# Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network
## 1 Introduction
Moreover, compared to the slight price fluctuations, which are similar to a random walk process, the temporal pattern before important breakout points is more likely to be caused by the real driving force itself. Consequently, we focus on the construction of a deep learning model that is capable of predicting important trading points. To address this challenge, we imitate the process of human stock traders looking for trading opportunities, which can be summarized in three principles:  
Figure 1: An example of the lagging phenomenon: prediction results for the S&P500 index from August 2017 to September 2018 using a long short-term neural network. **Focus on high-margin opportunities**: Even experienced human investors do not predict the stock price or trend at every time point; instead, they focus on the important trading points that are more likely to represent high-margin opportunities. This is not only because of the uncertainty and difficulty of the stock price forecasting task but also because the considerable transaction costs make slight price fluctuations meaningless. * **Keep track for some time**: A signal at a single time point is very likely not to be sufficiently informative. Furthermore, it may have different meanings in different contexts. Human traders usually comprehensively consider a sequence of recent data and consequently make a more reliable prediction of the subsequent stock trend. Within a sequential context, human traders can give different levels of attention to various parts according to their respective importance and influence. * **Diversify the investment portfolio**: Diversification is a management strategy that integrates different investments into a single portfolio. This is because diversified investments produce higher returns and face lower risks [33; 34]. To diversify portfolios, human investors typically look for asset classes that are less relevant to each other or negatively correlated so that if one asset class moves down, the other will counteract it. To capture the first and second principles of the human investment process, we define the time points that are followed by a significant stock price rise as important trading points (IPs) with a formula. In this way, the sequence of recent time points before an IP is labeled as the signal of the IP. An effective method to improve prediction is model fusion, where inherent characteristics of prediction models are exemplified to contribute towards improved forecasting [35]. In this work, we design a deep learning framework based on a hybrid convolutional recurrent neural network (HCRNN) to predict the IPs by analyzing and mining historical data to identify potential high-margin opportunities. In fact, the time points which are followed by a significant stock price drop form another kind of IP, and predicting this kind of IP is a very similar task. However, backtesting large sets of previous financial data requires an elaborate threshold search; thus, this study pays more attention to demonstrating the effectiveness and robustness of the proposed method. Inspired by existing research [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46], the convolutional neural network (CNN), which has shown great power in feature extraction, is used to perform convolution on the recent region to capture local fluctuation features, and long short-term memory (LSTM), which works well on sequence data, is utilized to learn the long-term temporal dependencies of both the raw data and the local features obtained by the CNN. To capture the third principle of the human analysis process, we use a set of stocks belonging to different sectors to build the dataset to achieve diversification.