Algorithmic Trading Using Long Short-Term Memory Network and Portfolio Optimization

Investors typically rely on a mix of experience, intuition, knowledge of economic fundamentals and real-time information to make informed choices and try to get as high a rate of return as possible. Their decisions are customarily more instinct-driven than methodical. Propelled by the need for numerically inspired judgments, ever stronger within the financial community, in recent years the usage of computational and mathematical tools has been taking root. In this work we used a Long Short-Term Memory (LSTM) Network trained on historical prices to predict future daily closing prices of several stocks listed on the Standard & Poor 500 (S&P500) index. We compared the predictions of our LSTM network with those produced by another state-of-the-art approach, the Hidden Markov Model (HMM), in order to validate our findings. We then fed our forecasts into aMarkowitz Portfolio Optimization (PO) procedure to identify the best trading strategy. The purpose of PO, which allows for simultaneous and optimal trading of multiple stocks, is to compute a set of daily weights representing the portion of initial capital to be invested in each company. Our empirical results highlight two facts: Firstly, our LSTM model achieves higher accuracy than the standard HMM approach. Secondly, by trading various stocks at the same time we can obtain a higher rate of return than is possible by using the single stock strategy, while also greatly enhancing the real-world applicability of our model

End-to-End Motion Classification Using Smartwatch Sensor Data

Analysis of smart devices’ sensor data for the classification of human activities has become increasingly targeted by industry and motion research. With the popularization of smartwatches, this data becomes available to everyone. The user’s data from accelerometers and gyroscopes is conventionally analyzed as a multivariate time series to obtain reliable information about the user’s activity at a specific moment. Due to the particular sampling rate instabilities of each device, previous approaches mainly work with feature extraction methods to generalize the information independently of the gear, which requires a lot of time and expertise. To overcome this problem, we present an end-to-end model for activity classification based on convolutional neural networks of different dimensions without extensive feature extraction. The data preprocessing is not computationally intensive and the model can deal with the irregularities of the data. By representing the input as twofold – both, interpolated 1D time series and encoded time series as images with the help of Gramian Angular Summation Fields – the use of computer vision techniques is enabled. In addition, an online prediction is possible and the accuracy is comparable to feature extraction approaches. The model is validated with random 10-fold and leave-one-user-out cross-validation showing improvement regarding the generalization of the task

Depression Diagnosis using Deep Convolutional Neural Networks

Depression is a prevalent psychiatric disorder that impacts the quality of life of 300 million people around the world. The complex nature of depression manifestations in patients and the lack of technological advances in the diagnosis process has left a lot of room for improvement in this particular domain. At present, the diagnosis is mainly made by physicians during a conversation comprising the exploration of the symptoms and the diagnostic criteria for depression. Recently, the electroencephalography (EEG) has regained interest as a promising approach to provide bio-markers which are of clinical value in the diagnostic process and for response prediction to therapy. In the present landscape, even the addition of EEG data has resulted in a semi-automated process, where the expert still has to heavily modify the raw data. This adds an inherent bias to the process based on the expert and incurs costs as well as time to the process of diagnosis. In this paper, we present a fast, effective and automated method that is able to quickly determine if the patient has depression while still maintaining a high accuracy of diagnosis. Our approach is built on using raw EEG-data, performing frequency domain preprocessing in order to split the data into its different frequency domains and to create EEG ’images’. These images are then treated by a convolutional neural network, which is a novel approach in this area. Experimental results have shown to provide outstanding results and to work without the need for feature engineering or any human interaction, which is a core strength of the model we are proposing