This　paper　presents　a　general　sparse　portfolio　selection　model　with　expectation,　chance　and　cardinality　constraints.　For　the　sparse　portfolio　selection　model,　we　derive　respectively　the　sample　based　reformulation　and　distributionally　robust　reformulation　with　mixture　distribution　based　ambiguity　set.　These　reformulations　are　mixed-integer　programming　problem　and　programming　problem　with　difference　of　convex　functions　(DC),　respectively.　As　an　application　of　the　general　model　and　its　reformulations,　we　consider　the　sparse　enhanced　indexation　problem　with　multiple　constraints.　Empirical　tests　are　conducted　on　the　real　data　sets　from　major　international　stock　markets.　The　results　demonstrate　that　the　proposed　model,　the　reformulations　and　the　solution　method　can　efficiently　solve　the　enhanced　indexation　problem　and　our　approach　can　generally　achieve　sparse　tracking　portfolios　with　good　out-of-sample　excess　returns　and　high　robustness.