Impute values for missing holiday values (or any values) by taking an average of the day before and the day after. This is rudimentary just to get going and can ultimately be modified/corrected via Intervention Detection procedures. Set your seasonality to 5 and jointly identify arima and latent deterministic effects like daily effects , pulses , step-level shifts , seasonal pulses i.e. changes in daily effects over time and local time trends following http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html and an iterative modelling process similar to https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf . Follow this thread for a practical example Simple method of forecasting number of guests given current and historical data .
You also might load up 11 monthly indicators to potentially capture monthly effects (and/or weekly dummies to capture possible weekly effects ) as you form your SARMAX model as here suggested here https://autobox.com/pdfs/SARMAX.pdf
Daily data usually has anthropogenic effects possibly reflecting systematic activity/behavior requiring inclusion of possible empirically developed deterministic factors. ARIMA memory is a poor substitute and widely over-used when this is the case. Only your data knows for sure as to how to form your model .. sometimes with domain knowledge in place.
Fitting a guessed model with a pre-specified # of fourier/trigometric terms and assumed forms of trends without detecting level shifts or changes in trends or changes in daily effects or changes in error variance should be studiously avoided whenever possible as it is not model identification but model specification/ guessing/presumption. Models like these do not lead to any reliable tests of significance or forecast error variance allowing for future anomalies to be considered.