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
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)
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)
class Metric(object):
''' Evaluate error of a solution in optimization '''
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)
def fitness(self, genes):
'''
Cost function in the optimization process
_________________________________________
Parameters
genes: list
Parameters to optimize
_________________________________________
Return
score: float
Error of the cost function ran with this solution
So the algo tries to minimize it (i.e. 0 is the best score)
'''
# No evoluation in manager (Constant) configuration
# We try here to optimize the algorithm parameters
strategie = {
'manager': {
'name': 'Xavier Bruhiere',
'load_backup': 0,
'max_weight': 0.4,
'buy_amount': 200,
'sell_amount': 100,
'connected': 0
},
'algorithm': {
'long_window': int(genes[0]),
'ma_rate': float(genes[1] / 10.0),
'threshold': genes[2]
}
}
try:
# Run backtest with all configuration dictionnaries
analyzes = self.engine.run(self.data, self.configuration,
strategie, self.context)
# Get back summary performance dictionnary
risk_metrics = analyzes.overall_metrics()
except:
import ipdb
ipdb.set_trace()
log.error('Exception caught while running cost function')
# Return worst result possible
return 1
return self.evaluate(risk_metrics)
def evaluate(self, risks):
'''
Define score from raw cost function results
'''
score = [
risks['Returns'], risks['Sharpe.Ratio'], risks['Max.Drawdown'],
risks['Volatility']
]
# Debug purpose
if score[0]:
log.notice(risks)
# Compute score from cummulative returns,
return 1 - score[0]
class Metric(object):
''' Evaluate error of a solution in optimization '''
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)
def fitness(self, genes):
'''
Cost function in the optimization process
_________________________________________
Parameters
genes: list
Parameters to optimize
_________________________________________
Return
score: float
Error of the cost function ran with this solution
So the algo tries to minimize it (i.e. 0 is the best score)
'''
# No evoluation in manager (Constant) configuration
# We try here to optimize the algorithm parameters
strategie = {'manager': {'name': 'Xavier Bruhiere',
'load_backup': 0,
'max_weight': 0.4,
'buy_amount': 200,
'sell_amount': 100,
'connected': 0},
'algorithm': {'long_window': int(genes[0]),
'ma_rate': float(genes[1] / 10.0),
'threshold': genes[2]}
}
try:
# Run backtest with all configuration dictionnaries
analyzes = self.engine.run(self.data, self.configuration, strategie, self.context)
# Get back summary performance dictionnary
risk_metrics = analyzes.overall_metrics()
except:
import ipdb; ipdb.set_trace()
log.error('Exception caught while running cost function')
# Return worst result possible
return 1
return self.evaluate(risk_metrics)
def evaluate(self, risks):
'''
Define score from raw cost function results
'''
score = [risks['Returns'], risks['Sharpe.Ratio'], risks['Max.Drawdown'], risks['Volatility']]
# Debug purpose
if score[0]:
log.notice(risks)
# Compute score from cummulative returns,
return 1 - score[0]
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 ____'''
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 ____'''
