The shape of your histogram appears to be potentially Log-Normal with a negative shift. I would shift all of these values positively by $|min(x) + \epsilon|$ so that your support is non-negative, then overlay a log-normal density to see if it matches (where $\epsilon$ is some arbitrarily small number).
Here's a histogram of some random lognormal data that I shifted by $-1000$. It's probably close enough to what you need show my point that you should shift your observations into the positives and try to fit a distribution after that.
This is a an exponentiated random normal distribution with mean 1 and standard deviation 0.20 premultiplied by 400.
R code I used:
x <- 400 * exp(rnorm(1000, mean = 1, sd = 0.20))
hist(x - 1000)