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 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
'''__________________________________________________________________ Setup ____'''
# Dedicated object for configuration setup
setup = Setup()
# General simulation behavior is defined using command line arguments
configuration = setup.parse_commandline()
# 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)
#TODO Parametric log handler and level
#log_setup = (utils.remote_setup if configuration['remote'] else
#utils.color_setup)
#FIXME Remote log broken here
log_setup = get_nestedlog(level=configuration['loglevel'], filename=configuration['logfile'])
with log_setup.applicationbound():
'''
TODO HUGE: Run multiple backtest with communication possibilities (ZMQ)
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'])
#TODO Externalize tests
def server_test():
server = ZMQ_Server()
server.run_forever(ports=5555, on_recv=handle_json)
def client_test():
client = ZMQ_Client(timeout=5)
client.connect(host='localhost', ports=[5555, 5555, 5555])
for request in range(1, 5):
reply = client.send('Hello', acknowledgment=True)
assert(reply)
def dealer_test():
client = ZMQ_Dealer(id='client_test')
client.run(host='127.0.0.1', port=5570)
client.receive()
for request in range(2):
client.noblock_recv()
time.sleep(0.5)
client.send('test number {}'.format(request), channel='dashboard', json=True)
client.send_to_android({'title': 'Buy signal on google', 'priority': 4,
'description': 'Google dual moving average crossed: you should buy 23 stocks with a risk of 0.23'})
if __name__ == '__main__':
log_setup = get_nestedlog(uri='tcp://127.0.0.1:5555')
with log_setup.applicationbound():
dealer_test()
'''__________________________________________________________________ Setup ____'''
# Dedicated object for configuration setup
setup = Setup()
# General simulation behavior is defined using command line arguments
configuration = setup.parse_commandline()
# 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)
#TODO Parametric log handler and level
#log_setup = (utils.remote_setup if configuration['remote'] else
#utils.color_setup)
#FIXME Remote log broken here
log_setup = get_nestedlog(level=configuration['loglevel'],
filename=configuration['logfile'])
with log_setup.applicationbound():
'''
TODO HUGE: Run multiple backtest with communication possibilities (ZMQ)
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(
client.connect(host='localhost', ports=[5555, 5555, 5555])
for request in range(1, 5):
reply = client.send('Hello', acknowledgment=True)
assert (reply)
def dealer_test():
client = ZMQ_Dealer(id='client_test')
client.run(host='127.0.0.1', port=5570)
client.receive()
for request in range(2):
client.noblock_recv()
time.sleep(0.5)
client.send('test number {}'.format(request),
channel='dashboard',
json=True)
client.send_to_android({
'title':
'Buy signal on google',
'priority':
4,
'description':
'Google dual moving average crossed: you should buy 23 stocks with a risk of 0.23'
})
if __name__ == '__main__':
log_setup = get_nestedlog(uri='tcp://127.0.0.1:5555')
with log_setup.applicationbound():
dealer_test()
