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ma_direction.py
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ma_direction.py
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#适于优矿
#当ma_short 向上 且 ma_long向上时买入； ma_short 向下 ma_long向下卖出
#加入止损策略 使用StopRate_on来控制开关
#加入分步建仓策略，BuyStep=0时 使用单次建仓；
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lib.mylib as mylib
from pandas import Series,DataFrame
from datetime import timedelta,date
sns.set_style('white')
start = '20120101' # 回测起始时间
end = '20160114' # 回测结束时间
benchmark = 'HS300' # 策略参考标准
#benchmark = '399006.ZICN'
universe = ['159915.XSHE'] # 证券池，支持股票和基金
#universe = ['510300.XSHG']
#universe = ['510050.XSHG']
commission = Commission(0.0,0.0)
capital_base = 100000 # 起始资金
freq = 'd' # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测
refresh_rate = 1 # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟
#self defined data
#最优参数，
#(3 20) (5 21)
window_short = 3
window_long = 20
#switch
bear_rush_on = 0
StopLoss_on = 1
BuyStep = 0 #第一次买入1/3；第二次买入1/3,当价格比第一次买入价格涨BuyStep%买入；当价格再涨BuyStep%，第三次买入满仓；BuyStep=0 采用单次建仓
#BuyStep_on = 1 #BuyStep=0 采用单次建仓
universe_tuple = tuple(universe)
global high_price,now_value,flag
global approximationAmount,b1st_price,avg_price
high_price =0
flag = 1
approximationAmount=0
b1st_price=0
stop_rate=90
def initialize(account): # 初始化虚拟账户状态
pass
def handle_data(account): # 每个交易日的买入卖出指令
hist = account.get_attribute_history('closePrice',window_long)
etf = universe_tuple[0]
today = account.current_date
preday = today + timedelta(days = -100)
yestoday = today + timedelta(days = -1)
global high_price,now_value,flag
global approximationAmount,b1st_price,avg_price
#计算指数
cIndex = DataAPI.MktIdxdGet(ticker='399006',beginDate=preday,endDate=yestoday,field=["tradeDate","closeIndex"],pandas="1")
maIndexShort = np.round(pd.rolling_mean(cIndex['closeIndex'],window=window_short),2)
maIndexLong = np.round(pd.rolling_mean(cIndex['closeIndex'],window=window_long),2)
today_index = cIndex['closeIndex'].values[-1]
yestoday_index = cIndex['closeIndex'].values[-2]
filter_std = np.std(cIndex['closeIndex'][-window_long:],axis=0) #not used
curPosition = account.position.secpos.get(etf, 0)
#计算价格
#print today_index.values[-1]
today_price = hist[universe_tuple[0]][-1]
#yestoday_price = hist[universe_tuple[0]][-2]
bear_loss = mylib.bear_rush(today_index, yestoday_index) #计算跌幅，大于5%时卖出
#计算止损&止盈
if high_price < today_price :
high_price = today_price
profit_level = high_price/avg_price-1
stopLoss_price = mylib.stop_level(profit_level,high_price)
if maIndexShort.values[-1] > maIndexShort.values[-2] and maIndexLong.values[-1] > maIndexLong.values[-2] and flag==1:
now_value = account.position.secpos.get(etf, 0)*hist[universe_tuple[0]][-1]
if curPosition == 0 :
if BuyStep != 0 :
approximationAmount = int(account.cash / (today_price*1.02)/1000.0) * 300
else :
approximationAmount = int(account.cash / (today_price*1.02)/100.0) * 100
b1st_price = today_price
avg_price = today_price
order(universe_tuple[0],approximationAmount)
elif curPosition == approximationAmount and today_price > (1+BuyStep/100.0)*b1st_price and BuyStep !=0 :
order(universe_tuple[0],approximationAmount)
elif curPosition == 2*approximationAmount and today_price > (1+2*BuyStep/100.0)*b1st_price and BuyStep !=0 :
lastAmount = int(account.cash / (today_price*1.02)/100.0) * 100
high_value = (lastAmount+account.position.secpos.get(etf, 0))*today_price
order(etf,lastAmount)
elif StopLoss_on ==1 and curPosition !=0:
#print curPosition
#分级移动止盈，分级的效果不明显，统一设置成交易后最高价的0.9
if (today_price < stopLoss_price and flag!=0) or (bear_loss > 0.05 and bear_rush_on ==1):
flag =0
high_price = 0;
order_to(etf,0)
elif maIndexShort.values[-1] < maIndexShort.values[-2] and maIndexLong.values[-1] < maIndexLong.values[-2] :
flag=1
if account.position.secpos.get(etf, 0) > 0:
order_to(etf,0)
else :
if (StopLoss_on ==1 and curPosition !=0) and ((today_price < stopLoss_price and flag!=0) or (bear_loss > 0.05 and bear_rush_on ==1)):
flag =0
high_price = 0;
order_to(etf,0)
# for paramenter opt
print 'window windows annualized_return sharpe max_drawdown BuyStep stop_rate StopRate_on '
for window_long in range(19,31,1):
for window_short in range(2,15,1):
for BuyStep in range(0,6,1):
#for stop_rate in range(1,20,1):
sim_params = quartz.sim_condition.env.SimulationParameters(start, end, benchmark, universe, capital_base)
strategy = quartz.sim_condition.strategy.TradingStrategy(initialize, handle_data)
idxmap_all, data_all = quartz.sim_condition.data_generator.get_daily_data(sim_params)
bt_test, acct = quartz.quick_backtest(sim_params, strategy, idxmap_all, data_all)
perf = quartz.perf_parse(bt_test, acct)
print '{0:2d} {1:2d} {2:>7.4}% {3:>7.4} {4:>7.4} {5:4d} {6:4d} {7:4d}'.format(window_long,window_short,perf['annualized_return']*100, perf['sharpe'], perf['max_drawdown'],BuyStep,stop_rate,StopRate_on)
#mylib
def bear_rush(today_index,yestoday_index) :
bear_rush = 1-today_index/yestoday_index
return bear_rush
def find_max(data_in):#not work
if max < data_in:
max = data_in
else :
max = max
def stop_level(profit_level,high_price) :
if profit_level < 0.2 :
stopLoss_price = high_price*0.9
elif 0.2 <= profit_level < 0.5 :
stopLoss_price = high_price*0.9
elif profit_level >= 0.5 :
stopLoss_price = high_price*0.9
return stopLoss_price