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)
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 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)