Medium frequency trading strategies include all trading activities, that do not require market microstructure analysis on one side and signicantly depend on market impact on the other side. The most important dierence from high frequency trading is the ability to analyze big amount of data using complex algorithms. Portfolio management in this case is the dynamic process, combination of signal (alpha) discovery and optimal execution on the level of trading scheduling. We used close price and trading volume time series for the list of S&P 500 companies that exist in an index since the beginning of 2008 at least. In this paper we present signal generation approaches as well as optimization of portfolio transactions. Formally the performances of medium frequency statistical arbitrage strategies are much better than the performance of their benchmarks, but they are very sensitive to the quality of trading engine and optimization software. In this minor revision we added the results of out-of-sample tests and explanations of terms and methodology.