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botvs
2016-10-20 09:24:06 +08:00
parent b43494f563
commit c5b8599407
4 changed files with 214 additions and 10 deletions
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CTP商品期货多品种海龟交易策略.js
+4 -4
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@@ -230,10 +230,10 @@ var TTManager = {
var amount = Math.min(insDetail.MaxLimitOrderVolume, positions[i].Amount);
if (positions[i].Type == PD_LONG || positions[i].Type == PD_LONG_YD) {
exchange.SetDirection(positions[i].Type == PD_LONG ? "closebuy_today" : "closebuy");
orderId = exchange.Sell(depth.Bids[0].Price - (insDetail.PriceTick * SlideTick), Math.min(amount, depth.Bids[0].Amount), obj.symbol, positions[i].Type == PD_LONG ? "平今" : "平昨", 'Bid', depth.Bids[0]);
orderId = exchange.Sell(_N(depth.Bids[0].Price - (insDetail.PriceTick * SlideTick), 2), Math.min(amount, depth.Bids[0].Amount), obj.symbol, positions[i].Type == PD_LONG ? "平今" : "平昨", 'Bid', depth.Bids[0]);
} else if (positions[i].Type == PD_SHORT || positions[i].Type == PD_SHORT_YD) {
exchange.SetDirection(positions[i].Type == PD_SHORT ? "closesell_today" : "closesell");
orderId = exchange.Buy(depth.Asks[0].Price + (insDetail.PriceTick * SlideTick), Math.min(amount, depth.Asks[0].Amount), obj.symbol, positions[i].Type == PD_SHORT ? "平今" : "平昨", 'Ask', depth.Asks[0]);
orderId = exchange.Buy(_N(depth.Asks[0].Price + (insDetail.PriceTick * SlideTick), 2), Math.min(amount, depth.Asks[0].Amount), obj.symbol, positions[i].Type == PD_SHORT ? "平今" : "平昨", 'Ask', depth.Asks[0]);
}
}
if (hasPosition) {
@@ -332,10 +332,10 @@ var TTManager = {
var orderId = null;
if (obj.task.action == ACT_LONG) {
exchange.SetDirection("buy");
orderId = exchange.Buy(depth.Asks[0].Price + (insDetail.PriceTick * SlideTick), Math.min(remain, depth.Asks[0].Amount), obj.symbol, 'Ask', depth.Asks[0]);
orderId = exchange.Buy(_N(depth.Asks[0].Price + (insDetail.PriceTick * SlideTick), 2), Math.min(remain, depth.Asks[0].Amount), obj.symbol, 'Ask', depth.Asks[0]);
} else {
exchange.SetDirection("sell");
orderId = exchange.Sell(depth.Bids[0].Price - (insDetail.PriceTick * SlideTick), Math.min(remain, depth.Bids[0].Amount), obj.symbol, 'Bid', depth.Bids[0]);
orderId = exchange.Sell(_N(depth.Bids[0].Price - (insDetail.PriceTick * SlideTick), 2), Math.min(remain, depth.Bids[0].Amount), obj.symbol, 'Bid', depth.Bids[0]);
}
// symbol not in trading or other else happend
if (!orderId) {
Dual Thrust_双轨突破_策略1.py
+200
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@@ -0,0 +1,200 @@
'''
策略出处: https://www.botvs.com/strategy/22983
策略名称: Dual Thrust_双轨突破_策略1
策略作者: 太极
策略描述:
策略越简单越好
'''
#!/usr/local/bin/python
#-*- coding: UTF-8 -*-
Enter_cycle=5 #周期数据
Open_price=0 #开盘价平均价
High_price=0 #最高价平均价
Low_price=0 #最低价平均价
Close_price=0 #收盘价平均价
N1Open_price=0 #开盘价
N1High_price=0 #最高价
N1Low_price=0 #最低价
N1Close_price=0 #收盘价
# import time
# import datetime
# 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 Record_Open_Close(cycle): #获取 开盘价 收盘价 平均价
global Open_price,High_price,Low_price,Close_price,N1High_price,N1Low_price,N1Open_price,N1Close_price
Open_list=[] #开盘价平均价
High_list=[] #最高价平均价
Low_list=[] #收盘平均价
Close_list=[] #收盘价平均价
#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_M5)#Record 历史数据
#ticker = _C(exchange.GetRecords)#Record 收盘价
#Log('当前数据:',ticker)
#因为无法回测所以不这样写
# minutes1_time=int(out_time(cycle)) #获取时间戳
#
# timeB1=str(int(minutes1_time))
# timeB2=timeB1[:len(timeB1)] #我们设定的收盘时间
#
# for colour in ticker:
# Log(colour.Time)
# timeA1=str(int(colour.Time))
# timeA2=timeA1[:len(timeA1)-3] #收盘时间
# if int(timeA2)>int(timeB2): #收盘时间大于我们设定的时间及为 真
# Log(colour.Time)
# #收集最高价 收盘 数据
# High_list.append(colour.High) #最高价平均价
# Low_list.append(colour.Low) #最低价平均价
#采集数据 N个周期
for i in range(len(ticker)-cycle, len(ticker)):
colour=ticker[i]
# timeA1=str(int(colour.Time))
# timeA2=timeA1[:len(timeA1)-3] #收盘时间
# Log(colour)
# Log(timeA2,"xxx",len(ticker),"rrrr",i)
Open_list.append(colour.Open) #开盘价
High_list.append(colour.High) #最高价
Low_list.append(colour.Low) #最低价
Close_list.append(colour.Close) #最低价
#上一个周期 最高价 最低价
for i in range(len(ticker)-1, len(ticker)):
colour=ticker[i]
N1Open_price=colour.Open #开盘价
N1High_price=colour.High #最高价
N1Low_price=colour.Low #最低价
N1Close_price=colour.Close #最低价
#计算平均价
if (len(High_list)>=cycle)and(len(Low_list)>=cycle)and(len(Open_list)>=cycle)and(len(Close_list)>=cycle):
Open_Close=_N(Volume_averages(Open_list,cycle),2) #开盘价平均价
High_Close=_N(Volume_averages(High_list,cycle),2) #最高价平均价
Low_Close=_N(Volume_averages(Low_list,cycle),2) #最低价平均价
Close_Close=_N(Volume_averages(Close_list,cycle),2) #收盘价平均价
Open_price=Open_Close #开盘价平均价
High_price=High_Close #最高价平均价
Low_price=Low_Close #最低价平均价
Close_price=Close_Close #收盘价平均价
else:
Open_price=0 #开盘价平均价
High_price=0 #最高价平均价
Low_price=0 #最低价平均价
Close_price=0 #收盘价平均价
###############################################################
lose_win={"lose":0,"win":0} #输：lose赢：win
def print_logxx():
global lose_win
#lose_win={"lose":35,"win":90} #输：lose赢：win
Log("赢:%s 输:%s"%(str(lose_win["win"]),str(lose_win["lose"])))
# if (float(lose_win["win"])>0 and float(lose_win["lose"])>0):
# SL="%.2f%%"%((float(lose_win["win"]-lose_win["lose"])/float(lose_win["win"]))*100)
# Log("赢:%s 输:%s=胜:%s"%(str(lose_win["win"]),str(lose_win["lose"]),str(SL)))
###############################################################
#low_minuN_Close= 0.1 #浮动开仓点
#high_minuN_Close= 0.3 #浮动平仓点
#lose_win={"lose":0,"win":0} #输：lose赢：win
def main():
#global low_minuN_Close,high_minuN_Close
global lose_win
global Open_price,High_price,Low_price,Close_price,N1High_price,N1Low_price,N1Open_price,N1Close_price
#LogProfitReset()# 清空所有收益日志, 可以带一个数字参数, 指定保留的条数
#LogReset()# 清空所有日志, 可以带一个数字参数, 指定保留的条数
state = 3 #开仓状态
open_price=0 #开仓价格
gains=0 #盈利
while True:
Record_Open_Close(Enter_cycle) #获取 开盘价 收盘价 平均价
tickerx = _C(exchange.GetTicker).Last#Ticker 市场行情 最后成交价
# Log('高价－收盘价:',High_price-Close_price)
# Log('收盘价－最低价:',Close_price-Low_price)
# Log('最高价，开盘价:',High_price,Open_price)
# Log('收盘价，最低价:',Close_price,Low_price)
# Log('最高价，最低价:',N1High_price,N1Low_price)
if High_price-Close_price>=Close_price-Low_price: #比较值计算2个值的浮动区间
floating_price=(High_price-Close_price)
else:
floating_price=(Close_price-Low_price)
#不知道如何获取当前开盘价采用上个周期收盘价最为开盘价
x_high=N1Open_price+(0.5*floating_price) #上轨
x_low=N1Open_price-(0.5*floating_price) #下轨
#Log('上轨%s=下轨%s=价格差%s=当前价格%s'%(str(x_high),str(x_low),str(x_high-x_low),str(tickerx)))
#if (float(tickerx)>=float(x_low-low_minuN_Close) and float(tickerx)<=float(x_low+low_minuN_Close)): #判断行情是否在指定区间内则开仓
#判断当前行情是否在上轨和下轨中
#突破上轨则开仓
#突破下轨则平仓
if (float(tickerx)<=float(x_high+5) and float(tickerx)>=float(x_low-5)): #判断当前行情是否在上轨和下轨中
if (state == 3): #空闲状态
if float(tickerx)>=float(x_high): #突破上轨则开仓
Log('开仓 上轨%s=下轨%s=价格差%s=当前价格%s'%(str(x_high),str(x_low),str(x_high-x_low),str(tickerx)))
state = 0 #开仓
if (state == 1): #开仓状态
if float(tickerx)<=float(x_low): #突破下轨则平仓
state = 2 #平仓
# if float(tickerx)<=float(x_low): #大于等于下轨 则开仓
# if (state == 3): #空闲状态
# state = 0 #开仓
# Log('开仓 上轨%s=下轨%s=价格差%s=当前价格%s'%(str(x_high),str(x_low),str(x_high-x_low),str(tickerx)))
if (state == 0): #开仓
if ext.Buy(0.5):
#开仓成功
open_price = tickerx
state = 1
if (state == 2): #平仓
#if (float(tickerx)>=float(x_high-high_minuN_Close) and float(tickerx)<=float(x_high+high_minuN_Close)): #判断行情是否在指定区间内则平仓
#if float(tickerx)>=float(x_high): #大于等于上轨道 则平仓
if ext.Sell(0.5):
#平仓成功
xx=tickerx-open_price #本次交易
gains=gains+xx
Log("本次盈利:%s--当前共盈利:%s"%(str(tickerx-open_price),str(gains)))
if float(tickerx-open_price)>=0:
lose_win["win"]=lose_win["win"]+1 #赢：win"胜"
else:
lose_win["lose"]=lose_win["lose"]+1#输：lose"负"
print_logxx() #信息输出
state = 3
open_price=0 #开仓价格
Sleep(1* 1000)
Python 机器学习之 SVM 预测买卖.py
+8 -5
View File
@@ -11,6 +11,10 @@ numpy pandas TA-Lib scipy statsmodels sklearn cvxopt hmmlearn pykalman arch matp
实盘需要在托管者所在机器安装策略需要的库
参数 默认值 描述
--------- ----- ----
SpreadVal 2 预测价差
'''
from sklearn import svm
@@ -22,7 +26,6 @@ def main():
success = 0
predict = None
pTime = None
spread = 2
marketPosition = 0
initAccount = exchange.GetAccount()
Log("Running...")
@@ -35,9 +38,9 @@ def main():
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:
if diff > SpreadVal:
success += 1 if predict == 0 else 0
elif diff < -spread:
elif diff < -SpreadVal:
success += 1 if predict == 1 else 0
else:
success += 1 if predict == 2 else 0
@@ -52,10 +55,10 @@ def main():
inputs_X.append([r[i].Open, r[i].Close])
Y = 0
diff = r[i+1].Close - r[i].Close
if diff > spread:
if diff > SpreadVal:
Y = 0
sets[0] = True
elif diff < -spread:
elif diff < -SpreadVal:
Y = 1
sets[1] = True
else:
README.md
+2 -1
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@@ -4,6 +4,8 @@
> 开源策略列表
- [CTP商品期货多品种海龟交易策略](https://www.botvs.com/strategy/17289)
- [Dual Thrust_双轨突破_策略1](https://www.botvs.com/strategy/22983)
- [Python 机器学习之 SVM 预测买卖](https://www.botvs.com/strategy/21370)
- [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)
@@ -11,7 +13,6 @@
- [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)
- [顺势_高频_交易_测试1](https://www.botvs.com/strategy/21742)
- [CTP商品期货多品种海龟交易策略](https://www.botvs.com/strategy/17289)
- [各大平台API可用率统计](https://www.botvs.com/strategy/4404)
- [Dual Thrust 商品期货](https://www.botvs.com/strategy/13011)
- [均线-趋势_数字货币策略V0.2](https://www.botvs.com/strategy/21369)