13_normalized_data.py

import os
import pandas as pd 
import matplotlib.pyplot as plt 
def symbol_to_path(symbol, base_dir = 'data'):
	return os.path.join(base_dir, "{}.csv".format(str(symbol)))

def dates_creator():
	start_date = '2013-01-01'
	end_date = '2013-12-31'
	dates = pd.date_range(start_date, end_date)
	return dates

def get_data(symbols, dates):
	df = pd.DataFrame(index = dates)
	if 'SPY' not in symbols: # adding SPY as the main reference
		symbols.insert(0, 'SPY')
	for symbol in symbols:
		df_temp = pd.read_csv(symbol_to_path(symbol),
			index_col = 'Date',
			parse_dates = True,
			usecols = ['Date', 'Adj Close'],
			na_values = ['nan'])
		df_temp = df_temp.rename(columns = {'Adj Close': symbol})
		df = df.join(df_temp)
	
		if symbol == 'SPY':
			df = df.dropna(subset = ['SPY'])

	#print(df)
	return df

def normalize_data(df):
	return df / df.ix[0,:]


def plot_data(df, title = 'Stock prices'):
	myplot = df.plot(title = title, fontsize = 2)
	myplot.set_xlabel('Date')
	myplot.set_ylabel('Price')
	plt.show()

def plot_selected_data(df, start, end, columns, title = 'Stock prices'):
	df = df.ix[start : end, columns]
	myplot = df.plot(title = title, fontsize = 10)
	myplot.set_xlabel('Date')
	myplot.set_ylabel('Price')
	plt.show()

symbols = ['AAPL', 'SPY' , 'IBM', 'GOOG', 'TSLA']

if __name__ == "__main__":
	dates = dates_creator()
	df = get_data(symbols, dates)
	df = normalize_data(df)

	plot_selected_data(df, '2013-01-01', '2013-12-31', ['SPY', 'IBM'])