and (code[:2] == '60' \
or code[:2] == '00' \
or code[:2] == '30'):
if code[:2] == '60':
stock_list.append("sh" + code)
else:
stock_list.append("sz" + code)
from DataProviders.DailyFullMarket2D import DailyFullMarket2D as Provider
from Models.ModelChange_AE import ModelChange as Model
low, high, step, samples = -9.5, 9.5, 1, 3500
data_segment = 'today_full'
result_cols = ['nextday_close']
provider = Provider(start_date, end_date, [])
model = Model()
cond = " `{0}` > {1} AND `{0}` < {2} ".format(result_cols[0], low, high)
results = provider.fetch_resultset(result_cols, cond)
results = provider.balance_result(result_cols[0], low, high, step, samples)
results = results[result_cols].as_matrix()
results = results[:, 0]
data = provider.fetch_dataset(data_segment)
# results = results * 0.1
# data = data[:10000]
# results = results[:10000]
count = data.shape[0]
exit(0)
elif len(sys.argv) == 2:
start_date = datetime.datetime.strptime(str(sys.argv[1]), "%Y-%m-%d").date()
end_date = datetime.datetime.strptime(str(sys.argv[1]), "%Y-%m-%d").date()
else:
start_date = datetime.datetime.strptime(str(sys.argv[1]), "%Y-%m-%d").date()
end_date = datetime.datetime.strptime(str(sys.argv[2]), "%Y-%m-%d").date()
import numpy as np
from DataProviders.DailyFullMarket2D import DailyFullMarket2D as Provider
from Models.ModelCNN_NDC import Model_CNN_NDC as Model
data_segment = 'today_full'
result_cols = ['nextday_close']
provider = Provider(start_date, end_date)
model = Model()
results = provider.fetch_resultset(result_cols)
data = provider.fetch_dataset(data_segment)
real_results = results[result_cols]
real_results = real_results.as_matrix()[:, 0]
# data = data[:1000]
# real_results = results[:1000]
print(("Evaluating {} samples".format(data.shape[0])))
pred_results = model.predict(data)
pred_results = pred_results.reshape(1, -1)
results = pd.DataFrame(results)
"%Y-%m-%d").date()
end_date = datetime.datetime.strptime(str(sys.argv[1]), "%Y-%m-%d").date()
else:
start_date = datetime.datetime.strptime(str(sys.argv[1]),
"%Y-%m-%d").date()
end_date = datetime.datetime.strptime(str(sys.argv[2]), "%Y-%m-%d").date()
import numpy as np
from DataProviders.DailyFullMarket2D import DailyFullMarket2D as Provider
from Models.ModelCNN_NDC import Model_CNN_NDC as Model
low, high, step, samples = -4, 4, 1, 2600
data_segment = 'today_full'
result_cols = ['nextday_close']
provider = Provider(start_date, end_date)
model = Model()
results = provider.fetch_resultset(result_cols)
results = provider.balance_result(result_cols[0], low, high, step, samples)
results = results[result_cols].as_matrix()
results = results[:, 0]
data = provider.fetch_dataset(data_segment)
# results = results * 0.1
# data = data[:10000]
# results = results[:10000]
count = data.shape[0]
[training_data, training_result], \
elif len(sys.argv) == 2:
start_date = datetime.datetime.strptime(str(sys.argv[1]),
"%Y-%m-%d").date()
end_date = datetime.datetime.strptime(str(sys.argv[1]), "%Y-%m-%d").date()
else:
start_date = datetime.datetime.strptime(str(sys.argv[1]),
"%Y-%m-%d").date()
end_date = datetime.datetime.strptime(str(sys.argv[2]), "%Y-%m-%d").date()
from DataProviders.DailyFullMarket2D import DailyFullMarket2D as Provider
low, high, step, samples = -9.5, 9.5, 1, 3500
data_segment = 'today_full'
result_cols = ['nextday_close']
provider = Provider(start_date, end_date, [])
cond = " `{0}` > {1} AND `{0}` < {2} ".format(result_cols[0], low, high)
results = provider.fetch_resultset(result_cols, cond)
results = provider.balance_result(result_cols[0], low, high, step, samples)
results = results[result_cols].as_matrix()
results = results[:, 0]
input = provider.fetch_dataset(data_segment)
# 先不缩放数据 只是观察
# psy_in = input[:, :, [68, 69]]
# amp_in = input[:, :, [20]] #vr 22 vol 21
mdi_in = input[:, :, [57, 58, 59, 60]]
input = mdi_in
input = np.nan_to_num(input)
exit(0)
elif len(sys.argv) == 2:
start_date = datetime.datetime.strptime(str(sys.argv[1]),
"%Y-%m-%d").date()
end_date = datetime.datetime.strptime(str(sys.argv[1]), "%Y-%m-%d").date()
else:
start_date = datetime.datetime.strptime(str(sys.argv[1]),
"%Y-%m-%d").date()
end_date = datetime.datetime.strptime(str(sys.argv[2]), "%Y-%m-%d").date()
from DataProviders.DailyFullMarket2D import DailyFullMarket2D as Provider
data_segment = 'today_full'
result_cols = ['nextday_close']
provider = Provider(start_date, end_date)
results = provider.fetch_resultset(result_cols)
low, high, step, samples = -7, 7, 1, 2600
# results = provider.balance_result(result_cols[0], low, high, step, samples)
results = results[result_cols].as_matrix()
results = results[:, 0]
dist = np.arange(low, high + step, step)
dist_count = []
results = pd.DataFrame(results)
results.columns = ['value']
report = pd.DataFrame(columns=['range', 'count'])
for i in range(len(dist) - 1):
low = dist[i]