class TraderTrainer:
def __init__(self,
config,
stockList,
featureList,
start_date,
end_date,
fake_data=False,
restore_dir=None,
save_path=None,
device="cpu",
agent=None):
"""
:param config: config dictionary
:param fake_data: if True will use data generated randomly
:param restore_dir: path to the model trained before
:param save_path: path to save the model
:param device: the device used to train the network
:param agent: the nnagent object. If this is provides, the trainer will not
create a new agent by itself. Therefore the restore_dir will not affect anything.
"""
self.config = config
self.train_config = config["training"]
self.input_config = config["input"]
self.save_path = save_path
self.best_metric = 0
np.random.seed(config["random_seed"])
self.__window_size = self.input_config["window_size"]
self.__batch_size = self.train_config["batch_size"]
self.__snap_shot = self.train_config["snap_shot"]
config["input"]["fake_data"] = fake_data
self.stockList = stockList
self.featureList = featureList
self.start_date = start_date
self.end_date = end_date
self.fake_data = fake_data
self._matrix = DataMatrices.create_from_config(config, stockList,
featureList, start_date,
end_date) #数据
self.test_set = self._matrix.get_test_set(
) #测试集 dict：{'X', 'y', 'last_w', 'setw'}
# X: (260, 4, 3, 31), y: (260, 4, 3), last_w: (260, 3)
# X: (test_length, feature_num, stock_num, time_windows)
if not config["training"]["fast_train"]:
self.training_set = self._matrix.get_training_set() #训练集
self.upperbound_validation = 1
self.upperbound_test = 1
tf.set_random_seed(self.config["random_seed"])
self.device = device
if agent:
self._agent = agent
else:
if device == "cpu":
os.environ["CUDA_VISIBLE_DEVICES"] = ""
with tf.device("/cpu:0"):
self._agent = NNAgent(config, stockList, featureList,
restore_dir, device)
else:
self._agent = NNAgent(config, stockList, featureList,
restore_dir, device)
def _evaluate(self, set_name, *tensors):
if set_name == "test":
feed = self.test_set
elif set_name == "training":
feed = self.training_set
else:
raise ValueError()
result = self._agent.evaluate_tensors(feed["X"],
feed["y"],
last_w=feed["last_w"],
setw=feed["setw"],
tensors=tensors)
# len: 6, result[5]: (260,4)
# 计算的是tensors的值，有几个tensor就相应的有几个输出
return result
@staticmethod
def calculate_upperbound(y):
array = np.maximum.reduce(y[:, 0, :], 1) #第1维的最大值
total = 1.0
for i in array:
total = total * i
return total
def log_between_steps(self, step):
fast_train = self.train_config["fast_train"]
tflearn.is_training(False, self._agent.session)
summary, v_pv, v_log_mean, v_loss, log_mean_free, weights= \
self._evaluate("test", self.summary,
self._agent.portfolio_value,
self._agent.log_mean,
self._agent.loss,
self._agent.log_mean_free,
self._agent.portfolio_weights)
self.test_writer.add_summary(summary, step)
if not fast_train:
summary, loss_value = self._evaluate("training", self.summary,
self._agent.loss)
self.train_writer.add_summary(summary, step)
# print 'ouput is %s' % out
logging.info('=' * 30)
logging.info('step %d' % step)
logging.info('-' * 30)
if not fast_train:
logging.info('training loss is %s\n' % loss_value)
logging.info('the portfolio value on test set is %s\nlog_mean is %s\n'
'loss_value is %3f\nlog mean without commission fee is %3f\n' % \
(v_pv, v_log_mean, v_loss, log_mean_free))
logging.info('=' * 30 + "\n")
if not self.__snap_shot:
self._agent.save_model(self.save_path)
elif v_pv > self.best_metric:
self.best_metric = v_pv
logging.info("get better model at %s steps,"
" whose test portfolio value is %s" % (step, v_pv))
if self.save_path:
self._agent.save_model(self.save_path)
self.check_abnormal(v_pv, weights)
def check_abnormal(self, portfolio_value, weigths):
if portfolio_value == 1.0:
logging.info("average portfolio weights {}".format(
weigths.mean(axis=0)))
def next_batch(self):
batch = self._matrix.next_batch()
batch_input = batch["X"]
batch_y = batch["y"]
batch_last_w = batch["last_w"]
batch_w = batch["setw"]
return batch_input, batch_y, batch_last_w, batch_w
def __init_tensor_board(self, log_file_dir):
tf.summary.scalar('benefit', self._agent.portfolio_value)
tf.summary.scalar('log_mean', self._agent.log_mean)
tf.summary.scalar('loss', self._agent.loss)
tf.summary.scalar("log_mean_free", self._agent.log_mean_free)
for layer_key in self._agent.layers_dict:
tf.summary.histogram(layer_key, self._agent.layers_dict[layer_key])
for var in tf.trainable_variables():
tf.summary.histogram(var.name, var)
grads = tf.gradients(self._agent.loss, tf.trainable_variables())
for grad in grads:
tf.summary.histogram(grad.name + '/gradient', grad)
self.summary = tf.summary.merge_all()
location = log_file_dir
self.network_writer = tf.summary.FileWriter(location + '/network',
self._agent.session.graph)
self.test_writer = tf.summary.FileWriter(location + '/test') #保存的是训练结果
self.train_writer = tf.summary.FileWriter(location + '/train')
def __print_upperbound(self):
upperbound_test = self.calculate_upperbound(self.test_set["y"]) #都乘起来
logging.info("upper bound in test is %s" % upperbound_test)
def train_net(self, log_file_dir="./tensorboard", index="0"):
"""
:param log_file_dir: logging of the training process
:param index: sub-folder name under train_package
:return: the result named tuple
"""
self.__print_upperbound()
if log_file_dir:
if self.device == "cpu":
with tf.device("/cpu:0"):
self.__init_tensor_board(log_file_dir) #初始化summary
else:
self.__init_tensor_board(log_file_dir)
starttime = time.time()
total_data_time = 0
total_training_time = 0
for i in range(self.train_config["steps"]): #训练步数
step_start = time.time()
x, y, last_w, setw = self.next_batch(
) #获取batch x:(109, 3, 11, 31) y:(109, 3, 11) last_w: (109,11) setw:function
if 'noise' in self.train_config:
if self.train_config['noise']:
noise = np.random.normal(0, 0.002, size=y.shape)
y = y + noise
finish_data = time.time()
total_data_time += (finish_data - step_start)
#训练智能体
self._agent.train(x, y, last_w=last_w, setw=setw)
total_training_time += time.time() - finish_data
if i % 50 == 0 and log_file_dir:
logging.info("average time for data accessing is %s" %
(total_data_time / 50))
logging.info("average time for training is %s" %
(total_training_time / 50))
total_training_time = 0
total_data_time = 0
#每隔固定的步数对测试集进行评估
self.log_between_steps(i)
if self.save_path:
self._agent.recycle() #
best_agent = NNAgent(self.config,
self.stockList,
self.featureList,
restore_dir=self.save_path)
self._agent = best_agent
pv, log_mean = self._evaluate("test", self._agent.portfolio_value,
self._agent.log_mean) #最后在评估一下
#logging.warning('the portfolio value train No.%s is %s log_mean is %s,'
# ' the training time is %d seconds' % (index, pv, log_mean, time.time() - starttime))
return self.__log_result_csv(index,
time.time() - starttime, self.save_path)
def __log_result_csv(self, index, time, path):
from pgportfolio.trade import backtest
dataframe = None
csv_dir = './train_package/train_summary.csv'
tflearn.is_training(False, self._agent.session)
v_pv, v_log_mean, benefit_array, v_log_mean_free = self._evaluate("test", self._agent.portfolio_value,\
self._agent.log_mean, self._agent.pv_vector,\
self._agent.log_mean_free)
backtest = backtest.BackTest(self.config.copy(),
self.stockList,
self.featureList,
self.start_date,
self.end_date,
self.fake_data,
net_dir=None,
result_path=path,
agent=self._agent) # 回测
backtest.start_trading()
result = Result(
test_pv=[v_pv],
test_log_mean=[v_log_mean],
test_log_mean_free=[v_log_mean_free],
test_history=[''.join(str(e) + ', ' for e in benefit_array)],
config=[json.dumps(self.config)],
net_dir=[index],
backtest_test_pv=[backtest.test_pv],
backtest_test_history=[
''.join(str(e) + ', ' for e in backtest.test_pc_vector)
],
backtest_test_log_mean=[np.mean(np.log(backtest.test_pc_vector))],
training_time=int(time))
new_data_frame = pd.DataFrame(result._asdict()).set_index("net_dir")
if os.path.isfile(csv_dir):
dataframe = pd.read_csv(csv_dir).set_index("net_dir")
dataframe = dataframe.append(new_data_frame)
else:
dataframe = new_data_frame
if int(index) > 0:
dataframe.to_csv(csv_dir)
return result
class TraderTrainer:
def __init__(self, config, fake_data=False, restore_dir=None, save_path=None, device="cpu",
agent=None):
"""
:param config: config dictionary
:param fake_data: if True will use data generated randomly
:param restore_dir: path to the model trained before
:param save_path: path to save the model
:param device: the device used to train the network
:param agent: the nnagent object. If this is provides, the trainer will not
create a new agent by itself. Therefore the restore_dir will not affect anything.
"""
self.config = config
self.train_config = config["training"]
self.input_config = config["input"]
self.save_path = save_path
self.best_metric = 0
np.random.seed(config["random_seed"])
self.__window_size = self.input_config["window_size"]
self.__coin_number = self.input_config["coin_number"]
self.__batch_size = self.train_config["batch_size"]
self.__snap_shot = self.train_config["snap_shot"]
config["input"]["fake_data"] = fake_data
self._matrix = DataMatrices.create_from_config(config) # obtain global_data
self.test_set = self._matrix.get_test_set() # obtain test_data
if not config["training"]["fast_train"]:
self.training_set = self._matrix.get_training_set() # obtain training_data
self.upperbound_validation = 1
self.upperbound_test = 1
tf.set_random_seed(self.config["random_seed"])
self.device = device
if agent:
self._agent = agent
else:
if device == "cpu":
os.environ["CUDA_VISIBLE_DEVICES"] = ""
with tf.device("/cpu:0"):
self._agent = NNAgent(config, restore_dir, device)
else:
self._agent = NNAgent(config, restore_dir, device)
def _evaluate(self, set_name, *tensors):
if set_name == "test":
feed = self.test_set
elif set_name == "training":
feed = self.training_set
else:
raise ValueError()
result = self._agent.evaluate_tensors(feed["X"],feed["y"],last_w=feed["last_w"],
setw=feed["setw"], tensors=tensors)
return result
@staticmethod
def calculate_upperbound(y):
array = np.maximum.reduce(y[:, 0, :], 1)
#print("array",array)
total = 1.0
for i in array:
total = total * i
return total
def log_between_steps(self, step):
# log the information per step
fast_train = self.train_config["fast_train"]
tflearn.is_training(False, self._agent.session)
summary, v_pv, v_log_mean, v_loss, log_mean_free, weights= \
self._evaluate("test", self.summary,
self._agent.portfolio_value,
self._agent.log_mean,
self._agent.loss,
self._agent.log_mean_free,
self._agent.portfolio_weights)
self.test_writer.add_summary(summary, step)
if not fast_train:
summary, loss_value = self._evaluate("training", self.summary, self._agent.loss)
self.train_writer.add_summary(summary, step)
# fast_train is ued to control the output of tensorboard.
# print 'ouput is %s' % out
logging.info('='*30)
logging.info('step %d' % step)
logging.info('-'*30)
if not fast_train:
logging.info('training loss is %s\n' % loss_value)
logging.info('the portfolio value on test set is %s\nlog_mean is %s\n'
'loss_value is %3f\nlog mean without commission fee is %3f\n' % \
(v_pv, v_log_mean, v_loss, log_mean_free))
logging.info('='*30+"\n")
if not self.__snap_shot:
self._agent.save_model(self.save_path)
elif v_pv > self.best_metric:
self.best_metric = v_pv
logging.info("get better model at %s steps,"
" whose test portfolio value is %s" % (step, v_pv))
if self.save_path:
self._agent.save_model(self.save_path)
self.check_abnormal(v_pv, weights)
def check_abnormal(self, portfolio_value, weigths):
if portfolio_value == 1.0:
logging.info("average portfolio weights {}".format(weigths.mean(axis=0)))
def next_batch(self):
batch = self._matrix.next_batch() # sampling batch based on experience
batch_input = batch["X"]
batch_y = batch["y"]
batch_last_w = batch["last_w"]
batch_w = batch["setw"]
return batch_input, batch_y, batch_last_w, batch_w
def __init_tensor_board(self, log_file_dir):
tf.summary.scalar('benefit', self._agent.portfolio_value)
tf.summary.scalar('log_mean', self._agent.log_mean)
tf.summary.scalar('loss', self._agent.loss)
tf.summary.scalar("log_mean_free", self._agent.log_mean_free)
for layer_key in self._agent.layers_dict:
tf.summary.histogram(layer_key, self._agent.layers_dict[layer_key])
for var in tf.trainable_variables():
tf.summary.histogram(var.name, var)
#grads = tf.gradients(self._agent.loss, tf.trainable_variables())
#for grad in grads:
# tf.summary.histogram(grad.name + '/gradient', grad)
self.summary = tf.summary.merge_all()
location = log_file_dir
self.network_writer = tf.summary.FileWriter(location + '/network',
self._agent.session.graph)
self.test_writer = tf.summary.FileWriter(location + '/test')
self.train_writer = tf.summary.FileWriter(location + '/train')
def __print_upperbound(self):
upperbound_test = self.calculate_upperbound(self.test_set["y"])
logging.info("upper bound in test is %s" % upperbound_test)
def train_net(self, log_file_dir="./tensorboard", index="0"):
"""
:param log_file_dir: logging of the training process
:param index: sub-folder name under train_package
:return: the result named tuple
"""
self.__print_upperbound()
if log_file_dir:
if self.device == "cpu":
with tf.device("/cpu:0"):
self.__init_tensor_board(log_file_dir)
else:
self.__init_tensor_board(log_file_dir)
starttime = time.time()
total_data_time = 0
total_training_time = 0
for i in range(self.train_config["steps"]):
step_start = time.time()
x, y, last_w, setw = self.next_batch()
finish_data = time.time()
total_data_time += (finish_data - step_start)
self._agent.train(x, y, last_w=last_w, setw=setw)
total_training_time += time.time() - finish_data
if i % 1000 == 0 and log_file_dir:
logging.info("average time for data accessing is %s"%(total_data_time/1000))
logging.info("average time for training is %s"%(total_training_time/1000))
total_training_time = 0
total_data_time = 0
self.log_between_steps(i) # log the information every 1000 rounds
if self.save_path:
self._agent.recycle()
best_agent = NNAgent(self.config, restore_dir=self.save_path)
self._agent = best_agent
pv, log_mean = self._evaluate("test", self._agent.portfolio_value, self._agent.log_mean) # execute test once
logging.warning('the portfolio value train No.%s is %s log_mean is %s,'
' the training time is %d seconds' % (index, pv, log_mean, time.time() - starttime))
return self.__log_result_csv(index, time.time() - starttime)
def __log_result_csv(self, index, time):
# execute backtest, and save the result in train_summary.csv
from pgportfolio.trade import backtest
dataframe = None
csv_dir = './train_package/train_summary.csv'
tflearn.is_training(False, self._agent.session)
v_pv, v_log_mean, benefit_array, v_log_mean_free, v_turn_over =\
self._evaluate("test",
self._agent.portfolio_value,
self._agent.log_mean,
self._agent.pv_vector,
self._agent.log_mean_free,
self._agent.turn_over)
backtest = backtest.BackTest(self.config.copy(),
net_dir=None,
agent=self._agent)
backtest.start_trading()
result = Result(test_pv=[v_pv],
test_log_mean=[v_log_mean],
test_log_mean_free=[v_log_mean_free],
test_history=[''.join(str(e)+', ' for e in benefit_array)],
test_turn_over = [v_turn_over],
config=[json.dumps(self.config)],
net_dir=[index],
backtest_test_pv=[backtest.test_pv],
backtest_test_history=[''.join(str(e)+', ' for e in backtest.test_pc_vector)],
backtest_test_log_mean=[np.mean(np.log(backtest.test_pc_vector))],
backtest_turn_over = [backtest.turn_over],
training_time=int(time))
new_data_frame = pd.DataFrame(result._asdict()).set_index("net_dir")
if os.path.isfile(csv_dir):
dataframe = pd.read_csv(csv_dir).set_index("net_dir")
dataframe = dataframe.append(new_data_frame)
else:
dataframe = new_data_frame
if int(index) > 0:
dataframe.to_csv(csv_dir)
return result
class TraderTrainer:
def __init__(self, config, fake_data=False, restore_dir=None, save_path=None, device="cpu",
agent=None):
"""
:param config: config dictionary
:param fake_data: if True will use data generated randomly
:param restore_dir: path to the model trained before
:param save_path: path to save the model
:param device: the device used to train the network
:param agent: the nnagent object. If this is provides, the trainer will not
create a new agent by itself. Therefore the restore_dir will not affect anything.
"""
self.config = config
self.train_config = config["training"]
self.input_config = config["input"]
self.save_path = save_path
self.best_metric = 0
np.random.seed(config["random_seed"])
self.__window_size = self.input_config["window_size"]
self.__coin_number = self.input_config["coin_number"]
self.__batch_size = self.train_config["batch_size"]
self.__snap_shot = self.train_config["snap_shot"]
config["input"]["fake_data"] = fake_data
self._matrix = DataMatrices.create_from_config(config)
self.test_set = self._matrix.get_test_set()
if not config["training"]["fast_train"]:
self.training_set = self._matrix.get_training_set()
self.upperbound_validation = 1
self.upperbound_test = 1
tf.set_random_seed(self.config["random_seed"])
self.device = device
if agent:
self._agent = agent
else:
if device == "cpu":
os.environ["CUDA_VISIBLE_DEVICES"] = ""
with tf.device("/cpu:0"):
self._agent = NNAgent(config, restore_dir, device)
else:
self._agent = NNAgent(config, restore_dir, device)
def _evaluate(self, set_name, *tensors):
if set_name == "test":
feed = self.test_set
elif set_name == "training":
feed = self.training_set
else:
raise ValueError()
result = self._agent.evaluate_tensors(feed["X"],feed["y"],last_w=feed["last_w"],
setw=feed["setw"], tensors=tensors)
return result
@staticmethod
def calculate_upperbound(y):
array = np.maximum.reduce(y[:, 0, :], 1)
total = 1.0
for i in array:
total = total * i
return total
def log_between_steps(self, step):
fast_train = self.train_config["fast_train"]
tflearn.is_training(False, self._agent.session)
summary, v_pv, v_log_mean, v_loss, log_mean_free, weights= \
self._evaluate("test", self.summary,
self._agent.portfolio_value,
self._agent.log_mean,
self._agent.loss,
self._agent.log_mean_free,
self._agent.portfolio_weights)
self.test_writer.add_summary(summary, step)
if not fast_train:
summary, loss_value = self._evaluate("training", self.summary, self._agent.loss)
self.train_writer.add_summary(summary, step)
# print 'ouput is %s' % out
logging.info('='*30)
logging.info('step %d' % step)
logging.info('-'*30)
if not fast_train:
logging.info('training loss is %s\n' % loss_value)
logging.info('the portfolio value on test set is %s\nlog_mean is %s\n'
'loss_value is %3f\nlog mean without commission fee is %3f\n' % \
(v_pv, v_log_mean, v_loss, log_mean_free))
logging.info('='*30+"\n")
if not self.__snap_shot:
self._agent.save_model(self.save_path)
elif v_pv > self.best_metric:
self.best_metric = v_pv
logging.info("get better model at %s steps,"
" whose test portfolio value is %s" % (step, v_pv))
if self.save_path:
self._agent.save_model(self.save_path)
self.check_abnormal(v_pv, weights)
def check_abnormal(self, portfolio_value, weigths):
if portfolio_value == 1.0:
logging.info("average portfolio weights {}".format(weigths.mean(axis=0)))
def next_batch(self):
batch = self._matrix.next_batch()
batch_input = batch["X"]
batch_y = batch["y"]
batch_last_w = batch["last_w"]
batch_w = batch["setw"]
return batch_input, batch_y, batch_last_w, batch_w
def __init_tensor_board(self, log_file_dir):
tf.summary.scalar('benefit', self._agent.portfolio_value)
tf.summary.scalar('log_mean', self._agent.log_mean)
tf.summary.scalar('loss', self._agent.loss)
tf.summary.scalar("log_mean_free", self._agent.log_mean_free)
self.summary = tf.summary.merge_all()
location = log_file_dir
self.network_writer = tf.summary.FileWriter(location + '/network',
self._agent.session.graph)
self.test_writer = tf.summary.FileWriter(location + '/test')
self.train_writer = tf.summary.FileWriter(location + '/train')
def __print_upperbound(self):
upperbound_test = self.calculate_upperbound(self.test_set["y"])
logging.info("upper bound in test is %s" % upperbound_test)
def train_net(self, log_file_dir="./tensorboard", index="0"):
"""
:param log_file_dir: logging of the training process
:param index: sub-folder name under train_package
:return: the result named tuple
"""
self.__print_upperbound()
if log_file_dir:
if self.device == "cpu":
with tf.device("/cpu:0"):
self.__init_tensor_board(log_file_dir)
else:
self.__init_tensor_board(log_file_dir)
starttime = time.time()
total_data_time = 0
total_training_time = 0
for i in range(self.train_config["steps"]):
step_start = time.time()
x, y, last_w, setw = self.next_batch()
finish_data = time.time()
total_data_time += (finish_data - step_start)
self._agent.train(x, y, last_w=last_w, setw=setw)
total_training_time += time.time() - finish_data
if i % 1000 == 0 and log_file_dir:
logging.info("average time for data accessing is %s"%(total_data_time/1000))
logging.info("average time for training is %s"%(total_training_time/1000))
total_training_time = 0
total_data_time = 0
self.log_between_steps(i)
if self.save_path:
self._agent.recycle()
best_agent = NNAgent(self.config, restore_dir=self.save_path)
self._agent = best_agent
pv, log_mean = self._evaluate("test", self._agent.portfolio_value, self._agent.log_mean)
logging.warning('the portfolio value train No.%s is %s log_mean is %s,'
' the training time is %d seconds' % (index, pv, log_mean, time.time() - starttime))
return self.__log_result_csv(index, time.time() - starttime)
def __log_result_csv(self, index, time):
from pgportfolio.trade import backtest
dataframe = None
csv_dir = './train_package/train_summary.csv'
tflearn.is_training(False, self._agent.session)
v_pv, v_log_mean, benefit_array, v_log_mean_free =\
self._evaluate("test",
self._agent.portfolio_value,
self._agent.log_mean,
self._agent.pv_vector,
self._agent.log_mean_free)
backtest = backtest.BackTest(self.config.copy(),
net_dir=None,
agent=self._agent)
backtest.start_trading()
result = Result(test_pv=[v_pv],
test_log_mean=[v_log_mean],
test_log_mean_free=[v_log_mean_free],
test_history=[''.join(str(e)+', ' for e in benefit_array)],
config=[json.dumps(self.config)],
net_dir=[index],
backtest_test_pv=[backtest.test_pv],
backtest_test_history=[''.join(str(e)+', ' for e in backtest.test_pc_vector)],
backtest_test_log_mean=[np.mean(np.log(backtest.test_pc_vector))],
training_time=int(time))
new_data_frame = pd.DataFrame(result._asdict()).set_index("net_dir")
if os.path.isfile(csv_dir):
dataframe = pd.read_csv(csv_dir).set_index("net_dir")
dataframe = dataframe.append(new_data_frame)
else:
dataframe = new_data_frame
if int(index) > 0:
dataframe.to_csv(csv_dir)
return result
