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# Make sure that you have all these libaries available to run the code successfully from pandas_datareader import data import matplotlib.pyplot as plt import pandas as pd import datetime as dt import urllib.request, json import os import numpy as np import tensorflow as tf # This code has been tested with TensorFlow 1.6 from sklearn.preprocessing import MinMaxScaler
data_source = 'kaggle' # alphavantage or kaggle if data_source == 'alphavantage': # ====================== Loading Data from Alpha Vantage ================================== api_key = '<your API key>' # American Airlines stock market prices ticker = "AAL" # JSON file with all the stock market data for AAL from the last 20 years url_string = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=%s&outputsize=full&apikey=%s"%(ticker,api_key) # Save data to this file file_to_save = 'stock_market_data-%s.csv'%ticker # If you haven't already saved data, # Go ahead and grab the data from the url # And store date, low, high, volume, close, open values to a Pandas DataFrame if not os.path.exists(file_to_save): with urllib.request.urlopen(url_string) as url: data = json.loads(url.read().decode()) # extract stock market data data = data['Time Series (Daily)'] df = pd.DataFrame(columns=['Date','Low','High','Close','Open']) for k,v in data.items(): date = dt.datetime.strptime(k, '%Y-%m-%d') data_row = [date.date(),float(v['3. low']),float(v['2. high']), float(v['4. close']),float(v['1. open'])] df.loc[-1,:] = data_row df.index = df.index + 1 print('Data saved to : %s'%file_to_save) df.to_csv(file_to_save) # If the data is already there, just load it from the CSV else: print('File already exists. Loading data from CSV') df = pd.read_csv(file_to_save) else: # ====================== Loading Data from Kaggle ================================== # You will be using HP's data. Feel free to experiment with other data. # But while doing so, be careful to have a large enough dataset and also pay attention to the data normalization df = pd.read_csv(os.path.join('Stocks','hpq.us.txt'),delimiter=',',usecols=['Date','Open','High','Low','Close']) print('Loaded data from the Kaggle repository')
Data saved to : stock_market_data-AAL.csv
# Sort DataFrame by date df = df.sort_values('Date') # Double check the result df.head()
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