Since you are new to this, I think it's best to walk through an example. Let's consider the case of a single risk $Z$ (i.e. a certain type of operational risk).
The Loss Distribution Approach can be described as:
$$Z=\sum_{i=1}^{N}X_{i}$$
where $N$ is the number of events (frequency) over one year and $X_{i}$ is the severity of loss $i$. $N$ is modelled as a discrete random variable with probability mass function:
$$\quad\quad\quad p_{k}=\text{Pr}[N=k],\,\,\,k=0,1,2,\ldots$$
$X_{i}$ are iid and modelled with a continuous distribution function $F_{X}(x)$. Now, it is important to note the assumption we make that $N$ and $X_{i}$ are independent for all $i$.
Now, based on your data, you can find suitable distributions to describe the frequency and severity of your losses. The exact method you use to find a suitable distribution will depend on the context, but finding the MLE is usually a good option.
I'll describe an example now. Let's assume we're considering a single (operational) risk $Z$. Let's assume (based on suitable fitting methods) that the distribution of severity of losses are independent and identical and follow:
$$X_{i}\sim \text{LN}(\mu=1,\sigma=2)$$
Similarly, we can say the frequency of losses follows:
$$N\sim \text{Poisson}(\lambda=1)$$
Now, I can only assume (having not watched the linked video) that your goal is to evaluate $E[Z]$, $\text{SD}(Z)$, $\text{VaR}_{q}[Z]$ and $\text{ES}_{q}[Z]$ etc. via Monte Carlo methods. Luckily for us there are closed-form, analytical solutions for the expectation and standard deviation (allowing us to check our simulation results).
To perform the simulations I used MATLAB with $K=10^{6}$ simulations.
%Set vector of number of simulations for loss Z:
K=10^6;
%Set parameters to be used for Lognormal and Poisson random variables:
lambda=1;
mu=1;
sigma=2;
%Initialize annual loss amount vector:
Z_vec=zeros(K,1);
%Iterate for size of annual loss sample:
for k=1:1:K
%Simulate Poisson value:
p_rnd=poissrnd(lambda);
%Initialize loss severity vector, if Poisson>0:
if p_rnd>0
X_vec=zeros(p_rnd,1);
for m=1:1:p_rnd
%Simulate Lognormal value:
X_vec(m,1)=lognrnd(mu,sigma);
end
%Otherwise, set severity vector to zero:
else
X_vec=0;
end
Z_vec(k,1)=sum(X_vec);
end
So the vector Z_vec contains the $10^{6}$ simulations for $Z$. From here it's all very straightforward, calculating the mean, standard deviation and whatever else you are interested in.
From my simulations, I obtained:
$$\begin{align}
E[Z]&=20.1318\\
\text{SD}(Z)&=143.7883
\end{align}$$
The analytical solutions are (simple compound distribution formulae):
$$\begin{align}
E[Z]&=E[N]E[X]=20.0855\\
\text{SD}(Z)&=\big(E[N]\text{Var}(X)+\text{Var}(N)E[X]^{2}\big)^{1/2}=148.4132
\end{align}$$
Similar calculations can be made for any sort of risk measure you would like. Keep in mind this method can be generalized to include more risks (i.e. $Z_{i}$, $i=1,2,\ldots$) and include dependencies between the $Z_{i}$.
In terms of your confusion about the time horizon of losses, the time horizon you set is purely up to you. If you want to consider yearly losses, then partition your 7-year period into $j$ yearly periods. For any given yearly period $j$, the observed frequency of losses, $n_{j}$, will be the count of losses. The $n_{j}$ go towards estimating the frequency distribution $N$. Similarly, the severity of those losses in all the yearly periods go towards estimating the severity distribution $X_{i}$.
Hopefully the following diagram illustrates the point well, where in this example there is a 3-year period split into $j=3$ 1-year periods. Each $n_{k}$, $k=\{1,\ldots,j\}$ contributes to estimating $N$ and there are 90 $X_{i}$ observed over the 3-year period which go toward estimating $X$.
