def main(modelPath,outpath,dt=None):
df = dataio.getLabelAndFeature(config.LABEL,config.FEATURE_SELECT);
turnoverFilter = dataio.getTurnoverRankFilter();
maxContinousCloseDayFilter = dataio.getMaxContinousCloseDayFilter();
secFilter = turnoverFilter & maxContinousCloseDayFilter;
idxFilter = np.asarray([True if st in secFilter else False \
for st in df.index]);
df = df[idxFilter]
dts = df.index.get_level_values('tradeDate').values;
if dt is None:
dt = np.max(dts);
pass;
df = df[dts==dt];
feature = df[config.FEATURE_SELECT].values;
cls = pickle.load(open(modelPath));
#pred = np.squeeze(cls.predict_proba(feature)[:,cls.classes_==1]);
pred = np.squeeze(cls.predict(feature));
dfout = pd.DataFrame(pred,
index=df.index,
columns = ['score']);
dfout = dfout[dfout.index.map(lambda i:i[0] not in config.BLACK_LIST).get_values()]
dfout.sort_values(['score'],inplace=True,ascending=False);
dfout.to_csv(outpath,float_format='%g');
pass;
def main(modelPath,startDt,endDt,initMoney,strategy,reportPath,testStartDt):
df = dataio.getLabelAndFeature(config.LABEL,config.FEATURE_SELECT);
turnoverFilter = dataio.getTurnoverRankFilter();
maxContinousCloseDayFilter = dataio.getMaxContinousCloseDayFilter();
secFilter = turnoverFilter & maxContinousCloseDayFilter;
idxFilter = np.asarray([True if st in secFilter else False \
for st in df.index]);
df = df[idxFilter]
feature = df[config.FEATURE_SELECT].values;
dts = df.index.get_level_values('tradeDate');
index = df.index.values;
dt = startDt;
while dt<=endDt:
print(dt);
filename = 'xgb_' + dt;
stdout = sys.stdout;
with open(os.path.join(reportPath,filename),'w') as fout:
sys.stdout = fout;
daily_test(feature,dts,index,initMoney,testStartDt,
os.path.join(modelPath,filename),
strategy);
sys.stdout = stdout;
pass;
dt = utils_common.dtAdd(dt,1);
pass;
def main(modelPath, outpath, dt=None):
df = dataio.getLabelAndFeature(config.LABEL, config.FEATURE_SELECT)
turnoverFilter = dataio.getTurnoverRankFilter()
maxContinousCloseDayFilter = dataio.getMaxContinousCloseDayFilter()
secFilter = turnoverFilter & maxContinousCloseDayFilter
idxFilter = np.asarray([True if st in secFilter else False \
for st in df.index])
df = df[idxFilter]
dts = df.index.get_level_values('tradeDate').values
if dt is None:
dt = np.max(dts)
pass
df = df[dts == dt]
feature = df[config.FEATURE_SELECT].values
cls = pickle.load(open(modelPath))
#pred = np.squeeze(cls.predict_proba(feature)[:,cls.classes_==1]);
pred = np.squeeze(cls.predict(feature))
dfout = pd.DataFrame(pred, index=df.index, columns=['score'])
dfout = dfout[dfout.index.map(
lambda i: i[0] not in config.BLACK_LIST).get_values()]
dfout.sort_values(['score'], inplace=True, ascending=False)
dfout.to_csv(outpath, float_format='%g')
pass
def main(modelPath, startDt, endDt, initMoney, strategy, reportPath,
testStartDt):
df = dataio.getLabelAndFeature(config.LABEL, config.FEATURE_SELECT)
turnoverFilter = dataio.getTurnoverRankFilter()
maxContinousCloseDayFilter = dataio.getMaxContinousCloseDayFilter()
secFilter = turnoverFilter & maxContinousCloseDayFilter
idxFilter = np.asarray([True if st in secFilter else False \
for st in df.index])
df = df[idxFilter]
feature = df[config.FEATURE_SELECT].values
dts = df.index.get_level_values('tradeDate')
index = df.index.values
dt = startDt
while dt <= endDt:
print(dt)
filename = 'xgb_' + dt
stdout = sys.stdout
with open(os.path.join(reportPath, filename), 'w') as fout:
sys.stdout = fout
daily_test(feature, dts, index, initMoney, testStartDt,
os.path.join(modelPath, filename), strategy)
sys.stdout = stdout
pass
dt = utils_common.dtAdd(dt, 1)
pass
def loadDataReg():
df = dataio.getLabelAndFeature(config.LABEL, config.FEATURE_SELECT)
df = df[df[config.LABEL] > -1]
turnoverFilter = dataio.getTurnoverRankFilter();
maxContinousCloseDayFilter = dataio.getMaxContinousCloseDayFilter();
secFilter = turnoverFilter & maxContinousCloseDayFilter;
idxFilter = np.asarray([True if st in secFilter else False \
for st in df.index]);
df = df[idxFilter]
dts = df.index.get_level_values('tradeDate')
label = np.squeeze(df[[config.LABEL]].values)
feature = df[config.FEATURE_SELECT].values
return label, feature, dts
def loadDataReg():
df = dataio.getLabelAndFeature(config.LABEL, config.FEATURE_SELECT)
df = df[df[config.LABEL] > -1]
turnoverFilter = dataio.getTurnoverRankFilter()
maxContinousCloseDayFilter = dataio.getMaxContinousCloseDayFilter()
secFilter = turnoverFilter & maxContinousCloseDayFilter
idxFilter = np.asarray([True if st in secFilter else False \
for st in df.index])
df = df[idxFilter]
dts = df.index.get_level_values('tradeDate')
label = np.squeeze(df[[config.LABEL]].values)
feature = df[config.FEATURE_SELECT].values
return label, feature, dts
from config import dataio;
import config;
import numpy as np;
import pandas as pd;
import sys;
if __name__=='__main__':
df = dataio.getLabelAndFeature(config.LABEL,config.FEATURE_SELECT);
df = df[df[config.LABEL]>-1];
turnoverFilter = dataio.getTurnoverRankFilter();
maxContinousCloseDayFilter = dataio.getMaxContinousCloseDayFilter();
secFilter = turnoverFilter & maxContinousCloseDayFilter;
idxFilter = np.asarray([True if st in secFilter else False \
for st in df.index]);
df.rename(columns = {config.LABEL:'label'}, inplace=True);
#df = df[idxFilter]
df.to_csv(sys.argv[1]);
pass;
from config import dataio
import config
import numpy as np
import pandas as pd
import sys
if __name__ == '__main__':
df = dataio.getLabelAndFeature(config.LABEL, config.FEATURE_SELECT)
df = df[df[config.LABEL] > -1]
turnoverFilter = dataio.getTurnoverRankFilter()
maxContinousCloseDayFilter = dataio.getMaxContinousCloseDayFilter()
secFilter = turnoverFilter & maxContinousCloseDayFilter
idxFilter = np.asarray([True if st in secFilter else False \
for st in df.index])
df.rename(columns={config.LABEL: 'label'}, inplace=True)
#df = df[idxFilter]
df.to_csv(sys.argv[1])
pass