feat(Models): added MAE & RMSE metrics, fixed NaN & Inf values in data,

model_regression_lstm.py

@@ -0,0 +1,116 @@
+#%% Import all the stuff, load data, define constants
+from sklearn.utils import shuffle
+from load_data import load_files, create_target_cum_forward_returns
+import pandas as pd
+from tensorflow import keras
+from utils.normalize import normalize
+import tensorflow as tf
+from utils.visualize import visualize_loss
+from sklearn.preprocessing import MinMaxScaler
+from utils.evaluate import print_metrics
+import numpy as np
+from utils.rolling import rolling_window
+
+
+data = load_files('data/', add_features=True, log_returns=False)
+data.reset_index(drop=True, inplace=True)
+data = data[[column for column in data.columns if not column.endswith('volume')]]
+data = data[["BTC_returns", "BTC_mom_10", "BTC_mom_20", "BTC_mom_30", "BTC_mom_60", "BTC_vol_10", "BTC_vol_20", "BTC_vol_60", "day_month", "day_week", "month"]]
+
+target_col = 'target'
+data = create_target_cum_forward_returns(data, 'BTC_returns', 10)
+
+learning_rate = 0.002
+batch_size = 64
+epochs = 100
+
+split_fraction = 0.715
+train_split = int(split_fraction * int(data.shape[0]))
+
+past = 10
+future = 1
+
+start = past + future
+end = start + train_split
+
+#%% split data into training - validation sets
+train_data = data.loc[0 : train_split - 1]
+val_data = data.loc[train_split:]
+
+#%% create features and target for training set & keras dataset
+feature_scaler = MinMaxScaler(feature_range= (-1, 1))
+target_scaler = MinMaxScaler(feature_range= (-1, 1))
+
+x_train = feature_scaler.fit_transform(train_data.drop(target_col, axis=1).values) # you get the mean and std
+y_train = target_scaler.fit_transform(data.iloc[start:end][target_col].values.reshape(-1, 1))
+
+dataset_train = keras.preprocessing.timeseries_dataset_from_array(
+    x_train,
+    y_train,
+    sequence_length=past,
+    batch_size=batch_size,
+)
+
+
+#%% create features and target for validation set & keras dataset
+x_end = len(val_data) - past - future
+label_start = train_split + past + future
+
+x_val = feature_scaler.transform(val_data.drop(target_col, axis=1).iloc[:x_end].values) # you use the training data's mean and std
+y_val = target_scaler.transform(data.iloc[label_start:][target_col].values.reshape(-1, 1))
+
+dataset_val = keras.utils.timeseries_dataset_from_array(
+    x_val,
+    y_val,
+    sequence_length=past,
+    batch_size=batch_size,
+    shuffle=False,
+)
+
+
+#%%
+
+for batch in dataset_train.take(10):
+    batch_inputs, batch_targets = batch
+
+print("Input shape:", batch_inputs.shape)
+print("Target shape:", batch_targets.shape)
+
+# print(batch_inputs)
+# print(batch_targets)
+
+# %%
+model = keras.Sequential()
+model.add(keras.layers.LSTM(units = 10, return_sequences = True, activation = 'sigmoid', input_shape=(batch_inputs.shape[1], batch_inputs.shape[2])))
+model.add(keras.layers.Dropout(0.4))
+model.add(keras.layers.Dense(units = 64, activation = 'sigmoid'))
+model.add(keras.layers.Dropout(0.4))
+model.add(keras.layers.Dense(units = 32, activation = 'sigmoid'))
+model.add(keras.layers.Dropout(0.4))
+model.add(keras.layers.Dense(units = 1))
+
+optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
+model.compile(optimizer=optimizer, loss="mean_squared_error")
+model.summary()
+
+# %%
+
+path_checkpoint = "model_checkpoint.h5"
+
+history = model.fit(
+    dataset_train,
+    epochs=epochs,
+    validation_data=dataset_val,
+)
+
+#%%
+
+pred = model.predict(rolling_window(x_val, 11))
+pred = pred.reshape(pred.shape[0], 1)
+pred = target_scaler.inverse_transform(pred)
+
+print_metrics(y_val, pred)
+
+#%%
+
+visualize_loss(history, "Training and Validation Loss")