本篇論文主要在探討如何利用近期發展之深度學習技術在於中文句子分散式表示法學習。近期深度學習受到極大的注目，相關技術也隨之蓬勃發展。然而相關的分散式表示方式，大多以英文為主的其他印歐語系作為主要的衡量對象，也據其特性發展。除了印歐語系外，另外漢藏語系及阿爾泰語系等也有眾多使用人口。還有獨立語系的像日語、韓語等語系存在，各自也有其不同的特性。中文本身屬於漢藏語系，本身具有相當不同的特性，像是孤立語、聲調、量詞等。近來也有許多論文使用多語系的資料集作為評量標準，但鮮少去討論各語言間表現的差異。 本論文利用句子情緒分類之實驗，來比較近期所發展之深度學習之技術與傳統詞向量表示法的差異，我們將以TF-IDF為基準比較其他三個PVDM、Siamese-CBOW及Fasttext的表現差異，也深入探討此些模型對於中文句子情緒分類之表現。 / The paper demonstrates how the deep learning methods published in recent years applied in Chinese sentence representation learning. Recently, the deep learning techniques have attracted the great attention. Related areas also grow enormously. However, the most techniques use Indo-European languages mainly as evaluation objective and developed corresponding to their properties. Besides Indo-European languages, there are Sino-Tibetan language and Altaic language, which also spoken widely. There are only some independent languages like Japanese or Korean, which have their own properties. Chinese itself is belonged to Sino-Tibetan language family and has some characters like isolating language, tone, count word...etc.Recently, many publications also use the multilingual dataset to evaluate their performance, but few of them discuss the differences among different languages. This thesis demonstrates that we perform the sentiment analysis on Chinese Weibo dataset to quantize the effectiveness of different deep learning techniques. We compared the traditional TF-IDF model with PVDM, Siamese-CBOW, and FastText, and evaluate the model they created.

本篇文章使用了增強學習與捲積深度學習結合的DQCN模型製作交易系統，希望藉由此交易系統能自行判斷是否買賣ETF，由於ETF屬於穩定性高且手續費高的衍生性金融商品，所以該系統不即時性的做買賣，採用每二十個開盤日進行一次買賣，並由這20個開盤日進行買賣的預測，希望該系統能最大化我們未來的報酬。 DQN是一種增強學習的模型，並在其中使用深度學習進行動作價值的預測，利用增強學習的自我更新動作價值的機制，再用深度學習強大的學習能力成就了人工智慧，並在其取得良好的成效。 / In this paper, we used DCQN model, which is combined with reinforcement learning and CNN to train a trading system and hope the trading system could judge whether buy or sell ETFs. Since ETFs is a derivative financial good with high stability and related fee, the system does not perform real-time trading and it performs every 20 trading day. The system predicts value of action based on data in the last 20 opening days to maximize our future rewards. DQN is a reinforcement learning model, using deep learning to predict value of actions in model. Combined with the RL's mechanism, which updates value of actions, and deep learning, which has a strong ability of learning, to finish an artificial intelligence. We got a perfect effect.

本研究主旨是希望提供一個智能金融演算法交易平台，以Django CMS作為網頁框架，區分成研發環境與交易環境，完整的功能包含用戶研發、用戶測試以及使用演算法服務。用戶研發與測試上採用IPython的互動式開發介面，利用JupyterHub進行管理與配置，能夠同時提供多個用戶存取平台，使得平台足以負載大規模用戶的使用；而演算法服務經由Celery包裝成任務，以利交付給後台進行分散式運算。搭上近年來深度學習的熱潮，平台額外擴充Tensorflow套件與GPU建置，支援多核及高速演算法運算。 面對存取大量、複雜且結構化的金融資料，本研究的資料庫採用HAWQ做為解決方案，利用其極大量平行化的架構，改善過往存取大數據所造成的系統複雜性與效能瓶頸，並搭配Ambari達到創建、監視及管理Hadoop分散式集群的功用，讓開發者在部署與維運上都將事半功倍。 由於採用新的資料庫HAWQ，傳統的資料表設計將不利反傷，因此本研究會針對程式端存取資料庫裡的金融資料，量身打造適合的資料表設計，並對其做效能評測，以確保資料能有效且迅速地被程式所取用。 / The purpose of this research is to provide a smartly algorithmic trading platform with financial data. I use Django CMS as a web framework and consisting of Develop environment and Trade environment. The entire functions of the platform include “User Research and Development”,” User Testing” and “Algorithmic Services”. “User Research and Development” and “User Testing” using IPython interactive development interface, with JupyterHub management and configuration, can simultaneously provide multiple user accessing and make the platform enough to support more and more users; “Algorithmic Services” using Celery to package algorithms into tasks can facilitate the delivery to the Server for distributed computing. By means of the growth of Deep Learning in recent years, the platform adds extra Tensorflow and GPU deployment to support multi-core and high-speed algorithm computing. In face of accessing large number of complex and structured financial data, I choose HAWQ as the database in this research. Its extremely massively parallel processing can alleviate the complexity of system and the bottlenecks of efficiency caused by accessing massive number of data. Combing HAWQ with Ambari can achieve the functions of creation, monitoring and management of Hadoop distributed cluster. The developers will do much more easily in deployment and maintenance. The traditional table design may not fit in with the new database HAWQ, so this research will design appropriate table, and evaluate its performance to ensure that data can be accessed effectively and quickly from programs.