@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.

@rishabmah5 Please use yfinance to download data from yahoo finance.

pip install yfinance

And use this code

import yfinance as yf
import backtrader as bt

data = bt.feeds.PandasData(dataname=yf.download('TSLA', '2018-01-01', '2019-01-01'))

cerebro = bt.Cerebro()
cerebro.adddata(data)

@rishabmah5 Please use yfinance to download data from yahoo finance.

pip install yfinance

And use this code

import yfinance as yf
import backtrader as bt

data = bt.feeds.PandasData(dataname=yf.download('TSLA', '2018-01-01', '2019-01-01'))

cerebro = bt.Cerebro()
cerebro.adddata(data)

@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.