Visual Knowledge Discovery and Machine Learning for Investment Strategy

Knowledge discovery is an important aspect of human cognition. The advantage of the visual approach is in opportunity to substitute some complex cognitive tasks by easier perceptual tasks. However for cognitive tasks such as financial investment decision making this opportunity faces the challenge that financial data are abstract multidimensional and multivariate, i.e., outside of traditional visual perception in 2D or 3D world. This paper presents an approach to find an investment strategy based on pattern discovery in multidimensional space of specifically prepared time series. Visualization based on the lossless Collocated Paired Coordinates (CPC) plays an important role in this approach for building the criteria in the multidimensional space for finding an efficient investment strategy. Criteria generated with the CPC approach allow reducing/compressing space using simple directed graphs with beginnings and the ends located in different time points. The dedicated subspaces constructed for time series include characteristics such as Bollinger Band, difference between moving averages, changes in volume etc. Extensive simulation studies have been performed in learning/testing context. Effective relations were found for one-hour EURUSD pair for recent and historical data. Also the method has been explored for one-day EURUSD time series n 2D and 3D visualization spaces. The main positive result is finding the effective split of a normalized 3D space on 4x4x4 cubes in the visualization space that leads to a profitable investment decision (long, short position or nothing). The strategy is ready for implementation in algotrading mode

Building an Artificial Stock Market Populated by Reinforcement-Learning Agents

In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup.agent-based financial modelling, artificial stock market, complex dynamical system, emergent properties, market efficiency, agent heterogeneity, reinforcement learning

Profiting from Dow Jones Industrial Index and Hang Seng Index using moving average and MACD optimization model

Before the internet, high-speed laptop computers, and big data became accessible and popular, academia on stock market trading concentrated on Efficient Market Hypothesis (EMH). EMH hinges on the idea that the market is efficient and there is no extra return that could be generated. With the dynamic development of the internet, big-data and computing technology, many researchers started to pay attention to Technical Analysis and its usage. Numerous academic papers claimed that technical analysis can enhance returns by using various technical tools. This paper explores in-depth the simulation model of Moving Average and Moving Average Convergence/Divergence (MACD) to come up with optimized parameters that will allow traders to profit from trading Dow Jones Industrial Index and Hang Seng Index

Predicting risk/reward ratio in financial markets for asset management using machine learning

Financial market forecasting remains a formidable challenge despite the surge in computational capabilities and machine learning advancements. While numerous studies have underscored the precision of computer-generated market predictions, many of these forecasts fail to yield profitable trading outcomes. This discrepancy often arises from the unpredictable nature of profit and loss ratios in the event of successful and unsuccessful predictions. In this study, we introduce a novel algorithm specifically designed for forecasting the profit and loss outcomes of trading activities. This is further augmented by an innovative approach for integrating these forecasts with previous predictions of market trends. This approach is designed for algorithmic trading, enabling traders to assess the profitability of each trade and calibrate the optimal trade size. Our findings indicate that this method significantly improves the performance of traditional trading strategies as well as algorithmic trading systems, offering a promising avenue for enhancing trading decisions

Forecasting etfs- price movements using convolutional neural networks - methodology and comparison of industries - focus on industrials etf

The aim of this paper is to achieve two goals. Firstly, build and apply a convolutional neural network to make predictions on historical data of the Vanguard Industrials ETF (VIS) in the form of Buy, Hold and Sell signals. Secondly, making comparisons among different indus triesin order to derive potential performance deviations. By using three image encoding tech niques and a randomly generated model for comparison purposes, some promising results have been achieved. Nevertheless, several classic strategies and the market performance could not be beaten, mainly because model predictions for Buy and Sell signals showed weaknesses

Forecasting financial asset price movements using convolutional neutral networks – application to the U.S. financial services sector and comparisson across industries

This thesis explores the applicability of CNNs as a price movement forecasting tool for ETFs, using a technical analysis approach and three different image encoding techniques. After developing a general methodology, the thesis focuses on the application to the U.S. financial services sector. Subsequently, the research draws comparisons to results obtained for other U.S. sector ETFs using the same model approach. Overall results show that the CNN models, while proving some potential and exceeding a random model in accuracy, show significant weaknesses for all industries in predicting Buy and Sell signals. Addressing these weaknesses, limitations of the approach are explored to suggest methods for model performance improvements

Forecasting oil & gas etfs´ price movements using convolutional neural networks

Thanks to advances in processing power, we have seen the revival of artificial intelligence after the 1980s, and algorithmic trading has become quite popular in the last two decades. In this paper, a convolutional neural network for image recognition was constructed. The CNN recognises patterns in 2D images generated from financial data and classifies them as BUY, SELL or HOLD. The analysed ETF, XLE, is from the Oil & Gas sector. The results are evaluated computationally and financially and compared to other industries. Overall, the CNN approach seems promising but generally, it was not possible to outperform the Buy&Hold strategy

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001