Why does Naive Forecast,Moving average,Simple Exponential Smothing model predictions flatten?
The naive forecast = random walk with no drift will always give a flat straight line as your forecasts. This is just the last observed data point in your time series extended forward n steps ahead. 

The moving average model with m =50 will be the average of the most 50 recent data points. As we can see, your time series is mostly flat with some erratic peaks that have been smoothed away by the relatively large window (50) that you have chosen. In general, the larger the window the more flat and smooth your forecasts. Try changing the window to be smaller and you will probably get more erratic behavior in your forecasts.

Simple exponential smoothing always gives a flat forecast since all forecasted values are equal to the first forecasted value (i.e. y(t+k) = y(t+k-1) =....y(t+1), for all k > 1). This can be proven quite easily using basic induction.

Holts Linear Trend model breaks up the forecasts into three different components; the trend, level, and seasonality. If your time series exhibits no seasonality and no level changes then all you will be left with is your trend which is a flat line in your case.

Your time series looks to be a flat line with unpredictable random fluctuations. Hence, in terms of forecasting error I am not too surprised to see that a flat line may give the best forecasts. Try calculating RMSE, MAE, or MASE (which compares to the naive method directly) and see for yourself. Often, a flat line is the best forecast especially for financial time series in particular.