def setUp(self):
''' called at the beginning of each test '''
self.log_handler = logbook.TestHandler()
self.log_handler.push_thread()
self.tickers = ['google', 'apple', 'starbucks']
self.fields = ['close', 'volume']
self.start = dt.datetime(2006, 1, 1, tzinfo = pytz.utc)
self.end = dt.datetime(2010, 12, 31, tzinfo = pytz.utc)
self.offset = pd.datetools.BMonthEnd()
self.agent = DataAgent()
def setUp(self):
''' called at the beginning of each test '''
self.tickers = ['starbucks']
self.fields = ['low', 'adj_close', 'volume']
self.start = dt.datetime(2004, 12, 1, tzinfo=pytz.utc)
self.end = dt.datetime(2012, 12, 31, tzinfo=pytz.utc)
self.offset = pd.datetools.BMonthEnd(4)
self.timestamp = pd.date_range(self.start, self.end, freq=self.offset)
self.agent = DataAgent()
self.agent.connectTo(['remote', 'database'],
db_name='stocks.db',
lvl='critical')
'''-------------------------------------- Notes
Zipline uses a benchmark data very interesting (see utils/factory.py and data/benchmark.py)
Modifying it to take in parameter an action and to process associated market ? only s&p500 at the moment
'''
if __name__ == '__main__':
''' ----------------------------------------------------------------- Data -----'''
# Thiz tzinfo is annoying ! default to provide if forgotten, or the one sent
#through the init of dataagent. See also zipline.loader_utils.py
timestamp = date_range(dt.datetime(2004,12,31, tzinfo=pytz.utc), \
dt.datetime(2005,12,31, tzinfo=pytz.utc), freq='M')
ticker = 'starbucks'
#TODO implement a how method wich give the same results as in table
#put in some DataAgent properties ?
db_agent = DataAgent('stocks.db')
data = db_agent.getQuotes([ticker], ['adj_close'], index=timestamp, \
save=False, reverse=True)
#ts = Series(data[ticker]['adj_close'], name=ticker)
ts = data['adj_close'][ticker]
#ts = Series([31.18, 27.0, 25.91, 25.83, 24.76, 27.4, 25.83, 26.27, \
#24.51, 25.05, 28.28, 30.45, 30.51], index=timestamp)
''' ----------------------------------------------------------------------------------'''
print
print('-' * 60)
#1 Simple monthly return between the end of December 2004 and the end of January 2005
compute = Quantitative(lvl='error')
monthly_ret = compute.returns(ts,
start=dt.datetime(2004, 12, 31),
sys.path.append(str(os.environ['QTRADE']))
from pyTrade.data.DataAgent import DataAgent
from pyTrade.calculus.engine import Backtester
from pyTrade.utils import LogSubsystem
import pandas as pd
import pytz
from zipline.data.benchmarks import *
from pyTrade.ai.genetic import Genetic, GeneticAlgorithm
start = pd.datetime(2008, 6, 20, 0, 0, 0, 0, pytz.utc)
end = pd.datetime(2010, 4, 1, 0, 0, 0, 0, pytz.utc)
delta = pd.datetools.timedelta(days=1)
dataobj = DataAgent('stocks.db')
data_tmp = dataobj.getQuotes(['google'], ['open'],
start=start,
end=end,
delta=delta,
reverse=True)
data = data_tmp['open']
def runBacktest(genes):
'''-------------------------------------------- Parameters -----'''
'''chromosome[short_window, long_window, buy_on_event, sell_on_event] '''
print('chromosome: {}'.format(genes))
short_window = genes['short_window']
long_window = round(short_window * genes['ma_rate'] / 2)
buy_on_event = genes['buy_n']
