def trade(mega_config):
'''
One function to rule them all
'''
from neuronquant.gears.engine import Simulation
from neuronquant.utils.logger import get_nestedlog
# General simulation behavior
#NOTE Portfolio server setup in Setup() object,
#if needed, create it manually here
configuration = mega_config['configuration']
strategie = mega_config['strategie']
# Remote: ZMQ based messaging, route logs on the network
# (catched by server's broker)
log_setup = get_nestedlog(level=configuration['loglevel'],
file=configuration['logfile'])
with log_setup.applicationbound():
# Backtest or live engine
engine = Simulation(configuration)
# Setup quotes data and financial context (location, market, ...)
# simulation from user parameters Wrap _configure_data() and
# _configure_context() you can use directly for better understanding
data, trading_context = engine.configure()
# See neuronquant/gears/engine.py for details of results which is an
# analyzes object
analyzes = engine.run(data, configuration, strategie, trading_context)
assert analyzes
def __init__(self):
# General backtest behavior configuration
self.configuration = {'algorithm' : 'DualMA',
'frequency' : 'daily',
'manager' : 'Constant',
'database' : 'test',
'tickers' : ['google', 'apple'],
'start' : pytz.utc.localize(datetime(2008, 1, 11)),
'end' : pytz.utc.localize(datetime(2010, 7, 3)),
'live' : False,
'port' : '5570',
'cash' : 100000,
'exchange' : 'nasdaq',
'remote' : False}
# Object use to run zipline backtest
self.engine = Simulation()
# Configure and return data used during backtest, and the TradingEnvironement
self.data, self.context = self.engine.configure(self.configuration)
for sophisticated multiple strategies strategy
- Available capital allocation
import ipdb; ipdb.set_trace() # XXX BREAKPOINT
- Strategies repartition
- Use of each-other signals behavior
- Global monitoring and evaluation
'''
# Fill strategie and manager parameters
# Localy, reading configuration file
# Remotely, listening gor messages through zmq socket
strategie = setup.get_strategie_configuration(
remote=configuration['remote'])
'''_________________________________________________________ Backtest ____'''
# Backtest or live engine
engine = Simulation(configuration)
# Setup quotes data and financial context (location, market, ...)
# simulation from user parameters Wrap _configure_data() and
# _configure_context() you can use directly for better understanding
data, trading_context = engine.configure()
# See neuronquant/gears/engine.py for details of results
#which is an Analyzes object
analyzes = engine.run(data, configuration, strategie, trading_context)
if analyzes is None:
log.error('** Backtest failed.')
sys.exit(1)
'''___________________________________________________________ Results ____'''
#analyzes.run_dashboard(portfolio=strategie['manager']['name'])
# Color_setup : Pretty print of errors, warning, and so on Remote_setup:
# ZMQ based messaging, route logs on the network (catched by server's
# broker)
log_setup = (utils.remote_setup if configuration['remote'] else
utils.color_setup)
with log_setup.applicationbound():
# Fill algorithm and manager parameters
# Localy, reading configuration file
# Remotely, listening gor messages through zmq socket
strategie = setup.get_strategie_configuration(remote=configuration['remote'])
'''____________________________________________________________________________________ Backtest ____'''
# Backtest or live engine
engine = Simulation()
# Setup quotes data and financial context (location, market, ...)
# simulation from user parameters Wrap _configure_data() and
# _configure_context() you can use directly for better understanding
data, context = engine.configure(configuration)
# See neuronquant/calculus/engine.py for details of results which is an
# analyzes object
analyzes = engine.run(data, configuration, strategie, context)
if analyzes is None:
utils.log.error('** Backtest failed, exiting')
sys.exit(1)
'''_______________________________________________________________________________________ Results ____'''
