摘要
量化交易是近幾年來被熱烈討論的議題，它是藉由歷史數據經數學模型的分析預測，帶來更高獲利的策略，並且已有多篇文獻探討利用機器學習的方法來預測期貨價格。例如: 支持向量機器、隨機森林、遞迴式神經網路等。整體學習(ensemble learning)是一個結合機器學習預測以達到優化結果的方法，透過使多個模型互相學習，來降低預測的誤差來得到更加精確的結果。在這篇論文中，我們運用整體學習，整合上面提到的三個方法，並且比較使用整體學習的改善前後結果。我們通常透過預測未來的收盤價，來預測股市走勢及價格。過去文獻中使用的數據，是由每日的交易資料中，只取了一筆收盤價。在這次研究中，我們將會提升交易紀錄的頻率，一日使用將近四十筆的交易紀錄，並使用這個比過去大三十倍的資料量，來詳細並精準預測未來期貨的價格。我們使用的資料為2014年香港期貨交易所初始的真實交易數據，我們將資料轉換成欲使用的高頻率交易資料，並透過整體學習的方法，預測過去中的未來價格，由真實資料反映出這篇研究結果的準確性。

The method of predicting futures price is popularly discussed in recent year. The algorithms of machine learning are used in past several researches and successfully predicting the trend of futures market. In this research, we use ensemble learning to predict the price of stock index futures. The feature is the historical trading data that is the raw data from Hong Kong futures Exchange. We transform the raw data to high frequency sequential data, then we apply ensemble learning to combine the result, bagging, boosting and random subspace are the three main ensemble methods adopt in this paper. We select support vector regression, random forest and recurrent neural network to construct the libraries of models, and then we improve the result of the libraries of models by ensembling the results. Observing the numerical result, the performance of ensemble learning is compared with the result of single model, and the predicting error of ensemble learning is lower than SVR, RNN and RF.

Content
Chapter 1 Introduction 5
Chapter 2 Literature Review 11
Chapter 3 Data Preprocessing 17
Chapter 4 Tools for predicting futures price 27
Chapter 5 Computational Experiments 42
Chapter 6 Conclusions 59
Chapter 7 References 61

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