def __init__(self, config, net_dir=None, agent=None, agent_type="nn"):
trader.Trader.__init__(self,
config["input"]["global_period"],
config,
0,
net_dir,
initial_BTC=1,
agent=agent,
agent_type=agent_type)
if agent_type == "nn":
data_matrices = self._rolling_trainer.data_matrices
elif agent_type == "traditional":
config["input"]["feature_number"] = 1
config["input"]["features_list"] = ["close"]
data_matrices = DataMatrices.create_from_config(config)
else:
raise ValueError()
#if config["brokerage"]:
# pass
self.__test_set = data_matrices.get_test_set()
self.__test_length = self.__test_set["X"].shape[0]
self._total_steps = self.__test_length
self.__test_pv = 1.0
self.__test_pc_vector = []
def __init__(self, config, device="cpu"):
"""
:param config: config dictionary
:param device: the device used to train the network
"""
self.config = config
self.train_config = config["training"]
self.input_config = config["input"]
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._matrix = DataMatrices.create_from_config(config)
self.stock_code = self._matrix.stock_code
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 device == "cpu":
os.environ["CUDA_VISIBLE_DEVICES"] = ""
with tf.device("/cpu:0"):
self._agent = NNAgent(config, device)
else:
self._agent = NNAgent(config, device)
def __init__(self,
config,
net_dir=None,
agent=None,
agent_type="nn",
initial_asset=10000):
trader.Trader.__init__(self,
0,
config,
0,
net_dir,
initial_asset=initial_asset,
agent=agent,
agent_type=agent_type)
if agent_type == "nn":
data_matrices = self._rolling_trainer.data_matrices
elif agent_type == "traditional":
config["input"]["feature_number"] = 1
data_matrices = DataMatrices.create_from_config(config)
else:
raise ValueError()
self.__test_set = data_matrices.get_test_set()
self.__test_length = self.__test_set["X"].shape[0]
self._total_steps = self.__test_length
self.__test_pv = 1.0
self.__test_pc_vector = []
def update_matrix(self):
now = time.time()
now_ymdhm = datetime.fromtimestamp(now).strftime("%Y/%m/%d %H:%M")
self.config['input']['end_date'] = now_ymdhm
self._matrix = DataMatrices.create_from_config(self.config,
_new_data=True)
self.test_set = self._matrix.get_test_set()
self.last_info = self._matrix.get_last_info()
print(self.last_info)
def __init__(self,
config,
main_config,
fake_data=False,
restore_dir=None,
save_path=None,
device="cpu",
agent=None,
index=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._matrix = DataMatrices.create_from_config(config)
self._coin_number = self._matrix.coin_number()
config['input']['coin_number'] = self._coin_number
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._asset_list = self._matrix.coin_list
self._time_index = self._matrix.global_weights.index
self._test_portion = self.input_config["test_portion"]
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 __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
#creates Datamatrices from net_config.json
self._matrix = DataMatrices.create_from_config(config)
#:test set :-windowsize+1
self.test_set = self._matrix.get_test_set()
#fast train: true without pre-training
if not config["training"]["fast_train"]:
self.training_set = self._matrix.get_training_set()
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
#needed for backtetst
if agent:
self._agent = agent
else:
#first run else is used to initialize NNAgent
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 get_test_data(config):
"""
:return : a 2d numpy array with shape(coin_number, periods),
each element the relative price
"""
config["input"]["feature_number"] = 1
config["input"]["norm_method"] = "relative"
config["input"]["global_period"] = config["input"]["global_period"]
price_matrix = DataMatrices.create_from_config(config)
test_set = price_matrix.get_test_set()["y"][:, 0, :].T
test_set = np.concatenate((np.ones((1, test_set.shape[1])), test_set), axis=0)
return test_set
def get_test_data(config):
"""
:return : a 2d numpy array with shape(coin_number, periods),
each element the relative price
"""
config["input"]["feature_number"] = 1
config["input"]["norm_method"] = "relative"
config["input"]["global_period"] = config["input"]["global_period"]
price_matrix = DataMatrices.create_from_config(config)
test_set = price_matrix.get_test_set()["y"][:, 0, :].T
test_set = np.concatenate((np.ones((1, test_set.shape[1])), test_set), axis=0)
return test_set
def __init__(self, config, net_dir=None, agent=None, agent_type="nn"):
trader.Trader.__init__(self, 0, config, 0, net_dir,
initial_BTC=1, agent=agent, agent_type=agent_type)
if agent_type == "nn":
data_matrices = self._rolling_trainer.data_matrices
elif agent_type == "traditional":
config["input"]["feature_number"] = 1
data_matrices = DataMatrices.create_from_config(config)
else:
raise ValueError()
self.__test_set = data_matrices.get_test_set()
self.__test_length = self.__test_set["X"].shape[0]
self._total_steps = self.__test_length
self.__test_pv = 1.0
self.__test_pc_vector = []
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 __init__(self,
config,
stockList,
featureList,
start_date,
end_date,
fake_data,
net_dir=None,
result_path=None,
agent=None,
agent_type="nn"):
trader.Trader.__init__(self,
0,
config,
stockList,
featureList,
start_date,
end_date,
fake_data,
0,
net_dir,
result_path,
initial_cash=1000000,
agent=agent,
agent_type=agent_type)
if agent_type == "nn":
data_matrices = self._rolling_trainer.data_matrices
elif agent_type == "traditional":
config["input"]["feature_number"] = 1
data_matrices = DataMatrices.create_from_config(config)
else:
raise ValueError()
self.__test_set = data_matrices.get_test_set()
self.__test_length = self.__test_set["X"].shape[0] #测试集的长度
self.__test_date = data_matrices.get_test_date()
self._total_steps = self.__test_length
self.__test_pv = 1.0
self.__test_pc_vector = []
self._window_size = config["input"]["window_size"]
def __init__(self, config, net_dir=None, agent=None, agent_type="nn"):
trader.Trader.__init__(self,
300,
config,
0,
net_dir,
initial_BTC=1,
agent=agent,
agent_type=agent_type)
self.config = config #My Addition can cause error TODO
if agent_type == "nn":
data_matrices = self._rolling_trainer.data_matrices
elif agent_type == "traditional":
config["input"]["feature_number"] = 1
data_matrices = DataMatrices.create_from_config(config)
else:
raise ValueError()
self.__test_set = data_matrices.get_test_set()
self.__test_length = self.__test_set["X"].shape[0]
self._total_steps = self.__test_length
self.__test_pv = 1.0
self.__test_pc_vector = []
self.__period = config['input']['global_period']
def __init__(self, config, net_dir=None, agent=None, agent_type="nn"):
trader.Trader.__init__(self,
0,
config,
0,
net_dir,
initial_BTC=1,
agent=agent,
agent_type=agent_type)
if agent_type == "nn":
data_matrices = self._rolling_trainer.data_matrices # construct the data and return matrix
elif agent_type == "traditional":
config["input"]["feature_number"] = 1
# traditional algorithms only need close prices
data_matrices = DataMatrices.create_from_config(config)
else:
raise ValueError()
self.__test_set = data_matrices.get_test_set(
) # backtest only needs test set
self.__test_length = self.__test_set["X"].shape[0]
self._total_steps = self.__test_length
self.__test_pv = 1.0 # total capital
self.__test_pc_vector = [] #portfolio change ratio
self.__turn_over = []
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 __init__(self, config):
self.__counter = 0
self.__start = parse_time(config["input"]["start_date"])
self.__end = parse_time(config["input"]["end_date"])
self.__number_of_coins = config["input"]["coin_number"]
self.__batch_size = config["training"]["batch_size"]
self.__window_size = config["input"]["window_size"]+1
span = self.__end - self.__start
self.__end = self.__end - config["input"]["test_portion"] * span
config2 = copy.deepcopy(config)
config2["input"]["global_period"] = 300
self._matrix2 = DataMatrices.create_from_config(config2)
self.__weightgarch = pd.DataFrame(index=range(0, self.__batch_size),
columns=range(0, self.__number_of_coins))
self.__weightgarch = self.__weightgarch.fillna(1.0 / self.__number_of_coins)
training_set = self._matrix2.get_training_set()
set = training_set["X"]
#good times sets for 3, 26: 1 and 2 are not functioning
set5 = set[-5000:, 0, :, 0]
set3 = set[-3000:, 0, :, 0]
set2 = set[-2000:, 0, :, 0]
#self.__abcdefg = set[:, 0]
self.__lastvalue1 = set5[0, 0]
self.__lastvalue2 = set5[0, 1]
self.__lastvalue3 = set2[0, 2]
self.__lastvalue4 = set5[0, 3]
self.__lastvalue5 = set3[0, 4]
self.__lastvalue6 = set3[0, 5]
self.__lastvalue7 = set3[0, 6]
self.__lastvalue8 = set5[0, 7]
self.__lastvalue9 = set5[0, 8]
self.__lastvalue10 = set5[0, 9]
self.__lastvalue11 = set3[0, 10]
#good times set5, bad times set3
#logreturns1 = np.log(set3[1:, 0] / set3[:-1, 0])
logreturns1 = np.log(set5[1:, 0] / set5[:-1, 0])
self.__lastsigma1 = np.sqrt(np.mean(logreturns1 ** 2))
self.__lastlogreturn1 = logreturns1[-1]
#good times set5, bad times set3
#logreturns2 = np.log(set3[1:, 1] / set3[:-1, 1])
logreturns2 = np.log(set5[1:, 1] / set5[:-1, 1])
self.__lastsigma2 = np.sqrt(np.mean(logreturns2 ** 2))
self.__lastlogreturn2 = logreturns2[-1]
logreturns3 = np.log(set2[1:, 2] / set2[:-1, 2])
self.__lastsigma3 = np.sqrt(np.mean(logreturns3 ** 2))
self.__lastlogreturn3 = logreturns3[-1]
logreturns4 = np.log(set5[1:, 3] / set5[:-1, 3])
self.__lastsigma4 = np.sqrt(np.mean(logreturns4 ** 2))
self.__lastlogreturn4 = logreturns4[-1]
logreturns5 = np.log(set3[1:, 4] / set3[:-1, 4])
self.__lastsigma5 = np.sqrt(np.mean(logreturns5 ** 2))
self.__lastlogreturn5 = logreturns5[-1]
logreturns6 = np.log(set3[1:, 5] / set3[:-1, 5])
self.__lastsigma6 = np.sqrt(np.mean(logreturns6 ** 2))
self.__lastlogreturn6 = logreturns6[-1]
logreturns7 = np.log(set3[1:, 6] / set3[:-1, 6])
self.__lastsigma7 = np.sqrt(np.mean(logreturns7 ** 2))
self.__lastlogreturn7 = logreturns7[-1]
logreturns8 = np.log(set5[1:, 7] / set5[:-1, 7])
self.__lastsigma8 = np.sqrt(np.mean(logreturns8 ** 2))
self.__lastlogreturn8 = logreturns8[-1]
logreturns9 = np.log(set5[1:, 8] / set5[:-1, 8])
self.__lastsigma9 = np.sqrt(np.mean(logreturns9 ** 2))
self.__lastlogreturn9 = logreturns9[-1]
logreturns10 = np.log(set5[1:, 9] / set5[:-1, 9])
self.__lastsigma10 = np.sqrt(np.mean(logreturns10 ** 2))
self.__lastlogreturn10 = logreturns10[-1]
logreturns11 = np.log(set3[1:, 10] / set3[:-1, 10])
self.__lastsigma11 = np.sqrt(np.mean(logreturns11 ** 2))
self.__lastlogreturn11 = logreturns11[-1]
self.__firstsigma1 = self.__lastsigma1
self.__firstsigma2 = self.__lastsigma2
self.__firstsigma3 = self.__lastsigma3
self.__firstsigma4 = self.__lastsigma4
self.__firstsigma5 = self.__lastsigma5
self.__firstsigma6 = self.__lastsigma6
self.__firstsigma7 = self.__lastsigma7
self.__firstsigma8 = self.__lastsigma8
self.__firstsigma9 = self.__lastsigma9
self.__firstsigma10 = self.__lastsigma10
self.__firstsigma11 = self.__lastsigma11
self.__firstlogreturn1 = self.__lastlogreturn1
self.__firstlogreturn2 = self.__lastlogreturn2
self.__firstlogreturn3 = self.__lastlogreturn3
self.__firstlogreturn4 = self.__lastlogreturn4
self.__firstlogreturn5 = self.__lastlogreturn5
self.__firstlogreturn6 = self.__lastlogreturn6
self.__firstlogreturn7 = self.__lastlogreturn7
self.__firstlogreturn8 = self.__lastlogreturn8
self.__firstlogreturn9 = self.__lastlogreturn9
self.__firstlogreturn10 = self.__lastlogreturn10
self.__firstlogreturn11 = self.__lastlogreturn11
self.__firstlastvalue1 = self.__lastvalue1
self.__firstlastvalue2 = self.__lastvalue2
self.__firstlastvalue3 = self.__lastvalue3
self.__firstlastvalue4 = self.__lastvalue4
self.__firstlastvalue5 = self.__lastvalue5
self.__firstlastvalue6 = self.__lastvalue6
self.__firstlastvalue7 = self.__lastvalue7
self.__firstlastvalue8 = self.__lastvalue8
self.__firstlastvalue9 = self.__lastvalue9
self.__firstlastvalue10 = self.__lastvalue10
self.__firstlastvalue11 = self.__lastvalue11
self.__theta1 = self.negative_log_likelihood(logreturns1, (1, 0.5, 0.5))
self.__theta1 = self.fitting(logreturns1)
print(self.__theta1)
self.__theta2 = self.fitting(logreturns2)
print(self.__theta2)
self.__theta3 = self.fitting(logreturns3)
print(self.__theta3)
self.__theta4 = self.fitting(logreturns4)
print(self.__theta4)
self.__theta5 = self.fitting(logreturns5)
print(self.__theta5)
self.__theta6 = self.fitting(logreturns6)
print(self.__theta6)
self.__theta7 = self.fitting(logreturns7)
print(self.__theta7)
self.__theta8 = self.fitting(logreturns8)
print(self.__theta8)
self.__theta9 = self.fitting(logreturns9)
print(self.__theta9)
self.__theta10 = self.fitting(logreturns10)
print(self.__theta10)
self.__theta11 = self.fitting(logreturns11)
print(self.__theta11)
else:
drawdown_list.append(1.0 - pv_array[i] / max_benefit)
return max(drawdown_list)
def port_value(pv_array):
p = np.array([np.prod(pv_array[:i + 1]) for i in range(pv_array.shape[0])])
return p
#%% load config & data
config_2 = load_config(2)
# config_2['input']['start_date'] = '2015/05/01'
# config_2['input']['end_date'] = '2017/04/27' # 自訂區間
data = DataMatrices.create_from_config(config_2)
#%% network structure
feature_number = config_2["input"]["feature_number"]
rows = config_2["input"]["coin_number"]
columns = config_2["input"]["window_size"]
input_tensor = tf.placeholder(tf.float32,
shape=[None, feature_number, rows,
columns]) #[None, 3, 11, 31]
previous_w = tf.placeholder(tf.float32, shape=[None, rows]) #[None, 11]
input_num = tf.placeholder(tf.int32, shape=[]) #[11]
y = tf.placeholder(tf.float32, shape=[None, feature_number, rows])
def allint(l):
