I would like to prove that (almost surely)
$$f_n(B_{\tau_1 } , \dots, B_{\tau_{n-1 }}, -1) < B_{\tau_{n-1 }} < f_n(B_{\tau_1 } , \dots, B_{\tau_{n-1 }}, 1)$$
Where the context is as follows: we have a martingale $(X_n )$ - with expectation equal to zero - such that for each $n \ge 1$ there exists a Borel measurable function $f_n: \ \mathbb {R } ^{-1 } \times \{-1, 1 \} \to \mathbb R$, and a $\{-1 , 1 \} $- valued random variable $D_n $ such that
$$X_n = f_n(X_1, \dots , X_{n-1 } , D_n )$$
Further we assume that for any $x_1, \dots, x_{n-1 } $
$$f_n(x_1, \dots, x_{n-1 } , -1 ) < f_n(x_1, \dots, x_{n-1 } , 1 )$$
Given a Brownian motion $(B_t)$ we define the stopping times $\tau_0 = 0$ and for $n \ge 1$
$$\tau_n = \inf \{t > \tau_{n-1 }: \ B_t \in \{f_n(B_{\tau_1 } ,\dots, B_{\tau_{n-1 } }, -1 ), f_n(B_{\tau_1 } ,\dots, B_{\tau_{n-1 } }, 1 ) \} \} $$
This is what I manage to do:
For $n=1 $ we have that $f_1 : \{-1, 1 \} \to \mathbb{R}$, and since $f_1(D_1)=X_1 $ and $E[X_1]=E[X_0]=0$, we get
$$0 = E[f_1(D_1)]=f_1(-1)P[D_1=-1] + f_1(d)P[D_1=1]$$
By the assumption that $f_1(-1) < f_1(1)$ this means that $f(-1)<0<f_1(1)$. And since $\tau_0 = 0$ and $B_0 = 0$ the claim holds for $n=1$.
For general $n $ we have again that
$$E[f_n(X_1, \dots, X_{n-1 } , D_n )] = 0$$
And hence
\begin{multline*} E[f_n(X_1, \dots, X_{n-1 } , D_n )] = \\ = E[f_n(X_1, \dots, X_{n-1 } , -1 )1_{\{D_n = -1\}}] + E[f_n(X_1, \dots, X_{n-1 } , 1 )1_{\{D_n = 1\}}] = 0 \end{multline*}
This means that one of the integrals must be negative and one positive [or both equal to zero]. But here I got stuck!
How is it possible to relate the value of $f_n(B_{\tau_1 } , \dots B_{\tau_{n-1 } } , \pm 1 ) $ to $B_{\tau_{n-1 } } $?
Much grateful for any help provided!
Combining the martingale property with the inequality $f_n(x_1, \dots, x_{n-1 } , -1 ) < f_n(x_1, \dots, x_{n-1 } , 1 )$ and assuming that $\mathrm P(D_n = 1\mid \mathcal F_{n-1})\notin \{0,1\}$ a.s., we have $$ X_{n-1} = \mathrm E[X_n \mid \mathcal F_{n-1}] = \mathrm E[f_n(X_1,\dots,X_{n-1},D_n) \mid \mathcal F_{n-1}] \\ = f_n(X_1,\dots,X_{n-1},1)\cdot \mathrm P(D_n = 1\mid \mathcal F_{n-1}) \\+ f_n(X_1,\dots,X_{n-1},-1)\cdot \mathrm P(D_n = -1\mid \mathcal F_{n-1})\\ < f_n(X_1,\dots,X_{n-1},1)\cdot \mathrm P(D_n = 1\mid \mathcal F_{n-1})\\ + f_n(X_1,\dots,X_{n-1},{\color{red}1})\cdot \mathrm P(D_n = -1\mid \mathcal F_{n-1})\\ = f_n(X_1,\dots,X_{n-1},1). $$ Similarly, $$ X_{n-1} > f_n(X_1,\dots,X_{n-1},-1)\cdot \mathrm P(D_n = 1\mid \mathcal F_{n-1})\\ + f_n(X_1,\dots,X_{n-1},-1)\cdot \mathrm P(D_n = -1\mid \mathcal F_{n-1}) = f_n(X_1,\dots,X_{n-1},-1). $$