Manipulation of the Bitcoin market: an agent-based study

Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market, both directly influencing the price of an asset or indirectly by misinforming other market participants. Such behavior can be a source of systemic risk and increasing distrust for the market participants, consequences that call for viable countermeasures. Building on the foundations provided by the extant literature, this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018. The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies, including a fraudulent agent, initialized from empirical data and who performs market manipulation. The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume. Simulation results provide a satisfactory fit to historical data. Several price dips and volume anomalies are explained by the actions of the fraudulent trader, completing the known body of evidence extracted from blockchain activity. The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period; without this agent, it would have been very unlikely that the price had reached the heights as it did in late 2017. The insights gained from the model, especially the connection between liquidity and manipulation efficiency, unfold a discussion on how to prevent illicit behavior

Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management

Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchange or cryptocurrencies, aiming to maximize the profit while minimizing the loss incurred by said operations. Deep Learning (DL) methods have been consistently excelling at various tasks and automated financial trading is one of the most complex one of those. This paper aims to provide insight into various DL methods for financial trading, under both the supervised and reinforcement learning schemes. At the same time, taking into consideration sentiment information regarding the traded assets, we discuss and demonstrate their usefulness through corresponding research studies. Finally, we discuss commonly found problems in training such financial agents and equip the reader with the necessary knowledge to avoid these problems and apply the discussed methods in practice

AutoTrading with reinforcement learning

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Eloi Puertas i Prats[en] Trading is the act of studying any financial market and making money with it through buying and selling of assets. In this project, I will try to automate the actions performed by a trader without having a thorough knowledge of the financial market or trading techniques. I will use algorithms based on reinforcement learning techniques used in other fields such as robotics without human interaction in the algorithm’s execution. The main objective of this project is to investigate the feasibility of using these techniques adapted to Deep Learning and the ability to cope with the volatility of cryptocurrency. Furthermore, to show the results of these algorithms, the cryptocurrency Bitcoin and ADA will be used as a market study by obtaining the historical and making market analysis

Two Models of Speculative Bubbles Dynamics for Cryptocurrency Prices

The problem of investing into a cryptocurrency market requires good understanding of the processes that regulate the price of the currency. In this paper we offer a view of the cryptocurrency market as an environment for realization of self-organized speculative schemes that result in the formation of a characteristic price bubble. We use a microscale, agent-based model to simulate the system behavior and derive a macroscale ordinary differential equation (ODE) model to estimate the price and the return rates observed in the simulated agent-based model. We provide a formula for the total risk of the system expressed as a sum of two independent components, one being characteristic of the price bubble and the other of the investor behavior

Forecasting financial time series with Boltzmann entropy through neural networks

Neural networks have recently been established as state-of-the-art in forecasting financial time series. However, many studies show how one architecture, the Long-Short Term Memory, is the most widespread in financial sectors due to its high performance over time series. Considering some stocks traded in financial markets and a crypto ticker, this paper tries to study the effectiveness of the Boltzmann entropy as a financial indicator to improve forecasting, comparing it with financial analysts’ most commonly used indicators. The results show how Boltzmann’s entropy, born from an Agent-Based Model, is an efficient indicator that can also be applied to stocks and cryptocurrencies alone and in combination with some classic indicators. This critical fact allows obtaining good results in prediction ability using Network architecture that is not excessively complex