def cal_rank_now(self, bias_now=None):
"""
给定一个值，计算相对bias
:param bias_now:
:return:
"""
if bias_now is None:
bias_now = self.cal_rt_bias()
if bias_now >= 0:
return relative_rank(self.bias_dict['bias_p'], bias_now)
else:
return relative_rank(self.bias_dict['bias_n'], bias_now) - 100
def cal_close_rank(df_):
"""
计算排名子函数
:param df_:
:return:
"""
close_history = df_['close']
close_now = close_history.values[-1]
return relative_rank(close_history, close_now)
def predict_tomorrow(stk_code,
label,
N_STEPS=N_STEPS,
feature_cols=feature_cols,
HIDDEN_SIZE=HIDDEN_SIZE,
NUM_LAYERS=NUM_LAYERS):
"""
:param stk_code: 例子 '300508'
:param label: 例子 'high'
:param N_STEPS:
:param feature_cols:
:param HIDDEN_SIZE:
:param NUM_LAYERS:
:return:
"""
""" ---------------------- 读取json中存储的极值 ---------------------- """
with open(rootPath + '\Function\LSTM\AboutLSTM\stk_max_min.json',
'r') as f:
max_min_info = json.load(f)
""" ---------------------- 获取实时数据 ---------------------- """
data_now = ts.get_k_data(stk_code)[-(N_STEPS + 30):]
# 增加M9 Rank
data_now['m9'] = data_now['close'].rolling(window=9).mean()
data_now['diff_m9'] = data_now.apply(lambda x:
(x['close'] - x['m9']) / x['close'],
axis=1)
data_now['rank'] = data_now.apply(lambda x: 100 - relative_rank(
max_min_info[stk_code]['m9_history'], x['diff_m9']),
axis=1)
# rootPath = 'C:/Users\paul\Desktop\软件代码\Git-Clone'
for c in ['close', 'high', 'low', 'open']:
data_now[c] = (data_now[c].values - max_min_info[stk_code]['p_min']
) / (max_min_info[stk_code]['p_max'] -
max_min_info[stk_code]['p_min'])
data_now['volume'] = (
data_now['volume'].values - max_min_info[stk_code]['v_min']) / (
max_min_info[stk_code]['v_max'] - max_min_info[stk_code]['v_min'])
# 进行归一化
input_normal = data_now.loc[:, feature_cols].tail(20).values
tf.reset_default_graph()
""" ---------------------- 创建模型 ---------------------- """
predictions, loss, train_op, X, y = lstm_model(n_steps=N_STEPS,
n_inputs=len(feature_cols),
HIDDEN_SIZE=HIDDEN_SIZE,
NUM_LAYERS=NUM_LAYERS)
# 创建保存器用于模型
saver = tf.train.Saver()
# 初始化
sess = tf.Session()
model_name = stk_code + '_' + label
model_dir = rootPath + 'Function\LSTM\AboutLSTM\modelDir/'
if os.path.exists(model_dir + model_name + '/' + model_name +
'.ckpt.meta'):
saver = tf.train.import_meta_graph(model_dir + model_name + '/' +
model_name + '.ckpt.meta')
saver.restore(sess,
tf.train.latest_checkpoint(model_dir + model_name + '/'))
# graph = tf.get_default_graph()
# 防报错
tf.reset_default_graph()
r_rela = sess.run([predictions], feed_dict={X:
[input_normal]})[0][0][0]
return max_min_info[stk_code]['p_min'] + (
max_min_info[stk_code]['p_max'] -
max_min_info[stk_code]['p_min']) * r_rela
else:
print('加载模型' + model_name + '失败！')
return -1
# encoding=utf-8
def cal_rank_sig(sig, total):
return relative_rank(total, sig)