為了讓一般的投資大眾能享有智慧型、系統化的策略交易環境，本研究計畫發展一個可供大量使用者共用、並且容易上手的策略交易平台。為了達到這個目的，此平台必須擁有快速且大量的運算能力，雲端運算所提供之大量且可擴充的運算能力，使之成為最適宜的平台。為滿足不同使用者不同的投資偏好，此平台提供多項常用之技術指標與K線型態辨識功能讓使用者利用基因演算法產生符合其偏好的交易策略。在策略產生之後，使用者可以在平台上檢視交易策略在不同商品、不同時間區間上的表現，並從最後的策略回測報告中加以評估，挑選出獲利能力、波動程度與交易頻率都符合需求的交易策略。

本研究利用雲端運算的技術，發展大量使用者使用的策略交易的系統。為滿足大量使用者的運算需求，本系統包括幾項特性： 1. 採用服務導向架構以充分使用雲端運算的特性。 2. 建立非同步事件控制機制以提供服務間非同步運算能力。 3. 採用集中式資料結構，提出收縮式肋骨網絡(SRN)資料結構，減少運算需求。 4. 提供基因演算模擬環境，讓使用者可以發展符合個人投資偏好的投資策略。 / In this study, we designed a algorithmic trading system for large numbers of users on a cloud computing plateform. So the main features of the algorithmic trading system have been as follows. 1. The use of Service-Oriented architecture in order to fully use the characteristics of cloud computing. 2. The establishment of asynchronous event control mechanism to provide services to non-synchronous computing power. 3. A centralized data structure, proposed Systolic Ribs Network (SRN) data structure, reducing the computing needs. 4. To provide the genetic algorithm simulation environment that allows users to develop in line with the investment strategy personal investment preferences.

本文以全球第一個演算法交易雲端平台－Quantopian進行研究，藉由平台社群討論區內公開之演算法交易策略，透過交易策略篩選和初步優化，以演算法交易策略為投資標的，搭配不同權重策略建構投資組合。權重策略部分，本文提出適用於組合式交易策略的績效指標加權 (Performance Index Weighted) 法，應用因子投資的觀念，融合排序相關性較低、不同面向之績效指標作為報酬率驅動因子，並參考Asness et al. (2013) 以因子排序作為權重計算依據，提供了簡單直覺、非最適化求解而且穩健的加權方式，更直接地將交易策略各面向績效的優劣反應在權重上。 根據數值分析，發現組合式交易策略長期而言，整體績效表現平均優於個別演算法交易策略，最小變異、績效指標加權和均等權重投資組合的風險亦明顯低於個別交易策略，且最小變異、績效指標加權和均等權重投資組合在降低投資組合風險的同時，並未犧牲過多報酬，風險調整後績效表現優於個別交易策略。而績效指標加權投資組合之年化報酬率、風險衡量和風險調整後績效表現皆優於最小變異、平均數－變異數、均等權重的加權投資組合，此種權重策略可使投資組合之夏普比率 (Sharpe ratio) 顯著提升，且投資組合的風險大幅降低，最大跌幅 (Max drawdown) 在四年半的實驗區間內降至10%以下的水準，風險調整後績效優異。 透過Quantopian社群演算法交易平台，個人投資者也能站在巨人的肩膀上學習，集合眾人的力量，憑藉量化交易創造出和機構法人一樣具有競爭力的投資組合。如Chan (2009) 所言，個人投資者也能憑藉量化交易，設計一套演算法交易策略。 / Quantopian is a crowd-sourced hedge fund which allows members on the platform to develop their own algorithmic strategies and even get capital allocations from Quantopian. In this paper, we constructed portfolios by Quantopian trading platform and proposed Performance Index Weighted method which generate consistently profit in our study. First, we filtered algorithmic trading strategies shared on the Quantopian community and improved the performance slightly. Second, we combined multiple algorithmic strategies with varied portfolio weight method, such as minimize-variance, performance index weighted, mean-variance, and equal weighed method to construct a portfolio. To elaborate, Performance Index Weighted portfolio is actually an application of factor investing, in which the portfolio weight depends on the ranking of performance index (factors), and these index measure returns, risk, and also risk-adjusted returns, which truly reflects how well the algorithmic strategy is. As a result, we used the performance index as a return driver and invested more in well-ranked strategies directly. Performance index weighted is a simple, robust, and fully intuitively way to construct a portfolio. In numerical analysis, we found that using multiple strategies to construct a portfolio could generate better performance than a single algorithm strategy on average. Moreover, the annual returns, risk measure, and risk-adjusted returns of Performance Index Weighted portfolio turn out to be better than minimize-variance portfolio, mean-variance portfolio, and equal weighted portfolio. As a result, Performance Index Weighted portfolio has significantly higher Sharpe ratio and lower Max Drawdown (lower than 10% in our out-of-sample test) than other portfolios, which shows excellent risk-adjusted performance. Most important of all, retail traders could learn more precisely by standing on the shoulders of giants through Quantopian trading platform. Also, by collecting wisdom from the crowd, we create an opportunity for retail traders to construct competitive portfolios just as institutional investors do.

摘要 自2008年金融風暴後，長期的經濟動盪造成顧客喪失對於傳統投資產業之信心。在這樣的環境下，從自動化投資管理、社群交易平台到零售演算法交易的興起，提供低成本與先進的替代方案取代傳統的投資管理產業。這種方式獲得廣大消費者的信賴，並使得顧客擁有更多投資管理之控制權。 本研究欲探討賦權投資者於金融科技的浪潮下，競爭者加入後所面臨之挑戰進行情境分析，了解投資者如何以自動化管理及報告、社群交易平台和零售演算法交易改變投資管理業之發展，並使得傳統以顧問諮詢為主的投資管理興起全自動化或財務顧問協助之新商業模式；再者，透過個案分析，分別探討自動化管理及報告為代表之機器人理財公司以及零售演算法交易平台Quantopian，並建議投資產業應善用金融科技結合兩者，因此，未來顧問所扮演的角色將轉型為從旁協助財務規劃之服務，不僅能夠降低成本，亦可大幅提升理專的效率，為更廣大的客群提供高價值之金融服務。 / Abstract The 2008 financial crisis was the worst economic disaster since it has caused public losing confidence in traditional investement management industry. As a result, the three key innovation clusters are booming─automated management and advice, retail algorithmic trading and social trading platform─that offer lower-cost and advanced alternatives to replace the traditional investement management industry. Additionally, those innovation clusters gain more trust to the masses and allow customers to control in their own investment portfolio. This study analyzes three scenarios how the empowered investors face the challenges under the new waves of Fintech. In particular, we consider the investment management industry transfer the traditional model to the new business models of fully automation or advisor-assistant. In the case studies, we compare six typical robo-advisor firms and retail algorithmic trading platform like Quantopian. Furthermore, we suggest that the investment industry should make good use of Fintech that combines both advantage of automated management and retail algorithmic trading;therefore, it can not only reduce costs but also improve the efficiency of financial services.