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# 克隆自聚宽文章：https://www.joinquant.com/post/28893
# 标题：股债波动平衡
# 作者：囚徒
# 导入函数库
from jqdata import *
# 初始化函数，设定基准等等
def initialize(context):
# 设定基准
set_benchmark('000300.XSHG')
# 开启动态复权模式(真实价格)
set_option("use_real_price", True)
log.set_level('order', 'error')
log.set_level('strategy','info')
### 场外基金相关设定 ###
# 设置账户类型: 场外基金账户
#set_subportfolios([SubPortfolioConfig(context.portfolio.cash, 'open_fund')])
# 设置赎回到账日
#set_redeem_latency(3, 'QDII_fund')
#set_order_cost(OrderCost(open_tax=0, close_tax=0, open_commission=0.00006, close_commission=0.00006, close_today_commission=0, min_commission=0.0), type='fund')
## 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）
# 开盘时运行
run_daily(market_open, time='open', reference_security='000300.XSHG')
# 收盘后运行
run_daily(after_market_close, time='after_close', reference_security='000300.XSHG')
g.buy_info = df = pd.DataFrame({'cash':[1000.0]},index=[context.current_dt.date()])
g.ratio_y = 1.0
# 波动率
def get_volatility(df,down=False):
# 无数据返回Nan
if len(df)==0:
return float(np.NaN)
#前一日收盘价
df['pre']=df.shift(1)
# 清除无效数据
df=df.dropna()
# 日收益率(当日收盘价/前一日收盘价，然后取对数)
df['day_volatility']=np.log(df.iloc[:,0]/df['pre'])
if down:
#avg = df[df.day_volatility<0.0].day_volatility.mean() df.loc[df.day_volatility>0.0,"day_volatility"] = 0
vol = df['day_volatility'].std()
mean = df['day_volatility'].mean()
df.loc[df.day_volatility>mean + 3*vol ,"day_volatility"] = mean + 3*vol
df.loc[df.day_volatility 0.05
def rebalance(context):
position = g.position
sells = []
for s in position.index.values:
p = position.position[s]
r = p
if s in context.portfolio.positions:
r = context.portfolio.positions[s].value / context.portfolio.total_value
if r > p:
order_target_value(s,context.portfolio.total_value*p)
sells.append(s)
for s in position.index.values:
p = position.position[s]
if s not in sells:
order_target_value(s,context.portfolio.total_value*p)
def market_open(context):
weekday = context.current_dt.isoweekday()
if weekday != 5 and len(context.portfolio.positions) > 0:
return
#富国天惠 | 兴全轻资 | 国债 | 纳指 | 标普 | 黄金 | 消费 | 医
stocks = ['161005.XSHE','163412.XSHE','511010.XSHG','513100.XSHG','513500.XSHG','518880.XSHG','159928.XSHE','512010.XSHG']
weights = [ 15.0,20.0,2.0,15.0,7.5,4.0,25.0,20.0 ]
waves = []
df = history(40, unit='1d', field='close', security_list=stocks, df=True, skip_paused=True, fq='post')
for s in stocks:
waves.append( get_volatility(df[[s]],False) )
g.position = pd.DataFrame(data={"weight":weights,"wave":waves},index=stocks)
g.position["position"] = g.position.weight / (g.position.wave ** 2)
g.position.position = g.position.position / g.position.position.sum()
print(g.position)
if context.portfolio.available_cash > 200.0 or need_balance(context):
rebalance(context)
#log.info(o)
# 赎回基金
'''elif weekday == 3:
o1 = redeem(s, 4000)
log.info(o1)
elif weekday == 4:
o2 = redeem(s, 3000)
log.info(o2)
'''
def compt_df_ratio(df,r,today):
total = 0.0
for d in df.index:
days = (today - d).days
total += df.cash[d] * power(r,days*1.0/365)
return total
def compt_ratio_y(total_value,r,today):
iter = 0.01
mincash = 1.0
min_r = max(r - 1.0,0.0)
max_r = r + 1.0
tv = compt_df_ratio(g.buy_info,r,today)
if tv > total_value:
max_r = r
min_r = max(r - 1.0,0)
while True:
tv = compt_df_ratio(g.buy_info,min_r,today)
#print("%.2f %.2f %.2f %.2f %.2f"%(total_value,tv,r,min_r,max_r))
if tv > total_value:
max_r = min_r
min_r = max(min_r - 1.0,0)
else:
break;
else:
min_r = r
max_r = r + 1.0
while True:
tv = compt_df_ratio(g.buy_info,max_r,today)
#print("%.2f %.2f %.2f %.2f %.2f"%(total_value,tv,r,min_r,max_r))
if tv < total_value:
min_r = max_r
max_r += 1.0
else:
break;
while True:
r = (min_r + max_r)/2.0
tv = compt_df_ratio(g.buy_info,r,today)
#print("%.2f %.2f %.2f %.2f %.2f"%(total_value,tv,r*100,min_r*100,max_r*100))
if abs( tv - total_value ) < mincash:
break;
if tv < total_value: min_r = r else: max_r = r return r ## 收盘后运行函数 def after_market_close(context): return # 查看融资融券账户相关相关信息(更多请见API-对象-SubPortfolio) days = (context.current_dt.date() - context.run_params.start_date).days p = context.portfolio.subportfolios[0] if days > 365:
g.ratio_y = compt_ratio_y(p.total_value,g.ratio_y,context.current_dt.date())
print("total_value %.2f total_incash %.2f ratio %.2f ratio_y : %.3f%%"%
(p.total_value,p.inout_cash,p.total_value/p.inout_cash*100,g.ratio_y*100))
record(ratio = g.ratio_y*100)
record(e=p.total_value/p.inout_cash*100)
'''record(total = p.total_value/p.inout_cash)
log.info('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -')
log.info('查看场外基金账户相关相关信息(更多请见API-对象-SubPortfolio)：')
log.info('场外基金持有份额：',p.long_positions['000311.OF'].closeable_amount)
log.info('账户所属类型：',p.type)
log.info('##############################################################')
'''
2025-02-20
