def update(self): """ """ wl = self.portfolio.watch_list for ticker, val in wl.items(): if ticker in ma_short and ticker in ma_long: ma_short[TIME.timestamp].append(mean(DATA.get_data_interval(ticker, TIME.timestamp, TIME.timestamp + timedelta(days=20), 'close', config.DAY_INTERVAL))) ma_long[TIME.timestamp].append(mean(DATA.get_data_interval(ticker, TIME.timestamp, TIME.timestamp + timedelta(days=150), 'close', config.DAY_INTERVAL))) else: ma_short[TIME.timestamp] = mean(DATA.get_data_interval(ticker, TIME.timestamp, TIME.timestamp + timedelta(days=20), 'close', config.DAY_INTERVAL)) ma_long[TIME.timestamp] = mean(DATA.get_data_interval(ticker, TIME.timestamp, TIME.timestamp + timedelta(days=150), 'close', config.DAY_INTERVAL))
def __init__(self, name, portfolio, shortterm, longterm):
"""
:param name: Name of Algorithm
"""
self.name = name
self.portfolio = portfolio
self.ma_short = {}
self.ma_long = {}
self.shortterm = shortterm
self.longterm = longterm
wl = self.portfolio.watch_list
for ticker, val in wl.items():
ma_short[ticker] = {}
ma_long[ticker] = {}
ma_short[ticker][TIME.timestamp] = mean(DATA.get_data_interval(ticker,
TIME.timestamp,
TIME.timestamp + timedelta(days=self.shortterm),
'close',
config.DAY_INTERVAL))
ma_long[ticker][TIME.timestamp] = mean(DATA.get_data_interval(ticker,
TIME.timestamp,
TIME.timestamp + timedelta(days=self.longterm),
'close',
config.DAY_INTERVAL))
def update_special(self):
dividend = DATA.get_data(self.ticker, TIME.date, 'dividend_amount')
split = DATA.get_data(self.ticker, TIME.date, 'split_coefficient')
if dividend is not None and split is not None:
self.dividend = dividend
self.split = split
else:
self.dividend = 0
self.split = 1
def get_quote(self, ticker, timestamp=None):
if timestamp is None:
timestamp = self.curr_time
quote = {}
quote['open'] = DATA.get_data(ticker, timestamp, 'open')
quote['high'] = DATA.get_data(ticker, timestamp, 'high')
quote['low'] = DATA.get_data(ticker, timestamp, 'low')
quote['close'] = DATA.get_data(ticker, timestamp, 'close')
quote['volume'] = DATA.get_data(ticker, timestamp, 'volume')
return quote
def get_quote(self, ticker, timestamp=None, daily=False):
if timestamp is None:
timestamp = self.curr_time
if timestamp > self.curr_time:
print('Request of future data\n')
quote = {}
quote['open'] = DATA.get_data(ticker, timestamp, 'open', daily)
quote['high'] = DATA.get_data(ticker, timestamp, 'high', daily)
quote['low'] = DATA.get_data(ticker, timestamp, 'low', daily)
quote['close'] = DATA.get_data(ticker, timestamp, 'close', daily)
quote['volume'] = DATA.get_data(ticker, timestamp, 'volume', daily)
return quote
def update_data(self):
for ticker, val in self.portfolio.watch_list.items():
self.dataset[ticker] = DATA.get_data_interval(ticker,
TIME.timestamp - timedelta(days=self.num_days),
TIME.timestamp,
'close',
INTERVAL)
self.train_set, self.test_set = dataproc.process_data(self.dataset)
def preprocess(self):
tickers = {}
wl = self.portfolio.watch_list
for ticker, val in wl.items():
tickers[ticker] = []
data = DATA.get_data_interval(ticker,
TIME.timestamp - timedelta(days=50),
TIME.timestamp,
'close',
INTERVAL)
train_split = len(data) * 0.9
test_split = len(data) * 0.1
train = data[train_split:]
test = data[:test_split]
for i, val in enumerate(data):
window = data[:5]
def set_quote(self):
quote = DATA.get_data(self.ticker, TIME.timestamp, 'close')
if quote is not None:
self.cur_quote = quote
return True
return False