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

High Frequency trading via convolutional neural networks

L'objectiu d'aquest projecte és desenvolupar i entrenar a una CNN capaç de realitzar intercanvis borsaris dins un HFT. La metodologia seguida va consistir en el disseny de models per pronosticar característiques particulars del LOB. El punt de partida va ser provar mètodes tradicionals en finances per tal de predir els preus de futurs. Aquests mètodes van ser rebutjats a través de l'experimentació. Posteriorment, es va realitzar un estudi sobre arquitectures de ANN per construir models capaços de predir la direcció de l'preu mitjà. A més, es van dur a terme experiments en diferents models, provant diferents representacions de l'LOB. Els resultats van mostrar com, amb un disseny d'entrada i d'arquitectura adequats, una CNN supera lleugerament a un MLP en la predicció de la direcció de l'preu. Finalment, es van introduir noves etiquetes per detectar quan l'intercanvi borsària produeix beneficis. Tot seguit, es van realitzar experiments per predir aquestes etiquetes. En aquest cas, tots dos models van tenir resultats positius, però CNN no va superar a MLP.El objetivo de este proyecto es desarrollar y entrenar a una CNN capaz de realizar intercambios bursátiles en un HFT. La metodología seguida consistió en el diseño de modelos para pronosticar características particulares del LOB. El punto de partida fue probar métodos tradicionales en finanzas con el fin de predecir los precios de futuros. Estos métodos fueron rechazados a través de la experimentación. Posteriormente, se realizó un estudio sobre arquitecturas de ANN para construir modelos capaces de predecir la dirección del precio medio. Además, se llevaron a cabo experimentos en diferentes modelos, probando diferentes representaciones del LOB. Los resultados mostraron como, con un diseño de entrada y de arquitectura adecuado, una CNN supera ligeramente a un MLP en la predicción de la dirección del precio. Finalmente, se introdujeron nuevas etiquetas para detectar cuándo el intercambio bursátil produce beneficios. Acto seguido, se realizaron experimentos para predecir estas etiquetas. En ese caso, ambos modelos tuvieron resultados positivos, pero CNN no superó a MLP.The objective of this project is to develop and train a CNN capable to trade in a HFT. The methodology followed consisted in design models to forecast particular features of the LOB. The start point was to test traditional methods in finance with the purpose of predicting futures prices. These methods were rejected through experimentation. Subsequently, a study on ANN architectures was conducted to build models capable of predicting the direction of the mid price. Furthermore, experiments were carried out on different models, testing different representations of the LOB. The results showed how, with proper input and architecture design, a CNN slightly outperforms a MLP in predicting price direction. Finally, new labels were introduced to detect when the trade has benefits. Thereupon, experiments were conducted to predict these labels. In that case, both models had positive results but CNN did not outperform MLP. Subsequently, a study on ANN architectures was conducted to build models capable of predicting the direction of the mid price. Furthermore, experiments were carried out on different models, testing different representations of the LOB. The results showed how, with proper input and architecture design, a CNN slightly outperforms a MLP in predicting price direction. Finally, new labels were introduced to detect when the trade has benefits. Thereupon, experiments were conducted to predict these labels. In that case, both models had positive results but CNN did not outperform MLP.Outgoin

Axial-LOB: High-Frequency Trading with Axial Attention

Previous attempts to predict stock price from limit order book (LOB) data are mostly based on deep convolutional neural networks. Although convolutions offer efficiency by restricting their operations to local interactions, it is at the cost of potentially missing out on the detection of long-range dependencies. Recent studies address this problem by employing additional recurrent or attention layers that increase computational complexity. In this work, we propose Axial-LOB, a novel fully-attentional deep learning architecture for predicting price movements of stocks from LOB data. By utilizing gated position-sensitive axial attention layers our architecture is able to construct feature maps that incorporate global interactions, while significantly reducing the size of the parameter space. Unlike previous works, Axial-LOB does not rely on hand-crafted convolutional kernels and hence has stable performance under input permutations and the capacity to incorporate additional LOB features. The effectiveness of Axial-LOB is demonstrated on a large benchmark dataset, containing time series representations of millions of high-frequency trading events, where our model establishes a new state of the art, achieving an excellent directional classification performance at all tested prediction horizons

Feature engineering for mid-price prediction with deep learning

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