Upload main.py

main.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sun Nov  6 18:29:53 2022
+
+@author: culli
+"""
+
+import pandas as pd
+import numpy as np
+import pandas_datareader.data as web
+import datetime as dt
+from datetime import timedelta
+from sklearn.preprocessing import MinMaxScaler
+import tensorflow as tf
+from tensorflow import lite
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, LSTM
+import matplotlib.pyplot as plt
+
+# Load Training Data
+company = "BTC-USD"
+start = dt.datetime(2015,1,1)
+end = dt.datetime(2022,1,1)
+data = web.DataReader(company, "yahoo", start, end)
+
+
+# Prepare Data
+scaler = MinMaxScaler(feature_range=(0,1))
+scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))
+
+prediction_days = 60
+
+x_train = []
+y_train = []
+
+for x in range(prediction_days, len(scaled_data)):
+    x_train.append(scaled_data[x-prediction_days:x, 0])
+    y_train.append(scaled_data[x, 0])
+
+
+x_train, y_train = np.array(x_train), np.array(y_train)
+x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
+
+
+# Build the model
+model = Sequential()
+
+model.add(LSTM(units=50, return_sequences=True, input_shape = (x_train.shape[1], 1)))
+model.add(Dropout(0.2))
+model.add(LSTM(units=50, return_sequences=True))
+model.add(Dropout(0.2))
+model.add(LSTM(units=50))
+model.add(Dropout(0.2))
+model.add(Dense(units=1))
+
+
+model.compile(optimizer="adam", loss="mean_squared_error")
+model.fit(x_train, y_train, epochs=25, batch_size=64)
+
+# Load Data
+test_start = dt.datetime(2022,1,1)
+test_end = dt.datetime.now()
+
+test_data = web.DataReader(company, "yahoo", test_start, test_end)
+actual_prices = test_data["Close"].values
+
+total_dataset = pd.concat((data["Close"], test_data["Close"]), axis = 0)
+
+
+
+model_inputs = total_dataset[len(total_dataset) - len(test_data) - prediction_days:].values # Important
+model_inputs = model_inputs.reshape(-1, 1)
+model_inputs = scaler.transform(model_inputs)
+
+# Loop for making predictions
+for x in range(prediction_days):
+    
+    
+    # Make Predictions on Test Data
+    x_test = []
+    
+    for x in range(prediction_days, len(model_inputs)+1):
+        x_test.append(model_inputs[x-prediction_days:x, 0])
+    
+    x_test = np.array(x_test)
+    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
+    
+    predicted_prices = model.predict(x_test)
+    predicted_prices = scaler.inverse_transform(predicted_prices)
+    
+    
+    # Predict Next Day
+    real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs+1), 0]]
+    real_data = np.array(real_data)
+    real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))
+    
+    # Formatting the prediction and printing it
+    prediction = model.predict(real_data)
+    prediction = scaler.inverse_transform(prediction)
+    print(f"Prediction: {prediction}")
+    
+    # Adding the prediction back into the model for the next prediction
+    model_prediction_input = prediction
+    model_prediction_input = model_prediction_input.reshape(-1, 1)
+    model_prediction_input = scaler.transform(model_prediction_input)
+    model_inputs = np.concatenate((model_inputs, model_prediction_input))
+
+
+plt.plot(actual_prices, color="black", label=f"Actual {company} Price")
+plt.plot(predicted_prices, color="green", label=f"Predicted {company} Price")
+plt.title(f"{company} Share Price")
+plt.xlabel("Days")
+plt.ylabel(f"{company} Share Price")
+plt.legend()
+plt.show()
+
+
+tf.keras.models.save_model(model,"model.pbtxt")
+converter = lite.TFLiteConverter.from_keras_model(model = model)
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+converter.experimental_new_converter=True
+converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
+tf.lite.OpsSet.SELECT_TF_OPS]
+model_tflite = converter.convert()
+open("BTCPrediction.tflite", "wb").write(model_tflite)
+