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Register a new userA Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data
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Qi Zhao
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This paper presents a deep learning framework based on Long Short-term Memory Network(LSTM) that predicts price movement of cryptocurrencies from trade-by-trade data. The main focus of this study is on predicting short-term price changes in a fixed time horizon from a looking back period. By carefully designing features and detailed searching for best hyper-parameters, the model is trained to achieve high performance on nearly a year of trade-by-trade data. The optimal model delivers stable high performance(over 60% accuracy) on out-of-sample test periods. In a realistic trading simulation setting, the prediction made by the model could be easily monetized. Moreover, this study shows that the LSTM model could extract universal features from trade-by-trade data, as the learned parameters well maintain their high performance on other cryptocurrency instruments that were not included in training data. This study exceeds existing researches in term of the scale and precision of data used, as well as the high prediction accuracy achieved.
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Understanding international trade is a fundamental problem in economics -- one standard approach is via what is commonly called the gravity equation, which predicts the total amount of trade $F_ij$ between two countries $i$ and $j$ as $$ F_{ij} = G frac{M_i M_j}{D_{ij}},$$ where $G$ is a constant, $M_i, M_j$ denote the economic mass (often simply the gross domestic product) and $D_{ij}$ the distance between countries $i$ and $j$, where distance is a complex notion that includes geographical, historical, linguistic and sociological components. We take the textit{inverse} route and ask ourselves to which extent it is possible to reconstruct meaningful information about countries simply from knowing the bilateral trade volumes $F_{ij}$: indeed, we show that a remarkable amount of geopolitical information can be extracted. The main tool is a spectral decomposition of the Graph Laplacian as a tool to perform nonlinear dimensionality reduction. This may have further applications in economic analysis and provides a data-based approach to trade distance.
Price movement forecasting aims at predicting the future trends of financial assets based on the current market conditions and other relevant information. Recently, machine learning(ML) methods have become increasingly popular and achieved promising results for price movement forecasting in both academia and industry. Most existing ML solutions formulate the forecasting problem as a classification(to predict the direction) or a regression(to predict the return) problem in the entire set of training data. However, due to the extremely low signal-to-noise ratio and stochastic nature of financial data, good trading opportunities are extremely scarce. As a result, without careful selection of potentially profitable samples, such ML methods are prone to capture the patterns of noises instead of real signals. To address the above issues, we propose a novel framework-LARA(Locality-Aware Attention and Adaptive Refined Labeling), which contains the following three components: 1)Locality-aware attention automatically extracts the potentially profitable samples by attending to their label information in order to construct a more accurate classifier on these selected samples. 2)Adaptive refined labeling further iteratively refines the labels, alleviating the noise of samples. 3)Equipped with metric learning techniques, Locality-aware attention enjoys task-specific distance metrics and distributes attention on potentially profitable samples in a more effective way. To validate our method, we conduct comprehensive experiments on three real-world financial markets: ETFs, the Chinas A-share stock market, and the cryptocurrency market. LARA achieves superior performance compared with the time-series analysis methods and a set of machine learning based competitors on the Qlib platform. Extensive ablation studies and experiments demonstrate that LARA indeed captures more reliable trading opportunities.
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we address this problem by designing a new set of handcrafted features and performing an extensive experimental evaluation on both liquid and illiquid stocks. More specifically, we implement a new set of econometrical features that capture statistical properties of the underlying securities for the task of mid-price prediction. Moreover, we develop a new experimental protocol for online learning that treats the task as a multi-objective optimization problem and predicts i) the direction of the next price movement and ii) the number of order book events that occur until the change takes place. In order to predict the mid-price movement, the features are fed into nine different deep learning models based on multi-layer perceptrons (MLP), convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks. The performance of the proposed method is then evaluated on liquid and illiquid stocks, which are based on TotalView-ITCH US and Nordic stocks, respectively. For some stocks, results suggest that the correct choice of a feature set and a model can lead to the successful prediction of how long it takes to have a stock price movement.
Stock and financial markets are examined from the perspective of communication-theoretical perspectives on the dynamics of information and meaning. The study focuses on the link between the dynamics of investors expectations and market price movement. This process is considered quantitatively in a model representation. On supposition that available information is differently processed by different groups of investors, market asset price evolution is described from the viewpoint of communicating the information and meaning generation within the market. A non-linear evolutionary equation linking investors expectations with market asset price movement is derived. Model predictions are compared with real market data.
We introduce a framework to infer lead-lag networks between the states of elements of complex systems, determined at different timescales. As such networks encode the causal structure of a system, infering lead-lag networks for many pairs of timescales provides a global picture of the mutual influence between timescales. We apply our method to two trader-resolved FX data sets and document strong and complex asymmetric influence of timescales on the structure of lead-lag networks. Expectedly, this asymmetry extends to trader activity: for institutional clients in our dataset, past activity on timescales longer than 3 hours is more correlated with future activity at shorter timescales than the opposite (Zumbach effect), while a reverse Zumbach effect is found for past timescales shorter than 3 hours; retail clients have a totally different, and much more intricate, structure of asymmetric timescale influence. The causality structures are clearly caused by markedly different behaviors of the two types of traders. Hence, market nanostructure, i.e., market dynamics at the individual trader level, provides an unprecedented insight into the causality structure of financial markets, which is much more complex than previously thought.
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