量化交易策略—鳄鱼法则交易系统股票策略
关于交易系统的介绍和相关概念见上一篇：《量化交易策略—鳄鱼法则交易系统》1.选股
选择沪深300指数成分股；沉睡的鳄鱼（选择连续20交易日处于沉睡状态的股票，建立buy_stock池）；每6个月更新股票池；benchmark沪深300指数。
2.入场
存在有效碎形；有效碎形被突破；AO指标连续5日上行；AC指标连续3日上行；收盘价高于前一日收盘价；按g.amount=总资金/股票池股数×乘数因子3买入。
3.止损&止盈
个股累计收益率（以成本价为基准）超过30%，全部卖出；
个股累计收益率下跌10%，全部卖出；
大盘3日累计收益率下跌3%，全部股票清仓。
4.加仓&减仓
AO连续5日下跌，AC连续3日下跌减仓10%；AO连续5日上行，AC连续3日上行加仓10%。
5.退出
存在有效向下碎形，向下碎形被突破，全部卖出。
源码：t numpy as np
def initialize(context):
g.up_price = {} #向上碎形最高价
g.low_price = {} #向下碎形最低价
g.up_fractal_exists = {} #判断有效向上碎形
g.down_fractal_exists = {} #判断有效向下碎形
g.AO_index = {} #存放连续的AO指标数据
g.cal_AC_index = {} #计算AC指标中转存储
g.AC_index = {} #存放连续的AC指标数据
g.amount = {} #满仓仓位
g.stock = get_index_stocks('000300.XSHG')
g.buy_stock = []
set_benchmark('000300.XSHG')
g.month = context.current_dt.month
run_monthly(select_universe,1,'open')
#重置全局变量
def reset_global():
g.up_price = {} #向上碎形最高价
g.low_price = {} #向下碎形最低价
g.up_fractal_exists = {} #判断有效向上碎形
g.down_fractal_exists = {} #判断有效向下碎形
g.AO_index = {} #存放连续的AO指标数据
g.cal_AC_index = {} #计算AC指标中转存储
g.AC_index = {} #存放连续的AC指标数据
g.amount = {} #满仓仓位
g.buy_stock = []
def initial_stock_global(stock):
g.up_price = 0
g.low_price = 0
g.up_fractal_exists = False
g.down_fractal_exists = False #判断有效向下碎形
g.AO_index = #存放连续的AO指标数据
g.cal_AC_index = #计算AC指标中转存储
g.AC_index = #存放连续的AC指标数据
g.amount = 0 #满仓仓位
#轮换选股后清空持仓
def reset_position(context):
for stock in g.buy_stock:
order_target(stock,0)
log.info("sell %s for reset position"%stock)
#选股
def select_universe(context):
#每三个月操作一次
month = context.current_dt.month
if month%6 != g.month%6:
return
#清空全局变量
reset_position(context)
reset_global()
hist = history(30,'1d','close',g.stock,df = False)
for stock in g.stock:
if is_sleeping_alligator(stock,hist,20):
g.buy_stock.append(stock)
#初始化该股票全局变量
initial_stock_global(stock)
print g.buy_stock
return None
#睡着的鳄鱼
def is_sleeping_alligator(stock,hist,nday):
for i in range(nday):
if is_struggle(stock,hist,i) == False:
return False
return True
#均线纠缠，BRG三线非常接近
def is_struggle(stock,hist,delta):
blue_line = hist[-21-delta:-8-delta].mean()
red_line = hist[-13-delta:-5-delta].mean()
green_line = hist[-8-delta:-3-delta].mean()
if abs(blue_line/red_line-1)<0.02 and abs(red_line/green_line-1)<0.02:
return True
else:
return False
#判断 向上 或 向下 碎形
def is_fractal(stock,direction):
hist = history(5,'1d',direction,,df = False)
if direction == 'high'\
and hist > hist\
and hist > hist\
and hist > hist\
and hist > hist:
g.up_price = hist
return True
elif direction == 'low'\
and hist < hist\
and hist < hist\
and hist < hist\
and hist < hist:
g.low_price = hist
return True
return False
#通过比较碎形与红线位置，判断碎形是否有效
def is_effective_fractal(stock, direction):
if is_fractal(stock,direction):
hist = history(13,'1d','close',,df = False)
red_line = hist[:-5].mean()
close_price = hist[-1]
if direction == 'high':
if close_price > red_line:
g.up_fractal_exists = True
else:
g.up_fractal_exists = False
elif direction == 'low':
if close_price < red_line:
g.down_fractal_exists = True
else:
g.down_fractal_exists = False
#N日内最高价格的N日线
def nday_high_point(stock,n):
hist = history(2*n,'1d','high',,df = False)
high_point = []
for i in range(n):
high_point.append(max(hist[-5-i:-1-i]))
return np.array(high_point).mean()
#N日内最低价格的N日线
def nday_low_point(stock,n):
hist = history(2*n,'1d','low',,df = False)
low_point = []
for i in range(n):
low_point.append(max(hist[-5-i:-1-i]))
return np.array(low_point).mean()
#AO=5日内（最高-最低）/2的5日移动平均-34日内（最高-最低）/2的34日移动平均
def AO_index(stock):
g.AO_index.append(nday_high_point(stock,5)/2 + nday_low_point(stock,5)/2\
- nday_high_point(stock,34)/2 - nday_low_point(stock,34)/2)
return None
#AO-AO的5日平均值的5日平均
def AC_index(stock):
AO_index(stock)
if len(g.AO_index) >= 5:
g.cal_AC_index.append(g.AO_index[-1] - np.array(g.AO_index[-5:]).mean())
if len(g.cal_AC_index) >=5:
g.AC_index.append(np.array(g.cal_AC_index[-5:]).mean())
#判断序列n日上行
def is_up_going(alist,n):
if len(alist) < n:
return False
for i in range(n-1):
if alist[-(1+i)] <= alist[-(2+i)]:
return False
return True
#判断序列n日下行
def is_down_going(alist,n):
if len(alist) < n:
return False
for i in range(n-1):
if alist[-(1+i)] >= alist[-(2+i)]:
return False
return True
#碎形被突破
def active_fractal(stock,direction):
close_price = history(1,'1d','close',,df=False)
if direction == 'up' and close_price > g.up_price:
return True
elif direction == 'down' and close_price < g.low_price:
return True
return False
#进场，初始仓位
def set_initial_position(stock,context):
close_price = history(1,'1d','close',,df=False)
g.amount = context.portfolio.cash/close_price/len(g.buy_stock)*3
order(stock, g.amount)
log.info("buying %s 股数为 %s"%(stock,g.amount))
g.down_fractal_exists = False
#卖出
def sell_all_stock(stock,context):
order_target(stock,0)
log.info("selling %s"%stock)
g.up_fractal_exists = False
#加仓
def adjust_position(stock,context,position):
order(stock,g.amount*position)
log.info("adjust position buying %s 股数为 %s"%(stock,g.amount*position))
# 计算股票前n日收益率
def security_return(days,security_code):
hist1 = attribute_history(security_code, days + 1, '1d', 'close',df=False)
security_returns = (hist1['close'][-1]-hist1['close'])/hist1['close']
return security_returns
# 止损，根据前n日收益率
def conduct_nday_stoploss(context,security_code,days,bench):
ifsecurity_return(days,security_code)<= bench:
for stock in g.buy_stock:
order_target_value(stock,0)
log.info("Sell %s for stoploss" %stock)
return True
else:
return False
# 计算股票累计收益率（从建仓至今）
def security_accumulate_return(context,data,stock):
current_price = data.price
cost = context.portfolio.positions.avg_cost
if cost != 0:
return (current_price-cost)/cost
else:
return None
# 个股止损，根据累计收益
def conduct_accumulate_stoploss(context,data,stock,bench):
if security_accumulate_return(context,data,stock) != None\
and security_accumulate_return(context,data,stock) < bench:
order_target_value(stock,0)
log.info("Sell %s for stoploss" %stock)
return True
else:
return False
# 个股止盈，根据累计收益
def conduct_accumulate_stopwin(context,data,stock,bench):
if security_accumulate_return(context,data,stock) != None\
and security_accumulate_return(context,data,stock) > bench:
order_target_value(stock,0)
log.info("Sell %s for stopwin" %stock)
return True
else:
return False
def handle_data(context,data):
#大盘止损
if conduct_nday_stoploss(context,'000300.XSHG',3,-0.03):
return
for stock in g.buy_stock:
#个股止损
if conduct_accumulate_stopwin(context,data,stock,0.3)\
or conduct_accumulate_stoploss(context,data,stock,-0.1):
return
#计算AO，AC指标
AC_index(stock)
#空仓时，寻找机会入场
if context.portfolio.positions.amount == 0:
#计算向上碎形
is_effective_fractal(stock,'high')
#有效向上碎形存在，并被突破，买入
if g.up_fractal_exists and active_fractal(stock,'up'):
close_price = history(5, '1d', 'close', ,df = False)
if is_up_going(g.AO_index,5)\
and is_up_going(g.AC_index,3)\
and is_up_going(close_price,2):
set_initial_position(stock,context)
#有持仓时，加仓或离场
else:
#计算向下碎形
is_effective_fractal(stock,'low')
#出场条件1：有效向下碎形存在，并被突破，卖出
if g.down_fractal_exists and active_fractal(stock,'down'):
sell_all_stock(stock,context)
return
#出场条件2：
#加仓10%：AO，AC同时5日上行，且收盘价走高
# if is_up_going(g.AO_index,5)\
# and is_up_going(g.AC_index,3)\
# and is_up_going(close_price,2):
# adjust_position(stock,context,0.1)
# #减仓10%：AO，AC同时3日下行，且收盘价走低
# if is_down_going(g.AO_index,5)\
# and is_down_going(g.AC_index,3)\
# and is_down_going(close_price,2):
# adjust_position(stock,context,-0.1)
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