def history_run(stocks, index, time_period, start_date, end_date=date.today()):
"""
Run PCA signal on desired history period with particular stocks and
benchmark index
:param stocks: list of stock names to run strategy on
:param index: name of benchmark index to compare risks with
:param time_period: size of time period, for which prices will be given
to signal function
:param start_date: beginning date to run from.
Important note: Some time from the beginning of this period is required for
the statistic gathering, so signal will be run with some lag from
start_date
:param end_date: final date to run signal on
:return: trinity of: dates - list of dates on which signal was run,
prices - list of lists of corresponding prices for all the stocks,
signals - list of signal results. Every of those is a list too.
"""
def backward_chunks(l, amount, size, pos):
result = []
for i in range((pos - size * amount), pos, size):
result.append(l[i:i+size])
return result
#gather and split price data in equal chunks to feed signal
dates, index_prices = _get_prices(index, start_date, end_date)
#split stock data in the same way
all_prices = []
# define position to be a starting history point. It should be shifted,
# because k preceding index price time periods are required for the
# first stock price time period
start_position = (pca.k - 1) * time_period
for stock in stocks:
history = _get_prices(stock, start_date, end_date)[1][start_position:]
all_prices.append(history)
# make successive calls to signal function with continuous time history
signals = []
stop_pos = len(all_prices[0]) + 1
for t in range(time_period, stop_pos):
# select prices of certain time period
prices = []
for stock_price_list in all_prices:
prices.append(stock_price_list[t - time_period:t])
# divide index list to chunks of length k, according to algorithm
# requirements
split_index = backward_chunks(index_prices, pca.k, time_period,
t + start_position)
# call algorithm function
try:
signals.append(pca.signal(prices, split_index))
except pca.WrongPricesError, pca.WrongParameterException:
raise
def simple_test_random_values(self):
#parameters
pca.k = 2
pca.H = 2
prices = [[1.0, 1.1, 1.2, 1.3], [2.0, 2.1, 2.2, 2.3], [3.0, 3.1, 3.2,
3.3]]
index = [[randrange(1, 20) for j in range(4)] for i in range(pca.k)]
results = pca.signal(prices, index)
self.assertEqual(results, ['sell', 'sell', 'sell'])
