def get_coin_name_list(config, online):
"""
:param online: boolean value to show if connected to internet,
if False, load data from database.
:return : list of coin names
"""
input_config = config["input"]
if not online:
start = parse_time(input_config["start_date"])
end = parse_time(input_config["end_date"])
volume_forward = get_volume_forward(
end - start,
input_config["test_portion"] + input_config["validation_portion"],
input_config["portion_reversed"])
else:
end = time()
volume_forward = 0
end = end - (end % input_config["trade_period"])
start = end - volume_forward - input_config["volume_average_days"] * DAY
end = end - volume_forward
coins = HistoryManager(input_config["coin_number"], end,
volume_forward=volume_forward,
volume_average_days=input_config["volume_average_days"],
online=online).\
select_coins(start, end)
return coins
def create_from_config(config):
"""main method to create the DataMatrices in this project
@:param config: config dictionary
@:return: a DataMatrices object
"""
config = config.copy()
input_config = config["input"]
train_config = config["training"]
start = parse_time(input_config["start_date"])
end = parse_time(input_config["end_date"])
return DataMatrices(
start=start,
end=end,
market=input_config["market"],
feature_number=input_config["feature_number"],
window_size=input_config["window_size"],
online=input_config["online"],
period=input_config["global_period"],
asset_filter=input_config["asset_number"],
is_permed=input_config["is_permed"],
buffer_bias_ratio=train_config["buffer_biased"],
batch_size=train_config["batch_size"],
volume_average_days=input_config["volume_average_days"],
test_portion=input_config["test_portion"],
portion_reversed=input_config["portion_reversed"],
)
def _extract_test(config):
global_start = parse_time(config["input"]["start_date"])
global_end = parse_time(config["input"]["end_date"])
span = global_end - global_start
start = global_end - config["input"]["test_portion"] * span
end = global_end
return start, end
def _extract_test(config):
global_start = parse_time(config["input"]["start_date"])
global_end = parse_time(config["input"]["end_date"])
span = global_end - global_start
start = global_end - config["input"]["test_portion"] * span
end = global_end
return start, end
def get_coin_name_list(config, online):
"""
:param online: boolean value to show if connected to internet,
if False, load data from database.
:return : list of coin names
"""
input_config = config["input"]
if not online:
start = parse_time(input_config["start_date"])
end = parse_time(input_config["end_date"])
volume_forward = get_volume_forward(end - start,
input_config["test_portion"]
+ input_config["validation_portion"],
input_config["portion_reversed"])
else:
end = time()
volume_forward = 0
end = end - (end % input_config["trade_period"])
start = end - volume_forward - input_config["volume_average_days"] * DAY
end = end - volume_forward
coins = HistoryManager(input_config["coin_number"], end,
volume_forward=volume_forward,
volume_average_days=input_config["volume_average_days"],
online=online).\
select_coins(start, end)
return coins
def create_from_config(config):
"""main method to create the DataMatrices in this project
@:param config: config dictionary
@:return: a DataMatrices object
"""
config = config.copy()
input_config = config["input"]
train_config = config["training"]
start = parse_time(input_config["start_date"])
end = parse_time(input_config["end_date"])
return DataMatrices(start=start,
end=end,
market=input_config["market"],
feature_number=input_config["feature_number"],
window_size=input_config["window_size"],
online=input_config["online"],
period=input_config["global_period"],
coin_filter=input_config["coin_number"],
is_permed=input_config["is_permed"],
buffer_bias_ratio=train_config["buffer_biased"],
batch_size=train_config["batch_size"],
volume_average_days=input_config["volume_average_days"],
test_portion=input_config["test_portion"],
portion_reversed=input_config["portion_reversed"],
)
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)
