We introduce a scenario based allocation model (SBAM) that uses entropy pooling for computing scenario probabilities. Compared to most other models that allow the investor to blend historical data with subjective views about the future, the SBAM does not require the investor to quantify a level of confidence in the subjective views.  A quantitative test is performed on a simulated systematic fund offered by the fund company Informed Portfolio Management in Stockholm, Sweden. The simulated fund under study consists of four individual systematic trading strategies and the test is simulated on a monthly basis during the years 1986-2010.  We study how the selection of views might affect the SBAM portfolios, creating three systematic views and combining them in different variations creating seven SBAM portfolios. We also compare how the size of sample data affects the results.   Furthermore, the SBAM is compared to more common allocation methods, namely an equally weighted portfolio and a portfolio optimization based only on historical data.  We find that the SBAM portfolios produced higher annual returns and information ratio than the equally weighted portfolio or the portfolio optimized only on historical data.