In this paper, we investigate the parametric knapsack problem, in which the item profits are affine functions depending on a real-valued parameter. The aim is to provide a solution for all values of the parameter. It is well-known that any exact algorithm for the problem may need to output an exponential number of knapsack solutions. We present a fully polynomial-time approximation scheme (FPTAS) for the problem that, for any desired precision $\varepsilon \in (0,1)$, computes $(1-\varepsilon)$-approximate solutions for all values of the parameter. This is the first FPTAS for the parametric knapsack problem that does not require the slopes and intercepts of the affine functions to be non-negative but works for arbitrary integral values. Our FPTAS outputs $\mathcal{O}(\frac{n^2}{\varepsilon})$ knapsack solutions and runs in strongly polynomial-time $\mathcal{O}(\frac{n^4}{\varepsilon^2})$. Even for the special case of positive input data, this is the first FPTAS with a strongly polynomial running time. We also show that this time bound can be further improved to $\mathcal{O}(\frac{n^2}{\varepsilon} \cdot A(n,\varepsilon))$, where $A(n,\varepsilon)$ denotes the running time of any FPTAS for the traditional (non-parametric) knapsack problem.