DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.Comment: 12 pages, 9 figure

Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis

Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.Comment: 12 pages, 4 figures, 3 table

Tensor Representation in High-Frequency Financial Data for Price Change Prediction

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.Comment: accepted in SSCI 2017, typos fixe

Dual State-Space Model of Market Liquidity: The Chinese Experience 2009-2010

This paper proposes and motivates a dynamical model of the Chinese stock market based on a linear regression in a dual state space connected to the original state space of correlations between the volume-at-price buckets by a Fourier transform. We apply our model to the price migration of executed orders by the Chinese brokerages in 2009-2010. Regulatory brokerage tapes were used to conduct a natural experiment assuming that tapes correspond to randomly assigned, informed and uninformed traders. Our analysis demonstrated that customers' orders were tightly correlated--in a highly nonlinear sense of the neural networks--with the Chinese market sentiment index, significantly correlated with the stock returns and exhibited no correlation with the bellwether bond of the Bank of China. We did not notice any spike of illiquidity transmitting from the US Flash Crash in May 2010 to trading in China.Comment: Only the abstract has been changed from the previous versio

Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading

We explore deep Reinforcement Learning(RL) algorithms for scalping trading and knew that there is no appropriate trading gym and agent examples. Thus we propose gym and agent like Open AI gym in finance. Not only that, we introduce new RL framework based on our hybrid algorithm which leverages between supervised learning and RL algorithm and uses meaningful observations such order book and settlement data from experience watching scalpers trading. That is very crucial information for traders behavior to be decided. To feed these data into our model, we use spatio-temporal convolution layer, called Conv3D for order book data and temporal CNN, called Conv1D for settlement data. Those are preprocessed by episode filter we developed. Agent consists of four sub agents divided to clarify their own goal to make best decision. Also, we adopted value and policy based algorithm to our framework. With these features, we could make agent mimic scalpers as much as possible. In many fields, RL algorithm has already begun to transcend human capabilities in many domains. This approach could be a starting point to beat human in the financial stock market, too and be a good reference for anyone who wants to design RL algorithm in real world domain. Finally, weexperiment our framework and gave you experiment progress

Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow

As a newly emerged asset class, cryptocurrency is evidently more volatile compared to the traditional equity markets. Due to its mostly unregulated nature, and often low liquidity, the price of crypto assets can sustain a significant change within minutes that in turn might result in considerable losses. In this paper, we employ an approach for encoding market information into images and making predictions of short-term realized volatility by employing Convolutional Neural Networks. We then compare the performance of the proposed encoding and corresponding model with other benchmark models. The experimental results demonstrate that this representation of market data with a Convolutional Neural Network as a predictive model has the potential to better capture the market dynamics and a better volatility prediction.Comment: Third International Workshop on Modelling Uncertainty in the Financial World (MUFin'23

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

High frequency trading via transformer deep neural networks

El Processament de Llenguatges Naturals es va revolucionar amb la introducció del “Transformer”, tal i com es pot veure amb l’impacte de Chat-GPT. D’altra banda, la Negociació d’Alta Freqüència és un tipus de negociació que es du a terme en l’ordre dels minuts, o encara menys, a diferència d’altres tipus de negociacions que es fan en l’ordre de dies o mesos. Una manera de guardar informació sobre la Negociació d’Alta Freqüència és amb un Llibre d’Ordres Límits, en el que un pot veure les ordres pendents. En aquest projecte, intentem aplicar aquesta nova arquitectura de Deep Learning de Processament de Llenguatges Naturals al món de la Negociació d’Alta Freqüència. Primer, introduïm i expliquem el Transformer i el seu característic mecanisme d’atenció. Després, expliquem alguns conceptes financers relatius al Llibre d’Ordres Límits. Finalment, apliquem el Transformer per resoldre dos problemes financers: predir el preu de tancament horari del Bitcoin i predir elmoviment del preu mitjà d’un Llibre d’Ordres Límits.El Procesamiento de Lenguajes Naturales se revolucionó con la introducción del “Transformer”, tal y como se puede ver con el impacto de Chat-GPT. Por otro lado, la Negociación de Alta Frecuencia es un tipo de negociación financiera que se lleva a cabo en el orden de minutos, o hasta menos, a diferencia de otros tipos de negociación que ocurren en el orden de días o meses. Una manera de guardar información sobre la Negociación de Alta Frecuencia es con un Libro de Órdenes Límites, en el que uno puede ver las órdenes pendientes. En este proyecto, intentamos aplicar esta nueva arquitectura de Deep Learning de Procesamiento de Lenguajes Naturales al mundo de la Negociación de Alta Frecuencia. Primero, introducimos y explicamos el Transformer y su característico mecanismo de atención. Después, explicamos algunos conceptos financieros relativos al Libro de Órdenes Límites. Finalmente, aplicamos el Transformer para resolver dos problemas financieros: predecir el precio de cierre horario del Bitcoin y predecir el movimiento del precio medio de un Libro de Órdenes Límites.Natural Language Processing has been revolutionized by the Transformer architecture, as can be seen by the impact of Chat-GPT. On the other hand, High Frequency Trading (HFT) is a type of financial trading that takes place within the order of minutes or even less, unlike other types of trading which take place in the order of days or months. One way of storing this High Frequency information is the Limit Order Book (LOB), where one can see pending bid and ask orders. In this project, we will try to apply this new Deep Learning NLP architecture to the world of HFT. We first introduced and explained the Transformer architecture and its characteristic attention mechanism. Then, we explained several financial concepts relating to HFT. Finally, we applied the transformer architecture to try to solve two financial problems: predicting the hourly closing price of Bitcoin and predicting the mid-price movement of a Limit Order Book.Outgoin

Deep Learning modeling of Limit Order Book: a comparative perspective

The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading, with a thorough review and analysis of the literature and state-of-the-art models. Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are compared on the same tasks, feature space, and dataset and clustered according to pairwise similarity and performance metrics. The underlying dimensions of the modeling techniques are hence investigated to understand whether these are intrinsic to the Limit Order Book's dynamics. It is possible to observe that the Multilayer Perceptron performs comparably to or better than state-of-the-art CNN-LSTM architectures indicating that dynamic spatial and temporal dimensions are a good approximation of the LOB's dynamics, but not necessarily the true underlying dimensions.Comment: 15 pages, 4 figures, 9 table

DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data

This work proposes DeepFolio, a new model for deep portfolio management based on data from limit order books (LOB). DeepFolio solves problems found in the state-of-the-art for LOB data to predict price movements. Our evaluation consists of two scenarios using a large dataset of millions of time series. The improvements deliver superior results both in cases of abundant as well as scarce data. The experiments show that DeepFolio outperforms the state-of-the-art on the benchmark FI-2010 LOB. Further, we use DeepFolio for optimal portfolio allocation of crypto-assets with rebalancing. For this purpose, we use two loss-functions - Sharpe ratio loss and minimum volatility risk. We show that DeepFolio outperforms widely used portfolio allocation techniques in the literature