本研究的目的為建立一套適用於台灣股市的主動式量化投資策略。本研究利用多因子 Alpha 模型為分析架構，試圖掌握多維度的股價影響因子，以資訊係數（Information Coefficient）、T-test of ICs、成功率（Success rate）以及 Quintile 累積報酬做因子有效性的檢定，篩選出穩定且有效解釋股價報酬的月頻率因子，再組合因子形成Alpha 股票評分，Alpha 可拆解成三部分，包括市場波動度、因子預測下一期報酬的能力以及因子的獲利能力。本論文以此評分做為股票投資權重的依據，建構一個以台灣中型 100 指數為標竿指數的投資組合。實證結果發現，此主動式量化投資策略能夠有效擊敗標竿指數，獲得平均每個月 3.7%的超額報酬。 　　本研究並嘗試以設定原始權重保留率的方法，控制追蹤誤差以降低週轉率與交易成本，實證結果發現，此方法可有效降低追蹤誤差，但隨著保留率提升，資訊比率（Information Ratio）與投資組合的超額報酬將降低。 / The objective of this study is to build an investment process of active quantitative stock selection model. In this study, we use the Alpha Multi-factor model to find a multitude of factors which are significantly relative to the stock return. The tests we conduct to select the factors that end up in the final multi-factor model are monthly Information Coefficient, T-test of ICs, success rate and quintile cumulative return. Then we examine how to optimally combine correlated factors and calculate the Alpha score for each stock for each period. Alpha is Volatility times IC times Score. Volatility is the cross-sectional volatility of the residual return. IC is the predictive power of the model. And Score are the cross-sectional scores for each stock. We utilize a simple method to construct the portfolio that uses the Alpha score to adjust the weight of component stocks in the benchmark. The empirical result reveals that this investment process successfully outperform the Taiwan Mid-Cap 100 Index benchmark. Moreover, this study tries to decrease the turnover rate and transaction costs by controlling the tracking error. We set the original weight retention rate of the benchmark to control the tracking error. The empirical result reveals that the method works. But as the retention rate rises, the Information ratio and the excess return drops.