@NikolaTesla

I figured a way to do that but the progress bar is being printed after every callback (i.e after every iteration of the optimization). Is there a way I could restrict it to one instance when it begins? I believe the reason is that I used a GLOBAL variable so that the function optimizer_callbacks(cb) would be able to recognize it. Is there a way I could pass the pbar to optimizer_callbacks without actually using it as a global variable?

def optimizer_callbacks(cb):
    pbar.update()

def strategy_optim(**kwargs):

    total = np.prod([len(value) for key,value in kwargs.items()])

    csv_file = FDaxCSVData(---data---)
    cerebro = bt.Cerebro()
    cerebro.adddata(csv_file)    
    cerebro.broker.setcash(500000.0)
    cerebro.broker.setcommission(commission=2.0)

    strats = cerebro.optstrategy(strategy_name, printlog = False, **kwargs)

    global pbar
    pbar = tqdm.tqdm(smoothing=0.05, desc='Optimization Runs', total=total)
    cerebro.optcallback(optimizer_callbacks)

    runnings = cerebro.run(optreturn=False, maxcpus=2)  

if __name__=="__main__":
    strategy_optim(periods = [100, 200, 300],  abs_margin= [25, 50, 75], trail_stop=[10, 20, 30, 40])

My bad, so if I were to use buy and sell orders along with trailing stops and if the respective conditions are True, something like

if not position:
     if buy_condition:
         self.buy(price, exectype=trailstop, trailstop value)

    if sell_condition:
         self.sell(price, exectype=trailstop, trailstop value)

else:
     if (open_price[0] > buy_price+target) or (open_price[0] < sell_price-target):
        self.order = self.close()

Then will I be able to use check if a position is closed using some condition?

@NikolaTesla said in progress bar / ETA during optimization:

@NikolaTesla said in progress bar / ETA during optimization:

I realized the repetition of the status bar in a new line after each iteration was an error related to VS Code, so this code should work just fine.

@tw00000 I guess this is useful if you want a progress bar for a single strategy, the tread has been all about a progress bar in the case of an optimization strategy.

@NikolaTesla said in progress bar / ETA during optimization:

@NikolaTesla said in progress bar / ETA during optimization:

@backtrader @vbs

I figured a way to do that but the progress bar is being printed after every callback (i.e after every iteration of the optimization). Is there a way I could restrict it to one instance when it begins? I believe the reason is that I used a GLOBAL variable so that the function optimizer_callbacks(cb) would be able to recognize it. Is there a way I could pass the pbar to optimizer_callbacks without actually using it as a global variable? My code is as follows, please have a look at it. Thank you for your time.

def optimizer_callbacks(cb):
    pbar.update()

def strategy_optim(**kwargs):

    total = np.prod([len(value) for key,value in kwargs.items()])

    csv_file = FDaxCSVData(---data---)
    cerebro = bt.Cerebro()
    cerebro.adddata(csv_file)    
    cerebro.broker.setcash(500000.0)
    cerebro.broker.setcommission(commission=2.0)

    strats = cerebro.optstrategy(strategy_name, printlog = False, **kwargs)

    global pbar
    pbar = tqdm.tqdm(smoothing=0.05, desc='Optimization Runs', total=total)
    cerebro.optcallback(optimizer_callbacks)

    runnings = cerebro.run(optreturn=False, maxcpus=2)  

if __name__=="__main__":
    strategy_optim(periods = [100, 200, 300],  abs_margin= [25, 50, 75], trail_stop=[10, 20, 30, 40])

@NikolaTesla said in progress bar / ETA during optimization:

@backtrader @vbs

I figured a way to do that but the progress bar is being printed after every callback (i.e after every iteration of the optimization). Is there a way I could restrict it to one instance when it begins? I believe the reason is that I used a GLOBAL variable so that the function optimizer_callbacks(cb) would be able to recognize it. Is there a way I could pass the pbar to optimizer_callbacks without actually using it as a global variable?

def optimizer_callbacks(cb):
    pbar.update()

def strategy_optim(**kwargs):

    total = np.prod([len(value) for key,value in kwargs.items()])

    csv_file = FDaxCSVData(---data---)
    cerebro = bt.Cerebro()
    cerebro.adddata(csv_file)    
    cerebro.broker.setcash(500000.0)
    cerebro.broker.setcommission(commission=2.0)

    strats = cerebro.optstrategy(strategy_name, printlog = False, **kwargs)

    global pbar
    pbar = tqdm.tqdm(smoothing=0.05, desc='Optimization Runs', total=total)
    cerebro.optcallback(optimizer_callbacks)

    runnings = cerebro.run(optreturn=False, maxcpus=2)  

if __name__=="__main__":
    strategy_optim(periods = [100, 200, 300],  abs_margin= [25, 50, 75], trail_stop=[10, 20, 30, 40])

@NikolaTesla

I figured a way to do that but the progress bar is being printed after every callback (i.e after every iteration of the optimization). Is there a way I could restrict it to one instance when it begins? I believe the reason is that I used a GLOBAL variable so that the function optimizer_callbacks(cb) would be able to recognize it. Is there a way I could pass the pbar to optimizer_callbacks without actually using it as a global variable?

def optimizer_callbacks(cb):
    pbar.update()

def strategy_optim(**kwargs):

    total = np.prod([len(value) for key,value in kwargs.items()])

    csv_file = FDaxCSVData(---data---)
    cerebro = bt.Cerebro()
    cerebro.adddata(csv_file)    
    cerebro.broker.setcash(500000.0)
    cerebro.broker.setcommission(commission=2.0)

    strats = cerebro.optstrategy(strategy_name, printlog = False, **kwargs)

    global pbar
    pbar = tqdm.tqdm(smoothing=0.05, desc='Optimization Runs', total=total)
    cerebro.optcallback(optimizer_callbacks)

    runnings = cerebro.run(optreturn=False, maxcpus=2)  

if __name__=="__main__":
    strategy_optim(periods = [100, 200, 300],  abs_margin= [25, 50, 75], trail_stop=[10, 20, 30, 40])

@backtrader
Is there a way to find out the total number of optimization cases without any manual inputs? From what I understand according to this thread, we can use the optcallback as an iterator function call but how exactly do we figure out the total number of cases in the optimization strategy(cerebro.optstrategy()) since you don't suggest using this

opt_count = len(list(itertools.product(*cerebro.strats)))

@vladisld said in I am struggling to save my backtrader optimizer results(params and analyzers) into a CSV file:

The best way of getting the optimization statistics is to use the analyzer objects that will collect and report such statistics. There are two benefits using the analyzers :

Modular and easy to understand design
Some performance benefits (up to 15% according to the docs ) in execution time.

What about this? Can you refer me to an example regarding this? I mean the analyzer objects which were used in some code or something? I am sorry but looking at an example helps me understand things better. Thank you for your time :)

@vladisld said in I am struggling to save my backtrader optimizer results(params and analyzers) into a CSV file:

By default the optreturn Cerebro parameter is set to True - meaning that run method will not return the list of Strategy objects but a stripped down OptReturn objects containing only the original strategy parameters and analyzers.

Hey, Thank you so much for your response. This totally solved the errors I was facing

Hello, I am trying to run a back trader strategy and right now working on optimizing it. Even after referring to https://community.backtrader.com/topic/174/create-a-csv-of-the-result-of-optstrategy/8 I find it difficult to print(and save to csv) the params and analyzers The problem I face is that after using the following code,

def stop(self):
    self.cash_start = self.broker.get_value()
    self.cash_final = self.broker.get_cash()
    self.draw_down = self.analyzers.drawdown.get_analysis()

    self.log(f"Period: {self.p.period}, Abs Margin is {self.p.abs_margin}, trailstop is {self.p.trail_stop}, Vol_Max is {self.p.Vol_Max} and the final value is {self.broker.getvalue()}", doprint=True)

def main():
   ..
   ..
   ..
    strats = cerebro.optstrategy(
        strategy_name, period = [10, 100, 100], ........ 
        )

    workers = cerebro.run(maxcpus=48)  

    all_strategies = [r[0] for r in workers]

    all_results = [str(s.cash_start) for s in all_strategies]
    print(all_results)

But the issue is that I get an Attribute error that OptRetur object has no attribute 'cash_start'. The error is as follows and can someone please help me figure out where am I going wrong??

AttributeError: 'OptReturn' object has no attribute 'cash_start'

My bad, so if I were to use buy and sell orders along with trailing stops and if the respective conditions are True, something like

if not position:
     if buy_condition:
         self.buy(price, exectype=trailstop, trailstop value)

    if sell_condition:
         self.sell(price, exectype=trailstop, trailstop value)

else:
     if (open_price[0] > buy_price+target) or (open_price[0] < sell_price-target):
        self.order = self.close()

Then will I be able to use check if a position is closed using some condition?