','low'])
    df_close = df_panel['close']
    df_open = df_panel['open']
    df_high_limit = df_panel['high_limit']
    df_pre_close = df_panel['pre_close']
    df_low = df_panel['low']
    high_limit_stock = []
    for stock in (stocks):
        _high_limit = (df_high_limit[stock].values)
        _close = (df_close[stock].values)
        _open =  (df_open[stock].values)
        _pre_close = (df_pre_close[stock].values)
        _low = (df_low[stock].values)
        if(stock[0:3] == '300'):
            continue
        if _high_limit == _close and _close > _pre_close * 1.02:
            df_panel_2 = get_price(stock, count = 2,end_date=pre_date, frequency='daily', fields=['open', 'close','high_limit','money','pre_close'],skip_paused=True)
            sum_plus_num_2 = (df_panel_2.loc[:,'close'] == df_panel_2.loc[:,'high_limit']).sum()
            
            df_panel_10 = get_price(stock, count = 10,end_date=pre_date, frequency='daily', fields=['open', 'close','high_limit','money','pre_close'],skip_paused=True)
            sum_plus_num_10 = (df_panel_10.loc[:,'close'] == df_panel_10.loc[:,'high_limit']).sum()
            if sum_plus_num_2 == 2 and sum_plus_num_10 <= 5:
                if _open > _pre_close * 1.04:
                    high_limit_stock.append(stock)
    return high_limit_stock

# def turn_ratio(stock,end_date):    
#     q = query(valuation.day,valuation.code,valuation.turnover_ratio).filter(valuation.code == stock,valuation.day == end_date).limit(1)
#     aa=finance.run_query(q)
#     #print(aa)
#     return aa   #换手率
    
##去除st的股票
def filter_st(codelist):
    current_data = get_current_data()
    codelist = [code for code in codelist if not current_data[code].is_st]
    return codelist

##过滤上市时间不满1080天的股票
def filter_stock_by_days(context, stock_list, days):
    tmpList = []
    for stock in stock_list :
        days_public=(context.current_dt.date() - get_security_info(stock).start_date).days
        if days_public > days:
            tmpList.append(stock)
    return tmpList

## 收盘后运行函数
def after_market_close(context):
    log.info(str('函数运行时间(after_market_close):'+str(context.current_dt.time())))
    #得到当天所有成交记录
    g.help_stock = []
    log.info('一天结束')
    log.info('##############################################################')

```

发布时间: 2025-02-26 20:58:17

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