Tutorial 18 The problem doesn't have a solution with actual input parameters

[fober02]

Hi!
I have a problem at 3.1 Optimizing Portfolios each Rebalancing Date. I tried with anaconda and with Colab but I couldn't make it run. When I run the code I get the following error:

The problem doesn't have a solution with actual input parameters

AttributeError Traceback (most recent call last)
File :25

File ~\anaconda3\lib\site-packages\vectorbt\portfolio\base.py:3919, in Portfolio.from_order_func(cls, close, order_func_nb, flexible, init_cash, cash_sharing, call_seq, segment_mask, call_pre_segment, call_post_segment, pre_sim_func_nb, pre_sim_args, post_sim_func_nb, post_sim_args, pre_group_func_nb, pre_group_args, post_group_func_nb, post_group_args, pre_row_func_nb, pre_row_args, post_row_func_nb, post_row_args, pre_segment_func_nb, pre_segment_args, post_segment_func_nb, post_segment_args, post_order_func_nb, post_order_args, ffill_val_price, update_value, fill_pos_record, row_wise, use_numba, max_orders, max_logs, seed, group_by, broadcast_named_args, broadcast_kwargs, template_mapping, wrapper_kwargs, freq, attach_call_seq, *order_args, **kwargs)
3917 if not use_numba and hasattr(simulate_func, 'py_func'):
3918 simulate_func = simulate_func.py_func
-> 3919 order_records, log_records = simulate_func(
3920 target_shape=target_shape_2d,
3921 group_lens=group_lens,
3922 init_cash=init_cash,
3923 cash_sharing=cash_sharing,
3924 call_seq=call_seq,
3925 segment_mask=segment_mask,
3926 call_pre_segment=call_pre_segment,
3927 call_post_segment=call_post_segment,
3928 pre_sim_func_nb=pre_sim_func_nb,
3929 pre_sim_args=pre_sim_args,
3930 post_sim_func_nb=post_sim_func_nb,
3931 post_sim_args=post_sim_args,
3932 pre_group_func_nb=pre_group_func_nb,
3933 pre_group_args=pre_group_args,
3934 post_group_func_nb=post_group_func_nb,
3935 post_group_args=post_group_args,
3936 pre_segment_func_nb=pre_segment_func_nb,
3937 pre_segment_args=pre_segment_args,
3938 post_segment_func_nb=post_segment_func_nb,
3939 post_segment_args=post_segment_args,
3940 order_func_nb=order_func_nb,
3941 order_args=order_args,
3942 post_order_func_nb=post_order_func_nb,
3943 post_order_args=post_order_args,
3944 close=broadcasted_args['close'],
3945 ffill_val_price=ffill_val_price,
3946 update_value=update_value,
3947 fill_pos_record=fill_pos_record,
3948 max_orders=max_orders,
3949 max_logs=max_logs,
3950 flex_2d=close.ndim == 2
3951 )
3953 # Create an instance
3954 return cls(
3955 wrapper,
3956 close,
(...)
3962 **kwargs
3963 )

File ~\anaconda3\lib\site-packages\vectorbt\portfolio\nb.py:2920, in simulate_nb(target_shape, group_lens, init_cash, cash_sharing, call_seq, segment_mask, call_pre_segment, call_post_segment, pre_sim_func_nb, pre_sim_args, post_sim_func_nb, post_sim_args, pre_group_func_nb, pre_group_args, post_group_func_nb, post_group_args, pre_segment_func_nb, pre_segment_args, post_segment_func_nb, post_segment_args, order_func_nb, order_args, post_order_func_nb, post_order_args, close, ffill_val_price, update_value, fill_pos_record, max_orders, max_logs, flex_2d)
2884 if call_pre_segment or segment_mask[i, group]:
2885 # Call function before the segment
2886 pre_seg_ctx = SegmentContext(
2887 target_shape=target_shape,
2888 group_lens=group_lens,
(...)
2918 call_seq_now=call_seq_now
2919 )
-> 2920 pre_segment_out = pre_segment_func_nb(pre_seg_ctx, *pre_group_out, *pre_segment_args)
2922 # Update open position stats
2923 if fill_pos_record:

Cell In[4], line 24, in pre_segment_func_nb(sc, find_weights_nb, rm, history_len, ann_factor, num_tests, srb_sharpe)
21 close = sc.close[sc.i - history_len:sc.i, sc.from_col:sc.to_col]
23 # Find optimal weights
---> 24 best_sharpe_ratio, weights = find_weights_nb(sc, rm, close, num_tests)
25 srb_sharpe[sc.i] = best_sharpe_ratio
27 # Update valuation price and reorder orders

Cell In[8], line 30, in opt_weights(sc, rm, close, num_tests)
27 l = 0 # Risk aversion factor, only useful when obj is 'Utility'
29 w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)
---> 30 weights = np.ravel(w.to_numpy())
31 shp = rp.Sharpe(w, port.mu, cov=port.cov, returns=returns, rm=rm, rf=0, alpha=0.05)
33 return shp, weights

AttributeError: 'NoneType' object has no attribute 'to_numpy'

Is there a way to solve it?