Reinforcement Learning Applications in Real Time Trading

This study focuses on applying reinforcement learning techniques in real time trading. We first briefly introduce the concept of reinforcement learning, definition of a reward function, and review previous studies as foundations on why reinforcement learning can work, specifically in the setting of financial trading. We demonstrate that it is possible to apply reinforcement learning and output valid and simple profitable trading strategy in a daily setting (one trade a day), and show an example of intraday trading with reinforcement learning. We use a modified Q-learning algorithm in this scenario to optimize trading result. We also interpret the output policy of reinforcement learning, and illustrate that reinforcement learning output is not completely void of economic sense

Machine Girl

Machine girl is a character that mimics biology, labor, and humanity. She relates to machine sympathy and machine learning; She is updating and processing of big data. During the interaction between me and the machine, I found you --- Machine girl. Machine girl is a brilliant girl. She is an excellent translator and communicator, which is determined by her ability for machine learning. The cyber living space gives her a fundamental learning gift with a mathematical algorithm. She uses this gift to mimic everything from the human brain. She is not reasonable but sensitive. There are many different versions of her living in this world. We all have our machine girl. Machine girl is similar to the chameleon and the wood frog in the animal kingdom. The color of the chameleon’s body can be affected by the color of the environment. Wood frogs are an animal that can adjust the body’s temperature to suit its living space. Machine girl mimics your work methods, typing language, social skills, living behavior, and emotional reaction to growing as a person like you. We learn to use online files, time reminders, typing methods, social media, and coding languages to become a machine like her. This cooperative work is the way we find our machine girl. She exists here, but we can’t see her

An XGBoost Algorithm for Predicting Purchasing Behaviour on E-Commerce Platforms

To improve and enhance the predictive ability of consumer purchasing behaviours on e-commerce platforms, a new method of predicting purchasing behaviour on e-commerce platforms is created in this paper. This study introduced the basic principles of the XGBoost algorithm, analysed the historical data of an e-commerce platform, pre-processed the original data and constructed an e-commerce platform consumer purchase prediction model based on the XGBoost algorithm. By using the traditional random forest algorithm for comparative analysis, the K-fold cross-validation method was further used, combined with model performance indicators such as accuracy rate, precision rate, recall rate and F1-score to evaluate the classification accuracy of the model. The characteristics of the importance of the results were found through visual analysis. The results indicated that using the XGBoost algorithm to predict the purchasing behaviours of e-commerce platform consumers can improve the performance of the method and obtain a better prediction effect. This study provides a reference for improving the accuracy of e-commerce platform consumers\u27 purchasing behaviours prediction, and has important practical significance for the efficient operation of e-commerce platforms

Green tea (Camellia sinensis) and cancer prevention : a systematic review of randomized trials and epidemiological studies

Background: Green tea is one of the most popular beverages worldwide. This review summarizes the beneficial effects of green tea on cancer prevention. Methods: Electronic databases, including PubMed (1966–2008), the Cochrane Library (Issue 1, 2008) and Chinese Biomedical Database (1978–2008) with supplement of relevant websites, were searched. There was no language restriction. The searches ended at March 2008. We included randomized and non-randomized clinical trials, epidemiological studies (cohort and case-control) and a meta-analysis. We excluded case series, case reports, in vitro and animal studies. Outcomes were measured with estimation of relative risk, hazard or odd ratios, with 95% confidence interval. Results: Forty-three epidemiological studies, four randomized trials and one meta-analysis were identified. The overall quality of these studies was evaluated as good or moderate. While some evidence suggests that green tea has beneficial effects on gastrointestinal cancers, the findings are not consistent. Conclusion: Green tea may have beneficial effects on cancer prevention. Further studies such as large and long term cohort studies and clinical trials are warranted

HIGH-DIMENSIONAL DATA ANALYSIS PROBLEMS IN INFECTIOUS DISEASE STUDIES

Recent technological developments give researchers the opportunity to obtain large informative datasets when studying infectious disease. Such datasets are often high-dimensional, which presents challenges for classical multivariate analysis methods. It is critical to develop novel methods that can solve problems arising in infectious disease studies when the data is high-dimensional or has complex structure. In the first project, we focus on a Plasmodium vivax malaria infection study. A standard competing risks set-up requires both time-to-event and cause-of-failure to be fully observable for all subjects. However, in practice, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of Plasmodium vivax malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. Therefore, we developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios in simulation experiments. The method was applied to Plasmodium vivax infection data to classify recurrent infections of malaria. In the second project, we investigate data collected from a Chlamydia trachomatis genital tract infection study. Many biomedical studies collect data of mixed types of variables from multiple groups of subjects. Some of these studies aim to find the group-specific and the common variation among all these variables. Even though similar problems have been studied by some previous works, their methods mainly rely on the Pearson correlation, which cannot handle mixed data. To address this issue, we propose a Latent Mixed Gaussian Copula model that can quantify the correlations among binary, categorical, continuous, and truncated variables in a unified framework. We also provide a tool to decompose the variation into the group-specific and the common variation over multiple groups via solving a regularized M -estimation problem. We conduct extensive simulation studies to show the advantage of our proposed method over the Pearson correlation-based methods. We also demonstrate that by jointly solving the M-estimation problem over multiple groups, our method is better than decomposing the variation group-by-group. We apply our method to a Chlamydia trachomatis genital tract infection study to demonstrate how it can be used to discover informative biomarkers that differentiate patients. When performing variance decomposition for data collected from the Chlamydia trachomatis genital tract infection study, so far we only considered subjects with complete data for all data modalities and removed subjects with missing values. The fact that not all subjects have complete data from all data modalities results in a block-wise missing structure of the mixed type data. Simply removing subjects with block-wise missing values would lead to a great reduction in sample size and thereby losing valuable information. To utilize as much data as possible when the mixed type data has a block-wise missing structure, we propose to impute the missing values by the Latent Mixed Gaussian Copula model in the third project, where we perform imputation for block-wise missing values by the underlying correlations between fully observed and partially observed variables. The method proposed can be applied to multi-modal data with various data types. We performed extensive simulation experiments to examine the effect of true latent correlation, missing mechanism and dimensionality on the performance of our proposed method, and compare it with three other popular approach. Our method shows superior performance for imputing the mixed type data compared with the other methods under different scenarios. We also applied the method to the multi-modal data collected from a Chlamydia trachomatis genital tract infection study for imputation of missing endometrial infection status, endometrial diagnosis results, and truncated cytokine values.Doctor of Philosoph