> For the complete documentation index, see [llms.txt](https://yuting0103.gitbook.io/quantbrains/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://yuting0103.gitbook.io/quantbrains/ping-jia-han-shu.md).
# 評價函數
原始的評價函數目的是對時間序列進行動能評估，或更直接白話地說，我們是**透過評價函數來對於股價或策略來丈量或統計過去某一段時間內的期望值 透過評價函數得出的分數**，我們可以大概理解或統計出過去某段時間內，該商品或策略的期望值(= 動能 = 績效)。
延伸閱讀: [淺談部位管理與資金(策略)管理](http://quantbrains.club/2017/03/19/%E6%B7%BA%E8%AB%87%E9%83%A8%E4%BD%8D%E7%AE%A1%E7%90%86%E8%88%87%E8%B3%87%E9%87%91%E7%AE%A1%E7%90%86/)
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那為什麼上面會說到"原始"，因為我們後來更進一步地擴展了評價函數的應用，透過評價函數來描述一個商品的特徵
延伸閱讀：[評價函數的穩健特性(Robustness)](http://quantbrains.club/2020/06/24/%E8%A9%95%E5%83%B9%E5%87%BD%E6%95%B8%E7%9A%84%E7%A9%A9%E5%81%A5%E7%89%B9%E6%80%A7/)\
[延伸閱讀：評價函數的進階應用 – 統計與機率變化的描述](http://quantbrains.club/2020/07/05/%E8%A9%95%E5%83%B9%E5%87%BD%E6%95%B8%E7%9A%84%E9%80%B2%E9%9A%8E%E6%87%89%E7%94%A8-%E7%B5%B1%E8%A8%88%E8%88%87%E6%A9%9F%E7%8E%87%E8%AE%8A%E5%8C%96%E7%9A%84%E6%8F%8F%E8%BF%B0/)\
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也就是說目前我們所使用的評價函數有兩大功能，一個是原始期望值的描述(QuantBrains\_Connect中的EVA區塊，目前範例為Optimal-F)，一個是用作商品特徵的描述(消除噪音，QuantBrains\_Connect中的範例為風險位階與DD管理)
延伸閱讀：[評價函數的選擇](http://quantbrains.club/2020/05/21/%e8%a9%95%e5%83%b9%e5%87%bd%e6%95%b8%e7%9a%84%e9%81%b8%e6%93%87/)\
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實務應用:\
[\[基礎篇\]策略與資金管理流程—連載(二)](http://quantbrains.club/2020/08/05/%e7%ad%96%e7%95%a5%e8%88%87%e8%b3%87%e9%87%91%e7%ae%a1%e7%90%86%e6%b5%81%e7%a8%8b-%e9%80%a3%e8%bc%89%e4%ba%8c/)\
[\[基礎篇\]策略與資金管理流程—連載(二) 實務補充](http://quantbrains.club/2020/08/18/%e7%ad%96%e7%95%a5%e8%88%87%e8%b3%87%e9%87%91%e7%ae%a1%e7%90%86%e6%b5%81%e7%a8%8b%ef%bc%88%e4%ba%8c%ef%bc%89%e5%af%a6%e5%8b%99%e8%a3%9c%e5%85%85/)\
[\[基礎篇\] 策略與資金管理流程—連載(二) DD管裡補述](http://quantbrains.club/2020/08/31/%e7%ad%96%e7%95%a5%e8%88%87%e8%b3%87%e9%87%91%e7%ae%a1%e7%90%86%e6%b5%81%e7%a8%8b%ef%bc%88%e4%ba%8c%ef%bc%89%e3%80%80dd%e7%ae%a1%e7%90%86%e5%af%a6%e5%8b%99%e6%b8%ac%e8%a9%a6/)\
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**所以目前我們就可以透過觀念 1來計算得到一個標準的單位部位(未加權時的滿艙部位)，然後透過觀念 2所算出來的分數進行加權，兩者相乘之後就是一個合理的部位數量**
**合理的部位數量= \[ 資金標準化過後的部位 ] \* \[評價分數]**
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