diff options
| -rwxr-xr-x | lstm.py | 13 |
1 files changed, 9 insertions, 4 deletions
@@ -22,7 +22,8 @@ from keras.layers import Dropout from keras.models import load_model window_len = 10 -split_date = "2018-03-01" +#split_date = "2018-03-01" +split_date = "2017.01.01" def SigHandler_SIGINT(signum, frame): print() @@ -57,7 +58,7 @@ def getData_CMC(crypto, crypto_short): return model_data def getData_Stock(name, period): - info = pd.from_csv(path="./data/"+name+"/"+period+".csv") + info = pd.read_csv("./data/"+name+"/"+period+".csv", encoding="utf-8") return info def get_sets(crypto, model_data): @@ -69,7 +70,7 @@ def get_sets(crypto, model_data): for i in range(len(training_set) - window_len): temp_set = training_set[i:(i+window_len)].copy() for col in norm_cols: - temp_set.loc[:, col] = temp_set[col]/temp_set[col].iloc[0] -1 + temp_set.loc[:, col] = temp_set[col]/temp_set[col].iloc[0] - 1 LSTM_training_inputs.append(temp_set) LSTM_training_outputs = (training_set["Close"][window_len:].values/training_set["Close"][:-window_len].values) - 1 LSTM_test_inputs = [] @@ -96,6 +97,9 @@ def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25, model.compile(loss=loss, optimizer=optimizer) return model +def stock(): + data = getData_Stock("irxo", "Daily") + def lstm_type_1(crypto, crypto_short): model_data = getData_CMC(crypto, crypto_short) np.random.seed(202) @@ -154,11 +158,12 @@ def load_models(crypto, crypto_short): def premain(argparser): signal.signal(signal.SIGINT, SigHandler_SIGINT) #here - lstm_type_1("ethereum", "ether") + #lstm_type_1("ethereum", "ether") #lstm_type_2("ethereum", "ether", 5, 20) #lstm_type_3("ethereum", "ether", 5, 20) #lstm_type_4("ethereum", "ether", "dogecoin", "doge") #load_models("ethereum", "eth") + stock() def main(): argparser = Argparser() |