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stratege.py
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stratege.py
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default_setting = '''backtest
start: 2019-06-10 00:00:00
end: 2019-08-10 00:00:00
period: 1m
exchanges: [{"eid":"Bitfinex","currency":"BTC_USD","balance":10000,"stocks":3}]'''
from fmz import *
import pandas as pd
from matplotlib.pylab import date2num ## 导入日期到数值一一对应的转换工具
import json
from datetime import datetime,timedelta
import matplotlib.pyplot as plt
import matplotlib
import random
from hmmlearn import hmm
from dateutil.parser import parse ## 导入转换到指定格式日期的工具
import numpy as np
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
matplotlib.rcParams['font.family']='sans-serif'
#解决负号'-'显示为方块的问题
matplotlib.rcParams['axes.unicode_minus'] = False
import mpl_finance as mpf
class PeriodTooLess(Exception):
def __init__(self):
pass
def TIME_STAMP(TIME,mode = 1):
'返回字符串时间戳'
if mode:
return int(time.mktime(time.strptime(TIME+' 08:00:00','%Y-%m-%d %H:%M:%S')))
else:
return int(time.mktime(time.strptime(TIME,'%Y-%m-%d %H:%M:%S')))
ALL_month = [
]
ALL_day = []
startday = datetime.strptime('2018-01-01 00:00:00', '%Y-%m-%d %H:%M:%S')
endday = datetime.strptime('2019-08-10 00:00:00', '%Y-%m-%d %H:%M:%S')
while True:
ALL_day.append(str(startday))
if str(startday) == str(endday):
break
lastday = startday - timedelta(days=1)
if lastday.month != startday.month:
ALL_month.append(str(startday))
startday = startday + timedelta(days=1)
class settings:
def __init__(self,start = "2019-06-10 00:00:00",end="2019-07-10 00:00:00",eid = "Bitfinex",currency ="BTC_USD",balance = "10000",stocks = "1",period = "1d"):
self.setting_str = default_setting
self.settings = {
"start":start,
"end" :end,
"eid" :eid,
"currency":currency,
"balance":str(balance),
"stocks":str(stocks),
"period":period
}
self.setting_str = self.setting_str.replace("2019-06-10 00:00:00",self.settings["start"])
self.setting_str = self.setting_str.replace("2019-08-10 00:00:00",self.settings["end"])
self.setting_str = self.setting_str.replace("Bitfinex",self.settings["eid"])
self.setting_str = self.setting_str.replace("BTC_USD",self.settings["currency"])
self.setting_str = self.setting_str.replace("10000",self.settings["balance"])
self.setting_str = self.setting_str.replace("stocks:3","stocks:{}".format(float(self.settings["stocks"])))
self.setting_str = self.setting_str.replace("1m",self.settings["period"])
def export(self):
return self.setting_str
def get_day(timestamp):
'返回月份 与 日'
format_time = time.localtime(timestamp/1000)
return (format_time[0],format_time[1],format_time[2])
class strategy:
def __init__(self):
pass
def init(self,PRINT = False):
self.task = VCtx(self.setting)
self.init_account = exchange.GetAccount()
self.realizible_profit = 0
if PRINT:
print("Strategy Name:",self.name)
print("Setting:", self.setting)
#print(self.setting)
#print("初始账户信息:",self.init_account)
def main(self):
while True:
self.onTick()
Sleep(1000)
def exit_f(self):
pass
def format_outcome(self):
for key in self.outcome:
if key == "RuntimeLogs":
continue
if type(self.outcome[key]) != type([]) and type(self.outcome[key]) != type({}):
print(key, self.outcome[key])
elif type(self.outcome[key]) == type({}):
print(key)
for small_key in self.outcome[key]:
print(' ', small_key, self.outcome[key][small_key])
else:
print(key)
for item in self.outcome[key]:
for small_key in item:
print(' ', small_key, item[small_key])
#for Log in self.outcome['RuntimeLogs']:
#print(Log)
def run(self,PRINT = False):
'''运行回测然后得到收益'''
self.init(PRINT)
try:
self.main()
except EOFError:
#print("回测结束\n")
self.exit_f()
self.outcome = json.loads(self.task.Join())
for Log in self.outcome['RuntimeLogs']:
Log[1] = get_day(Log[1])
#print(Log)
Snapshort = self.outcome['Snapshort'][0]
balance_change = Snapshort['Balance'] - self.init_account['Balance']
stock_change = Snapshort['Stocks'] - self.init_account['Stocks']
self.realizible_profit = balance_change + stock_change * Snapshort['Symbols']['BTC_USD_Bitfinex']['Last'] - Snapshort['Commission']
self.commission = float(Snapshort['Commission'])
#print(Snapshort)
def Multiperiodbacktest(self,periods,filename = time.strftime("%b_%d_%Y_%H_%M_%S", time.localtime(time.time()))):
result = {
"period":[],
"profit":[],
"commission":[],
}
if len(periods) <= 1:
raise PeriodTooLess
labels = []
this_dir = self.dir +filename + "/"
try:
os.mkdir(this_dir)
except:
pass
write = pd.ExcelWriter(this_dir+ "result.xls")
for period in periods:
start = period[0]
end = period[1]
if start.endswith("00:00:00"):
start = start[:10]
if end.endswith("00:00:00"):
end = end[:10]
key = start
labels.append(start)
print('period:',start + " - " + end)
result['period'].append(start + " - " + end)
self.backtest(start,end,False,False)
result['profit'].append(self.realizible_profit)
result['commission'].append(self.commission)
pd.DataFrame(result).to_excel(write,sheet_name="具体收益")
average_profit = sum(result['profit'])/len(result['profit'])
samplenum = len(result['profit'])
inflowmonthnum = sum([1 if profit > 0 else 0 for profit in result['profit']])
metrics = {
"average_profit":[average_profit],
"极差":[max(result['profit']) - min(result['profit'])],
"正收益月数":[inflowmonthnum],
"正收益月数比率":[round(inflowmonthnum/samplenum,2)],
"平均手续费":[round(sum(result['commission'])/samplenum,2)],
"参数:":[" "]
}
for key in self.param:
metrics[key] = [self.param[key]]
pd.DataFrame(metrics).to_excel(write, sheet_name="指标")
write.save()
fig = plt.figure(figsize=(20,8),frameon =False)
ax = plt.gca()
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left') # 指定下边的边作为 x 轴 指定左边的边为 y 轴
ax.spines['bottom'].set_position(('data', 0)) # 指定 data 设置的bottom(也就是指定的x轴)绑定到y轴的0这个点上
ax.spines['left'].set_position(('data', 0))
plt.plot()
plt.plot(list(range(samplenum)),result['profit'],label = 'profit')
plt.plot(list(range(samplenum)),[average_profit]*samplenum,label = "average = {}".format(average_profit))
plt.legend(loc = 'upper right')
plt.xlabel("period")
plt.ylabel("profit")
plt.xticks(list(range(samplenum)),labels,rotation = 45)
plt.title("profit curve")
plt.savefig(this_dir + "profitcurve.png")
print("done!:",filename)
return result
def dailybacktest(self,gap):
period = []
for i in range(len(ALL_day) - gap):
period.append((ALL_day[i],ALL_day[i+gap]))
self.Multiperiodbacktest(period,"{}日内交易回测".format(gap))
def monthlybacktest(self,gap):
period = []
for i in range(len(ALL_month) - gap):
period.append((ALL_month[i],ALL_month[i+gap]))
suffix = ""
for key in self.param:
suffix += str(key) +"="+str(self.param[key]) + "&"
self.Multiperiodbacktest(period,str(gap) + "月内交易回测(param_" + suffix[:-1]+")")
def backtest(self,start,end,PRINTSETTING=True,PRINTLOG = False):
#self.param["start"] = start
#self.param['end'] = end
if not start.endswith("00:00:00"):
start+= " 00:00:00"
if not end.endswith("00:00:00"):
end += " 00:00:00"
self.setting = settings(start = start,end = end,period='1d').export()
self.run(PRINT=PRINTSETTING)
print('profit', self.realizible_profit)
if PRINTLOG:
for log in self.outcome['RuntimeLogs'][::-1]:
print(log)
@property
def param_suffix_str(self):
suffix = ""
for key in self.param:
suffix += str(key) +"="+str(self.param[key]) + "&"
suffix = suffix[:-1]
return suffix
@ property
def setting(self):
try:
return self._setting
except AttributeError:
self._setting = settings().export()
return self._setting
@ setting.setter
def setting(self,value):
self._setting = value
@property
def param(self):
try:
return self._param
except AttributeError:
self._param = {}
return self._param
@param.setter
def param(self,value):
self._param = value
def __getitem__(self, item):
return self._param[item]
def __setitem__(self, key, value):
self._param[key] = value
@property
def name(self):
if self._name not in locals().keys():
self._name = "Unnamed Strategy"
return self._name
@name.setter
def name(self,value):
self._name = value
self.dir = self._name + "/"
if os.path.exists(self.dir) == False:
os.mkdir(self.dir)
@staticmethod
def ticktime(tick):
return (time.strftime("%b %d %Y %H:%M:%S", time.localtime(int(str(tick['Time'])[:-3]))))
class R_breaker(strategy):
'''
p : 每次日内交易的btc个数
'''
def load_param(self,p = 0.1):
self
self.p = p
def main(self):
LastDay = get_day(exchange.GetRecords(PERIOD_D1)[-1].Time - 1000)
LastTimeStamp = exchange.GetRecords(PERIOD_D1)[-1].Time / 1000
yestoday = 0
BreakSsteup = False
BreakBsteup = False
STATE = "IDLE"
Fan = False
while True:
ticker = exchange.GetTicker()
NowDay = get_day(ticker.Time)
if NowDay != LastDay:
STATE = "IDLE"
yestoday = exchange.GetRecords(PERIOD_D1)[-2] # -1是今日
BreakSsteup = False
BreakBsteup = False
Log(NowDay)
Fan = False
NowPrice = ticker.Last
High = yestoday.High
Low = yestoday.Low
Close = yestoday.Close
Open = yestoday.Open
TodayHigh = ticker.High
TodayLow = ticker.Low
Ssteup = High + 0.35 * (Close - Low) # 观察卖出价
Bsteup = Low - 0.35 * (High - Close) # 观察买入价
Senter = 1.07 / 2 * (High + Low) - 0.07 * Low # 反转卖出价
Benter = 1.07 / 2 * (High + Low) - 0.07 * High # 反转买入价
Bbreak = Ssteup + 0.25 * (Ssteup - Bsteup) # 突破买入价
Sbreak = Bsteup - 0.25 * (Ssteup - Bsteup) # 突破卖出价
if TodayHigh > Ssteup:
BreakSsteup = True
if TodayLow < Bsteup:
BreakBsteup = True
'''
空仓的情况下:趋势跟踪
1.如果盘中价格超过突破买入价，则采取趋势策略，即在该点位开仓做多
2.如果盘中价格跌破突破卖出价，则采取趋势策略，即在该点位开仓做空
持仓的情况下:
1.当日内最高价超过观察卖出价后，盘中价格出现回落，且进一步跌破反转卖出价构成的支撑线时，采取反转策略，即在该点位（反手、开仓）做空；
2.当日内最低价低于观察买入价后，盘中价格出现反弹，且进一步超过反转买入价构成的阻力线时，采取反转策略，即在该点位（反手、开仓）做多；
'''
if STATE == "IDLE":
if NowPrice > Bbreak:
Log("价格超过突破买入价，趋势跟踪开仓做多")
exchange.Buy(ticker.Sell * 1.001, self.p)
STATE = "LONG"
elif NowPrice < Sbreak:
Log("价格跌破突破卖出价，趋势跟踪开仓做空")
exchange.Sell(ticker.Buy * 0.999, self.p)
STATE = "SHORT"
else:
if BreakSsteup and NowPrice < Senter and STATE == "LONG" and Fan == False:
Log("当日内最高价超过观察卖出价，盘中价格回落跌破反转卖出价,反手做空")
exchange.Sell(ticker.Buy * 0.999, 2 * self.p)
STATE = "SHORT"
Fan = True
if BreakBsteup and NowPrice > Benter and STATE == "SHORT"and Fan == False:
Log("当日内最低价低于观察买入价，盘中价格回落跌破反转卖出价,反手做多")
exchange.Buy(ticker.Sell * 1.001, 2 * self.p)
STATE = "LONG"
Fan = True
'''每日收盘前2分钟,进行平仓'''
if ticker.Time / 1000 - LastTimeStamp >= 86250:
LastTimeStamp = LastTimeStamp + 86400
if STATE == "LONG":
Log("每日收盘平仓")
exchange.Sell(ticker.Buy * 0.999, self.p) # 做多后做空平仓
if STATE == "SHORT":
Log("每日收盘平仓")
exchange.Buy(ticker.Sell * 1.001, self.p) # 做空后做多平仓
Log(exchange.GetAccount())
LastDay = NowDay
Sleep(500)
class Dual_Thrust(strategy):
def __init__(self):
self.name = 'DualThrust'
self.param = {
"N":5,
"k1":0.9,
"k2":0.5,
"p":1
}
def main(self):
Histroy_record = exchange.GetRecords()
dopen = Histroy_record[-1].Open # 每日的开盘价
LastDay = get_day(Histroy_record[-1].Time / 1000) # 上一次获取行情的日期
Nperiod = Histroy_record[-1 * self.param['N'] - 1:][:-1] # 前N日的K数据
Track = self.get_track(Nperiod,dopen) # 获取上下轨数据
LastState = 'IDLE'
while True:
ticker = exchange.GetTicker()
#print(time.strftime("%b %d %Y %H:%M:%S",time.localtime(int(str(ticker['Time'])[:-3]))))
NowPrice = ticker.Last # 当前市场最后成交价格
NowDay = get_day(ticker.Time) # 当前日期
if (NowDay != LastDay):
# 进入了新的一天，重新更新前N天数据与今日开盘价,与今日上下轨点数
Histroy_record = exchange.GetRecords(PERIOD_D1)
#for tick in Histroy_record:
# print(time.strftime("%b %d %Y %H:%M:%S",time.localtime(int(str(tick['Time'])[:-3]))))
Nperiod = Histroy_record[-1 * self.param['N'] - 1:][:-1]
dopen = Histroy_record[-1].Open
Track = self.get_track(Nperiod,dopen)
Log(NowDay, ':', Log(exchange.GetAccount()))
if (NowPrice > Track['uptrack'] and LastState != 'LONG'):
Log('当前市价格', NowPrice, '突破做多触发价:', Track['downtrack'])
# cancel_pending_orders(ORDER_TYPE_SELL) # 撤回所有卖单
if LastState == "IDLE":
exchange.Buy(ticker.Sell * 1.001, self.param['p'])
else:
exchange.Buy(ticker.Sell * 1.001, 2 * self.param['p'])
# days_operation = True
LastState = 'LONG'
if (NowPrice < Track['downtrack'] and LastState != 'SHORT'):
Log('当前市价格', NowPrice, '突破做空触发价:', Track['downtrack'])
# cancel_pending_orders(ORDER_TYPE_BUY) # 撤回所有买单
if LastState == "IDLE":
exchange.Sell(ticker.Buy * 0.999, self.param['p'])
else:
exchange.Sell(ticker.Buy * 0.999, 2 * self.param['p'])
LastState = 'SHORT'
LastDay = NowDay # 更新上次获取行情的日期
def get_track(self,Nperiod,dopen):
'获得上下轨点数'
HH = max([day.High for day in Nperiod])
LC = min([day.Close for day in Nperiod])
HC = max([day.Close for day in Nperiod])
LL = min([day.Low for day in Nperiod])
RANGE = max(HH - LC, HC - LL)
return {'uptrack': dopen + self.param['k1'] * RANGE, 'downtrack': dopen - self.param['k2'] * RANGE}
class 菲阿里四价(strategy):
def __init__(self):
self.name = "菲阿里四价"
self.param['p'] = 0.1
def main(self):
STATE = "IDLE"
LastDay = get_day(exchange.GetRecords(PERIOD_D1)[-1].Time)
LastTimeStamp = exchange.GetRecords(PERIOD_D1)[-1].Time / 1000
while True:
p = self.param['p']
ticker = exchange.GetTicker()
NowDay = get_day(ticker.Time)
#TODAY = exchange.GetRecords(PERIOD_D1)[-1]
yestoday = exchange.GetRecords(PERIOD_D1)[-2] # -1是今日
uptrack = yestoday.High
downtrack = yestoday.Low
NowPrice = ticker.Last
if NowDay != LastDay:
STATE = "IDLE"
Log(NowDay)
'''日内价格突破上下轨，进行做多或者做空'''
if STATE == "IDLE" and NowPrice > uptrack:
exchange.Buy(ticker.Sell * 1.001, p)
STATE = "LONG"
if STATE == "IDLE" and NowPrice < downtrack:
exchange.Sell(ticker.Buy * 0.999, p)
STATE = "SHORT"
'''每日收盘前2分钟,进行平仓'''
if ticker.Time / 1000 - LastTimeStamp >= 86250:
LastTimeStamp = LastTimeStamp + 86400
if STATE == "LONG":
exchange.Sell(ticker.Buy * 0.999, p) # 做多后做空平仓
if STATE == "SHORT":
exchange.Buy(ticker.Sell * 1.001, p) # 做空后做多平仓
LastDay = NowDay
Sleep(500)
class skypark(strategy):
def load_param(self,p = 0.1,k1=1.01,k2=0.99):
self.p = p
self.k1 = k1
self.k2 = k2
def main(self):
p = self.p
k1 = self.k1
k2 = self.k2
History = exchange.GetRecords(PERIOD_D1)
TODAY = History[-1]
Yestoday = History[-2]
LastTimeStamp = TODAY.Time / 1000
LastDay = get_day(TODAY.Time - 1000)
DayFirstCandle = 0 # 每日第一个k线
DayOpen = TODAY.Open
LastOpen = Yestoday.Open
STATE = "IDLE"
while True:
ticker = exchange.GetTicker()
NowDay = get_day(ticker.Time)
if LastDay != NowDay:
'进入新的更新每日第一条k线与昨日和今日的开盘价'
DayFirstCandle = ticker
History = exchange.GetRecords(PERIOD_D1)
TODAY = History[-1]
Yestoday = History[-2]
DayOpen = TODAY.Open
LastOpen = Yestoday.Open
if DayOpen > LastOpen * k1:
Log("今日高开", "今日开盘价:", DayOpen, "昨日开盘价", LastOpen)
if DayOpen < LastOpen * k2:
Log("今日低开", "今日开盘价:", DayOpen, "昨日开盘价", LastOpen)
STATE = "IDLE"
Nowprice = ticker.Last
if DayOpen > LastOpen * k1 and STATE == "IDLE" and Nowprice > DayFirstCandle.High and ticker.Time / 1000 - LastTimeStamp < 86250:
Log("价格", Nowprice, "突破上轨", DayFirstCandle.High, "，买入开仓")
exchange.Buy(ticker.Sell * 1.001, p)
STATE = "LONG"
if DayOpen < LastOpen * k2 and STATE == "IDLE" and Nowprice < DayFirstCandle.Low and ticker.Time / 1000 - LastTimeStamp < 86250:
Log("价格", Nowprice, "突破下轨", DayFirstCandle.High, "，卖入开仓")
exchange.Sell(ticker.Buy * 0.999, p)
STATE = "SHORT"
'''每日收盘前2分钟,进行平仓'''
if ticker.Time / 1000 - LastTimeStamp >= 86250:
LastTimeStamp = LastTimeStamp + 86400
if STATE == "LONG":
Log("做多后做空平仓")
exchange.Sell(ticker.Buy * 0.999, p) # 做多后做空平仓
if STATE == "SHORT":
Log("做空后做多平仓")
exchange.Buy(ticker.Sell * 1.001, p) # 做空后做多平仓
LastDay = NowDay # 更新获取上一次行情的日期
class Dual_Thrust_improved(strategy):
def __init__(self):
self.name = 'DualThrust_improved'
self.defaultparam = {
"N": 5,
"k1": 0.4,
"k2": 0.4,
"p": 0.1,
'stop': 1000,
"KDJ_N":9,
}
self.param = self.defaultparam.copy()
self.k = {}
self.d = {}
self.j = {}
def main(self):
Histroy_record = exchange.GetRecords(PERIOD_D1)
dopen = Histroy_record[-1].Open # 每日的开盘价
LastDay = get_day(Histroy_record[-1].Time / 1000) # 上一次获取行情的日期
Nperiod = Histroy_record[-1 * self.param['N'] - 1:][:-1] # 前N日的K数据
Track = self.get_track(Nperiod,dopen) # 获取上下轨数据
LastState = 'IDLE'
self.k[LastDay] = 50
self.d[LastDay] = 50
self.price = 0
while True:
ticker = exchange.GetTicker()
#print(time.strftime("%b %d %Y %H:%M:%S",time.localtime(int(str(ticker['Time'])[:-3]))))
#print(ticker.Last)
NowPrice = ticker.Last # 当前市场最后成交价格
NowDay = get_day(ticker.Time) # 当前日期
#print(time.strftime("%b %d %Y %H:%M:%S", time.localtime(int(str(ticker['Time'])[:-3]))))
if (NowDay != LastDay):
# 进入了新的一天，重新更新前N天数据与今日开盘价,与今日上下轨点数
Histroy_record = exchange.GetRecords(PERIOD_D1)
#for tick in Histroy_record:
# print(time.strftime("%b %d %Y %H:%M:%S", time.localtime(int(str(tick['Time'])[:-3]))))
#exit()
Nperiod = Histroy_record[-1 * self.param['N'] - 1:][:-1]
dopen = Histroy_record[-1].Open
self.KDJ(NowDay)
if self.k[NowDay] > 50 and self.d[NowDay] > 50 and self.j[NowDay] > 50:
self.param['k1'] = self.defaultparam['k1'] - 0.1
self.param['k2'] = self.defaultparam['k2']
#为做多市场，调整下轨
if self.k[NowDay] < 50 and self.d[NowDay] < 50 and self.j[NowDay] < 50:
self.param['k1'] = self.defaultparam['k1']
self.param['k2'] = self.defaultparam['k2'] - 0.1
Track = self.get_track(Nperiod, dopen)
Log(NowDay, ':', Log(exchange.GetAccount()))
#止损
if LastState == 'LONG' and self.price - ticker.Buy*0.999 >= self.param['stop']:
exchange.Sell(ticker.Buy * 0.999, 1 * self.param['p'])
LastState = "IDLE"
if LastState == 'SHORT' and ticker.Sell*1.001 - self.price >= self.param['stop']:
exchange.Buy(ticker.Sell *1.001, 1 * self.param['p'])
LastState = "IDLE"
#KDJ条件判断市场
if (NowPrice > Track['uptrack'] and LastState != 'LONG'):
Log('当前市价格', NowPrice, '突破做多触发价:', Track['downtrack'])
# cancel_pending_orders(ORDER_TYPE_SELL) # 撤回所有卖单
if LastState == "IDLE":
exchange.Buy(ticker.Sell * 1.001, self.param['p'])
else:
exchange.Buy(ticker.Sell * 1.001, 2 * self.param['p'])
# days_operation = True
self.price = ticker.Sell * 1.001
LastState = 'LONG'
if (NowPrice < Track['downtrack'] and LastState != 'SHORT'):
Log('当前市价格', NowPrice, '突破做空触发价:', Track['downtrack'])
# cancel_pending_orders(ORDER_TYPE_BUY) # 撤回所有买单
if LastState == "IDLE":
exchange.Sell(ticker.Buy * 0.999, self.param['p'])
else:
exchange.Sell(ticker.Buy * 0.999, 2 * self.param['p'])
self.price = ticker.Buy * 0.999
LastState = 'SHORT'
LastDay = NowDay # 更新上次获取行情的日期
def get_track(self,Nperiod,dopen):
'获得上下轨点数'
HH = max([day.High for day in Nperiod])
LC = min([day.Close for day in Nperiod])
HC = max([day.Close for day in Nperiod])
LL = min([day.Low for day in Nperiod])
RANGE = max(HH - LC, HC - LL)
return {'uptrack': dopen + self.param['k1'] * RANGE, 'downtrack': dopen - self.param['k2'] * RANGE}
def RSV(self,Nperiod):
Cn = Nperiod[-1].Close
Ln = min([day.Close for day in Nperiod])
Hn = max([day.High for day in Nperiod])
return (Cn - Ln) / (Hn - Ln) * 100
def KDJ(self,today):
records = exchange.GetRecords(PERIOD_D1);
kdj = TA.KDJ(records,self.param['KDJ_N'], 3, 3);
#Log("k:", kdj[0], "d:", kdj[1], "j:", kdj[2]);
self.k[today] = kdj[0][-1]
self.d[today] = kdj[0][-1]
self.j[today] = kdj[0][-1]
class HMM(strategy):
def __init__(self):
self.name = "Hiden Markov "
self.winratio = []
self.param = {
"p":0.1,
'gap':2,
"days":200,
}
def testmetric(self):
global ALL_day
periods = []
total_real = []
total_metr= []
ALL_day = ALL_day[-10:]
for i in range(len(ALL_day)-1):
periods.append((ALL_day[i],ALL_day[i+1]))
labels = []
this_dir = self.dir + self.param['metrics']+ "/"
try:
os.mkdir(this_dir)
except:
pass
for period in periods:
start = period[0]
end = period[1]
if start.endswith("00:00:00"):
start = start[:10]
if end.endswith("00:00:00"):
end = end[:10]
print('period:',start + " - " + end)
self.backtest(start,end,False,False)
labels.append(start)
total_real += self.real
total_metr += self.metrics
samplenum = len(total_real)
fig = plt.figure(figsize=(30,8))
ax = plt.gca()
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left') # 指定下边的边作为 x 轴 指定左边的边为 y 轴
ax.spines['bottom'].set_position(('data', 0)) # 指定 data 设置的bottom(也就是指定的x轴)绑定到y轴的0这个点上
ax.spines['left'].set_position(('data', 0))
ax.plot(list(range(samplenum)), total_real, label='真实成交价变动')
ax2 = ax.twinx()
ax2.set_xlabel("sample")
ax2.set_ylabel("number")
# ax2.set_ylim((0,100))
plt.title(self.param['title'])
if self.param['metrics'] == 'MACD':
DIF = []
DEA = []
MACD = []
for item in total_metr:
DIF.append(item[0])
DEA.append(item[1])
MACD.append(item[2])
ax2.plot(list(range(samplenum)), DIF, color='red',
label="DIF")
ax2.plot(list(range(samplenum)), DEA, color='blue',
label="DEA")
ax2.plot(list(range(samplenum)), MACD, color='green',
label="MACD")
ax2.legend(loc='upper right')
plt.savefig(this_dir +self.param['title'] + ".png")
print("done!:")
if self.param['metrics'] == 'RSI':
ax2.plot(list(range(samplenum)), total_metr, color='red',
label="该时刻前{}条1分钟k线所求出{}值".format(self.param['metrics_period'], self.param['metrics']))
ax2.legend(loc='upper right')
plt.savefig(this_dir + str(self.param['metrics_period'])+'_'+self.param['title'] + ".png")
print("done!:")
if self.param['metrics'] == 'OBV':
ax2.plot(list(range(samplenum)), total_metr, color='red',
label="前一时刻所求出{}值".format(self.param['metrics']))
ax2.legend(loc='upper right')
plt.savefig(this_dir +self.param['title'] + ".png")
print("done!:")
def testall(self):
global ALL_day
periods = []
total_real = []
total_pred = []
ratio = []
profits = []
ALL_day = ALL_day[-1*self.param['days']:]
for i in range(len(ALL_day)-1):
periods.append((ALL_day[i],ALL_day[i+1]))
labels = []
this_dir = self.dir + "algorithm2" + "/"
try:
os.mkdir(this_dir)
except:
pass
if self.param['gap'] != None:
this_dir = self.dir +"algorithm2" + "/" + str(self.param['gap']) + '/'
else:
this_dir = self.dir + "algorithm2" + "/"
try:
os.mkdir(this_dir)
except:
pass
for period in periods:
start = period[0]
end = period[1]
if start.endswith("00:00:00"):
start = start[:10]
if end.endswith("00:00:00"):
end = end[:10]
print('period:',start + " - " + end)
self.backtest(start,end,False,False)
self.predict = self.predict[:len(self.real)]
if len(self.predict) == 0:
print('跳过一天')
continue
labels.append(start)
total_real += self.real
total_pred += self.predict
ratio.append(self.cal_trend_correct_ratio(self.real,self.predict))
profits.append(self.realizible_profit)
sum_profit = [profits[0]]
for i in range(1,len(profits)):
sum_profit.append(sum_profit[-1] + profits[i])
samplenum = len(total_real)
fig = plt.figure(figsize=(30,5))
above_figure, axs = plt.subplots(2,1)
change_ax = axs[0]
correct_ax = axs[1]
ax = change_ax
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left') # 指定下边的边作为 x 轴 指定左边的边为 y 轴
ax.spines['bottom'].set_position(('data', 0)) # 指定 data 设置的bottom(也就是指定的x轴)绑定到y轴的0这个点上
ax.spines['left'].set_position(('data', 0))
ax.plot(list(range(samplenum)),total_real,label = '真实成交价变动')
ax.plot(list(range(samplenum)),total_pred,label = "前一时刻指标值")
ax.legend(loc = 'upper right')
ax.set_xlabel("sample")
ax.set_ylabel("number")
ax.set_title("{}日内成交价真实变动与预测变动曲线".format(self.param['days']))
ax = correct_ax
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left') # 指定下边的边作为 x 轴 指定左边的边为 y 轴
ax.spines['bottom'].set_position(('data', 0)) # 指定 data 设置的bottom(也就是指定的x轴)绑定到y轴的0这个点上
ax.spines['left'].set_position(('data', 0))
ax.plot(list(range(len(ratio))),ratio,label = '趋势预测正确率')
ax.plot(list(range(len(ratio))),[np.average(ratio)]*len(ratio),label = '样本平均预测正确率')
ax.legend(loc = 'upper right')
ax.set_xlabel("sample")
ax.set_ylabel("ratio")
ax.set_title("{}日预测正确率".format(self.param['days']))
plt.savefig(this_dir + "compare_change_and_correct_ratio_{}.png".format(self.param_suffix_str))
fig = plt.figure()
ax = plt.gca()
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left') # 指定下边的边作为 x 轴 指定左边的边为 y 轴
ax.spines['bottom'].set_position(('data', 0)) # 指定 data 设置的bottom(也就是指定的x轴)绑定到y轴的0这个点上
ax.spines['left'].set_position(('data', 0))
plt.plot(list(range(len(labels))),profits,label = '每日收益曲线')
plt.plot(list(range(len(labels))),sum_profit,label = "累积收益曲线")
plt.legend(loc = 'upper left')
plt.xlabel("day")
plt.ylabel("number")
plt.title("{}日收益曲线".format(self.param['days']))
plt.savefig(this_dir + "profit_curve_{}.png".format(self.param_suffix_str))
print("done!:")
def testprofit(self):
global ALL_day
periods = []
profits = []
labels = []
ALL_day = ALL_day[-100:]
for i in range(len(ALL_day)-1):
periods.append((ALL_day[i],ALL_day[i+1]))
for period in periods:
start = period[0]
end = period[1]
if start.endswith("00:00:00"):
start = start[:10]
if end.endswith("00:00:00"):
end = end[:10]
print('period:',start + " - " + end)
labels.append(start)
self.backtest(start,end,False,False)
profits.append(self.realizible_profit)
this_dir = self.dir +"algorithm2" + "/"
sum_profit = [profits[0]]
for i in range(1,len(profits)):
sum_profit.append(sum_profit[-1] + profits[i])
try:
os.mkdir(this_dir)
except:
pass
samplenum = len(labels)
fig = plt.figure()
ax = plt.gca()
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left') # 指定下边的边作为 x 轴 指定左边的边为 y 轴
ax.spines['bottom'].set_position(('data', 0)) # 指定 data 设置的bottom(也就是指定的x轴)绑定到y轴的0这个点上
ax.spines['left'].set_position(('data', 0))
plt.plot(list(range(samplenum)),profits,label = '每日收益曲线')
plt.plot(list(range(samplenum)),sum_profit,label = "累积收益曲线")
plt.legend(loc = 'upper right')
plt.xlabel("day")
plt.ylabel("number")
plt.title("100日收益曲线")
plt.savefig(this_dir + "profit_curve_{}.png".format(self.param_suffix_str))
def plot(self):
pass
def exit_f2(self):
self.predict = self.predict[:len(self.real)]
self.winratio.append(self.cal_trend_correct_ratio(self.real,self.predict))
def exit_f1(self):
fig = plt.figure(figsize=(12,8))
plt.plot(self.real,label = "真实的变化")
plt.plot(self.preditct,label = "预测的变化")
plt.title("用10点-12点的数据喂模型")
plt.legend(loc = "upper_left")
suffix = ""
for key in self.param:
suffix += str(key) +"="+str(self.param[key]) + "&"
g = lambda x: 1 if x > 0 else -1
f = lambda x,y: 1 if x == y else 0
trend_real = [g(x) for x in self.real]
trend_predic = [g(x) for x in self.preditct]
ratio = sum([f(trend_real[i],trend_predic[i]) for i in range(len(trend_predic))])/(len(trend_predic))
suffix += "趋势正确预测率_"+str(round(ratio,2))
plt.savefig(self.dir + "10点-12点数据训练模型_"+suffix + ".png")
def cal_trend_correct_ratio(self,real,predict):
g = lambda x: 1 if x > 0 else -1
f = lambda x, y: 1 if x == y else 0
trend_real = [g(x) for x in real]
trend_predic = [g(x) for x in predict]
if len(trend_real) == 0:
return np.nan
ratio = sum([f(trend_real[i], trend_predic[i]) for i in range(len(trend_predic))]) / (len(trend_predic))
return round(ratio,2)
def algorithm1(self):
'''用早上10点-12点的数据喂HMM'''
self.param['n'] = 10
self.param['obeserve'] = 6
self.param['threhold'] = 70
LastDay = ""
STATE = 'IDLE'
X = []
History = exchange.GetRecords(PERIOD_M1)
lastticker = History[-2]
lts = lastticker.Time / 1000
train_model = False
self.accumulate_profit = 0
self.real = []
self.predict = []
self.total_ratio = []
COMPARE = False
self.profits = []
NeedStop = False
self.stop = {
"LONG":[],
"SHORT":[],
}
hastart = False
while True:
ticker = exchange.GetTicker()
#优先止损 亏损保持在5美元以内
self.stop["LONG"] = list(sorted(self.stop["LONG"],key = lambda x:x[0]))
self.stop["SHORT"] = list(sorted(self.stop["SHORT"], key=lambda x: x[0],reverse=True))
for item in self.stop["LONG"]:
if ticker.Buy * 0.9999 - item[0] >= -1*item[1] * 5 / 0.1:
exchange.Sell(ticker.Buy * 0.9999, 1 * item[1])
self.stop["LONG"].remove(item)
for item in self.stop["SHORT"]:
if item[0] - ticker.Sell * 1.0001 >= -1*item[1] * 5 / 0.1:
exchange.Buy(ticker.Sell * 1.0001, 1 * item[1])
self.stop["SHORT"].remove(item)
tickertime = strategy.ticktime(ticker)
NowDay = get_day(ticker.Time)
if NowDay != LastDay:
Log("进入新的一天")
dts = ticker.Time / 1000
LastDay = NowDay
train_model = False
O = []
if ticker.Time/1000 - dts >= 36000 and ticker.Time/1000 - dts < 43200:
"十点至十二点的数据喂养模型"
change = ticker.Last - lastticker.Last
X.append([change])
else:
if not hastart:
last_check_ticker = ticker
hastart = True
if ticker.Time/1000 - dts >= 43200 and ticker.Time/1000 - dts < 64800:
if ticker.Time/1000 - last_check_ticker.Time/1000 < self.param['gap']*60:
#需要跳过
continue
lastticker = last_check_ticker
change = ticker.Last - lastticker.Last
O.append([change])
"十二点到下午六点进行交易"
if COMPARE:
self.real.append(change)
#print("预测:", self.predict[-1], " 实际:", change,end = " ")
#print("当前成交价:",ticker.Last ," 上一时刻成交价:",lastticker.Last)
#print("买一:",ticker.Buy," 卖一:",ticker.Sell,end=" ")
if STATE == 'LONG':
print("买: ",price)
if price - ticker.Buy * 0.9999 >= self.param['p'] * 30 / 0.1 :
print("变动过大,止损,有仓位需要后续止损")
self.stop[STATE].append((price,self.param['p']))
else:
exchange.Sell(ticker.Buy * 0.9999, 1 * self.param['p'])
print("收益:",ticker.Buy * 0.9999 - price)
self.profits.append(ticker.Buy * 0.9999 - price)
STATE = "IDLE"
if STATE == 'SHORT':
print("卖: ", price)
if ticker.Sell * 1.0001 - price>= self.param['p'] * 30 / 0.1 :
print("变动过大,止损,有仓位需要后续止损")
self.stop[STATE].append((price,self.param['p']))
else:
exchange.Buy(ticker.Sell * 1.0001, 1 * self.param['p'])
print("收益:",price - ticker.Sell * 1.000 )
self.profits.append(price - ticker.Sell * 1.000)
STATE = "IDLE"
COMPARE = False
#print()
if not train_model:
"先训练模型"
m = hmm.GaussianHMM(n_components=self.param['n'],covariance_type="full")
seen = np.array(X).reshape(-1, 1)
try:
m.fit(seen)
except:
return 0
train_model = True
trans = m.transmat_
else:
"训练好模型,观察到5个就交易,就对下一个进行交易"
if len(O) == self.param['obeserve'] and len(self.stop["LONG"]) + len(self.stop["SHORT"]) <= 2:
"最多只能有两个未止损"
try:
I = m.predict_proba(O)
except:
return 0
TRAN = I[-1:]
next_state_pro = (TRAN @ trans).T
distriution_means = m.means_
expected_change = (next_state_pro.T @ distriution_means)[0][0]
if expected_change > self.param['threhold'] and STATE == "IDLE":
exchange.Buy(ticker.Sell *1.0001, 1 * self.param['p'])
STATE = "LONG"
price = ticker.Sell *1.0001
if expected_change < -1*self.param['threhold'] and STATE == "IDLE":
exchange.Sell(ticker.Buy *0.9999, 1 * self.param['p'])
STATE = "SHORT"
price = ticker.Buy *0.9999
self.predict.append(expected_change)
COMPARE = True
O = O[1::]
#O = []
last_check_ticker = ticker
if ticker.Time/1000 - dts >= 86250:
Log("进入一天末尾准备平仓")
for item in self.stop["LONG"]:
exchange.Sell(ticker.Buy * 0.9999, 1 * item[1])
self.stop["LONG"].remove(item)
for item in self.stop["SHORT"]:
exchange.Buy(ticker.Sell * 1.0001, 1 * item[1])
self.stop["SHORT"].remove(item)
STATE = 'IDLE'
Log(exchange.GetAccount())
print(self.profits)
print(sum(self.profits))
lastticker = ticker
def algorithm2(self):
'''用早上10点-12点的数据喂HMM'''
self.param['n'] = 18
self.param['obeserve'] = 5
self.param['threhold'] = 20
self.param['stop'] = True
LastDay = ""
STATE = 'IDLE'
X = []
History = exchange.GetRecords(PERIOD_M1)
lastticker = History[-2]
lts = lastticker.Time / 1000
train_model = False
self.accumulate_profit = 0
self.real = []
self.predict = []
self.total_ratio = []
COMPARE = False
self.profits = []
NeedStop = False
self.stop = {
"LONG": [],
"SHORT": [],
}
hastart = False
hastrainstart = False
while True:
ticker = exchange.GetTicker()
# 优先止损 亏损保持在5美元以内
'''
self.stop["LONG"] = list(sorted(self.stop["LONG"], key=lambda x: x[0]))
self.stop["SHORT"] = list(sorted(self.stop["SHORT"], key=lambda x: x[0], reverse=True))
for item in self.stop["LONG"]: