def get_portfolio_by_id(id: str):
portfolios = list()
connect()
portfolio = Portfolio.objects(_id=id).first()
portfolios.append(portfolio)
close()
LOG.info('Loading %d portfolios' % len(portfolios))
return portfolios
def process_result_of_ga(results_frame, stocks, gmean):
try:
id_sharpe = results_frame['sharpe'].idxmax()
except TypeError as exc:
id_sharpe = maxId(results_frame, 'sharpe')
try:
max_sharpe_port = results_frame.iloc[id_sharpe]
except TypeError as exc:
id_sharpe = maxId(results_frame, 'sharpe')
max_sharpe_port = results_frame.iloc[id_sharpe]
try:
id_stdev = results_frame['stdev'].idxmin()
except TypeError as exc:
id_stdev = minId(results_frame, 'stdev')
if id_stdev == np.NaN:
id_stdev = minId(results_frame, 'stdev')
try:
min_vol_port = results_frame.iloc[id_stdev]
except TypeError as exc:
id_stdev = minId(results_frame, 'stdev')
min_vol_port = results_frame.iloc[id_stdev]
LOG.info('Max Sharpe ratio: \n%s' % str(max_sharpe_port))
LOG.info('Min standard deviation: \n%s' % str(min_vol_port))
db.connect()
solved = Portfolio()
max_item = parse_solved_portfolio(max_sharpe_port, stocks)
min_item = parse_solved_portfolio(min_vol_port, stocks)
solved.max_item = max_item
solved.min_item = min_item
solved.date = datetime.datetime.today()
solved.total_sum = 0
for stock in max_item.stocks:
solved.total_sum += stock.value
if gmean is None:
solved.gmean = 0.0
else:
solved.gmean = gmean
solved.save()
db.close()
return max_item.sharpe_ratio, solved._id
def get_n_first_portfolios(count: int):
portfolios = list()
connect()
today = datetime.today() - timedelta(hours=12)
for p in Portfolio.objects(date__gt=today).order_by('-max_item.sharpe_ratio')[:count]:
portfolios.append(p)
close()
LOG.info('Loading %d portfolios' % len(portfolios))
return portfolios
def parallel_solve(all_stocks, type_ga, curr, count):
LOG.info('Start parallel solve in %s' % str(threading.get_ident()))
range_stock = len(all_stocks)
number = list()
stocks = []
for position in range(15):
pos = random.randint(0, range_stock)
while pos in number:
pos = random.randint(0, range_stock)
stock = all_stocks[pos - 1]
while len(stock.day_history) < 66:
pos = random.randint(0, range_stock)
stock = all_stocks[pos - 1]
stocks.append(stock)
# download daily price data for each of the stocks in the portfolio
data = get_stock_price(stocks)
returns = data.pct_change()
# calculate mean daily return and covariance of daily returns
mean_daily_returns = returns.mean()
cov_matrix = returns.cov()
days = len(stocks[0].day_history)
iterations = 50000
start = time.time()
if type_ga == GA_SIMPLE:
results_frame = simple.solve(stocks, iterations, mean_daily_returns,
cov_matrix, days)
if type_ga == GA_NSGAII:
results_frame = NSGAII.solve(stocks, iterations, mean_daily_returns,
cov_matrix, days)
if type_ga == GA_NSGAIII:
results_frame = NSGAII.solve_nsgaiii(stocks, iterations,
mean_daily_returns, cov_matrix,
days)
duration = time.time() - start
LOG.info('Duration solved: %s' % duration)
sharpe_id_min = results_frame['sharpe'].idxmin()
max_sharpe_port = results_frame.iloc[sharpe_id_min]
stdev_id_min = results_frame['stdev'].idxmax()
min_vol_port = results_frame.iloc[stdev_id_min]
LOG.info('Max Sharpe ratio: %s' % str(max_sharpe_port))
LOG.info('Min standard deviation: %s' % str(min_vol_port))
db.connect()
solved = Portfolio()
max_item = parse_solved_portfolio(max_sharpe_port, stocks)
min_item = parse_solved_portfolio(min_vol_port, stocks)
solved.max_item = max_item
solved.min_item = min_item
solved.date = datetime.datetime.today()
solved.save()
db.close()
LOG.info('Save %d portfolio from %d in thread %s' %
(curr + 1, count, str(threading.get_ident())))
return 'Duration %s' % str(duration)
def get_n_random_portfolios(number=10):
portfolios = list()
connect()
today = datetime.today() - timedelta(hours=12)
found_portfolios = Portfolio.objects(date__gt=today).order_by('-max_item.sharpe_ratio')
all_found = len(found_portfolios)
for x in range(number):
position = random.randint(0, all_found - 1)
portfolios.append(found_portfolios[position])
close()
LOG.info('Loading %d portfolios' % len(portfolios))
return portfolios