价值投资改进版-6年9.5倍

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/29038
# 标题：价值投资改进版-6年9.5倍
# 作者：叶松

# 请选择 python 2 来进行回测

'''
投资程序：
霍华．罗斯曼强调其投资风格在于为投资大众建立均衡、且以成长为导向的投资组合。选股方式偏好大型股，
管理良好且为领导产业趋势，以及产生实际报酬率的公司；不仅重视公司产生现金的能力，也强调有稳定成长能力的重要。
总市值大于等于50亿美元。
良好的财务结构。
较高的股东权益报酬。
拥有良好且持续的自由现金流量。
稳定持续的营收成长率。
优于比较指数的盈余报酬率。
'''

import pandas as pd
import numpy as np
import jqdata
# 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')
    #策略参数设置
    #操作的股票列表
    g.buy_list = []
    # 最大建仓数量
    g.max_hold_stocknum = 4
    g.num = 0
    ### 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')
    
    # 每月第5个交易日进行操作
    
    # 开盘前运行
    run_monthly(before_market_open,1,time='before_open', reference_security='000300.XSHG') 
    #止盈止损
    run_weekly(stop_loss,5,time='open', reference_security='000300.XSHG') 
    # 开盘时运行
    run_monthly(market_open,1,time='open', reference_security='000300.XSHG')
    
## 开盘前运行函数     
def before_market_open(context):
    if not g.num%2:

#获取满足条件的股票列表
        temp_list = get_stock_list(context)
        log.info('================满足条件的股票有%s只================'%len(temp_list))
        #按市值进行排序
        g.buy_list = get_check_stocks_sort(context,temp_list)
        
    g.num+=1

## 开盘时运行函数
def market_open(context):
    if not g.num%2:
        #卖出不在买入列表中的股票
        sell(context,g.buy_list)
        #买入不在持仓中的股票，按要操作的股票平均资金
        if not judge_More_average('000300.XSHG'):
            buy(context,g.buy_list)
#交易函数 - 买入
def buy(context, buy_lists):
    current_data = get_current_data()
    # 获取最终的 buy_lists 列表
    Num = g.max_hold_stocknum - len(context.portfolio.positions)
    buy_lists = buy_lists[:Num]
    # 买入股票
    if len(buy_lists)>0:
        #分配资金
        cash = context.portfolio.available_cash/(len(buy_lists)*1.1)
        # 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 not judge_More_average(stock) and current_data[stock].last_price*120<cash:
                if not e_5<-0.1:
                    result = order_value(stock,cash)
                    if not result ==None:
                        log.info("买入：%s %s"%(current_data[stock].name,stock))
       
# 交易函数 - 出场
def sell(context, buy_lists):
    current_data = get_current_data()
    # 获取 sell_lists 列表
    hold_stock = context.portfolio.positions.keys()
    for s in hold_stock:
        #卖出不在买入列表中的股票
        if s not in buy_lists:
            order_target_value(s,0)   
            log.info('卖出：',current_data[s].name,s)

#按市值进行排序   
#从大到小
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
    
'''
1.总市值≧市场平均值*1.0。
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）。
3.近四季股东权益报酬率（roe）≧市场平均值。
4.近五年自由现金流量均为正值。（cash_flow.net_operate_cash_flow - cash_flow.net_invest_cash_flow）
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(%)
6.近四季盈余成长率介于8%至50%。(eps比值)
'''
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()]
    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)
    #print 'test'
    #print 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(l1 & 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    
 # 清仓
def sell_clear(context):
    if judge_More_average('000300.XSHG'):
        # 获取 sell_lists 列表
        hold_stock = context.portfolio.positions.keys()
        for s in hold_stock:
            if context.portfolio.positions[s].closeable_amount>0:
                order_target_value(s,0)   
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:
        if context.portfolio.positions[security].closeable_amount>0:
            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<-0.10 :
                result = order_target(security, 0) 
                if not result == None:
                    log.info('个股止损0.10 卖出：',security,current_data[security].name,earn)
            elif e_5<-0.13:
                result = order_target(security, 0)  
                if not result == None:
                    log.info('5天回撤0.10  卖出：',security,current_data[security].name,earn)
            # if index_5<-0.13:
            #     result = order_target(security, 0)  
            #     if not result == None:
            #         log.info('5天大盘跌0.13卖出：',security,current_data[security].name,earn)
	    
```

发布时间: 2025-02-26 20:58:19

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