# 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*120df_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 MA5MA30 :
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)
2025-02-20
