def get_today_more_data_for_MLP(code):
'''
:param code:股票代码
:return: 今日预测的X
'''
import numpy as np
stock_data_path = "./data/stock_data/"
open_price, oneDayLine, volume, ma5, vma5, turnover, dates = load_fq_open_close_volume_ma5_vma5_turnover_from_tushare(
stock_data_path + str(code) + '_fq.csv')
if (str(code)[0] == '6'):
# 上证指数
open_index, close_index, volume_index, ma5_index, vma5_index, dates_index = load_index_open_close_volume_ma5_vma5_from_tushare(
stock_data_path + '../sh.csv')
else:
# 深证指数
open_index, close_index, volume_index, ma5_index, vma5_index, dates_index = load_index_open_close_volume_ma5_vma5_from_tushare(
stock_data_path + '../sz.csv')
daynum = 5
X_clf = []
ef = Extract_Features()
for i in range(daynum, len(dates)):
#以大盘为标准
p = dates_index.index(dates[i])
# 组装数据
X_delta = [(oneDayLine[k] - oneDayLine[k - 1]) / oneDayLine[k - 1] for k in range(i - daynum, i)] + \
[(volume[k] - volume[k - 1]) / volume[k - 1] for k in range(i - daynum, i)] + \
[turnover[k] for k in range(i - daynum, i)] + \
[(ma5[i] - ma5[i - 1]) / ma5[i - 1]] + \
[(vma5[i] - vma5[i - 1]) / vma5[i - 1]] + \
[(open_index[p] - open_index[p - 1]) / open_index[p - 1]] + \
[(close_index[p] - close_index[p - 1]) / close_index[p - 1]] + \
[(volume_index[p] - volume_index[p - 1]) / volume_index[p - 1]] + \
[(ma5_index[p] - ma5_index[p - 1]) / ma5_index[p - 1]] + \
[(vma5_index[p] - vma5_index[p - 1]) / vma5_index[p - 1]] + \
covert_bin(ef.parse_weekday(dates[i]), 3) \
# + [(ef.MoneySupply(dates[i]) - ef.MoneySupply(dates[i - 22])) / ef.MoneySupply(dates[i - 22])]
# covert_bin(ef.lunar_month(dates[i]), 5) + \
if i == 5:
X_delta[0] = X_delta[5] = 0.0 # 调整第一个百分比
X_clf.append(X_delta)
X_clf = preprocessing.MinMaxScaler().fit_transform(X_clf)
# return np.array(X_clf)
return X_clf[-1]