Strategy optimization never finishes

amit.kehat

HI,

When trying to use the optstrategy method, it never finish... run forever. No error is observed.
My code:

from datetime import datetime
import backtrader as bt
import matplotlib.pyplot as plt
%matplotlib inline

class SmaCross(bt.Strategy):
    
    params = (('fast', 10),
              ('slow', 30)
             )
    
    def __init__(self):
        self.sma_fast = bt.ind.SMA(period = self.params.fast)
        self.sma_slow = bt.ind.SMA(period = self.params.slow)
        self.crossover = bt.ind.CrossOver(self.sma_fast, self.sma_slow)


    def start(self): #runs after the init in the beginning
        pass
        
    def next(self): #runs in each iteration
        if self.position.size == 0:
            if self.sma_fast > self.sma_slow:
                self.buy()
        elif self.position.size != 0:
            if self.sma_fast < self.sma_slow:
                self.sell()
    
    def stop(self): #runs at the end
        pass

contract_history = pd.read_csv('APPL-hourly.csv')
contract_history['dt'] = pd.to_datetime(contract_history['date'])
contract_history = contract_history.set_index('dt')
data_pd = bt.feeds.PandasData(dataname = contract_history, fromdate = datetime(2020, 11, 11),
                                todate = datetime(2020,12,1))

        
cerebro = bt.Cerebro()
cerebro.adddata(data_pd)
cerebro.addsizer(bt.sizers.PercentSizer, percents = 90)

cerebro.optstrategy(SmaCross, fast = (5, 10), slow = (20, 25))
cerebro.run()

Does someone maybe know how to resolve this issue?

Thanks,
Amit

run-out

@amit-kehat said in Strategy optimization never finishes:

Does someone maybe know how to resolve this issue?
Thanks,
Amit

I just ran this using daily data and it's fine. You may wish to add in timeframe and compression. Also, there are no outputs in your code. Perhaps this is why you are not seeing anything?



timeframe (default: TimeFrame.Days)

Potential values: Ticks, Seconds, Minutes, Days, Weeks, Months and Years

compression (default: 1)

Number of actual bars per bar. Informative. Only effective in Data Resampling/Replaying.