def test_draw_portfolio_ref_compare():
symbols = ['AAPL', 'GOOG', 'IBM', 'MSFT']
ref_symbol = '^GSPC'
basic_portfolio = BasicPortfolio(symbols, dt.datetime(2014, 1, 1),
dt.datetime(2014, 12, 31))
basic_portfolio.plot_with_reference([0.4, 0.4, 0.0, 0.2],
ref_symbol,
source='yahoo')
def test_small_portfolio():
symbols = ["AIR.PA", "LG.PA", "GLE.PA", "DG.PA"]
ref_symbol = '^FCHI'
end_date = dt.datetime.today()
start_date = end_date - dt.timedelta(days=365)
basic_portfolio = BasicPortfolio(symbols, start_date, end_date)
optimize(basic_portfolio, ref_symbol, filename=None, target_return=0.012)
def main():
"""main function"""
# Construct the two symbol lists SP 500 of 2008 and 2012
dataobj = da.DataAccess('Yahoo')
symbols12 = dataobj.get_symbols_from_list("sp5002012")
symbols12.append('SPY')
lookbackdates = 20
basic_portfolio = BasicPortfolio(symbols12, dt.datetime(2008, 1, 1),
dt.datetime(2009, 12, 31))
print 'Start Looking for the events'
df_events = BollingerEventTest(basic_portfolio, lookbackdates)
print 'Start retrieving data from local Yahoo'
d_data = load_local_data_from_yahoo(basic_portfolio.start_date,
basic_portfolio.end_date,
basic_portfolio.tickers)
filename = "BollingerEventStudy12.9.pdf"
ep.eventprofiler(df_events,
d_data,
i_lookback=20,
i_lookforward=20,
s_filename=filename,
b_market_neutral=True,
b_errorbars=True,
s_market_sym='SPY')
print 'Generate orders with the events'
df_event_trading = GenerateTradingWithEvents(df_events)
df_event_trading.to_csv("ordersbollinger5d.csv", index=False, header=False)
def test_cac40_portfolio():
ref_symbol = '^FCHI'
end_date = dt.datetime.today()
start_date = end_date - dt.timedelta(days=365)
cac40 = load_valid_cac40_names()
basic_portfolio = BasicPortfolio(cac40.index, start_date, end_date,
cac40.values)
optimize(basic_portfolio, ref_symbol, filename="EquitiesvFrontier2015.pdf")
def test():
"""Main Function"""
ls_symbols = ['AAPL', 'GOOG', 'IBM', 'MSFT']
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2010, 12, 31)
basic_portfolio = BasicPortfolio(ls_symbols, start_date, end_date)
period = 20
compute_bollinger_band(basic_portfolio, period, source='local')
def get_correlations_cac40(filename='result/correlationCAC40.csv', target_ticker=None):
symbols = load_valid_cac40_names()
end_date = dt.datetime.today()
start_date = end_date - dt.timedelta(days=365)
basic_portfolio = BasicPortfolio(symbols.index, start_date, end_date, symbols.values)
correlations = basic_portfolio.get_correlations()
if isinstance(correlations, pd.DataFrame):
print 'Write the correlation matrix into file: ', filename
correlations.to_csv(filename)
if target_ticker is not None and target_ticker in symbols.index:
ticker_name = symbols[target_ticker]
target_correlation = correlations[ticker_name]
target_correlation = target_correlation.drop(ticker_name)
print 'The stock %s is least correlated to %s with correlation %f' % \
(ticker_name, target_correlation.argmin(), target_correlation.min())
print 'The stock %s is most correlated to %s with correlation %f' % \
(ticker_name, target_correlation.argmax(), target_correlation.max())
def testing(self, target_return=0.011):
"""
The method of back testing
@param target_return: target return of the portfolio
"""
best_allocation = get_frontier(BasicPortfolio(self.symbols,
self.start_date,
self.mid_date,
self.symbol_names),
self.reference_symbol,
filename=None,
target_return=target_return)
BasicPortfolio(self.symbols, self.start_date, self.mid_date, self.symbol_names)\
.plot_with_reference(best_allocation, self.reference_symbol,
filename='result/portvCAC40-target.before.pdf')
BasicPortfolio(self.symbols, self.mid_date, self.end_date, self.symbol_names)\
.plot_with_reference(best_allocation, self.reference_symbol,
filename='result/portvCAC40-target.forecast.pdf')
def get_correlations_cac40(filename='result/correlationCAC40.csv',
target_ticker=None):
symbols = load_valid_cac40_names()
end_date = dt.datetime.today()
start_date = end_date - dt.timedelta(days=365)
basic_portfolio = BasicPortfolio(symbols.index, start_date, end_date,
symbols.values)
correlations = basic_portfolio.get_correlations()
if isinstance(correlations, pd.DataFrame):
print 'Write the correlation matrix into file: ', filename
correlations.to_csv(filename)
if target_ticker is not None and target_ticker in symbols.index:
ticker_name = symbols[target_ticker]
target_correlation = correlations[ticker_name]
target_correlation = target_correlation.drop(ticker_name)
print 'The stock %s is least correlated to %s with correlation %f' % \
(ticker_name, target_correlation.argmin(), target_correlation.min())
print 'The stock %s is most correlated to %s with correlation %f' % \
(ticker_name, target_correlation.argmax(), target_correlation.max())
def test_best_allocation():
"""
This function is to remove
Test that it is possible to find the best portfolio (highest sharpe ratio)
given a list of symbols using the methods above.
"""
# symbols = ['BRCM', 'TXN', 'IBM', 'HNZ']
symbols = ['AAPL', 'GOOG', 'IBM', 'MSFT']
# ['GOOG','AAPL','GLD','XOM']
basic_portfolio = BasicPortfolio(symbols, dt.datetime(2014, 1, 1),
dt.datetime(2014, 12, 31))
alloc = range(4)
sharpe_max = 0
alloc_max = alloc[:]
for i in range(11):
alloc[0] = i * 0.1
for j in range(11 - i):
alloc[1] = j * 0.1
for k in range(11 - i - j):
alloc[2] = k * 0.1
alloc[3] = (10 - i - j - k) * 0.1
vol, daily_ret, sharpe, cum_ret = \
basic_portfolio.analyze(alloc)
if sharpe > sharpe_max:
sharpe_max = sharpe
alloc_max = alloc[:]
print 'Best sharpe ratio is ', sharpe_max
print 'Best allocation is', alloc_max
ref_symbol = '$SPX'
basic_portfolio.plot_with_reference(alloc_max, ref_symbol, source='local')
def test_best_allocation():
"""
This function is to remove
Test that it is possible to find the best portfolio (highest sharpe ratio)
given a list of symbols using the methods above.
"""
# symbols = ['BRCM', 'TXN', 'IBM', 'HNZ']
symbols = ['AAPL', 'GOOG', 'IBM', 'MSFT']
# ['GOOG','AAPL','GLD','XOM']
basic_portfolio = BasicPortfolio(symbols, dt.datetime(2014, 1, 1), dt.datetime(2014, 12, 31))
alloc = range(4)
sharpe_max = 0
alloc_max = alloc[:]
for i in range(11):
alloc[0] = i * 0.1
for j in range(11 - i):
alloc[1] = j * 0.1
for k in range(11 - i - j):
alloc[2] = k * 0.1
alloc[3] = (10 - i - j - k) * 0.1
vol, daily_ret, sharpe, cum_ret = \
basic_portfolio.analyze(alloc)
if sharpe > sharpe_max:
sharpe_max = sharpe
alloc_max = alloc[:]
print 'Best sharpe ratio is ', sharpe_max
print 'Best allocation is', alloc_max
ref_symbol = '$SPX'
basic_portfolio.plot_with_reference(alloc_max, ref_symbol, source='local')
def main():
"""Main Function"""
# Construct the two symbol lists SP 500 of 2008 and 2012
dataobj = da.DataAccess('Yahoo')
symbols08 = dataobj.get_symbols_from_list("sp5002008")
symbols08.append('SPY')
symbols12 = dataobj.get_symbols_from_list("sp5002012")
symbols12.append('SPY')
start_date = dt.datetime(2008, 1, 1)
end_date = dt.datetime(2009, 12, 31)
basic_portfolio08 = BasicPortfolio(symbols08, start_date, end_date)
basic_portfolio08.event_profiler(source='local',
filename="MyEventStudy08.9.chg.pdf")
basic_portfolio12 = BasicPortfolio(symbols12, start_date, end_date)
basic_portfolio12.event_profiler(source='local',
filename="MyEventStudy12.9.chg.pdf")
def main():
"""Main Function"""
# Construct the two symbol lists SP 500 of 2008 and 2012
dataobj = da.DataAccess('Yahoo')
symbols08 = dataobj.get_symbols_from_list("sp5002008")
symbols08.append('SPY')
symbols12 = dataobj.get_symbols_from_list("sp5002012")
symbols12.append('SPY')
start_date = dt.datetime(2008, 1, 1)
end_date = dt.datetime(2009, 12, 31)
basic_portfolio08 = BasicPortfolio(symbols08, start_date, end_date)
basic_portfolio08.event_profiler(source='local', filename="MyEventStudy08.9.chg.pdf")
basic_portfolio12 = BasicPortfolio(symbols12, start_date, end_date)
basic_portfolio12.event_profiler(source='local', filename="MyEventStudy12.9.chg.pdf")
"""
Thi file contains test for the portfolio analysis.
"""
import datetime as dt
import pandas as pd
from portfolio import BasicPortfolio
basic_portfolio = BasicPortfolio(['AAPL', 'IBM', 'MSFT'], dt.datetime(2011, 12, 1),
dt.datetime(2011, 12, 31))
def test_get_correlations():
correlations = basic_portfolio.get_correlations()
assert isinstance(correlations, pd.DataFrame)
assert correlations.shape == (3, 3)
def test_analyze():
vol, ave_daily_ret, sharpe, cum_ret = \
basic_portfolio.analyze([0.1, 0.4, 0.5], source='local', debug=True)
eps = 0.0001
assert abs(vol - 0.00941707081886) * 100 < eps
assert abs(sharpe - 0.561924531129) * 100 < eps
assert abs(ave_daily_ret - 0.000333344702654) * 10000 < eps
assert abs(cum_ret - 0.00608928693) * 10000 < eps
if __name__ == '__main__':
test_get_correlations()
test_analyze()
def test_draw_portfolio_ref_compare():
symbols = ['AAPL', 'GOOG', 'IBM', 'MSFT']
ref_symbol = '^GSPC'
basic_portfolio = BasicPortfolio(symbols, dt.datetime(2014, 1, 1), dt.datetime(2014, 12, 31))
basic_portfolio.plot_with_reference([0.4, 0.4, 0.0, 0.2], ref_symbol, source='yahoo')