HFDataView
如果出现读取不了数据文件的问题，将原来的版本删掉，安装新的jaqs_fxdayu：
pip install git+https://github.com/xingetouzi/jaqs-fxdayu.git
min_data数据下载：
链接：https://pan.baidu.com/s/1OBTX-zc2GfsGutlEeoznJg 提取码：uz91
如果放在同一目录下使用 path = r'./min_data/OANDA_M1'
如果放在不同目录下使用或者指定路径 path = r'../min_data/OANDA_M1'
1) path : hdf5文件上级目录
'.\min_data\VnTrader_1Min_Db' 与 '.\min_data\OANDA_M1'
binance后缀的品种在VnTrader_1Min_Db、oanda后缀的品种在OANDA_M1的位置
2) props :
配置项包括symbol, start_date, end_date , field, freq
start_date/end_date : 20180601000000 (int, 精确到秒，共14位数字)
symbol :str/list
'BTCUSDT:binance,ETHUSDT:binance,EOSUSDT:binance' 与 'AUD_USD:oanda,EUR_USD:oanda,GBP_USD:oanda,HK33_HKD:oanda,NAS100_USD:oanda, NZD_USD:oanda,USD_CAD:oanda,USD_CHF:oanda,USD_CNH:oanda,USD_JPY:oanda,WTICO_USD:oanda,XAU_USD:oanda'
field : str/list
'open,high,low,close,volume'
freq : str/list
'1Min, 5Min, 15Min, 30Min, 1H, 4H, 6H, 8H, 12H'
配置项均可为空，代表该项读全部
3）resample_rules : 时间变周期（resample）规则
import warnings
warnings.filterwarnings('ignore')from jaqs_fxdayu.data.dataservice import LocalDataService
ds = LocalDataService()
from time import time
## 加freq参数
start = time()
path = r'./min_data/OANDA_M1'
props = {'fields': 'open,high,low,close,volume','symbol': \
'USD_CNH:oanda,WTICO_USD:oanda,XAU_USD:oanda',
'freq': '15Min,1H',
'start_date':20180601000000}
Time_dict = ds.bar_reader(path,props) #读取数据
print(time()-start)23.927146434783936
# Time_dict#调用模块
from jaqs_fxdayu.data.hf_dataview import HFDataView
dv15M = HFDataView()
dv1H = HFDataView()# dv1H.datadv15M.create_init_dv(Time_dict['15Min'].dropna().set_index(["trade_date","symbol"]))
dv1H.create_init_dv(Time_dict['1H'].dropna().set_index(["trade_date","symbol"]))Initialize dataview success.
Initialize dataview success.
# dv1H.get_ts('close', date_type='datetime')Gap1H = dv1H.add_formula('gap', 'close-Delay(open, 1)',add_data=True)Gap15 = dv15M.add_formula('gap', 'close-Delay(open, 1)',add_data=True)#观察价差
import matplotlib.pyplot as plt
plt.plot(dv15M.get_ts('gap', date_type='datetime')['USD_CNH:oanda'])
plt.show()
MA20和MA55金叉死叉信号
进场信号：
long:如果出现，当20日均线向上穿过55日均线以及前一个小时的20日均线向下穿过55日均线时的信号时，赋值2表示做多，下多单。
short:如果出现，当20日均线向下穿过55日均线以及前一个小时的20日均线向上穿过55日均线时的信号时，赋值-2表示做空，下空单。
出场信号：
close_long：判断当前市场是否出现新的信号可以开空单，那么就平掉原来的多仓，否则继续持有多仓。赋值为1，表示平多仓。
close_short：判断当前市场是否出现新的信号可以开多单，那么就平掉原来的空仓，否则继续持有空仓。赋值为-1，表示平空仓。
# 进场信号
long = dv1H.add_formula("long","If((Ts_Mean(close,20)>Ts_Mean(close,55)) && (Delay(Ts_Mean(close,20),1)<=Delay(Ts_Mean(close,55),1)),2,0)", add_data=True)
short = dv1H.add_formula("short","If((Ts_Mean(close,20)<Ts_Mean(close,55)) && (Delay(Ts_Mean(close,20),1)>=Delay(Ts_Mean(close,55),1)),-2,0)", add_data=True)
# 出场信号
close_long = dv1H.add_formula("closeLong","If(short==-2,1,0)", add_data=True)
close_short = dv1H.add_formula("closeShort","If(long==2,-1,0)", add_data=True)dv1H.get_ts('long').stack()[dv1H.get_ts('long').stack()==2]#观察出现做多信号的时间点trade_date symbol
20180607220000 USD_CNH:oanda 2.0
20180612160000 USD_CNH:oanda 2.0
20180614210000 USD_CNH:oanda 2.0
20180705180000 USD_CNH:oanda 2.0
20180710220000 USD_CNH:oanda 2.0
20180713180000 USD_CNH:oanda 2.0
20180717180000 USD_CNH:oanda 2.0
20180723180000 USD_CNH:oanda 2.0
20180726190000 USD_CNH:oanda 2.0
20180731050000 USD_CNH:oanda 2.0
20180801170000 USD_CNH:oanda 2.0
20180807010000 USD_CNH:oanda 2.0
20180809200000 USD_CNH:oanda 2.0
20180822160000 USD_CNH:oanda 2.0
20180829080000 USD_CNH:oanda 2.0
20180904150000 USD_CNH:oanda 2.0
20180907160000 USD_CNH:oanda 2.0
20180914140000 USD_CNH:oanda 2.0
dtype: float64
#通过测试报告，看多空信号的绩效，这里是定义报告格式
from jaqs_fxdayu.research import TimingDigger
tdMa = TimingDigger(output_folder=".", output_format='pdf',signal_name='Ma')
def TimingSignal(td, dv, long='long', short='short',closeLong='closeLong', closeShort='closeShort'\
, mhp=None,sl=None,sp=None):
# step 1：实例化TimingDigger 通过output_folder和output_format指定测试报告的输出路径和输出格式，通过signal_name指定绩效文件名称
#多空信号分别计算一遍 输出汇总结果
td.process_signal(
enter_signal=dv.get_ts(long),
exit_signal=dv.get_ts(closeLong),
sig_type="long", # 信号类型 long/short
price=dv.get_ts('close'),
max_holding_period=mhp, # 最大持有天数 可为空
stoploss=-sl, # 止损百分比 负数 可为空
stopprofit=sp, # 止盈百分比 正数 可为空
)
td.process_signal(
enter_signal=dv.get_ts(short),
exit_signal=dv.get_ts(closeShort),
sig_type="short", # 信号类型 long/short
price=dv.get_ts("close"),
max_holding_period=mhp, # 最大持有天数 可为空
stoploss=-sl, # 止损百分比 负数 可为空
stopprofit=sp, # 止盈百分比 正数 可为空
)TimingSignal(tdMa, dv1H, mhp=100, sl=0.05, sp=0.3)#调取数据Nan Data Count (should be zero) : 0; Percentage of effective data: 0%
Nan Data Count (should be zero) : 0; Percentage of effective data: 0%
主要看的是win_ratio和win_mean/loss_mean,其他是基础的统计指标。 胜率win_ratio最好在0.4以上，盈亏比：win_mean/loss_mean在1以上，具体看两者比例。
tdMa.create_event_report(sig_type="long")*****-Summary-*****
Event Analysis
win loss all
t-stat 2.437 -4.674 -0.852
p-value 0.071 0.001 0.406
mean 0.005 -0.004 -0.001
std 0.004 0.003 0.005
info_ratio 1.218 -1.349 -0.207
skewness -0.061 -0.333 0.926
kurtosis -1.742 -1.214 0.223
pct5 0.000 -0.008 -0.007
pct25 0.001 -0.006 -0.004
pct50 0.006 -0.003 -0.002
pct75 0.010 -0.002 0.000
pct95 0.010 -0.000 0.010
occurance 5.000 13.000 18.000
win_ratio NaN NaN 0.278
win_mean/loss_mean NaN NaN 1.521
tdMa.create_event_report(sig_type="short")*****-Summary-*****
Event Analysis
win loss all
t-stat NaN -8.571 -6.710
p-value NaN 0.000 0.000
mean 0.002000 -0.005 -0.004
std 0.000000 0.002 0.003
info_ratio inf -2.213 -1.677
skewness 0.000000 0.000 0.517
kurtosis -3.000000 -0.803 -0.018
pct5 0.002000 -0.008 -0.008
pct25 0.002000 -0.007 -0.007
pct50 0.002000 -0.004 -0.004
pct75 0.002000 -0.004 -0.004
pct95 0.002000 -0.001 -0.000
occurance 1.000000 16.000 17.000
win_ratio NaN NaN 0.059
win_mean/loss_mean NaN NaN 0.393
tdMa.create_event_report(sig_type="long_short") #同时计算多空头信号的绩效*****-Summary-*****
Event Analysis
win loss all
t-stat 2.532 -9.093 -3.529
p-value 0.052 0.000 0.001
mean 0.005 -0.004 -0.003
std 0.004 0.002 0.004
info_ratio 1.132 -1.718 -0.605
skewness 0.264 -0.041 1.344
kurtosis -1.654 -1.066 1.869
pct5 0.000 -0.008 -0.008
pct25 0.001 -0.007 -0.006
pct50 0.004 -0.004 -0.004
pct75 0.009 -0.002 -0.001
pct95 0.010 -0.000 0.007
occurance 6.000 29.000 35.000
win_ratio NaN NaN 0.171
win_mean/loss_mean NaN NaN 1.133
-
文档：http://www.fxdayu.com/static/standalone/tutorials.html?article=245,
https://jaqs.readthedocs.io/zh_CN/latest/user_guide.html#dataview
-
源码查阅：Pycharm
for dvAdd in [dv15M, dv1H]:
dvAdd.add_formula('alpha1_C','Ts_Argmax(SignedPower(If(Return(close,1)<0, StdDev(Return(close,1),20),close),2),5)',add_data=True)import matplotlib.pyplot as plt
def Plot_Mean(dv, alpha, chart_name='dv_alpha_ma', ShortPeriod=20, LongPeriod=60, n=0):
fig,(ax,ax1) = plt.subplots(2,1,sharex=True, figsize=(15,15))
ax.plot(dv.get_ts('close', date_type='datetime').iloc[:,0].iloc[n:])
ax.set_title(chart_name,fontsize=24,color='b')
ax1.plot(alpha.iloc[n:], alpha=0.1)
ax1.plot(pd.Series(alpha.rolling(ShortPeriod).mean(), index=alpha.index).iloc[n:])
ax1.plot(pd.Series(alpha.rolling(LongPeriod).mean(), index=alpha.index).iloc[n:])
# plt.savefig(chart_name)
plt.show()dv_dict = {'alpha1_dv15M':dv15M, 'alpha1_dv1H':dv1H}
alpha1_dict = {n: dv.get_ts('alpha1_C', date_type='datetime') for n, dv in dv_dict.items()}# alpha1_dictimport pandas as pd
#alpha1_dv15M是15分钟线，1H是1小时线，ma30和ma60代表对信号进行均线处理和观察是否与价格存在涨跌关系
for name, alpha in alpha1_dict.items():
Plot_Mean(globals()[name.split('_')[1]], alpha.iloc[:,0], chart_name=name+'_ma30_ma60',ShortPeriod=30, LongPeriod=60, n=-1000)#橘色短线 绿色长线
# 进场信号
long = dv1H.add_formula("longAlpha1","If((Ts_Mean(alpha1_C,20)>Ts_Mean(alpha1_C,55)) && (Delay(Ts_Mean(alpha1_C,20),1)<=Delay(Ts_Mean(alpha1_C,55),1)),2,0)", add_data=True)
short = dv1H.add_formula("shortAlpha1","If((Ts_Mean(alpha1_C,20)<Ts_Mean(alpha1_C,55)) && (Delay(Ts_Mean(alpha1_C,20),1)>=Delay(Ts_Mean(alpha1_C,55),1)),-2,0)", add_data=True)
# 出场信号
close_long = dv1H.add_formula("closeLongAlpha1","If(shortAlpha1==-2,1,0)", add_data=True)
close_short = dv1H.add_formula("closeShortAlpha1","If(longAlpha1==2,-1,0)", add_data=True)dv1H.get_ts('longAlpha1').stack()[dv1H.get_ts('longAlpha1').stack()==2].tail()trade_date symbol
20180919100000 USD_CNH:oanda 2.0
20180919160000 USD_CNH:oanda 2.0
20180920120000 USD_CNH:oanda 2.0
20180921080000 USD_CNH:oanda 2.0
20180921140000 USD_CNH:oanda 2.0
dtype: float64
from jaqs_fxdayu.research import TimingDigger
tdAlpha1 = TimingDigger(output_folder=".", output_format='pdf',signal_name='Alpha001')
TimingSignal(tdAlpha1, dv1H,'longAlpha1','shortAlpha1','closeLongAlpha1','closeShortAlpha1', 100, 0.05, 0.3)Nan Data Count (should be zero) : 0; Percentage of effective data: 1%
Nan Data Count (should be zero) : 0; Percentage of effective data: 1%
观察有无加入alpha的绩效区别
主要看的是win_ratio和win_mean/loss_mean,其他是基础的统计指标。 胜率win_ratio最好在0.4以上，盈亏比：win_mean/loss_mean在1以上，具体看两者比例。
tdAlpha1.create_event_report(sig_type="long_short")*****-Summary-*****
Event Analysis
win loss all
t-stat 6.496 -12.362 -1.690
p-value 0.000 0.000 0.093
mean 0.003 -0.002 -0.000
std 0.003 0.002 0.003
info_ratio 0.968 -1.184 -0.136
skewness 1.394 -2.036 1.319
kurtosis 1.545 5.004 3.949
pct5 0.000 -0.005 -0.004
pct25 0.000 -0.002 -0.002
pct50 0.002 -0.001 -0.001
pct75 0.005 -0.001 0.000
pct95 0.010 -0.000 0.006
occurance 46.000 110.000 156.000
win_ratio NaN NaN 0.295
win_mean/loss_mean NaN NaN 1.626
tdMa.create_event_report(sig_type="long_short")*****-Summary-*****
Event Analysis
win loss all
t-stat 2.532 -9.093 -3.529
p-value 0.052 0.000 0.001
mean 0.005 -0.004 -0.003
std 0.004 0.002 0.004
info_ratio 1.132 -1.718 -0.605
skewness 0.264 -0.041 1.344
kurtosis -1.654 -1.066 1.869
pct5 0.000 -0.008 -0.008
pct25 0.001 -0.007 -0.006
pct50 0.004 -0.004 -0.004
pct75 0.009 -0.002 -0.001
pct95 0.010 -0.000 0.007
occurance 6.000 29.000 35.000
win_ratio NaN NaN 0.171
win_mean/loss_mean NaN NaN 1.133