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隐马尔科夫模型收益曲线2017.py
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隐马尔科夫模型收益曲线2017.py
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# -*- coding: utf-8 -*-
"""
@author: ty
"""
from hmmlearn.hmm import GaussianHMM
import pandas as pd
import numpy as np
from pandas import Series,DataFrame
import matplotlib.pyplot as plt
import scipy.stats as sts
import matplotlib.dates as dates
import seaborn as sns
from datetime import datetime
start=datetime.now()
PosSizeL=1
PosSizeS=1
data1=pd.read_csv('rb888_2015.csv',parse_dates=True,index_col='time')
data1.reset_index(inplace=True)
#定义MACD函数
def EMA_MACO(data,d):
test=pd.Series(index=range(len(data)))
test=pd.ewma(data, span=d)
return test
def MACD(data,FastLength,SlowLength,MACDLength):
data['Diff']=''
data['Diff']=EMA_MACO(data['open'],FastLength)-EMA_MACO(data['open'],SlowLength)
data['DEA']=''
data['DEA']=EMA_MACO(data['Diff'],MACDLength)
data['MACD']=''
data['MACD']=data['Diff']-data['DEA']
return data
for h,k in [(5,20),(15,20),(5,10),(5,15),(10,15)]:
data1['fast_line']=''
data1['slow_line']=''
data1['fast_line']=pd.rolling_mean(data1['close'],h)
data1['slow_line']=pd.rolling_mean(data1['close'],k)
data1['fast_line']=data1['fast_line'].fillna(value=pd.expanding_mean(data1['close']))
data1['slow_line']=data1['slow_line'].fillna(value=pd.expanding_mean(data1['close']))
data1['dist_%s_%s'%(k,h)]=data1['fast_line']-data1['slow_line']
for h in range(10,26,5):
data1['fast_line']=''
data1['slow_line']=''
data1['fast_line']=pd.rolling_max(data1['high'].shift(1),h)
data1['slow_line']=pd.rolling_min(data1['low'].shift(1),h)
data1['fast_line']=data1['fast_line'].fillna(value=pd.expanding_max(data1['high']))
data1['slow_line']=data1['slow_line'].fillna(value=pd.expanding_min(data1['low']))
data1['dist_high_%s'%h]=data1['high']-data1['fast_line']
data1['dist_low_%s'%h]=data1['low']-data1['slow_line']
data1=MACD(data1,12,26,9)
data2=pd.read_csv('rb888_2017.csv',parse_dates=True,index_col='time')
data2.reset_index(inplace=True)
data2['log_return']=np.log(data2['close']/data2['close'].shift(1))
data2['log_return']=data2['log_return'].fillna(0)
for h,k in [(5,20),(15,20),(5,10),(5,15),(10,15)]:
data2['fast_line']=''
data2['slow_line']=''
data2['fast_line']=pd.rolling_mean(data2['close'],h)
data2['slow_line']=pd.rolling_mean(data2['close'],k)
data2['fast_line']=data2['fast_line'].fillna(value=pd.expanding_mean(data2['close']))
data2['slow_line']=data2['slow_line'].fillna(value=pd.expanding_mean(data2['close']))
data2['dist_%s_%s'%(k,h)]=data2['fast_line']-data2['slow_line']
for h in range(10,26,5):
data2['fast_line']=''
data2['slow_line']=''
data2['fast_line']=pd.rolling_max(data2['high'].shift(1),h)
data2['slow_line']=pd.rolling_min(data2['low'].shift(1),h)
data2['fast_line']=data2['fast_line'].fillna(value=pd.expanding_max(data2['high']))
data2['slow_line']=data2['slow_line'].fillna(value=pd.expanding_min(data2['low']))
data2['dist_high_%s'%h]=data2['high']-data2['fast_line']
data2['dist_low_%s'%h]=data2['low']-data2['slow_line']
data2=MACD(data2,12,26,9)
factor_list=['dist_low_20','MACD','dist_20_15','dist_10_5','dist_15_5','dist_15_10','dist_20_5']
hidden_states=[]
forward=100
count=0
result=pd.DataFrame(columns=['因子','交易次数','累积净利','最大回撤','收益风险比','胜率'],index=range(100))
for i in factor_list:
X = np.column_stack([data1[i]])
model = GaussianHMM(n_components=3, covariance_type="diag", n_iter=1000,random_state=0).fit(X)
Y = np.column_stack([data2[i]])
hidden_states=model.predict(Y)
if i=='dist_low_20':
hidden_states=np.array(hidden_states)
signal=np.where(hidden_states==1,-1,np.where(hidden_states==2,1,0))
elif i=='MACD':
hidden_states=np.array(hidden_states)
signal=np.where(hidden_states==1,-1,np.where(hidden_states==2,1,0))
elif i=='dist_20_15':
hidden_states=np.array(hidden_states)
signal=np.where(hidden_states==1,-1,np.where(hidden_states==2,1,0))
elif i=='dist_10_5':
hidden_states=np.array(hidden_states)
signal=np.where(hidden_states==1,-1,np.where(hidden_states==2,1,0))
elif i=='dist_15_5':
hidden_states=np.array(hidden_states)
signal=np.where(hidden_states==1,-1,np.where(hidden_states==2,1,0))
elif i=='dist_20_5':
hidden_states=np.array(hidden_states)
signal=np.where(hidden_states==1,-1,np.where(hidden_states==2,1,0))
else:
hidden_states=np.array(hidden_states)
signal=np.where(hidden_states==2,-1,hidden_states)
signal=np.append(0,signal[:-1])
data2['signal']=signal
buy=[]
sell=[]
Type=[]
Hand=[]
SetTime=[]
OpenPosition=[]
CoverTime=[]
NetProfit=[]
CoverPosition=[]
StaticRights=[]
Rights=0
BarRights=Rights
DynamicRights=[]
ProfitShares=0
position=0
for index in data2.index:
if data2['signal'][index]==1 and position==0:
ep=data2['open'][index]
buy.append(-ep*PosSizeL*10)
position=1
Type.append('多头')
Hand.append(PosSizeL)
SetTime.append(data2['time'][index])
OpenPosition.append(ep)
if data2['signal'][index]==0 and position==1:
ep=data2['open'][index]
buy.append(ep*PosSizeL*10)
position=0
CoverTime.append(data2['time'][index])
NetProfit.append(ep*10-OpenPosition[-1]*10)
CoverPosition.append(ep)
StaticRights.append(sum(NetProfit))
if data2['signal'][index]==-1 and position==1:
ep=data2['open'][index]
CoverTime.append(data2['time'][index])
NetProfit.append(ep*10-OpenPosition[-1]*10)
CoverPosition.append(ep)
StaticRights.append(sum(NetProfit))
buy.append(ep*PosSizeL*10)
sell.append(ep*PosSizeS)
position=-1
Type.append('空头')
Hand.append(PosSizeS)
SetTime.append(data2['time'][index])
OpenPosition.append(ep)
if data2['signal'][index]==-1 and position==0:
ep=data2['open'][index]
sell.append(ep*PosSizeS)
position=-1
Type.append('空头')
Hand.append(PosSizeS)
SetTime.append(data2['time'][index])
OpenPosition.append(ep)
if data2['signal'][index]==0 and position==-1:
ep=data2['open'][index]
sell.append(-ep*PosSizeS)
position=0
CoverTime.append(data2['time'][index])
NetProfit.append((-ep+OpenPosition[-1])*10)
CoverPosition.append(ep)
StaticRights.append(sum(NetProfit))
if data2['signal'][index]==1 and position==-1:
ep=data2['open'][index]
sell.append(-ep*PosSizeS)
buy.append(-ep*PosSizeL*10)
position=0
CoverTime.append(data2['time'][index])
NetProfit.append((-ep+OpenPosition[-1])*10)
CoverPosition.append(ep)
StaticRights.append(sum(NetProfit))
position=1
Type.append('多头')
Hand.append(PosSizeL)
SetTime.append(data2['time'][index])
OpenPosition.append(ep)
if position==1:
BarRights=Rights+sum(buy)+data2.close[index]*10+sum(sell)*10
DynamicRights.append(BarRights)
if position==0:
DynamicRights.append(BarRights)
if position==-1:
BarRights=Rights+(sum(sell)-data2.close[index])*10+sum(buy)
DynamicRights.append(BarRights)
if position==1:
buy.append(data2.close[index]*10)
CoverTime.append(data2['time'][index])
CoverPosition.append(data2.close[index])
NetProfit.append((data2.close[index]-OpenPosition[-1])*10)
StaticRights.append(sum(NetProfit))
if position==-1:
sell.append(-data2.close[index])
CoverTime.append(data2['time'][index])
CoverPosition.append(data2.close[index])
NetProfit.append((-data2.close[index]+OpenPosition[-1])*10)
StaticRights.append(sum(NetProfit))
trade_info=pd.DataFrame(index=range(1,len(OpenPosition)+1))
trade_info['建仓时间']=SetTime
trade_info['建仓价格']=OpenPosition
trade_info['平仓时间']=CoverTime
trade_info['平仓价格']=CoverPosition
trade_info['数量']=PosSizeL
trade_info['净利']=NetProfit
trade_info['累计净利']=StaticRights
trade_info['收益率']=trade_info['净利']/trade_info['建仓价格']
trade_info['累积收益率']=trade_info['收益率'].cumsum()
trade_info.to_csv('%s因子交易记录2017.csv'%i,index=False)
capital_change=pd.DataFrame(index=data2['time'])
capital_change['动态权益']=DynamicRights
def max_drawdown(date_line,capital_line):
df=pd.DataFrame({'date':date_line,'capital':capital_line})
df.sort('date',inplace=True)
df.reset_index(drop=True,inplace=True)
df['max2here']=pd.expanding_max(df['capital'])
df['dd2here']=df['max2here']-df['capital']
temp=df.sort('dd2here',ascending=False).iloc[0][['date','dd2here']]
max_dd=temp['dd2here']
end_date=temp['date']
df=df[df['date']<=end_date]
start_date=df.sort('capital',ascending=False).iloc[0]['date']
return max_dd#'最大回撤为：%f,开始日期：%s,结束日期：%s'%(max_dd,start_date, end_date)
date_line=capital_change.index
capital_line=capital_change['动态权益']
#trade_info['累计净利'].plot()
#capital_change['动态权益'].plot()
for index in range(len(NetProfit)):
if NetProfit[index]>0:
ProfitShares=ProfitShares+1
WinRate=ProfitShares/len(NetProfit)
plt.figure(figsize=(15, 8))
plt.plot(trade_info['平仓时间'],trade_info['累计净利'])
plt.savefig('C:/Users/Public/Documents/Python Scripts/隐马尔科夫模型收益曲线2017/%s.png'%(i))
end=datetime.now()
period=(end-start).seconds
profit=sum(NetProfit)
day=(data2['time'][data2.index[-1]]-data2['time'][0]).days
maxdrawdown=max_drawdown(date_line,capital_line)
annual_return=trade_info['累计净利'].iloc[-1]/day*356
ratio_of_return_and_risk=annual_return/maxdrawdown
result['因子'][count]=i
result['交易次数'][count]=len(SetTime)
result['累积净利'][count]=profit
result['最大回撤'][count]=maxdrawdown
result['收益风险比'][count]=ratio_of_return_and_risk
result['胜率'][count]=WinRate
count=count+1
result.to_csv('隐马尔科夫模型绩效回测表2017.csv',index=False)