Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov 18-21, 201

CREATION OF A NEURAL NETWORK ALGORITHM FOR AUTOMATED COLLECTION AND ANALYSIS OF STATISTICS OF EXCHANGE QUOTES GRAPHICS

Currently, the problem of automated data analysis and statistics collection from stock quotation charts has not been fully resolved. Most of the analysis of visual data falls on the physical work of the analyst, or on obsolete software solutions. The process of summarizing the information received from financial markets still requires physical attention and labor, which increases the risks associated primarily with the human factor and corresponding errors. An algorithm has been developed and tested for the automated collection of statistics from graphs of stock quotes, including data on the development and context of various figures (patterns) of technical analysis, as well as an improved adaptation and tracking system for the trend. The modeling process, analysis and the results of applying the analysis algorithm and statistics collection are presented. The developed algorithm works in conjunction with the previously created neural network pattern detector, which allows to automatically search for the exact boundaries of technical analysis figures of various sizes, analyze the context in front of them and play the patterns. This makes it possible to obtain important statistics that allow one to determine the degree of confidence in emerging patterns, taking into account their type, context, and other factors. In terms of accuracy and efficiency, the developed algorithm meets the existing challenges in the financial markets and can significantly increase the efficiency of the trader or investor through the automated processing of graphic and visual data. The created solution is universal in nature and can be applied to any capital market, regardless of the location and nature of the assets placed. The results can be used both to improve the accuracy of existing trading strategies, and for the analytical work of financial market participants. The use of new technologies for statistical processing of information can significantly improve the accuracy of investment and trade decision

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. in this study, we investigate a framework with a set of advanced machine learning methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.Comment: 8 figures, 16 page

Hybrid deep neural networks for mining heterogeneous data

In the era of big data, the rapidly growing flood of data represents an immense opportunity. New computational methods are desired to fully leverage the potential that exists within massive structured and unstructured data. However, decision-makers are often confronted with multiple diverse heterogeneous data sources. The heterogeneity includes different data types, different granularities, and different dimensions, posing a fundamental challenge in many applications. This dissertation focuses on designing hybrid deep neural networks for modeling various kinds of data heterogeneity. The first part of this dissertation concerns modeling diverse data types, the first kind of data heterogeneity. Specifically, image data and heterogeneous meta data are modeled. Detecting Copy Number Variations (CNVs) in genetic studies is used as a motivating example. A CNN-DNN blended neural network is proposed to authenticate CNV calls made by current state-of-art CNV detection algorithms. It utilizes hybrid deep neural networks to leverage both scatter plot image signal and heterogeneous numerical meta data for improving CNV calling and review efficiency. The second part of this dissertation deals with data of various frequencies or scales in time series data analysis, the second kind of data heterogeneity. The stock return forecasting problem in the finance field is used as a motivating example. A hybrid framework of Long-Short Term Memory and Deep Neural Network (LSTM-DNN) is developed to enrich the time-series forecasting task with static fundamental information. The application of the proposed framework is not limited to the stock return forecasting problem, but any time-series based prediction tasks. The third part of this dissertation makes an extension of LSTM-DNN framework to account for both temporal and spatial dependency among variables, common in many applications. For example, it is known that stock prices of relevant firms tend to fluctuate together. Such coherent price changes among relevant stocks are referred to a spatial dependency. In this part, Variational Auto Encoder (VAE) is first utilized to recover the latent graphical dependency structure among variables. Then a hybrid deep neural network of Graph Convolutional Network and Long-Short Term Memory network (GCN-LSTM) is developed to model both the graph structured spatial dependency and temporal dependency of variables at different scales. Extensive experiments are conducted to demonstrate the effectiveness of the proposed neural networks with application to solve three representative real-world problems. Additionally, the proposed frameworks can also be applied to other areas filled with similar heterogeneous inputs