技術面分析是使用過去市場資料包含股票價格與交易量來預測未來市場動態。技術分析將股價與交易量經由數學轉換成易懂且能繪製成圖表的技術分析指標，幫助技術分析投資人預測未來股價。本文的決策過程有別於傳統的技術面分析，使用相似度模型以貼近現實技術分析投資人的決策過程。此策略使用多個技術指標作為相似度技術指標交易策略的依據，用以捕捉市場動態與預測未來股價報酬，且即便不同的技術指標提供不同的買賣訊號，技術分析投資人依然可以藉由相似度技術指標交易策略進行投資決策。相似度技術指標交易策略所預測的未來報酬是根據過往價格圖形出現相似情境的報酬加權平均作為未來預測報酬。當預測報酬為正則買；預測報酬為負則賣。本文使用S&P500指數期貨來檢測相似度技術指標交易策略的獲利能力，發現在不同的技術指標下，相似度技術指標交易策略報酬顯著異於零也高於S&P500指數期貨在樣本期間內的B/H報酬。為使本文相似度技術指標交易策略更能模擬現實投資人的真實情況，導入機器學習改善相似度技術指標交易策略，分別使用貪婪演算法與模擬淬鍊法(Simulated Annealing)來模擬現實投資人會根據交易策略表現的好壞變更決策過程的策略。其報酬顯著異於零也高於S&P500指數期貨在樣本期間內的B/H報酬。本研究發現投資人會參考不同的混合技術指標策略，且會依照不同混合策略的過往績效，篩選出參考策略，進而決定投資策略，這也呼應混合技術指標的相似度技術指標交易策略比單一技術指標的相似度技術指標交易策略擁有較好的預測能力。因此使用混合技術指標的相似度技術指標交易策略作為機器學習篩選的策略可有效的改善原本的相似度技術指標交易策略。

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