def check_single_stk_middle_level(stk_code, dict):
"""
输入代码，返回level
:param stk_code:
:return:
"""
# 获取当前价格
current_price = get_RT_price(stk_code, source='jq')
if stk_code in dict.keys():
l = relativeRank(dict[stk_code], current_price)
else:
df_hour = get_k_data_JQ(stk_code,
count=None,
start_date=add_date_str(
get_current_date_str(), -60),
freq='60m')
dict[stk_code] = list(df_hour['close'].values)
l = relativeRank(list(df_hour['close'].values), current_price)
with open(data_dir + 'middlePeriodHourData.json', 'w') as f:
json.dump(dict, f)
return l
df = df.dropna(how='any', axis=0)
"""
画图语句
df.plot('date', ['upper', 'lower', 'high', 'low'], style=['-', '-', '*', '*'])
"""
# 计算短期波动率（3天）
near_days = 3
df['low_near'] = df['low'].rolling(window=near_days).min()
df['high_near'] = df['high'].rolling(window=near_days).max()
df['wave_near'] = df.apply(lambda x: x['high_near'] - x['low_near'], axis=1)
df['wave_near_rank'] = df.apply(lambda x: 100 - relativeRank(df['wave_near'], x['wave_near']), axis=1)
"""
df.plot('date', ['close', 'wave_near_rank', 'wave_near'], style=['--*', '--*'], subplots=True)
"""
"""
如何调节买卖比例：
用移动均线-未成熟随机值（RSV）来对买卖比例进行分配，
实现在上涨时，易买难卖，在下跌时，易卖难买，最终能够实现随着趋势线调整仓位重心的目的。
避免在迅速下跌时爆仓，在迅速上涨时空仓的情况。
"""
#
# def updateRSVRecord():
# try:
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: relativeRank(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
"""
画图语句
df.plot('date', ['upper', 'lower', 'high', 'low'], style=['-', '-', '*', '*'])
"""
# 计算短期波动率（3天）
near_days = 3
df['low_near'] = df['low'].rolling(window=near_days).min()
df['high_near'] = df['high'].rolling(window=near_days).max()
df['wave_near'] = df.apply(lambda x: x['high_near'] - x['low_near'],
axis=1)
df['wave_near_rank'] = df.apply(
lambda x: 100 - relativeRank(df['wave_near'], x['wave_near']), axis=1)
"""
df.plot('date', ['close', 'wave_near_rank', 'wave_near'], style=['--*', '--*'], subplots=True)
"""
"""
如何调节买卖比例：
用移动均线-未成熟随机值（RSV）来对买卖比例进行分配，
实现在上涨时，易买难卖，在下跌时，易卖难买，最终能够实现随着趋势线调整仓位重心的目的。
避免在迅速下跌时爆仓，在迅速上涨时空仓的情况。
"""
#
# def updateRSVRecord():
# try:
# (conn_opt, engine_opt) = genDbConn(localDBInfo, 'stk_opt_info')