Celery 讓平台裡的任務可以跨層傳輸以及遠端運行；JupyterHub 提供使用者熟悉 的IPython 頁面；Docker Spawner 將 docker 虛擬化的概念深植平台裡，使得各個 服務平均分佈在分散式架構中，減輕單一機器負擔；HAWQ 極大量平行運算的 功能，更是突破大量資料存儲速度過慢的問題。此架構具有以下之優勢：
l 高擴充性
Ambari 作為 Hadoop 平台上的搭建利器，只需經由瀏覽器即可進行 Hadoop 叢集部署。圖形化及一目瞭然的參數設定介面，讓開發者可以 快速新增節點及其他Hadoop 上的開源軟體，如 YARN、Spark 等，有 利於平台進行水平擴充與垂直擴充。
l 資源隔離
利用 Docker 容器化的技術，將原本 JupyterHub 生成的 Python 實例 轉變成容器使用，由於容器間彼此都是獨立的環境，讓使用者個人配置
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以HAWQ 作為資料庫，並每日排程化爬取金融資料存進資料庫 裡，讓使用者不需在此額外下功夫，且IPython 的開發介面也對開發者 來說較為熟悉、容易上手。
第二節 未來展望
為了搭上近幾年深度學習的熱潮，本研究擴增Tensorflow 套件及 GPU 建 置，目的是希望讓演算法交易更多樣化且加速運算速度，不過Tensorflow 函式 庫過多且較複雜，對於剛接觸的開發者上手較為不易，因此，本研究未來可能 會採用較簡易的套件Keras，或者是將 Tensorflow 的重要函式包裝成程式所需的 API，讓未來的開發者有更多選擇，並開發出更多有用的演算法交易。
針對平台上的交易，未來想採用區塊鏈的技術，透過區塊鏈去中心化及資 料無法被竄改的優勢，將平台裡的每筆交易公開、透明且安全地儲存下來，讓 平台上的開發者及買服務者能更具保障。
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