def test_1_6(self):
daily_returns = self.get_daily_returns()
stocks().plot_data(daily_returns,
title='Daily returns') #, ylabel='Daily returns')
daily_returns.hist(bins=20)
plt.show()
daily_returns['SPY'].hist(bins=20, label='SPY')
daily_returns['XOM'].hist(bins=20, label='XOM')
daily_returns['GLD'].hist(bins=20, label='GLD')
plt.legend(loc='upper right')
plt.show()
def test_1_7_sharp_rate(self):
#Sharper rate
s = stocks()
df = s.get_datas(['SPY', 'MSFT'],
pd.date_range('2012-01-01', '2012-12-30'))
daily_returns = s.compute_daily_returns(df)
s.plot_data(daily_returns)
def get_daily_returns(self):
s = stocks()
dates = pd.date_range('2009-01-01', '2012-12-31')
symbols = ['SPY', 'XOM', 'GLD']
df = s.get_datas(symbols, dates)
#self.plot_data(df)
return s.compute_daily_returns(df)
def test_cache(self):
"""
need implement a cache for visited stocks
:return:
"""
s = stocks()
s.get_data("MSFT")
self.assertTrue(s.is_cached("MSFT"))
def test_incompletdata_01_05(self):
s = stocks()
s.get_data('JAMN',
start=datetime(2005, 12, 31),
end=datetime(2014, 12, 07))
spy = s.get_data('SPY')
print('-----------------')
print(spy)
pass
def pullData(self, stock):
"""
use pandas datareader to read history stock data from yahoo
:param stock: stock symbol
:return: None
"""
s = stocks()
currstock = s.get_data(stock)
print(len(currstock))
def build_stocks(symbols):
from stocks import stocks
stock = stocks()
for i in symbols:
stock.add(i)
return stock
def test_1_7_exercises(self):
#portfolio values, cumulative return, average daily return, sharp_rate
allocations = {'SPY': 0.4, 'XOM': 0.4, 'MSFT': 0.1, 'IBM': 0.1}
buy_date = datetime(2012, 1, 1)
s = stocks()
dates = pd.date_range('2012-01-01', '2012-12-20')
df = s.get_datas(allocations.keys(), dates)
df = df.div(
df.ix[0]
) #http://stackoverflow.com/questions/12007406/python-pandas-divide-dataframe-by-first-row
df = 1000000 * df.multiply([0.4, 0.4, 0.1, 0.1]) #allocations
daily_port_vals = df.sum(axis=1)
s.plot_data(daily_port_vals)
def test_plot_spy(self):
s = stocks()
spydata = s.get_data('SPY')
#print(type(spydata))
print(spydata.axes)
#spydata.plot()
#plt.show()
dates = pd.date_range('2012-01-01', '2012-12-31')
df = pd.DataFrame(index=dates)
print(df.axes)
spydata.rename(columns={'Adj Close': 'SPY'})
df.join(spydata)
spydata.to_csv('data/{}.csv'.format('SPY'))
spydata.plot()
plt.show()
def assess_portfolio(self, sd, ed, syms, allocs, sv, rfr, sf, gen_plot):
"""
http://quantsoftware.gatech.edu/MC1-Project-1#API_specification
:param sd: A datetime object that represents the start date
:param ed: A datetime object that represents the end date
:param symb: A list of symbols that make up the portfolio (note that your code should support any symbol in the data directory)
:param allocs: A list of allocations to the stocks, must sum to 1.0
:param sv: Start value of the portfolio
:param rfr: The risk free return per sample period for the entire date range (a single number, not an array).
:param sf: Sampling frequency per year
:param gen_plot: If True, create a plot named plot.png
:return: (cr, adr, sddr, sr, ev)
cr: Cumulative return
adr: Average period return (if sf == 252 this is daily return)
sddr: Standard deviation of daily return
sr: Sharpe ratio
ev: End value of portfolio
"""
symbols = list(syms)
s = stocks()
dates = pd.date_range(sd,ed)
df = s.get_datas(syms,dates)
df = df.div(df.ix[0])
df = df.multiply([1.0] + allocs)
df['portfolio'] = df[symbols].sum(axis=1)
df.drop(symbols, axis=1, inplace=True)
#s.plot_data(df)
cr = df['portfolio'][-1] - 1
adr = cr / len(df)
df = df * sv
#s.plot_data(df)
daily_return = s.compute_daily_returns(df)
sddr = daily_return.std()['portfolio']
sr = np.sqrt(sf) * (daily_return['portfolio'].mean() - rfr) / sddr
ev = df['portfolio'][-1]
return (cr, adr, sddr, sr, ev)
def test_get_cmcm(self):
s = stocks()
s.get_data('CMCM')
self.assertTrue(s.is_cached('CMCM'))
def test_data_to_csv(self):
import os
s = stocks()
s.get_data_to_csv("MSFT")
self.assertTrue(os.path.isfile(s.symbol_to_path("MSFT")))
def test1(self):
self.assertEqual(stocks.stocks([9.5, 3.4, 3.3, 5.6, 7.0], 50), [3.3, 7.0])
def test3(self):
self.assertEqual(stocks.stocks([100.3, 4.5, 34.5, 23.4, 6.8, 19.2], 0.7), [])
def test2(self):
self.assertEqual(stocks.stocks([2.9, 3.0, 5.0, 3.4, 2.2, 4.4], 3), [2.2, 4.4])