Can't apply optimizer to another simple strategy!

magenta.grimer

Hi!
I'm trying to use the optimizer to optimize the very simple Blank Templeate for development strategy. It optimizer runs but I don't get sharpe ratios and always 0 as mean return.

Where's the error?

Cell1

import xarray as xr

import qnt.ta as qnta
import qnt.backtester as qnbt
import qnt.data as qndata



def single_pass_strategy1(data, parameter1=20, parameter2=10):
    wma = qnta.lwma(data.sel(field='close'), parameter1)
    sroc = qnta.roc(wma, parameter2)
    weights = xr.where(sroc > 0, 1, 0)
    weights = weights / len(data.asset) # normalize weights so that sum=1, fully invested
    with qnlog.Settings(info=False, err=False): # suppress log messages
        weights = qnout.clean(weights, data, debug=False) # check for problems
    return weights

Cell 2

def single_pass_strategy2(data,parameter1=10, parameter2=1):
    close = data.sel(field='close')
    ma_slow = close.rolling(time=parameter1).mean().isel(time=-1) # qnta.sma(close, 200).isel(time=-1)
    ma_fast = close.rolling(time=parameter2).mean().isel(time=-1) # qnta.sma(close, 20).isel(time=-1)
    
    weights= xr.where(ma_fast > ma_slow, 1, -1)
    
    weights = weights / len(data.asset) # normalize weights so that sum=1, fully invested
    
    with qnlog.Settings(info=False, err=False): # suppress log messages
        weights = qnout.clean(weights, data, debug=True) # check for problems
    
    
    return weights

Cell 3

import qnt.data as qndata
import qnt.ta as qnta
import qnt.output as qnout
import qnt.stats as qns
import qnt.log as qnlog
import qnt.optimizer as qnop
import qnt.backtester as qnbt

import xarray as xr


#DEBUG#
# evaluator will remove all cells with this tag before evaluation

data = qndata.futures.load_data(min_date='2004-01-01') # indicators need warmup, so prepend data

result = qnop.optimize_strategy(
    data,
    single_pass_strategy2,
    qnop.full_range_args_generator(
        parameter1=range(1, 200, 20), # min, max, step
        parameter2=range(1, 200, 20)   # min, max, step
    ),
    workers=1 # you can set more workers when you run this code on your local PC to speed it up
)

qnop.build_plot(result) # interactive chart in the notebook

print("---")
print("Best iteration:")
display(result['best_iteration']) # as a reference, display the iteration with the highest Sharpe ratio

single pass strategy 1 works (should be exactly the same used in the optimizer example) whereas single pass strategy 2 gives no sharpe and flat returns

magenta.grimer

@magenta-grimer @support
I coded this way so they look the most possible the same and to test one or another I just change the 1 with the 2. With the 1 it seems to work, with 2 no

support

@magenta-grimer

Hello.

Remove .isel(time=-1).

ma_slow = close.rolling(time=parameter1).mean() #.isel(time=-1) 
ma_fast = close.rolling(time=parameter2).mean()#.isel(time=-1)

It selects the last day, you need an entire series.

Regards.