In [1]:
import numpy as np
import pandas as pd
import TAF
import datetime
import talib
import matplotlib.pylab as plt
import seaborn as sns
% matplotlib inline
In [2]:
factors = pd.read_csv('HS300_15m.csv')
index = factors['index']
High = factors.high.values
Low = factors.low.values
Close = factors.close.values
Open = factors.open.values
Volume = factors.volume.values
factors = TAF.get_factors(index, Open, Close, High, Low, Volume, drop=True)
factors = factors.iloc[-700 * 16 - 11 * 16:]
In [3]:
start_date = factors.index[11*16][:10]
end_date = factors.index[-1][:10]
print ('开始时间', start_date)
print ('结束时间', end_date)
In [4]:
rolling = 88
targets = pd.read_csv('HS300_1d.csv')
targets.rename(columns={'Unnamed: 0':'tradeDate'}, inplace=True)
targets['returns'] = targets.close.shift(-5)/ targets.close - 1.
targets['labels'] = 1
targets['upper_boundary']= targets.returns.rolling(rolling).mean() + 0.5 * targets.returns.rolling(rolling).std()
targets['lower_boundary']= targets.returns.rolling(rolling).mean() - 0.5 * targets.returns.rolling(rolling).std()
targets.dropna(inplace=True)
targets.loc[targets['returns']>=targets['upper_boundary'], 'labels'] = 2
targets.loc[targets['returns']<=targets['lower_boundary'], 'labels'] = 0
targets.set_index('tradeDate', inplace=True)
targets= targets.loc[start_date:end_date, 'labels']
In [5]:
inputs = np.array(factors).reshape(-1, 1, 58)
def dense_to_one_hot(labels_dense):
"""标签 转换one hot 编码
输入labels_dense 必须为非负数
2016-11-21
"""
num_classes = len(np.unique(labels_dense)) # np.unique 去掉重复函数
raws_labels = labels_dense.shape[0]
index_offset = np.arange(raws_labels) * num_classes
labels_one_hot = np.zeros((raws_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
targets = dense_to_one_hot(targets)
targets = np.expand_dims(targets, axis=1)
train_inputs = inputs[:-100*16]
test_inputs = inputs[-100*16 - 11 * 16:]
train_targets = targets[:-100]
test_targets = targets[-100:]
train_gather_list = np.arange(train_inputs.shape[0])
train_gather_list = train_gather_list.reshape([-1,16])[11:]
train_gather_list = train_gather_list[:,-1]
test_gather_list = np.arange(test_inputs.shape[0])
test_gather_list = test_gather_list.reshape([-1,16])[11:]
test_gather_list = test_gather_list[:,-1]
In [6]:
import tensorflow as tf
from DNCore import DNCoreDeepLSTM
In [7]:
class Classifier_DNCoreDeepLSTM(object):
def __init__(self,
inputs,
targets,
gather_list=None,
batch_size=1,
hidden_size=10,
memory_size=10,
num_reads=3,
num_writes=1,
learning_rate = 1e-4,
optimizer_epsilon = 1e-10,
l2_coefficient = 1e-3,
max_gard_norm = 50,
reset_graph = True):
if reset_graph:
tf.reset_default_graph()
# 控制参数
self._tmp_inputs = inputs
self._tmp_targets = targets
self._in_length = None
self._in_width = inputs.shape[2]
self._out_length = None
self._out_width = targets.shape[2]
self._batch_size = batch_size
# 声明会话
self._sess = tf.InteractiveSession()
self._inputs = tf.placeholder(
dtype=tf.float32,
shape=[self._in_length, self._batch_size, self._in_width],
name='inputs')
self._targets = tf.placeholder(
dtype=tf.float32,
shape=[self._out_length, self._batch_size, self._out_width],
name='targets')
self._RNNCoreCell = DNCoreDeepLSTM(
dnc_output_size=self._out_width,
hidden_size=hidden_size,
memory_size=memory_size,
word_size=self._in_width,
num_read_heads=num_reads,
num_write_heads=num_writes)
self._initial_state = \
self._RNNCoreCell.initial_state(batch_size)
output_sequences, _ = \
tf.nn.dynamic_rnn(cell= self._RNNCoreCell,
inputs=self._inputs,
initial_state=self._initial_state,
time_major=True)
self._original_output_sequences = output_sequences
if gather_list is not None:
output_sequences = tf.gather(output_sequences, gather_list)
# L2 正则化测试 2017-09-03
self._trainable_variables = tf.trainable_variables()
_l2_regularizer = tf.add_n([tf.nn.l2_loss(v) for v in self._trainable_variables])
self._l2_regularizer = _l2_regularizer * l2_coefficient / len(self._trainable_variables)
rnn_cost = tf.nn.softmax_cross_entropy_with_logits(
labels=self._targets, logits=output_sequences)
self._rnn_cost = tf.reduce_mean(rnn_cost)
self._cost = self._rnn_cost + self._l2_regularizer
train_pred = tf.nn.softmax(output_sequences, dim=2)
correct_pred = tf.equal(tf.argmax(train_pred,2), tf.argmax(self._targets,2))
self._accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Set up optimizer with global norm clipping.
trainable_variables = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(
tf.gradients(self._cost, trainable_variables), max_gard_norm)
global_step = tf.get_variable(
name="global_step",
shape=[],
dtype=tf.int64,
initializer=tf.zeros_initializer(),
trainable=False,
collections=[tf.GraphKeys.GLOBAL_VARIABLES, tf.GraphKeys.GLOBAL_STEP])
optimizer = tf.train.RMSPropOptimizer(
learning_rate=learning_rate, epsilon=optimizer_epsilon)
self._train_step = optimizer.apply_gradients(
zip(grads, trainable_variables), global_step=global_step)
self._sess.run(tf.global_variables_initializer())
self._variables_saver = tf.train.Saver()
def fit(self,
training_iters =1e2,
display_step = 5,
save_path = None,
restore_path = None):
if restore_path is not None:
self._variables_saver.restore(self._sess, restore_path)
for scope in range(np.int(training_iters)):
self._sess.run([self._train_step],
feed_dict = {self._inputs:self._tmp_inputs, self._targets:self._tmp_targets})
if scope % display_step == 0:
loss, acc, l2_loss, rnn_loss = self._sess.run(
[self._cost, self._accuracy, self._l2_regularizer, self._rnn_cost],
feed_dict = {self._inputs:self._tmp_inputs, self._targets:self._tmp_targets})
print (scope, ' loss--', loss, ' acc--', acc, ' l2_loss', l2_loss, ' rnn_cost', rnn_loss)
print ("Optimization Finished!")
loss, acc, l2_loss, rnn_loss = self._sess.run(
[self._cost, self._accuracy, self._l2_regularizer, self._rnn_cost],
feed_dict = {self._inputs:self._tmp_inputs, self._targets:self._tmp_targets})
print ('Model assessment loss--', loss, ' acc--', acc, ' l2_loss', l2_loss, ' rnn_cost', rnn_loss)
# 保存模型可训练变量
if save_path is not None:
self._variables_saver.save(self._sess, save_path)
def close(self):
self._sess.close()
print ('结束进程，清理tensorflow内存/显存占用')
def pred(self, inputs, gather_list=None, restore_path=None):
output_sequences = self._original_output_sequences
if gather_list is not None:
output_sequences = tf.gather(output_sequences, gather_list)
probability = tf.nn.softmax(output_sequences)
classification = tf.argmax(probability, axis=-1)
return self._sess.run([probability, classification],feed_dict = {self._inputs:inputs})
def restore_trainable_variables(self, restore_path):
self._variables_saver.restore(self._sess, restore_path)
def score(self, inputs, targets, gather_list=None):
acc = self._sess.run(
self._accuracy,
feed_dict = {self._inputs:self._tmp_inputs,
self._targets:self._tmp_targets})
return acc
In [8]:
a = Classifier_DNCoreDeepLSTM(train_inputs, train_targets, train_gather_list)
a.fit(training_iters = 5)
a.close()
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