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Zero
2016-10-17 18:15:14 +08:00
parent c8220a4017
commit afe77ec7f7
6 changed files with 592 additions and 8 deletions
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Python API 测试.py
+29 -7
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@@ -17,9 +17,16 @@ import matplotlib
matplotlib.use("Agg")
import numpy as np
import matplotlib.pyplot as plt, StringIO, base64
import matplotlib.pyplot as plt, io, base64
import mpl_toolkits.mplot3d
def saveImage(fig=None):
if fig is None:
fig = plt
dataStream = io.BytesIO()
fig.savefig(dataStream, format="png")
return '`data:image/png;base64,%s`'%(base64.b64encode(dataStream.getvalue()))
def main():
testGlobal = [
'Version', ['Log', 'ok', 3.5, True], ['Sleep', 100], ['LogProfit', 10.5],
@@ -77,10 +84,27 @@ def main():
# test plot image
plt.plot([3,6,2,4,7,1])
dataStream = StringIO.StringIO()
plt.savefig(dataStream, format="png")
Log('支持base64图片 `data:image/png;base64,%s`'%(base64.b64encode(dataStream.getvalue())))
Log('支持base64图片 ' + saveImage())
fig = plt.figure()
ax = fig.add_subplot(111)
t = ax.scatter(np.random.rand(20), np.random.rand(20))
Log(saveImage(fig))
x = np.linspace(0, 10, 1000)
y = np.sin(x)
z = np.cos(x**2)
plt.figure(figsize=(8,4))
plt.plot(x,y,label="$sin(x)$",color="red",linewidth=2)
plt.plot(x,z,"b--",label="$cos(x^2)$")
plt.xlabel("Time(s)")
plt.ylabel("Volt")
plt.title("PyPlot Example")
plt.ylim(-1.2,1.2)
plt.legend()
Log(saveImage())
x,y=np.mgrid[-2:2:20j,-2:2:20j]
z=x*np.exp(-x**2-y**2)
ax=plt.subplot(111,projection='3d')
@@ -88,10 +112,8 @@ def main():
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
dataStream = StringIO.StringIO()
plt.savefig(dataStream, format="png")
# plot image to status bar
LogStatus('支持base64图片 `data:image/png;base64,%s`'%(base64.b64encode(dataStream.getvalue())))
LogStatus(saveImage())
# test plot chart
x = Chart({
Python API测试.py
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@@ -0,0 +1,73 @@
'''
策略出处: https://www.botvs.com/strategy/22600
策略名称: Python API测试
策略作者: iamjayson
策略描述:
需要下载最新的托管者, 托管者机器个需要安装python, (Linux自带无需安装)
参数 默认值 描述
---- ------- -------------------
a 1.2 number
b 你好hello str
c true bool
d 0 select: abc|def|xxx
'''
import time
import talib
def main():
_G("ok", 123)
_G(None)
Log(_G("ok"))
EnableLog(True)
SetErrorFilter("net")
LogStatus('这是一个普通的状态提示');
ticker = exchange.GetTicker()
Log('ticker buy', ticker.Buy, ticker['Buy']);
r = exchange.GetRecords()
Log(len(r))
Log(r[0])
Log(r[-1])
Log(TA.Highest(r, 10, 'High'))
Log(TA.Lowest(r, 10, 'Low'))
for e in exchanges:
Log(e.GetName(), e.GetRate(), e.GetCurrency(), e.GetFee())
Log(e.GetAccount())
Log(_C(e.GetOrders))
Log(e.GetOrder(10))
Log(e.CancelOrder(10000))
Log(e.IO("api", "userinfo"))
Log(e.IO("api", "order_info", "symbol=btc_usd&order_id=3"))
Log(e.GetUSDCNY())
#Log(e.GetPosition())
#Log(e.SetContractType("next_week"))
Log(e.GetTicker())
Log('Asks:', len(e.GetDepth().Asks))
#Log(e.SetMarginLevel(10))
#Log(e.SetDirection("buy"))
#Log(e.SetContractType("quarter"))
#Log(e.GetRecords(PERIOD_M30)[0])
Log(e.GetRecords()[0])
x = Chart({
'title' : { 'text' : 'test chart'},
'xAxis': { 'type': 'datetime'},
'series' : [{'name' : 'Buy', 'data' : []}, {'name' : 'Sell', 'data' : []}]
})
x.reset()
Log("策略将每10秒更新一次ticker");
for i in range(100):
ts = int(time.time() * 1000)
ticker = _C(exchange.GetTicker)
x.add(0, [ts, ticker.Buy])
x.add(1, [ts, ticker.Sell])
LogStatus(ticker)
Sleep(10000)
Python 机器学习之 SVM 预测买卖.py
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@@ -0,0 +1,106 @@
'''
策略出处: https://www.botvs.com/strategy/21370
策略名称: Python 机器学习之 SVM 预测买卖
策略作者: Zero
策略描述:
Python入门简单策略 sklearn 机器学习库的使用
回测系统自带的库有
numpy pandas TA-Lib scipy statsmodels sklearn cvxopt hmmlearn pykalman arch matplotlib
实盘需要在托管者所在机器安装策略需要的库
'''
from sklearn import svm
import numpy as np
def main():
preTime = 0
n = 0
success = 0
predict = None
pTime = None
spread = 2
marketPosition = 0
initAccount = exchange.GetAccount()
Log("Running...")
while True:
r = exchange.GetRecords()
if len(r) < 60:
continue
bar = r[len(r)-1]
if bar.Time > preTime:
preTime = bar.Time
if pTime is not None and r[len(r)-2].Time == pTime:
diff = r[len(r)-2].Close - r[len(r)-3].Close
if diff > spread:
success += 1 if predict == 0 else 0
elif diff < -spread:
success += 1 if predict == 1 else 0
else:
success += 1 if predict == 2 else 0
pTime = None
LogStatus("预测次数", n, "成功次数", success, "准确率:", '%.3f %%' % round(float(success) * 100 / n, 2))
else:
Sleep(1000)
continue
inputs_X, output_Y = [], []
sets = [None, None, None]
for i in xrange(1, len(r)-2, 1):
inputs_X.append([r[i].Open, r[i].Close])
Y = 0
diff = r[i+1].Close - r[i].Close
if diff > spread:
Y = 0
sets[0] = True
elif diff < -spread:
Y = 1
sets[1] = True
else:
Y = 2
sets[2] = True
output_Y.append(Y)
if None in sets:
Log("样本不足, 无法预测 ...")
continue
n += 1
clf = svm.LinearSVC()
clf.fit(inputs_X, output_Y)
predict = clf.predict(np.array([bar.Open, bar.Close]).reshape((1, -1)))
pTime = bar.Time
Log("预测当前Bar结束:", bar.Time, ['涨', '跌', '横'][predict])
if marketPosition == 0:
if predict == 0:
exchange.Buy(initAccount.Balance/2)
marketPosition = 1
elif predict == 1:
exchange.Sell(initAccount.Stocks/2)
marketPosition = -1
else:
nowAccount = exchange.GetAccount()
if marketPosition > 0 and predict != 0:
exchange.Sell(nowAccount.Stocks - initAccount.Stocks)
nowAccount = exchange.GetAccount()
marketPosition = 0
elif marketPosition < 0 and predict != 1:
while True:
dif = initAccount.Stocks - nowAccount.Stocks
if dif < 0.01:
break
ticker = exchange.GetTicker()
exchange.Buy(ticker.Sell + (ticker.Sell-ticker.Buy)*2, dif)
while True:
Sleep(1000)
orders = exchange.GetOrders()
for order in orders:
exchange.CancelOrder(order.Id)
if len(orders) == 0:
break
nowAccount = exchange.GetAccount()
marketPosition = 0
if marketPosition == 0:
LogProfit(_N(nowAccount.Balance - initAccount.Balance, 4), nowAccount)
README.md
+4 -1
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@@ -4,11 +4,14 @@
> 开源策略列表
- [Python API测试](https://www.botvs.com/strategy/22600)
- [Python 机器学习之 SVM 预测买卖](https://www.botvs.com/strategy/21370)
- [这是为什么，请看first_bar 为什么后面的first_bar 变了](https://www.botvs.com/strategy/16309)
- [顺势_高频_交易_测试3](https://www.botvs.com/strategy/22183)
- [Python API 测试](https://www.botvs.com/strategy/21365)
- [均线策略（python版）](https://www.botvs.com/strategy/21157)
- [python版现货数字货币交易类库](https://www.botvs.com/strategy/21104)
- [顺势_高频_交易_测试2——失败](https://www.botvs.com/strategy/21938)
- [Python 机器学习之 SVM 简单预测是涨是跌还是横](https://www.botvs.com/strategy/21370)
- [顺势_高频_交易_测试1](https://www.botvs.com/strategy/21742)
- [均线策略改版（python版） (复制)](https://www.botvs.com/strategy/21890)
- [CTP商品期货多品种海龟交易策略](https://www.botvs.com/strategy/17289)
这是为什么，请看first_bar 为什么后面的first_bar 变了.js
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@@ -0,0 +1,28 @@
/*
策略出处: https://www.botvs.com/strategy/16309
策略名称: 这是为什么，请看first_bar 为什么后面的first_bar 变了
策略作者: 青铜战国级机器人
策略描述:
xxx
*/
/* 非常感谢满意沟通的解决方案，对于GetRecords 得到的数据类型来说，数组中的元素依然是类似字典
元素，是可变对象，slice只是进行了浅拷贝，数组中的元素是索引，而回测是最后一条bar是修改而来，所以造成first_bar也改变了，
加上deepCopy 能够解决这个问题*/
function deepCopy(Ele) {
return JSON.parse(JSON.stringify(Ele));
}
function main(){
var records, fist_bar, sec_bar;
records = exchange.GetRecords();
first_bar = deepCopy(records.slice(-1)[0]);
Log("first_bar:", first_bar);
Sleep(10000);
records = exchange.GetRecords();
sec_bar = deepCopy(records.slice(-1)[0]);
Log("sec_bar:", sec_bar);
Log("first_bar:", first_bar);
}
顺势_高频_交易_测试3.py
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@@ -0,0 +1,352 @@
'''
策略出处: https://www.botvs.com/strategy/22183
策略名称: 顺势_高频_交易_测试3
策略作者: 太极
策略描述:
根据历史收盘价 小周期均价-大周期均价=值
根据上边的值来进行开仓
'''
#!/usr/local/bin/python
#-*- coding: UTF-8 -*-
Interval_s=30 #轮询(采集周期/秒)
Enter_large_cycle=10 #开仓 大周期
Enter_Small_cycle=3 #开仓 小周期
large1_Period=-1 #开仓大周期差值1
large2_Period=-1.5 #开仓大周期差值2
PositionRatio=0.7 #仓位比例
Exit_large_cycle=3 #平仓 大周期
Exit_Small_cycle=5 #平仓 小周期
ExitPeriod=2 #平仓观察期(开仓买入 大于 当前)
Exit_stop=4 #当完成这个开仓周期后 暂停几个周期不开仓
Small1_Period=0.4 #平仓小周期差值1
import time
import datetime
start_timexx =time.localtime(time.time()) #time.clock()
start_time=time.strftime("%Y-%m-%d %H:%M:%S",start_timexx)
buy_price=0 #买入价格
buy_qty=0 #买入数量
gains=0 #盈利
########################################################
def out_time(x_time): #获取当前时间 前10分钟时间戳
#d0=time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(time.time()))
d1=datetime.datetime.now()
#d1=time.mktime(time.strptime(d0,'%Y-%m-%d %H:%M:%S')) #转换成时间戳
d3 = str(d1 - datetime.timedelta(minutes =x_time)) #days日 hours时 minutes分 seconds秒
d4=d3[:len(d3)-7] #清除垃圾数据
return time.mktime(time.strptime(d4,'%Y-%m-%d %H:%M:%S'))
def Volume_averages(Ticker_listxxx,x): #均价
try:
Volume=0
if len(Ticker_listxxx)<x:
return 0
for colour in Ticker_listxxx:
Volume+=colour
return Volume/len(Ticker_listxxx)
except BaseException, e:
return 0
def Close2_Close(Close2_list): #小周期 差值
Result_list=[] #结果
#去第一个 第3个减去第2个
for i in range(0, len(Close2_list)):
#print i, Close2_list[i]
if i<len(Close2_list)-1:
A=Close2_list[i+1]-Close2_list[i]
Result_list.append(A)
#print Result_list
x=0
for i in range(0, len(Result_list)):
#print i, Result_list[i]
if i==0:
x=Result_list[i+1]-Result_list[i]
else:
if i<len(Result_list)-1:
x=Result_list[i+1]-x
else:
x=Result_list[i]-x
return _N(x,3)
def add_min_list(Ticker_list,name,min_size): #添加秒级历史数据
try:
#global min_size
#ticker = _C(exchange.GetTicker).Last#Ticker 市场行情 最后成交价
if (int(name)>10):
if len(Ticker_list)>=min_size:
Ticker_list.pop(0) #返回并清除第一个数组
Ticker_list.append(name) #追加元素
else:
Ticker_list.append(name) #追加元素
return 1,Ticker_list
except BaseException, e:
return 0,[]
########################################################
def Caltime(date1,date2):
try:
date1=time.strptime(date1,"%Y-%m-%d %H:%M:%S")
date2=time.strptime(date2,"%Y-%m-%d %H:%M:%S")
date1=datetime.datetime(date1[0],date1[1],date1[2],date1[3],date1[4],date1[5])
date2=datetime.datetime(date2[0],date2[1],date2[2],date2[3],date2[4],date2[5])
return date2-date1
except Exception,ex:
Log('except Exception Caltime:',ex)
return "except Exception"
outAccount = ext.GetAccount() #初始化信息
outtickerx = _C(exchange.GetTicker).Last#Ticker 市场行情 最后成交价
def print_log(k_p,data=""):
try:
name=""
if k_p:
name="开仓"
else:
name="平仓"
global outAccount,outtickerx
tickerx = _C(exchange.GetTicker).Last#Ticker 市场行情 最后成交价
endAccount = ext.GetAccount() #当前账户信息
######################################
#盈利计算方法
#盈利计算方法 浮动利润： 按 （现在币 - 初始币）x 现在的价格 + （现在的钱 - 初始的钱）
diff_stocks=endAccount.Stocks-outAccount.Stocks #比的差值
diff_balance=endAccount.Balance-outAccount.Balance #钱的差值
new_end_balance=diff_stocks * tickerx + diff_balance #实现盈亏 #当前的盈利
#盈利计算方法 账面利润 ： （现在币 x 现在价格+现在钱） - （初始币 x 初始价格 + 初始钱）
new_end_balance2=(endAccount.Stocks*tickerx+endAccount.Balance)-(outAccount.Stocks*outtickerx+outAccount.Balance)
#当前账户RMB
end_rmb=(endAccount.Stocks*tickerx)+endAccount.Balance
######################################
date1=time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(time.time()))
LogStatus("初始化投入2016/9/24 投入0.2个币=等于行情800RMB\r\n",
"本次初始化状态:",outAccount,
"\r\n当前运 行状态:",endAccount,
"\r\n本次开始运行时间:%s 已运行:%s\r\n"%(start_time,Caltime(start_time,date1)),
"浮动利润:%s(RMB)\r\n"%(str(_N(new_end_balance,3))),
"账面利润:%s(RMB)\r\n"%(str(_N(new_end_balance2,3))),
"当前状态:%s--钱:%s--币:%s=%s(RMB)\r\n"%(str(name),str(endAccount.Balance),str(endAccount.Stocks),str(end_rmb)),
"更新时间:%s\r\n"%(date1),
"%s"%(data)
) # 测试
except Exception,ex:
Log('except Exception print_log:',ex)
def my_buy(): #开仓
try:
global buy_price,buy_qty
initAccount = ext.GetAccount() #交易模板的导出函数， 获得账户状态，保存策略运行前账户初始状态
opAmount=1
#开仓之前判断有币没有没有先进行买入
if int(initAccount.Stocks)>1:
if buy_price<1:
buy_price=_C(exchange.GetTicker).Last
buy_qty=initAccount.Stocks
Log('开仓信息1 仓内还有比:',initAccount.Stocks,'进行清空','--开仓详情:',initAccount)
return 1
if int(initAccount.Stocks)<1:
if initAccount.Stocks>=0.03: #保证满足交易量
# Log('不满足最小交易量:',initAccount.Stocks)
#return 1 #当前价格一定要大于 开仓价格
#if int(str(initAccount.Stocks).replace('0.',''))>=3:
if buy_price<1:
buy_price=_C(exchange.GetTicker).Last
buy_qty=initAccount.Stocks
Log('开仓信息2 仓内还有比:',initAccount.Stocks,'进行清空','--开仓详情:',initAccount)
return 1
#if int(str(initAccount.Stocks).replace('0.',''))==0:
#这个地方有缺陷 仓位比例有缺陷
#账户总钱数/开仓比例 *当前行情价
tickerx = _C(exchange.GetTicker).Last#Ticker 市场行情 最后成交价
scale=(initAccount.Balance/PositionRatio)*tickerx
opAmount = _N(scale,3) #买入数量
Log("开仓没有币先进行 开仓买入%s元"%(str(opAmount))) #生成LOG日志
Dict = ext.Buy(opAmount) #买入ext.Buy
if(Dict):#确认开仓成功
buy_price=Dict['price'] #买入价格 #{'price': 4046.446, 'amount': 1.5}
buy_qty=Dict['amount'] #买入数量
#LogProfit(_N(gains,4),'开仓信息 钱:',initAccount.Balance,'--币:',initAccount.Stocks,'--开仓详情:',Dict)
print_log(1)
return 1
return 0
except Exception,ex:
Log('except Exception my_buy:',ex)
return 0
def my_sell(): #平仓
try:
global buy_price,buy_qty,gains,ExitPeriod
nowAccount = ext.GetAccount() #交易模板的导出函数 获取账户信息
if nowAccount.Stocks<=0.03: #保证满足交易量
Log('不满足最小交易量:',nowAccount.Stocks)
return 1 #当前价格一定要大于 开仓价格
Dict = ext.Sell(nowAccount.Stocks)
#Dict ={"price":_C(exchange.GetTicker).Last}
if(Dict):
sell_gains=(Dict['price']-buy_price)*Dict['amount']
gains=gains+sell_gains
buy_price=0 #买入价格
buy_qty=0 #买入数量
LogProfit(_N(gains,4),'平仓信息 钱:',nowAccount.Balance,'--币:',nowAccount.Stocks,'--平仓详情:',Dict)#收益曲线
print_log(0)
return 1
return 0
except Exception,ex:
Log('except Exception my_sell:',ex)
return 0
########################################################
def main():
global buy_price
n=0
state = 3 #开仓状态
value1_data=0 #大周期 差值
value2_data=0 #小周期 差值
Log("run ",outAccount) #输出初始账户信息
Close1_list=[] #开仓 大周期
Close2_list=[] #开仓 小周期
Close3_list=[] #平仓 大周期
Close4_list=[] #平仓 小周期
min_size=10 #获取秒级数据量
min_list=[] #秒级均价
min_size_list=[] #秒级 采集样本
current_Last=0 #当前历史均价
b=0 #开仓
a=0 #平仓
while True:
try:
#nowAccountx = ext.GetAccount() #交易模板的导出函数 获取账户信息
tickerx = _C(exchange.GetTicker).Last#Ticker 市场行情 最后成交价
# add_bool,f_list=add_min_list(min_size_list,tickerx,min_size) #添加秒级历史数据
# if add_bool:
# min_size_list=f_list
# current_Last=int(_N(Volume_min_averages(min_size_list),2)) #当前历史均价
#更新历史数据
if n<int(Interval_s): #记录更新过一次
n+=1
if n>=int(Interval_s): #重置
n=0
###########################
#PERIOD_M1 指1分钟,#PERIOD_M5 指5分钟,#PERIOD_M15 指15分钟,#PERIOD_M30 指30分钟,#PERIOD_H1 指1小时,#PERIOD_D1 指一天
ticker = _C(exchange.GetRecords,PERIOD_M1)#Record 收盘价
#Log('当前数据:',ticker)
minutes1_time=int(out_time(Enter_large_cycle)) #开仓 大周期 获取当前时间 获取时间戳
minutes2_time=int(out_time(Enter_Small_cycle)) #开仓 小周期 获取当前时间 获取时间戳
minutes3_time=int(out_time(Exit_large_cycle)) #平仓 大周期
minutes4_time=int(out_time(Exit_Small_cycle)) #平仓 小周期
timeB1=str(int(minutes1_time))
timeB2=timeB1[:len(timeB1)] #我们设定的收盘时间 大周期
#timeB_time=time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(int(timeB2))) #时间戳 转换成日期
timeC1=str(int(minutes2_time))
timeC2=timeC1[:len(timeC1)] #我们设定的收盘时间 大周期
timeD1=str(int(minutes3_time))
timeD2=timeC1[:len(timeD1)] #我们设定的收盘时间
timeE1=str(int(minutes4_time))
timeE2=timeC1[:len(timeE1)] #我们设定的收盘时间
for colour in ticker:
timeA1=str(int(colour.Time))
timeA2=timeA1[:len(timeA1)-3] #收盘时间
#timeA_time=time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(int(timeA2))) #时间戳 转换成日期
#开仓数据
if int(timeA2)>int(timeB2): #收盘时间大于我们设定的时间及为 真
#Log('大周期:',timeA_time,"|",timeA2,",",timeB_time,"|",timeB2)
Close1_list.append(colour.Close) #大周期
if int(timeA2)>int(timeC2): #收盘时间大于我们设定的时间及为 真
#Log('小周期:',timeA_time,"|",timeA2,",",timeC_time,"|",timeC2)
Close2_list.append(colour.Close) #小周期
#平仓数据
if int(timeA2)>int(timeD2): #收盘时间大于我们设定的时间及为 真
Close3_list.append(colour.Close) #平仓 大周期
if int(timeA2)>int(timeE2): #收盘时间大于我们设定的时间及为 真
Close4_list.append(colour.Close) #平仓 大周期
#########################################################################
#开仓条件
if (len(Close1_list)+1>=Enter_large_cycle and len(Close2_list)+1>=Enter_Small_cycle):
#说明数据收集完毕准备开始操作
minu1_Close=_N(Volume_averages(Close1_list,Enter_large_cycle),2) #大周期 历史均价
minu2_Close=_N(Volume_averages(Close2_list,Enter_Small_cycle),2) #小周期 历史均价
if (minu1_Close>10 and minu2_Close>10):
minuN_Close=_N(minu2_Close-minu1_Close,3)
data="小周期均价减去大周期均价:%s"%(str(minuN_Close))
print_log(0,data)
if (float(large1_Period)>=float(minuN_Close) and float(minuN_Close)>=float(large2_Period)): #判断这个差值在这个区间内则开仓
#Log("ssssssssssssssssssssss",state)
if (state == 3): #
state = 0 #开仓
#########################################################################
EnterPeriod=1 #开仓观察期
ExitPeriod=1 #平仓观察期
if (state == 0): #
n = tickerx
#data="开仓 xxx检测成交价%s"% \
# (str(tickerx))
#Log(data)
#if n<0: #确定当前为死叉
#if b>=int(n): #说明现在还是在下跌涨趋势
if ((n >= b-0.5) and (n <=b+0.5)):
b=int(n)
else: #开始下跌 开仓
if(int(n)>=int(b)+int(EnterPeriod)): #确认上行走势 至自己定义的点
if my_buy(): #开仓
#buy_price=tickerx
data="开仓 当前成交价%s"% \
(str(tickerx))
Log(data)
b=0
state = 1
if (state == 1): #
n = tickerx
#if n>0: #确定当前为金叉
#计算2个值的差值 还是 +- 指定范围
#if a<=int(n): #说明现在还是在上涨趋势
if ((n >= a-0.5) and (n <=a+0.5)):
a=int(n)
else: #开始下跌 平仓
#if(int(n)<=int(a)-int(ExitPeriod)): #确认下行走势 至自己定义的点
if tickerx>buy_price+ExitPeriod: #
if my_sell(): #平仓
data="平仓 开仓价格%s 当前成交价%s"% \
(str(buy_price),str(tickerx))
Log(data)
a=0
state = 3
if Exit_stop: #当完成这个开仓周期后 暂停几个周期不开仓
Log('完成当前一次交易----暂停几个周期不开仓')
Sleep((Interval_s*Exit_stop)* 1000)
#################################################
#Log('xxxx大周期:',len(Close1_list))
#Log('xxxx小周期:',len(Close2_list))
Close1_list=[] #大周期 清空
Close2_list=[] #小周期 清空
Close3_list=[] #平仓 大周期
Close4_list=[] #平仓 小周期
except Exception,ex:
Log('except Exception main:',ex)
Sleep(1000)