def main(argv):
del argv
random.seed(123)
show = np_common.Show(True)
if FLAGS.r:
data_source = tushare_data.DataSource(20000101, '', '', 1, 20000101,
20200306)
while True:
code_index = random.randint(0, len(data_source.code_list) - 1)
ts_code = data_source.code_list[code_index]
print(ts_code)
code_name = data_source.name_list[code_index]
pp_data = data_source.LoadStockPPData(ts_code, True)
if len(pp_data) == 0:
return
day_index = random.randint(0, len(pp_data) - 1)
show.SetTitle('%s - %s' % (ts_code, code_name))
RandShow(pp_data, show, day_index)
else:
data_source = tushare_data.DataSource(0, '', '', 1, 20000101, 20200603)
ts_code = FLAGS.c
code_name = data_source.StockName(ts_code)
data_source.DownloadStockData(ts_code)
data_source.UpdateStockPPData(ts_code)
pp_data = data_source.LoadStockPPData(ts_code, True)
show.SetTitle('%s - %s' % (ts_code, code_name))
ShowAStockLowLevel(pp_data, show, [PPI_open, PPI_vol])
def ShowAStock_(ts_code,
show_values=[
PPI_open, PPI_close, PPI_close_5_avg, PPI_close_10_avg,
PPI_close_30_avg, PPI_close_100_avg, PPI_vol,
PPI_vol_10_avg, PPI_vol_100_avg
]):
plt, mdate, _, _, zhfont = base_common.ImportMatPlot()
# current_date_int = int(base_common.CurrentDate())
current_date_int = 20200403
o_data_source = tushare_data.DataSource(current_date_int, '', '', 1, 0,
current_date_int)
o_data_source.DownloadStockData(ts_code)
o_data_source.UpdateStockPPData(ts_code)
pp_data = o_data_source.LoadStockPPData(ts_code)
preprocess.PPRegionCompute(pp_data, PPI_close, PPI_close_5_avg, 500,
len(pp_data), np.mean)
stock_name = o_data_source.StockName(ts_code)
if len(pp_data) == 0:
return
title = "%s - %s" % (ts_code, stock_name)
title = unicode(title, "utf-8")
fig1 = plt.figure(dpi=70, figsize=(32, 10))
ax1 = fig1.add_subplot(1, 1, 1)
ax1.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d'))
plt.title(title, fontproperties=zhfont)
plt.xlabel('date')
plt.ylabel('price')
date_str_arr = np.array(["%.0f" % x for x in pp_data[:, PPI_trade_date]])
xs = [datetime.strptime(d, '%Y%m%d').date() for d in date_str_arr]
plt.grid(True)
vol_list = [
PPI_vol, PPI_vol_5_avg, PPI_vol_10_avg, PPI_vol_30_avg, PPI_vol_100_avg
]
print(set(show_values) - set(vol_list))
if len(set(show_values) - set(vol_list)) > 0:
close_max = max(pp_data[:, PPI_close])
vol_max = max(pp_data[:, PPI_vol])
vol_ratio = 1.0 / vol_max * close_max / 2
else:
vol_ratio = 1.0
for col_index in show_values:
name = ''
if col_index in vol_list:
# plt.bar(xs, pp_data[:, col_index] * vol_ratio, 0.8)
plt.plot(xs,
pp_data[:, col_index] * vol_ratio,
label=PPI_name[col_index],
linewidth=1)
else:
plt.plot(xs,
pp_data[:, col_index],
label=PPI_name[col_index],
linewidth=1)
plt.gcf().autofmt_xdate()
plt.legend()
#plt.ylim([0,5])
plt.show()
plt.pause(1000)
def main(argv):
del argv
o_data_source = tushare_data.DataSource(20000101, '', '', 1, 20120101, 20200106, False, False, True)
o_feature = feature.Feature(30, feature.FUT_D5_NORM, 1, False, False)
# o_feature = feature.Feature(30, feature.FUT_D5_NORM, 1, False, False)
# o_feature = feature.Feature(30, feature.FUT_5REGION5_NORM, 5, False, False)
# o_feature = feature.Feature(30, feature.FUT_2AVG5_NORM, 5, False, False)
o_wave = ExtremeWave(o_data_source, o_feature, 2, 2, False, 0, 0.1, 5)
split_date = 20180101
o_dl_model = dl_model.DLModel('%s_%u' % (o_wave.setting_name, split_date),
o_feature.feature_unit_num,
o_feature.feature_unit_size,
32, 10240, 0.004, 'mean_absolute_tp0_max_ratio_error')
if FLAGS.mode == 'data':
o_data_source.DownloadData()
o_data_source.UpdatePPData()
elif FLAGS.mode == 'testall':
o_wave.TradeTestAll()
elif FLAGS.mode == 'test':
o_wave.TradeTestStock(FLAGS.c, FLAGS.show)
elif FLAGS.mode == 'show':
o_wave.ShowTradePP(FLAGS.c)
elif FLAGS.mode == 'train':
tf, tl, vf, vl, td = o_wave.GetDataset(split_date)
tl = tl * 100.0
vl = vl * 100.0
o_dl_model.Train(tf, tl, vf, vl, FLAGS.epoch)
elif FLAGS.mode == 'rtest':
tf, tl, tf, tl, ta = o_wave.GetDataset(split_date)
o_dl_model.LoadModel(FLAGS.epoch)
o_wave.RTest(o_dl_model, tf, ta, False)
exit()
def main(argv):
del argv
o_data_source = tushare_data.DataSource(20000101, '', '', 1, 20000101,
20200403, False, False, True)
o_feature = feature.Feature(30, feature.FUT_D5_NORM_PCT, 1, False, False)
# o_feature = feature.Feature(30, feature.FUT_D5_NORM, 1, False, False)
# o_feature = feature.Feature(30, feature.FUT_5REGION5_NORM, 5, False, False)
# o_feature = feature.Feature(30, feature.FUT_2AVG5_NORM, 5, False, False)
o_trade = Breakup(o_data_source, o_feature, PPI_close_100_avg, 10, 10, 3.0,
0.1)
split_date = 20180101
o_dl_model = dl_model.DLModel('%s_%u' % (o_trade.setting_name, split_date),
o_feature.feature_unit_num,
o_feature.feature_unit_size, 32, 10240,
0.004, 'mean_absolute_tp0_max_ratio_error')
if FLAGS.mode == 'data':
o_data_source.DownloadData()
o_data_source.UpdatePPData()
elif FLAGS.mode == 'testall':
o_trade.TradeTestAll(True, FLAGS.show)
elif FLAGS.mode == 'test':
o_data_source.DownloadStockData(FLAGS.c)
o_data_source.UpdateStockPPData(FLAGS.c)
start_time = time.time()
# o_trade.TradeTestStock(FLAGS.c, FLAGS.show)
o_trade.Test(FLAGS.c)
elif FLAGS.mode == 'train':
tf, tl, vf, vl, td = o_trade.GetDataset(split_date)
tl = tl * 100.0
vl = vl * 100.0
o_dl_model.Train(tf, tl, vf, vl, FLAGS.epoch)
elif FLAGS.mode == 'rtest':
tf, tl, tf, tl, ta = o_trade.GetDataset(split_date)
o_dl_model.LoadModel(FLAGS.epoch)
o_trade.RTest(o_dl_model, tf, ta, False)
elif FLAGS.mode == 'dsw':
dataset = o_trade.ShowDSW3DDataset()
elif FLAGS.mode == 'show':
dataset = o_trade.ShowTradePP(FLAGS.c)
exit()
def main(argv):
del argv
o_data_source = tushare_data.DataSource(20000101, '', '', 1, 20100101, 20200306, False, False, True)
o_feature = feature.Feature(7, feature.FUT_D5_NORM, 1, False, False)
o_vol_wave = VolWave(o_data_source, o_feature, 0.1)
# split_date = 20180101
# o_dl_model = dl_model.DLModel('%s_%u' % (o_vol_wave.setting_name, split_date),
# o_feature.feature_unit_num,
# o_feature.feature_unit_size,
# 32, 10240, 0.004, 'mean_absolute_tp0_max_ratio_error')
if FLAGS.mode == 'data':
o_data_source.DownloadData()
o_data_source.UpdatePPData()
elif FLAGS.mode == 'testall':
o_vol_wave.TradeTestAll(True, FLAGS.show)
elif FLAGS.mode == 'test':
o_data_source.DownloadStockData(FLAGS.c)
o_data_source.UpdateStockPPData(FLAGS.c)
start_time = time.time()
o_vol_wave.TradeTestStock(FLAGS.c, FLAGS.show)
print(time.time() - start_time)
elif FLAGS.mode == 'train':
tf, tl, vf, vl, td = o_vol_wave.GetDataset(split_date)
tl = tl * 100.0
vl = vl * 100.0
o_dl_model.Train(tf, tl, vf, vl, FLAGS.epoch)
elif FLAGS.mode == 'rtest':
tf, tl, tf, tl, ta = o_vol_wave.GetDataset(split_date)
o_dl_model.LoadModel(FLAGS.epoch)
o_vol_wave.RTest(o_dl_model, tf, ta, False)
elif FLAGS.mode == 'dsw':
dataset = o_vol_wave.ShowDSW3DDataset()
elif FLAGS.mode == 'show':
dataset = o_vol_wave.ShowTradePP(FLAGS.c)
exit()
(self.code_index_map[ts_code], ts_code))
def CreateDSFa3DDataset(self):
dataset_file_name = self.FileNameDSFa3DDataset()
self.dataset = np.zeros(
(len(self.date_list), len(self.code_list), self.feature.unit_size))
# base_common.ListMultiThread(CreateDSFa3DSplitMTFunc, self, 1, self.code_list)
for ts_code in self.code_list:
self.CreateDSFa3DSplitStock(ts_code)
base_common.MKFileDirs(dataset_file_name)
np.save(dataset_file_name, self.dataset)
self.dataset = None
def GetDSFa3DDataset(self):
dataset_file_name = self.FileNameDSFa3DDataset()
if not os.path.exists(dataset_file_name):
self.CreateDSFa3DDataset()
return np.load(dataset_file_name)
if __name__ == "__main__":
data_source = tushare_data.DataSource(20000101, '', '', 1, 20000101,
20200106, False, False, True)
data_source.ShowStockCodes()
o_feature = feature.Feature(30, feature.FUT_D5_NORM, 1, False, False)
o_dataset = DSFa3DDataset(data_source, o_feature)
temp_dataset = o_dataset.GetDSFa3DDataset()
print("dataset: {}".format(temp_dataset.shape))
def main(argv):
del argv
end_date = 20200306
split_date = 20100101
o_data_source = tushare_data.DataSource(20000101, '', '', 10, 20000101,
end_date, False, False, True)
o_feature = feature.Feature(10, feature.FUT_D5_NORM, 1, False, False)
obj = OpenClose(o_data_source, o_feature, not FLAGS.overlap_feature)
o_dl_model = dl_model.DLModel(
'%s_%u' % (obj.setting_name, split_date),
o_feature.feature_unit_num,
o_feature.feature_unit_size,
# 32, 10240, 0.04, 'mean_absolute_tp0_max_ratio_error') # rtest<0
# 4, 10240, 0.04, 'mean_absolute_tp0_max_ratio_error') # rtest<0
# 4, 10240, 0.01, 'mean_absolute_tp0_max_ratio_error') # rtest:0.14
32,
10240,
0.03,
'mean_absolute_tp_max_ratio_error_tanhmap',
50) # rtest:0.62
# 16, 10240, 0.01, 'mean_absolute_tp0_max_ratio_error') # rtest<0
# 16, 10240, 0.01, 'mean_absolute_tp_max_ratio_error_tanhmap', 100)
if FLAGS.mode == 'datasource':
o_data_source.DownloadData()
o_data_source.UpdatePPData()
elif FLAGS.mode == 'dataset':
obj.CreateDataSet()
elif FLAGS.mode == 'public_dataset':
obj.CreateDataSet()
public_dataset = obj.PublicDataset()
file_name = './public/data/dataset.npy'
np.save(file_name, public_dataset)
elif FLAGS.mode == 'train':
tf, tl, vf, vl, td = obj.GetDataset(split_date)
# tf, tl, vf, vl, va = obj.GetDatasetRandom(0.5)
train_epoch = FLAGS.epoch if FLAGS.epoch > 0 else 250
o_dl_model.Train(tf, tl, vf, vl, train_epoch)
elif FLAGS.mode == 'rtest':
tf, tl, vf, vl, va = obj.GetDataset(split_date)
# tf, tl, vf, vl, va = obj.GetDatasetRandom(0.5)
o_dl_model.LoadModel(FLAGS.epoch)
obj.RTest(o_dl_model, vf, va, False)
# elif FLAGS.mode == 'dqntest':
# o_dl_model.LoadModel(FLAGS.epoch)
# o_dsfa = dsfa3d_dataset.DSFa3DDataset(o_data_source, o_feature)
# o_dqn_test = dqn_test.DQNTest(o_dsfa, split_date, o_dl_model)
# o_dqn_test.Test(1, FLAGS.pt, True, FLAGS.show)
# elif FLAGS.mode == 'dqntestall':
# o_dl_model.LoadModel(FLAGS.epoch)
# o_dsfa = dsfa3d_dataset.DSFa3DDataset(o_data_source, o_feature)
# o_dqn_test = dqn_test.DQNTest(o_dsfa, split_date, o_dl_model)
# o_dqn_test.TestAllModels(1, FLAGS.pt)
# elif FLAGS.mode == 'predict':
# o_dl_model.LoadModel(FLAGS.epoch)
# o_data_source.SetPPDataDailyUpdate(20180101, 20200323)
# o_dsfa = dsfa3d_dataset.DSFa3DDataset(o_data_source, o_feature)
# o_dqn_test = dqn_test.DQNTest(o_dsfa, split_date, o_dl_model)
# o_dqn_test.Test(1, FLAGS.pt, True, FLAGS.show)
elif FLAGS.mode == 'dsw':
dataset = obj.ShowDSW3DDataset()
elif FLAGS.mode == 'show':
dataset = obj.ShowTradePP(FLAGS.c)
elif FLAGS.mode == 'showlabel':
dataset = obj.ShowLabel()
elif FLAGS.mode == 'debug':
dataset = np.load(obj.FileNameDataset())
print("dataset: {}".format(dataset.shape))
dataset = np_common.Sort2D(dataset, [obj.index_increase], [False])
dataset = dataset[:5]
obj.ShowDataSet(dataset, 'dataset')
elif FLAGS.mode == 'clean':
obj.Clean()
o_dl_model.Clean()
elif FLAGS.mode == 'pp':
o_data_source.ShowStockPPData(FLAGS.c, FLAGS.date)
elif FLAGS.mode == 'vol':
o_data_source.ShowAvgVol(100000)
exit()
def main(argv):
del argv
code_filter = '\
000001.SZ,\
000002.SZ,\
000063.SZ,\
000538.SZ,\
000541.SZ,\
000550.SZ,\
000560.SZ,\
000561.SZ,\
000584.SZ,\
000625.SZ,\
000650.SZ,\
000651.SZ,\
000721.SZ,\
000800.SZ,\
000802.SZ,\
000858.SZ,\
000898.SZ,\
000913.SZ,\
000927.SZ,\
000932.SZ,\
000937.SZ,\
000938.SZ,\
000951.SZ,\
000959.SZ,\
001696.SZ,\
600000.SH,\
600006.SH,\
600085.SH,\
600104.SH,\
600109.SH,\
600115.SH,\
600137.SH,\
600177.SH,\
600198.SH,\
600199.SH,\
600600.SH,\
600601.SH,\
600609.SH,\
600612.SH,\
600623.SH,\
600624.SH,\
600664.SH,\
600679.SH,\
600702.SH,\
600718.SH,\
600809.SH'
o_data_source = tushare_data.DataSource(20000101, '', code_filter, 1,
20130101, 20200106, False, False,
True)
o_feature = feature.Feature(30, feature.FUT_D5_NORM, 1, False, False)
# o_feature = feature.Feature(30, feature.FUT_D5_NORM, 1, False, False)
# o_feature = feature.Feature(30, feature.FUT_5REGION5_NORM, 5, False, False)
# o_feature = feature.Feature(30, feature.FUT_2AVG5_NORM, 5, False, False)
o_avg_wave = AvgWave(o_data_source, o_feature, PPI_close_30_avg, MODE_GRAD,
0, 0, 0.1)
split_date = 20180101
o_dl_model = dl_model.DLModel(
'%s_%u' % (o_avg_wave.setting_name, split_date),
o_feature.feature_unit_num, o_feature.feature_unit_size, 32, 10240,
0.004, 'mean_absolute_tp0_max_ratio_error')
if FLAGS.mode == 'data':
o_data_source.DownloadData()
o_data_source.UpdatePPData()
elif FLAGS.mode == 'testall':
o_avg_wave.TradeTestAll(True, FLAGS.show)
elif FLAGS.mode == 'test':
o_data_source.DownloadStockData(FLAGS.c)
o_data_source.UpdateStockPPData(FLAGS.c)
start_time = time.time()
o_avg_wave.TradeTestStock(FLAGS.c, FLAGS.show)
print(time.time() - start_time)
elif FLAGS.mode == 'train':
tf, tl, vf, vl, td = o_avg_wave.GetDataset(split_date)
tl = tl * 100.0
vl = vl * 100.0
o_dl_model.Train(tf, tl, vf, vl, FLAGS.epoch)
elif FLAGS.mode == 'rtest':
tf, tl, tf, tl, ta = o_avg_wave.GetDataset(split_date)
o_dl_model.LoadModel(FLAGS.epoch)
o_avg_wave.RTest(o_dl_model, tf, ta, False)
elif FLAGS.mode == 'dsw':
dataset = o_avg_wave.ShowDSW3DDataset()
elif FLAGS.mode == 'show':
dataset = o_avg_wave.ShowTradePP(FLAGS.c)
exit()
def main(argv):
del argv
code_filter = '\
000001.SZ,\
000002.SZ,\
000005.SZ,\
000006.SZ,\
000009.SZ,\
000012.SZ,\
000016.SZ,\
000021.SZ,\
000027.SZ,\
000031.SZ,\
000036.SZ,\
000039.SZ,\
000050.SZ,\
000059.SZ,\
000060.SZ,\
000063.SZ,\
000066.SZ,\
000069.SZ,\
000078.SZ,\
000088.SZ,\
000089.SZ,\
000401.SZ,\
000402.SZ,\
000410.SZ,\
000413.SZ,\
000422.SZ,\
000425.SZ,\
000503.SZ,\
000507.SZ,\
000510.SZ,\
000518.SZ,\
000520.SZ,\
000528.SZ,\
000540.SZ,\
000554.SZ,\
000559.SZ,\
000563.SZ,\
000571.SZ,\
000572.SZ,\
000592.SZ,\
000598.SZ,\
000601.SZ,\
000616.SZ,\
000625.SZ,\
000627.SZ,\
000629.SZ,\
000630.SZ,\
000636.SZ,\
000650.SZ,\
000651.SZ,\
000652.SZ,\
000656.SZ,\
000659.SZ,\
000667.SZ,\
000670.SZ,\
000680.SZ,\
000682.SZ,\
000683.SZ,\
000686.SZ,\
000690.SZ,\
000709.SZ,\
000717.SZ,\
000718.SZ,\
000720.SZ,\
000723.SZ,\
000727.SZ,\
000728.SZ,\
000735.SZ,\
000750.SZ,\
000751.SZ,\
000758.SZ,\
000767.SZ,\
000768.SZ,\
000776.SZ,\
000778.SZ,\
000783.SZ,\
000789.SZ,\
000793.SZ,\
000795.SZ,\
000800.SZ,\
000806.SZ,\
000807.SZ,\
000816.SZ,\
000822.SZ,\
000823.SZ,\
000825.SZ,\
000829.SZ,\
000830.SZ,\
000831.SZ,\
000839.SZ,\
000851.SZ,\
000858.SZ,\
000859.SZ,\
000868.SZ,\
000876.SZ,\
000877.SZ,\
000878.SZ,\
000882.SZ,\
000886.SZ,\
000897.SZ,\
000898.SZ,\
000917.SZ,\
000927.SZ,\
000930.SZ,\
000931.SZ,\
000932.SZ,\
000933.SZ,\
000937.SZ,\
000959.SZ,\
600000.SH,\
600006.SH,\
600060.SH,\
600063.SH,\
600067.SH,\
600068.SH,\
600069.SH,\
600078.SH,\
600089.SH,\
600100.SH,\
600103.SH,\
600104.SH,\
600108.SH,\
600109.SH,\
600110.SH,\
600111.SH,\
600115.SH,\
600122.SH,\
600123.SH,\
600125.SH,\
600127.SH,\
600151.SH,\
600153.SH,\
600157.SH,\
600158.SH,\
600160.SH,\
600162.SH,\
600166.SH,\
600169.SH,\
600170.SH,\
600171.SH,\
600175.SH,\
600176.SH,\
600177.SH,\
600183.SH,\
600186.SH,\
600187.SH,\
600188.SH,\
600196.SH,\
600198.SH,\
600200.SH,\
600206.SH,\
600208.SH,\
600209.SH,\
600210.SH,\
600212.SH,\
600216.SH,\
600219.SH,\
600220.SH,\
600221.SH,\
600226.SH,\
600239.SH,\
600266.SH,\
600601.SH,\
600606.SH,\
600609.SH,\
600611.SH,\
600624.SH,\
600630.SH,\
600635.SH,\
600642.SH,\
600643.SH,\
600649.SH,\
600651.SH,\
600652.SH,\
600653.SH,\
600660.SH,\
600662.SH,\
600664.SH,\
600667.SH,\
600673.SH,\
600674.SH,\
600675.SH,\
600677.SH,\
600688.SH,\
600690.SH,\
600703.SH,\
600704.SH,\
600705.SH,\
600711.SH,\
600717.SH,\
600718.SH,\
600720.SH,\
600736.SH,\
600737.SH,\
600739.SH,\
600740.SH,\
600741.SH,\
600755.SH,\
600759.SH,\
600770.SH,\
600776.SH,\
600777.SH,\
600782.SH,\
600787.SH,\
600789.SH,\
600795.SH,\
600797.SH,\
600800.SH,\
600804.SH,\
600805.SH,\
600808.SH,\
600811.SH,\
600812.SH,\
600816.SH,\
600820.SH,\
600837.SH,\
600839.SH,\
600846.SH,\
600851.SH,\
600863.SH,\
600868.SH,\
600871.SH,\
600872.SH,\
600873.SH,\
600874.SH,\
600875.SH,\
600877.SH,\
600879.SH,\
600881.SH,\
600886.SH,\
600887.SH,\
600895.SH,\
601607.SH\
'
# end_date = 20200106
end_date = 20200306
# end_date = 20190221
split_date = 20100101
o_data_source = tushare_data.DataSource(20000101, '', '', 2, 20000101,
end_date, False, False, True)
# o_feature = feature.Feature(30, feature.FUT_D5_NORM, 1, False, False)
o_feature = feature.Feature(20, feature.FUT_D5_NORM, 1, False, False)
# o_feature = feature.Feature(7, feature.FUT_5REGION5_NORM, 5, False, False)
# o_feature = feature.Feature(30, feature.FUT_5REGION5_NORM, 5, False, False)
# o_feature = feature.Feature(30, feature.FUT_D3_NORM, 1, False, False)
# o_dqn_fix = DQNFix(o_data_source, o_feature, 6, DECAY_MODE_EXP, 0.6, not FLAGS.overlap_feature)
o_dqn_fix = DQNFix(o_data_source, o_feature, 20, False, DECAY_MODE_EXP,
0.9, not FLAGS.overlap_feature)
# o_dqn_fix = DQNFix(o_data_source, o_feature, 30, DECAY_MODE_EXP, 0.9, not FLAGS.overlap_feature)
o_dl_model = dl_model.DLModel(
'%s_%u' % (o_dqn_fix.setting_name, split_date),
o_feature.feature_unit_num,
o_feature.feature_unit_size,
# 32, 10240, 0.04, 'mean_absolute_tp0_max_ratio_error') # rtest<0
# 4, 10240, 0.04, 'mean_absolute_tp0_max_ratio_error') # rtest<0
# 4, 10240, 0.01, 'mean_absolute_tp0_max_ratio_error') # rtest:0.14
0,
10240,
0.03,
'mean_absolute_tp_max_ratio_error_tanhmap',
50) # rtest:0.62
# 16, 10240, 0.01, 'mean_absolute_tp0_max_ratio_error') # rtest<0
# 16, 10240, 0.01, 'mean_absolute_tp_max_ratio_error_tanhmap', 100)
if FLAGS.mode == 'datasource':
o_data_source.DownloadData()
o_data_source.UpdatePPData()
elif FLAGS.mode == 'dataset':
o_dqn_fix.CreateDataSet()
elif FLAGS.mode == 'public_dataset':
o_dqn_fix.CreateDataSet()
public_dataset = o_dqn_fix.PublicDataset()
file_name = './public/data/dataset.npy'
np.save(file_name, public_dataset)
elif FLAGS.mode == 'train':
tf, tl, vf, vl, td = o_dqn_fix.GetDataset(split_date)
# tf, tl, vf, vl, va = o_dqn_fix.GetDatasetRandom(0.5)
train_epoch = FLAGS.epoch if FLAGS.epoch > 0 else 250
o_dl_model.Train(tf, tl, vf, vl, train_epoch)
elif FLAGS.mode == 'rtest':
tf, tl, vf, vl, va = o_dqn_fix.GetDataset(split_date)
# tf, tl, vf, vl, va = o_dqn_fix.GetDatasetRandom(0.5)
o_dl_model.LoadModel(FLAGS.epoch)
o_dqn_fix.RTest(o_dl_model, vf, va, False)
elif FLAGS.mode == 'dqntest':
o_dl_model.LoadModel(FLAGS.epoch)
o_dsfa = dsfa3d_dataset.DSFa3DDataset(o_data_source, o_feature)
o_dqn_test = dqn_test.DQNTest(o_dsfa, split_date, o_dl_model)
o_dqn_test.Test(1, FLAGS.pt, True, FLAGS.show)
elif FLAGS.mode == 'dqntestall':
o_dl_model.LoadModel(FLAGS.epoch)
o_dsfa = dsfa3d_dataset.DSFa3DDataset(o_data_source, o_feature)
o_dqn_test = dqn_test.DQNTest(o_dsfa, split_date, o_dl_model)
o_dqn_test.TestAllModels(1, FLAGS.pt)
elif FLAGS.mode == 'predict':
o_dl_model.LoadModel(FLAGS.epoch)
o_data_source.SetPPDataDailyUpdate(20180101, 20200323)
o_dsfa = dsfa3d_dataset.DSFa3DDataset(o_data_source, o_feature)
o_dqn_test = dqn_test.DQNTest(o_dsfa, split_date, o_dl_model)
o_dqn_test.Test(1, FLAGS.pt, True, FLAGS.show)
elif FLAGS.mode == 'dsw':
dataset = o_dqn_fix.ShowDSW3DDataset()
elif FLAGS.mode == 'show':
dataset = o_dqn_fix.ShowTradePP(FLAGS.c)
elif FLAGS.mode == 'showlabel':
dataset = o_dqn_fix.ShowLabel()
elif FLAGS.mode == 'debug':
dataset = np.load(o_dqn_fix.FileNameDataset())
print("dataset: {}".format(dataset.shape))
dataset = np_common.Sort2D(dataset, [o_dqn_fix.index_increase],
[False])
dataset = dataset[:5]
o_dqn_fix.ShowDataSet(dataset, 'dataset')
elif FLAGS.mode == 'clean':
o_dqn_fix.Clean()
o_dl_model.Clean()
elif FLAGS.mode == 'pp':
o_data_source.ShowStockPPData(FLAGS.c, FLAGS.date)
elif FLAGS.mode == 'vol':
o_data_source.ShowAvgVol(100000)
exit()