def temp():
# data
start = dt(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
end = dt(2017, 9, 29, 0, 0, 0, 0, pytz.utc)
data = web.DataReader("종가", "yahoo", start, end)
data = data[['Adj Close']]
data.columns = ['종가']
# data = data.tz_localize('UTC')
# algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
# result = algo.run(data)
result = run_algorithm(start,
end,
initialize,
100000000.0,
handle_data,
data_frequency='daily',
data=data)
# plt.plot(result.index, result.portfolio_value)
# plt.show()
plt.plot(result.index, result.ma5)
plt.plot(result.index, result.ma20)
plt.legend(loc='best')
# plt.plot(result.ix[result.buy == True].index, result.ma5[result.buy == True], '^')
# plt.plot(result.ix[result.sell == True].index, result.ma5[result.sell == True], 'v')
plt.show()
def test_algo_init(self):
'''
test init symbol checking
'''
def initialize(context):
symbols = ['AAPL', 'GE', 'JNJ', 'THEBADONE']
return init(context, 'test', symbols=symbols)
def handle_data(context, data, s=self):
s.context = context
end = get_calendar('NYSE').previous_close(Timestamp.utcnow())
start = get_calendar('NYSE').previous_close(end - DateOffset(days=1))
res = run_algorithm(
start=start,
end=end,
initialize=initialize,
handle_data=handle_data,
capital_base=float('1.0e5'),
)
self.assertListEqual(
['AAPL', 'GE', 'JNJ'],
[s.symbol for s in self.context.sbot['symbols']],
)
self.assertEqual(
self.context.sbot['capital_ppt'],
1.0 / len(self.context.sbot['symbols']),
)
def backtest():
df = utils.get_dataframe_with_code("005440")
# df = utils.add_이동평균선_to_dataframe(df, [5, 20])
# df은 오름차순으로 되어 있다.
# serialdatetime = [pd.to_datetime(bb, utc="UTC", format="%Y%m%d") for bb in df.index]
serialdatetime = [dt.strptime(str(bb), "%Y%m%d") for bb in df.index]
# dtstart = serialdatetime[0].astimezone(pytz.utc)
# dtend = serialdatetime[-1].astimezone(pytz.utc)
# dtstart = serialdatetime[0]
# dtend = serialdatetime[-1]
# dtstart = dt(2001, 1, 2, 0, 0, 0, 0, pytz.utc)
# dtend = dt(2017, 9, 29, 0, 0, 0, 0, pytz.utc)
start = str(df.index[0]) + "%s"
dtstart = dt.strptime(start % ("000000"),
"%Y%m%d%H%M%S").astimezone(pytz.utc)
end = str(df.index[-1]) + "%s"
dtend = dt.strptime(end % ("000000"), "%Y%m%d%H%M%S").astimezone(pytz.utc)
# dtstart = dt(2001, 1, 2)
# dtend = dt(2017, 9, 29 )
#
# indexnew = pd.date_range('2001/1/2', periods=4119)
# indexnew = indexnew.tz_localize(None)
df = df.set_index(pd.DatetimeIndex(serialdatetime))
data = df[["현재가"]]
data.columns = ['종가']
# data = data.tz_localize(pytz.utc)
print("data length :%s" % (len(data)))
dfresult = run_algorithm(dtstart,
dtend,
initialize,
100000000.0,
handle_data,
data_frequency='daily',
data=data)
#
# algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
# dfresult = algo.run(data)
plt.plot(dfresult.index, dfresult.portfolio_value)
plt.show()
def test_adx_sar_so(self):
'''
ensure adx_sar_so trading system runs without exceptions
'''
end = get_calendar('NYSE').previous_close(Timestamp.utcnow())
start = get_calendar('NYSE').previous_close(end - DateOffset(months=2))
res = run_algorithm(
start=start,
end=end,
initialize=adx_sar_so.initialize,
handle_data=adx_sar_so.handle_data,
capital_base=float('1.0e5'),
)
self.assertNotEqual(res['alpha'][-1], 0.0)
def test_zipline(self):
'''
a do-nothing algo to test zipline through latest close
'''
def initialize(context):
pass
end = get_calendar('NYSE').previous_close(Timestamp.utcnow())
start = get_calendar('NYSE').previous_close(end - DateOffset(years=1))
res = run_algorithm(
start=start,
end=end,
initialize=initialize,
capital_base=float('1.0e5'),
)
self.assertTrue(start in res.index and end in res.index)
def temp2():
df = pd.read_hdf("test100.hdf", "day")
datelen = len(df)
# start = dt.strptime("20170101%s"%("000000"), "%Y%m%d%H%M%S").astimezone(pytz.utc)
# end = dt.strptime("20170410%s"%("000000"), "%Y%m%d%H%M%S").astimezone(pytz.utc)
start = dt(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
end = dt(2017, 4, 10, 0, 0, 0, 0, pytz.utc)
# start = datetime.datetime(2017, 1, 1)
# end = datetime.datetime(2017, 4, 10)
# data = web.DataReader("종가", "yahoo", start, end)
# rawdata = [ aa for aa in range(datelen)]
rawdata = df["현재가"].tolist()
# daterange = pd.date_range('2017/1/1', periods=datelen)
daterange = pd.date_range(start, end)
data = pd.DataFrame(rawdata, daterange, ['종가'])
# data = data.tz_localize('UTC')
result = run_algorithm(start,
end,
initialize,
100000000.0,
handle_data,
data_frequency='daily',
data=data)
# algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
# result = algo.run(data)
plt.plot(result.index, result.ma5)
plt.plot(result.index, result.ma20)
plt.legend(loc='best')
plt.show()
def main():
args = parse_arguments()
# Set tz to avoid TypeError: Cannot compare tz-naive and tz-aware timestamps pandas issue
# This shold be fixed in 0.23
start = (pd.to_datetime('2017-08-01') + BDay(1)).tz_localize('UTC')
end = (pd.to_datetime('2018-08-28') + BDay(1)).tz_localize('UTC')
# TODO: Add start/end date setup
# start = pd.Timestamp(args.from_date, tz='utc') if args.from_date else None
# end = pd.Timestamp(args.to_date, tz='utc') if args.to_date else None
backtest = run_algorithm(start,
end,
initialize,
args.capital_base,
before_trading_start=before_trading_start,
analyze=analyze,
bundle='iex-csvdir-bundle')
returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(
backtest)
# TODO: The backtest benchmark graphs/data are not looking good, check why is this happening.
benchmark_rets = None # backtest.benchmark_period_return
plotting.create_simple_tear_sheet(returns,
positions=positions,
transactions=transactions,
benchmark_rets=benchmark_rets,
file_name=os.path.join(
working_dir,
export_file_format.format('simple')))
plotting.create_returns_tear_sheet(
returns,
positions=positions,
transactions=transactions,
benchmark_rets=benchmark_rets,
file_name=os.path.join(working_dir,
export_file_format.format('returns')))
order(symbol('002450'), 300, limit_price=buy_price)
buy_status = True
if __name__ == '__main__':
from zipline.utils.cli import Date
from zipline.utils.run_algo import run_algorithm
from zipline.gens.brokers.tdx_shipane_broker import TdxShipaneBroker
from zipline.gens.shipane_client import ShipaneClient
import pandas as pd
import os
import datetime
if platform.architecture()[0] == '32bit':
client_uri = 'config.json'
else:
client_uri = "tcp://127.0.0.1:4242"
shipane_client = ShipaneClient(client_key="1")
broker = TdxShipaneBroker(client_uri, shipane_client)
if not os.path.exists('tmp'):
os.mkdir('tmp')
realtime_bar_target = 'tmp/real-bar-{}'.format(str(pd.to_datetime('today').date()))
state_filename = 'tmp/live-state'
start = Date(tz='utc', as_timestamp=True).parser('2017-10-01')
end = Date(tz='utc', as_timestamp=True).parser(datetime.datetime.now().strftime("%Y-%m-%d"))
run_algorithm(start, end, initialize, 10e6, handle_data=handle_data, bundle='tdx',
trading_calendar='SHSZ', data_frequency="minute", output='out.pickle',
broker=broker, state_filename=state_filename, realtime_bar_target=realtime_bar_target)