import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as web
import datetime as dt

from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import  Dense, Dropout, LSTM

#Load Data
company = "SPY"

start = dt.datetime(2012, 1, 1)
end = dt.datetime(2021, 10, 7)

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)) #Prediction of the next closing value

    model.compile(optimizer = "adam", loss = "mean_squared_error")
    model.fit(x_train, y_train, epochs = 25, batch_size = 32)

    '''Test the model accuracy'''

    #Load Test Data
    test_start = dt.datetime(2020, 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
    model_inputs = model_inputs.reshape(-1, 1)
    model_inputs = scaler.transform(model_inputs)

    # Make Predictions on Test Data

    x_test = []

    for x in range(prediction_days, len(model_inputs)):
        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)

        # Plot the Test Predictions
        plt.plot(actual_prices, color = "black", label = f"Actual {company} Price")
        plt.plot(predicted_prices, color = "green", label = f"Actual {company} Price")
        plt.title(f"{company} Share Price")
        plt.xlabel("Time")
        plt.ylabel(f"{company} Share Price")
        plt.legend()
        plt.show()

        #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))

    prediction = model.predict(real_data)
    prediction = scaler.inverse_transform(prediction)
    print(f"Prediction:  {prediction}")