价值投资策略-大盘择时

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/29173
# 标题：价值投资策略-大盘择时
# 作者：叶松

# 克隆自聚宽文章：https://www.joinquant.com/post/25496
# 标题：收益狂飙，年化收益100%，11年1700倍，绝无未来函数
# 作者：jqz1226

# 请选择 python 2 来进行回测

# 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
    g.base = '000300.XSHG'
    set_benchmark(g.base)
    # 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
    # 中小板 "399101.XSHE"
    g.security_universe_index = "399101.XSHE"
    g.buy_stock_count = 5

g.risk_control = RiskControl(g.base)

### 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')
    # before_market_open(context)
    ## 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_monthly(before_market_open,1,time='before_open', reference_security='000300.XSHG')
    # 定时运行
    run_weekly(trade,1, time='14:40', reference_security=g.base)
    run_weekly(stop_loss,3,time='open', reference_security='000300.XSHG')
    #止盈止损
    run_weekly(stop_loss,3,time='open', reference_security='000300.XSHG')

## 开盘前运行函数     
def before_market_open(context):

#获取满足条件的股票列表
    g.stock_list = get_stock_list(context)
    check_out_lists = g.stock_list
    # 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_paused_stock(check_out_lists)
    # 取需要的只数
    g.stock_list = get_check_stocks_sort(context,check_out_lists)
        
## 开盘时运行函数
def trade(context):
    # 买卖
    adjust_position(context, g.stock_list)

log.info('__'*15)

# 交易
def adjust_position(context, buy_stocks):
    # 交易函数 - 出场
    current_data = get_current_data()
    # 获取 sell_lists 列表
    hold_stock = context.portfolio.positions.keys()
    for stock in hold_stock:
        #卖出不在买入列表中的股票
        if stock not in buy_stocks:
            order_target_value(stock,0)   
            log.info('卖出：',current_data[stock].name,stock)
    #买入
    if check_for_benchmark(context):
        Num = g.buy_stock_count - len(context.portfolio.positions)
        buy_lists = buy_stocks[:Num]
        if len(buy_lists) > 0:
            #分配资金
            cash = context.portfolio.available_cash / (len(buy_lists))
            # 进行买入操作
            for stock in buy_lists:
                close_data = attribute_history(stock, 5, '1d', ['close'])
                e_5 = (close_data['close'][-1] -
                       close_data['close'][0]) / close_data['close'][0]
                if current_data[
                        stock].last_price * 120 < cash and not judge_More_average(
                            stock):
                    if not e_5 < -0.1 and stock in g.stock_list:
                        result = order_value(stock, cash)
                        if not result == None:
                            log.info("买入：%s %s" %
                                     (current_data[stock].name, stock))
#止盈止损
def stop_loss(context):
    current_data = get_current_data()
    close_index = attribute_history('000300.XSHG', 5, '1d', ['close'])
    index_5 = (close_index['close'][-1]-close_index['close'][0])/close_index['close'][0]
    
    for security in context.portfolio.positions:
        closeable_amount= context.portfolio.positions[security].closeable_amount
        if closeable_amount:
            close_data = attribute_history(security, 5, '1d', ['close'])
            e_5 = (close_data['close'][-1]-close_data['close'][0])/close_data['close'][0]
            earn = (current_data[security].last_price-context.portfolio.positions[security].avg_cost)/context.portfolio.positions[security].avg_cost
            if earn>.35:
                result = order_target(security, 0)  
                if not result == None:
                    log.info('止赢：%s %s %.2f'%(current_data[security].name,security,earn))
            if e_5<-0.1:
                result = order_target(security, 0)  
                if not result == None:
                    log.info('回撤：%s %s %.2f'%(current_data[security].name,security,earn))
            
# 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\跌停的股票
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
        or last_prices[stock][-1] >= current_data[stock].low_limit
    ]

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):

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 = 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
def get_check_stocks_sort(context,check_out_lists):
    df = get_fundamentals(query(valuation.circulating_cap,valuation.pe_ratio,valuation.code).filter(valuation.code.in_(check_out_lists)),date=context.previous_date)
    #asc值为0，从大到小
    df = df.sort('circulating_cap',ascending=0)
    out_lists = list(df['code'].values)
    return out_lists
def get_stock_list(context):
    temp_list = list(get_all_securities(types=['stock']).index)    
    #剔除停牌股
    all_data = get_current_data()
    temp_list = [stock for stock in temp_list if not all_data[stock].paused]
    #获取多期财务数据
    panel = get_data(temp_list,4)
    #1.总市值≧市场平均值*1.0。
    df_mkt = panel.loc[['circulating_market_cap'],3,:]
    df_mkt = df_mkt[df_mkt['circulating_market_cap']>df_mkt['circulating_market_cap'].mean()*1.2]
    l1 = set(df_mkt.index)
    
    #2.最近一季流动比率≧市场平均值（流动资产合计/流动负债合计）。
    df_cr = panel.loc[['total_current_assets','total_current_liability'],3,:]
    #替换零的数值
    df_cr = df_cr[df_cr['total_current_liability'] != 0]
    df_cr['cr'] = df_cr['total_current_assets']/df_cr['total_current_liability']
    df_cr_temp = df_cr[df_cr['cr']>df_cr['cr'].mean()]
    l2 = set(df_cr_temp.index)

#3.近四季股东权益报酬率（roe）≧市场平均值。
    l3 = {}
    for i in range(4):
        roe_mean = panel.loc['roe',i,:].mean()
        df_3 = panel.iloc[:,i,:]
        df_temp_3 = df_3[df_3['roe']>roe_mean]
        if i == 0:    
            l3 = set(df_temp_3.index)
        else:
            l_temp = df_temp_3.index
            l3 = l3 & set(l_temp)
    l3 = set(l3)

#4.近3年自由现金流量均为正值。（cash_flow.net_operate_cash_flow - cash_flow.net_invest_cash_flow）
    y = context.current_dt.year
    l4 = {}
    for i in range(1,4):
        df = get_fundamentals(query(cash_flow.code,cash_flow.statDate,cash_flow.net_operate_cash_flow , \
                                    cash_flow.net_invest_cash_flow),statDate=str(y-i))
        if len(df) != 0:
            df['FCF'] = df['net_operate_cash_flow']-df['net_invest_cash_flow']
            df = df[df['FCF']>1000000]
            l_temp = df['code'].values
            if len(l4) != 0:
                l4 = set(l4) & set(l_temp)
            l4 = l_temp
        else:
            continue
    l4 = set(l4)
    #5.近四季营收成长率介于6%至30%（）。    'IRYOY':indicator.inc_revenue_year_on_year, # 营业收入同比增长率(%)
    l5 = {}
    for i in range(4):
        df_5 = panel.iloc[:,i,:]
        df_temp_5 = df_5[(df_5['inc_revenue_year_on_year']>15) & (df_5['inc_revenue_year_on_year']<50)]
        if i == 0:    
            l5 = set(df_temp_5.index)
        else:
            l_temp = df_temp_5.index
            l5 = l5 & set(l_temp)
    l5 = set(l5)
    
    #6.近四季盈余成长率介于8%至50%。(eps比值)
    l6 = {}
    for i in range(4):
        df_6 = panel.iloc[:,i,:]
        df_temp = df_6[(df_6['eps']>0.08) & (df_6['eps']<0.5)]
        if i == 0:    
            l6 = set(df_temp.index)
        else:
            l_temp = df_temp.index
            l6 = l6 & set(l_temp)
    l6 = set(l6)
    
    return list(l2 &l3 &l4&l5&l6)
    
#去极值（分位数法）  
def winsorize(se):
    q = se.quantile([0.025, 0.975])
    if isinstance(q, pd.Series) and len(q) == 2:
        se[se < q.iloc[0]] = q.iloc[0]
        se[se > q.iloc[1]] = q.iloc[1]
    return se
    
#获取多期财务数据内容
def get_data(pool, periods):
    q = query(valuation.code, income.statDate, income.pubDate).filter(valuation.code.in_(pool))
    df = get_fundamentals(q)
    df.index = df.code
    stat_dates = set(df.statDate)
    stat_date_stocks = { sd:[stock for stock in df.index if df['statDate'][stock]==sd] for sd in stat_dates }

def quarter_push(quarter):
        if quarter[-1]!='1':
            return quarter[:-1]+str(int(quarter[-1])-1)
        else:
            return str(int(quarter[:4])-1)+'q4'

q = query(valuation.code,valuation.code,valuation.circulating_market_cap,balance.total_current_assets,balance.total_current_liability,\
    indicator.roe,cash_flow.net_operate_cash_flow,cash_flow.net_invest_cash_flow,indicator.inc_revenue_year_on_year,indicator.eps
              )

stat_date_panels = { sd:None for sd in stat_dates }

for sd in stat_dates:
        quarters = [sd[:4]+'q'+str(int(sd[5:7])/3)]
        for i in range(periods-1):
            quarters.append(quarter_push(quarters[-1]))
        nq = q.filter(valuation.code.in_(stat_date_stocks[sd]))
        pre_panel = { quarter:get_fundamentals(nq, statDate = quarter) for quarter in quarters }
        for thing in pre_panel.values():
            thing.index = thing.code.values
        panel = pd.Panel(pre_panel)
        panel.items = range(len(quarters))
        stat_date_panels[sd] = panel.transpose(2,0,1)

final = pd.concat(stat_date_panels.values(), axis=2)

return final.dropna(axis=2)
#均线
def judge_More_average(security):
    close_data = attribute_history(security, 5, '1d', ['close'])
    MA5 = close_data['close'].mean()
    close_data = attribute_history(security, 10, '1d', ['close'])
    MA10 = close_data['close'].mean()
    close_data = attribute_history(security, 15, '1d', ['close'])
    MA20 = close_data['close'].mean()
    close_data = attribute_history(security, 25, '1d', ['close'])
    MA30 = close_data['close'].mean()
    if MA5 < MA20 and MA10 < MA30:  #and MA20>MA30 :
        return True
    return False

## 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('————'*15)

```

发布时间: 2025-02-26 20:58:19

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