The prospect of algorithmic trading in China is magnificent due to the exigent demand for reducing trading cost from institutional investors． This paper builds a Socially Embedded Multi Agent( SEMA) model to investigate the impact of algorithmic trading on execution costs，market quality，and trading system． The approach integrates the order book information of real world with a simulation of an artificial financial market to enhance the value and usefulness of the simulation． The research indicates that: ( 1) The average execution costs of VWAP and IS algorithm are lower than those of institutional investors; ( 2) Algorithmic trading can both decrease market volatility by reducing the impact of large orders and improve market liquidity by updating real-time limit orders; ( 3) The growing message traffic caused by algorithmic trading will not exceed the system capacity of Shanghai Stock Exchange in the prometa phase．