diff --git a/model_ff_10days.py b/model_ff_10days.py
deleted file mode 100644
index cf6349e..0000000
--- a/model_ff_10days.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#%% Import all the stuff, load data, define constants
-from load_data import load_files
-import pandas as pd
-from tensorflow import keras
-from utils.normalize import normalize
-import tensorflow as tf
-from utils.visualize import visualize_loss
-
-data = load_files('data/', add_features=True, log_returns=True)
-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_vol_10", "BTC_mom_20", "BTC_vol_20"]]
-
-ticker_to_predict = 'BTC_mom_10'
-
-learning_rate = 0.002
-batch_size = 128
-epochs = 100
-
-split_fraction = 0.715
-train_split = int(split_fraction * int(data.shape[0]))
-
-past = 100
-future = 11
-
-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
-
-x_train = normalize(train_data).values
-# x_train = normalize(train_data).drop(ticker_to_predict, axis=1).values
-y_train = normalize(data).iloc[start:end][ticker_to_predict].values
-
-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 = normalize(val_data).iloc[:x_end].values
-# x_val = normalize(val_data).iloc[:x_end].drop(ticker_to_predict, axis=1).values
-y_val = normalize(data).iloc[label_start:][ticker_to_predict].values
-
-dataset_val = keras.utils.timeseries_dataset_from_array(
-    x_val,
-    y_val,
-    sequence_length=past,
-    batch_size=batch_size,
-)
-
-
-#%%
-
-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.Dense(units = 50, 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 = 10, activation = 'sigmoid'))
-model.add(keras.layers.Dropout(0.4))
-model.add(keras.layers.Dense(units = 3, activation = 'sigmoid'))
-model.add(keras.layers.Dense(units = 1))
-
-optimizer = keras.optimizers.Adam(learning_rate=learning_rate, clipnorm=1.0)
-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,
-)
-# %%
-
-visualize_loss(history, "Training and Validation Loss")
\ No newline at end of file