Dynamic modeling of mean-reverting spreads for statistical arbitrage

Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is a noisy realization of some hidden states. Real-time estimation of the unobserved spread process can reveal temporary market inefficiencies which can then be exploited to generate excess returns. Building on previous work, we embrace the state-space framework for modeling spread processes and extend this methodology along three different directions. First, we introduce time-dependency in the model parameters, which allows for quick adaptation to changes in the data generating process. Second, we provide an on-line estimation algorithm that can be constantly run in real-time. Being computationally fast, the algorithm is particularly suitable for building aggressive trading strategies based on high-frequency data and may be used as a monitoring device for mean-reversion. Finally, our framework naturally provides informative uncertainty measures of all the estimated parameters. Experimental results based on Monte Carlo simulations and historical equity data are discussed, including a co-integration relationship involving two exchange-traded funds.Comment: 34 pages, 6 figures. Submitte

THE POTENTIAL FOR REAL-TIME TESTING OF HIGH FREQUENCY TRADING STRATEGIES THROUGH A DEVELOPED TOOL DURING VOLATILE MARKET CONDITIONS

In this study, the authors propose a method for testing high frequency trading (HFT) algorithms on the GPU using kernel parallelization, code vectorization, and multidimensional matrices. The method is applied to various algorithmic trading methods on cryptocurrencies during volatile market conditions, specifically during the COVID-19 pandemic. The results show that the method is effective in evaluating the efficiency and profitability of HFT strategies, as demonstrated Sharp ratio of 2.29 and Sortino ratio of 2.88. The authors suggest that further study on HFT testing methods could be conducted using a tool that directly connects to electronic marketplaces, enabling real-time receipt of high-frequency trading data and simulation of trade decisions

Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies

This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. Finally, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that the new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.Comment: 12 pages, 6 figure

A Background and Chronological Take on High Frequency Trading

The purpose of this thesis is to analyze, in context of high frequency trading, potential market manipulation techniques (i.e. market making arbitrage, statistical arbitrage, market structure arbitrage, and directional strategies), and to review the subject from a chronological perspective from the 1960s onward, covering topics not limited to Regulation National Market System, the flash crash of May 6, 2010, and the August 24, 2015 market crash. To date, high frequency trading’s effects on the United States market place have been well documented. This thesis will speculate about the true nature—whether adverse or beneficial-- of this fascinating, evolutionary, highly scrutinized topic

Algorithmic trading with cryptocurrencies - does twitter sentiment impact short-term price fluctuations in bitcoin

Since its inception in 2009, Bitcoin has gained popularity and importance in financial markets. The Bitcoin price is highly volatile entailing high risk and chances of high returns for traders. This work is part of a work project, which performs a holistic approach to build an intra day Bitcoin trading algorithm based on predictive analysis of Machine Learning models. This part performs a Sentiment Analysis on Twitter data, showing a Granger causal relationship between the extracted Sentiment and the Bitcoin price