Using brute optimisaton with Scipy on an objective function that uses a dataframe-CodePudding

I have a function an objective function which takes a data frame of time series data which it uses to calculate an estimated return on investment over the time data. This function is shown below:

def bwp(x, processed):
    pred,real = x
    money = 1000
    for i in range(0, len(processed)):
        if processed[i, 3] > pred and processed[i-1,4] > real:
            money = money   (money * processed[i, 4])
    return money * -1

I want to minimize this function, it is a cheap and discontinuous so I want to use a brute method to optimize this.

I am trying to optimize this with the following code:

def opt_2(processed):
    bounds = (slice(-1, 1), slice(-1, 1))
    resbrute = optimize.brute(bwp, ranges=bounds, args=processed, full_output=True, finish=optimize.fmin)
    print(resbrute[0])
    print(resbrute[1])

the dataframe 'proceessed' is a dataset containing 3757 rows. The error I get when running this is:

TypeError: bwp() takes 2 positional arguments but 3757 were given

I am not sure why this doesn't work as I have run another function with Scipy using the 'nelder-mead' method and provided the dataframe to 'args' in the same way as I did above and it was able to pass the dataframe through to the function.

Does anyone have experience with this?

CodePudding user response：

Shouldn't the signature of bwp be:

# HERE ----v
def bwp(x, *processed):
    ...

rather than:

def bwp(x, processed):
    ...