-
Notifications
You must be signed in to change notification settings - Fork 11
/
Copy pathxgboost_6factor_test _2.py
185 lines (168 loc) · 9.53 KB
/
xgboost_6factor_test _2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
# -*- coding: utf-8 -*-
from atrader import *
from atrader.enums import *
import numpy as np
import pandas as pd
import datetime
import xgboost as xgb
from sklearn.model_selection import KFold, train_test_split, GridSearchCV
from sklearn.metrics import confusion_matrix, mean_squared_error
from sklearn.datasets import load_iris, load_digits, load_boston
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold,StratifiedKFold
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
import warnings
from sklearn.metrics import accuracy_score
"""
2016-03-31至2018-03-31训练，2018-03-31至2019-03-31测试,滚动1个月
"""
def init(context: Context):
set_backtest(initial_cash=1e6)
context.faclist = ['MktValue',
'NegMktValue',
'LFLO',
'LINEARREG_INTERCEPT',
'HT_TRENDLINE',
'KAMA'
]
context.tarlist=get_code_list('hs300')
reg_factor(context.faclist)
reg_kdata(frequency='month', fre_num=1, adjust=False)
context.ratio = 0.9 #初始权重设为0.9
context.val_num = 1 # 测试1个月
begin_date = '2016-03-01'
end_date = '2019-03-31'
context.cal=pd.Series(get_trading_days(market='sse', begin_date=begin_date, end_date=end_date))
# get_trading_days()以列表的形式获取2016-03-31到2018-03-31的交易日期（包含2018-03-31）
# len取其长度，作为后面获取factor的天数
context.days = len(get_trading_days(market='sse', begin_date=begin_date, end_date=end_date))
# 设置开仓的最大资金量
def on_data(context: Context):
print(context.now)
# 实时模式返回当前本地时间, 回测模式返回当前回测时间
now = context.now
# begin_date1(每月1号), end_date1 相差一个月
begin_date1=now.replace(day=1)
if now.month==12:
if now.year == 2016:
end_date1=begin_date1.replace(year=2017).replace(month=1)
if now.year == 2017:
end_date1 = begin_date1.replace(year=2018).replace(month=1)
if now.year == 2018:
end_date1 = begin_date1.replace(year=2019).replace(month=1)
else:
end_date1=begin_date1.replace(month=(begin_date1.month+1))
# call提取begin_date1与end_date1之间的时间
cal1=context.cal[context.cal<end_date1]
cal1=cal1[cal1>begin_date1]
orderlist=get_order_info(order_list=())
print(cal1) # call为当前一个月的所有交易日
# cal[-2:-1]为本月最后一个交易日
if now<datetime.datetime(2018,4,30): # 取2016-03-31 12个月后的日期开始交易，前11个月的数据用于训练，第13个月的为标签。如此滚动。
pass
else:
if now<cal1.iloc[-2]: # 日期小于本月交易日的倒数第二天
# 查询当前价格进行止损，减小最大回撤的方式
#price0=get_reg_kdata(reg_idx=context.reg_kdata[0], target_indices=(), length=1, fill_up=True, df=True)
positions = context.account().positions
idx_long_list = positions.loc[positions['volume_long']>0,'target_idx']
for idx in idx_long_list:
#price_idx = price0.loc[price0['target_idx']==idx,'close']
amount_long = positions['amount_long'].iloc[idx] # 多头持仓金额
fpnl_long = positions['fpnl_long'].iloc[idx] # 多头持仓的浮动盈亏
if -fpnl_long>amount_long*0.1:
# 全部卖出
order_volume(account_idx=0, target_idx=idx,
volume=int(positions['volume_long'].iloc[idx]),
side=2, position_effect=2, order_type=2, price=0)
else:
pass
else:
# price， target_idx取值为0，1，2，3（代表不同的股票）
price=get_reg_kdata(reg_idx=context.reg_kdata[0], target_indices=(), length=25, fill_up=True, df=True) #获取13个月的price
price = price[price['close']!=0]
price['ret_month'] = price.groupby('target_idx')['close'].apply(lambda x:(x-x.shift())/x.shift()) # 在相同的target_idx内计算ret
price.loc[price['ret_month']>=0.03,'label'] = 1 # 盈利率>3%, 标记为1
price.loc[price['ret_month']<0.03,'label'] = 0 # 盈利率<3%, 标记为0
price_month1 = price[['target_idx','time','ret_month','close','label']]
print(price_month1)
price_month1['month'] = price_month1['time'].apply(lambda x:int(str(x)[0:4]+str(x)[5:7])) # 增加month列，201701，201702，只记录月份，不记录日时分秒
price_month1['ret_nextmonth'] = price_month1.groupby('target_idx')['ret_month'].shift(-1) # 添加下个月的盈利
price_month1['label'] = price_month1.groupby('target_idx')['label'].shift(-1) # 对应平移标签，因为ret_month计算的是本月的盈利
# factor的注册频率默认为日频
factor=get_reg_factor(reg_idx=context.reg_factor[0], target_indices=(), length=500, df=True)
factor = factor.dropna(subset =['date']) # 删除非法日期
factor['code'] = factor['target_idx'].apply(lambda x:context.target_list[x]) # 将用0，1，2，3等表示的股票换成对应的股票代码
factor['month'] = factor['date'].apply(lambda x:int(str(x)[0:4]+str(x)[5:7])) # 增加month列，2017-01，2017-02，只记录月份，不记录日时分秒
factor_name = factor['factor'].drop_duplicates().tolist() # 以列表的形式取出因子名称
# 将factor按['target_idx','month','factor']分组，分别取每组的最后一行
# 即取出各股票每个月末的所有因子值
factor_month = factor.groupby(['target_idx','month','factor']).apply(lambda x:x.iloc[-1])[['date','value']].reset_index()
# 添加所有因子名作为新的列
factor_month1 = factor_month.groupby(['target_idx','month']).apply(deal).reset_index()
df = pd.merge(factor_month1,price_month1,on=['target_idx','month'],how='right')
"""
训练集取最初到最后的前一个月，[:-1]
测试集取最后一个月[-1]
"""
train = df.groupby('target_idx').apply(lambda x: x.iloc[:-1 * context.val_num - 1])
test = df.groupby('target_idx').apply(lambda x: x.iloc[-1 * context.val_num - 1:-1])
scaler=StandardScaler() # 标准化
# 提取因子值train[factor_name]作为特征
X_train = train[factor_name]
X_train = X_train.fillna(0).values
X_train = scaler.fit_transform(X_train) # 因子标准化
# 提取train['label']作为标签，
# .fillna(0)NaN补0，
# .values转化成array形式
y_train = train['label'].fillna(0).values
X_test = test[factor_name]
y_test = test['label'].values
X_test = X_test.fillna(0).values
#X_test=scaler.fit_transform(X_test) # 因子标准化
X_test = scaler.transform(X_test) # 因子标准化
# 预测
model = XGBClassifier().fit(X_train, y_train)
y_pred = model.predict(X_test)
y_pred1 = pd.DataFrame(y_pred,columns=['label'])
idx_list = list(y_pred1[y_pred1['label']==1].index)
print(idx_list)
positions = context.account().positions
if len(idx_list)==0: # 没有一只股票在标的池，则卖出全部股票
for target_idx in positions.loc[positions['volume_long']>0,'target_idx'].astype(int):
order_volume(account_idx=0, target_idx=target_idx,
volume=int(positions['volume_long'].iloc[target_idx]),
side=2, position_effect=2, order_type=2, price=0)
else:
# 平不在标的池的股票
for target_idx in positions.target_idx.astype(int):
if target_idx not in idx_list:
if positions['volume_long'].iloc[target_idx] > 0:
order_volume(account_idx=0, target_idx=target_idx,
volume=int(positions['volume_long'].iloc[target_idx]),
side=2, position_effect=2, order_type=2, price=0)
# 获取股票的权重
percent_b = context.ratio / len(idx_list)
#print(percent_b)
# 买在标的池中的股票
for target_idx in idx_list:
order_target_percent(account_idx=0, target_idx=target_idx, target_percent=percent_b, side=1, order_type=2)
print(positions.loc[positions['volume_long']>0,'code'].tolist())
def deal(df):
factor_name = df['factor'].tolist()
df1 = pd.DataFrame(columns = factor_name)
for i in factor_name:
df1[i] = df.loc[df['factor']==i,'value'].values
return df1
if __name__ == "__main__":
# 投资域
begin_date = '2016-03-01'
end_date = '2019-03-31'
cal=get_trading_days(market='sse', begin_date=begin_date, end_date=end_date)
warnings.filterwarnings("ignore")
start = datetime.datetime.now()
tarlist = get_code_list('hs300')
a = run_backtest('XGBoost_2018_2019',target_list=tarlist['code'].tolist(),file_path='.', begin_date=begin_date,
end_date=end_date,frequency='month', fq=1)