def train_real_data(symbol="SPX"):
dm_real = dc.DataManagerRealData(symbol)
X_train, y_train = dm_real.get_training_data()
X_test, y_test = dm_real.get_test_data()
# X_train, y_train, X_test, y_test = dm_real.get_random_training_test_data(n_samples=150000)
scaler = preprocessing.StandardScaler()
scaler.fit(X_train, y_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
# list_activations = [["softsign", "sigmoid"], ["softsign", "sigmoid", "relu"], ["elu", "relu"],["relu", "elu"], 2*["relu"]]
# list_n_nodes = [[300, 150], [300, 150, 33], [300, 150],[300, 150],[300, 150]]
list_activations = [2 * ['softplus'], 2 * ["softsign"], 2 * ['elu']]
list_n_nodes = 3 * [[300, 150]]
# # activations = ["softsign", "sigmoid"]
# # activations = ["softsign", "sigmoid", "relu"]
# activations = 3 * ['relu']
# # activations = ["elu", "relu"]
for activations, nodes in zip(list_activations, list_n_nodes):
model = build_nn_model(X_train.shape[1], nodes, activations)
history = model.fit(X_train,
y_train,
batch_size=1000,
epochs=50,
verbose=2,
validation_data=(X_test, y_test))
print(f"Activations {activations} -- Nodes {nodes}")
print(history.history)
def train_real_data(symbol="SPX"):
dm = dc.DataManagerRealData(symbol=symbol, test_month=9)
X_train, y_train = dm.get_training_data()
X_test, y_test = dm.get_test_data()
rf_model = RandomForestRegressor(n_estimators=300,
max_features="auto",
n_jobs=8)
rf_model.fit(X_train, y_train)
y_pred = rf_model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"{symbol} - MSE= {mse}")
def train_real_data(symbol="SPX"):
kernel = "rbf"
C = 50
dm_real = dc.DataManagerRealData(symbol)
X_train, y_train = dm_real.get_training_data()
X_test, y_test = dm_real.get_test_data()
train_index = X_train.sample(n=100000).index
X_train, y_train = X_train.loc[train_index], y_train.loc[train_index]
svr_model = SVR(cache_size=3000, kernel=kernel, C=C)
svr_model.fit(X_train, y_train)
y_pred = svr_model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"{symbol} - MSE: {mse}")
def main_real_data():
kernel = Matern()
for symbol in ["SPX", "SPXPM", "SX5E"]:
dm_real = dc.DataManagerRealData(symbol)
X_train, y_train = dm_real.get_training_data()
train_index = X_train.sample(n=5000).index
X_train, y_train = X_train.loc[train_index], y_train.loc[train_index]
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
gpr_model = gaussian_process.GaussianProcessRegressor(kernel=kernel)
gpr_model.fit(X_train, y_train)
X_test, y_test = dm_real.get_test_data()
X_test = scaler.transform(X_test)
y_pred = gpr_model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"{symbol} - MSE: {mse}")
