# Research on Quantitative Investment Strategies Based on Deep Learning
## 1 Introduction  
Financial innovation improvement drives the rise of quantitative trading in the Chinese financial market. As major parts of the national market economy, the crucial roles stocks, futures, and options markets attract an increasing number of researchers for the study of market price behavior. Instead of introducing numerous premise assumptions, the adoption of deep learning techniques makes it possible to directly hand over rules mining tasks to computers. In this way, the study of stocks, futures and options price behavior is essentially a predictive work that is related to the future price trend of trading objects in the market. At present, most researches target the stock market [1; 2; 3], however, in China, traders can only buy stocks first, which means that traders can only hold a long position. Besides, T + 0 trading isalso forbidden, which means traders cannot sell the amount of stocks they bought within a same day. These rules contribute to the particularity of Chinese stock market, which further negatively prompts the utilization of prediction based on deep learning in this realm. Therefore, this article focuses on an options market with four trading strategies (Long Call, Short Call, Long Put, Short Put) where deep learning prediction results can be fully employed, in order to mirror the impact of its accuracy on quantitative trading strategies in a straightforward way. However, financial markets tend to present tremendous noise, non-stationarity, and non-linearity [4; 5; 6]. Furthermore, technical indicators of the traditional financial market cannot fully reflect the situation, which lead to problems such as greater delay and lower accuracy. Meanwhile, conventional econometric equations hardly perform well in analyzing high-dimensional, complex, and noisy financial market data. In sharp contrast, deep learning-based data mining models succeed in avoiding drawbacks mentioned above, and they are more likely to obtain a more accurate result [5]. Deep learning models [7] have attracted huge attention from researchers and investors, due to their superiority in dealing with extremely complex problems [2; 5]. Many high-tech companies, such as Microsoft, Baidu, and Google, have already invested heavily in deep learning models, in order to take leading positions. Financial market prediction is actually a high-dimensional time series forecast, since differences always exist between contiguous moments. For time-series forecasting, [PERSON] et al. [8] proposed a novel temporal-aware neural bag-of-features (BoF) model, which is tailored to the needs of time-series forecasting by using high frequency limit order book data that captures both long-term and short-term behavior in order to handle the complex situation and to improve the prediction ability. Researchers have already discussed quite a few econometrical and statistical models, such as the Autoregressive Integrated Moving Average (ARIMA) model [9] and the Vector Autoregressive (VAR) model [10] etc., in financial time series predictions, but these models cannot perfectly fit the financial time series, due to their non-stationarity and nonlinearity. Given these characteristics, researchers have subsequently turned to artificial neural networks (ANNs) [11], deep learning [6; 12], and other models. Results show that these models perform better in reflecting real financial situations compared to the linear ones. Based on the structural risk minimization principle, Support Vector Machine (SVM) is an approach that involves training of the polynomial or radial basis function neural network. Compared with other methods, it has better generalization performance [13], which has gained favor from numerous researchers. For instance, [PERSON] [4] and [PERSON] [14] took advantage of SVM for financial forecasting, and both achieved good results. However, one model approach may ignore other features of the problem, which brings about the multiple models approach.