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Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

License: GNU General Public License v3.0

Jupyter Notebook 100.00%
stock-market-data nyse using-lstm stock-market-prediction stock-price-prediction stock-forcast newyork-stock keras tensorflow-gpu

nyse-stock_market-analysis-using-lstm's Introduction

Research Paper of Stock Prediction of NYSE
Learn More Technique for this project Click on Here

This dataset is a playground for fundamental and technical analysis. It is said that 30% of traffic on stocks is already generated by machines, can trading be fully automated? If not, there is still a lot to learn from historical data.

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
from keras.models import load_model
import keras
import h5py
import requests
import os
import seaborn as sns
sns.set()

Read data and transform them to pandas dataframe

df = pd.read_csv("nyse/prices-split-adjusted.csv", index_col = 0)
df["adj close"] = df.close # Moving close to the last column
df.drop(['close'], 1, inplace=True) # Moving close to the last column
df.head()
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symbol open low high volume adj close
date
2016-01-05 WLTW 123.430000 122.309998 126.250000 2163600.0 125.839996
2016-01-06 WLTW 125.239998 119.940002 125.540001 2386400.0 119.980003
2016-01-07 WLTW 116.379997 114.930000 119.739998 2489500.0 114.949997
2016-01-08 WLTW 115.480003 113.500000 117.440002 2006300.0 116.620003
2016-01-11 WLTW 117.010002 114.089996 117.330002 1408600.0 114.970001 
df2 = pd.read_csv("nyse/fundamentals.csv")
df2.head()
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Unnamed: 0 Ticker Symbol Period Ending Accounts Payable Accounts Receivable Add'l income/expense items After Tax ROE Capital Expenditures Capital Surplus Cash Ratio ... Total Current Assets Total Current Liabilities Total Equity Total Liabilities Total Liabilities & Equity Total Revenue Treasury Stock For Year Earnings Per Share Estimated Shares Outstanding
0 0 AAL 2012-12-31 3.068000e+09 -222000000.0 -1.961000e+09 23.0 -1.888000e+09 4.695000e+09 53.0 ... 7.072000e+09 9.011000e+09 -7.987000e+09 2.489100e+10 1.690400e+10 2.485500e+10 -367000000.0 2012.0 -5.60 3.350000e+08
1 1 AAL 2013-12-31 4.975000e+09 -93000000.0 -2.723000e+09 67.0 -3.114000e+09 1.059200e+10 75.0 ... 1.432300e+10 1.380600e+10 -2.731000e+09 4.500900e+10 4.227800e+10 2.674300e+10 0.0 2013.0 -11.25 1.630222e+08
2 2 AAL 2014-12-31 4.668000e+09 -160000000.0 -1.500000e+08 143.0 -5.311000e+09 1.513500e+10 60.0 ... 1.175000e+10 1.340400e+10 2.021000e+09 4.120400e+10 4.322500e+10 4.265000e+10 0.0 2014.0 4.02 7.169154e+08
3 3 AAL 2015-12-31 5.102000e+09 352000000.0 -7.080000e+08 135.0 -6.151000e+09 1.159100e+10 51.0 ... 9.985000e+09 1.360500e+10 5.635000e+09 4.278000e+10 4.841500e+10 4.099000e+10 0.0 2015.0 11.39 6.681299e+08
4 4 AAP 2012-12-29 2.409453e+09 -89482000.0 6.000000e+05 32.0 -2.711820e+08 5.202150e+08 23.0 ... 3.184200e+09 2.559638e+09 1.210694e+09 3.403120e+09 4.613814e+09 6.205003e+09 -27095000.0 2012.0 5.29 7.328355e+07

5 rows × 79 columns

Extract all symbols from the list

symbols = list(set(df.symbol))
len(symbols)
501

symbols[:11]
['TSCO', 'NOV', 'DVA', 'QRVO', 'FB', 'LUK', 'CAG', 'ECL', 'MA', 'SJM', 'TWX']

df = df[df.symbol == 'GOOG']
df.drop(['symbol'],1,inplace=True)
df.head()
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open low high volume adj close
date
2010-01-04 312.304948 310.955001 313.580158 3927000.0 312.205308
2010-01-05 312.419511 309.610028 312.748278 6031900.0 310.830459
2010-01-06 311.761979 302.048370 311.761979 7987100.0 302.994813
2010-01-07 303.562685 295.218951 303.861575 12876600.0 295.941242
2010-01-08 294.895159 293.455551 300.499172 9483900.0 299.886470 
df.info()
<class 'pandas.core.frame.DataFrame'>
Index: 1762 entries, 2010-01-04 to 2016-12-30
Data columns (total 5 columns):
open         1762 non-null float64
low          1762 non-null float64
high         1762 non-null float64
volume       1762 non-null float64
adj close    1762 non-null float64
dtypes: float64(5)
memory usage: 82.6+ KB

df.plot(figsize=(23,8),title = "Google Stock Price Analysis")
plt.subplot(411)
plt.plot(df.open, label='Original')
plt.legend(loc='best')
plt.subplot(412)
plt.plot(df.low, label='Trend')
plt.legend(loc='best')
plt.subplot(413)
plt.plot(df.high,label='Seasonality')
plt.legend(loc='best')
plt.subplot(414)
plt.plot(df.volume, label='Residuals')
plt.legend(loc='best')
plt.tight_layout()
plt.show()

png

Normalize the data

def normalize_data(df):
    min_max_scaler = preprocessing.MinMaxScaler()
    df['open'] = min_max_scaler.fit_transform(df.open.values.reshape(-1,1))
    df['high'] = min_max_scaler.fit_transform(df.high.values.reshape(-1,1))
    df['low'] = min_max_scaler.fit_transform(df.low.values.reshape(-1,1))
    df['volume'] = min_max_scaler.fit_transform(df.volume.values.reshape(-1,1))
    df['adj close'] = min_max_scaler.fit_transform(df['adj close'].values.reshape(-1,1))
    return df
df = normalize_data(df)
df.plot(figsize=(23,10))
plt.show()
plt.subplot(411)
plt.plot(df.open, label='Original')
plt.legend(loc='best')
plt.subplot(412)
plt.plot(df.low, label='Trend')
plt.legend(loc='best')
plt.subplot(413)
plt.plot(df.high,label='Seasonality')
plt.legend(loc='best')
plt.subplot(414)
plt.plot(df.volume, label='Residuals')
plt.legend(loc='best')
plt.tight_layout()
plt.show()

png

Create Training Set and Testing Set

def load_data(stock, seq_len):
    amount_of_features = len(stock.columns) # 5
    data = stock.as_matrix() 
    sequence_length = seq_len + 1 # index starting from 0
    result = []
    
    for index in range(len(data) - sequence_length): # maxmimum date = lastest date - sequence length
        result.append(data[index: index + sequence_length]) # index : index + 22days
    
    result = np.array(result)
    row = round(0.9 * result.shape[0]) # 90% split
    train = result[:int(row), :] # 90% date, all features 
    
    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]

Build the structure of model

  • dropout = 0.3
  • epochs = 90
  • LSTM 256
  • LSTM 256 > Relu 32 > Linear 1
def build_model(layers):
    d = 0.3
    model = Sequential()
    
    model.add(LSTM(256, input_shape=(layers[1], layers[0]), return_sequences=True))
    model.add(Dropout(d))
        
    model.add(LSTM(256, input_shape=(layers[1], layers[0]), return_sequences=False))
    model.add(Dropout(d))
        
    model.add(Dense(32,kernel_initializer="uniform",activation='relu'))        
    model.add(Dense(1,kernel_initializer="uniform",activation='linear'))
    
    # adam = keras.optimizers.Adam(decay=0.2)
        
    start = time.time()
    model.compile(loss='mse',optimizer='adam', metrics=['accuracy'])
    print("Compilation Time : ", time.time() - start)
    return model

Train Model

window = 22
X_train, y_train, X_test, y_test = load_data(df, window)
print (X_train[0], y_train[0])
[[0.15704696 0.16116746 0.15638998 0.1317225  0.15939908]
 [0.15723843 0.15888449 0.15499506 0.20246902 0.15709185]
 [0.15613951 0.14604929 0.15334121 0.26818406 0.14394234]
 [0.14243617 0.13445699 0.14009362 0.43252209 0.13210528]
 [0.12795029 0.13146379 0.13445546 0.3184921  0.13872603]
 [0.13832355 0.1356323  0.13546617 0.48640628 0.1379653 ]
 [0.13265404 0.13052526 0.13020388 0.3271972  0.12907917]
 [0.11503787 0.1186032  0.12203482 0.43807453 0.12624527]
 [0.12120685 0.12613696 0.12689613 0.2858228  0.12855249]
 [0.12906589 0.12210374 0.12636158 0.36641044 0.12031836]
 [0.11895908 0.12062405 0.12373877 0.29099208 0.12668832]
 [0.12293853 0.11977851 0.12003013 0.21906583 0.12066114]
 [0.12082392 0.1172081  0.12073176 0.42532947 0.1228095 ]
 [0.10505596 0.0855935  0.10718348 0.45857351 0.09524822]
 [0.09014547 0.08614311 0.08987641 0.29795279 0.08688037]
 [0.08296912 0.08680264 0.08964255 0.29361033 0.08890336]
 [0.08571648 0.08597403 0.08801372 0.26721945 0.08863587]
 [0.08839717 0.08199153 0.08747078 0.21760714 0.08210705]
 [0.08340204 0.0777723  0.08245074 0.27910072 0.07847068]
 [0.08016357 0.08173786 0.07812399 0.15159969 0.0810454 ]
 [0.08046326 0.07946339 0.07741402 0.27611276 0.07945711]
 [0.07522667 0.07998762 0.08337794 0.20208586 0.08756581]] 0.07582908201368749

model = build_model([5,window,1])
Compilation Time :  0.03211355209350586

model.fit(X_train,y_train,batch_size=2048,epochs=50,validation_split=0.1,verbose=1)
Train on 1408 samples, validate on 157 samples
Epoch 1/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.9183e-04 - acc: 7.1023e-04 - val_loss: 0.0066 - val_acc: 0.0000e+00
Epoch 2/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0011 - acc: 7.1023e-04 - val_loss: 0.0078 - val_acc: 0.0000e+00
Epoch 3/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0082 - val_acc: 0.0000e+00
Epoch 4/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0076 - val_acc: 0.0000e+00
Epoch 5/50
1408/1408 [==============================] - 4s 3ms/step - loss: 9.8577e-04 - acc: 7.1023e-04 - val_loss: 0.0065 - val_acc: 0.0000e+00
Epoch 6/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0061 - val_acc: 0.0000e+00
Epoch 7/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.6095e-04 - acc: 7.1023e-04 - val_loss: 0.0069 - val_acc: 0.0000e+00
Epoch 8/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0078 - val_acc: 0.0000e+00
Epoch 9/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0073 - val_acc: 0.0000e+00
Epoch 10/50
1408/1408 [==============================] - 4s 3ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0063 - val_acc: 0.0000e+00
Epoch 11/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.2948e-04 - acc: 7.1023e-04 - val_loss: 0.0059 - val_acc: 0.0000e+00
Epoch 12/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0011 - acc: 7.1023e-04 - val_loss: 0.0065 - val_acc: 0.0000e+00
Epoch 13/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0073 - val_acc: 0.0000e+00
Epoch 14/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0071 - val_acc: 0.0000e+00
Epoch 15/50
1408/1408 [==============================] - 4s 3ms/step - loss: 9.9832e-04 - acc: 7.1023e-04 - val_loss: 0.0061 - val_acc: 0.0000e+00
Epoch 16/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.2213e-04 - acc: 7.1023e-04 - val_loss: 0.0054 - val_acc: 0.0000e+00
Epoch 17/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.8422e-04 - acc: 7.1023e-04 - val_loss: 0.0054 - val_acc: 0.0000e+00
Epoch 18/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.9123e-04 - acc: 7.1023e-04 - val_loss: 0.0068 - val_acc: 0.0000e+00
Epoch 19/50
1408/1408 [==============================] - 4s 3ms/step - loss: 9.9868e-04 - acc: 7.1023e-04 - val_loss: 0.0075 - val_acc: 0.0000e+00
Epoch 20/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0064 - val_acc: 0.0000e+00
Epoch 21/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0053 - val_acc: 0.0000e+00
Epoch 22/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.4797e-04 - acc: 7.1023e-04 - val_loss: 0.0054 - val_acc: 0.0000e+00
Epoch 23/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.7492e-04 - acc: 7.1023e-04 - val_loss: 0.0063 - val_acc: 0.0000e+00
Epoch 24/50
1408/1408 [==============================] - 4s 3ms/step - loss: 9.6473e-04 - acc: 7.1023e-04 - val_loss: 0.0068 - val_acc: 0.0000e+00
Epoch 25/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.3285e-04 - acc: 7.1023e-04 - val_loss: 0.0062 - val_acc: 0.0000e+00
Epoch 26/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.6644e-04 - acc: 7.1023e-04 - val_loss: 0.0053 - val_acc: 0.0000e+00
Epoch 27/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.2052e-04 - acc: 7.1023e-04 - val_loss: 0.0052 - val_acc: 0.0000e+00
Epoch 28/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.7233e-04 - acc: 7.1023e-04 - val_loss: 0.0060 - val_acc: 0.0000e+00
Epoch 29/50
1408/1408 [==============================] - 4s 3ms/step - loss: 9.1919e-04 - acc: 7.1023e-04 - val_loss: 0.0065 - val_acc: 0.0000e+00
Epoch 30/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.6803e-04 - acc: 7.1023e-04 - val_loss: 0.0059 - val_acc: 0.0000e+00
Epoch 31/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.4760e-04 - acc: 7.1023e-04 - val_loss: 0.0050 - val_acc: 0.0000e+00
Epoch 32/50
1408/1408 [==============================] - 4s 3ms/step - loss: 9.7521e-04 - acc: 7.1023e-04 - val_loss: 0.0051 - val_acc: 0.0000e+00
Epoch 33/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.9890e-04 - acc: 7.1023e-04 - val_loss: 0.0062 - val_acc: 0.0000e+00
Epoch 34/50
1408/1408 [==============================] - 4s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0063 - val_acc: 0.0000e+00
Epoch 35/50
1408/1408 [==============================] - 3s 2ms/step - loss: 0.0010 - acc: 7.1023e-04 - val_loss: 0.0053 - val_acc: 0.0000e+00
Epoch 36/50
1408/1408 [==============================] - 3s 2ms/step - loss: 8.8524e-04 - acc: 7.1023e-04 - val_loss: 0.0049 - val_acc: 0.0000e+00
Epoch 37/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.5803e-04 - acc: 7.1023e-04 - val_loss: 0.0052 - val_acc: 0.0000e+00
Epoch 38/50
1408/1408 [==============================] - 4s 3ms/step - loss: 9.0167e-04 - acc: 7.1023e-04 - val_loss: 0.0056 - val_acc: 0.0000e+00
Epoch 39/50
1408/1408 [==============================] - 3s 2ms/step - loss: 8.9501e-04 - acc: 7.1023e-04 - val_loss: 0.0054 - val_acc: 0.0000e+00
Epoch 40/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.0009e-04 - acc: 7.1023e-04 - val_loss: 0.0047 - val_acc: 0.0000e+00
Epoch 41/50
1408/1408 [==============================] - 3s 2ms/step - loss: 8.8031e-04 - acc: 7.1023e-04 - val_loss: 0.0046 - val_acc: 0.0000e+00
Epoch 42/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.0014e-04 - acc: 7.1023e-04 - val_loss: 0.0052 - val_acc: 0.0000e+00
Epoch 43/50
1408/1408 [==============================] - 4s 3ms/step - loss: 9.4034e-04 - acc: 7.1023e-04 - val_loss: 0.0057 - val_acc: 0.0000e+00
Epoch 44/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.1925e-04 - acc: 7.1023e-04 - val_loss: 0.0050 - val_acc: 0.0000e+00
Epoch 45/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.0978e-04 - acc: 7.1023e-04 - val_loss: 0.0047 - val_acc: 0.0000e+00
Epoch 46/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.2713e-04 - acc: 7.1023e-04 - val_loss: 0.0051 - val_acc: 0.0000e+00
Epoch 47/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.3294e-04 - acc: 7.1023e-04 - val_loss: 0.0051 - val_acc: 0.0000e+00
Epoch 48/50
1408/1408 [==============================] - 4s 3ms/step - loss: 9.0042e-04 - acc: 7.1023e-04 - val_loss: 0.0046 - val_acc: 0.0000e+00
Epoch 49/50
1408/1408 [==============================] - 3s 2ms/step - loss: 9.2293e-04 - acc: 7.1023e-04 - val_loss: 0.0050 - val_acc: 0.0000e+00
Epoch 50/50
1408/1408 [==============================] - 3s 2ms/step - loss: 8.8114e-04 - acc: 7.1023e-04 - val_loss: 0.0053 - val_acc: 0.0000e+00





<keras.callbacks.History at 0x160f748bc88>

# print(X_test[-1])
diff=[]
ratio=[]
p = model.predict(X_test)
print (p.shape)
# for each data index in test data
for u in range(len(y_test)):
    # pr = prediction day u
    pr = p[u][0]
    # (y_test day u / pr) - 1
    ratio.append((y_test[u]/pr)-1)
    diff.append(abs(y_test[u]- pr))
    # print(u, y_test[u], pr, (y_test[u]/pr)-1, abs(y_test[u]- pr))
    # Last day prediction
    # print(p[-1]) 
(174, 1)

Denormalize the data

df = pd.read_csv("nyse/prices-split-adjusted.csv", index_col = 0)
df["adj close"] = df.close # Moving close to the last column
df.drop(['close'], 1, inplace=True) # Moving close to the last column
df = df[df.symbol == 'GOOG']
df.drop(['symbol'],1,inplace=True)

# Bug fixed at here, please update the denormalize function to this one
def denormalize(df, normalized_value): 
    df = df['adj close'].values.reshape(-1,1)
    normalized_value = normalized_value.reshape(-1,1)
    
    #return df.shape, p.shape
    min_max_scaler = preprocessing.MinMaxScaler()
    a = min_max_scaler.fit_transform(df)
    new = min_max_scaler.inverse_transform(normalized_value)
    return new

newp = denormalize(df, p)
newy_test = denormalize(df, y_test)
def model_score(model, X_train, y_train, X_test, y_test):
    trainScore = model.evaluate(X_train, y_train, verbose=0)
    print('Train Score: %.5f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))

    testScore = model.evaluate(X_test, y_test, verbose=0)
    print('Test Score: %.5f MSE (%.2f RMSE)' % (testScore[0], math.sqrt(testScore[0])))
    return trainScore[0], testScore[0]


model_score(model, X_train, y_train, X_test, y_test)
Train Score: 0.00107 MSE (0.03 RMSE)
Test Score: 0.00791 MSE (0.09 RMSE)





(0.001071222130054483, 0.007908337535443663)

import matplotlib.pyplot as plt2
plt.figure(figsize=(25,8))
plt2.plot(newp,color='red', label='Prediction')
plt2.plot(newy_test,color='blue', label='Actual')
plt2.legend(loc='best')
plt2.show()

png

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