高收益低回撤的小市值策略

策略作者: 水滴

```python
# 风险及免责提示：该策略由聚宽用户分享，仅供学习交流使用。
# 原文一般包含策略说明，如有疑问建议到原文和作者交流讨论。
# 克隆自聚宽文章：https://www.joinquant.com/post/28824
# 标题：高收益低回撤的小市值策略
# 作者：曹经纬

# 导入函数库
from jqdata import *
from kuanke.wizard 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')
    
    # 股票池
    g.security_universe_index = "399101.XSHE"  # 中小板
    g.buy_stock_count = 5
    
    g.risk_control = RiskControl('000300.XSHG')
    
    ### 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）
    # 定时运行
    run_daily(my_trade, time='14:40', reference_security='000300.XSHG')
    # 收盘后运行
    run_daily(after_market_close, time='after_close', reference_security='000300.XSHG')

## 开盘时运行函数
def my_trade(context):
    # 选取中小板中市值最小的若干只
    check_out_lists = get_index_stocks(g.security_universe_index)
    q = query(valuation.code).filter(
        valuation.circulating_market_cap < 100
    ).filter(
        valuation.code.in_(check_out_lists)
    ).order_by(
        valuation.circulating_market_cap.asc()
    ).limit(
        int(g.buy_stock_count * 2.0)
    )
    
    check_out_lists = list(get_fundamentals(q).code)
    # 过滤: 三停（停牌、涨停、跌停）及st,*st,退市
    check_out_lists = filter_st_stock(check_out_lists)
    check_out_lists = filter_limitup_stock(context, check_out_lists)
    check_out_lists = filter_limitdown_stock(context, check_out_lists)
    check_out_lists = filter_paused_stock(check_out_lists)
    # 取需要的只数
    check_out_lists = check_out_lists[:g.buy_stock_count]
    # 买卖
    adjust_position(context, check_out_lists)

## 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
    trades = get_trades()
    for _trade in trades.values():
        log.info('成交记录：' + str(_trade))
    log.info('一天结束')
    log.info('##############################################################')

# 自定义下单
# 根据Joinquant文档，当前报单函数都是阻塞执行，报单函数（如order_target_value）返回即表示报单完成
# 报单成功返回报单（不代表一定会成交），否则返回None
def order_target_value_(security, value):
    if value == 0:
        log.debug("Selling out %s" % (security))
    else:
        log.debug("Order %s to value %f" % (security, value))
    
    # 如果股票停牌，创建报单会失败，order_target_value 返回None
    # 如果股票涨跌停，创建报单会成功，order_target_value 返回Order，但是报单会取消
    # 部成部撤的报单，聚宽状态是已撤，此时成交量>0，可通过成交量判断是否有成交
    return order_target_value(security, value)

# 开仓，买入指定价值的证券
# 报单成功并成交（包括全部成交或部分成交，此时成交量大于0），返回True
# 报单失败或者报单成功但被取消（此时成交量等于0），返回False
def open_position(security, value):
    order = order_target_value_(security, value)
    if order != None and order.filled > 0:
        return True
    return False

# 平仓，卖出指定持仓
# 平仓成功并全部成交，返回True
# 报单失败或者报单成功但被取消（此时成交量等于0），或者报单非全部成交，返回False
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

# 交易
def adjust_position(context, buy_stocks):
    if not check_for_benchmark(context):
        for stock in context.portfolio.positions:
            position = context.portfolio.positions[stock]
            close_position(position)
        return
    
    for stock in context.portfolio.positions:
        if stock not in buy_stocks:
            log.info("stock [%s] in position is not buyable" % (stock))
            position = context.portfolio.positions[stock]
            close_position(position)
        else:
            log.info("stock [%s] is already in position" % (stock))
    
    # 根据股票数量分仓
    # 此处只根据可用金额平均分配购买，不能保证每个仓位平均分配
    position_count = len(context.portfolio.positions)
    if g.buy_stock_count > position_count:
        value = context.portfolio.cash / (g.buy_stock_count - position_count * 0.33)
        #value = context.portfolio.cash / g.buy_stock_count
        
        for stock in buy_stocks:
            if context.portfolio.positions[stock].total_amount == 0:
                if open_position(stock, value):
                    if len(context.portfolio.positions) == g.buy_stock_count:
                        break

# 过滤停牌股票
def filter_paused_stock(stock_list):
    current_data = get_current_data()
    return [stock for stock in stock_list if not current_data[stock].paused]

# 过滤ST及其他具有退市标签的股票
def filter_st_stock(stock_list):
    current_data = get_current_data()
    return [stock for stock in stock_list
            if not current_data[stock].is_st
            and 'ST' not in current_data[stock].name
            and '*' not in current_data[stock].name
            and '退' not in current_data[stock].name]

# 过滤涨停的股票
def filter_limitup_stock(context, stock_list):
    last_prices = history(1, unit='1m', field='close', security_list=stock_list)
    current_data = get_current_data()
    
    # 已存在于持仓的股票即使涨停也不过滤，避免此股票再次可买，但因被过滤而导致选择别的股票
    return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
            or last_prices[stock][-1] < current_data[stock].high_limit]

# return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
#    or last_prices[stock][-1] < current_data[stock].high_limit * 0.995]

# 过滤跌停的股票
def filter_limitdown_stock(context, stock_list):
    last_prices = history(1, unit='1m', field='close', security_list=stock_list)
    current_data = get_current_data()
    
    return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
            or last_prices[stock][-1] > current_data[stock].low_limit]
            
            
#自定义函数
def check_for_benchmark(context):
    #security = '000300.XSHG'
    #could_trade1 = RSI_judge_qujian(security, (50,99), 15)
    #could_trade2 = RSI_judge_qujian(security, (50,99), 90)
    #could_trade3 = RSI_judge_qujian(security, (47,99), 60)
    #return (could_trade1 and could_trade2) or could_trade3
    
    return g.risk_control.check_for_benchmark(context)

#============================================================================================      
class RiskControlStatus(Enum):
    RISK_WARNING = 1
    RISK_NORMAL = 2
    
class RiskControl(object):
    def __init__(self, symbol):
        self.symbol = symbol
        self.status = RiskControlStatus.RISK_NORMAL
    
    def check_for_ma_rate(self, period, ma_rate_min, ma_rate_max, show_ma_rate):
        ma_rate = self.compute_ma_rate(period, show_ma_rate) 
        return (ma_rate_min < ma_rate <  ma_rate_max)
        
    def compute_ma_rate(self, period, show_ma_rate):
        #hst = attribute_history(self.symbol, period, '1d', ['close'])
        hst = get_bars(self.symbol, period, '1d', ['close'])
        close_list = hst['close']
        if (len(close_list) == 0):
            return -1.0
 
        if (math.isnan(close_list[0]) or math.isnan(close_list[-1])):
            return -1.0
            
        period = min(period, len(close_list))
        if (period < 2):
            return -1.0
            
        #ma = close_list.sum() / len(close_list)
        ma = talib.MA(close_list, timeperiod=period)[-1] 
        ma_rate = hst['close'][-1] / ma
        if (show_ma_rate):
            record(mar=ma_rate)
            
        return ma_rate
        
    def check_for_rsi(self, period, rsi_min, rsi_max, show_rsi):
        hst = attribute_history(self.symbol, period + 1, '1d', ['close'])
        close = [float(x) for x in hst['close']]
        if (math.isnan(close[0]) or math.isnan(close[-1])):
            return False
        
        rsi = talib.RSI(np.array(close), timeperiod=period)[-1]  
        if (show_rsi):
            record(RSI=max(0,(rsi-50)))  
 
        return (rsi_min < rsi < rsi_max)
    
    def check_for_benchmark_v1(self, context):
        could_trade_ma_rate = self.check_for_ma_rate(10000, 0.75, 1.50, True)
        
        could_trade = False
        if (could_trade_ma_rate):
            could_trade = self.check_for_rsi(90, 35, 99, False)
        else:
            could_trade = self.check_for_rsi(15, 50, 70, False)
            
        return could_trade

def check_for_benchmark(self, context):
        ma_rate = self.compute_ma_rate(1000, False)
        if (ma_rate <= 0.0):
            return False
        
        if (self.status == RiskControlStatus.RISK_NORMAL):
            if ((ma_rate > 2.5) or (ma_rate < 0.30)):
                self.status = RiskControlStatus.RISK_WARNING
        elif (self.status == RiskControlStatus.RISK_WARNING):
            if (0.35 <= ma_rate <= 0.7):
                self.status = RiskControlStatus.RISK_NORMAL
        
        could_trade = False
       
        if (self.status == RiskControlStatus.RISK_WARNING):       
        #if (self.status == RiskControlStatus.RISK_WARNING) or not(self.check_for_usa_intrest_rate(context)):
            #could_trade = self.check_for_rsi(15, 55, 90, False) and self.check_for_rsi(90, 50, 90, False)
            could_trade = self.check_for_rsi(60, 47, 99, False)
            #record(status=2.5)
        elif (self.status == RiskControlStatus.RISK_NORMAL):
            #could_trade = self.check_for_rsi(60, 50, 99, False)
            could_trade = True
            #record(status=0.7)
            
        
        return could_trade

```

发布时间: 2025-02-26 20:58:19

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水滴

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