diff --git a/ai_data_pipeline.py b/ai_data_pipeline.py
new file mode 100644
index 0000000..bebdcc5
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+++ b/ai_data_pipeline.py
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+from models.autoencoder import AutoEncoder
+from models.nnmodel import NNModel
+from models.rfmodel import RFModel
+from data_processor.data_processing import DataProcessing
+from data_processor.feature_engineering import AdvancedFeatureEngineering
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.ensemble import StackingClassifier
+import matplotlib.pyplot as plt
+from data_processor.base_bars import BaseBars
+import joblib
+
+# Define constants
+RAW_DATA_PATH = "sample_data/raw_data/price_vol.csv"
+PROCESSED_DATA_PATH = "sample_data/processed_data/"
+TICK_BARS_PATH = PROCESSED_DATA_PATH + "price_bars/tick_bars.csv"
+DOLLAR_BARS_PATH = PROCESSED_DATA_PATH + "price_bars/dollar_bars.csv"
+VOLUME_BARS_PATH = PROCESSED_DATA_PATH + "price_bars/volume_bars.csv"
+AUTOENCODER_DATA_PATH = PROCESSED_DATA_PATH + "autoencoder_data/"
+NN_DATA_PATH = PROCESSED_DATA_PATH + "nn_data/"
+RF_DATA_PATH = PROCESSED_DATA_PATH + "rf_data/"
+FULL_X_PATH = AUTOENCODER_DATA_PATH + "full_x.csv"
+FULL_Y_PATH = AUTOENCODER_DATA_PATH + "full_y.csv"
+TRAIN_X_PATH = RF_DATA_PATH + "train_x.csv"
+TRAIN_Y_PATH = RF_DATA_PATH + "train_y.csv"
+TEST_X_PATH = RF_DATA_PATH + "test_x.csv"
+TEST_Y_PATH = RF_DATA_PATH + "test_y.csv"
+
+print('Creating tick bars...')
+base = BaseBars(RAW_DATA_PATH, TICK_BARS_PATH, "tick", 10)
+base.batch_run()
+
+print('Creating dollar bars...')
+base = BaseBars(RAW_DATA_PATH, DOLLAR_BARS_PATH, "dollar", 20000)
+base.batch_run()
+
+print('Creating volume bars...')
+base = BaseBars(RAW_DATA_PATH, VOLUME_BARS_PATH, "volume", 50)
+base.batch_run()
+
+print('Processing data...')
+preprocess = DataProcessing(0.8)
+df = preprocess.make_features(file_path=DOLLAR_BARS_PATH, window=20,  
+    csv_path=AUTOENCODER_DATA_PATH, save_csv=True)
+fulldata, y_values, train_x, train_y, test_x, test_y = preprocess.make_train_test(df_x=df, df_y=None, window=1, 
+csv_path=AUTOENCODER_DATA_PATH, save_csv=True)
+
+print('Loading data...')
+a_train_x = pd.read_csv(TRAIN_X_PATH, index_col=0)
+a_train_y = pd.read_csv(TRAIN_Y_PATH, index_col=0)
+a_test_x = pd.read_csv(TEST_X_PATH, index_col=0)
+a_test_y = pd.read_csv(TEST_Y_PATH, index_col=0)
+print(a_train_x.head())
+print(a_train_x.shape)
+
+print('Scaling data...')
+scaler = MinMaxScaler(feature_range=(-1, 1))
+x_train_a = scaler.fit_transform(a_train_x.iloc[:, 1:])
+x_test_a = scaler.transform(a_test_x.iloc[:, 1:])
+
+autoencoder = AutoEncoder(20, x_train_a.shape[1])
+autoencoder.build_model(100, 50, 50, 100)
+
+print('Training model...')
+autoencoder.train_model(autoencoder.autoencoder, x_train_a, epochs=20, model_name='autoencoder')
+
+print('Testing model...')
+autoencoder.test_model(autoencoder.autoencoder, x_test_a)
+
+print('Encoding data...')
+a_full_data = pd.read_csv(FULL_X_PATH, index_col=0)
+a_scaled_full = pd.DataFrame(scaler.transform(a_full_data.iloc[:, 1:]))
+autoencoder.encode_data(a_scaled_full, csv_path=NN_DATA_PATH + 'full_x.csv')
+
+print('Processing data...')
+preprocess = DataProcessing(0.8)
+df1 = pd.read_csv(NN_DATA_PATH + "full_x.csv", index_col=0) 
+df2 = pd.read_csv(FULL_Y_PATH, index_col=0)
+fulldata, y_values, train_x, train_y, test_x, test_y = preprocess.make_train_test(df_x=df1, df_y=df2, window=1, 
+csv_path=RF_DATA_PATH, has_y=True, binary_y=True, save_csv=True)
+y = pd.read_csv(RF_DATA_PATH + 'full_y.csv', index_col=0)
+preprocess.check_labels(y)
+
+print('Loading data...')
+train_x = pd.read_csv(TRAIN_X_PATH, index_col=0)
+train_y = pd.read_csv(TRAIN_Y_PATH, index_col=0)
+test_x = pd.read_csv(TEST_X_PATH, index_col=0)
+test_y = pd.read_csv(TEST_Y_PATH, index_col=0)
+print(train_x.head())
+print(train_y.shape)
+
+print('Scaling data...')
+scaler = MinMaxScaler(feature_range=(-1, 1))
+x_train = scaler.fit_transform(train_x)
+x_test = scaler.transform(test_x)
+
+print('Training Random Forest model...')
+rfmodel = RFModel(x_train.shape[1])
+rfmodel.make_model(300, -1, verbose=1)
+rfmodel.train_model(x_train, train_y)
+rfmodel.test_model(x_test, test_y)
+
+print('Training AutoEncoder-based Random Forest model...')
+rfmodel = RFModel(x_train_a.shape[1])
+rfmodel.make_model(300, -1, verbose=1)
+rfmodel.train_model(x_train_a, train_y)
+rfmodel.test_model(x_test_a, test_y)
+
+# Save models
+joblib.dump(rfmodel, 'rfmodel.pkl')
+joblib.dump(autoencoder, 'autoencoder.pkl')
+
+print('Models saved.')
+