@d416 Thank you for point to the Gradient-Free-Optimizers. They are amazing and do optimization super fast!
In my tests they are faster 40x times than build-in brute force optimization algo.
I have prepared optimization statistics for different optimizers from Gradient-Free-Optimizers.
I did my tests on the following simple strategy (just for proof):
class SmaCross(bt.SignalStrategy):
params = (
('fast', 10),
('slow', 30),
)
def __init__(self):
sma1, sma2 = bt.ind.SMA(period=self.p.fast), bt.ind.SMA(period=self.p.slow)
crossover = bt.ind.CrossOver(sma1, sma2)
self.signal_add(bt.SIGNAL_LONG, crossover)
And the test parameters were:
"fast": np.arange(5, 150, 2),
"slow": np.arange(50, 150, 2)
The most interesting is the results for optimization times and scores:
Build-in brute force: score:10035.94200515747, time:207.48 s, para: {"fast":57, "slow":56}
HillClimbingOptimizer: score:10023.95000076294, time:7.72 s, para:{'fast': 129, 'slow': 54}
RepulsingHillClimbingOptimizer: score:10023.95000076294, time:15.99 s, para:{'fast': 119, 'slow': 60}
SimulatedAnnealingOptimizer: score:10023.95000076294, time:7.24 s, para:{'fast': 131, 'slow': 52}
RandomSearchOptimizer: score:10026.352001190186, time:20.60 s, para:{'fast': 61, 'slow': 56}
RandomRestartHillClimbingOptimizer: score:10023.95000076294, time:10.14 s, para:{'fast': 127, 'slow': 52}
RandomAnnealingOptimizer: score:10023.95000076294, time:8.63 s, para:{'fast': 125, 'slow': 56}
ParallelTemperingOptimizer: score:10021.9880027771, time:15.08 s, para:{'fast': 147, 'slow': 50}
ParticleSwarmOptimizer: score:10030.186000823975, time:15.71 s, para:{'fast': 61, 'slow': 54}
EvolutionStrategyOptimizer: score:10023.95000076294, time:14.98 s, para:{'fast': 131, 'slow': 52}
DecisionTreeOptimizer: score:10035.94200515747, time:5.05 s, para:{'fast': 57, 'slow': 56}
To summarize:
Build-in brute force: score:10035.94200515747, time:207.48 s
DecisionTreeOptimizer: score:10035.94200515747, time:5.05 s
DecisionTreeOptimizer was 40x times faster!
You could check my calculations on github.
