diff --git a/tutorial_lstm.py b/tutorial_lstm.py
new file mode 100644
index 0000000..e4e02fe
--- /dev/null
+++ b/tutorial_lstm.py
@@ -0,0 +1,110 @@
+#%%
+from math import sqrt
+from numpy import concatenate
+import numpy as np
+from matplotlib import pyplot
+from pandas import read_csv
+from pandas import DataFrame
+from pandas import concat
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import mean_squared_error
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import LSTM
+#%%
+
+# convert series to supervised learning
+def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
+	n_vars = 1 if type(data) is list else data.shape[1]
+	df = DataFrame(data)
+	cols, names = list(), list()
+	# input sequence (t-n, ... t-1)
+	for i in range(n_in, 0, -1):
+		cols.append(df.shift(i))
+		names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
+	# forecast sequence (t, t+1, ... t+n)
+	for i in range(0, n_out):
+		cols.append(df.shift(-i))
+		if i == 0:
+			names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
+		else:
+			names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
+	# put it all together
+	agg = concat(cols, axis=1)
+	agg.columns = names
+	# drop rows with NaN values
+	if dropnan:
+		agg.dropna(inplace=True)
+	return agg
+ 
+#%% load dataset
+from load_data import load_files
+data = load_files('data/', 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_vol_10"]]
+
+values = data.values
+# ensure all data is float
+# values = values.astype('float32')
+
+#%%
+np.isposinf(values).sum()
+
+#%% normalize features
+scaler = MinMaxScaler(feature_range=(-1, 1))
+scaled = scaler.fit_transform(values)
+
+
+# specify the number of lag hours
+past = 10
+n_features = 8
+#%% frame as supervised learning
+reframed = series_to_supervised(scaled, past, 1)
+print(reframed.shape)
+ 
+#%% split into train and test sets
+values = reframed.values
+n_train_hours = 365 * 24
+train = values[:n_train_hours, :]
+test = values[n_train_hours:, :]
+# split into input and outputs
+n_obs = past * n_features
+train_X, train_y = train[:, :n_obs], train[:, -n_features]
+test_X, test_y = test[:, :n_obs], test[:, -n_features]
+print(train_X.shape, len(train_X), train_y.shape)
+# reshape input to be 3D [samples, timesteps, features]
+train_X = train_X.reshape((train_X.shape[0], past, n_features))
+test_X = test_X.reshape((test_X.shape[0], past, n_features))
+print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
+ 
+#%% design network
+model = Sequential()
+model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
+model.add(Dense(1))
+model.compile(loss='mae', optimizer='adam')
+
+#%% fit network
+history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)
+# plot history
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+ 
+# make a prediction
+yhat = model.predict(test_X)
+test_X = test_X.reshape((test_X.shape[0], n_hours*n_features))
+# invert scaling for forecast
+inv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1)
+inv_yhat = scaler.inverse_transform(inv_yhat)
+inv_yhat = inv_yhat[:,0]
+# invert scaling for actual
+test_y = test_y.reshape((len(test_y), 1))
+inv_y = concatenate((test_y, test_X[:, -7:]), axis=1)
+inv_y = scaler.inverse_transform(inv_y)
+inv_y = inv_y[:,0]
+# calculate RMSE
+rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
+print('Test RMSE: %.3f' % rmse)
\ No newline at end of file