def word_distance(cls, token_list, sentence_list):
token_pairs = list(combinations(token_list, size=2))
ret = {}
for sentence in sentence_list:
for a, b in token_pairs:
dist_a = cls.locate_word(a, sentence)
if dist_a < 0:
continue
dist_b = cls.locate_word(b, sentence)
if dist_b < 0:
continue
if dist_a > dist_b:
dist = dist_a - dist_b
else:
dist = dist_b - dist_a
#logging.info((a,b,dist ))
if a not in ret:
ret[a] = {}
if b not in ret[a]:
ret[a][b] = []
if b not in ret:
ret[b] = {}
if a not in ret[b]:
ret[b][a] = []
ret[a][b].append(dist)
ret[b][a].append(dist)
return ret
def getStocks(cls, stock_list):
minimum_portfolio_size = len(stock_list) - 2
count = len(stock_list) - 1
while count > minimum_portfolio_size:
data = EXTRACT.load(stock_list)
stock_list = sorted(data.keys())
for subset in combinations(stock_list, count):
logging.info((len(subset), sorted(subset)))
yield data, sorted(subset)
stock_list = TRANSFORM_STOCK.truncate(stock_list, count)
count = len(stock_list) - 1
def stocks(cls, data):
stock_list, minimum_portfolio_size = cls.validate(data)
if len(stock_list) == 0:
return
if len(stock_list) <= 3:
yield stock_list
return
yield stock_list
count = len(stock_list) - 1
while count > minimum_portfolio_size:
for subset in combinations(stock_list, count):
yield sorted(subset)
stock_list = cls.reduceRisk(data, count)
count = len(stock_list) - 1
if count < 3:
break
def stocks(self, data) :
ret = data.drop(labels=self.columns_drop,errors='ignore')
stock_list, minimum_portfolio_size = TRANSFORM.validate(ret)
if len(stock_list) == 0 :
return
if len(stock_list) <= 3 :
yield stock_list
return
yield stock_list
count = len(stock_list)-1
while count > minimum_portfolio_size :
for subset in combinations(stock_list,count) :
yield sorted(subset)
stock_list = BACKGROUND.reduceRisk(ret,count)
count = len(stock_list)-1
if count < 3 :
break
def main(file_list, stock_list):
stock_list = init(*ini_list)
name, spy_data = prep(*file_list)
annual = MonteCarlo.YEAR()
stock_data = pd.DataFrame()
stock_data[name] = spy_data['Adj Close']
max_sharp, min_vol = annual([name], stock_data)
logging.info(max_sharp)
logging.info(min_vol)
stock_name = []
for name, stock in read(file_list, stock_list):
stock_name.append(name)
stock_data[name] = stock['Adj Close']
for subset in combinations(stock_name):
logging.info(subset)
max_sharp, min_vol = annual(subset, stock_data)
logging.info(max_sharp)
logging.info(min_vol)