def test_get_returns_and_pl_average_return(self):
"""
Run on simple 2 period time schema and verify that average return is
being processed correctly. No positions are closed.
"""
all_prices = [[1., 2.], [3., 4.], [5., 6.]]
signals = [['buy', 'buy', 'sell'],
[None, None, None]]
returns, p_and_l = stockdata.get_returns_and_pl(all_prices, signals)
self.assertEqual(returns, [0, -0.38077249551779302])
self.assertEqual(p_and_l, [0, 0])
def test_get_returns_and_pl_closing_trades(self):
"""
Run on simple 2 period time schema and verify that trades are closed
and P&L is counted correctly.
"""
all_prices = [[1., 2.], [3., 4.], [5., 6.]]
signals = [['buy', 'buy', 'sell'],
['sell', 'sell', 'buy']]
returns, p_and_l = stockdata.get_returns_and_pl(all_prices, signals)
self.assertEqual(returns, [0, -0.38077249551779302])
self.assertEqual(p_and_l, [0, 1.0])
def test_get_returns_and_pl_3_days_range(self):
"""
Run on 2 period time schema and verify that double 'buy' doesn't
produce any changes. After closing long position another 'sell' is
made on day 3.
"""
all_prices = [[1., 2., 2.6, 2.9], [3., 4., 4.4, 4.9], [5., 6., 7.13,
7.18]]
signals = [['buy', 'buy', 'sell'],
['sell', 'buy', 'buy'],
['sell', None, 'sell'],
['buy', None, 'buy']]
returns, p_and_l = stockdata.get_returns_and_pl(all_prices, signals)
self.assertEqual(returns, [0, -0.38077249551779302,
-0.76376474777389891, -0.95015550861867115])
self.assertEqual(p_and_l, [0, 0.0, 0.0, -0.34999999999999964])
def run(request):
def graph_img(x, y, name):
def y_precise():
result = ["chd=t:"]
for elem in y:
result.append(str(elem))
result.append(',')
result[-1] = '&'
return ''.join(result)
G = Line(y, encoding='text')
G.axes.type('xy')
min_y = min(y)
max_y = max(y)
G.axes.range(1, min_y, max_y)
G.scale(min_y, max_y)
G.axes.label(0, x[0], x[-1])
G.axes.label(1, min_y, max_y)
G.title(name)
image_code = G.img()
norm_img = re.sub(r'chd=t.*?\&', y_precise(), image_code)
if len(norm_img) > 2000:
if len(image_code) > 2000:
raise LongPeriodException()
else:
return image_code
return norm_img
try:
# getting parameters
start_date = datetime.strptime(request.POST['start_date'],
'%Y-%m-%d').date()
end_date = datetime.strptime(request.POST['end_date'],
'%Y-%m-%d').date()
stock_amount = int(request.POST['stock_amount'])
time_period = int(request.POST['time_period'])
k = int(request.POST['k'])
H = int(request.POST['H'])
regime_switcher = request.POST['regime_switcher']
except KeyError:
raise
# starting our job
pca.k = k
pca.H = H
if regime_switcher == 'off':
pca.regime_switcher = False
else:
pca.regime_switcher = True
index = '^GSPC'
source = os.path.join(settings.BASE_DIR,
'strategy_runner/stock_names/'
'NASDAQ.csv')
stocks = stockdata.get_stock_names(source, stock_amount)
try:
dates, prices, signals = stockdata.history_run(stocks, index,
time_period, start_date,
end_date)
except (pca.WrongParameterException, pca.WrongPricesError) as e:
return render(request, 'strategy_runner/base_history.html',
{'error_message': str(e)})
date_strings = [date.strftime('%Y-%m-%d') for date in dates]
returns, pl = stockdata.get_returns_and_pl(prices, signals)
#prepare img tags with graphs to pass to the template
graph_imgs = []
try:
for i, price_list in enumerate(prices):
graph_imgs.append(graph_img(date_strings, price_list, stocks[i]))
graph_imgs.append(graph_img(date_strings, returns,
'Cumulative Log Returns'))
graph_imgs.append(graph_img(date_strings, pl, r'Profit/Loss'))
except LongPeriodException as e:
return render(request, 'strategy_runner/base_history.html',
{'error_message': str(e)})
# NamedPrices = namedtuple('NamedPrices', ['stock', 'prices'])
# named_prices = []
# for i, stock in enumerate(stocks):
# named_prices.append(NamedPrices(stock, prices[i]))
return render(request,
'strategy_runner/base_history.html',
{'graph_imgs': graph_imgs})
