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)
Пример #2
0
    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)
Пример #3
0
    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)