#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import datetime
import math, time
import itertools
from sklearn import preprocessing
import datetime
from operator import itemgetter
from sklearn.metrics import mean_squared_error
from math import sqrt
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.recurrent import LSTM


# * Get the stock data from Yahoo finance as a CSV for any company. 
# 
# * Drop all other columns (Volume, Year, Month and Day) and retain Open, High and Close - since I'm just predicting the close value for a day

# In[2]:


def get_stock_data(stock_name, normalized=0):
    url = 'http://chart.finance.yahoo.com/table.csv?s=%s&a=11&b=15&c=2011&d=29&e=10&f=2016&g=d&ignore=.csv' % stock_name

    col_names = ['Date','Open','High','Low','Close','Volume','Adj Close']
    stocks = pd.read_csv(url, header=0, names=col_names) 
    df = pd.DataFrame(stocks)
    date_split = df['Date'].str.split('-').str
    df['Year'], df['Month'], df['Day'] = date_split
    df["Volume"] = df["Volume"] / 10000
    #df.drop(df.columns[[0,3,5,6, 7,8,9]], axis=1, inplace=True) 
    return df


# In[3]:


stock_name = 'GOOGL'
df = get_stock_data(stock_name,0)
df.head()


# In[4]:


df.drop(df.columns[[0,3,5,6, 7,8,9]], axis=1, inplace=True)
df.head()


# * Let me save the data to a CSV file so that I can refer later (if needed :) ) 

# In[16]:


today = datetime.date.today()
filename = stock_name+'_stock_%s.csv' % today
df.to_csv(filename)


# Normalizing

# In[5]:


df['Open'] = df['Open']/100
df['High'] = df['High']/100
df['Close'] = df['Close']/100
df.head()


# In[6]:


#Load the data
def load_data(stock, seq_len):
    amount_of_features = len(stock.columns)
    data = stock.as_matrix() #pd.DataFrame(stock)
    sequence_length = seq_len + 1
    result = []
    for index in range(len(data) - sequence_length):
        result.append(data[index: index + sequence_length])

    result = np.array(result)
    row = round(0.9 * result.shape[0])
    train = result[:int(row), :]
    x_train = train[:, :-1]
    y_train = train[:, -1][:,-1]
    x_test = result[int(row):, :-1]
    y_test = result[int(row):, -1][:,-1]

    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], amount_of_features))
    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], amount_of_features))  

    return [x_train, y_train, x_test, y_test]


# In[7]:


#Build the model
def build_model(layers):
    model = Sequential()

    model.add(LSTM(
        input_dim=layers[0],
        output_dim=layers[1],
        return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(
        layers[2],
        return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(
        output_dim=layers[2]))
    model.add(Activation("linear"))

    start = time.time()
    model.compile(loss="mse", optimizer="rmsprop",metrics=['accuracy'])
    print("Compilation Time : ", time.time() - start)
    return model

def build_model2(layers):
        d = 0.2
        model = Sequential()
        model.add(LSTM(128, input_shape=(layers[1], layers[0]), return_sequences=True))
        model.add(Dropout(d))
        model.add(LSTM(64, input_shape=(layers[1], layers[0]), return_sequences=False))
        model.add(Dropout(d))
        model.add(Dense(16,init='uniform',activation='relu'))        
        model.add(Dense(1,init='uniform',activation='linear'))
        model.compile(loss='mse',optimizer='adam',metrics=['accuracy'])
        return model


# Divide as Training and Test set

# In[8]:


window = 22
X_train, y_train, X_test, y_test = load_data(df[::-1], window)
print("X_train", X_train.shape)
print("y_train", y_train.shape)
print("X_test", X_test.shape)
print("y_test", y_test.shape)


# In[9]:


# model = build_model([3,lag,1])
model = build_model2([3,window,1])


# Execute the trained model

# In[ ]:


model.fit(
    X_train,
    y_train,
    batch_size=512,
    nb_epoch=500,
    validation_split=0.1,
    verbose=1)


# Train on 1055 samples, validate on 118 samples
# 
# Epoch 1/500
# 1055/1055 [==============================] - 6s - loss: 54.5698 - acc: 0.0000e+00 - val_loss: 56.6067 - val_acc: 0.0000e+00
# Epoch 2/500
# 1055/1055 [==============================] - 4s - loss: 54.2361 - acc: 0.0000e+00 - val_loss: 56.1267 - val_acc: 0.0000e+00
# Epoch 3/500
# 1055/1055 [==============================] - 3s - loss: 53.7573 - acc: 0.0000e+00 - val_loss: 55.5052 - val_acc: 0.0000e+00
# Epoch 4/500
# 1055/1055 [==============================] - 3s - loss: 53.1317 - acc: 0.0000e+00 - val_loss: 54.6624 - val_acc: 0.0000e+00
# Epoch 5/500
# 1055/1055 [==============================] - 3s - loss: 52.2918 - acc: 0.0000e+00 - val_loss: 53.5170 - val_acc: 0.0000e+00
# Epoch 6/500
# 1055/1055 [==============================] - 3s - loss: 51.1431 - acc: 0.0000e+00 - val_loss: 52.0444 - val_acc: 0.0000e+00
# Epoch 7/500
# 1055/1055 [==============================] - 3s - loss: 49.7124 - acc: 0.0000e+00 - val_loss: 50.2901 - val_acc: 0.0000e+00
# Epoch 8/500
# 1055/1055 [==============================] - 3s - loss: 48.0304 - acc: 0.0000e+00 - val_loss: 48.4176 - val_acc: 0.0000e+00
# Epoch 9/500
# 1055/1055 [==============================] - 3s - loss: 46.2073 - acc: 0.0000e+00 - val_loss: 46.4271 - val_acc: 0.0000e+00
# Epoch 10/500
# 1055/1055 [==============================] - 3s - loss: 44.2880 - acc: 0.0000e+00 - val_loss: 44.3134 - val_acc: 0.0000e+00
# 
# Epoch 490/500
# 1055/1055 [==============================] - 6s - loss: 3.3352 - acc: 0.0000e+00 - val_loss: 0.2747 - val_acc: 0.0000e+00
# Epoch 491/500
# 1055/1055 [==============================] - 6s - loss: 3.3220 - acc: 0.0000e+00 - val_loss: 0.3347 - val_acc: 0.0000e+00
# Epoch 492/500
# 1055/1055 [==============================] - 6s - loss: 3.3430 - acc: 0.0000e+00 - val_loss: 0.3876 - val_acc: 0.0000e+00
# Epoch 493/500
# 1055/1055 [==============================] - 6s - loss: 3.3882 - acc: 0.0000e+00 - val_loss: 0.3919 - val_acc: 0.0000e+00
# Epoch 494/500
# 1055/1055 [==============================] - 6s - loss: 3.3117 - acc: 0.0000e+00 - val_loss: 0.3515 - val_acc: 0.0000e+00
# Epoch 495/500
# 1055/1055 [==============================] - 7s - loss: 3.4429 - acc: 0.0000e+00 - val_loss: 0.3073 - val_acc: 0.0000e+00
# Epoch 496/500
# 1055/1055 [==============================] - 7s - loss: 3.3748 - acc: 0.0000e+00 - val_loss: 0.2877 - val_acc: 0.0000e+00
# Epoch 497/500
# 1055/1055 [==============================] - 6s - loss: 3.3394 - acc: 0.0000e+00 - val_loss: 0.2870 - val_acc: 0.0000e+00
# Epoch 498/500
# 1055/1055 [==============================] - 6s - loss: 3.3265 - acc: 0.0000e+00 - val_loss: 0.2725 - val_acc: 0.0000e+00
# Epoch 499/500
# 1055/1055 [==============================] - 6s - loss: 3.3056 - acc: 0.0000e+00 - val_loss: 0.2654 - val_acc: 0.0000e+00
# Epoch 500/500
# 1055/1055 [==============================] - 6s - loss: 3.3673 - acc: 0.0000e+00 - val_loss: 0.2697 - val_acc: 0.0000e+00

# In[11]:


trainScore = model.evaluate(X_train, y_train, verbose=0)
print('Train Score: %.2f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))

testScore = model.evaluate(X_test, y_test, verbose=0)
print('Test Score: %.2f MSE (%.2f RMSE)' % (testScore[0], math.sqrt(testScore[0])))


# Predictions vs Real results

# In[24]:


import matplotlib.pyplot as plt2

plt2.plot(p,color='red', label='prediction')
plt2.plot(y_test,color='blue', label='y_test')
plt2.legend(loc='upper left')
plt2.show()