附：交并集投资组合回测结果
import pandas as pd
Union = pd.read_excel("Union.xlsx", index_col="trade_date")
Intersection = pd.read_excel("Intersection.xlsx", index_col="trade_date")Union| 000001.SZ | 000002.SZ | 000008.SZ | 000009.SZ | 000012.SZ | 000024.SZ | 000027.SZ | 000039.SZ | 000046.SZ | 000060.SZ | ... | 601992.SH | 601997.SH | 601998.SH | 603000.SH | 603160.SH | 603288.SH | 603699.SH | 603858.SH | 603885.SH | 603993.SH | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| trade_date | |||||||||||||||||||||
| 20140103 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140106 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140107 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140108 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140109 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140110 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140113 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140114 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140115 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140116 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140117 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140120 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140121 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140122 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140123 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140124 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140127 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140128 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140129 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140130 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140207 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140210 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140211 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140212 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140213 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140214 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140217 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140218 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140219 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20140220 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20170821 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170822 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170823 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170824 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170825 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170828 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170829 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170830 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170831 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170901 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170904 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170905 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170906 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170907 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170908 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170911 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170912 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170913 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170914 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170915 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170918 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170919 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170920 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170921 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170922 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170925 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170926 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170927 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170928 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20170929 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
916 rows × 456 columns
from datetime import datetime, timedelta
import numpy as np
def change_columns_index(signal):
new_names = {}
for c in signal.columns:
if c.endswith('SZ'):
new_names[c] = c.replace('SZ', 'XSHE')
elif c.endswith('SH'):
new_names[c] = c.replace('SH', 'XSHG')
signal = signal.rename_axis(new_names, axis=1)
signal.index = pd.Index(map(lambda x: datetime.strptime(str(x),"%Y%m%d") , signal.index))
signal.index = pd.Index(map(lambda x: x+timedelta(hours=15) , signal.index))
return signal
def get_daily_weight(data,period):
data = data.fillna(0)
weight_list = []
for time_index, weight in data.iterrows():
weight_result = (weight/weight.sum())
weight_list.append(weight_result.to_dict())
stock_df = pd.DataFrame(weight_list, index=data.index)
if period>1:
stock_df = pd.concat([pd.DataFrame(0,columns=stock_df.columns,index=list(range(1,period))),stock_df],axis=0)
target = (pd.rolling_apply(stock_df, period, sum).dropna(how="all")/period).replace(0,np.nan)
return targetUnion_weight = get_daily_weight(change_columns_index(Union),period=30)
Intersection_weight = get_daily_weight(change_columns_index(Intersection),period=30)
Union_weight.head()/home/xinger/anaconda3/envs/IIA/lib/python3.6/site-packages/ipykernel/__main__.py:25: FutureWarning: pd.rolling_apply is deprecated for DataFrame and will be removed in a future version, replace with
DataFrame.rolling(window=30,center=False).apply(func=<builtin_function_or_method>,args=<tuple>,kwargs=<dict>)
| 000001.XSHE | 000002.XSHE | 000008.XSHE | 000009.XSHE | 000012.XSHE | 000024.XSHE | 000027.XSHE | 000039.XSHE | 000046.XSHE | 000060.XSHE | ... | 601992.XSHG | 601997.XSHG | 601998.XSHG | 603000.XSHG | 603160.XSHG | 603288.XSHG | 603699.XSHG | 603858.XSHG | 603885.XSHG | 603993.XSHG | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-01-03 15:00:00 | 0.000412 | 0.000412 | NaN | NaN | NaN | 0.000412 | NaN | NaN | NaN | NaN | ... | 0.000412 | NaN | 0.000412 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2014-01-06 15:00:00 | 0.000412 | 0.000823 | NaN | NaN | NaN | 0.000823 | NaN | NaN | NaN | NaN | ... | 0.000823 | NaN | 0.000412 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2014-01-07 15:00:00 | 0.000412 | 0.001235 | NaN | NaN | 0.000412 | 0.001235 | NaN | NaN | NaN | NaN | ... | 0.001235 | NaN | 0.000412 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2014-01-08 15:00:00 | 0.000412 | 0.001622 | NaN | NaN | 0.000799 | 0.001622 | NaN | NaN | NaN | NaN | ... | 0.001622 | NaN | 0.000412 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2014-01-09 15:00:00 | 0.000412 | 0.002024 | NaN | NaN | 0.000799 | 0.002024 | NaN | NaN | NaN | NaN | ... | 0.002024 | NaN | 0.000412 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 456 columns
并集组合（不对冲）
import rqalpha
from rqalpha.api import *
#读取文件位置
def init(context):
context.codes = Union_weight
context.stocks = None
context.pos_weight = 0.9
scheduler.run_daily(find_pool)
def find_pool(context, bar_dict):
try:
codes = context.codes.loc[context.now].dropna()
context.stocks = codes
except:
context.stocks = None
def handle_bar(context, bar_dict):
buy(context, bar_dict)
def buy(context, bar_dict):
pool = context.stocks
if pool is not None:
for ins, percentage in pool.items():
order_target_percent(ins, percentage*context.pos_weight)
for ins in context.portfolio.positions:
if ins not in context.stocks:
order_target_percent(ins, 0)
config = {
"base": {
"start_date": "2014-01-03",
"end_date": "2018-01-31",
"accounts": {'stock':10000000},
"benchmark": "000300.XSHG"
},
"extra": {
"log_level": "error",
},
"mod": {
"sys_analyser": {
#"enabled": True,
"plot": True
}
}
}
rqalpha.run_func(init=init, handle_bar=handle_bar, config=config)WARNING: better_exceptions will only inspect code from the command line
when using: `python -m better_exceptions'. Otherwise, only code
loaded from files will be inspected!
{'sys_analyser': {'benchmark_portfolio': cash market_value static_unit_net_value total_value \
date
2014-01-03 745.3 9999254.70 1.000 10000000.00
2014-01-06 745.3 9771663.60 1.000 9772408.90
2014-01-07 745.3 9768870.00 0.977 9769615.30
2014-01-08 745.3 9785937.15 0.977 9786682.45
2014-01-09 745.3 9699990.30 0.979 9700735.60
2014-01-10 745.3 9624170.25 0.970 9624915.55
2014-01-13 745.3 9575413.20 0.962 9576158.50
2014-01-14 745.3 9659090.25 0.958 9659835.55
2014-01-15 745.3 9642023.10 0.966 9642768.40
... ... ... ... ...
2018-01-17 745.3 18543043.80 1.859 18543789.10
2018-01-18 745.3 18644748.30 1.854 18645493.60
2018-01-19 745.3 18705771.00 1.865 18706516.30
2018-01-22 745.3 18929259.00 1.871 18930004.30
unit_net_value units
date
2014-01-03 1.000000 10000000.0
2014-01-06 0.977241 10000000.0
2014-01-07 0.976962 10000000.0
2014-01-08 0.978668 10000000.0
2014-01-09 0.970074 10000000.0
2014-01-10 0.962492 10000000.0
2014-01-13 0.957616 10000000.0
2014-01-14 0.965984 10000000.0
2014-01-15 0.964277 10000000.0
2014-01-16 0.965543 10000000.0
... ... ...
2018-01-16 1.858897 10000000.0
2018-01-17 1.854379 10000000.0
2018-01-18 1.864549 10000000.0
2018-01-19 1.870652 10000000.0
2018-01-22 1.893000 10000000.0
[991 rows x 6 columns],
'portfolio': cash market_value static_unit_net_value total_value \
date
2014-01-03 9736148.000 2.634470e+05 1.000 9.999595e+06
2014-01-06 9429722.000 5.606790e+05 1.000 9.990401e+06
2014-01-07 9125957.000 8.627950e+05 0.999 9.988752e+06
2014-01-08 8829838.000 1.151396e+06 0.999 9.981234e+06
2014-01-09 8517260.115 1.455680e+06 0.998 9.972940e+06
... ... ... ... ...
2018-01-15 2080687.653 2.057292e+07 2.266 2.265361e+07
2018-01-16 2080687.653 2.087998e+07 2.265 2.296067e+07
2018-01-17 2080687.653 2.087841e+07 2.296 2.295910e+07
2018-01-18 2080687.653 2.102167e+07 2.296 2.310235e+07
2018-01-19 2080687.653 2.104398e+07 2.310 2.312467e+07
2018-01-22 2080687.653 2.114470e+07 2.312 2.322539e+07
unit_net_value units
date
2014-01-03 0.999960 10000000.0
2014-01-06 0.999040 10000000.0
2014-01-07 0.998875 10000000.0
2014-01-08 0.998123 10000000.0
... ... ...
2018-01-11 2.259800 10000000.0
2018-01-12 2.265512 10000000.0
2018-01-15 2.265361 10000000.0
2018-01-16 2.296067 10000000.0
2018-01-17 2.295910 10000000.0
2018-01-18 2.310235 10000000.0
2018-01-19 2.312467 10000000.0
2018-01-22 2.322539 10000000.0
[991 rows x 6 columns],
'stock_account': cash dividend_receivable market_value total_value \
date
2014-01-03 9736148.000 0.0 2.634470e+05 9.999595e+06
2014-01-06 9429722.000 0.0 5.606790e+05 9.990401e+06
2014-01-07 9125957.000 0.0 8.627950e+05 9.988752e+06
2014-01-08 8829838.000 0.0 1.151396e+06 9.981234e+06
2014-01-09 8517260.115 0.0 1.455680e+06 9.972940e+06
2014-01-10 8220735.115 0.0 1.743951e+06 9.964686e+06
2014-01-13 7923490.365 0.0 2.039548e+06 9.963038e+06
2014-01-14 7641301.300 0.0 2.343817e+06 9.985118e+06
2014-01-15 7330628.300 0.0 2.639467e+06 9.970095e+06
... ... ... ... ...
2018-01-17 2080687.653 0.0 2.087841e+07 2.295910e+07
2018-01-18 2080687.653 0.0 2.102167e+07 2.310235e+07
2018-01-19 2080687.653 0.0 2.104398e+07 2.312467e+07
2018-01-22 2080687.653 0.0 2.114470e+07 2.322539e+07
transaction_cost
date
2014-01-03 405.000
2014-01-06 415.000
2014-01-07 410.000
2014-01-08 425.000
2014-01-09 440.885
2014-01-10 455.000
2014-01-13 441.750
... ...
2018-01-12 0.000
2018-01-15 0.000
2018-01-16 0.000
2018-01-17 0.000
2018-01-18 0.000
2018-01-19 0.000
2018-01-22 0.000
[991 rows x 5 columns],
'stock_positions': avg_price last_price market_value order_book_id quantity symbol
date
2014-01-03 11.930 11.93 3579.0 000001.XSHE 300.0 平安银行
2014-01-03 7.840 7.84 3136.0 000002.XSHE 400.0 万科A
2014-01-03 20.190 20.19 2019.0 000024.XSHE 100.0 招商地产
2014-01-03 14.070 14.07 2814.0 000063.XSHE 200.0 中兴通讯
2014-01-03 5.150 5.15 3605.0 000069.XSHE 700.0 华侨城A
2014-01-03 5.330 5.33 3198.0 000157.XSHE 600.0 中联重科
2014-01-03 18.330 18.33 3666.0 000338.XSHE 200.0 潍柴动力
2014-01-03 7.500 7.50 3000.0 000425.XSHE 400.0 徐工机械
2014-01-03 9.740 9.74 2922.0 000630.XSHE 300.0 铜陵有色
2014-01-03 30.990 30.99 3099.0 000651.XSHE 100.0 格力电器
2014-01-03 7.920 7.92 3168.0 000656.XSHE 400.0 金科股份
2014-01-03 12.100 12.10 3630.0 000776.XSHE 300.0 广发证券
2014-01-03 4.680 4.68 3276.0 000933.XSHE 700.0 神火股份
2014-01-03 7.150 7.15 3575.0 000937.XSHE 500.0 冀中能源
2014-01-03 10.500 10.50 3150.0 000960.XSHE 300.0 锡业股份
2014-01-03 6.920 6.92 3460.0 000961.XSHE 500.0 中南建设
2014-01-03 6.820 6.82 3410.0 000983.XSHE 500.0 西山煤电
2014-01-03 19.060 19.06 1906.0 002001.XSHE 100.0 新和成
2014-01-03 9.640 9.64 2892.0 002299.XSHE 300.0 圣农发展
2014-01-03 24.600 24.60 2460.0 002375.XSHE 100.0 亚厦股份
2014-01-03 9.140 9.14 3656.0 600000.XSHG 400.0 浦发银行
2014-01-03 4.980 4.98 3486.0 600011.XSHG 700.0 华能国际
2014-01-03 7.610 7.61 3044.0 600016.XSHG 400.0 民生银行
2014-01-03 4.010 4.01 3609.0 600019.XSHG 900.0 宝钢股份
2014-01-03 2.990 2.99 3588.0 600027.XSHG 1200.0 华电国际
2014-01-03 4.380 4.38 3504.0 600028.XSHG 800.0 中国石化
2014-01-03 2.700 2.70 3510.0 600029.XSHG 1300.0 南方航空
2014-01-03 8.110 8.11 3244.0 600048.XSHG 400.0 保利地产
2014-01-03 17.650 17.65 3530.0 600066.XSHG 200.0 宇通客车
2014-01-03 3.940 3.94 3546.0 600068.XSHG 900.0 葛洲坝
... ... ... ... ... ... ...
2018-01-22 18.690 19.79 57391.0 600362.XSHG 2900.0 江西铜业
2018-01-22 7.375 6.88 129344.0 601919.XSHG 18800.0 中远海控
2018-01-22 58.643 60.74 218664.0 601336.XSHG 3600.0 新华保险
2018-01-22 7.653 6.64 14608.0 601727.XSHG 2200.0 上海电气
2018-01-22 21.661 20.59 78242.0 601688.XSHG 3800.0 华泰证券
2018-01-22 6.402 6.65 202825.0 600688.XSHG 30500.0 上海石化
2018-01-22 18.511 18.22 34618.0 600739.XSHG 1900.0 辽宁成大
2018-01-22 5.875 5.06 23782.0 600369.XSHG 4700.0 西南证券
2018-01-22 13.940 13.40 8040.0 002736.XSHE 600.0 国信证券
2018-01-22 17.568 21.20 19080.0 600030.XSHG 900.0 中信证券
2018-01-22 15.532 14.59 23344.0 601788.XSHG 1600.0 光大证券
2018-01-22 6.455 5.98 25714.0 601118.XSHG 4300.0 海南橡胶
2018-01-22 3.720 3.36 25536.0 600157.XSHG 7600.0 永泰能源
2018-01-22 5.982 5.38 48420.0 601018.XSHG 9000.0 宁波港
2018-01-22 3.413 3.21 149265.0 600795.XSHG 46500.0 国电电力
2018-01-22 7.766 8.09 33978.0 601600.XSHG 4200.0 中国铝业
2018-01-22 7.296 6.17 15425.0 000959.XSHE 2500.0 首钢股份
2018-01-22 19.104 18.89 24557.0 000776.XSHE 1300.0 广发证券
2018-01-22 11.978 13.84 53976.0 002024.XSHE 3900.0 苏宁云商
2018-01-22 9.861 12.06 20502.0 601766.XSHG 1700.0 中国中车
2018-01-22 10.324 12.89 41248.0 600352.XSHG 3200.0 浙江龙盛
2018-01-22 8.500 9.35 8415.0 601225.XSHG 900.0 陕西煤业
2018-01-22 27.897 30.94 64974.0 601628.XSHG 2100.0 中国人寿
2018-01-22 15.446 14.63 16093.0 002299.XSHE 1100.0 圣农发展
2018-01-22 7.346 6.85 71925.0 600050.XSHG 10500.0 中国联通
2018-01-22 8.600 6.42 6420.0 601718.XSHG 1000.0 际华集团
2018-01-22 12.398 10.19 49931.0 600100.XSHG 4900.0 同方股份
2018-01-22 22.260 23.31 6993.0 000623.XSHE 300.0 吉林敖东
2018-01-22 14.789 13.97 32131.0 600837.XSHG 2300.0 海通证券
2018-01-22 31.334 32.19 16095.0 600547.XSHG 500.0 山东黄金
[159399 rows x 6 columns],
'summary': {'STOCK': 10000000.0,
'alpha': 0.060999999999999999,
'annualized_returns': 0.23100000000000001,
'benchmark': '000300.XSHG',
'benchmark_annualized_returns': 0.17000000000000001,
'benchmark_total_returns': 0.89300000000000002,
'beta': 0.94699999999999995,
'cash': 2080687.6529999999,
'downside_risk': 0.035999999999999997,
'end_date': '2018-01-22',
'information_ratio': 0.95199999999999996,
'max_drawdown': 0.40799999999999997,
'run_type': 'BACKTEST',
'sharpe': 0.81999999999999995,
'sortino': 5.4530000000000003,
'start_date': '2014-01-03',
'strategy_file': 'strategy.py',
'strategy_name': 'strategy',
'total_returns': 1.323,
'total_value': 23225390.339000002,
'tracking_error': 0.056000000000000001,
'unit_net_value': 2.323,
'units': 10000000.0,
'volatility': 0.23899999999999999},
'trades': commission exec_id last_price last_quantity \
datetime
2014-01-03 15:00:00 5.0000 1516788432 11.93 300.0
2014-01-03 15:00:00 5.0000 1516788433 7.84 400.0
2014-01-03 15:00:00 5.0000 1516788434 20.19 100.0
2014-01-03 15:00:00 5.0000 1516788435 14.07 200.0
2014-01-03 15:00:00 5.0000 1516788436 5.15 700.0
2014-01-03 15:00:00 5.0000 1516788437 5.33 600.0
2014-01-03 15:00:00 5.0000 1516788438 18.33 200.0
2014-01-03 15:00:00 5.0000 1516788439 7.50 400.0
2014-01-03 15:00:00 5.0000 1516788440 9.74 300.0
2014-01-03 15:00:00 5.0000 1516788441 30.99 100.0
2014-01-03 15:00:00 5.0000 1516788442 7.92 400.0
2014-01-03 15:00:00 5.0000 1516788443 12.10 300.0
2014-01-03 15:00:00 5.0000 1516788444 4.68 700.0
2014-01-03 15:00:00 5.0000 1516788445 7.15 500.0
... ... ... ... ...
2017-09-29 15:00:00 6.4160 1516879312 8.02 1000.0
2017-09-29 15:00:00 5.0000 1516879313 4.05 200.0
2017-09-29 15:00:00 7.0312 1516879314 7.99 1100.0
2017-09-29 15:00:00 6.6144 1516879315 6.89 1200.0
2017-09-29 15:00:00 5.0000 1516879316 7.99 200.0
2017-09-29 15:00:00 7.2512 1516879317 4.12 2200.0
2017-09-29 15:00:00 5.0000 1516879318 6.35 700.0
order_book_id order_id position_effect side symbol \
datetime
2014-01-03 15:00:00 000001.XSHE 1516788430 None BUY 平安银行
2014-01-03 15:00:00 000002.XSHE 1516788431 None BUY 万科A
2014-01-03 15:00:00 000024.XSHE 1516788432 None BUY 招商地产
2014-01-03 15:00:00 000063.XSHE 1516788433 None BUY 中兴通讯
2014-01-03 15:00:00 000069.XSHE 1516788434 None BUY 华侨城A
2014-01-03 15:00:00 000157.XSHE 1516788435 None BUY 中联重科
2014-01-03 15:00:00 000338.XSHE 1516788436 None BUY 潍柴动力
2014-01-03 15:00:00 000425.XSHE 1516788437 None BUY 徐工机械
2014-01-03 15:00:00 000630.XSHE 1516788438 None BUY 铜陵有色
2014-01-03 15:00:00 000651.XSHE 1516788439 None BUY 格力电器
2014-01-03 15:00:00 000656.XSHE 1516788440 None BUY 金科股份
... ... ... ... ... ...
2017-09-29 15:00:00 601857.XSHG 1516885076 None SELL 中国石油
2017-09-29 15:00:00 601919.XSHG 1516885077 None BUY 中远海控
2017-09-29 15:00:00 601933.XSHG 1516885078 None SELL 永辉超市
2017-09-29 15:00:00 601988.XSHG 1516885079 None BUY 中国银行
2017-09-29 15:00:00 601992.XSHG 1516885080 None SELL 金隅集团
tax trading_datetime transaction_cost
datetime
2014-01-03 15:00:00 0.000 2014-01-03 15:00:00 5.0000
2014-01-03 15:00:00 0.000 2014-01-03 15:00:00 5.0000
2014-01-03 15:00:00 0.000 2014-01-03 15:00:00 5.0000
2014-01-03 15:00:00 0.000 2014-01-03 15:00:00 5.0000
2014-01-03 15:00:00 0.000 2014-01-03 15:00:00 5.0000
2014-01-03 15:00:00 0.000 2014-01-03 15:00:00 5.0000
2014-01-03 15:00:00 0.000 2014-01-03 15:00:00 5.0000
... ... ... ...
2017-09-29 15:00:00 0.000 2017-09-29 15:00:00 7.2512
2017-09-29 15:00:00 4.445 2017-09-29 15:00:00 9.4450
[90887 rows x 12 columns]}}
交集组合（不对冲）
import rqalpha
from rqalpha.api import *
#读取文件位置
def init(context):
context.codes = Intersection_weight
context.stocks = None
context.pos_weight = 0.9
scheduler.run_daily(find_pool)
def find_pool(context, bar_dict):
try:
codes = context.codes.loc[context.now].dropna()
context.stocks = codes
except:
context.stocks = None
def handle_bar(context, bar_dict):
buy(context, bar_dict)
def buy(context, bar_dict):
pool = context.stocks
if pool is not None:
for ins, percentage in pool.items():
order_target_percent(ins, percentage*context.pos_weight)
for ins in context.portfolio.positions:
if ins not in context.stocks:
order_target_percent(ins, 0)
config = {
"base": {
"start_date": "2014-01-03",
"end_date": "2018-01-31",
"accounts": {'stock':10000000},
"benchmark": "000300.XSHG"
},
"extra": {
"log_level": "error",
},
"mod": {
"sys_analyser": {
#"enabled": True,
"plot": True
},
}
}
rqalpha.run_func(init=init, handle_bar=handle_bar, config=config)
{'sys_analyser': {'benchmark_portfolio': cash market_value static_unit_net_value total_value \
date
2014-01-03 745.3 9999254.70 1.000 10000000.00
2014-01-06 745.3 9771663.60 1.000 9772408.90
2014-01-07 745.3 9768870.00 0.977 9769615.30
2014-01-08 745.3 9785937.15 0.977 9786682.45
2014-01-09 745.3 9699990.30 0.979 9700735.60
2014-01-10 745.3 9624170.25 0.970 9624915.55
2014-01-13 745.3 9575413.20 0.962 9576158.50
2014-01-14 745.3 9659090.25 0.958 9659835.55
2014-01-15 745.3 9642023.10 0.966 9642768.40
2014-01-16 745.3 9654681.60 0.964 9655426.90
... ... ... ... ...
2018-01-09 745.3 18286294.50 1.816 18287039.80
2018-01-10 745.3 18367090.65 1.829 18367835.95
2018-01-11 745.3 18357400.35 1.837 18358145.65
2018-01-12 745.3 18442125.00 1.836 18442870.30
2018-01-15 745.3 18443172.60 1.844 18443917.90
2018-01-16 745.3 18588221.55 1.844 18588966.85
2018-01-17 745.3 18543043.80 1.859 18543789.10
2018-01-18 745.3 18644748.30 1.854 18645493.60
2018-01-19 745.3 18705771.00 1.865 18706516.30
2018-01-22 745.3 18929259.00 1.871 18930004.30
unit_net_value units
date
2014-01-03 1.000000 10000000.0
2014-01-06 0.977241 10000000.0
2014-01-07 0.976962 10000000.0
2014-01-08 0.978668 10000000.0
2014-01-09 0.970074 10000000.0
2014-01-10 0.962492 10000000.0
2014-01-13 0.957616 10000000.0
2014-01-14 0.965984 10000000.0
2014-01-15 0.964277 10000000.0
... ... ...
2018-01-12 1.844287 10000000.0
2018-01-15 1.844392 10000000.0
2018-01-16 1.858897 10000000.0
2018-01-17 1.854379 10000000.0
2018-01-18 1.864549 10000000.0
2018-01-19 1.870652 10000000.0
2018-01-22 1.893000 10000000.0
[991 rows x 6 columns],
'portfolio': cash market_value static_unit_net_value total_value \
date
2014-01-03 9706814.639 292951.0 1.000 9.999766e+06
2014-01-06 9400250.633 588600.0 1.000 9.988851e+06
2014-01-07 9097252.082 888252.0 0.999 9.985504e+06
2014-01-08 8790795.296 1184237.0 0.999 9.975032e+06
2014-01-09 8489710.813 1481302.0 0.998 9.971013e+06
2014-01-10 8182080.859 1779354.0 0.997 9.961435e+06
2014-01-13 7880399.282 2080951.0 0.996 9.961350e+06
2014-01-14 7599941.843 2387894.0 0.996 9.987836e+06
... ... ... ... ...
2018-01-18 2606790.021 25697584.0 2.799 2.830437e+07
2018-01-19 2606790.021 25778644.6 2.830 2.838543e+07
2018-01-22 2606790.021 25703953.4 2.839 2.831074e+07
unit_net_value units
date
2014-01-03 0.999977 10000000.0
2014-01-06 0.998885 10000000.0
2014-01-07 0.998550 10000000.0
2014-01-08 0.997503 10000000.0
2014-01-09 0.997101 10000000.0
2014-01-10 0.996143 10000000.0
2014-01-13 0.996135 10000000.0
... ... ...
2018-01-19 2.838543 10000000.0
2018-01-22 2.831074 10000000.0
[991 rows x 6 columns],
'stock_account': cash dividend_receivable market_value total_value \
date
2014-01-03 9706814.639 0.0 292951.0 9.999766e+06
2014-01-06 9400250.633 0.0 588600.0 9.988851e+06
2014-01-07 9097252.082 0.0 888252.0 9.985504e+06
2014-01-08 8790795.296 0.0 1184237.0 9.975032e+06
... ... ... ... ...
2018-01-18 2606790.021 0.0 25697584.0 2.830437e+07
2018-01-19 2606790.021 0.0 25778644.6 2.838543e+07
2018-01-22 2606790.021 0.0 25703953.4 2.831074e+07
transaction_cost
date
2014-01-03 234.361
2014-01-06 254.006
... ...
2018-01-15 0.000
2018-01-16 0.000
2018-01-17 0.000
2018-01-18 0.000
2018-01-19 0.000
2018-01-22 0.000
[991 rows x 5 columns],
'stock_positions': avg_price last_price market_value order_book_id quantity symbol
date
2014-01-03 14.070 14.07 12663.0 000063.XSHE 900.0 中兴通讯
2014-01-03 6.920 6.92 12456.0 000961.XSHE 1800.0 中南建设
2014-01-03 6.820 6.82 12958.0 000983.XSHE 1900.0 西山煤电
2014-01-03 4.980 4.98 12948.0 600011.XSHG 2600.0 华能国际
... ... ... ... ... ... ...
2018-01-22 25.885 33.08 258024.0 600036.XSHG 7800.0 招商银行
2018-01-22 8.089 7.68 205824.0 601669.XSHG 26800.0 中国电建
2018-01-22 7.894 9.29 140279.0 601009.XSHG 15100.0 南京银行
2018-01-22 3.320 3.21 68694.0 600795.XSHG 21400.0 国电电力
[63607 rows x 6 columns],
'summary': {'STOCK': 10000000.0,
'alpha': 0.112,
'annualized_returns': 0.29199999999999998,
'benchmark': '000300.XSHG',
'benchmark_annualized_returns': 0.17000000000000001,
'benchmark_total_returns': 0.89300000000000002,
'beta': 0.88800000000000001,
'cash': 2606790.0210000002,
'downside_risk': 0.047,
'end_date': '2018-01-22',
'information_ratio': 1.242,
'max_drawdown': 0.34200000000000003,
'run_type': 'BACKTEST',
'sharpe': 1.0369999999999999,
'sortino': 5.077,
'start_date': '2014-01-03',
'strategy_file': 'strategy.py',
'strategy_name': 'strategy',
'total_returns': 1.831,
'total_value': 28310743.421,
'tracking_error': 0.076999999999999999,
'unit_net_value': 2.831,
'units': 10000000.0,
'volatility': 0.23000000000000001},
'trades': commission exec_id last_price last_quantity \
datetime
2014-01-03 15:00:00 10.1304 1516792677 14.07 900.0
2014-01-03 15:00:00 9.9648 1516792678 6.92 1800.0
2014-01-03 15:00:00 10.3664 1516792679 6.82 1900.0
2014-01-03 15:00:00 10.3584 1516792680 4.98 2600.0
... ... ... ... ...
2017-09-29 15:00:00 15.2984 1516832861 14.71 1300.0
2017-09-29 15:00:00 5.0000 1516832862 6.30 100.0
2017-09-29 15:00:00 43.4592 1516832863 15.09 3600.0
order_book_id order_id position_effect side symbol \
datetime
2014-01-03 15:00:00 000063.XSHE 1516792676 None BUY 中兴通讯
2014-01-03 15:00:00 000961.XSHE 1516792677 None BUY 中南建设
2014-01-03 15:00:00 000983.XSHE 1516792678 None BUY 西山煤电
2014-01-03 15:00:00 600011.XSHG 1516792679 None BUY 华能国际
2014-01-03 15:00:00 600019.XSHG 1516792680 None BUY 宝钢股份
2014-01-03 15:00:00 600028.XSHG 1516792681 None BUY 中国石化
2014-01-03 15:00:00 600123.XSHG 1516792682 None BUY 兰花科创
2014-01-03 15:00:00 600157.XSHG 1516792683 None BUY 永泰能源
2014-01-03 15:00:00 600188.XSHG 1516792684 None BUY 兖州煤业
2014-01-03 15:00:00 600348.XSHG 1516792685 None BUY 阳泉煤业
... ... ... ... ... ...
2017-09-29 15:00:00 601997.XSHG 1516834906 None BUY 贵阳银行
2017-09-29 15:00:00 601998.XSHG 1516834907 None BUY 中信银行
2017-09-29 15:00:00 600690.XSHG 1516834908 None SELL 青岛海尔
tax trading_datetime transaction_cost
datetime
2014-01-03 15:00:00 0.000 2014-01-03 15:00:00 10.1304
2014-01-03 15:00:00 0.000 2014-01-03 15:00:00 9.9648
... ... ... ...
2017-09-29 15:00:00 0.000 2017-09-29 15:00:00 5.0000
2017-09-29 15:00:00 4.748 2017-09-29 15:00:00 9.7480
2017-09-29 15:00:00 0.000 2017-09-29 15:00:00 58.4584
2017-09-29 15:00:00 0.000 2017-09-29 15:00:00 57.6720
2017-09-29 15:00:00 0.000 2017-09-29 15:00:00 5.0000
2017-09-29 15:00:00 0.000 2017-09-29 15:00:00 5.0000
2017-09-29 15:00:00 0.000 2017-09-29 15:00:00 59.9872
2017-09-29 15:00:00 0.000 2017-09-29 15:00:00 15.2984
2017-09-29 15:00:00 0.000 2017-09-29 15:00:00 5.0000
2017-09-29 15:00:00 54.324 2017-09-29 15:00:00 97.7832
[40187 rows x 12 columns]}}