Expected position of a random walk with a biased coin directly after two consecutive heads are observed Suppose a coin is tossed repeatedly and independently, and the probability of observing a heads on any toss is 0.6. Now suppose that a one-dimensional simple random walk $\{\sigma_1,\sigma_2,\cdots\}$ is designed based on the coin tossing. For $i=1,2,\cdots,$ a coin is tossed and 
$$ 
\sigma_i = 
\left\{
\begin{array}{ll}
      1, & \text{if a head is observed} \\
      -1, & \text{if a tails is observed} \\
\end{array} 
\right. 
$$
The random walk starts at the origin. Compute the expected position of the random walk when a string of 2 consecutive heads is observed for the first time. That is compute 
$$ E\left(\sum_{i=1}^T \sigma_i\right) $$
where $\{T=i \in \mathbb{N}: \text{A string of 2 consecutive heads is observed for the first time after toss i}\} $.

So I figured I should first compute T, the expected number of flips to get two consecutive heads.
Basically, I did this by conditioning as shown below:
$$E(T) = (1+E(T))\frac{2}{5}+(2+E(T)\frac{3}{5}\frac{2}{5}+2\frac{3}{5}\frac{3}{5}$$
Solving for E(T) I then get E(T) = $\frac{40}{9}$.
Thus, I'm assuming when I calculate $ E\left(\sum_{i=1}^T \sigma_i\right) $, have T=5 since it is the next natural number.
Now even with this assumption I am stuck on what to do next. I guess I could just brute force it by writing out the terms of the sum from the standard expectation formula, but I think there might be an easier way. Any insight would be appreciated.
 A: I actually think I just came up with the answer another way using Wald's equation:
$$E\left(\sum_{i=1}^T \sigma_i \right) = E(T) E(\sigma_1)= \frac{40}{9}*\left(1*.6-1*.4\right)=\frac{8}{9}$$ 
This is exactly what Marcus Andrews got above. Thanks for the help 
A: Let $E_{\text{prev}=H}$ be the expected number of total flips to get two consecutive heads, given that the previous flip was a head. Let $E_{\text{prev}\neq H}$ be the expected number of total flips to get two consecutive heads, given that the previous flip was not a head. 
$E_{\text{prev}=H} = \frac{6}{10}(1 + 0) + \frac{4}{10}(1 + E_{\text{prev}\neq H})$
$E_{\text{prev}\neq H} = \frac{6}{10}(1 + E_{\text{prev}=H}) + \frac{4}{10}(1 + E_{\text{prev}\neq H})$
We use $E_{\text{prev}\neq H}$ to get the total number of flips. Labeling $E_{\text{total}} = E_{\text{prev}\neq H}$ and combining equations, we have:
$E_{\text{total}} = \frac{6}{10}(1 + \frac{6}{10}(1 + 0) + \frac{4}{10}(1 + E_{\text{total}})) + \frac{4}{10}(1 + E_{\text{total}})$
Solving for $E_{\text{total}}$ we get $\frac{40}{9}$.
However, what we really need is the expected number of heads and the expected number of tails during this time, as this will tell us the expected value of the walk. Since the equation above counts both heads and tails, we can modify the equation to exclude what we don't want to count.
The expected number of heads:
$E_{\text{heads}} = \frac{6}{10}(\color{blue}1 + \frac{6}{10}(\color{blue}1 + 0) + \frac{4}{10}(\color{red}0 + E_{\text{heads}})) + \frac{4}{10}(\color{red}0 + E_{\text{heads}})$
The expected number of tails:
$E_{\text{tails}} = \frac{6}{10}(\color{red}0 + \frac{6}{10}(\color{red}0 + 0) + \frac{4}{10}(\color{blue}1 + E_{\text{tails}})) + \frac{4}{10}(\color{blue}1 + E_{\text{tails}})$
The red designates coin flips that have been removed from the equations, and blue designates the flips we want to count. We get $\frac{8}{3}$ heads and $\frac{16}{9}$ tails on average.
Then the expected value of the walk is $\frac{8}{3}- \frac{16}{9} = \frac{8}{9}$.
Another way to look at it: Since the probability distribution of a single flip also extends to the whole (due to the flips being independent and identically distributed), then if we know that the total number of flips is $\frac{40}{9}$ on average, then $\frac{6}{10}$ of these flips will be heads, with the rest tails. Therefore we get $\frac{40}{9}(\frac{6}{10} - \frac{4}{10}) = \frac{8}{9}$ again for the expected value of the walk.
