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xgboost_6factor_test _3.py
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# -*- 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
import pickle
"""
2016-03-01至2018-03-31训练，2018-03-31至2019-03-31测试,全分开，不滚动
"""
def init(context: Context):
set_backtest(initial_cash=1e6, stock_cost_fee=30)
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
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<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: #浮动盈亏大于持仓金额的10%
# 全部/50% 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:
# factor的注册频率默认为日频
factor=get_reg_factor(reg_idx=context.reg_factor[0], target_indices=(), length=30, 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()
"""
取最后一个月(当前时间)
"""
test = factor_month1.groupby('target_idx').apply(lambda x: x.iloc[-1])
scaler = StandardScaler() # 标准化
X_test = test[factor_name]
X_test = X_test.fillna(0).values
#X_test=scaler.fit_transform(X_test) # 因子标准化
X_test = scaler.fit_transform(X_test) # 因子标准化
# 预测
model = pickle.load(open("XGboost_ret0.03.pickle.dat", "rb"))
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)