def fitness(self, genes):
'''
Cost function in the optimization process
_________________________________________
Parameters
genes: list(3)
ordered parameters to optimize
_________________________________________
Return
score: float(1)
Error of the cost function ran with this solution
'''
# No evoluation in manager (Constant) configuration so reads it statically from file
# We want here to optimize the algorithm parameters
engine = Simulation()
try:
engine.configure(bt_cfg=self.bt_cfg,
a_cfg={'long_window': genes[0], 'ma_rate': float(genes[1] / 10.0), 'threshold': genes[2]},
m_cfg=None)
results = engine.run_backtest()
risk_metrics = engine.overall_metrics()
except:
pdb.set_trace()
log.error('Exception caught while running cost function')
return 1
return self.evaluate(results, risk_metrics)
import sys
import os
import pylab as plt
sys.path.append(os.environ['QTRADE'])
from neuronquant.calculus.engine import Simulation
from neuronquant.utils import color_setup, remote_setup, log
if __name__ == '__main__':
# use 'setup' configuration for logging
with remote_setup.applicationbound():
'''------------------------------------------------------------------------------------------- Backtest ----'''
# Backtest or live engine used
engine = Simulation()
# Read local (.cfg files and command line args) or remote (ZMQ Messages) backtest, algorithm and manager configuration
args = engine.configure()
# See neuronquant/calculus/engine.py or zipline for details on results dataframe
results = engine.run_backtest()
'''--------------------------------------------------------------------------------------------- Results ----'''
log.info('Portfolio returns: {}'.format(results.portfolio_value[-1]))
if args['live'] or results.portfolio_value[-1] == 100000:
# Currently tests don't last more than 20min, analysis is not relevant, neither backtest without orders
sys.exit(0)
#TODO Implement in datafeed a generic save method (which could call the correct database save method)
import sys
import os
import pylab as plt
sys.path.append(os.environ['QTRADE'])
from neuronquant.calculus.engine import Simulation
from neuronquant.utils import color_setup, remote_setup, log
if __name__ == '__main__':
# use 'setup' configuration for logging
with color_setup.applicationbound():
'''------------------------------------------------------------------------------------------- Backtest ----'''
# Backtest or live engine used
engine = Simulation()
# Read local (.cfg files and command line args) or remote (ZMQ Messages) backtest, algorithm and manager configuration
args = engine.configure()
# See neuronquant/calculus/engine.py or zipline for details on results dataframe
results = engine.run_backtest()
'''--------------------------------------------------------------------------------------------- Results ----'''
log.info('Portfolio returns: {}'.format(results.portfolio_value[-1]))
if args['live'] or results.portfolio_value[-1] == args['cash']:
# Currently tests don't last more than 20min, analysis is not relevant, neither backtest without orders
sys.exit(0)
#TODO Implement in datafeed a generic save method (which could call the correct database save method)
