The problem of prediction future event given an individual 
sequence of past events is considered. Predictions are given 
in form of real numbers $p_n$ which are computed by some algorithm 
$\varphi$ using initial fragments $\omega_1,\dots, \omega_{n-1}$ 
of an individual binary sequence $\omega=\omega_1,\omega_2,\dots$ 
and can be interpreted as probabilities of the event $\omega_n=1$
given this fragment. 
According to Dawid's {\it prequential framework} 
%we do not consider
%numbers $p_n$ as conditional probabilities generating by some 
%overall probability distribution on the set of all possible events.
we consider partial forecasting algorithms $\varphi$ which are 
defined on all initial fragments of $\omega$ and can 
be undefined outside the given sequence of outcomes. 
We show that even for this large class of forecasting algorithms
combining outcomes of coin-tossing and transducer algorithm
it is possible to efficiently generate with probability close 
to one sequences 
for which any partial forecasting algorithm is failed by the
method of verifying called {\it calibration}.